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Relationships among Span, Time Allocation, and Leadership of First-Line Managers and Nurse and Team Outcomes
by
Raquel Marie Meyer
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Graduate Department of Nursing Science
University of Toronto
© Copyright by Raquel Marie Meyer 2010
ii
Relationships among Span, Time Allocation, and Leadership of First-Line Managers and Nurse and Team Outcomes
Raquel Marie Meyer
Doctor of Philosophy
Graduate Department of Nursing Science University of Toronto
2010
Abstract
Comparisons of raw span (i.e., number of staff who report directly to a manager) within and
across organizations can misrepresent managerial capacity to support staff because managers
may not allocate the same amount of time to staff contact. The purpose was to examine the
influence of alternative measures of managerial span on nurse satisfaction with manager’s
supervision and on multidisciplinary teamwork. The alternative measures were (a) raw span as a
measure of reporting structure and (b) time in staff contact as a measure of closeness of contact
by the manager. The main effects of the alternative measures, leadership, hours of operation, and
other covariates on outcomes were examined. The interaction effects of the alternative measures
with leadership and hours of operation were investigated. The study framework was based on
Open System Theory and the boundary spanning functions of managers. A descriptive,
correlational design was used to collect survey and administrative data from employees,
managers, and organizations. Managerial time allocation data were collected through self-
logging and validated through observation. Acute care hospitals were selected through purposive
sampling. For supervision satisfaction, the final sample size was 31 first-line managers and 558
nurses. For teamwork, the final sample size was 30 first-line managers and 754 staff. The
Leadership Practices Inventory, the Satisfaction with my Supervisor Scale, and the Relational
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Coordination Scale were used. Hierarchical linear modeling was the main type of analysis
conducted. Raw span interacted with leadership and hours of operation to explain supervision
satisfaction. Teamwork was explained by leadership, clinical support roles, hours of operation,
total areas, and non-direct reports, but not by raw span or time in staff contact. Large acute care
hospitals can improve satisfaction with supervision and teamwork by modifying first-line
management positions.
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Acknowledgements
Success as a doctoral student depends on the efforts of many people. I am deeply grateful to the many
individuals and organizations that enabled this journey and extend my sincerest thanks.
To my thesis supervisor and mentor Dr. Linda O’Brien-Pallas, for sharing your vast expertise and
experience, for believing in me, and for fostering my success. I will always hold you in high and fond
regard. To my committee members Dr. Diane Doran, Dr. David Streiner, Dr. Mary Ferguson-Paré, and
Dr. Christine Duffield, for sharing your expert knowledge and humour to skillfully guide this thesis. To
my thesis reviewers Dr. Linda McGillis Hall and Dr. Carol Brewer, for your constructive feedback. To
the staff at the Nursing Health Services Research Unit, for facilitating this thesis.
To the nurse managers, who graciously allowed me into their busy worlds, thank you so much for your
insights and for the vital contributions you make every day to our healthcare system. You made this
dissertation possible and I will always be grateful. To the front-line nurses and health care providers,
administrative staff, senior nurse leaders, and hospitals who also very generously participated, I am
extremely appreciative.
To the following, thank you for financial support: the Canadian Institutes for Health Research, the
Nursing Health Services Research Unit, the Canadian Health Services Research Foundation/Canadian
Institutes for Health Research Chair in Nursing/Health Human Resources, the Ontario Training Program
in Health Services and Policy Research, the Ontario Nursing Leadership Network, the Registered Nurses
Foundation of Ontario, the Nursing Research Interest Group, and the Ontario Nursing Informatics Group
as well as awards through the Lawrence S. Bloomberg Faculty of Nursing and University of Toronto.
To my father Ken Meyer and stepmother Lynn Brown, for your steadfast encouragement and for
supporting me in a myriad of ways. To my mother Cam Duhaime and stepfather Don Kishibe, for
instilling me with a strong work ethic and for cheering me on. To Amy, Louise, and Bob, for believing in
me and for the on-line support. To Barb Mildon, Christine Covell, Kim Sears, Joan Almost, Jessica
Peterson, and Doris Leung for friendship and laughter during the doctoral program adventure.
Finally and ever so importantly, to my husband Paul Barber, for unwavering love, support, and humour
and for weathering this journey alongside me – you are the wind beneath my wings.
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Table of Contents
Abstract.......................................................................................................................................... ii
Acknowledgements ...................................................................................................................... iv
Chapter 1: The Problem............................................................................................................... 1
Background of the Problem ............................................................................................. 1
Problem Statement............................................................................................................ 2
Purpose of the Study......................................................................................................... 3
Literature Review ............................................................................................................. 3
Managerial Span ............................................................................................................... 4
Measurement and Analytical Issues ....................................................................... 11
Studies of Span as Reporting Structure and Staff Outcomes .............................. 13
Studies of Span as Closeness of Contact and Staff Outcomes.............................. 14
Managerial Time Allocation .......................................................................................... 16
Measurement Issues ................................................................................................. 16
Leadership ....................................................................................................................... 22
Studies of Managerial Span, Leadership, and Staff Outcomes ........................... 25
Gaps in the Literature and Study Rationale ................................................................ 26
Chapter 2: Theory and Study Framework............................................................................... 28
Theory .............................................................................................................................. 28
Assumptions.............................................................................................................. 28
Open System Theory Applied to Large Scale Organizations............................... 29
Boundary Spanning ................................................................................................. 32
Outcomes................................................................................................................... 33
Study Framework ........................................................................................................... 38
First-Order Relationships ....................................................................................... 42
Manager Level Covariates ...................................................................................... 46
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Staff Level Covariates.............................................................................................. 51
Chapter 3: Method...................................................................................................................... 53
Design............................................................................................................................... 53
Design and Data Collection Overview.................................................................... 53
Power......................................................................................................................... 54
Setting and Sample................................................................................................... 55
Data Collection Procedures ..................................................................................... 57
Risk and Benefits...................................................................................................... 59
Administrative Data........................................................................................................ 60
Managerial Work Logs................................................................................................... 62
Instrumentation............................................................................................................... 64
Leadership Practices Inventory .............................................................................. 64
Nurse Satisfaction with Manager’s Supervision ................................................... 65
Relational Coordination Scale ................................................................................ 68
Data Analyses .................................................................................................................. 68
Data Entry and Cleaning......................................................................................... 68
Levels of Analysis ..................................................................................................... 69
Study Objectives....................................................................................................... 70
Knowledge Translation Plan.......................................................................................... 71
Chapter 4: Results....................................................................................................................... 73
Instruments...................................................................................................................... 73
Leadership Practices Inventory .............................................................................. 73
Satisfaction with my Supervisor Scale ................................................................... 74
Relational Coordination Scale ................................................................................ 74
Sample Description ......................................................................................................... 74
Descriptive Statistics of the Study Variables................................................................ 77
Satisfaction Findings....................................................................................................... 84
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Objective 1: Main Effects for Satisfaction ............................................................. 84
Objective 2: Interaction Effects for Satisfaction ................................................... 87
Objective 3: Model Explaining Most Variation in Satisfaction ........................... 92
Teamwork Findings ........................................................................................................ 93
Objective 1: Main Effects for Teamwork .............................................................. 93
Objective 2: Interaction Effects for Teamwork .................................................... 96
Objective 3: Model Explaining Most Variation in Teamwork ............................ 98
Chapter 5: Discussion of Findings........................................................................................... 100
Descriptive Findings ..................................................................................................... 100
Manager Sample..................................................................................................... 100
Outcomes................................................................................................................. 102
Raw Span and Outcomes ............................................................................................. 102
Time in Staff Contact and Outcomes .......................................................................... 103
Leadership and Outcomes............................................................................................ 103
Hours of Operation and Outcomes ............................................................................. 104
Three-Way Interaction Effects .................................................................................... 105
Covariates and Outcomes............................................................................................. 107
Implications for Boundary Spanning.......................................................................... 109
Study Limitations and Strengths................................................................................. 110
Future Knowledge Translation Plan........................................................................... 112
Chapter 6: Summary, Recommendations, and Conclusions ................................................ 113
Summary........................................................................................................................ 113
Recommendations for Research .................................................................................. 114
Recommendations for Theory Development .............................................................. 116
Recommendations for Organizational Policy and Managerial Practice ................. 116
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Conclusions.................................................................................................................... 120
References.................................................................................................................................. 121
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List of Tables
Table 1. Span at the Manager Level ............................................................................................... 6
Table 2. Operational Definitions of Span ..................................................................................... 12
Table 3. Components of the Literature Used to Derive the Study Framework ............................ 41
Table 4. Data Collection Flow Chart ............................................................................................ 54
Table 5. Managers’ Average Raw Span, Total Areas Assigned, and Surveys Analyzed............. 75
Table 6. Managers’ Age, Tenure, Years of Experience, and Education....................................... 76
Table 7. Nurses’ Designation, Age, Years of Experience, and Education ................................... 76
Table 8. Team Surveys by Occupation ......................................................................................... 77
Table 9. Team Surveys by Highest Education.............................................................................. 77
Table 10. Descriptive Statistics of Study Variables...................................................................... 78
Table 11. One-Way Analysis of Variance Model for Satisfaction ............................................... 84
Table 12. Fixed-Coefficient Regression Model Level-1 for Satisfaction..................................... 85
Table 13. Fixed-coefficient Regression Model Level-1 for Satisfaction: Reduced Model .......... 85
Table 14. Raw Span. Intercepts-as-Outcomes Model for Satisfaction ......................................... 86
Table 15. Time in Staff Contact. Intercepts-as-Outcomes Model for Satisfaction....................... 86
Table 16. Raw Span with Two-Way Interactions for Extended Hours of Operation. Intercepts-as-
Outcomes Model for Satisfaction ................................................................................. 87
Table 17. Raw Span with Two-Way Interactions for Compressed and Mixed Hours of Operation.
Intercepts-as-Outcomes Model for Satisfaction ........................................................... 88
Table 18. Time in Staff Contact Two-Way Interactions for Extended Hours of Operation.
Intercepts-as-Outcomes Model for Satisfaction ........................................................... 88
Table 19. Time in Staff Contact with Two-Way Interactions for Compressed and Mixed Hours of
Operation. Intercepts-as-Outcomes Model for Satisfaction ......................................... 89
Table 20. Raw Span with Three-Way Interaction for Extended Hours of Operation. Intercepts-as-
Outcomes Model for Satisfaction ................................................................................. 89
Table 21. Holm Procedure for Three-Way Interaction for Two Alternative Measures for
Satisfaction.................................................................................................................... 90
Table 22. Raw Span with Three-Way Interaction for Compressed and Mixed Hours of Operation.
Intercepts-as-Outcomes Model for Satisfaction ........................................................... 91
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Table 23. Summary of Between-Manager Variance Explained in Satisfaction Models with
Alternative Span Measures ........................................................................................... 93
Table 24. One-Way Analysis of Variance Model for Teamwork................................................. 93
Table 25. Fixed-coefficient Regression Model Level-1 for Teamwork ....................................... 94
Table 26. 95% Confidence Intervals of Pairwise Differences in Mean Teamwork Scores.......... 95
Table 27. Raw Span. Level-2 Covariate Model Parameter Estimates for Teamwork.................. 96
Table 28. Time in Staff Contact. Level-2 Covariate Model Parameter Estimates for Teamwork 96
Table 29. Time in Staff Contact with Two-Way Interactions for Extended Hours of Operation.
Level-2 Covariate Model Parameter Estimates for Teamwork .................................... 97
Table 30. Time in Staff Contact with Two-Way Interactions for Compressed and Mixed Hours of
Operation. Level-2 Covariate Model Parameter Estimates for Teamwork .................. 98
Table 31. Summary of Between-Manager Variance Explained in Teamwork Models with
Alternative Measures .................................................................................................... 98
Table 32. Level-2 Covariate Model Parameter Estimates for Teamwork .................................... 99
Table 33. Characteristics of Study Managers Compared to Other Studies................................ 101
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List of Figures Figure 1. Large scale organization as open system (based on Katz & Kahn, 1978). ................... 30
Figure 2. Study framework. .......................................................................................................... 39
Figure 3. Distribution of raw span values..................................................................................... 79
Figure 4. Distribution of time in staff contact values. .................................................................. 80
Figure 5. Distribution of leadership scores. .................................................................................. 81
Figure 6. Distribution of level-1 nurse satisfaction with manager’s supervision scores. ............. 82
Figure 7. Distribution of level-1 teamwork scores. ...................................................................... 83
Figure 8. Plot of supervision satisfaction on raw span and leadership for extended hours of
operation........................................................................................................................ 91
Figure 9. Plot of supervision satisfaction on raw span and leadership for compressed and mixed
hours of operation.......................................................................................................... 92
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List of Appendices
Appendix A. Studies of Span at the Manager Level and Staff Outcomes.................................. 139
Appendix B Information and Consent Letter and Survey for Managers .................................... 150
Appendix C. Information and Consent Letter and Survey for Employees ................................. 156
Appendix D. Pilot Work ............................................................................................................. 161
Appendix E. Pearson Correlations of Study Variables............................................................... 170
Appendix F. Letters of Permission to Use Instruments .............................................................. 172
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1
Chapter 1: The Problem
Background of the Problem
Although intended to streamline services and reduce costs, Canadian health care restructuring
initiatives during the 1990s were paradoxically accompanied by increases in the prevalence and
cost of employee turnover, casualisation, absenteeism, and injury (Baumann et al., 2001;
Canadian Nursing Advisory Committee, 2002; O’Brien-Pallas, Thomson et al., 2003; Shamian,
O’Brien-Pallas, Thomson, Alksnis & Kerr, 2003). These challenges, which are the domain of
managers, have been compounded not only by a growing shortage of health care professionals
(O’Brien-Pallas, Alksnis & Wang, 2003) but also by increasingly heavy managerial workloads
and by greater numbers of direct reports per manager (i.e., wider raw spans) despite a lack of
evidence to support these organizational changes (Baumann; Canadian College of Health Service
Executives, 2001; Canadian Nursing Advisory Committee; Commission on the Future of Health
Care in Canada, 2002; O’Brien-Pallas, Thomson et al., 2003). In Canada, the proportion of the
Registered Nurse workforce reporting employment in management positions across all levels
declined from 8.9% in 1997 to 7.1% in 2007 (Canadian Institute for Health Information, 2002,
2008). Until recently, the fiscal and human consequences of this trend and the effect of
increasing workloads and spans of first-line managers have remained relatively unexamined.
Although scientific inquiry has been directed to the workload and productivity of nurses
(O’Brien-Pallas, Meyer & Thomson, 2004; O’Brien-Pallas, Thomson et al., 2004), less attention
has been devoted to the span, workload, and productivity of managers. Evidence that explains
and optimizes the influence of managerial work design on patient, staff, manager, and system
outcomes can assist decision makers in organizations and in health care policy to evaluate
managerial deployment decisions. The span and workload of first-line managers in relation to
patient, staff, and system outcomes has yet to be studied in depth.
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Problem Statement
Research on managerial span, for the most part, is limited by conflicting conceptual and
operational definitions and by the failure to consider both the complexity of the organizational
environment and the characteristics and competing demands of employees, consumers, and the
organization. Managerial span has yet to be considered in relation to the total workload of the
manager in order to more comprehensively measure and evaluate the contributions of managers
to the health care system. Measurement of managerial span and work allows the relationships
between managerial inputs and outputs to be investigated. Based on the literature reviewed,
studies examining managerial span in relation to employee and organizational outcomes are
emerging. However, limited research has been conducted on managerial span in the health care
sector in relation to patient, employee, and organizational outcomes.
This thesis aimed both to explore the influence of alternative measures of first-line managerial
span (i.e., raw span and time in staff contact) on nurse and team outcomes in the hospital sector
and to examine the curvilinear relationship between span and outcomes originally proposed by
Van Fleet and Bedeian (1977). A curvilinear relationship would indicate that when raw spans are
very narrow or very wide, outcomes may be subject to diminishing or increasing returns. Meier
and Bohte (2000) and Theobald and Nicholson-Crotty (2005) examined the curvilinear
relationship between span and outcomes in the education sector. However, preliminary pilot
work for this thesis in the acute care hospital setting demonstrated that an examination of the
curvilinear relationship was not feasible for the reasons below. Unlike the education sector,
hours of operation in acute care hospitals are not constant across managers and areas. For
example, clinics may run weekdays only whereas as inpatient units run 24 hours a day, 7 days a
week. Hours of operation are relevant to two concepts central to this study: raw span and time
allocation. Variation in hours of operation in health care alters the density of staff relative to the
manager’s workday and the coverage of service hours by the manager relative to his/her
workweek. Consequently, the curvilinear relationship between span and outcomes could not be
explored, and hours of operation was integrated into the study framework because of its potential
interaction with raw span and time allocation.
3
Purpose of the Study
The purpose of the study was to examine the influence of alternative measures of managerial
span on nurse and team outcomes in the hospital sector. The alternative measures were (a) raw
span as a measure of reporting structure and (b) time in staff contact as a measure of closeness of
contact by the manager. Specifically, the study objectives were:
1. to examine the main effects of the alternative measures of managerial span on outcomes
for nurses and teams;
2. to examine the interaction effects of the alternative measures of managerial span with
leadership and hours of operation on outcomes for nurses and teams; and
3. to determine the extent to which the alternative measures of managerial span explain
variation in outcomes for nurses and teams.
The focus was on the first-line manager’s capacity to supervise and support staff. The outcomes
were nurse satisfaction with manager’s supervision and teamwork. The design and execution of
the study was guided by an in-depth review of the literature.
Literature Review
The literature review to inform this study was inclusive of three specific areas. First, a critical
appraisal explored the pragmatic utility of the span of management concept. The historical,
conceptual, and measurement issues related to span of management at the manager level are
reported here as well as studies of managerial span and outcomes. The alternative measures of
managerial span proposed in this study were derived from this review. Next issues in the
measurement of managerial time allocation were appraised to select the time measurement
technique employed in this study. Finally, reviews of the leadership paradigms and studies of
leadership, span, and staff outcomes informed the choice of leadership model for this study and
the extent of progress in this topic area. Study outcomes are addressed in Chapter 2.
4
Managerial Span
A critical appraisal of the literature was first conducted to explore the pragmatic utility of the
span of management concept (Meyer, 2008). The goal of this type of analysis is to determine the
usefulness of an abstract concept to science by clarifying the purpose of the inquiry, ensuring
validity, maintaining bibliographic records, identifying significant analytical questions, and
synthesizing results (Morse, 2000). Literature was retrieved from the disciplines of business,
psychology, health care, and nursing. The sample was derived from online searches of Business
Source Premier, PsychINFO, and CINAHL. The following related terms were identified: span of
control, span of authority, span of responsibility, supervisory ratio, supervisory span, chain of
command, scalar chain, organizational structure, and work group size. After reviewing the
keywords in each database, the search terms were narrowed to span of control, span of
management, organizational structure, hierarchy, supervisory ratio, and work group size.
Ancestry and invisible college approaches were also used to track cited references and
unpublished works (Cooper, 1982). The search was limited to English language, peer-reviewed
publications between 1975 and 2007 in order to address conceptual and methodological advances
since Van Fleet and Bedian’s (1977) historical review. Relevant seminal works were also
included. Citations numbered 78 for Business Source Premier, 21 for PsychINFO, and 11 for
CINAHL. Using a computerized system, 51 manuscripts were reviewed and catalogued. The
search resulted in 17 citations related to conceptual or methodological issues about the span of
management. The critical appraisal revealed that conceptual approaches to span vary according
to the question posed, the purpose of the inquiry, and the level of analysis (i.e., organizational,
manager, work group, and employee levels), and the full concept analysis is reported elsewhere
(Meyer, 2008).
Consistent with the study purpose, the literature discussed in this chapter addresses the span of
management at the level of analysis of the manager (i.e., measures of ‘managerial span’). In this
section, a historical overview of span is presented; three conceptual approaches to span at the
manager level are highlighted; and, determinants of span at the manager level are identified.
Issues in the measurement and analysis of managerial span are discussed. Finally, studies of
managerial span and staff outcomes are reviewed.
5
Derived from the Germanic word ‘spanne’, span refers to breadth, extent, or range which can be
measured along different axes: spatially (e.g., distance), numerically (e.g., quantity, diversity), or
temporally (e.g., time period; Oxford University Press, 2005). Span of control may be described
as “the area of activity, number of functions or subordinates, etc., for which an individual or
organization is responsible” (Oxford University Press). Fervently debated since the advent of the
Industrial Revolution, the concept of span has been variously labeled span of control, span of
management, span of authority, span of responsibility, supervisory ratio, supervisory span, chain
of command, and scalar chain (Fayol, 1937; Gittell, 2001; Ouchi & Dowling, 1974; Van Fleet &
Bedeian, 1977).
Approaches to understanding span have evolved over the past century (Van Fleet & Bedian,
1977). During the industrial revolution, classical theorists debated the maximum number of staff
whose work and interactions the manager could supervise, direct, coordinate, and control
(Graicunas, 1937; Gulick, 1937; Urwick, 1937). This approach to span, also known as ‘limited
span’, delimits the maximum number of workers that one superior can oversee (Van Fleet &
Bedeian). For example, Graicunas quantified the potential exponential increase in direct-single,
direct-group, and cross relationships that a manager must supervise as the number of
subordinates increase. This approach, which reflected the growing emphasis on quantification
within scientific management circles, considered neither the influence of supervisory
effectiveness (Van Fleet & Bedian) nor the frequency and intensity of the interactions between
managers and their staff (Koontz, O’Donnell & Weihrich, 1980). Subsequently, span was
theorized in relation to organizational structures, with wider and narrower spans deemed
appropriate at lower and higher organizational levels respectively (Van Fleet & Bedian). Greater
diversity in the number of specialties supervised has been associated with narrower spans for
lower level managers (Dewar & Simet, 1981; Meier & Bohte, 2003). During the 1950s, studies
were conducted to validate the concept of an ‘optimum span’ which recognized that spans which
were too wide or too narrow could alter the effectiveness of supervision (Van Fleet & Bedian).
During the 1960s, the influence of contextual factors on span was acknowledged (Koontz et al.)
and later on, Ouchi and Dowling (1974) argued that it was necessary to factor in how much time
managers devote to employees.
6
Prior to the 1980s, most empirical studies of span were largely descriptive and measured span as
an outcome variable reflective of organizational characteristics (Van Fleet & Bedeian, 1977).
The question asked was: What organizational characteristics predict span within and across
different organizations? These studies examined the relationships between existing spans and
organizational characteristics. The organizations studied were generally assumed to be
successful, and objective performance measures were rarely used. More recently, researchers
have shifted the emphasis to span as an intermediary variable and its influence on organizational
outcomes. Span has been tested as a predictor, moderator, and outcome variable. Studies of span
have had limited success in explaining the influence of span on outcomes.
Ouchi and Dowling (1974) argued that conceptualization and measurement of span vary
according to the research question posed. Measures of span also reflect the level of analysis
(Meyer, 2008). The question, purpose of the inquiry and the level of analysis reflect underlying
conceptual approaches, each of which calls for a different measure of span. Table 1 presents
these facets of span at the manager level (Meyer, 2008).
Table 1. Span at the Manager Level
Question Purpose Level of Analysis Concept Measurement Example For how many employees is the
manager responsible? Lines of accountability Manager Reporting
structure number of employeesa
per manager
How much time does the manager spend supervising &
supporting employees?
Proxy for interaction between manager &
employee
Manager Closeness of contact by manager
number of employeesb % of time by manager
What is the breadth of the manager’s responsibilities &
roles?
Proxy for managerial capacity
Manager Scope of managerial role
multifactor toolsc
Notes. aMcCutcheon, 2004. bAdapted from Ouchi & Dowling, 1974. cMorash, Brintnell & Rodger., 2005; Stieglitz, 1962. The full article published by Blackwell Publishing is available in the Journal of Advanced Nursing (Meyer, 2008).
Span as reporting structure. Span of management has been conceptualized at the managerial
level as a measure of the reporting structure. Ouchi and Dowling (1974) referred to raw span,
which encompasses the “limits of hierarchical authority exercised by a single manager” (p. 357).
The question to be answered is: For how many employees does the manager have some
authority, control, or responsibility? (Ouchi & Dowling). The emphasis is on reporting
structures, lines of accountability, and communication. The measure is commonly
operationalized as the number of employees reporting directly to the manager (i.e., the number of
direct report staff). However, such measures fail to consider the frequency, intensity, purpose,
7
quality, amount, and outcomes of manager-staff interactions (House & Miner, 1969; Koontz et
al., 1980). Further, the supervisory demands associated with nonpermanent positions (e.g.,
nursing students) or independent contractors (e.g., agency nurses) are excluded from this
traditional definition. For this thesis, a measure of raw span (i.e., the number of employees
reporting directly to the manager) was used as a proxy for span as reporting structure.
Wider raw spans are theorized to be appropriate when workers are highly skilled or specialized,
because their extensive knowledge of the work process requires less supervision (Meier & Bohte,
2003). When first-line managers are responsible for teams of regulated healthcare professionals,
this argument is relevant. In contrast, unregulated workers may require more hands-on
supervisory activity by the nurse manager. Unit size in combination with mandatory staffing
ratios can determine the minimum number of direct report staff per manager. Overly wide spans
may hinder access to the manager, delay communication by staff, and overextend the manager
(Alidina & Funke-Furber, 1988). Wide spans may impede highly skilled workers from
communicating with and providing feedback to management to complete complex work
processes (Blau, 1968). Wide spans may also indicate increased managerial job complexity
because larger subunit size necessitates greater division of labor within the group and hence
greater coordination of interdependent tasks within and between subunits (Mia & Goyal, 1991).
Given that nurses and other healthcare professionals work in complex environments, the purpose,
amount, context, and outcomes of managerial activity need to be considered in addition to the
number of direct report staff.
Narrow raw spans are theorized to facilitate horizontal communication when coordination of the
main work processes occurs horizontally (Gittell, 2003). Indeed, nursing teams generally deliver
services with limited vertical coordination required across management layers (McCutcheon,
2004). At the extreme end, overly narrow spans, which may stifle worker autonomy through
increased supervision and reduced delegation, may contribute to lower worker autonomy and
morale (Alidina & Funke-Furber, 1988; House & Miner, 1969).
Span as closeness of contact by the manager. Theorists have recognized that in addition to
integrating and coordinating human resources, managers are often assigned other job functions
(Altaffer, 1998; Ouchi & Dowling, 1974). Competing demands for the manager’s time indicate
8
that, although important, span as a measure of manager-employee interaction may only be one
aspect of the manager’s total workload. Ouchi and Dowling argue that as managerial job
complexity increases, the amount of time managers have available for staff decreases. Even if
managers have the same number of direct report staff, the amount of support provided to
employees may vary relative to the amount of time each manager allocates to interaction with
staff. Comparisons of the number of staff who report directly to a manager within and across
organizations can misrepresent managerial capacity to support staff because managers may not
allocate the same amount of time to staff contact (Ouchi & Dowling). Measures that factor in
how much time managers spend in supervisory support facilitate comparisons of managers
across units and organizations (Ouchi & Dowling). How managers spend their time may
influence the relationship between span and outcomes when the outcome is sensitive to time
allocation across management activities.
With respect to span as closeness of contact, the question to be answered is: How much time
does a manager spend supervising employees? (Ouchi & Dowling, 1974). The emphasis is on the
relational aspects of the interactions between the manager and employees (Ouchi & Dowling).
Napier and Ferris (1993) suggested that span can limit the interaction between supervisors and
workers. In more contemporary terms, the question to be answered is: Given the number of
direct report staff assigned, how much attention, support, clinical supervision, and feedback can
the manager provide to each employee? For this thesis, time in staff contact (i.e., the average
daily amount of time spent by the manager interacting with staff) was used as a proxy for span as
closeness of contact by the manager.
Amount and allocation of managerial resources across an organization may also influence how
individual managers spend their time. Greater managerial resources, whether through taller
structures (i.e., multiple management layers) or through re-distribution of management functions
to other roles or departments, may influence the division of work amongst management
positions. For example, in a study by Drach-Zahavy and Dagan (2002), head nurses within a
four-tier nursing management chain focused their activities on internal unit coordination and
spent less time than anticipated in external coordination, which was taken on by higher level
nurse managers. A shift from clinical to management activities has been observed as first-line
management functions such as training, supervision, and care delivery management have
9
devolved to other roles (Duffield, Donoghue & Pelletier, 1996; Duffield, Pelletier & Donoghue,
1994). Not only do managers in healthcare navigate one of the most complex industries, but
restructuring and decentralization of the Canadian health system during the 1990s was associated
with increased responsibilities for managers. The accountability of first-line nurse managers for
patient care on a single unit broadened to the management of finances, operations, human
resources, and patient care across multiple units (Hollett, 2001; McGillis Hall & Donner, 1997).
Thus, the degree of contact that first-line nurse managers have with staff varies according to
competing work demands. How much time a manager spends interacting with employees is
partially dependent on other competing demands and the overall distribution of managerial
resources.
Span as managerial scope. Span has been also conceptualized as the scope of responsibility of
the manager. This approach considers the breadth of the manager’s position by weighting the
complexity and diversity of the assigned functions and responsibilities. The question to be
answered is: What is the scope of the manager’s responsibilities, in addition to direct report
staff? For example, Altaffer (1998) examined the budgetary responsibility and number of
assistants allocated to the manager, as well as employee full-time equivalents. In 1962, Stieglitz
described a weighted scale to optimize seven factors influencing managerial span in a
manufacturing setting. These factors included the diversity and complexity in the work and
workers, geographical proximity, coordinating and planning activities, as well as organizational
assistance received. Morash, Brintnell, and Rodger (2005) developed a tool based on literature,
focus groups and expert consensus in which factors influencing the roles, responsibilities, and
span of control of clinical hospital managers along unit, staff, and program dimensions were
weighted. Multiple variables were used to operationalize budgetary responsibility, program
diversity, complexity of material management, and characteristics of staff. A field study
demonstrated that the tool adequately reflected variation among managers in one hospital
(Morash et al.). These tools reflect the broader responsibilities assigned to a manager by
considering organizational characteristics and work demands in addition to the number of direct
report staff, but have yet to be evaluated in relation to outcomes.
A review of the literature revealed that factors influencing the scope of the manager’s role were
proposed in both the theoretical and empirical literature. As early as 1937, Gulick theorized three
10
key determinants of raw span: diversification of function, stability, and space. For Gulick,
diversification of function entailed differences in the technologies used by subordinates, as for
example when subordinates have different disciplines. Stability referred to the changing nature
of the organization. The element of space described the dispersion of subordinates relative to the
superior. It is important to note that Gulick was discussing span within the context of early 20th
century manufacturing industries. Meier and Bohte (2003) expanded on the determinants
described by Gulick. Variation and unpredictability along these dimensions are theorized to
place greater demands on the manager.
Diversification of function entails the different types of workers, functions, or inputs used. Little
variation in workers, inputs, and the work performed should allow the manager to supervise
more employees since the workplace technologies are constant and predictable (Meier & Bohte,
2003). Greater diversity in employee categories or disciplines may generate differing needs for
supervision, support, resources, and information and thus may place greater demands on the
manager.
Stability reflects the constancy of workers, organizational inputs, and the environment, which in
turn, supports routinization (Meier & Bohte, 2003). Worker experience is theorized to reduce the
need for supervision because workers have greater fluency in the job (Meier & Bohte). Stability
of organizational inputs, such as the inflow of patients and funding, contributes to routinization
and facilitates long term planning which reduces the demands on managers and allows for wider
spans. Organizations experiencing turbulent funding or restructuring may, for pragmatic reasons,
merge clinical areas or services, which could easily increase the number of direct reports, reduce
time available to interact with staff, or broaden the scope of the manager’s responsibilities, if
additional resources are not allocated to support the manager.
Space refers to the physical separation of the manager from workers. Greater distance between
the manager and the employees, as may be occasioned by supervising employees in different
locations or in large organizations, reduces the face-to-face interaction between the two parties.
Distance can influence the quality of manager-employee relationships and communication
(Stogdill & Bass, 1981) and the frequency of opportunities for task and social exchange (Stogdill
& Bass). Bearing in mind the manufacturing context assumed by Gulick (1937), greater physical
11
distance was presumed to increase requirements for supervision. Thus, when similar workers
perform routine work using standard inputs in a confined space, a manager will be able to
supervise a greater number of employees (Gulick; Meier & Bohte, 2003). However, in large
organizations that rely on specialists (e.g., regulated health care professionals), the need for
immediate supervision may be less than in organizations that employ generalists (Stogdill &
Bass; Meier & Bohte).
In terms of other factors influencing the scope of the manager’s role, authors have identified the
potential importance of variables such as administrative support and technology (Van Fleet &
Bedian, 1977) as well as the extent of supervision, coordination, and planning required of the
manager (Stieglitz, 1962). In the health care industry, several contextual factors that may
influence managerial span have been proposed. These variables include: characteristics of the
manager (leadership, skills, experience, training, knowledge in domains of responsibility,
amount of non-supervisory managerial work, support roles; proximity); employees (volume,
stability, skill mix, training, competence, education, satisfaction); units (care delivery models,
occupancy rates, operational complexity, hours of operation, demands for fiscal control and
efficiency); programs (reporting and accountability structures, budget); and patient populations
(acuity and immediacy of decisions, complexity and quality of care, degree of coordination;
Alidina & Funke-Furber, 1998; Altaffer, 1998; Doran et al., 2004; Kramer et al., 2007; Mahon &
Young, 2006; McCutcheon, 2004; Morash et al., 2005; Pabst, 1993). For this thesis, the
following variables were used to represent select characteristics of the manager’s role: manager
education, manager experience, manager position tenure, hours of operation, support roles,
number of areas assigned, diversity of jobs reporting to the manager, employee tenure, full-time
employment, and non-direct reports. The rationale for including these variables in the study
framework is detailed in Chapter 2.
Measurement and Analytical Issues
Managerial span is typically measured as a ratio (Ouchi & Dowling, 1974). Units of
measurement include headcounts, full-time equivalent positions or time spent in supervisory
activity by full-time equivalent position (Ouchi & Dowling; Pabst, 1993). Variation in
12
operational definitions has impeded comparisons of span across studies (Ouchi & Dowling).
Examples of operational definitions of span at the manager level are presented in Table 2. Table 2. Operational Definitions of Span
Author (Year) Operational Definition Altaffer (1998) Raw span:
number of full-time equivalent employees per manager
Graicunas (1937) Span of control: Series of formulas to calculate number of direct single, direct group, and cross relationships assigned to manager
Ouchi & Dowling (1974)
Raw span: number of employees
per manager
Ouchi & Dowling Adjusted span for time spent in supervisory activity by manager only:a number of employees
% time spent by manager on the selling floor Note. aAdapted from the organizational level to the managerial level
Ratios are advantageous because the calculations are transparent, easily compared, and serve as
rudimentary indicators of management structures. However, the numerator, denominator, and
context of the ratio are assumed to be standard. Simple span ratios assume that all employees
have similar needs for managerial support and that all managers provide equal amounts,
frequency, and quality of support within comparably resourced and spatially designed
environments. However, measures of span are complicated by several factors. For example,
allocation of managerial time to human resource activities will vary according to competing
workload demands. Also, employee outcomes sensitive to span may be influenced by
supervisory support provided by other roles (e.g., charge nurse, clinical educator). Thus
comparisons of simple span ratios across and within organizations may be less accurate and less
sensitive to the outcomes of interest (Ouchi & Dowling, 1974).
Use of full-time employee equivalents versus employee headcounts. When employees are
counted as full-time equivalents rather than headcounts in the calculation of span, Ouchi and
Dowling (1974) argued that this measure reflects the span of responsibility. The question to be
answered is: For how many 40 hour work weeks is the manager responsible? The authors
suggested that employee full-time equivalents represent an average number of employees for
whom management is responsible, but do not capture the resources allocated per employee
related to scheduling, evaluation, and orientation. Using full-time equivalents as the metric of
analysis is theorized to be less sensitive to outcomes that are based on closeness of contact
13
(Ouchi & Dowling) and may be less appropriate when the focus is on manager-employee dyads.
This thesis used a headcount as the metric for counting employees.
Use of full-time managerial equivalents. Some calculations of span use managerial full-time
equivalents, rather than headcounts, to partition out the proportion of time managers allocate to
supervisory activity or, when full-time equivalents are the only available data (e.g., Ouchi &
Dowling, 1974; Pabst, 1993). However, in salaried positions, managers’ worked hours may
exceed a standard full-time equivalent. Thus use of full-time equivalents may be influenced by
variation in the length of the manager’s work week. This thesis used a headcount as the metric
for counting the manager.
Relative versus absolute time spent. Ouchi and Dowling (1974) recommend calculating the
percentage of the manager’s full-time equivalent spent in supervisory activity. Use of a
percentage implies a relative value whereby time spent varies relative to the length of the
manager’s work week (i.e., not all managers work 40 hours per week). For this thesis, absolute
time spent by the manager was used and worked hours were examined as a covariate to control
for differences in the length of the workweek.
Analytical methods. For the most part, studies examining the relationships between span and
outcomes have used correlations or regression without attending to the hierarchical nature of the
data. More recently, hierarchical linear modeling has been applied to studies of span and
leadership to address the nested structure of the data and cross level interactions within data
structures (Castro, 2002). Hierarchical linear modeling was used in this thesis to account for the
nested structure of the data set.
Studies of Span as Reporting Structure and Staff Outcomes
Of 10 studies examining the main effects of managerial span on staff outcomes, 5 were
conducted in health care settings and 2 attended to hierarchical levels within the data (Appendix
A). The 2 studies attending to levels-of-analysis are reviewed here. The first study investigated
raw span (by headcount) among Canadian hospital nurse managers (Doran et al., 2004;
McCutcheon, 2004; McCutcheon, Doran, Evans, McGillis Hall & Pringle, 2009). Wide spans
were associated with a lower proportion of staff nurses surviving the first year on the unit
14
(McCutcheon), higher staff nurse turnover (Doran et al.; McCutcheon), and lower patient
satisfaction (Doran et al.; McCutcheon et al.). No association between span and staff nurse job
satisfaction was observed. For every 10 person increment in the manager’s span of control, unit
turnover of nursing staff increased by 1.6%. Thus a span of 100 would be expected to result in a
16% turnover rate (Doran et al; McCutcheon).
In a second study, in the airline industry, the raw spans (by full-time equivalent) of airline flight
departure supervisors were examined in relation to group performance (Gittell, 2001). Wide and
narrow spans were associated with lower and higher levels of group performance respectively.
Wide spans were also significantly associated with less timely communication; lower levels of
problem solving, shared goals, and shared knowledge; and with lower levels of helping, and
mutual respect. The level of teamwork mediated the relationship between span and outcomes.
The remaining eight studies that investigated the main effects of span on staff outcomes,
although not addressing levels-of-analysis issues, also generally supported this trend whereby
wider spans were associated with decreased performance (Bohte & Meier, 2001; Meier & Bohte,
2000), less employee engagement (Cathcart et al., 2004), lower staff empowerment (Spreitzer,
1994), more negative staff nurse perceptions of the work environment (McGillis Hall et al.,
2006) and increased accidents and unsafe behaviors (Hechanova-Alampay & Beehr, 2001).
However in a study of performance evaluation with nurse-supervisor dyads, a non-significant
relationship between raw span and performance ratings was observed (Judge & Ferris, 1993).
Theobald and Nicholson-Crotty (2005) also observed that when multiple outcomes were
considered, optimal span values conflicted.
Studies of Span as Closeness of Contact and Staff Outcomes
Variation in managerial time allocation has been documented across industries and management
positions (e.g., Carroll & Gillen, 1987; Hass, Porat & Vaughan, 1969; Mahoney, Jerdee &
Carroll, 1963; Penfield, 1974). In the healthcare sector, Dunn and Schilder (1993) using work
observations techniques found that head nurses spent 20% of their time in scheduling,
orientation, staff development, employee evaluation, and counseling activities. Fox, Fox, and
Wells (1999) used job description categories to determine that nurse managers in one hospital
logged nearly 8.6% of their time in personnel management activities.
15
Although time spent with employees by managers varies, no research was located that explicitly
quantified these variations in relation to staff outcomes. However, two studies offer indirect
support. In a hospital study of performance evaluations between supervisor and nurse dyads,
Judge and Ferris (1993) observed that variation in performance evaluations, although not
predicted by raw span, was predicted by staff nurse perceptions’ of their supervisors’
opportunities to observe their performance. The authors conjectured that raw span may be an
insufficient measure of the contact between supervisors and staff because supervisors may
differentially allocate their time to non-supervisory work and may enact their roles with varying
degrees of efficiency. Hence managerial time allocation could be a more sensitive predictor of
performance evaluations than raw span alone. In a study of airline departure teams, the positive
influence of narrow spans on teamwork was potentially attributed to supervisors having greater
time available to work alongside, coach, and provide feedback to team members based on
qualitative observations (Gittell, 2001).
In summary, three conceptual approaches are evident in the literature related to span at the level
of the manager. These include span as: reporting structure, closeness of contact by manager, and
managerial scope. Ouchi and Dowling (1974) proposed that measures of span as closeness of
contact would be more sensitive to staff outcomes than measures of span as reporting structure.
This thesis addressed this argument by determining the extent to which alternative measures of
managerial span explained variation in outcomes. Specifically, the two alternative measures in
this thesis were raw span as a measure of reporting structure and time in staff contact as a
measure of closeness of contact by the manager. Factors influencing the scope of the managerial
role were also included in the study framework. Measures of raw span using headcounts and
measures of time allocation using absolute measures of time were identified as appropriate for
this thesis. Despite differences in design, methods, and analytical approaches, the review of the
research suggested that span as reporting structure (e.g., raw span) is an important predictor of
staff outcomes. Research linking span as closeness of contact (e.g., time spent in staff contact)
with staff outcomes is limited.
16
Managerial Time Allocation
In this section managerial work is defined and approaches to describing managerial work are
overviewed. Time allocation is a key predictor in this study and therefore issues in the
measurement of managerial time allocation are examined.
Inquiry into the nature and purpose of managerial work has a long-standing history (Urwick,
1937). Mahoney et al. (1963) succinctly delineated the basic purpose of management. They
observed that:
some form of management is inherent in all formal group activity which involves
cooperation in the pursuit of a common goal. Cooperation in this setting involves the
allocation of labor or responsibilities for action among group members in such a way that
individual actions sum to meaningful group activity… Management is that ingredient
which … effectively translates the activities of a collection of individuals into purposeful
group activity. (p.3)
In the management literature, the work of those in management roles has typically been
described at the level of observable tasks (e.g., doing paperwork, phoning, attending meetings),
in relation to contacts (i.e., with whom) or, in relation to purpose (Hales, 1986; McCall,
Morrison & Hannan, 1978). The work activities of managers are often classified using structured
observation categories (e.g., Arman, Dellve, Wikström & Törnström, 2009; Mintzberg, 1970,
1971, 1973; Stewart, 1982), job descriptions (e.g., Fox et al., 1999; Dunn & Schilder, 1993), or
the classical management functions (e.g., Carroll & Gillen, 1984, 1987; Mahoney et al., 1963;
Singler, 1982). Studies indicate that managerial work is characterized by variety, interruption,
fragmentation, and brevity (Arman et al., 2009; Martinko & Gardner, 1985; McCall et al.).
Measurement Issues
Time measurement techniques for managerial work have included structured observation, work
sampling, and time studies. Additional considerations include: the classification system, data
collectors and reporting methods, as well as the measurement time frame. These measurement
issues are explored in this section.
17
In Mintzberg’s (1973) review of empirical studies from the 1950s and 1960s, descriptions of
managerial work were predominantly derived from self-report diaries in samples of senior and
middle managers. Beginning in the 1960s, structured observation techniques were introduced to
determine how managers spend their time. The trend amongst health services researchers has
been to use observational techniques such as work sampling and time studies to investigate how
practitioners spend their time (Finkler, Knickman, Hendrickson, Lipkin & Thompson, 1993;
Pelletier & Duffield, 2003). These techniques, which characterize industrial and management
engineering approaches, focus on the observable tasks or groups of tasks (i.e., activities)
performed, without necessarily considering the context (including power relations), quality,
purpose, and outcomes of the work, nor the skill proficiency and mental processes (e.g.,
problem-solving) of the worker (Kerlinger, 1986; Pelletier & Duffield; Willmott, 1987).
O’Brien-Pallas (2004) noted that researchers must consider the purpose of the work
measurement, the level of rigor required, and the resources available in selecting a work
measurement technique.
Structured observation, work sampling, and time studies. To determine which type of work
measurement technique to apply, the purpose of the research and the resources available for the
project should be considered (Finkler et al., 1993; O’Brien-Pallas, 2004; Pelletier & Duffield,
2003). If the study purpose is to describe managerial activity levels (e.g., frequency of activities,
contacts, and interruptions) and interaction patterns (e.g., type, recipient, and location of
communication) in relation to performance criteria and the organizational context, then
structured observation may be an appropriate technique (Martinko & Gardner, 1985;
Noordegraaf & Stewart, 2000). Structured observation has proven helpful for generating
inductive descriptions of the work of managers. This method relies on independent observation
to classify managerial work according to a structured categorization system without the use of
random sampling procedures (Martinko & Gardner) and is generally feasible for small sample
sizes (McCall et al., 1978). Structured observation has also been used to describe time spent by
activity (Martinko & Gardner); however, these descriptions focused on observable tasks (e.g.,
doing paperwork, phoning, and attending meetings; McCall et al.).
If the study purpose is to describe the types of and proportion of time spent per activity, then
work sampling is an acceptable method. This technique samples work activities using random or
18
systematic intervals to extrapolate a distribution of activities which are assumed to be
representative of the phenomena (Finkler et al., 1993; Kerlinger, 1986). Due to the instantaneous
nature of the observations, task duration cannot be determined (Finkler et al.). Infrequent
activities may not be captured unless the sampling net is sufficiently large (Kerlinger). The larger
sample size enhances external validity (Finkler et al.). Given the large number of observations
that may be required to achieve precision, work sampling may also be more feasible when the
average distribution of the work activities amongst a group of workers is of interest and
observations can be divided amongst many workers.
If the purpose is to describe the type, frequency, and duration of time per task as well as the
proportion of total time spent by task, then time studies are an appropriate approach. Time
studies involve continuous observation of work activity to record the number and duration of
activities performed (Finkler et al., 1993). Detailed data, including infrequent activities, are more
likely to be captured (Finkler et al.). Time studies are labor intensive, and usually only a small
sample size can be supported, which limits external validity. If the predictor variables of interest
or unit of analysis are at the level of the individual worker, then time studies can provide data
about the time allocation of a particular worker.
This thesis aimed to explore the relationship between the time allocation of first-line managers
and staff outcomes. Structured observation was not chosen for this thesis because of its focus on
observable behaviors and its feasibility with small samples only. Nor was a work sampling
design used in this study because dividing the observations of managerial work across managers
would provide information on how managers spend their time on average, but not on how
particular time allocation patterns may be associated with variation in outcomes between
managers. Instead, a time study was selected as the measurement technique because the
associations of interest between the time allocation patterns of particular managers and their
respective staff outcomes could be examined. Common issues in the reliability and validity of
time study techniques are now discussed. These include the classification system, data collectors,
observation method, and the time frame.
Classification system. Classification systems should comprise exhaustive and mutually exclusive
categories (Kerlinger, 1986), be readable (Burns & Grove, 2001), and clear (Pelletier & Duffield,
19
2003). In determining categories, broad definitions may improve validity because more aspects
of the construct are considered; however, greater observer judgment is required and reliability
may be lowered (Kerlinger). In contrast, more specific operational definitions which require less
inference, may improve reliability of observations but the operational list may exclude important
aspects of the construct (Kerlinger). Content validity is enhanced by using previously validated
tools, but researchers have tended to modify existing or create new tools which limit
comparisons across studies (Pelletier & Duffield). Literature reviews and pilot testing can
enhance content validity of the data collection instrument (Cardona, Tappen, Terrill, Acosta &
Eusebe, 1997; Ross, Rideout & Carson, 1994). Pre and post interviews with subjects can clarify
recording issues with self-report tools (Ross et al.). In this thesis, the classification system was
pre-tested.
Data collectors. Observations may be made by an independent rater or by self-observation using
a structured instrument (Burke et al., 2000; Pelletier & Duffield, 2003). Although reliability
increases as the work measurement approach advances from self-logging to work sampling to
direct observation, so too do costs (O’Brien-Pallas, 2004). Regardless of whether the
observations are self-reported or made by independent data collectors, participants are at risk for
the Hawthorne effect, whereby they alter their behaviors when under observation (Burns &
Grove, 2001; Burman, 1995; Finkler et al., 1993). To minimize this effect, the self-report method
should minimize disruptions to work flow and independent data collectors should be carefully
selected to be neutral, unobtrusive observers and trained using trial runs (McNiven, O’Brien-
Pallas & Hodnett, 1993; Pelletier & Duffield). Interrater reliability of greater than 90% randomly
tested throughout the data collection period is considered acceptable and should be conducted for
both self-observation and independent methods (Pelletier & Duffield). Although costly,
independent data collectors are effective when the activity is observable in nature.
Self-observation may enhance validity in situations when workers multi-task because workers
can identify the primary activity performed (Rutter, 1994), or when the activity is not directly
observable (Carroll & Gillen, 1987; McCall et al., 1978). Since managerial work is characterized
by mental, rather than observable activity, self-observation by managers is recommended in
order for the manager to identify the purpose or subject of the activity (Carroll & Gillen). In this
20
thesis, managers engaged in self-observation to collect data. This approach was supplemented by
examining inter-observer agreement.
Self-report methods. Common self-report methods in the management literature include
estimations and work diaries. Estimates of time allocation, although easily collected, are subject
to recall bias. Early seminal work by Stogdill and Shartle (1955) found high correlations between
logged and estimated times in a 3-day, continuous self-observation logging exercise by naval
officers. However, because the participants estimated their time in activities after completing the
logging exercise, the high correlations for observable activities may have resulted from a priming
effect because the data were easily retrieved from memory. Estimated and logged times did not
correlate highly when the activities were mental in nature (e.g., planning, reflecting) or
infrequent. In a study of time spent per activity by managers, a three way comparison of
estimates of self-report, self-observation, and work sampling by independent observer produced
comparable results across participants on average (Carroll & Taylor, 1968). Similarly, Penfield
(1974) reported an average agreement coefficient of .81 between managers’ estimates of time
spent and independent observations. In a review of approaches to studying managerial work,
McCall et al. (1978) found that managers poorly estimated their work activities. This finding is
supported by more recent studies in the computer industry and in employment surveys which
indicate that self-reported estimates of time allocation at the level of the individual tended to
regress towards the group mean with respondents who spent little time in the activity
overestimating time spent and those who engaged extensively in the activity underestimating
time spent (Collopy, 1996; Jacob, 1998).
Work logs are another self-report data collection technique used to identify and record job
activities (Freda, Senkewicz & AT&T, 1988). However, self-report techniques can be subject to
biases such as social desirability. Social desirability bias occurs because participants generally
want to appear as favorably as possible and tend to over- or under- report behaviors deemed to
be more or less acceptable by researchers and employers (Donaldson & Grant-Vallone, 2002;
Podsakoff, MacKenzie, Lee & Podsakoff, 2003). Varying data collection techniques in terms of
timing, sources, method, and location can assist in minimizing these biases (Podsakoff et al.;
Podsakoff & Organ, 1986). Also, assuring respondents of the confidentiality of responses and
21
that answers are neither right, nor wrong, may reduce the likelihood of socially desirable
responses (Podsakoff et al.).
Work log techniques may also increase the participant’s sensitivity and awareness about the
behavior under study which could potentially result in over-reporting or changes in behaviors
(Burns & Grove, 2001). Participants may become fatigued or bored which might result in
underreporting (Burns & Grove). During busy periods, logging may become onerous, entries
may be skipped, and participants could rely on memory to complete the missing entries, thus
hindering data accuracy (Pelletier & Duffield, 2003). The potential to falsify responses also
exists (Pelletier & Duffield).
In comparison to retrospective interviews, self-report diaries enhance internal validity by
generating more data and reducing recall error (Verbrugge, 1980). Work diaries are feasible for
larger samples, less costly than independent observation, and can track time distribution across
predetermined activities (McCall et al., 1978). In a synthesis of studies using health diaries,
Verbrugge reported high rates of patient participation (86-98%) and completion (88-100%).
Health diary data were found to be complete and of high quality when participants were
monitored and encouraged during the data collection period (Verbrugge). However, participant
sensitization and fatigue over time can negatively influence data quality of health diaries, with a
5-25% decline observed in responses over a 2 to 3 month period (Verbrugge). The burden on the
respondent must therefore be considered. To minimize fatigue and improve response rates, Freda
et al. (1998) recommend a maximum of two work logging days per week. Activity logging at
half hourly intervals is also advised to enhance the accuracy of the data and minimize recall bias
(Freda et al., 1988). In this thesis, time allocation work logs were completed prospectively by
managers to enhance the quality of the data as compared to data derived from estimates or
retrospective interviews. Strategies to reduce the burden of self-report work logs were considered
and are described in Chapter 3.
Time frame. Sampling error can be reduced by increasing the number of observations and by
ensuring the time frame is wide enough to capture all activities (Kerlinger, 1986; McNiven et al.,
1993). The time frame for work diary completion should consider the cyclical nature of the
22
behavior under study whereby the length of the time study should at least equal the longest
period of the cyclical behavior of interest (Brisley, 2001; Freda et al., 1988).
Studies of nurse managers indicate that cycles vary for different human resource activities.
Hospital managers in three U.S. teaching hospitals self reported the frequency of a set of
management activities along the following scale: at least annually, at least monthly, at least
weekly, at least daily, and repeatedly daily (Baskin, 1996; Furman, 1995; Hudak, 1995). The
frequency of human resource activity in these studies mostly occurred daily, weekly, or monthly
(Baskin; Furman; Hudak). Only orientation and planning of orientation occurred annually, with
managers in one hospital also reporting hiring on an annual basis (Baskin; Furman; Hudak). In
this thesis, the time frame for collecting time allocation data considered that the cyclical peaks in
managerial activity may occur on a wide time frame (e.g., monthly or yearly, rather than hourly).
Given the resources available for the study and the burden of data collection on the managers,
the time frame chosen for the study reflected a one month cycle of work activity.
In summary, approaches to measuring time allocation are diverse. Selection of a time allocation
measurement technique needs to consider the question to be answered, the unit of analysis, and
the resources available. For this thesis, a time study approach was selected to measure
managerial time allocation for a one month cycle of work activity. Prospective, self-reported
work logs were completed by managers and were supplemented by examining inter-observer
agreement.
Leadership
Leadership is one of the most studied attributes of managers and was considered in this study
because of its numerous associations with staff outcomes. In this section leadership is defined;
methodological issues are briefly considered; the main leadership paradigms are introduced; and
the rationale for selecting the leadership model used in this study is presented. Finally, studies
examining leadership in relation to managerial span and staff outcomes are reviewed.
A fundamental theme in the management and health care literature, leadership has been studied
along a variety of dimensions including leadership traits, styles, behaviors, skills, and
relationships (House & Aditya, 1997; Patrick & White, 2005; Vance & Larson, 2002).
Traditionally, leadership theories have mainly focused inward on the leader-follower
23
relationship, largely disregarding the type of organization, the context, and the network of
relationships in which leadership is enacted (House & Aditya). Most definitions reflect the idea
that leadership is a process which involves, motivates, and gains the commitment of people in
the completion of tasks to achieve mutual goals (Wylie, 1994).
Studies of leadership have also been plagued by conceptual and methodological challenges, often
resulting in conflicting or inconsistent findings (House & Aditya, 1997; Yammarino, Dionne,
Chun & Dansereau, 2005). A review of leadership research in organizational settings located
only 19 empirical publications in the last decade that appropriately theorized and analyzed the
data according to its hierarchical structure (Yammarino et al.). In a review of leadership studies
between 1970 and 1999 in health care and business settings, only 5.2% of studies progressed
beyond anecdotes and descriptive designs to examine correlations between leadership and
measurable outcomes (Vance & Larson, 2002). In this study, the relationships between
managerial leadership and staff outcomes were tested, and the hierarchical structure of the data
set was accounted for by using hierarchical linear modeling with staff outcomes nested under
managerial predictors.
Leadership theory has evolved along four main paradigms: trait, behavioral, contingency, and
neo-charismatic (House & Aditya, 1997). Early trait theories searched for universal
characteristics of individuals that were associated with effective leadership (Patrick & White,
2005). An emphasis on the personal abilities and characteristics of the leader implied that leaders
were born, rather than made; however, support for the heritability of personality traits is
inconsistent (House & Aditya). Positive leadership traits in managers have been associated with
nurse retention and job satisfaction (Patrick & White).
Under the behavioral paradigm, two broad categories of leader behaviors were identified and
studied in relation to leader effectiveness (House & Aditya, 1997). These comprised task- and
person-oriented behaviors. The former focus on the structure and organization of the work,
whereas the latter build relationships and address the concerns and job satisfaction of employees
(Ferguson-Paré, Mitchell, Perkin & Stevenson, 2002; Patrick & White, 2005; Wylie, 1994). The
search for universal traits and universally effective leadership behaviors met with limited success
because research on both trait and behavioral approaches to leadership was mainly inductive,
24
lacking a theoretical base, and fraught with measurement and study design limitations (House &
Aditya). Furthermore, the influence of context and constraints placed upon the leader was
ignored (House & Aditya).
Subsequently, contingency theories focused attention on the contextual influences (e.g.,
situational moderators) that interact with leader characteristics and behaviors (House & Aditya,
1997). Although the resulting findings were ambiguous, these studies stimulated further theory
development (House & Aditya). To date, only a few studies have examined the moderating
effects of organizational structures on the relationship between leadership and outcomes in
nursing. McCutcheon et al. (2009) reported a significant moderating effect for span on the
relationship between leadership and nurse job satisfaction and patient satisfaction. The influence
of nurse managers’ emotional intelligence on staff nurse empowerment has also been shown to
be conditional on wide or narrow spans (Lucas, Laschinger & Wong, 2008). Stordeur,
Vandenberghe, and D’Hoore (2000) observed a moderating effect for hierarchical level on the
relationship between leadership and perceived nursing unit effectiveness. Cummings et al.
(2008) observed indirect effects of relational leadership on nurse job satisfaction through work
environment variables related to organizational structure, namely staff development programs,
staffing levels, and staff participation in policy decisions.
Finally, House and Aditya (1997) describe a paradigm shift toward neo-charismatic leadership
theories during the 1970s. Theories within this genre explain how visionary, transformational,
and charismatic leader behaviors generate affective responses by followers, including high levels
of motivation and commitment, and foster outstanding accomplishments within organizations
(House & Aditya). These theories emphasize visionary and charismatic behaviors, but also
address person-oriented, and to a lesser extent, task-oriented behaviors (House & Aditya; Wylie,
1994). In the health care sector, measures of transformational and visionary leadership have been
associated with staff nurse and organizational outcomes, including job satisfaction (Dunham-
Taylor, 2000; McCutcheon, 2004; McCutcheon et al., 2009; McNeese-Smith, 1995; Medley &
Larochelle, 1995; Morrison, Jones & Fuller, 1997; Loke, 2001; Stordeur et al., 2000),
organizational commitment (Loke, 2001; McNeese-Smith), extra effort (Dunham-Taylor;
Stordeur et al.), perceived unit effectiveness (Stordeur et al.), self-reported productivity
(McNeese-Smith), and turnover (Houser, 2003; McCutcheon).
25
Given the plethora of approaches to leadership, Vance and Larson (2002) recommended that
researchers select a definition that corresponds with the theoretical, methodological, or
substantive aspect of leadership under study. Because of their specialized knowledge and
professional accountability, health care professionals may not require or benefit from high levels
of task structuring by managers, which could potentially hinder their autonomy and decision-
making capacity in the provision of patient care. Leadership that emphasizes visionary and
relationship oriented behaviors rather than task supervision and control may therefore be
particularly suited to the management of health care professionals. For these reasons, a theory of
transformational leadership was chosen for this study. Specifically, Kouzes and Posner’s (2002)
leadership model was selected and the instrument is described in Chapter 3.
Studies examining Kouzes and Posner’s (2002) leadership model in the health care industry have
reported significant associations between managerial leadership practices and staff outcomes.
Specifically, McNeese-Smith (1995) found that the variation in U.S. hospital staff outcomes
explained by managerial leadership practices ranged from 9 to 15% for self-reported
productivity, 11 to 27% for job satisfaction, and 16 to 29% for organizational commitment.
Similarly in a study of Singapore hospital nurses, Loke (2001) observed that 9% of self-reported
productivity, 29% of job satisfaction, and 22% of organizational commitment were explained by
managers’ leadership practices. Houser (2003) also determined that managerial leadership
practices had a moderate inverse effect on nurse turnover in long-term care facilities. These
studies provide empirical validation of Kouzes and Posner’s leadership model with regard to
predicting staff outcomes in the health care sector.
Studies of Managerial Span, Leadership, and Staff Outcomes
In the health care sector, two studies have examined the relationships among span as reporting
structure, leadership, and outcomes (Appendix A). McCutcheon et al. (2009) found that raw span
(by headcount) moderated the relationship between leadership style and nurse job satisfaction
and patient satisfaction, whereby even highly transformational leaders could not overcome spans
that were too wide. Wider spans reduced the positive effects of transformational and
transactional leadership on nurse and patient satisfaction and increased the negative effects of
management-by-exception and laissez-faire leadership on nurses’ job satisfaction. Similarly,
26
Lucas et al. (2008) observed that the beneficial influence of emotionally intelligent leadership by
nurse managers on staff nurse empowerment was lessened under wider raw spans (by
headcount).
In the airline industry, although teamwork mediated the relationship between raw span (by full-
time equivalent) and group performance, qualitative observations indicated that the benefits
achieved by narrow spans were dependent upon whether the supervisor’s style was facilitative or
coercive (Gittell, 2001). Performance was enhanced when supervisors provided coaching and
feedback to employees. In a banking sector study, raw span (by full-time equivalent) moderated
the relationship between quality of leader-member exchange and organizational commitment, but
not supervisors’ ratings of staff performance (Schriesheim, Castro & Yammarino, 2000). This
study may have been challenged by a small sample, with only 2 employees per manager
participating. Overall, these studies indicate that span interacts with leadership to influence staff
outcomes.
In summary, several approaches to leadership theory have evolved in the literature. Given the
study setting and sample, a theory of transformational leadership was identified as suitable to the
study purpose. In the research reviewed, managerial span has been shown to moderate the
influence of leadership on nurse outcomes.
Gaps in the Literature and Study Rationale
A review of the literature assists in ascertaining the extent of progress in the theoretical and
empirical domains of interest, while allowing an assessment of future areas for research and the
potential for methodological improvements. The literature revealed limited research in the areas
of managerial span and work, particularly in the health care industry. Although research is
emerging, further inquiry related to managers, managerial work, work contexts, and outcomes is
needed to build an evidence base that allows for the synthesis of findings to inform
organizational and heath system policy.
This thesis sought to advance the science related to managerial work and the management of
hospital services in several ways. Few studies have investigated managerial span and time
allocation in relation to objective outcome measures. This was one of the first studies to consider
time allocation at the level of the manager in relation to staff outcomes. This dissertation
27
examined the extent to which managerial span and time allocation explained variation in two
staff outcomes while assessing the manager’s leadership practices. In addition, the influence of
other factors and key determinants of managerial span were considered in the study framework.
Furthermore, to address the multi-disciplinary responsibilities of the first-line hospital manager,
a measure of multi-disciplinary team functioning was utilized. Given recent analytical advances
that address the hierarchical nature of data sets, the nested structure of the data in this
dissertation was also taken into account. This thesis aimed to explore the work of managers in a
manner that allows organizations to assess how best to deploy managers, given the capacity of
managers to produce positive outcomes under varying spans.
28
Chapter 2: Theory and Study Framework
Theory
The study framework of first-line manager span was based on Open System Theory as applied to
large scale organizations by Katz and Kahn (1978). The concept of boundary spanning, which
evolved from an open system view of organizations (Gittell, 2003), was used to examine the
work activities of managers within hospitals. This chapter first outlines the assumptions
underpinning the research inquiry and the basic principles of Open System Theory as applied to
large scale organizations (Katz & Kahn). The concept of boundary spanning and the
characteristics of effective boundary spanners are described. The two study outcomes, nurse
satisfaction with manager’s supervision and teamwork, are introduced and the influence of
boundary spanning by managers on these selected outcomes is explained. Finally, the study
framework is presented, the study variables are operationally defined, and the empirical rationale
for their inclusion in the framework is examined.
Assumptions
This research inquiry assumed the existence of an objective external reality whereby the
phenomenon of an “organization” was approached as an object with identifiable and measurable
characteristics (Hatch & Cunliffe, 2006). Consistent with a post-positivist paradigm, the goals
were to describe and explain relationships and variations in the phenomena of interest. Notably
these patterns are understood in a probabilistic sense; that is, relationships are subject to
contingencies and to context (Cook & Campbell, 1979; Ford-Gilboe, Campbell & Berman,
1995). Post positivists acknowledge that while the truth is never completely knowable, whether
through sensory or experiential modes, rigorous methodology and triangulation of different types
of evidence contribute to theory testing (Ford-Gilboe et al.). This study was intended to
contribute to the growing and eclectic understanding of organizations and organizational theory
that have been advanced through modern, symbolic, and post modern perspectives alike.
Additionally, although the work functions and characteristics of managers were important foci of
the study, it was recognized that the work of managers and managers themselves are embedded
within larger, complex environments. As such, their work and leadership practices may be
29
shaped or constrained by organizational values, priorities, resources, and power relations, as well
as by political and economic forces (e.g., labor market trends; Willmott, 1987). Thus, although
span, time allocation, and leadership were selected as significant predictors of managerial
influence on satisfaction and teamwork, other important predictors may also exist.
Open System Theory Applied to Large Scale Organizations
In this study, the organization was conceptualized as a social structure with the properties of an
open system (Katz & Kahn, 1978). Open System Theory recognizes the hierarchical nature of
entities whereby each level of a system consists of a ‘subsystem’ of interrelated parts. A large
scale organization is an open system comprised of supportive, maintenance, adaptive,
production, and management subsystems. A simplified representation is illustrated in Figure 1.
The supportive, maintenance, and adaptive subsystems import people, materials, and energies
through transactions at the organizational boundaries; balance internal work structures relative to
human inputs by formalizing activities and socializing and rewarding members; and deal with
problems of adjustment to external forces by recommending and incorporating change (Katz &
Kahn). Production subsystems transform the energy (e.g., people, material, resources) of the
organization by dividing the labor to accomplish tasks and generate output (Katz & Kahn).
Overall organizational functioning and adjustment to external demands are coordinated and
integrated by the management subsystem which crosscuts and directs all subsystems and
negotiates conflict across hierarchical levels.
30
Figure 1. Large scale organization as open system (based on Katz & Kahn, 1978).
In terms of functioning, the subsystems do not operate in isolation, but rather, are interdependent
and interact dynamically as part of a greater, complex whole. Wholeness implies that an entity is
a function of the behaviors of all the elements and that change in the entity is greater than the
sum of the changes amongst its elements (i.e., change is not necessarily linear; Bertalanffy,
1950). An organization consists of repeating cycles of interconnecting events and sub-events
within or between subsystems (Katz & Kahn, 1978).
A fundamental characteristic of an open system is its ability to import, transform, and export
energy; an open system is thus considered an ‘energic’ entity (Katz & Kahn, 1978). Katz and
Kahn proposed that a social organization constitutes an energic input-output system that must
import and renew energy to sustain its functioning. An organization is dependent on its
supporting environment for continued inputs to ensure its sustainability. Various forms of energy
(e.g., materials, information, resources, staff, and patients) are imported across the organization’s
31
external, semi-permeable boundary and are redistributed to its subsystems. To survive, an
organization must overcome entropy which is an inevitable process of disorder and dissolution
caused by the loss of inputs or by an inability to transform energies (Katz & Kahn). An open
system must acquire negative entropy, usually through some form of storage capacity, to ensure
its continued existence (Katz & Kahn). For organizations, negentropy can involve the renewal of
inputs, the storage of energy, the creation of slack resources, or the maximization of imported
energy relative to exported energy (Galbraith, 1974; Katz & Kahn). Organizations also
counteract entropy by adapting system functioning in response to informational signals and
feedback from the environment.
An energic input-output system must exchange energy to export outputs (Katz & Kahn, 1978).
The reorganization and transformation of energy into outputs is known as throughput. Within
production subsystems, the energic inputs are processed through the recurring and patterned
activities and interactions of individuals to yield outputs (Katz & Kahn). Thus an organization is
essentially a social structure that “consists of other people and their behavior and the products of
their activities” (Katz & Kahn, p. 5). These outputs must be used by an outside system or group
and must reactivate the organization itself (Katz & Kahn). Renewal of a social structure may be
generated by its output (e.g., revenues).
Regularity in energy inflow, transformation, and outflow allows the system to achieve both a
steady state, in that the character of the system is maintained, and a dynamic homeostasis, in that
the system continuously anticipates and adjusts to disturbances in energy inflow (Katz & Kahn,
1978). Adjustment in a social system often requires the importation of additional resources and
multiplication of subsystems which result in system growth (Katz & Kahn). An open system
becomes more complex as its subsystems multiply and specialize in function (Katz & Kahn).
Greater differentiation and proliferation of subsystems requires processes to unify system
functioning (Katz & Kahn). There is no single way for an open system to achieve its final state.
An organization can achieve its end state from various initial conditions and through different
means (Katz & Kahn). This suggests that there is no single right way to structure an
organization.
32
Boundary Spanning
The study was based upon an understanding of the boundary spanning activities of managers
within an organization. Boundary spanning evolved from a system view of organizations
whereby open systems with semi-permeable boundaries must negotiate the inflow and outflow of
energy between subsystems and with the external environment (Gittell, 2003). Boundaries exist
internally within an organization, such as those that occur between subsystems (Katz & Kahn,
1978), hierarchical levels (Ancona & Caldwell, 1992; Katz & Kahn, 1978), functional groups, or
spatial divisions (Gittell, 2003). The boundary function relates a unit or subsystem to its external
structure or environment and is often charged to those in leadership roles (Katz & Kahn).
Although “interconnected groups may form the technical or production subsystem of an
organization … these behavior patterns are crisscrossed by cycles of behavior from the
managerial subsystem” (Katz & Kahn, p. 3-4). In social structures, the managerial subsystem
serves to resolve conflicts between hierarchical levels, coordinates substructures, and balances
external demands with internal resources and needs (Katz & Kahn). At all levels in the hierarchy,
managers contribute to organizational functioning by coordinating and integrating system
functioning across suprasystem and subsystem boundaries. Senior executives are most likely to
negotiate the external suprasystem boundaries, whereas first-line managers are more likely to
manage internal and production subsystem boundaries. This study focused on first-line managers
of production subsystems.
Managers span boundaries by coordinating and integrating inputs (e.g., information, materials,
and human resources), throughput processes, and outputs across interrelated subsystems,
hierarchical levels, functional groups, and spatial divides. Boundary spanning can involve
acquiring, filtering, and importing information from outside entities for distribution to internal
users (Tushman & Scanlan, 1981); regulating the flow of inputs and negotiating for needed
resources (Ancona & Caldwell, 1992); and engaging in relationship management to buffer and
influence relations that occur externally and internally to the unit (Gittell, 2003; Ancona &
Caldwell).
Effective boundary spanning requires individuals to understand the vocabulary, semantics,
shared beliefs, and context of both the internal unit and its external environment (Tushman &
33
Scanlan, 1981). Effective boundary spanners are more likely to interpret and translate language
and contextual information on both sides of the unit boundary to meet the contrasting
information needs and demands of external areas (Tushman & Scanlon). Appropriate
interpretation and translation of messaging and contextual cues by the boundary spanner prevents
the distortion and bias that can occur as communication crosses boundaries (Tushman &
Scanlan). Boundary spanners with strong connections to and communication patterns with
external information areas are also more likely to effectively link an internal unit to external
areas (Tushman & Scanlon). Boundary spanners with technical competence and work expertise
are more likely to understand internal work processes, demands, and roles and to be consulted by
internal system users (Tushman & Scanlon).
Effective boundary spanners foster high quality communication and relationships with and
among people through physical movement, interpersonal skills, and conversation which require
time on the part of the boundary spanner (Gittell, 2003). Coordination of communication and
relationships is a time- and relationship-intensive endeavor for managers who provide coaching
and feedback to members through direct supervision (Gittell). Charnes and Tewksbury (1993)
suggested that “within each organizational unit, coordination among members of the unit is
facilitated by patterns of interaction and by common goals, rewards, cognitive and interpersonal
orientations, and supervision” (p. 25). Coaching alongside the employee is a boundary spanning
activity that offers opportunities to enhance worker respect for and knowledge about the work of
others and to reinforce how the work completed individually serves a larger, shared goal (e.g.,
patient centered care; Gittell).
Outcomes
The study examined the contributions of managers to care delivery with respect to two outcomes:
nurse satisfaction with manager’s supervision and multidisciplinary teamwork. This section
defines the outcomes and theorizes how boundary spanning by first-line managers influences
these outcomes. Empirical support for the influence of managers on the outcomes and the
relevance of the outcomes to health care organizations are presented.
Nurse satisfaction with manager’s supervision. Nurse satisfaction with manager’s supervision
was selected as an outcome of managerial work for this study. Nurses’ perceptions of their
34
manager’s abilities with respect to supervision were assessed. A facet specific measure, the
Satisfaction with my Supervisor Scale (Scarpello & Vandenberg, 1987) was used. Conceptually,
the scale reflects three areas of supervisory ability: technical, human relations, and administrative
skills (Mann, 1965). Effective supervision requires the manager to understand the technical
content of the work performed. Technically proficient supervisors can better assist in problem-
solving and work completion. Human relations skills reflect the manager’s ability to listen to
workers’ concerns and ideas, to deal with mistakes made by staff, to recognize staff
accomplishments, to treat staff in a consistent manner, and to back up staff with other
management. Effective supervisors also demonstrate administrative abilities in the assignment
and delegation of work, in the fair appraisal of worker performance, and in the communication of
organizational changes to workers.
Supervision generates boundary spanning work for managers across subsystem, functional,
spatial, and hierarchical boundaries. Nurses do not provide nursing care in isolation; their work is
reliant on the flow of inputs (e.g., patients, equipment), on other subsystems (e.g., admissions
department, laboratories), and on other healthcare providers (i.e., the multidisciplinary team) to
accomplish interdependent work goals. Managers coordinate energy flow and work processes
across departments and patient care areas, professions, and roles to facilitate nurses’ work.
Spatial boundaries are inherent in the physical space created by an organization. Managers travel
physically within the organization to engage in face-to-face communication, observation, and
direct supervision of nurses.
Managers also span hierarchical boundaries in relation to the supervision of nurses. By virtue of
the formal reporting structure of the organization (i.e., hierarchy), management positions are
vested with the authority to direct the actions and the norms expected of subordinate positions
(Weber, 1978) and are assigned responsibility for aspects of staff and system functioning and
performance. Reporting relationships are important to ensure employees are held accountable for
the work assigned (Jaques, 1990), to create channels of appeal (Weber), and to ensure that staff
can access organizational resources and managerial support (Blau, 1968; Kanter, 1977) thereby
ensuring the flow of information and resources.
35
Nurses’ satisfaction with the supervisory relationship is an increasingly important concern for
health care organizations that wish to retain nurses in an era of worsening shortages of nurses
(O’Brien-Pallas, Thomson et al., 2003). Many nurses do not feel respected or supported by their
managers (Laschinger, 2004; O’Brien-Pallas, Tomblin Murphy et al., 2005; Pellico, Brewer &
Kovner, 2009). Communication and relationship with supervisor are significant predictors of
nurse job satisfaction (Blegen, 1993; Buccheri, 1986; Cummings et al., 2008; Hall, 2007; Irvine
& Evans, 1995; Kovner, Brewer, Yow-Wu, Cheng & Suzuki, 2006). Evidence suggests that
managers can influence nurses’ satisfaction. For instance, nurse job satisfaction has been
significantly predicted by the manager’s leadership or management style (Duffield et al., 2009;
Dunham-Taylor, 2000; Leveck & Jones, 1988; Loke, 2001; McCutcheon, 2004; McCutcheon et
al., 2009; McGillis Hall et al., 2001; McGilton, McGillis Hall, Wodchis & Petroz, 2007;
McNeese-Smith, 1995; Medley & Larochelle, 1995; Morrison et al., 1997; Stordeur et al., 2000)
as well as by boundary spanning activities such as management communication (Blegen; Irvine
& Evans; Laschinger & Finegan, 2005) and performance feedback (Irvine & Evans; Larson,
1984; Tonges, Rothstein & Carter, 1998). In turn, nurse satisfaction is a pertinent indicator for
health care organizations because of its positive associations with patient satisfaction (McGillis
Hall, 2003; McGillis Hall et al., 2001) and nurse retention (Leveck & Jones) as well as its
negative associations with nurses’ intentions to leave and nurse turnover (Duffield et al., 2007;
Irvine & Evans; Price & Mueller, 1986; Shields & Ward, 2001).
For this study, the outcome of supervision satisfaction was examined only from the perspective
of nurses. As a result of the shift to program management in Ontario hospitals, health care
professionals and other non-nursing service providers often have dual reporting relationships
with both a first-line unit manager and a department head (Leatt, Lemieux-Charles & Aird,
1994). Ultimately, the satisfaction of these non-nurse employees with supervision may be related
to factors beyond the control and influence of the first-line manager. In contrast, staff nurses are
generally only supervised by one manager.
Teamwork. The Relational Coordination Scale for General Healthcare Settings (Gittell, 2006)
was used to measure teamwork. Teamwork is defined as the frequency, accuracy, timeliness, and
problem-solving orientation of the communication between care providers, as well as the shared
knowledge, shared goals, and mutual respect among group members (Gittell, 2003). A team is
36
composed of “multiple health disciplines with diverse knowledge and skills who share an
integrated set of goals and who utilize interdependent collaboration that involves
communication, sharing of knowledge and coordination of services to provide services to
patients and their caregiving systems” (Drinka & Ray, 1987, p. 44). Communication,
coordination, and shared decision-making are key sub-concepts underpinning definitions of
teams and teamwork (Doran, 2005). Coordination of workers’ activities, which is central to
achieving organizational goals, is a primary function of managers (Katz & Kahn, 1978; Mahoney
et al., 1963). Managers also integrate system functioning by fostering shared norms and values
(Katz & Kahn).
In developing a measure of teamwork, Gittell (2003) attended to the relational aspects of
teamwork. Known as ‘relational coordination’, the measure is premised on the theory that highly
interdependent work processes are most effectively coordinated through high quality
communication and high quality relationships. Relational coordination is theorized to improve
performance in settings characterized by interdependence, uncertainty, and time constraints
(Gittell, 2000; 2003). Unlike sequential or pooled work processes, interdependent work occurs
when work activities are inter-reliant and workers must engage in mutually supportive
interactions to accomplish the work goals (Gittell et al., 2000). Interdependency characterizes the
work of health care providers in hospital settings who often work in multidisciplinary teams to
provide patient-centered care (Gittell et al., 2004). Relational coordination is a competency that
is “carried out by front-line workers with an awareness of their relationship to the overall work
process and to other participants in that process” (Gittell, 2000, p. 518). The focus is on the
informal mechanisms of coordination, as reflected by communication patterns and relationships,
rather than formal structures such as routines (e.g., clinical pathways) or meetings (e.g., patient
rounds; Gittell, 2000; 2002a).
Teamwork generates boundary spanning work for managers across subsystem, hierarchical,
functional, and spatial boundaries. Subsystem boundary work is fundamental to the work of
managers because production subsystems do not function in isolation of the organization
suprasystem to produce patient care services. To accomplish work goals, health care teams rely
on and interact with other subsystems (e.g., to negotiate the flow of inputs and outputs) and must
respond to changing work demands and standards required by the organizational suprasystem.
37
As boundary spanners, managers coordinate the bidirectional flow of information, resources, and
work processes between the team and other subsystems. They also attempt to influence external
opinions and buffer the team from external demands (Ancona & Caldwell, 1992). At times, this
boundary work spans hierarchical levels as the manager interacts with more senior management
positions (e.g., to secure additional resources).
The professional and role boundaries inherent in multidisciplinary teams also create boundary
work for managers who must integrate interdependent work processes. Because health care
providers are generally educated in professional silos, their understanding of the roles of other
team members may be limited (Charnes & Tewksbury, 1993). Professional affiliations can
magnify functional divisions since members of a functional group (e.g., nursing) interact more
frequently, develop social relationships, are supervised and evaluated from within the group, and
conform to professional standards (Charnes & Tewksbury). Coordination of activities may be
challenging when group members have functionally based differences in “work goals, thought
worlds, and status” (Gittell, 2003, p. 294). When work is highly interdependent, managers also
assist in coordinating the efforts of team members by building connections with and among
workers (Gittell). In health care, managers may need to manage the relationships amongst
professional groups and roles to achieve integrated, patient-centered care (Charnes &
Tewksbury). Managers travel physically across spatial divides within the organization to engage
in face-to-face communication, observation, and direct supervision, including coaching. This
movement enables managers to create connections among team members, and between team
members and external parties.
Evidence suggests that managers can influence multidisciplinary teamwork and inter-
professional relationships. Manager leadership style and skills have been associated with
teamwork (Gittell, 2001; Kramer et al., 2007) and related concepts such as extra effort (Dunham-
Taylor, 2000; Stordeur et al., 2000), perceived unit effectiveness (Stordeur et al.), access to
resources, and nurse-physician relationships (O’Brien-Pallas, Tomblin Murphy et al., 2005).
Both Blau (1968) and Kanter (1977) theorized that managers enable access to information and
resources. Organizational support in the form of information, feedback, and resources has been
associated with improved team communication, cooperation, and decision-making (Kennedy,
Loughry, Klammer & Beyerlein, 2009).
38
Teamwork remains a salient concern for health care organizations. The demand for improved
interdisciplinary communication and collaboration can be attributed to reductions in hospital
stays, as well as increased patient acuity and complexity and increased frequency of exchanges
amongst multi-disciplinary health care providers (O’Brien-Pallas, Hiroz, Cook & Mildon, 2005).
Teamwork is an important informal coordinating mechanism in health care because of its
influence on patient, staff, and system outcomes. Higher levels of teamwork have been
associated with improved patient outcomes, including reduced postoperative pain, improved
postoperative functioning, shorter length of stay (Gittell et al., 2000), satisfaction and intent to
recommend (Gittell, 2002b), improved patient functional status (McGillis Hall et al., 2001),
lower fall rates (Kalisch, Curley & Stefanov, 2007), and fewer adverse events (Houser, 2003).
Strong group functioning has also been associated with improvements in outcomes for
continuous quality improvement projects by interdisciplinary health care teams (Doran et al.,
2002), lower nurse turnover (Kalisch et al.; Mohr, Burgess & Young, 2008; Shortell et al., 1994)
and lower nurse intent to leave (Estryn-Béhar et al., 2007).
Study Framework
This section presents the study framework, defines the variables, and provides a rationale for the
variables selected. The study purpose was to examine the influence of alternative measures of
managerial span on nurse and team outcomes. The alternative measures of managerial span were
(a) raw span and (b) time in staff contact. Figure 2 depicts the main and interaction effects
examined.
39
Figure 2. Study framework.
The specific study objectives were:
1. to examine the main effects of the alternative measures of managerial span on outcomes
for nurses and teams;
2. to examine the interaction effects of the alternative measures of managerial span with
leadership and hours of operation on outcomes for nurses and teams; and
3. to determine the extent to which the alternative measures of managerial span explain
variation in outcomes for nurses and teams.
The level-2 variables in the study framework were chosen based on the boundary spanning
function in large scale organizations, the characteristics of effective boundary spanners, and key
40
determinants of span (i.e., diversification of function, stability, and space), as well as other
factors influencing managerial span that were proposed in the health care literature. Level-2
covariates were also included. The level-1 variables were associated with variation in the study
outcomes as identified in the literature or were included as control variables. Table 3 identifies
the components of the literature used to derive the study framework.
41
Table 3. Components of the Literature Used to Derive the Study Framework Level Literature Reference(s) Manager
First-Order Relationships Raw Span Boundary spanning Ancona & Caldwell, 1992; Katz & Kahn, 1978 Empirical literature Alidina & Funke-Furber, 1988; Blau, 1968; Cathcart et al., 2004; Doran et al.,
2004; Bohte & Meier, 2001; Gittell, 2001; Hechanova-Alampay & Beehr, 2001; Jaques, 1990; McCutcheon, 2004; McCutcheon et al., 2009; McGillis Hall et al., 2006; Meier & Bohte, 2000; Spreitzer, 1994
Time in Staff Contact
Empirical literature Gittell 2001, 2003; Ferguson-Paré, 1997; Ouchi & Dowling, 1974
Leadership Boundary spanners Katz & Kahn; Tushman & Scanlan, 1981 Empirical literature Gittell, 2000, 2001; Kouzes & Posner, 2002; McCutcheon; McCutcheon et al.;
Lucas et al., 2008; Schriesheim et al., 2000 Hours of Operation Empirical literature Morash et al., 2005 Covariates Education Boundary spanners Tushman & Scanlan, 1981 Empirical literature Alidina & Funke-Furber; Duffield & Franks, 2001; Mahon & Young, 2006;
McGillisHall & Donner, 1997; Reyna, 1992; Smith & Friedland, 1998; Synowiez, 1987
Experience Boundary spanners Tushman & Scanlan Empirical literature Alidina & Funke-Furber; Doran et al.; Dunn & Schilder, 1993; Englebardt,
1993; Meier & Bohte, 2003; Reyna, 1992 Position Tenure Empirical literature Doran et al. Worked Hours Measurement issue Identified as potential confounder Administrative
Support Roles Empirical literature Alidina & Funke-Furber; Altaffer, 1998; Drach-Zahavy & Dagan, 2002;
Duffield et al., 1996; Kramer et al., 2007; Ouchi & Dowling; Pabst, 1993; Van Fleet & Bedian, 1977
Clinical Support Roles
Empirical literature Alidina & Funke-Furber; Altaffer; Drach-Zahavy & Dagan; Duffield et al., 1994; Gittell, 2002a; Kramer et al.; McCutcheon; McCutcheon et al.; Ouchi & Dowling; Pabst; Van Fleet & Bedian
Total Areas Boundary spanning Gittell, 2003; Katz & Kahn Key determinants Gulick, 1937; Meier & Bohte, 2003 Empirical literature Altaffer; Morash et al.
Occupational Diversity
Boundary spanning Gittell, 2003
Key determinants Gulick, 1937; Meier & Bohte, 2003 Empirical literature Alidina & Funke-Furber; Morash et al. Employee Tenure Key determinants Gulick; Meier & Bohte, 2003 Full-time
Employment Key determinants Gulick; Meier & Bohte, 2003
Empirical literature Edwards & Robinson, 2004; Grinspun, 2002; Kalisch & Begeny, 2005 Non-Direct Reports Boundary spanning Gittell, 2003 Key determinants Gulick; Meier & Bohte, 2003 Empirical literature Burke, 1996; Green et al., 1996; Hechanova-Alampay & Beehr, 2001; Mullen
et al., 1989 Staff
Covariates Nurse Age Empirical literature Blegen & Mueller, 1987; Kalleberg & Loscocco, 1983; O’Brien-Pallas,
Tomblin Murphy et al., 2005; Price & Mueller, 1986; Shields & Ward, 2001 Nurse Day Shift
Employment Empirical literature Blegen & Mueller, 1987; McCutcheon; Shields & Ward, 2001
Nurse Education Empirical literature O’Brien-Pallas, Tomblin Murphy et al.; Shields & Ward; Ward, 2002 Nurse Registration Empirical literature O’Brien-Pallas, Tomblin Murphy et al.; Shields & Ward; Ward, 2002 Occupational Group Empirical literature Manser, 2009 Full-time Status Empirical literature Edwards & Robinson, 2004; Kalisch & Lee, 2009
42
First-Order Relationships
Raw span was the number of assigned employees who report directly to a manager. Managers
who have larger raw spans are less effective boundary spanners by virtue of the number of staff
members for whom they have some authority and responsibility. The hierarchical structure of the
organization determines the number of direct reports assigned to a manager. Direct report
relationships enable managers to supervise and coordinate the work performed in assigned areas.
Managers can assign or delegate responsibility for work performance to staff, as well as coach
staff in the performance of their role. Direct report staff members are, in turn, liable for work
performance and accountable to the manager (Jaques, 1990). As discussed under boundary
spanning and outcomes, managers span the hierarchical boundaries (Ancona & Caldwell, 1992;
Katz & Kahn, 1978) within the organizational suprasystem to coordinate the supervision of staff
as well as teamwork.
Although very low spans have been posited to stifle worker autonomy (Alidina & Funke-Furber,
1988) and reduce staff empowerment (Spreitzer, 1994), first-line nurse managers in healthcare
typically do not have the very narrow spans observed in other industries (e.g., raw spans as low
as 1 to 10; Hales, 2005). In two Ontario studies of acute care hospitals, minimum raw spans for
first-line nurse managers numbered 40 (McGillis Hall et al., 2006) and 36 (Doran et al., 2004)
respectively. Because a truncated range of span values was anticipated in this study, the effects
of very narrow raw spans, as described in the business literature, were not expected in this study.
However, moderate and high raw spans were anticipated in the study sample. Overly wide spans
are theorized to negatively impact staff outcomes because they may hinder access to the
manager, delay communication by staff, and overextend the manager (Alidina & Funke-Furber,
1988). Wide spans may also impede skilled workers from accessing managerial support and
organizational resources needed to complete complex work processes (Blau, 1968). Wider
managerial raw spans have been associated with lower levels of staff stability (McCutcheon,
2004), consumer satisfaction (McCutcheon et al., 2009), teamwork (Gittell, 2001), performance
(Bohte & Meier, 2001; Gittell; Meier & Bohte, 2000), staff engagement (Cathcart et al., 2004),
and staff empowerment (Spreitzer, 1994). Wider raw spans have also been associated with
higher staff turnover (Doran et al., 2004; McCutcheon), increased accidents and unsafe behaviors
43
(Hechanova-Alampay & Beehr, 2001), and more negative staff perceptions of the work
environment (McGillis Hall et al., 2006).
Time in staff contact was the average daily amount of time spent by the manager interacting
with staff and physicians working in the area(s) assigned to the manager. Contact included
verbal, written, and email communication with staff and person-to-person interaction. Managers
who spend greater time in staff contact are more effective boundary spanners by virtue of their
efforts to communicate and to develop relationships with staff and team members. The
importance of interpersonal dynamics within a social structure can therefore not be
underestimated. Managers who engage in a holistic approach to people by showing personal
interest in people, addressing psychosocial and spiritual concerns, and using touch to interact
enhance professional nursing autonomy (Ferguson-Paré, 1997). The presence of the manager is
necessary to offer this support to staff. Similarly, Gittell (2003) proposed that supervisors must
spend sufficient time with staff to establish and maintain high quality communication and
relationships with and among workers.
Even if managers have the same number of direct report staff (i.e., raw span), the amount of
supervisory support provided to employees may vary relative to the amount of time each
manager allocates to interaction with staff. Ouchi and Dowling (1974) argued that a measure of
span adjusted for the time spent by the manager in contact with staff more accurately represents
the manager’s capacity to engage staff (i.e., closeness of contact) and thus facilitates
comparisons of managers across units and organizations. No studies were located that quantified
the associations between managerial time allocation and staff outcomes. However based on
qualitative observations of airline departure teams, Gittell (2001) surmised that narrow spans
increase teamwork levels by virtue of supervisors having greater time available to coach and
provide feedback to team members.
Leadership practices were the ratings of leadership behaviors of the manager as measured by the
Leadership Practices Inventory (Kouzes & Posner, 2002; Appendices H & I). The five leadership
practices are: challenging the process, inspiring a shared vision, enabling others to act, modeling
the way, and encouraging the heart. Katz and Kahn (1978) argued that human effort and
motivation are essential inputs to the continued existence of a social organization. Managers
44
integrate system functioning by fostering shared norms and values amongst its members. As
discussed under boundary spanning, Tushman and Scanlan (1981) theorized that effective
boundary spanners have excellent communication skills and understand contextual cues,
vocabulary, semantics, and shared beliefs both within and across subsystems. Furthermore,
effective boundary spanners are technically competent which allows them to meaningfully
coordinate work processes, roles, and demands among internal users. Consistent with the
characteristics of effective boundary spanners, Kouzes and Posner’s leadership practices enable
the manager to establish effective communication and relationships as described below.
Managers with highly transformational leadership styles are more effective boundary spanners
by virtue of their leadership behaviors which include challenging the process. This is because
managers who challenge the process seek and are positive about the ideas of others, encourage
action by setting clear work goals, responsibilities, and manageable time frames, allow staff to
master work activities before assigning new work, and discuss work changes ahead of time with
staff (Kouzes & Posner, 2002). These managers also allow staff to take risks, back up staff with
other management, and treat errors as learning opportunities. These practices enhance team
members’ “awareness of their relationship to the overall work process and to other participants in
that process” (Gittell, 2000, p. 518).
Managers with highly transformational leadership styles are more effective boundary spanners
by virtue of their leadership behaviors which include inspiring a shared vision and enabling
others to act. Managers who inspire a shared vision listen to nurses and get to know nurses by
understanding their career aspirations (Kouzes & Posner, 2002). Managers who inspire a shared
vision will create shared goals among team members. Managers who enable others to act foster
accountability and trust by not supervising work in an over-controlling manner, by engaging in
face-to-face interactions, and by building relationships (Kouzes & Posner). Enabling managers
listen to and coach staff. Enabling managers foster interdependence among team members by
ensuring each member understands how their role contributes to the larger goal; by establishing
norms of reciprocity to ensure fairness, predictability, and stability of relationships; and by
rewarding joint efforts (Kouzes & Posner). In this way, team members are more likely to
understand and respect each others’ roles. Durable and frequent face-to-face interactions,
informal interactions, and the sharing of influence, information, and resources also foster
45
collaboration and trust. Enabling managers strengthen the problem solving abilities and
confidence of staff through coaching, increasing choice, and delegating authority (Kouzes &
Posner).
Managers with highly transformational leadership styles are more effective boundary spanners
by virtue of their leadership behaviors which include modeling the way and encouraging the
heart. Managers who model the way demonstrate competence and align actions with values by
following through to solve problems and achieve results; by assisting staff in meeting work
requirements when needed; by behaving consistently towards staff; by using mistakes as learning
opportunities; and by providing feedback (Kouzes & Posner, 2002). Managers who model the
way exhibit and encourage high quality communication and shared values among team members.
Managers who encourage the heart do so by having clear expectations with appropriate
feedback; by providing frequent individualized recognition; and by creating a spirit of
community enhanced by public and social celebrations of shared successes, values, and
outcomes (Kouzes & Posner).
Empirical research has shown that the beneficial influence of positive leadership on outcomes
may be conditional on the number of staff reporting directly to the manager. McCutcheon et al.
(2009) and Lucas et al. (2008) observed that no matter how strong the leadership style, managers
with overly wide spans were unable to positively influence nurse job satisfaction and
empowerment, respectively. Similarly, Gittell (2001) also observed that the beneficial influence
of smaller spans on teamwork was dependent on whether the supervisor’s style with staff was
facilitative or coercive. In contrast, Schriesheim et al. (2000) found that higher levels of leader-
member exchange were associated with higher staff organizational commitment under wider raw
spans.
Hours of operation was the average weekly hours of operation per manager weighted by the
number of direct reports in each assigned area. Variation in the hours of operation alters the
density of staff relative to the manager’s workday and the extent to which the manager’s
workweek covers the serviced hours. Longer hours of operation are thought to reflect increased
complexity of the areas assigned to managers (Morash et al., 2005). Hours of operation are an
important contextual factor that alters the meaning of two concepts central to this study: raw
46
span and time allocation. Variation in hours of operation in health care alters the density of staff
relative to the manager’s workday and the coverage of service hours by the manager relative to
his/her workweek. For these reasons, hours of operation was included as a key predictor.
Manager Level Covariates
Manager education was measured as the highest educational qualification held by the manager
and was dichotomized as graduate degree versus less than graduate degree. Managers with
graduate education are more effective boundary spanners by virtue of their greater depth of
knowledge about organizations and health care systems. As discussed under boundary spanning,
Tushman and Scanlan (1981) theorized that effective boundary spanners have expertise that
enables them to negotiate external subsystems and to effectively communicate with and meet the
needs of internal and external users. Management training and knowledge related to financial and
human resource management are thought to influence the manager’s supervisory capacity
(Alidina & Funke-Furber, 1998; Mahon & Young, 2006). A graduate level degree serves as a
proxy for advanced organizational and leadership knowledge related to health care operations.
Educational preparation for the managerial role has been posited to influence health care
outcomes (Duffield & Franks, 2001; McGillis Hall & Donner, 1997). Managerial education level
has been associated with risk taking propensity (Smith & Friedland, 1998), greater autonomy
(Synowiez, 1987), and staff nurse motivation (Reyna, 1992).
Manager experience was the number of years’ of experience of the manager in a first-line
manager position. Managers with more first-line management experience are more effective
boundary spanners by virtue of their background experience and skills. As discussed under
boundary spanning, Tushman and Scanlan (1981) theorized that effective boundary spanners
have expertise that enables them to negotiate external subsystems and to effectively
communicate with and meet the needs of internal and external users. Management experience
and knowledge related to clinical operations, finances, and human resource management are
posited to influence managerial span (Alidina & Funke-Furber, 1998). Years of management
experience serves as a proxy for the background knowledge and practice that managers have
accumulated related to the management role.
47
Managerial experience has been associated with positive work unit climate and work relations
(i.e., work involvement, peer cohesion, and supervisory support; Englebardt, 1993); higher staff
nurse motivation (Reyna, 1992); and lower nurse turnover (Doran et al., 2004). Managerial
experience also has implications for differences in the work styles and skill levels of managers.
For example, although novice and expert head nurses allocated time similarly across work
activities, those with more than two years’ experience operated at a more controlled and calm
pace; were able to multi-task more fluidly; oversaw, rather than micro-managed unit activities;
and more effectively filtered and channelled information than their frantic novice counterparts
(Dunn & Schilder, 1993). Less experienced managers worked nearly twice the amount of
overtime as experts (Dunn & Schilder). Experienced managers have greater job fluency (Meier
& Bohte, 2003) and practice in terms of management skills, role demands, and organization
structures.
Manager position tenure was the years in the current position and served as a proxy for the
length of the relationship between the manager and staff. Managers with longer tenure in
position are more effective boundary spanners by virtue of greater opportunities to establish
working relationships with staff over time. Based on position tenure, a manager may alter the
amount of time spent in staff contact in a given area if, for example, previous investments of
time have successfully established good working relationships with staff. Unit tenure of the
manager has been associated with lower nurse turnover in hospitals (Doran et al., 2004). Position
tenure serves as a proxy for the length of the relationship between manager and staff and was
included in the study framework as a potential control variable.
Manager worked hours was the average number of hours worked by the manager daily.
Managers who work more hours per day on average have the potential to allocate more time to
staff contact than managers who work fewer hours. The amount of time in staff contact may vary
relative to the hours worked by the manager. Worked hours was included in the study framework
as a potential control variable.
Administrative support roles was the total number of full-time equivalent positions in the
manager’s assigned area(s) that enacted supervisory functions (e.g., secretary, scheduling
coordinator). Managers with more administrative support roles are more effective boundary
48
spanners by virtue of reduced supervisory demands (i.e., substitution effect) related to staffing
and scheduling activities. Subsequently, managers are assumed to reinvest their energy into other
managerial boundary spanning activities. The amount and types of work performed by managers
is partially dependent on the work performed by other roles with administrative functions
(Drach-Zahavy & Dagan, 2002; Duffield et al., 1996; Kramer et al., 2007).Variation in the
number of administrative support roles across managers has been observed in health care settings
(Altaffer, 1998; Pabst, 1993). Support roles are posited to allow for broader managerial spans
(Alidina & Funke-Furber, 1988; Altaffer; Ouchi & Dowling, 1974; Pabst; Van Fleet & Bedian,
1977) although this has yet to be linked to staff outcomes.
Clinical support roles was the total number of full-time equivalent positions in the manager’s
assigned area(s) that enacted clinical support functions (e.g., clinical nurse educator, discharge
planner). Managers with more clinical support roles are more effective boundary spanners by
virtue of reduced supervisory demands (i.e., substitution effect) related to activities such as
training, coaching, hands-on supervision and work coordination, and evaluation of staff. These
roles enhance interdependent work processes by fostering positive interactions amongst workers
across functional boundaries (Gittell, 2002a). Subsequently, managers are assumed to reinvest
their energy into other managerial boundary spanning activities. The amount and types of work
performed by managers is partially dependent on the work performed by other roles with clinical
functions (Drach-Zahavy & Dagan, 2002; Duffield et al., 1994; Kramer et al., 2007). The
number of clinical support roles tends to vary across managers in health care settings (Altaffer,
1998; McCutcheon, 2004; McCutcheon et al., 2009; Pabst, 1993). Support roles are thought to
allow for wider managerial spans (Alidina & Funke-Furber, 1988; Altaffer; Ouchi & Dowling,
1974; Pabst; Van Fleet & Bedian, 1977). Boundary spanner roles such as case managers have
been associated with improved relational coordination and patient care quality as well as reduced
lengths of hospital stay (Gittell). McCutcheon also observed that for front-line nurse managers,
an increasing number of staff resource roles (e.g., clinical nurse educators and specialists) was
modestly associated with more nurses surviving the first year on the nursing unit.
Total areas was the number of units, clinics, and services assigned to the manager. Managers
assigned fewer areas are more effective boundary spanners by virtue of reduced demands across
fewer subsystem boundaries. From a boundary spanning perspective, multiple assigned areas
49
divide the manager’s focus among production subsystems with varied relational dynamics, work
content, work processes, and physical locations. These represent increased functional and spatial
boundaries that the manager must negotiate (Gittell, 2003; Katz & Kahn, 1978). Based on
Gulick’s (1937) and Meier and Bohte’s (2003) proposed determinants of span, the grouping of
production subsystems within management positions creates variation in workplace technologies
(i.e., diversification of function) thereby increasing the demands on the manager. Managers with
fewer areas can investigate and resolve issues more easily than dealing with diverse issues across
multiple areas. The numbers of units, sites, or locations have been proposed as potential factors
influencing managerial span (Altaffer, 1998; Morash et al., 2005).
Occupational diversity referred to the number of different job titles that report directly to the
manager. Managers assigned lower occupational diversity are more effective boundary spanners
by virtue of reduced coordination and integration demands across functional boundaries. From a
boundary spanning perspective, job titles reflect functional boundaries. Managers must
coordinate work activities within and across job functions (Gittell, 2003). Diversity in staff
functions has been proposed as an influential factor on managerial span (Alidina & Funke-
Furber, 1988; Morash et al., 2005). In terms of span determinants, constant and predictable
workplace technologies (e.g., roles) allow the manager to supervise more employees (Gulick,
1937; Meier & Bohte, 2003). Although McCutcheon (2004) observed wider raw spans for front-
line nurse managers as the number of staff categories increased, no significant associations were
observed between number of staff categories and either nurse turnover or the proportion of
nurses surviving the first year on the nursing unit.
Employee tenure was the average number of years employees reporting to the manager have
worked for the organization. Managers assigned areas with greater employee tenure are more
effective boundary spanners because of reduced coordination and supervisory demands. Based
on Gulick’s (1937) and Meier and Bohte’s (2003) proposed determinants of span, staffing
stability enhances routinization of work processes because of reduced planning and coordination
demands on the manager and because workers have greater job fluency which lessen the need for
supervision (Meier & Bohte). Employee tenure serves as a proxy for staffing stability under the
manager.
50
Full-time employment was the percentage of employees reporting to the manager with full time
employment status. Managers assigned areas with higher proportions of full-time staff are more
effective boundary spanners because of reduced supervision and scheduling demands. Gulick
(1937) and Meier and Bohte (2003) suggested that stability of inputs is a key determinant of span
because it fosters routinization that reduces coordination demands on mangers. A full-time
staffing complement stabilizes the worker inputs under the manager and supports routinization.
Others also suggest that full-time employment by organizations enhances continuity of care
delivery (Grinspun, 2002). Greater continuity of care is likely to decrease coordination demands
on the manager. Research indicates that when group membership includes proportionately
greater part-time or casual staff, coordination needs are increased because the team composition
from shift-to-shift may be unstable and staff may be less familiar, less invested, and less
accountable for work group routines, standards, goals, and outputs (Kalisch & Begeny, 2005).
Roughly 40% of managers perceived that part-time employment of staff nurses resulted in
communication and information exchange difficulties with the team and co-workers (Edwards &
Robinson, 2004).
Non-direct reports was the number of employees, physicians, and medical residents routinely
working in the manager’s assigned area(s) but who did not report directly to the manager’s
position. Managers must coordinate work processes between direct and non-direct reports. From
a boundary spanning perspective, non-direct reports exemplify the functional and spatial
boundaries identified by Gittell (2003). Non-direct reports typically represent different
professions (e.g., allied health, medicine) which engender functional boundaries. The manager
may also need to travel physically within the organization because non-direct reports are not
always physically co-located in the production subsystem. Similarly in identifying space as a
determinant of span, Gulick (1937) and Meier and Bohte (2003) proposed that a lack of physical
proximity between the manager and workers can increase the supervisory demands on the
manager.
Although no studies of non-direct reports were located, research on work group size offers
indirect support for the influence of large numbers of workers on staff outcomes. Larger work
groups have been associated with lower employee ratings of supervisory competence, overall
satisfaction with the firm, unit morale, and due process (Burke, 1996) and lower quality of
51
leader-member exchange (Green et al., 1996) as well as increased employee intent to quit
(Burke), dissatisfaction (Mullen et al., 1989), and accidents and unsafe behaviours (Hechanova-
Alampay & Beehr, 2001).
Staff Level Covariates
Variations in nurse job satisfaction or in satisfaction with manager’s supervision have been
associated with age, day shift employment, and education. These level-1 nurse variables were
treated as control variables in the supervision satisfaction models. Nurse age was the age in
years of the nurse reporting to the manager. Older employees tend to report higher job
satisfaction (Blegen & Mueller, 1987; Kalleberg & Loscocco, 1983; O’Brien-Pallas, Tomblin
Murphy et al., 2005; Price & Mueller, 1986; Shields & Ward, 2001). Price and Mueller found
that other personal characteristics such as occupation, gender, full- and part-time status, tenure,
and opportunity did not explain significant variation in job satisfaction among hospital
employees.
Nurse day shift employment referred to the nurse’s regular employment on a day shift schedule
(as opposed to a rotating schedule). Day shift employment is a significant predictor of nurse job
satisfaction (Blegen & Mueller, 1987; McCutcheon, 2004; Shields & Ward, 2001). Day shift
employment may influence the amount of interaction between the manager and staff or staff
perceptions of accessibility to the manager.
Nurse education level was the highest level of nursing education achieved by the nurse.
Education level has been negatively associated with nurse job satisfaction (O’Brien-Pallas,
Tomblin Murphy et al., 2005; Shields & Ward, 2001). With respect to satisfaction with
supervisor specifically, Ward (2002) also reported variation by educational level but not by age,
gender, or race.
Nurse registration was the registration status of the nurse as a Registered Nurse or Registered
Practical Nurse. Registered Nurses have tended to report lower job satisfaction than Registered
Practical Nurses in Canada (O'Brien-Pallas, Tomblin Murphy et al., 2004).
Variation in teamwork was considered in relation to occupation and employment status of the
team member. These level-1 staff variables were treated as control variables in the teamwork
52
models. Occupation was the current occupation of the staff respondent which was categorized as
nursing (inclusive of registered practical nurses and registered nurses in staff and in advance
practice roles), other regulated health professional, or as unregulated care provider. Research
indicates that team members from different occupations tend to perceive the quality of teamwork
differently (Manser, 2009).
Full-time status was the employment status of the team member as full-time employee versus
other status (e.g., part-time or casual). Full-time employees coordinate work processes and
manage relationships on an ongoing basis. Full-time employees may take on the burden of care
continuity and thus may perceive greater challenges to teamwork. For example, part-time nursing
staff has been associated with communication and information exchange difficulties with the
team and co-workers (Edwards & Robinson, 2004), and part-time nurses are likely to rate
teamwork more highly (Kalisch & Lee, 2009).
Chapter 3 details the study design, data collection processes, instrumentation, data analyses, and
knowledge translation approach.
53
Chapter 3: Method
Design
This chapter begins by addressing study design considerations. These include an overview of the
design and data collection process, a discussion of power, settings, and sample sizes, and a
detailed description of the data collection procedures, including ethical considerations. Next, the
derivation of some of the study variables from administrative and managerial work log data is
explained. The established instruments utilized in the study are also examined. The approach to
data analysis is subsequently documented in regards to data preparation, levels of analysis,
analytical techniques, and the study objectives. Finally, knowledge translation processes are
considered.
Design and Data Collection Overview
A descriptive, correlational design with cross-sectional and longitudinal components was used to
collect survey and administrative data. Cross-sectional survey data were collected for leadership
practices, managerial job characteristics, supervision satisfaction, and teamwork. Data on raw
span, time allocation, and administrative data related to direct reports were collected
longitudinally.
Data collection was a phased process and data were collected from multiple sources (Table 4).
The separation of data collection for predictor and outcome variables, both temporally (i.e.,
Phases I and II) and methodologically (i.e., from different sources in different places using
different methods), helped to reduce common method bias (Podsakoff et al., 2003). Pilot work
was done to pre-test the work log method and to establish the metrics related to time allocation.
The pre-test assessed the feasibility of the work log procedures and classification system. The
researcher shadowed the managers and checked inter-observer agreement during the pre-test.
Phase I entailed a survey of managers related to demographics (i.e., age, occupation, education,
experience, tenure), job characteristics (i.e., number of areas, support roles, budget), and self-
reported leadership practices.
During Phase II, managers self-reported time allocation in work logs and tracked their worked
hours. As managers work logged, the researcher also completed inter-observer agreement
54
ratings. In addition, a subset of managers was shadowed by the manager to observe managerial
work flow. Staff surveys were collected during Phase II. All staff surveys included demographics
(e.g., age, occupation, education, employment status, tenure, day shift employment). Direct
report nurses completed a measure of supervision satisfaction. A separate subset of nurses and
directors completed a measure of the manager’s leadership practices. Nurses and other health
care providers working in the manager’s assigned area completed the teamwork measure. The
human resource departments provided administrative data (i.e., area, job title, date of hire, year
of birth, and employment status) about managers’ direct reports for three consecutive monthly
data points. Data on hours of operation and number and types of non-direct reports were
collected from the managers by the researcher. Table 4. Data Collection Flow Chart Phase Source Managers Employees Human Resource
Department Researcher
Pre-test • pre-test of work logs • observation of
managers • inter-observer
agreement Phase I • survey
• leadership practices (LPI-self)
Phase II • work logs of time in
staff contact • worked hours
• supervision satisfaction (SWMSS)
• leadership practices (LPI-other)
• teamwork (RCS) • demographics
• administrative data about direct reports
• inter-observer agreement
• observation of subset of managers
• hours of operation & non-direct reports
Note. LPI = Leadership Practices Inventory; SWMSS = Satisfaction with My Supervisor Scale; RCS = Relational Coordination Scale
The pre-test began in February 2007 with recruitment of first-line managers at the 4 participating
organizations occurring between May 2007 and February 2008. Phases I and II were completed
between June 2007 and June 2008. Data analysis finished in June 2009.
Power
Hierarchical linear modeling (HLM) is a relatively recent statistical technique. Although
increasing attention has been paid to the assumptions and applications of HLM, at the time this
study was proposed limited guidance was available for determining sample sizes needed to
achieve power, particularly in two level and three level models with more than one outcome
(Raudenbush & Bryk, 2002). In a review of two key HLM sampling studies, Kreft and De
55
Leeuw (1998) recommended a sample of at least 30 groups with 30 observations per group for
assessing cross-level interactions. Overall, collection of data from many groups, as opposed to
from many individuals, is preferable for detecting cross-level interaction effects, and as the
number of higher level units increases, fewer lower level units are needed (Kreft & De Leeuw).
This thesis proposed to examine interaction effects within the same analytical level, but not
between levels (i.e., cross-level interaction effects). To achieve power in this study, a minimum
sample of 30 managers was targeted. For cross-level interactions, Kreft and De Leeuw’s
guidelines indicated that fewer than 30 lower level units (i.e., staff respondents) were needed
given 30 upper level units (i.e., managers). However, the exact number of lower level units (i.e.,
less than 30) could not be determined. Target samples for outcome measures were set at 15
nurses for supervision satisfaction (target n = 450) and 15 nurses plus up to 5 other health care
providers for teamwork (target n = 600) where possible.
Setting and Sample
Hospitals. Acute care hospitals in a large urban city were selected through purposive sampling.
Of 6 hospitals invited to participate in the study, 4 (66.6%) agreed. The non-participating
organizations were also located in urban cities; one was an academic teaching hospital, the other
was a large academic-affiliated community hospital. Non-participating organizations indicated
that planned changes to the scope of first-line management positions were imminent. Three of
the participating hospitals were academic teaching hospitals and one was a large academic-
affiliated community hospital. Two hospitals were multi-site organizations.
Managers. A convenience sample of managers was recruited from managers of patient care
units, clinics, and services within the participating hospitals who met the inclusion criteria.
Inclusion criteria for managers included: (a) first-line management position (i.e., direct reports
were non-management, other than assistant managers), (b) employment in current position for at
least 3 months, and (c) management of at least one area where health care providers directly
deliver patient care services. The 3 month parameter was specified to reflect, at least in part, the
current manager’s influence on employee supervision satisfaction and teamwork (as opposed to
the previous manager’s influence).
56
Across the 4 participating organizations, from approximately 105 first-line management
positions, 35 (33.3%) managers agreed to participate. Of the 35 managers who consented to
participate, 31 (88.6%) completed the study. Managers who did not complete the study reported
insufficient time available in their work week to participate or misplaced work logs. The 31
managers were assigned 81 areas in total. Nurse satisfaction with manager’s supervision was
surveyed among 31 managers in 51 (63%) areas. Teamwork was surveyed among 30 managers
in 54 (67%) areas. Reasons for excluding assigned areas included: no nurses or direct reports
were employed in the area, or patient care was not delivered in a locally situated work group
context (e.g., hospital wide service).
Employees. A convenience sample of employees was recruited in each participating area where
outcomes could be collected. The inclusion criterion for staff members was employment by the
organization in the current position for at least 3 months. Agency staff and students were
excluded. Although the study planned to sample staff physicians to complete the measure of
teamwork, difficulties in accessing physicians across the participating sites resulted in this group
being excluded as well.
An examination of the administrative raw span data supplied by human resource departments
and data on non-direct reports supplied by managers provides an estimate of the total number of
potential staff participants working in the managers’ areas. However these estimates do not
exclude employees with less than 3 months experience in their current position. For supervision
satisfaction, of the estimated 1,786 nurses working under the study managers in areas where the
survey was administered (excluding the LPI-other subset described below), 31.2% completed
surveys (n = 558). The mean response rate per manager was 33.6% (range: 10.8 - 57.7%). For
teamwork, the estimated total number of potential staff participants included direct and non-
direct report nurses, allied health professionals, and other care providers working in the areas
assigned to managers where the survey was administered. Of the estimated 2,484 health care
providers, 30.4% completed teamwork surveys (n = 754). The mean response rate per manager
was 35% (range: 11.8 - 72.9%). Staff members who did not participate indicated that they were
too busy, did not wish to participate in research, or feared their employment would be placed at
risk. The final sample sizes numbered 558 for supervision satisfaction and 754 for teamwork.
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A sub-sample of nurses as well as managers’ directors were recruited to complete ratings of
managerial leadership practices (LPI-other). The inclusion criterion for staff members was
employment by the organization in the current position for at least 3 months. The proposed target
sample was set at 9 observers which included the manager’s supervisor, one peer manager, and
seven randomly selected nursing staff across the manager’s areas. This target was higher than
other studies using subordinates to rate managers’ leadership styles in hospital (n = 3; Houser,
2003) and bank (n = 2; Schriesheim et al., 2000) settings. Due to difficulty recruiting managers
and the data collection demands imposed on participating managers, peer managers were not
recruited. Staff nurses who completed the LPI-other were excluded from completing the outcome
measures to reduce common method variance (Podsakoff & Organ, 1986). A subset of nurses,
rather than a subset of employees from other disciplines, was chosen to complete the LPI-other,
because nurses are typically the largest disciplinary cohort. This allowed other health care
professionals, who are typically fewer in number, to complete the outcome measure of
teamwork. Two to 7 LPI-other surveys were submitted for each manager (M = 3.8). Of the final
117 LPI-other instruments completed, 97% were rated by nurses.
Data Collection Procedures
Upon defense of the thesis proposal, this study was submitted to the participating hospitals and
the University of Toronto for ethical review prior to data collection. Each participating hospital
approved the research protocol. Ethical approval for data storage at the Nursing Health Services
Research Unit was received from the University of Toronto. Unless otherwise noted, all data
collection forms will be stored for 7 years in the Nursing Health Services Research Unit’s locked
data storage unit and then destroyed.
Managers were recruited by the researcher through manager staff meetings and third party email
distributions. Refreshments were provided at the meetings. The research protocol and nature of
the study were reviewed with managers prior to data collection. A letter of introduction attached
to the Phase I manager survey package highlighted the objectives and background of the study,
participants’ rights, and the confidentiality of both the respondents themselves and the data
(Appendix B). Managers who agreed to participate in the study completed a written consent form
(Appendix B) and were briefly oriented to the work log procedure by the researcher (e.g., less
58
than 30 minute session). A nominal, weekly thank you token (e.g., coffee coupon) was provided
to managers who completed work log data collection on a weekly basis during Phase II.
A 6 digit study number assigned to each manager was recorded on the manager survey and work
logs. Each area assigned to the manager was also given a study code. These codes were used to
link manager-level data with staff-level data without recording the manager’s name or area on
the surveys to enable data collection and analyses of the nested data set. The 6 digit study
number, names, and assigned areas for each manager were recorded in a confidential master log
that was kept and available only to the research team. Only code numbers appeared in the data
files. The master log was stored in a password protected file on a password protected server in a
locked office within the locked Nursing Health Services Research Unit until the final defense of
the thesis.
Given that work log data were self-recorded, managers were asked to carry and store the work
logs securely to ensure their confidentiality. Managers faxed the work logs to the Nursing Health
Services Research Unit on a weekly basis. This allowed the researcher to clarify work log entries
as needed with the manager, to monitor manager participation, and to distribute the weekly thank
you token for work log completion. The researcher was periodically on-site to collect the work
logs and worked hours data from the manager.
Employees were recruited by the researcher or research assistant through staff meetings or
information sessions and refreshments were provided. To minimize possible disruptions to care
delivery, staff recruitment sessions were offered in areas based on work levels (as determined by
attending morning bed meetings or by communicating with staffing coordinators or team leaders)
and on recruitment targets. For the sub-sample of staff completing the LPI-other, nurses were
recruited during staff information sessions in the participating areas and directors were recruited
via third party email. At staff recruitment sessions, the nature of the study was reviewed with
employees. Employees who indicated an interest in participating in the study received an
information and consent sheet attached to the Phase II employee survey package which
highlighted the objectives and background of the study, participants’ rights, and the assurance of
confidentiality of both the respondents themselves and the data (Appendix C). Submission of the
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completed employee survey to the research team indicated that the employee had read,
understood, and consented to participate in the study.
Staff surveys were distributed, completed, and collected during the recruitment session or on the
same day to ensure that surveys were correctly coded as follows. Staff participants identified
their area and manager to the researcher who noted this on a confidential, numbered list that
corresponded to the survey numbers. Once returned, the researcher discretely transcribed the
manager and area codes onto the survey which was then stored at the Nursing Health Services
Research Unit.
Administrative data provided by the human resource departments excluded employee names.
Once received, the electronic files were further anonymized using the manager and area study
codes and were password protected. These anonymized files were stored on the password
protected server within the locked Nursing Health Services Research Unit or on the researcher’s
password protected laptop computer.
Risk and Benefits
Voluntary participation, amount of risk, anticipated benefits, and assurance of confidentiality
were discussed with managers and employees. Opportunities to ask questions were available. All
managers and employees were made aware of their right to withdraw from the study at any time
without penalty. Subjects were assured that participation in research was voluntary and that they
could refuse to answer any question(s) or to stop responding to the survey or withdraw from the
study at any time. Potential participants were informed that although there were no direct
benefits related to their participation, the study would contribute to an understanding how
organizations and policy makers can optimize the work of managers in the hospital system.
Managers were informed that, beyond the period of time required to complete the survey,
orientation and work logs, there may be minimal discomfort associated with being shadowed by
the researcher and that there may be employment risks associated with participation (e.g., in the
event that the results of individual managers were to become known). Further by agreeing to
participate, the manager was allowing the research team to ask (a) nurses, a peer manager and
their director to consider completing a questionnaire about their leadership practices, and (b)
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staff in the units, clinics and services that they manage to consider completing questionnaires of
nurse satisfaction with manager and teamwork. Managers were advised that the questionnaires
about leadership behaviours, nurse satisfaction with the manager’s supervision, and teamwork
may lead the respondents to reflect on issues they might not have otherwise.
Staff members were also informed that beyond the period of time required to complete the
survey, there were no known risks associated with their participation in this study. However, one
hospital also required employment risk to be declared in the event that the results of the
individual staff surveys were to become known.
All participants were informed that the research team would do everything possible to maintain
the confidentiality of all surveys. Only the research team would have access to the surveys and
raw data. No participant would be given access to the surveys of other participants (e.g.,
directors would not have access to the surveys of other staff). All findings would be rolled up to
group, hospital, or study levels to protect against identification. For example, if only 3 palliative
care units participated, the findings from these units would be rolled up into the results for
medical units. All paper surveys were stored at the Nursing Health Services Research Unit.
Administrative Data
During Phase II, anonymized data about direct report employees assigned to study managers
were extracted by the human resource department of each participating organization and
provided to the researcher in Excel spreadsheets. The data elements provided at a case level for
each direct report employee and extracted at 3 consecutive monthly time points were: area, job
title, date of hire, year of birth, and employment status. These data were used by the researcher to
derive an average mean (based on the 3 data collection time points) for the following manager-
level study variables: raw span, occupational diversity, employee tenure, and full-time
employment. The monthly counts of direct report employees stratified by area, job title, and
employment status were verified by participating managers. Two managers identified
discrepancies in that allied health professionals (n = 12) who formerly reported to the director
level now reported to their position. Thus nearly complete case level data were received (99.6%).
The area, job title, and employment status for the missing direct reports were obtained from the
managers and subsequently added to the data set.
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Using the case level data provided by the human resource departments, raw span was calculated
as the monthly average of the number of direct report employees (by headcount) assigned to the
manager. Occupational diversity was calculated as the monthly average of the number of job
titles reporting directly to the manager. For employee tenure, the difference (in years) between
the date of hire and the date of data extraction was calculated and then used to compute the
monthly average of the organizational tenure (in years) of all direct report employees assigned to
the manager. Full-time employment was the monthly average of the percentage of full-time
direct report employees (by headcount) assigned to the manager. The percentage was derived by
dividing the number of full-time direct report employees by the total number of direct report
employees assigned to the manager.
The researcher also asked managers to identify the number of non-direct reports by job title (by
headcount). This variable was calculated as the sum of the number of physicians, medical
students, and other staff (e.g., allied health) not reporting directly to the manager but who
routinely worked in the manager’s assigned areas. Where non-direct reports rotated through the
assigned area(s), the estimated average number at any one time was used. That is, this was a
cross-sectional count, not a cumulative count. In some areas, a float pool service provided
coverage on an as-needed basis (e.g., respiratory therapy for intensive care units) and this staff
was excluded from the count of non-direct reports.
Hours of operation were obtained by the researcher from the managers. A measure of weekly
operational hours weighted by the number of direct reports per assigned area was created at the
manager level. Hours of operation was calculated as:
∑manager number of direct reports per area * number of hours of operation weekly per area
total number of direct reports
The weighted value was then classified as extended (i.e., 24 hours per day, 7 days a week with
less than 3.5% of direct reports working in a compressed area), compressed (<106 hours per
week), or mixed (i.e., combination of extended and compressed hours of operation assigned to
the manager with more than 3.5% of direct reports working in an area with compressed hours of
operation).
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Also, two covariates initially proposed in the study framework were subsequently excluded due
to unavailability of data. These variables measured variation in patient case mix groups in the
manager’s assigned area(s) and the percent change in the budget assigned to the manager
between the previous and current fiscal year. These covariates would have served as proxies for
the stability of inputs which is a key determinant of raw span proposed by Gulick (1937) and
Meier and Bohte (2003).
Managerial Work Logs
A time study measurement technique was used to quantify the time allocation of managers for a
one month cycle of work activity. Prospective, self-reported work logs were completed by
managers and were supplemented by inter-observer agreement. The work logging method and
the classification system were pre-tested and this process is detailed in Appendix D.
Measures of time allocation and the associated metrics were piloted and this work is described in
Appendix D. Time in staff contact was the final measure of time allocation. Time in staff contact
was the average daily amount of time (in hours) spent by the manager interacting with direct and
non-direct report staff, physicians, and students working in the area(s) assigned to the manager.
Contact included verbal, written, and email communication and person-to-person interaction.
Time in staff contact was calculated as the sum of the daily total hours in staff contact divided by
the total number of work log days.
During Phase II, managers self-reported time spent in staff contact using the work logs. The
work logging time frame for this study was determined based on the availability of resources, the
data collection burden placed on managers, and the cyclical peaks in managerial activity. Ideally,
measurement of managerial time allocation would be stratified across a one year period to
capture seasonal variation in the work performed (e.g., performance review deadlines) in order to
improve accuracy. However, a 12 month sampling time frame was beyond the resources of this
thesis and would have placed a significant burden on managers. Instead the time frame chosen
for the study reflected a one month cycle of work activity.
Given that managers typically work weekdays only, a one month cycle consists of an average of
21.7 weekdays per month (i.e., 52 weeks x 5 weekdays / 12 months = 21.7). The target number
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of work log days was set at 20 days. Twenty data collection days allowed for four weekdays each
to be sampled (i.e., four Mondays, four Tuesdays, etc.). To minimize fatigue and improve
response rates, Freda et al. (1998) recommend a maximum of two work logging days per week.
Therefore, managers were asked to complete the work logs for 2 days a week for 10 weeks. A
random stratified sample of weekdays for each manager was determined by the researcher so that
managers would not self-select heavier or lighter days in terms of staff contact thereby biasing
the work log data. Half hourly entries were used to minimize recall bias (Freda et al.) and work
flow interruptions. For each half hourly log entry, managers recorded the number of minutes
spent in staff contact. In terms of work log completion, the mean number of work log days per
manager was 18.7 (range: 13 - 20) with 71% of managers completing 19 or more work log days.
This generated a total of 578 manager work log days.
During Phase II, the researcher contacted the manager by phone, email, or in-person to answer
questions, to provide support regarding the work logging procedure, and to clarify weekly
submissions and work log entries as needed. Inter-observer agreement of the work log data was
also evaluated for each manager. The manager and the researcher completed the inter-observer
agreement ratings for 2 half days on days when the manager was assigned to work log; once near
the beginning and once near the mid-point of the manager’s work log period. Cohen’s kappa,
which factors in chance agreement, was used to assess inter-observer agreement (Norman &
Streiner, 2000). The manager and the researcher rated the presence or absence of time in staff
contact at half hourly intervals. Managers often engaged in multiple activities and interactions
during an observation interval. Disagreement on any one activity or interaction during the half
hour period resulted in the interval being coded as disagreement (i.e., agree/disagree or
disagree/agree). Based on Cicchetti’s (1981) guideline, for kappa sample sizes with two
categories (e.g., agree/disagree) approximately 16 observations are sufficient to estimate this
parameter. The mean number of half hourly observations per manager was 12.7 (range: 8 - 19).
Cohen’s Kappa averaged 0.82 (range: 0.43 - 1.00).
To supplement the work log data, a sub-sample of managers was observed for an additional day
to provide the researcher with an opportunity to understand the manager's workflow. The sub-
sample of managers had raw spans of less than 60 (n = 3), 60 to 100 (n = 3), and 100 to 175 (n =
3).
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During Phase II, managers also self-reported the hours they worked on a daily basis for 10
weeks. Worked hours was the average number of hours worked by the manager per weekday.
Overtime hours were recorded also and included in the count for weekday hours. On-call hours
and lunch breaks were excluded. Vacation days were also excluded. If the manager worked on a
weekend, those weekend hours were divided by the number of weekdays worked for that
particular week and added to the weekday values. Worked hours was calculated as the sum of the
worked hours per weekday divided by the number of valid weekday entries. On average,
managers recorded worked hours for 34 weekdays (range: 12 - 50).
Instrumentation
Leadership Practices Inventory
The Leadership Practices Inventory (LPI) is a 30 item instrument rated using a ten point scale
that can be completed by either or both the manager (LPI-Self version) and other persons
familiar with the manager’s behavior (LPI-Other version; Posner & Kouzes, 1988). Both the self
and other versions of the LPI were used in this study. The LPI was initially derived from
qualitative case studies (n = 650) and in-depth interviews (n = 38) of managers’ personal best
experiences in leading a project (Posner & Kouzes). The qualitative data were subjected to
content analysis and validation by external raters (Posner & Kouzes). The five fundamental
leadership practices which emerged were: challenging the process, inspiring shared vision,
enabling others to act, modeling the way, and encouraging the heart (Posner & Kouzes). Each
leadership practice entailed two basic strategies. Challenging the process involved searching for
opportunities and experimenting and taking risks. Managers who inspired a shared vision were
able to envision the future and enlist the support of others. Those who enabled others to act both
fostered collaboration and strengthened others. By modeling the way, managers set the example
and planned small wins. Managers who encouraged the heart recognized contributions and
celebrated accomplishments. These five leadership practices accounted for 80% of the behaviors
and strategies described in the qualitative data (Posner & Kouzes).
The LPI scale was subsequently developed using iterative feedback from respondents, including
over 2,100 managers and their employees, and factor analyses of sets of behaviorally-based
statements (Posner & Kouzes, 1988). This was supported by two validation studies. The first
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study involved 708 managers and 2,168 of their employees (Posner & Kouzes, 1988). The
second study included 5,298 managers and 30,913 of their subordinates (Posner & Kouzes,
1993).
Internal reliabilities of the LPI ranged from .70 to .90 in 1988 and from .80 to .91 in 1993
(Posner & Kouzes, 1988). LPI-self reliabilities ranged from .70 to .84 in 1988 and from .70 to
.85 in 1993 (Posner & Kouzes, 1993). LPI-other reliabilities ranged from .81 to .91 in 1988 and
from .81 to .92 in 1993 (Posner & Kouzes, 1988, 1993). However, use of the LPI-self with nurse
managers in an Ontario study resulted in Cronbach alpha values lower than .70 for two of the
leadership practices (Tourangeau, Lemonde, Luba, Dakers & Alksnis, 2003). Test-retest
reliabilities averaged approximately .94 in both the 1988 and 1993 studies (Posner & Kouzes,
1988, 1993). Social desirability response bias tests using the Marlowe-Crowne Personal Reaction
Inventory demonstrated no statistically significant correlations in a sample of 30 managers
(Posner & Kouzes, 1988). Factor analysis has consistently revealed 5 factors with eigenvalues
greater than 1.0 which explained 59.9% to 60.2% of the variance (Posner & Kouzes, 1988,
1993). When ratings by managers to the ratings of others were compared, LPI-self scores were
likely to be higher than LPI-other scores (p < .001), although the relative rank ordering of the
practices was identical by both managers and others in both studies (Posner & Kouzes, 1988,
1993). Research on the LPI indicates little variation in leadership behaviors among managers
related to demographic (e.g., age, marital status, experience, education) and organizational
characteristics (e.g., size; Posner, 2002).
For this study, exploratory factor analysis of the LPI was planned, and if more than one factor
(i.e., subscale) was observed, then confirmatory factor analysis (Norman & Streiner, 2000)
would be used to test a global score of leadership. Previous research that conducted confirmatory
factor analysis demonstrated that the LPI assessed an over-arching construct of transformational
leadership (Carless, 2001).
Nurse Satisfaction with Manager’s Supervision
Job satisfaction represents an employee’s affective reaction or cognitive appraisal of the job and
work context based on beliefs, feelings, and behavioral intentions (Fields, 2002; McShane,
2009). Facet, global, and composite measures are commonly employed to assess job satisfaction.
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Selection of a job satisfaction scale should be based on the purpose of the measurement and the
specificity of the criterion (Ironson, Smith, Brannick, Gibson & Paul, 1989; Smith, 1976)
because facet, global, and composite measures are not necessarily equivalent (Ironson et al.;
Jackson & Corr, 2002; Rice, McFarlin & Gentile, 1991).
Global and composite scales of job satisfaction are intended to represent general, overall
reactions to the job (Ironson et al., 1989). These general measures are often used as an index of
organizational effectiveness (Ironson et al.). Global measures, which elicit a single, integrated
response, assume that an individual identifies, processes, and consolidates various aspects of the
job (Ironson et al.). An advantage of global scales is that the individual is free to select those
aspects of the job that are most relevant and important, thereby allowing individuals to view the
situation in their own unique way (Ironson et al.). Composite measures, which are constructed
using single items to represent each component of job satisfaction, assume that “the whole is
equal to the sum of its principal parts” (Ironson et al., p. 194). In a comparison of global, facet,
and composite scales, Ironson et al. found that composite measures represent a unitary construct
and differ from facet and global measures.
Facet scales assess employee reactions to separate, homogeneous aspects of the job. Although
mutually exclusive, facets tend to be intercorrelated (Ironson et al., 1989). Facet measures are
useful for distinguishing various aspects of job satisfaction, and can, for example, assist
organizations in identifying areas for improvement (Ironson et al.). However, facet measures
may underestimate job satisfaction by excluding important, or by including unimportant, aspects
of the concept (Ironson et al.). Linear addition of facets may also inaccurately reflect the
individual’s perceptions or weighting of various aspects of job satisfaction (Ironson et al.).
Respondents are likely to apply different cognitive heuristics to facet scales as compared to
global scales, leading to the non-equivalence of these different types of scales (Jackson & Corr,
2002; Rice et al., 1991).
Given that the main focus of the dissertation is managerial time spent in staff contact, a single
facet scale, the Satisfaction with My Supervisor Scale (SWMSS; Scarpello &Vandenberg, 1987)
was utilized. Nurses’ perceptions of the technical, administrative, and relational abilities of the
manager with respect to supervision were assessed. The SWMSS is an 18 item instrument rated
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using a 5 point Likert scale that is completed by subordinates of the supervisor (Scarpello &
Vandenberg). The SWMSS was initially derived from focus groups with supervisors and
employees (n = 308) in a U.S. manufacturing plant. Of those factors identified by focus group
participants as contributing to job satisfaction, 23 items specific to the supervisor were used to
construct preliminary scale items. Conceptually, the scale reflects the three areas of supervisory
skill outlined by Mann (1965) which involve technical, human relations, and administrative
skills.
These preliminary items were subsequently embedded in a larger 180 item multi-facet scale of
job satisfaction (Satisfaction with the Quality of Employment Survey) along with 2 composite
items of supervisory satisfaction from the short-form Minnesota Satisfaction Questionnaire
(MSQ) and 2 global items each for general job satisfaction and general supervisory satisfaction
(Scarpello & Vandenberg, 1987). Over a 3 year period, data were collected from 2,101
employees in 7 U.S. manufacturing plants. Factor analyses across plants revealed a 2 factor
solution with the 2 composite items from the MSQ loading on the first factor. Predictive power
of the scale was assessed by regressing the items on the global item of supervisory satisfaction
and the 2 MSQ composite items and 5 of the preliminary SWMSS items were dropped, resulting
in an 18 item scale. Coefficient alpha across the plants ranged from .95 to .96. Convergent and
discriminant validity were demonstrated by assessing SWMSS items against 6 global measures
of various job satisfaction facets. Predictive and content validity were successfully evaluated by
regressing global supervisory satisfaction on the SWMSS items. The 18 item SWMSS was
subsequently tested with 1,104 employees of a U.S. insurance company and explained 85% of
the variance in supervisory satisfaction (p < .001; Scarpello & Vandenberg).
Although a two factor solution was obtained across all plants and the insurance company,
Scarpello and Vandenberg (1987) proposed that this reflected a primary general factor
accompanied by a lesser secondary factor which supported the integrated nature of the
supervisor’s technical, human relations, and administrative skills. Furthermore, in a repeated test
at one of the plants, adding the word ‘supervisor’ to the second factor items (which did not
contain the word supervisor unlike the items loading on the primary factor) resulted in a single-
factor solution.
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Later validation of the scale among management information systems professionals (n = 100)
resulted in coefficient alphas of .95 and test-retest reliability of .78 with two administrations of
the scale at a 5 month interval (Vandenberg & Scarpello, 1992). Convergent validity of the scale
was supported. The SWMSS also differed from measures of departmental, organizational, and
occupational commitment supporting discriminant validity. In a study of over 600 government
employees, Cronbach’s alpha for the SWMSS was .88 (Jones, Scarpello & Bergmann, 1999).
Relational Coordination Scale
Teamwork was measured using the Relational Coordination Survey for General Health Care
Setting. This 7-item instrument by Gittell (2006) appraises healthcare providers’ perceptions of
the quality of communication and the extent to which goals, knowledge, and respect are shared
amongst team members. Self-report bias is minimized by having respondents rate how well other
group members communicate with them (rather than how well the respondent communicates
with others). As noted by Podsakoff and Organ (1986), the tendency toward self-attribution
among respondents can mean that ratings about self are likely to be more favorable than ratings
about others or external factors. Frequency, timeliness, accuracy, and problem-solving
orientation of communication are rated, as well as the levels of shared knowledge and goals and
mutual respect (Gittell). Respondents rate each type of care provider on the team using a 5-point
scale. Cronbach’s alpha ranged from .72 to .84 for the individual dimensions and was .85 for the
overall index in a study of relational coordination in health care teams (Gittell et al., 2000).
Data Analyses
Data Entry and Cleaning
Data were entered using double data entry to minimize errors and were analyzed using the
Statistical Package for the Social Sciences (SPSS) version 16.0. The univariate frequency
distributions of variables to be included in the models were examined. The variables were fairly
normally distributed. Outliers for predictor variables were identified by box plots and by
examining the distribution of z-scores. Z-scores that exceeded three standard deviations from the
mean were treated as outliers (Norman & Streiner, 2000). For worked hours, total areas,
administrative support roles, and non-direct reports, identified outliers were reduced (or
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increased) to the value of the nearest score plus one unit (Tabachnick & Fidell, 1996). Missing
data for nurse day shift (0.5%), nurse education (0.5%), and nurse employment status (0.7%)
were assigned an average unit value. Missing values for nurse age (10%) were imputed by
regressing years in occupation on nurse age. Reliability and factor analyses of the instruments
were conducted.
Levels of Analysis
In this study, employees were nested within defined areas (e.g., units, clinics, labs, or services)
which in turn, were nested under managers. Although a three-level hierarchical linear model was
anticipated, nearly one-third of the managers in the sample had only one area in which outcomes
could be collected. Because variation in outcomes between areas could not be examined for these
managers, area could not be treated as a separate level in the models. Therefore, only two-level
models were tested with individual employees (level-1) nested under managers (level-2). For this
reason, one of the covariates initially proposed as a unit level variable in the study framework
was subsequently excluded. Distance between the manager’s office and each assigned area was
proposed as a proxy for space, one of the key determinants of raw span proposed by Gulick
(1937) and Meier and Bohte (2003). This measure could not be meaningfully aggregated to the
manager level and was therefore omitted.
The two alternative measures of managerial span, leadership, and hours of operation were level-2
predictors. The goal was to examine the main effects of level-2 managerial predictors on level-1
staff outcome variables. Hierarchical linear modeling (HLM) was used to analyse the nested data
set because it permits the simultaneous examination of relationships between and across
hierarchical levels (Raudenbush & Bryk, 2002). HLM resolves problems of analysis such as
aggregation bias, misestimated precision, and heterogeneity of regression which emerge when
the nested structure of the data set is ignored (Raudenbush & Bryk). Aggregation bias occurs if
the meaning or effect of a variable is assumed to be the same when measured at different
organizational levels (Raudenbush & Bryk). Misestimated precision results when dependence
between individual responses in clusters is not taken into account, and is resolved through HLM
by incorporating a unique random effect for each cluster (Raudenbush & Bryk). Heterogeneity of
regression is observed when relationships between individual characteristics and outcomes vary
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across organizations (Raudenbush & Bryk). This difficulty is addressed through HLM by
estimating a separate set of regression coefficients for each cluster and then modeling the
variation among the clusters in their sets of coefficients as multivariate outcomes to be explained
by organizational factors (Raudenbush & Bryk).
Study Objectives
The study objectives included an examination of main effects, interaction effects, and amount of
variance explained. The analytical approach for each objective is explored below. In addition, the
treatment of covariates included in the analytical framework is considered.
For study objective 1, the main effects of the alternative measures of managerial span,
leadership, and hours of operation on both study outcomes were examined. The analytical
strategy for main effects typically follows a four step process (Hoffman, 1997; Raudenbush &
Bryk, 2002): (a) one-way analysis of variance (ANOVA), (b) random coefficient regression
model, (c) intercepts-as-outcomes model, and (d) slopes-as-outcomes model. In this thesis, the
first three steps were conducted to meet the study objectives. First, an ANOVA was conducted to
assess systematic within and between group variance in the outcome (Hoffman). Second, a
random coefficient regression model was conducted to determine if significant variance is
observed in the intercepts and slopes across groups (Hoffman). Assuming significant variance
was observed in the intercept term in the second step, the third step was to conduct an intercepts-
as-outcomes model to examine whether variance in the level-1 outcome was significantly related
to the level-2 predictor (Hoffman). Step 4, the slopes-as-outcomes model, was not conducted. A
slopes-as-outcome model determines whether the level-1 predictor (i.e., slope) varies in relation
to a level-2 predictor (Hoffman; Raudenbush & Bryk). Essentially, a slopes-as-outcome model
assesses whether a level-2 predictor moderates the relationship between a level-1 predictor and
the level-1 outcome. This model is appropriate for testing cross-level interactions. Step 4 was not
necessary to answer the study objectives.
For study objective 2, the three-way interaction effects between each alternative measure of
managerial span (i.e., raw span, time in staff contact) with leadership and hours of operation
were also examined. These interactions were among variables from the same level of analysis
(i.e., these interactions were not cross-level interactions). Testing of a three-way interaction first
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involves a test of the main effects of the predictors on the outcomes, then a test of the products of
each pair of predictors on the outcome (i.e., two-way interactions), followed by the three-way
interaction (Aiken & West, 1991). All continuous variables were mean-centered prior to forming
cross-product terms to minimize collinearity between the main effects and cross-product terms
(Aiken & West). Categorical variables were dummy coded. The two-way and three-way
interaction terms were added to step 3, the intercepts-as-outcomes model, as described for
objective 1.
For study objective 3, to determine the extent to which the alternative measures of managerial
span explained variation in outcomes for nurses and teams, the main and interaction effects
models with significant effects for raw span or time allocation were compared to the
unconditional model for each outcome. For each outcome, the alternative measure of managerial
span in the model that most reduced the between-manager variance was determined to explain
more variation in the outcome.
The study framework also proposed other level-2 covariates to reflect characteristics of the
managerial role. Covariates consist of confounding, moderating, and control variables. A
confounding variable must affect variation in both the predictor and outcome variables.
Confounding and control variables were entered as covariates. A variable that affects only
variation in the outcome variable, but not the predictor variable, is a moderator. First-order
moderator variables were analyzed as interaction terms.
Knowledge Translation Plan
Engaging with key policy players at an early stage in the doctoral endeavor (and hence, early in
the researcher’s career) helps to build a relational foundation to facilitate the processes of
increasing public awareness, engaging political support, and activating interest groups. Effective
policy-research linkages are prerequisite to supporting success in each of these steps (Lomas,
1997; Shamian, Skelton-Green & Villeneuve, 2002). Fortunately, as a doctoral fellow at the
Nursing Health Services Research Unit, I was actively involved with policy makers. For
example, as a student intern at the Dorothy M. Wylie Nursing Leadership Institute the doctoral
thesis took on particular focus as senior management nurses identified a need to measure and
optimize managerial span. Indeed, Lomas argued these linkages should be part of scientific
72
training and are necessary to shaping the entire research process to effectively define, design,
conduct, and disseminate policy-relevant research. Lomas noted that engaging policy makers in
the entire research process fosters their investment in the research question and process,
outcomes, and uptake of findings. This approach has been adopted by the Canadian Health
Services Research Foundation (2005) which encourages policy makers to be involved in all
stages of the research process. Consistent with this approach, a decision-maker who is a Chief
Nurse Officer and actively involved in several professional associations was a thesis committee
member. Her input into the research process and advice on how to tailor messages for key
audiences will enhance knowledge exchange activities.
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Chapter 4: Results
This study used a descriptive, correlational design comprising cross-sectional and longitudinal
components to collect survey and administrative data from employees, managers, and
administrative sources. The purpose was to examine the influence of alternative measures of
managerial span on nurse and team outcomes in the hospital sector. This chapter presents the
study results in four sections: 1) reliability analyses of instruments; followed by 2) sample
description; 3) descriptive statistics of study variables; as well as, 4) findings for each outcome
by study objective.
Instruments
Leadership Practices Inventory
Two versions of the LPI were used in the study; the LPI-self was completed by managers (n =
31) and the LPI-other was filled in by nurses and directors (n = 117). The correlation between
LPI-self and LPI-other scores was low (r = .24, p < .05) suggesting that managers were tapping
different phenomena than other raters. The sample of LPI-self scores was also very small (n =
31) and was therefore excluded from the analysis.
Of the 117 LPI-other surveys submitted, 88.0% were complete with the remaining surveys
missing 1 to 8 items. Exploratory factor analysis using principal components methods was then
conducted for the LPI-other scores. Individual responses (n = 117) used in the factor analysis
were not independent and may therefore be correlated; however, this process was consistent with
the analysis done by Posner and Kouzes (1993). Sampling was adequate as indicated by
acceptable values for the Kaiser-Meyer-Olkin measure. Varimax rotation was applied. The scree
plot indicated a single factor solution explaining 59.9% of the variance. Confirmatory factor
analysis was subsequently not conducted as much larger sample sizes are recommended for this
purpose (Dixon, 2001). A mean LPI-other score was calculated to represent an over-arching
construct of transformational leadership consistent with Carless (2001). Cronbach’s alpha was
.98. An intraclass correlation coefficient of .577 indicated that 57.7% of the item variation was
between raters, rather than within raters (i.e., inconsistently rated items by a given rater).
74
Satisfaction with my Supervisor Scale
Of the 558 Satisfaction with My Supervisor Scales filled in by nurses, 85.8% were complete and
9.7% were missing one item. Twenty-five responses (4.4%) were missing 2 to 9 items. A mean
overall score was calculated for each respondent (n = 558) based on the number of items for
which there were responses. Cronbach’s alpha was .97. This result is consistent with or higher
than the reliability alphas reported by Scarpello and Vandenberg (1987), Vandenberg and
Scarpello (1992) and Jones et al. (1999). An intraclass correlation coefficient of .653 indicated
that 65.3% of the item variation was between raters, rather than within raters (i.e., inconsistently
rated items by a given rater).
Relational Coordination Scale
Of the 754 Relational Coordination Scales submitted by nurses, other regulated health care
providers, and unregulated care providers, 95.4% were complete with the remaining teamwork
surveys missing 1 to 4 items. A mean overall teamwork score was calculated for each respondent
(n = 754) based on the number of items for which there were responses. Cronbach’s alpha for the
7 items was .89 which is consistent with the alpha reliability reported by Gittell et al. (2000). An
intraclass correlation coefficient of .543 indicated that 54.3% of the item variation was between
raters, rather than within raters (i.e., inconsistently rated items by a given rater).
Sample Description
Table 5 shows the raw span values of the managers as well as the numbers of areas assigned and
staff participants per manager.
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Table 5. Managers’ Average Raw Span, Total Areas Assigned, and Surveys Analyzed Manager Average Raw Span Total Areas Nurse Surveys Team Surveys
1 60.3 2 13 24 2 104.7 3 25 43 3 68.7 4 15 20 4 76.3 2 15 18 5 29.0 1 12 12 6 90.0 3 23 30 7 89.0 2 25 26 8 136.0 2 15 21 9 78.0 1 14 20
10 38.0 3 12 17 11 59.7 3 18 24 12 157.3 1 15 21 13 163.0 5 36 44 14 54.0 1 14 16 15 174.3 11 15 N/A 16 123.3 1 14 18 17 76.0 3 29 36 18 75.7 2 24 32 19 75.7 1 15 20 20 66.7 3 15 21 21 64.0 1 15 20 22 85.0 2 27 34 23 63.3 3 10 16 24 91.7 5 17 25 25 127.0 1 15 30 26 77.7 2 21 27 27 106.7 3 20 23 28 102.3 4 43 51 29 34.7 2 8 13 30 84.0 1 12 17 31 52.7 3 6 35
Note. N/A = not applicable.
Of the 31 managers, all were Registered Nurses and 93.5% were female (Table 6). In terms of
highest educational credential achieved, 38.7% held Master degrees, 45.2% held undergraduate
degrees, and 16.1% held college diplomas. On average, managers were aged 46.1 years and had
worked for 23.5, 16.6, and 3.0 years in the profession, hospital, and position respectively.
Managers averaged 6.9 years of management experience. For the 2007/2008 fiscal year, the
average assigned budget was 6.7 million Canadian dollars.
76
Table 6. Managers’ Age, Tenure, Years of Experience, and Education Manager Characteristics (n = 31)
% Mean Range Age 46 29-63
Position Tenure 3 .3-9 Hospital Tenure 17 .5-35
Nursing Experience 24 6-39 Management Experience 7 .25-19
Registered Nurse Diploma 16 Undergraduate Degree 45
Graduate Degree 39
Nurse Supervision Satisfaction Respondents. Of the 558 respondents, 87.8% were Registered
Nurses and 12.2% were Registered Practical Nurses (Table 7). On average, respondents were
aged 42.4 years and had worked for 16.0, 10.6, and 7.6 years in the profession, hospital, and area
respectively. Of these nurses, 91.4% were female, 35.3% held a baccalaureate nursing degree or
higher, and 26.9% worked day shift only. Most were employed full-time (79.2%), followed by
part-time (16.3%) and casual (4.5%). The most common position held was staff nurse (93.28%),
followed by team leaders (4.9%) and other (1.9%).
Table 7. Nurses’ Designation, Age, Years of Experience, and Education Nurse Characteristics Satisfaction Models (n = 558)
% Mean Range Registered Nurse 88
Age 42 22-67 Unit Experience 8 .25-33
Hospital Experience 11 .25-37 Nursing Experience 16 .33-46
RPN Certificate/Diploma 9 Registered Nurse Hospital School 2
Registered Nurse Diploma 54 Undergraduate Degree 33
Graduate Degree 2
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Team Respondents. The occupations of the 754 respondents reflected a variety of
multidisciplinary roles (Table 8). Nearly 74% were nurses.
Table 8. Team Surveys by Occupation Frequency % Registered Nurse 476 63.2 Registered Practical Nurse 72 9.6 Physiotherapist/Physiotherapist Assistant 39 5.2 Unregulated Care Provider 27 3.6 Technician/Technologist 25 3.3 Social Worker 23 3.1 Clerical 23 3.1 Occupational Therapist/Assistant & Speech Therapist 14 1.9 Registered Dietician/Registered Dietary Assistant 13 1.7 Pharmacist 11 1.5 Respiratory Therapist 10 1.3 Advanced Practice Nurse 8 1.1 Other 7 0.9 Counsellor/Case worker 5 0.7
As shown in Table 9, 48.7% of team respondents held a university degree and only 1.3% held a
secondary school credential as their highest level of education.
Table 9. Team Surveys by Highest Education Frequency % College Diploma 314 42.1 Undergraduate degree 211 28.3 Graduate degree 152 20.4 College Certificate 54 7.2 High School 10 1.3 Hospital School of Nursing 5 0.7
Team respondents were mainly female (86.1%). On average, respondents were aged 41.0 years
and had worked for 14.1, 9.92, and 6.9 years in the occupation, hospital, and area respectively.
Most were employed full-time (81.3%), followed by part-time (14.2%) and casual (4.5%).
Descriptive Statistics of the Study Variables
78
Table 10 presents the descriptive statistics for predictors and the outcome variables.
Table 10. Descriptive Statistics of Study Variables Predictors % Mean SD Level-2 (Manager) Raw Span 86.6 36.2 Time in Staff Contact (minutes per weekday) 192 84 Leadership Practices – Other 7.6 1.0 Hours of Operation (weekly hours) Extended 61.3 Compressed and Mixed 38.7 Education (graduate degree) 38.7 Experience (years) 6.9 5.5 Position Tenure (years) 3.0 2.4 Worked Hoursa (per weekday) 8.9 0.9 Total Areasa 2.5 1.3 Administrative Support Rolesa (full-time equivalents) 0.9 0.9 Clinical Support Roles (full-time equivalents) 3.4 1.8 Occupational Diversity 9.1 4.0 Employee Tenure (years) 9.5 2.4 Full-time Employment (%) 59.5 8.5 Non-Direct Reportsa 33.3 20.5 Level-1 (Staff) Nurse Age 42.4 10.5 Nurse Day Shift Employment (not day shift) 73.1 Nurse Education Level (college diploma) 53.9 Nurse Registration (Registered Nurse) 87.8 Occupational Group Nursing 73.7 Other Regulated Health Professional 14.6 Unregulated Care Provider 11.7 Full-Time Status 81.3 Outcome Variables Level-1 (Staff) Nurse Satisfaction with Manager’s Supervision 3.8 0.8 Teamwork 3.9 0.5 Note. aOutlier(s) corrected.
Raw span values ranged from 29.0 to 174.3. Direct report staff numbered less than 66.7 for one-
third of managers and 90 or more for one-third of managers. The distribution of raw span values
is presented in Figure 3.
79
Figure 3. Distribution of raw span values.
Daily time in staff contact ranged from 1.4 to 7.2 hours. On average, one-third of managers spent
less than 2.4 hours in staff contact per day and one-third spent 3.9 hours or more in staff contact
per day. The distribution of time in staff contact values is presented in Figure 4. Relative to the
mean daily worked hours of managers, 36% of the workday was spent in staff contact on
average.
80
Figure 4. Distribution of time in staff contact values.
Leadership scores ranged from 5.3 to 9.2. The mean leadership score was 7.6, indicating that on
average, managers fairly often or usually engaged in leadership behaviors as measured by the
Leadership Practices Inventory - Other (Kouzes & Posner, 2003). On average, one-third of
managers scored 7.1 or lower and one-third scored 8.2 or higher. The distribution of leadership
scores is presented in Figure 5.
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Figure 5. Distribution of leadership scores.
Of the managers, 12.9% (n = 4) covered area(s) with compressed hours of operation only, 25.8%
(n = 8) covered areas that were a mix of compressed and extended hours of operation, and 61.3%
(n = 19) covered area(s) with extended hours of operation only. In terms of highest educational
qualification, managers had completed graduate degrees (38.7%), undergraduate degrees
(45.2%), or college diplomas (16.1%). Years of management experience ranged from 0.25 to 3.9
for the least experienced one-third of managers and 7.5 to 19 for the most experienced one-third
of managers. Years of position tenure ranged from 0.25 to 1.0 for the least tenured one-third of
managers and 4.17 to 9.25 years for the most tenured one-third of managers. Of the managers,
29% worked less than 8.5 hours per day and 19% worked more than 9.5 hours per day. The total
areas assigned to managers numbered from one (29%), two (26%), three (29%), or four or more
(15%). Thus 71% of managers had more than one assigned unit, clinic or service. The proportion
of managers with three or more administrative and clinical support roles full-time equivalents
(including direct and non-direct report staff) was 6.5% and 68% respectively. Overall, 74% of
managers had three or more administrative and clinical full-time equivalent positions combined.
Over three-quarters of managers (77%) had 6 or more job titles reporting directly to their
82
position. Average employee tenure of direct report staff was less than 9 years for 36% managers
and greater than 11 years for 39% managers. The proportion of full-time employment for direct
report staff averaged 60%, ranging from 40% to 74%. Non-direct reports working in the assigned
areas numbered 20 or fewer for one-third of managers and 37 or more for one-third of the
managers.
Nurse satisfaction with manager’s supervision scores ranged from 1.0 to 5.0. One-third of the
scores were at or below 3.53 and one-third of the scores were at or above 4.17. The distribution
of supervision satisfaction scores is presented in Figure 6.
Figure 6. Distribution of level-1 nurse satisfaction with manager’s supervision scores. Teamwork scores ranged from 1.88 to 5.0. One-third of the scores were at or below 3.68 and
one-third of the scores were at or above 4.14. The distribution of teamwork scores is presented in
Figure 7.
83
Figure 7. Distribution of level-1 teamwork scores.
A weak correlation (r = .29, p < .01, n = 558) between supervision satisfaction and teamwork
scores indicated that the scales measured different phenomena in this study sample. Bivariate
Pearson correlations of study variables are presented in Appendix E.
84
Satisfaction Findings
Objective 1: Main Effects for Satisfaction
In Step 1, a one-way ANOVA with random effects (i.e., unconditional means model) was
conducted to determine how much variation in supervision satisfaction scores existed within and
between managers and the proportion of total variance residing between groups. As shown in
Table 11, the supervision satisfaction grand mean was 3.82. Variance components indicated
significant variability at the between-manager (.12) and within-manager (.55) levels.
Table 11. One-Way Analysis of Variance Model for Satisfaction
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.82 .07 28.81 53.63 .001 3.67 3.96 Estimates of covariance parametersa Residual .55 .03 16.20 .001 .49 .63 INTERCEPT (subject variance) .12 .04 2.97 .003 .06 .24 aDependent variable = Satisfaction
A modest range of plausible values for the supervision satisfaction means among managers was
observed with 95% of the means falling between 3.13 and 4.5. Using the variance components,
the intraclass correlation coefficient was computed as (.121979/[.121979 + .554057]) = .18,
indicating that 18% of the variance in supervision satisfaction was between managers. On
average, the sample means were fairly reliable as indicators of the true manager means (λ hat =
.78).
In Step 2, a random-coefficient regression model was conducted. At level-1 (the nurse model),
the supervision satisfaction score for each nurse under a given manager was regressed on nurse
age, nurse day shift employment, nurse education level, and nurse registration status (Table 12).
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Table 12. Fixed-Coefficient Regression Model Level-1 for Satisfaction 95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.82 .072 28.84 53.15 .001 3.67 3.96 Nurse Age 3.02x10-4 3.34x10-3 549.80 .09 .928 -6.26x10-3 6.86x10-3 Nurse Day Shift .04 .08 555.55 .51 .613 -.12 .20 Nurse Education .03 .07 542.61 .47 .637 -.11 .18 Nurse Registration -.28 .11 554.87 -2.48 .013 -.50 -.06 Estimates of covariance parametersa Residual .55 .03 16.20 .001 .48 .62 INTERCEPT (subject variance) .13 .04 2.99 .003 .06 .24 aDependent variable = Satisfaction
Of these level-1 covariates, only nurse registration explained significant variation in the
supervision satisfaction outcome. Nurse registration was retained as a control variable in
subsequent models (i.e., the level-1 slope was fixed to be invariant across level-2 models). A
reduced model was estimated using nurse registration (Table 13). The inclusion of nurse
registration reduced the within-manager variance by 1.2%.
Table 13. Fixed-coefficient Regression Model Level-1 for Satisfaction: Reduced Model
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.82 .07 28.90 53.32 .001 3.67 3.96 Nurse Registration -.27 .11 551.61 -2.42 .016 -.48 -.05 Estimates of covariance parametersa Residual .55 .03 16.21 .001 .49 .62 INTERCEPT [subject = mgr] .12 .04 2.99 .003 .06 .24 aDependent variable = Satisfaction
In Step 3, intercepts-as-outcome models were conducted to determine whether the level-1
intercept varied in relation to level-2 predictors. Fixed level-2 raw span and time in staff contact
covariates were added and examined in separate intercepts-as-outcome models; the level-1
model remained the same. Predictors were held constant and centered on the grand mean. All
proposed covariates were first examined in separate models. Following the step-up strategy
recommended by Raudenbush and Bryk (2002), five manager level covariates, namely
leadership, hours of operation, administrative support roles, clinical support roles, and worked
hours were tested separately and in combination with raw span and time in staff contact.
Raudenbush and Bryk recommend that level-2 predictors with small estimated effects and t
ratios near or less than 1 be excluded. On this basis, administrative support roles, clinical support
86
roles, and worked hours variables were excluded. Hours of operation, leadership, and nurse
registration were included in all supervision satisfaction models (Tables 14 and 15). As shown in
Table 14, no significant main effects were observed for raw span and operational hours.
Leadership practices were positively associated with supervision satisfaction. Registered Nurses
were less satisfied than Registered Practical Nurses with their manager’s supervision.
Table 14. Raw Span. Intercepts-as-Outcomes Model for Satisfaction
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.80 .08 30.73 47.50 .001 3.64 3.96 Raw Span -8.47x10-4 1.83x10-3 33.45 -.46 .647 -4.58x10-3 2.88x10-3 Leadership .22 .07 33.14 3.20 .003 .08 .36 Hours of Operation .039 .13 30.94 .30 .767 -.23 .30 Nurse Registration -.24 .11 548.82 -2.22 .027 -.46 -.03 Estimates of covariance parametersa Residual .55 .03 16.23 .001 .48 .62 INTERCEPT (subject variance) .09 .03 2.82 .005 .04 .17 aDependent variable = Satisfaction
As shown in Table 15, no significant main effects were observed for time in staff contact and
operational hours. Leadership practices were positively associated with supervision satisfaction,
and Registered Nurses were less satisfied than Registered Practical Nurses.
Table 15. Time in Staff Contact. Intercepts-as-Outcomes Model for Satisfaction
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.80 .08 31.14 46.91 .001 3.64 3.97 Time in Staff Contact 3.69x10-3 .05 28.01 .08 .936 -9.03x10-3 .10 Leadership .22 .07 32.87 3.14 .004 .08 .36 Hours of Operation .03 .13 30.88 .22 .825 -.24 .30 Nurse Registration -.25 .11 545.20 -2.28 .023 -.46 -.03 Estimates of covariance parametersa Residual .55 .03 16.23 .001 .48 .62 INTERCEPT (subject variance) .09 .03 2.83 .005 .04 .18 aDependent variable = Satisfaction
In summary for Objective 1, main effects on supervision satisfaction were observed for
leadership, but not for raw span, time in staff contact, and hours of operation.
87
Objective 2: Interaction Effects for Satisfaction
Following the procedure outlined by Aiken and West (1991), the two-way interactions were first
examined in a combined model for each alternative measure of managerial span. Of the two-way
interactions tested, only raw span by hours of operation (Tables 16 and 17) and time in staff
contact by hours of operation (Table 18) were significant. As shown in Table 16, raw span
interacted with extended hours of operation and of the conditional first order effects, only
leadership had a significant positive effect on supervision satisfaction.
Table 16. Raw Span with Two-Way Interactions for Extended Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa Intercept 3.82 .07 29.16 52.67 .001 3.67 3.97 Raw Span 4.80x10-3 2.73x10-3 31.82 1.76 .088 -7.54x10-4 .01 Leadership .19 .09 29.57 2.20 .036 .01 .36 Hours of Operation (extended) .08 .12 29.81 .66 .517 -.16 .32 Raw Span * Leadership -3.41x10-3 2.47x10-3 28.24 -1.38 .179 -8.46x10-3 1.65x10-3 Raw Span * Hours of Operation -8.86x10-3 3.52x10-3 30.29 -2.51 .017 -.02 -1.67x10-3 Leadership * Hours of Operation -.06 .13 30.91 -.45 .656 -.33 .21 Nurse Registration -.24 .11 534.93 -2.24 .025 -.45 -.03 Estimates of covariance parametersa Residual .55 .03 16.21 .001 .49 .62 INTERCEPT (subject variance) .06 .03 2.49 .013 .03 .14 aDependent variable = Satisfaction
With compressed and mixed hours of operation as the referent, raw span interacted with hours of
operation and none of the conditional first order effects were significant (Table 17).
88
Table 17. Raw Span with Two-Way Interactions for Compressed and Mixed Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa Intercept 3.90 .10 30.23 40.65 .001 3.70 4.09 Raw Span -4.06x103 2.25x103 30.95 -1.80 .082 -8.66x103 5.51x104 Leadership .13 .12 29.04 1.08 .289 -.12 .37 Hours of Operation (compressed
& mixed) -.08 .12 29.81 -.66 .517 -.32 .16
Raw Span * Leadership -3.41x103 2.47x103 28.24 -1.38 .179 -8.46x103 1.65x103 Raw Span * Hours of Operation 8.86x103 3.52x103 30.29 2.51 .017 1.67 .02 Leadership * Hours of Operation .06 .13 30.91 .45 .656 -.21 .33 Nurse Registration -.24 .11 534.93 -2.24 .025 -.45 -.03 Estimates of covariance parametersa Residual .55 .03 16.21 .001 .49 .62 INTERCEPT (subject variance) .06 .03 2.49 .013 .03 .14 aDependent variable = Satisfaction
As shown in Table 18, time in staff contact also interacted with extended hours of operation. Of
the conditional first order effects, only leadership had a significant positive effect on supervision
satisfaction.
Table 18. Time in Staff Contact Two-Way Interactions for Extended Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa Intercept 3.82 .08 31.50 50.06 .001 3.66 3.97 Time in Staff Contact .10 .06 29.71 1.61 .118 -.03 .23 Leadership .25 .09 32.48 2.81 .008 .07 .43 Hours of Operation (extended) .03 .12 30.59 .25 .803 -.22 .28 Time in Staff Contact *
Leadership .06 .08 28.90 .72 .476 -.11 .22
Time in Staff Contact * Hours of Operation
-.20 .10 28.19 -2.14 .041 -.40 -8.64x10-3
Leadership * Hours of Operation -.09 .15 31.73 -.64 .528 -.39 .20 Nurse Registration -.25 .11 536.80 -2.30 .022 -.46 -.04 Estimates of covariance parametersa Residual .55 .03 16.23 .001 .48 .62 INTERCEPT (subject variance) .07 .03 2.64 .008 .03 .15 aDependent variable = Satisfaction
With compressed and mixed hours of operation as the referent, time in staff contact interacted
with hours of operation; however, none of the conditional first order effects were significant
(Table 19).
89
Table 19. Time in Staff Contact with Two-Way Interactions for Compressed and Mixed Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa Intercept 3.85 .09 29.67 40.68 .001 3.66 4.04 Time in Staff Contact -.10 .07 27.11 -1.56 .130 -.24 .03 Leadership .16 .11 32.15 1.49 .146 -.06 .37 Hours of Operation (compressed
& mixed) -.03 .12 30.59 -.25 .803 -.28 .22
Time in Staff Contact * Leadership
.058 .08 28.90 .72 .476 -.11 .22
Time in Staff Contact * Hours of Operation
.20 .10 28.19 2.14 .041 8.64x103 .40
Leadership * Hours of Operation .09 .15 31.73 .64 .528 -.20 .39 Nurse Registration -.25 .11 536.79 -2.30 .022 -.46 -.04 Estimates of covariance parametersa Residual .55 .03 16.23 .001 .48 .62 INTERCEPT (subject variance) .07 .03 2.64 .008 .03 .15 aDependent variable = Satisfaction
The interaction effects were explored further in a three-way interaction. Three-way interactions
between hours of operation, leadership, and both alternative measures (i.e., raw span and time in
staff contact) were tested. The three-way interaction term was statistically significant for the raw
span model only (Table 20). Data are not shown for the time in staff contact model.
Table 20. Raw Span with Three-Way Interaction for Extended Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa Intercept 3.80 .07 29.16 56.49 .001 3.66 3.94 Raw Span 2.33x10-3 2.72x10-3 32.85 .86 .398 -3.21x10-3 7.88x10-3 Leadership .25 .08 29.68 3.00 .005 .08 .42 Hours of Operation (extended) .16 .11 29.41 1.43 .163 -.07 .40 Raw Span * Leadership 1.66x10-3 3.11x10-3 32.21 .53 .599 -4.69x10-3 8.00x10-3 Raw Span * Hours of Operation -6.31x10-3 3.42x10-3 31.65 -1.85 .074 -.01 6.56x10-4 Leadership * Hours of Operation -.28 .15 25.58 -1.84 .077 -.59 .03 Raw Span * Leadership * Hours
of Operation -.01 4.54x10-3 27.28 -2.39 .024 -.02 -1.56x10-3
Nurse Registration -.24 .11 517.10 -2.22 .027 -.45 -.03 Estimates of covariance parametersa Residual .55 .03 16.20 .001 .48 .62 INTERCEPT (subject variance) .05 .02 2.24 .025 .02 .12 aDependent variable = Satisfaction
To account for multiple comparisons (i.e., between raw span and time in staff contact), the Holm
procedure was used to determine a more stringent alpha level (Table 21; Norman & Streiner,
2000) for the significant three-way interaction. The Holm procedure applies a graduated
correction based on the number of tests. The smallest p value is compared to the most stringent
90
alpha level (α/total number of tests), the next larger p value is compared to critical value
factoring in the total number of tests less one (α/(total number of tests-1)), etc. The observed p
value for the three-way interaction between raw span, hours of operation, and leadership was
below the critical number and was thus considered significant.
Table 21. Holm Procedure for Three-Way Interaction for Two Alternative Measures for Satisfaction Model p Levels Calculation Critical Number Corrected p Level Raw Span (Table 22) .024 α/T .025 Significant Time in Staff Contacta .817 α/(T-1) .05 Not Significant Note. α = .05; T = number of tests was two. aModel not presented.
With extended hours of operation as the referent (Table 20), the conditional first order effects for
raw span and hours of operation were not significant. The conditional first order effect of
leadership was significant and positive at mean values for raw span and for extended hours of
operation. The conditional two-way interaction effects were not significant. The three predictor
interaction was casted into a series of simple regression equations to plot the interaction, using
values one standard deviation above and below the mean (Aiken & West, 1991). Figures 8 and 9
illustrate the two-dimensional view of the three-way interaction for extended hours of operation
versus compressed and mixed hours of operation respectively.
As shown in Figure 8, when managers were assigned extended hours of operation, nurses were
more satisfied under higher leadership in combination with higher raw span. The relationship
between extended hours of operation, leadership, raw span, and supervision satisfaction tended
to be stronger for managers with higher leadership. In other words, when managers were
assigned extended hours of operation, higher leadership enhanced nurse satisfaction with
manager’s supervision and this effect was more pronounced under higher raw spans.
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3.91
3.353.67
3.30
1.00
2.00
3.00
4.00
5.00
Low (1 SD below = 6.7) High (1 SD above = 8.6)Leadership Practices
Sat
isfa
ctio
n
High Raw Span (1 SD above = 115.5)Low Raw Span (1 SD below = 53.2)
Figure 8. Plot of supervision satisfaction on raw span and leadership for extended hours of operation.
With mixed and compressed hours of operation as the referent (Table 22), none of the
conditional first order effects for raw span, leadership, and hours of operation were significant.
Of the conditional two-way interactions, only raw span by leadership was significant.
Table 22. Raw Span with Three-Way Interaction for Compressed and Mixed Hours of Operation. Intercepts-as-Outcomes Model for Satisfaction
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa Intercept 3.96 .09 29.35 42.97 .001 3.77 4.15 Raw Span -3.97x10-3 2.08x10-3 30.59 -1.91 .066 -8.21x10-3 2.71x10-4 Leadership -.03 .13 24.15 -.24 .816 -.29 .23 Hours of Operation (compressed
& mixed) -.16 .11 29.41 -1.43 .163 -.40 .07
Raw Span * Leadership -9.21x10-3 3.30x10-3 23.77 -2.79 .010 -.02 -2.39x10-3 Raw Span * Hours of Operation 6.31x10-3 3.41x10-3 31.65 1.85 .074 -6.56x10-4 .01 Leadership * Hours of Operation .28 .15 25.58 1.84 .077 -.03 .59 Raw Span * Leadership * Hours
of Operation .01 4.54x10-3 27.28 2.39 .024 1.56x10-3 .02
Nurse Registration -.24 .11 517.10 -2.22 .027 -.45 -.03 Estimates of covariance parametersa Residual .55 .03 16.20 .001 .48 .62 INTERCEPT (subject variance) .05 .02 2.24 .025 .02 .12 aDependent variable = Satisfaction
As shown in Figure 9, when managers were assigned compressed and mixed hours of operation,
nurse supervision satisfaction varied by raw span and leadership. Under low raw spans,
supervision satisfaction was higher with higher leadership. Under high raw spans, supervision
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satisfaction was lower with higher leadership. In other words, no matter how highly
transformational the leadership style, when managers were assigned compressed and mixed
hours of operation, they could not overcome high raw spans to positively influence nurse
satisfaction with manager’s supervision.
3.10
4.294.00
3.51
1.00
2.00
3.00
4.00
5.00
Low (1 SD below = 6.6) High (1 SD above = 8.6)Leadership Practices
Sat
isfa
ctio
n
High Raw Span (1 SD above = 134.5)Low Raw Span (1 SD below = 45.7)
Figure 9. Plot of supervision satisfaction on raw span and leadership for compressed and mixed hours of operation.
In summary for Objective 2, the three-way interaction for supervision satisfaction was significant
with raw span, but not with time in staff contact.
Objective 3: Model Explaining Most Variation in Satisfaction
A summary of the models with the alternative measures of managerial span is presented in Table
23. Of the alternative measures of managerial span, only raw span in a three-way interaction with
leadership and hours of operation (Table 20) explained significant variation in supervision
satisfaction. Compared to the unconditional model (Table 11), this model reduced the between-
manager variance by ([.121979- .048062]/.121979) = .606, indicating that the model explained
60.6% of the variation in supervision satisfaction between managers. For this final model, the
residuals were normally distributed across level-1 and level-2 units and level-1 residuals were
fairly homogeneous across level-2 units.
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Table 23. Summary of Between-Manager Variance Explained in Satisfaction Models with Alternative Span Measures Objectives & Terms (Table) Variance Explained by Model Main Effects Raw Span (16) NS Time in Staff Contact (17) NS Three-Way Interactions Raw Span x Leadership Practices x Hours of Operation (18) 60.6% Time in Staff Contact x Leadership Practices x Hours of Operationa NS Note. NS = Alternative measure was not statistically significant in the model. aModel not presented.
In summary for Objective 4, raw span interacted with leadership and hours of operation to
explain the most variation in supervision satisfaction between managers. Time in staff contact
did not explain significant variation in supervision satisfaction between managers.
Teamwork Findings
Objective 1: Main Effects for Teamwork
The analytical steps used for the supervision satisfaction models were repeated for the teamwork
models. In Step 1, a one-way ANOVA with random effects (i.e., unconditional means model)
was conducted to determine how much variability in teamwork scores existed between and
within managers and the proportion of total variance residing between groups. As shown in
Table 24, the teamwork grand mean was 3.92. Variance components suggested significant
variability at the between-manager (.03) and within-manager (.25) levels.
Table 24. One-Way Analysis of Variance Model for Teamwork
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.92 0.04 30.5 107.93 .001 3.85 4.00 Estimates of covariance parametersa Residual 0.25 0.01 19.04 .001 0.22 0.27 INTERCEPT (subject variance) 0.03 0.01 2.86 .004 0.01 0.06 aDependent variable = Teamwork
A modest range of plausible values for the teamwork means among managers was observed with
95% of the means falling between 3.59 and 4.26. Using the variance components, the intraclass
correlation coefficient was computed as (.028856/[.028856 + .247929]) = .10, indicating that
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10% of total teamwork score variability occurred between managers. On average, the sample
means were fairly reliable as indicators of the true manager means λ hat = .73.
In Step 2, a random-coefficient regression model was conducted. At level-1 (the individual team
member model), the teamwork score for each team member under a given manager was
regressed on occupational group and full-time status (data not shown). Because teamwork scores
varied by occupational group and by full-time status of the respondents, these variables were
retained as control variables in subsequent models (i.e., the level-1 slopes were fixed to be
invariant across level-2 models; Table 25). The inclusion of occupational group and full-time
status reduced the within-manager variance by 7.4% compared to the unconditional model.
Table 25. Fixed-coefficient Regression Model Level-1 for Teamwork 95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.9 .04 30.72 104.86 .001 3.85 4.00 Full-Time Status -.14 .05 741.85 -2.95 .003 -.23 -.05 Occupational Group -.20 .03 750.25 -7.06 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.04 .001 .21 .25 INTERCEPT (subject variance) .03 .01 3.00 .003 .02 .06 aDependent variable = Teamwork
Full-time respondents perceived lower levels of teamwork than their part-time and casual
counterparts. A one-way analysis of variance was conducted to evaluate the relationship between
occupational group and teamwork. The occupational group factor included three levels: nurses,
other regulated health care providers, and unregulated care providers. The ANOVA was
significant, F(2, 751) = 18.64, p < .001. The strength of the relationship between occupational
groups and teamwork was small as assessed by η2 with the occupational group factor accounting
for 4.7% of the variance in teamwork. Follow up tests using Dunnett’s C test were conducted to
evaluate pairwise differences among the means. There were no significant differences between
other regulated health professionals and unregulated care providers. However, nurses differed
significantly from other regulated health professionals and from unregulated care providers.
Nurses reported significantly higher levels of teamwork in comparison to the other occupational
groups (Table 26). Both occupational group and full-time status were retained as level-1
covariates in subsequent models.
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Table 26. 95% Confidence Intervals of Pairwise Differences in Mean Teamwork Scores Occupational Group M SD Nursing Other Regulated Health Professional Nursing 3.97 .50 Other Regulated Health Professional 3.73 .45 .13 to .35* Unregulated Care Provider 3.69 .69 .10 to .47* -.16 to .25 Note. An asterisk indicates that the 95% confidence interval does not contain zero, and therefore the difference in means is significant at the .05 significance using Dunnett’s C procedure.
In Step 3, intercepts-as-outcome models were conducted to determine whether the level-1
intercept varied in relation to level-2 predictors. Fixed level-2 raw span and time in staff contact
predictors were added and examined in separate models; the level-1 model remained the same.
Predictors were held constant and centered on the grand mean. Proposed covariates were
examined in separate models. Following the step-up strategy recommended by Raudenbush and
Bryk (2002), nine manager level covariates were tested separately and in combination with span
and time in staff contact. The manager level covariates were: hours of operation, leadership,
experience, worked hours, total areas, clinical support roles, occupational diversity, employee
tenure, full-time employment, and non-direct reports. Raudenbush and Bryk recommend that
level-2 predictors with small estimated effects and t ratios near or less than one be excluded. On
this basis, experience, worked hours, occupational diversity, employee tenure, and full-time
employment variables were excluded. Five level-2 covariates consistently explained significant
variation in teamwork and were retained (Tables 27 and 28).
As shown in Table 27, raw span had no main effect on teamwork. Total areas was negatively
associated with teamwork. Teamwork was positively associated with leadership and with
compressed and mixed hours of operation. Clinical support roles and non-direct reports had no
significant main effects on teamwork in this model.
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Table 27. Raw Span. Level-2 Covariate Model Parameter Estimates for Teamwork 95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.82 .05 33.15 75.84 .001 3.72 3.92 Raw Span -2.99x10-4 1.51x10-3 33.45 -.20 .844 -3.36x10-3 2.76x10-3 Leadership .08 .04 37.94 2.17 .037 5.28x10-3 .15 Hours of Operation (extended) .17 .08 33.28 2.10 .044 5.36x10-3 .34 Total Areas -.08 .03 33.68 -2.32 .026 -.15 -9.92x10-3 Clinical Support Roles .04 .02 33.32 1.84 .074 -4.61x10-3 .09 Non-Direct Reports -3.40x10-3 1.80x10-3 31.29 -1.89 .068 -7.07x10-3 2.73 x10-4 Full-Time Status -.14 .05 745.43 -3.00 .003 -.23 -.05 Occupational Group -.20 .03 746.42 -7.02 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.09 .001 .21 .25 INTERCEPT (subject variance) .02 6.70x10-3 2.73 .006 8.93x10-3 .04 aDependent variable = Teamwork
As shown in Table 28, time in staff contact had no main effect on teamwork. Total areas and
non-direct reports were negatively associated with teamwork. Teamwork was positively
associated with leadership, compressed and mixed hours of operation, and clinical support roles.
Table 28. Time in Staff Contact. Level-2 Covariate Model Parameter Estimates for Teamwork
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.82 .05 33.48 77.72 .001 3.72 3.92 Time in Staff Contact -.01 .02 32.81 -.58 .565 -.06 .03 Leadership .08 .03 38.10 2.32 .026 .01 .15 Hours of Operation (extended) .18 .08 34.71 2.24 .031 .02 .35 Total Areas -.08 .03 34.56 -2.31 .027 -.15 -9.32x10-3 Clinical Support Roles .04 .02 33.95 2.29 .028 4.98x10-3 .08 Non-Direct Reports -3.54x10-
3 1.63x10-3 31.49 -2.17 .038 -6.87x10-3 -2.13 x10-4
Full-Time Status -.14 .05 745.80 -3.01 .003 -.23 -.05 Occupational Group -.20 .03 742.84 -7.00 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.09 .001 .21 .25 INTERCEPT (subject variance) .02 6.63x10-3 2.72 .007 8.76x10-3 .04 aDependent variable = Teamwork
In summary for Objective 1, main effects on teamwork were observed for leadership and hours
of operation, but not for raw span and time in staff contact.
Objective 2: Interaction Effects for Teamwork
Following the procedure outlined by Aiken and West (1991), the two-way interactions were first
examined in a combined model for each alternative measure of managerial span. None of the
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two-way interaction terms in the raw span model was significant (data not shown). For time in
staff contact, the two-way interaction for time in staff contact by extended hours of operation
was conditional on the first order effect of leadership at mean values for raw span and for
extended hours of operation (Table 29).
Table 29. Time in Staff Contact with Two-Way Interactions for Extended Hours of Operation. Level-2 Covariate Model Parameter Estimates for Teamwork
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.90 .04 35.58 92.61 .001 3.82 3.99 Time in Staff Contact .045 .03 33.66 1.29 .206 -.03 .11 Leadership .12 .05 36.35 2.40 .022 .02 .22 Hours of Operation (extended) .07 .07 32.74 1.02 .314 -.07 .21 Time in Staff Contact *
Leadership .07 .04 32.02 1.51 .142 -.02 .16
Time in Staff Contact * Hours of Operation
-.13 .05 31.11 -2.41 .022 -.24 -.02
Leadership * Hours of Operation
-.14 .08 34.01 -1.76 .088 -.30 .02
Full-Time Status -.14 .05 743.13 -2.98 .003 -.23 -.05 Occupational Group -.20 .03 744.61 -7.01 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.06 .001 .21 .25 INTERCEPT (subject variance) .02 .01 2.74 .006 .01 .04 aDependent variable = Teamwork
However with compressed and mixed hours of operation as the referent (Table 30), the
significant two-way interaction for raw span by compressed and mixed hours of operation was
conditional on the first order effect of time in staff contact. These findings were explored further
in a three-way interaction.
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Table 30. Time in Staff Contact with Two-Way Interactions for Compressed and Mixed Hours of Operation. Level-2 Covariate Model Parameter Estimates for Teamwork
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.97 .05 30.60 74.16 .001 3.87 4.08 Time in Staff Contact -.08 .04 29.61 -2.24 .033 -.16 -7.47x10-3 Leadership -.02 .06 32.60 -.39 .701 -.14 .09 Hours of Operation (compressed
or mixed) -.07 .07 32.74 -1.02 .314 -.21 .07
Time in Staff Contact * Leadership
.07 .04 32.02 1.51 .142 -.02 .16
Time in Staff Contact * Hours of Operation
.13 .05 31.11 2.41 .022 .02 .24
Leadership * Hours of Operation .14 .08 34.01 1.76 .088 -.02 .30 Full-Time Status -.14 .05 743.13 -2.98 .003 -.23 -.05 Occupational Group -.20 .03 744.61 -7.01 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.06 .001 .21 .25 INTERCEPT (subject variance) .02 7.64x10-3 2.74 .006 .01 .04 aDependent variable = Teamwork
The three-way interactions between each alternative measure (i.e., raw span and time in staff
contact) and hours of operation and leadership were examined. The three-way interaction term
was not significant in either model (data not shown). In summary for Objective 2, the three-way
interactions for raw span and for time in staff contact were not significant for teamwork.
Objective 3: Model Explaining Most Variation in Teamwork
A summary of the models with the alternative measures is presented in Table 31. Of the
alternative measures of managerial span, neither raw span nor time in staff contact explained
between-manager variation in teamwork.
Table 31. Summary of Between-Manager Variance Explained in Teamwork Models with Alternative Measures Objectives & Key Terms (Table) Variance Explained Main Effects Raw Span (29) NS Time in Staff Contact (30) NS Three-Way Interactions Raw Span x Hours of Operation x Leadership Practicesa NS Time in Staff Contact x Hours of Operation x Leadership Practicesa NS Note. NS = Alternative measure was not statistically significant in the model. aModel not presented.
However, other level-2 manager variables explained significant variation in teamwork between
managers. As shown in Table 32, total areas and non-direct reports were negatively associated
with teamwork. Leadership, compressed and mixed hours of operation, and clinical support roles
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were positively associated with teamwork. Compared to the unconditional model (Table 24), this
model reduced the between-manager variance by ([.028856 - .018342]/.028856) = .364,
indicating that the model explained 36.4% of the variation in teamwork between managers. For
this final model, the residuals were normally distributed across level-1 and level-2 units and the
level-1 residuals were homogeneous across level-2 units.
Table 32. Level-2 Covariate Model Parameter Estimates for Teamwork
95% Confidence Interval
Parameter Estimate SE df t Wald Z Significance Lower Bound
Upper Bound
Estimates of fixed effectsa INTERCEPT 3.82 .05 33.51 77.27 .001 3.72 3.92 Leadership .08 .03 38.08 2.24 .031 7.46x10-3 .15 Hours of Operation (extended) .18 .08 34.64 2.20 .035 .01 .34 Total Areas -.08 .03 33.92 -2.44 .020 -.15 -.01 Clinical Support Roles .04 .02 34.60 2.21 .034 3.25x10-3 .08 Non-Direct Reports -3.54x10-3 1.64x10-3 31.56 -2.16 .039 -6.89x10-3 -1.94x10-4 Full-Time Status -.14 .05 745.66 -3.00 .003 -.23 -.05 Occupational Group -.20 .03 742.87 -7.02 .001 -.25 -.14 Estimates of covariance parametersa Residual .23 .01 19.09 .001 .21 .25 INTERCEPT (subject variance) .02 6.71 x10-3 2.73 .006 8.96x10-3 .04 aDependent variable = Teamwork
In summary for Objective 4, of the alternative measures of managerial span, neither raw span nor
time in staff contact explained significant variation in teamwork. However, leadership, hours of
operation, and other level-2 covariates (i.e., total areas, clinical support roles, and non-direct
reports) explained significant variation in teamwork.
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Chapter 5: Discussion of Findings
The purpose of the study was to examine the influence of alternative measures of managerial
span (i.e., raw span and time in staff contact) on nurse and team outcomes in the hospital sector.
Raw span interacted with leadership and hours of operation to explain significant variation in
supervision satisfaction between managers. Time in staff contact and most other manager level
covariates, with the exception of leadership, did not explain variation in supervision satisfaction.
Neither span nor time in staff contact explained variation in teamwork. Leadership, hours of
operation, total areas, clinical support roles, and non-direct reports explained variation in
teamwork between managers. In this chapter, the study findings are discussed in relation to
previous research and literature. Theoretical implications for boundary spanning and the
strengths and limitations of the study are also considered.
Descriptive Findings
Manager Sample
The study sample was compared to other published samples of first-line nurse managers to
determine the extent to which it reflected the population of interest. Difficulties in recruiting
managers may have been associated with self-selection biases. In terms of manager
characteristics, the sample was of similar age and gender but had less position tenure, less
management experience, and more graduate level education compared to first-line nurse
managers in academic and community hospitals in recent studies in Ontario and Canada (Table
33). Urban versus rural location of hospitals was not reported in these comparative studies. The
participating hospitals in this thesis were located in a large urban city. Higher levels of graduate
education in the sample could reflect increasing organizational support for or demand for more
educated managers, or possibly greater access to education programs in urban areas.
Alternatively, more highly educated managers may be more willing to participate in research.
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Table 33. Characteristics of Study Managers Compared to Other Studies Sample Characteristic Thesis Sample McCutcheon et al. (2009) Laschinger et al. (2008)
Region Ontario Ontario Canada N 31 41 780
Manager Characteristic Mean (%) Age 46 45 47
Female (94%) n/a (95%) Position Tenurea 3 5 7
Management Experience 7 10 11 Registered Nurse Diploma (16%) (22%) (11%)
Undergraduate Degree (45%) (51%) (71%) Graduate Degree (39%) (27%) (17%)
Note. n/a = unavailable; a. For McCutcheon et al. = unit experience; For Laschinger et al. = role experience
Comparisons with other studies also indicate that managers in this sample tended to be assigned
wider raw spans, more varied hours of operation, greater administrative and clinical support
roles, and greater diversity in the number of job titles reporting to their position. Specifically, the
mean (86.6) and median (78) raw spans in this study were slightly higher than other reported
Ontario means of 77 (n = 40; McCutcheon et al., 2009) and 77.5 (n = 16; Lucas et al., 2008; H.
K. S. Laschinger, personal communication, March 16, 2009), as well as the Ontario median
value of 70 (Laschinger et al., 2008). The raw span values were substantially higher than those
sampled by McGillis Hall et al. (2006) where 81% (n = 13) of acute care managers had 40-59
direct reports. The median span value in this study was also higher than those reported for
Western Canada (65), Atlantic Canada (55), and Quebec (63; Laschinger et al.).
A higher proportion of managers in the sample (38.7%) were assigned compressed or mixed
hours of operation compared to 10% (Doran et al., 2004) and 2% (Lucas et al., 2008) in other
Ontario studies. Overall, 74% of participating managers had three or more administrative and
clinical support full-time equivalent positions combined, which was higher than the 47%
reported by McCutcheon et al. (2009). Likewise, 77% of study managers had more than 6 job
titles reporting directly to their position compared to 60% in McCutcheon et al.’s Ontario study.
Finally, managers worked an average of 8.9 hours per weekday which was similar to the mean
9.0 hours (n = 10) reported by Arman et al. (2009) in a Swedish study. No other reports of
worked hours by managers were located.
Overall the study sample differed in terms of managerial experience, tenure, and education.
Participating managers also tended to be assigned higher raw spans, greater numbers of support
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roles and job categories as well as more varied hours of operation. Of these variables, only raw
span, hours of operation, and support roles explained variation in the study outcomes.
Outcomes
In order to contextualize the findings, the mean supervision satisfaction and teamwork scores
were compared to findings in the literature. Out of a possible range of 1 to 5, the mean
supervision satisfaction score in this study was 3.82 (SD = 0.8). This was consistent with the
mean score of 3.8 (SD = 0.8) reported in Ward’s (2002) study of direct care providers in mental
health settings. The study scores for supervision satisfaction are somewhat higher than mean
scores of 3.5 and 3.6 for a computer company (Keller & Dansereau, 1995), multinational firm
(Vandenberg & Nelson, 1999), and county government (Jones et al., 1999). These comparisons
suggest that supervision satisfaction may be higher among health care workers than workers in
other industries.
For teamwork, the average relational coordination score in this study was 3.9 (SD = 0.5) out of a
possible range of 1 to 5. This score was similar to the mean of 4.0 (SD = 0.5) reported by Gittell
et al. (2000) in a multi-site study of acute care health care professionals. This is higher than mean
relational coordination scores reported between formal health care providers and informal
caregivers for postsurgical knee replacement patients (M = 2.8 on a 5 point scale; Weinberg,
Lusenhop, Gittell & Kautz, 2007), nursing home staff (M = 2.0 on a 4 point scale; Gittell,
Weingberg, Pfefferle & Bishop, 2008) and airline staff (M = 3.0 on a 5 point scale; Gittell,
2000). These comparisons indicate that relational coordination may be higher in acute care
hospital settings and that teamwork ratings in this study were comparable to those of other acute
care hospital staff.
The study framework examined main and interaction effects among raw span, time in staff
contact, leadership, and hours of operation on supervision satisfaction and on teamwork. The
study findings provide partial support for the proposed relationships which are discussed below.
Raw Span and Outcomes
Raw span did not have main effects on supervision satisfaction or on teamwork. The main effect
result specific to raw span and satisfaction with supervision is consistent with McCutcheon
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(2004), Doran et al. (2004) and McCutcheon et al. (2009) who did not observe a direct
relationship between raw span and nurses’ job satisfaction in acute care hospitals. However,
McGillis Hall et al. (2006) found that nurses’ in acute care were more satisfied with the quality
of their work and working conditions when first-line unit managers had narrower raw spans.
Because the direct report supervisory relationship is fundamental to the employment contract,
managers must attend to each direct report staff regardless of their raw span. Thus satisfaction
with supervision may be less likely to be influenced by the total number of direct reports
assigned to a manager. Alternatively, the relationship between span and supervision satisfaction
may be more complex and this is discussed in the next section on interaction effects.
Raw span was also not a significant predictor of teamwork. This finding differs from Gittell
(2001) who documented lower levels of teamwork with increasing numbers of full-time
equivalent staff per supervisor in the airline industry. However, in acute care hospitals settings,
the team frequently includes significant numbers of non-direct report team members (e.g.,
physicians, allied health professionals). Because the unit-level of analysis was not examined in
the models, the influence of work group size (i.e., the number of direct and non-direct report
team members in an area) on teamwork in the presence of wide raw spans was not tested.
However, the influence of non-direct reports under the manager is considered in the section on
covariates and outcomes.
Time in Staff Contact and Outcomes
Time in staff contact did not have main effects on satisfaction with supervision or on teamwork.
Ouchi and Dowling’s (1974) proposition that time allocation to staff contact is a more sensitive
predictor (than raw span) of staff outcomes was not supported in the sample studied.
Observations of managerial work flow in the acute care hospital setting revealed that time in
staff contact varies relative to the hours of operation assigned to the manager which influence the
density of staff on the manager’s workday and the manager’s coverage of the service hours.
Potential interaction effects among time allocation, leadership, and hours of operation were
examined and did not explain variation in outcomes. No comparative studies were found that
examined the relationship between time in staff contact and satisfaction or teamwork.
Leadership and Outcomes
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Leadership had positive main effects on supervision satisfaction and on teamwork. As boundary
spanners, when managers usually engaged in transformational leadership practices, nurses were
more likely to be satisfied with their manager’s supervision and team members were more likely
to rate teamwork highly. These findings are consistent with recent research on the relationship
between leadership and nurses’ job satisfaction (Duffield et al., 2007; Hall, 2007; McCutcheon et
al., 2009; McGillis Hall & Doran, 2007; McGilton et al., 2007), nurses’ psychological
empowerment (Laschinger, Finegan & Wilk, 2009), and nurses’ ability to work to full scope of
practice (Oelke et al., 2008) as well as between leadership and teamwork (Gittell, 2001;
Oandasan et al., 2006). Transformational leadership practices enable managers to establish
personal connections and build trust with staff, to support professional autonomy, and to
strengthen interdependent work processes among team members.
Hours of Operation and Outcomes
Hours of operation had no main effect on supervision satisfaction, but were associated with
teamwork. In this study, hours of operation were classified as extended or as compressed and
mixed. Comparable studies examining the relationship between hours of operation and nurse
satisfaction were not located. Teamwork was lower when managers were assigned extended
hours of operation (i.e., 24 hours, 7 days a week). No comparative studies examining the
relationship between hours of operation and teamwork were found. Two alternative explanations
for the teamwork finding are explored. First, a manager covers much less of the serviced hours
when hours of operation are extended. The manager’s availability to staff seeking access to
information, resources, support, or problem-resolution is also constrained by a lower density of
staff during the manager’s workday. In contrast, managers assigned compressed and mixed hours
of operation provide greater coverage because they are more likely to be working at the same
times as staff and staff density is higher during the manager’s workday.
A second alternative explanation is that the lower teamwork scores associated with extended
hours of operation may reflect more challenging internal team work conditions. Team members
are spread out across differing and rotating shifts resulting in temporal and physical dislocation.
This may increase the coordination demands across team members and across interdependent
roles, making it more difficult for managers and team members to establish and consistently
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engage in high quality communication and relationships. With compressed and mixed hours of
operation, team members reported higher teamwork scores. It may be that the benefits of
temporal and physical co-location compensated for higher coordination demands associated with
high volume throughput (e.g., clinic visits, procedures) in these areas, as compared to lower
volume throughput (e.g., in-patients) in areas with extended hours of operation.
Three-Way Interaction Effects
A three-way interaction between raw span, leadership, and hours of operations was found for
supervision satisfaction, but not for teamwork. The significant three-way interaction for
satisfaction with supervision lends partial support to the two-way interactions observed between
raw span and leadership on nurse job satisfaction (McCutcheon et al., 2009) and on nurse
empowerment (Lucas et al., 2008). McCutcheon et al. observed that no matter how highly
transformational the leadership style, managers could not overcome raw spans that were too wide
to positively influence nurse job satisfaction. Similarly, Lucas et al. found that no matter how
emotionally intelligent the leadership style, managers could not overcome raw spans that were
too wide to positively influence nurse empowerment. Consistent with these findings, a two-way
interaction was observed in this study for nurse satisfaction with supervision when managers
were assigned compressed and mixed hours of operation (but not for managers assigned
extended hours of operation). When managers were assigned compressed and mixed hours of
operation, nurse satisfaction with supervision was lower under transformational managers with
wide raw spans. This interaction makes sense because with compressed and mixed hours of
operation, staff density is high relative to the manager’s workday and the manager covers more
of the serviced hours. Two alternative explanations are explored. One interpretation is that high
numbers of staff may overwhelm the capacity of even highly transformational managers to
positively influence supervision satisfaction when they are assigned compressed and mixed
hours of operation. That is, no matter how highly transformational the leadership style, when
managers were assigned compressed and mixed hours of operation, they could not overcome raw
spans that were too wide to positively influence nurse satisfaction with supervision.
However, this interaction also suggests that some nurses were more satisfied with supervision
when managers assigned compressed and mixed hours of operation had less transformational
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leadership styles in combination with wide spans. An alternative interpretation is that under wide
spans, managers could have engaged in other types of behaviors (e.g., more task-oriented
leadership) to resolve structural work process and relational issues that impeded nurses’
workflow, hence enhancing nurses’ satisfaction with supervision. Managers assigned
compressed and mixed hours of operation often oversaw areas characterized by high volume
throughput (e.g., clinic visits, procedures). Shorter and frequent work cycles intensify
coordination demands between work roles and groups. Nurses may have been more satisfied
with supervision by managers who were more focused on tasks or who managed by exception,
which could have in turn, enhanced nurses’ workflow and productivity.
An important consideration is that the Leadership Practices Inventory was initially developed in
the context of managers’ best experiences leading projects, not managing high intensity
operations. A possible conjecture is that leadership styles other than transformational leadership
may be effective under these conditions. In their meta-analysis, Mullen, Symons, Hu, and Salas
(1989) noted that increases in either consideration (person-focused) or initiating structure (task-
focused) leadership behaviors were associated with high staff job satisfaction. It may be that
task-oriented behaviors, rather than the person-oriented behaviors associated with
transformational leadership, were conducive to meeting nurses’ needs for supervision in these
settings when managers had wide raw spans.
However, this interpretation is not consistent with McCutcheon’s (2004) finding that the positive
effect of transactional leadership (i.e., more task-focused orientation) on job satisfaction was
lessened under wide spans. Of interest however, is that although McCutcheon (2004) also
observed the overall negative influence of management-by-exception leadership on job
satisfaction, in some cases, the negative relationship was attenuated under wider raw spans. It
may be that in fast paced environments, managing by exception enabled nurses to maintain high
volume throughput or, that managers delegated authority thus enhancing nurse autonomy and
supervision satisfaction. These conjectures would require further inquiry.
In contrast, the three-way interaction with regard to extended hours of operation indicated that
more transformational leadership was associated with higher nurse supervision satisfaction in
combination with wider raw spans. More transformational leadership enhanced nurse satisfaction
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with manager’s supervision and this effect was more pronounced under higher raw spans. This
finding is similar to a study in the banking industry by Schriesheim et al. (2000) whereby higher
supervisor ratings of leader-member exchange were associated with higher staff organizational
commitment under wider raw spans. However, the thesis result differs from the constraining
effect of wide raw spans that has been observed on leadership and nurse job satisfaction
(McCutcheon et al., 2009) and on leadership and nurse empowerment (Lucas et al., 2008).
Managers in these 2 studies were predominantly assigned extended hours of operation in hospital
settings. Similarly, McGillis Hall et al. (2006) reported less nurse satisfaction with the work
environment with wider raw spans.
A possible explanation for this contrary thesis finding is that the outcomes of nurse job
satisfaction, empowerment, and work environment satisfaction reflect broader constructs than
nurse satisfaction with manager’s supervision. That is, job satisfaction, empowerment, and work
environment satisfaction represent multi-faceted aspects of nurses’ work experiences. In contrast,
nurse supervision satisfaction is a narrower construct focused on the direct-report relationship
which is enhanced by higher staff density under extended hours of operation. This is consistent
with Blau (1968) who theorized that access to managerial support for resolution of complex
work problems is a central underlying mechanism of an effective manager-staff reporting
structure, especially when employees are skilled professionals. However, this study finding is
inconsistent with previous nursing studies and warrants future investigation and replication. This
finding also suggests that trade-offs may exist in the staff outcomes associated with wide versus
narrow raw spans (i.e., some outcomes may improve under wider raw spans).
Covariates and Outcomes
No other covariates were significant predictors of nurse satisfaction with manager’s supervision.
Teamwork however was significantly associated with other manager-level covariates, namely
clinical support roles, total areas, and non-direct reports. Each of these is discussed below.
A greater number of full-time equivalent clinical support roles working in the manager’s
assigned area(s) was significantly associated with higher teamwork scores (i.e., more frequent,
timely, accurate, and problem-solving communication and greater likelihood of shared goals,
shared knowledge, and mutual respect). Examples of clinical support roles in this study included
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permanent team leaders, advanced practice nurses, and case managers. These roles enact
boundary spanning functions that create linkages among people and that move across functional,
professional, spatial, and organizational boundaries. For instance, permanent team leaders and
advanced practice nurses (e.g., nurse practitioners, clinical nurse educators) frequently liaise with
physicians, families, allied health professionals, and nursing staff to coordinate clinical care and
to educate team members about new clinical advances and organizational directives. Case
managers establish and maintain relationships, exchange information, and negotiate resources
with parties internal and external to the organization to facilitate patient care across occupations,
services, sectors, funding agencies, and locations. The study finding is consistent with emerging
research suggesting a positive association between clinical support roles and team processes. For
example, specialist nursing support or nurse educators or clinical leader roles have been
associated with fewer interventions left undone or delayed, fewer adverse events on units
(Duffield et al., 2007), more nurses surviving the first year on the nursing unit (McCutcheon,
2004), and improved patient and physician satisfaction with nursing care (Smith, Manfredi,
Hagos, Drummond-Huth & Moore, 2006). Case manager roles have been positively associated
with enhanced physician collaboration and satisfaction with nursing care, care outcomes, access
to resources, inter-professional relationships, and teamwork (Gittell, 2002a; Reimanis, Cohen &
Redman, 2001). Thus clinical support roles have the potential to enhance teamwork in acute care
settings by virtue of enacting boundary spanning functions across functional, spatial, and sub-
system boundaries.
In addition, the greater the number of units, clinics, and services assigned to the manager, the
lower the level of teamwork under that manager. Studies examining the scope of responsibility
assigned to managers in relation to outcomes were not located. This study finding supports
Gittell’s (2004) contention that focus in working relationships can enhance performance, as well
as Meier and Bohte’s (2003) proposition that variation in workplace technologies increases
demands on managers. Multiple assigned areas divide the manager’s focus among production
subsystems with varied relational dynamics, work content, work processes, and physical
locations. These represent increased functional and spatial boundaries that the manager must
negotiate. Under these conditions, managers may find it more challenging to foster shared norms
and values (Katz & Kahn, 1978) and high quality communication and relationships with and
among staff (Gittell, 2003).
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The higher the number of non-direct reports working in the areas assigned to the manager, the
lower the level of teamwork under that manager. This suggests that work functions,
relationships, and information communication were more effectively integrated by managers
when fewer non-direct reports were involved in interdependent work processes. Direct report
relationships enable managers to supervise and coordinate the work performed in assigned areas.
Managers can assign or delegate responsibility for work performance to staff, as well as coach
staff in the performance of their role. Direct report staff members are, in turn, liable for work
performance and accountable to the manager (Jaques, 1990). Non-direct reports engendered
functional and spatial boundaries for the managers in this study as these workers often
represented distinct professional groups (e.g., social work, occupational therapy, medicine) and
were not likely to be continuously co-located in the production subsystem. Physical co-location
can foster spontaneous interactions and relationships across functional and professional divides,
resulting in knowledge sharing, problem-solving, and innovation (Galbraith, Downey & Kates,
2002). As boundary spanners, fostering shared norms and values (Katz & Kahn, 1978), building
high quality relationships amongst team members, and communicating information (Ancona &
Caldwell, 1992; Gittell, 2003; Tushman & Scanlan, 1981) may be more difficult when managers
are required to negotiate relationships with increasing numbers of non-direct reports. Although
no directly comparable studies were located, the finding is consistent with previous health care
studies which found inverse associations between work group size and work responsibility,
problem solving, goal setting, and conflict resolution (Stahelski & Tsukuda, 1990) as well as
increased intent to quit and lower morale (Burke, 1996), less employee engagement (Cathcart et
al., 2004), declines in organizational commitment (Green et al., 1996), and less satisfaction
(Burke, 1996; Green et al.; Mullen et al., 1989).
Implications for Boundary Spanning
Many authors have theorized that the personal characteristics and activities of boundary spanners
can influence their effectiveness across boundaries which occur between interrelated subsystems,
hierarchical levels, functional groups, and spatial divides (Ancona & Caldwell, 1992; Gittell,
2003; Katz & Kahn, 1978; Tushman & Scanlan, 1981). In this study, the capacity of managers to
influence supervision satisfaction and teamwork varied not only in relation to their leadership
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practices, but also in relation to subsystem, hierarchical, functional, and spatial boundaries.
These boundaries reflect the scope of responsibility assigned to managers by the organization.
In addition, the study results suggest that managers span another type of boundary, namely time.
The purpose of the management subsystem is to coordinate work processes and to integrate
shared norms and values within and across organizational subsystems. As observations of
managerial work flow revealed, these functions were enacted over time. That is, managers
coordinated work processes and maintained relationships with staff across time. This was
particularly evident for managers assigned extended hours of operation. Even though these
managers typically covered only 26% of the weekly hours of operation (relative to their
workweek), they were responsible for work processes and staff at all times (i.e., 24 hours a day,
7 days per week). The study results suggest that time is another type of boundary that managers
must span to ensure organizational functioning. Indeed, temporal boundaries, as measured by
hours of operation, influenced both supervision satisfaction and teamwork. Thus, the personal
attributes of boundary spanners and the types of boundaries encountered by managers remain
theoretically and empirically relevant to future studies of managerial work.
Study Limitations and Strengths
A convenience sample of managers and staff participated voluntarily in the study. Participants
may have differed in significant ways from non-participants. For example, managers in this
study were less tenured, less experienced, and more likely to hold graduate degrees, than their
counterparts in recent Canadian studies. However, these covariates were examined and did not
explain variation in either study outcome. Study results should only be generalized to first-line
managers working in similar settings, namely academic teaching hospitals or large teaching-
affiliated community hospitals in urban centres.
This study was limited by a small manager (i.e., level-2) sample size. Small sample sizes lower
statistical power and increase the likelihood of a Type II error (i.e., the conclusion that a
difference does not exist, when it does; Norman & Streiner, 2000). That is, non-significant
findings could be related to the small number of participating managers. It is possible that
because of the small sample size, time in staff contact did not vary sufficiently across levels of
leadership and categories of hours of operation. At the time this study was proposed, limited
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information was available for determining sample sizes needed to achieve power in hierarchical
linear models (Raudenbush & Bryk, 2002). Since then, additional guidance about sample sizes
for these models has been advanced. For multilevel models examining fixed effects, Scherbaum
and Ferreter (2009) estimated that, with a sample size of 30 level-2 units, a minimum of 15 level-
1 units would be required to detect a medium effect size with a power of .80. Further they note
that the inclusion of level-1 covariates can increase power. In this thesis, the level-2 units (i.e.,
managers) numbered 31 for supervision satisfaction and 30 for teamwork. The number of level-1
units (i.e., employees) averaged 18 for supervision satisfaction and 25 for teamwork and level-1
covariates were included. Relative to Scherbaum and Ferreter’s (2009) estimates, this suggests
that the sample size was adequate to achieve a power of .80 in this study.
As well, because multiple statistical tests were conducted, the risk of a Type 1 error (i.e., the
conclusion that a difference exists, when it does not) must also be considered (Norman &
Streiner, 2000). Although a more stringent alpha was applied for multiple comparisons, the
three-way interaction effect for supervision satisfaction must be interpreted cautiously as this
interaction may have occurred by chance. On the other hand, the power to detect differences is
lessened with higher order terms in interactions (Aiken & West, 1991) and the three-way
interaction for supervision satisfaction achieved statistical significance. Future replication of this
study with a larger sample size is warranted given the number of covariates and relationships as
well as the higher order interaction effects tested in the analytical models.
Common method bias, which is a source of measurement error, was reduced by collecting
predictor and outcome data from different sources using different methods at different times.
Williams, Cote, and Buckley (1989) cautioned that common method bias may account for up to
25% of shared variance between self-reported predictor and outcome variables. Good reliability
of the Satisfaction with My Supervisor Scale, the Relational Coordination Scale, and the
Leadership Practices Inventory – Other in this sample also enhanced the ability to detect true
differences. The work log data were collected prospectively to avoid recall error and were fairly
reliable in this sample. However, the expectancies of the researcher as the observer could have
influenced inter-observer agreement. Self-report bias, maturation effects, or hypothesis-guessing
could also have occurred among managers, with managers changing their time allocation
patterns or altering their responses.
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Although some data were collected prospectively, the study design was cross-sectional. Thus
cause and effect relationships cannot be established. Level-of-analysis issues were encountered.
Too few hospitals participated to consider the influence of contextual variables at the hospital
level. As well, unit level covariates were not tested in the hierarchical linear models resulting in
the loss of potentially meaningful unit level variation in the outcomes. For the teamwork
outcome in particular, which is dependent on the members and context of a particular work
group, this loss of information may have reduced the variability in teamwork. Indeed, after
controlling for level-1 covariates, variability in teamwork was higher between units (16%) than
between managers (12%).
Future Knowledge Translation Plan
The goal of public awareness is to broadly and specifically target audiences to inform them of
the issue and the solutions proposed by the research (Shamian et al., 2002). For this doctoral
work, the Canadian Council of Health Services Accreditation represents a key legislative
audience with the potential to publicly regulate management standards (Lomas, 1990).
Administrative audiences such as employers, Boards of Directors, Chief Nursing Officers, and
managers will be targeted as these groups would be responsible for adopting and implementing
policies (Lomas). Finally, linkages with interest groups such as the Academy of Canadian
Executive Nurses, Nursing Leadership Network of Ontario, and nursing and allied health
associations will be explored to find supportive audiences. Key knowledge translation vehicles
will include a two page fact sheet, presentations at conferences and organizations, as well as
academic publications. A summary of the study, as well as recommendations and conclusions,
are presented in the next chapter.
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Chapter 6: Summary, Recommendations, and Conclusions
Summary
De-layering of management structures is a frequent strategy to reduce costs in healthcare
organizations. As a result, managers remaining in the organization are typically assigned broader
responsibilities, increased work demands, and wider raw spans although evidence is lacking to
support these work redesign strategies. The overall goal of this study was to examine factors
influencing the capacity of first-line managers in acute care hospitals to support front-line staff.
The literature review revealed that studies of managerial work have often over-emphasized the
personal attributes of the manager without due consideration of the organizational context and
the demands placed on managers. Time allocation of managers was identified by Ouchi and
Dowling (1974) as a key factor impeding comparisons of span. Specifically, they argued that
comparisons of raw span within and across organizations have the potential to misrepresent
managerial capacity to support staff because managers may not allocate the same amount of time
to interaction with staff.
The purpose of the study was to examine the influence of alternative measures of managerial
span on nurse and team outcomes in the hospital sector. The two alternative measures of
managerial span were raw span as a measure of the reporting structure and time in staff contact
as a measure of closeness of contact by the manager. The framework was based on Open System
Theory and the boundary spanning functions of managers. The main effects of the alternative
measures of managerial span on supervision satisfaction and teamwork were investigated. The
interaction effects of the alternative measures of managerial span with leadership and hours of
operation on supervision satisfaction and teamwork were also examined.
A descriptive, correlational design comprising cross-sectional and longitudinal components was
used to collect survey and administrative data from employees, managers, and administrative
sources. Managerial time allocation data were collected prospectively through self-logging and
validated through inter-observer agreement. Acute care hospitals were selected through
purposive sampling. For supervision satisfaction, the final sample size was 31 first-line managers
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and 558 nurses. For teamwork, the final sample size was 30 first-line managers and 754 staff.
The Leadership Practices Inventory, the Satisfaction with my Supervisor Scale, and the
Relational Coordination Scale were used. Hierarchical linear modeling was the main type of
analysis conducted.
Span interacted with leadership and hours of operation to explain 60.6% of the between-manager
variation in nurse satisfaction with supervision. Hours of operation were classified as extended or
compressed and mixed. With extended hours of operation, higher transformational leadership
enhanced supervision satisfaction and this effect was more pronounced under wider spans. With
compressed and mixed hours of operation, supervision satisfaction varied by span and
leadership. When managers were assigned compressed and mixed hours of operation, the
positive effects of transformational leadership on supervision satisfaction were observed under
narrower spans. In other words, no matter how highly transformational their leadership style,
managers assigned compressed and mixed hours of operation could not overcome wide raw
spans to positively influence nurse satisfaction with manager’s supervision. Variation in
teamwork was not explained by raw span or by time in staff contact. Of the between-manager
variation in teamwork, 36.4% was explained by the negative effects of total areas and non-direct
reports and the positive effects of transformational leadership, compressed and mixed hours of
operation, and clinical support roles.
Based on the study results, the following recommendations are advanced related to research,
theory development, and organizational policy and managerial practice.
Recommendations for Research
1. Replication of study. Replication of this study using a larger sample size is warranted to
overcome the error rate problem, to examine interactions among variables, and to adequately
sample managers assigned varied hours of operation.
2. Study other staff outcomes sensitive to managerial coverage and to staff density at the
manager level. Hours of operation was as a key variable influencing the density of staff
relative to the manager’s workday and the coverage provided by the manager relative to the
serviced hours. In areas with extended hours of operation, the study findings suggested that
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the greater the density of the staff during the manager’s weekday, the more satisfied the
nursing staff is with supervision. However, in areas with compressed and mixed areas of
operation, high staff density (i.e., wide raw spans) impeded highly transformational
managers from positively influencing nursing staff satisfaction with supervision. Given that
the manager is the interface between the nurse and the organization, other outcomes may also
be sensitive to the interaction between hours of operation, raw span, and leadership. Potential
staff outcomes sensitive to managerial coverage and staff density that could be considered
include: supervisor-related commitment, organizational commitment, and organizational
citizenship behaviors.
3. Time allocation. Future studies testing the time allocation of managers need to control for
varied operational hours by sampling managers with the same hours of operation, or
alternatively, by increasing the sample size across all categories of operational hours.
4. Leadership effectiveness relative to area type. Studies are needed to test which leadership
style or management skills are effective under different operational conditions. In this study,
less transformational managers assigned compressed and mixed areas in combination with
wide raw spans achieved higher satisfaction with supervision than more transformational
managers with wide raw spans. The leadership and management skills needed to effectively
oversee areas with high volume throughput or high uncertainty may differ by area type.
5. Shift work. The influence of staff shift work on the capacity of managers to support staff
could be considered in future research. Managers whose staff work 12 hour shifts or
permanent nights or weekends are disadvantaged because these staff work fewer days
through the year or may work opposing shifts to the manager. Nursing work groups limited
to one shift length as well as nurses working 8- or 10-hour shifts or night shift only also tend
to report higher teamwork than nursing groups with mixed shift lengths or nurses working
12-hour shifts and combinations of 8- and 12-hour shifts (Kalisch, Begeny & Anderson,
2008; Kalisch & Lee, 2009). Future studies could examine the impact of staff shift work on
managers’ ability to supervise staff and to foster teamwork.
6. Teamwork. Future studies of teamwork incorporating key unit level predictors such as work
group size, the proportion of full-time staff, the mix of regulated and unregulated staff, and
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care delivery models are warranted as these unit level variables influence communication and
care coordination processes (Kalisch & Begeny, 2005; McGillis Hall & Doran, 2004). Also,
for areas with extended hours of operation, there is a need to identify specific mechanisms or
supports to enhance teamwork across work group members who may work infrequently or
inconsistently with colleagues because of rotational shifts.
7. Subsequent studies in other settings. Future studies examining the work of first-line
managers are also needed for other hospital types (e.g., community, complex continuing
care) and in other sectors (e.g., home care, public health) to extend the generalizability of the
results. The context for other hospital types may differ in terms of resources (e.g., limited
clinical support roles) and organizational structure (e.g., layers of management). In other
sectors such as home care, geography may pose special challenges for managers.
Recommendations for Theory Development
1. Theory development. The study framework was based on the boundary spanning functions of
managers in open systems. However, more theoretical work is needed to explicate the work
of managers within an open systems approach to large scale organizations and to integrate
existing research related to concepts such as empowerment.
2. Manager sensitive outcomes. Further exploratory and theoretical work is needed to identify
the outcomes sensitive to managerial work (e.g., outcomes related to quality assurance, risk
management, and change management) and to clarify the theoretical mechanisms by which
managers influence these outcomes.
Recommendations for Organizational Policy and Managerial Practice
Open system theory posits that to survive, an organization must acquire negentropy to offset the
loss of inputs or the inability to transform energies, which otherwise lead to disorder and to the
dissolution of the organization (Katz & Kahn, 1978). For organizations, negentropy can involve
renewing inputs, storing energy, creating slack resources, or maximizing imported energy
relative to exported energy (Galbraith, 1974; Katz & Kahn). Based on the study findings, the
following recommendations for organizations and managers offer strategies by which
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organizations can acquire negentropy with the goals of improving throughput efficiency (i.e.,
teamwork) and human resource outcomes (i.e., supervision satisfaction).
1. Leadership. In this study, transformational leadership had an overall positive influence on
satisfaction with supervision and on teamwork. Given that transformational leadership
practices can be learned (Kouzes & Posner, 2002), managers and organizations need to
continue to seek opportunities to enhance the leadership skills of first-line managers. This
strategy maximizes existing managerial inputs without increasing the number of managerial
inputs.
i. With extended hours of operation, the positive influence of transformational leadership
on satisfaction with supervision was even more pronounced under wider spans. Thus,
highly transformational managers assigned extended hours of operation can more
positively enhance satisfaction with supervision when assigned wider raw spans. Under
these specific conditions, wider raw spans maximize existing managerial inputs.
However, caution is warranted given that previous research indicates that wider raw
spans negatively influence other staff outcomes (e.g., staff engagement, work
environment satisfaction; Cathcart et al., 2004; McGillisHall et al., 2006) even when
managers have strong leadership skills (e.g., work empowerment, job satisfaction; Lucas
et al., 2008; McCutcheon et al., 2009).
ii. In contrast, with compressed and mixed hours of operation, wider spans for highly
transformational managers were detrimental to satisfaction with supervision. Hence there
are limits to maximizing managerial inputs when managers are assigned compressed and
mixed hours of operation. Organizations and managers are encouraged to identify
whether staffing issues sensitive to staff density or managerial coverage are occurring in
these areas and if so, to consider ways in which these issues could be alleviated by
altering the work assigned to the manager. Potential strategies are offered below.
2. Increased support by front-line management and clinical support roles.
i. Increases to first-line management support through co-management models could
enhance managerial coverage and accessibility during hours of operation. Co-manager
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models could be considered in the following situations: (a) when teamwork levels are
low and managers are assigned extended operating hours and high numbers of non-direct
reports, and (b) when nursing staff are dissatisfied with supervision and managers are
assigned compressed and mixed hours of operation in combination with wide raw spans.
This role design strategy has been proposed by others with the goals of enhancing
manager role satisfaction, coping, health outcomes, empowerment, and clinical
competence and of reducing staff turnover (Carroll, Lacey & Cox, 2004; Shirey, 2009).
This study extends the recommendation under specific conditions to improve
multidisciplinary teamwork and nurse satisfaction with supervision. The trade-offs of co-
manager models are likely to include dual reporting relationships, new coordination
demands between co-managers (e.g., communication, consistency), and added up-front
costs to employ more managers. On the other hand, the potential cost savings related to
throughput and output improvements (e.g., teamwork, staff retention) would also need to
be considered. Organizations that implement co-manager models are essentially creating
slack resources by importing additional managerial inputs.
ii. If the issue is managerial coverage and staff density during the manager’s workday, then
managers who can bridge shifts during their workday can interface with more staff.
Similarly, managers who work shifts and weekends could also increase coverage and
access. This strategy maximizes existing managerial inputs without increasing the
number of managerial inputs. However, a co-manager model (which increases the
number of managerial inputs) would likely better support shift work rotations by
managers.
iii. Alternatively, increasing the number of full-time equivalent clinical support roles could
also enhance teamwork processes. This strategy requires organizations to import
additional staffing inputs to production subsystems to intensify the boundary spanning
function within and among subsystems, functional groups, and spatial boundaries.
3. Non-direct reports. Reducing the number of non-direct reports working in a manager’s
assigned area(s) can potentially enhance teamwork.
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i. In the case of non-direct reports who are employees (e.g., allied health professionals or
unregulated workers with no direct report relationship to the manager), this might be
accomplished by establishing direct reporting relationships with the manager. The
underlying rationale is that when more work group members are accountable to the
manager, they are more likely to receive consistent communication about expectations
and feedback, and the manager has the authority to coordinate and integrate the work
processes and outcomes of diverse team roles. This strategy allows organizations to
benefit from the boundary spanning function of the first-line management subsystem
thereby maximizing existing managerial inputs without increasing managerial inputs.
However the trade-offs could include dual reporting relationships (e.g., in matrix
structures) which can be ambiguous and lead to conflict (Charnes & Tewksbury, 1993) or
the isolation of allied health professionals (e.g., in program management structures)
which has been associated with lower job satisfaction and fewer professional
development opportunities (Young, Charnes & Heeren, 2004).
ii. In the case of non-direct reports who are not employees of the organization (e.g.,
physicians, students), efforts could be directed at enhancing team continuity by limiting
the numbers of physicians and student rotations (i.e., reducing inputs), by extending the
length of physician and student rotations (i.e., creating slack resources by extending lead
times), or by promoting consistency in student rotations through dedicated learning units
(i.e., creating slack resources by reducing the number of exceptions). Alternatively, when
the number of non-direct reports is high and the manager is assigned more than one area,
the number of areas assigned to the manager could be reduced or co-manager models
could be considered (i.e., slack resources could be created).
4. Areas assigned to managers. The greater the number of areas assigned to managers, the
lower the teamwork, suggesting that splitting the focus of the manager may be detrimental to
fostering inter-professional collaboration. The scope of managerial roles could be narrowed
by assigning fewer areas. Organizations that subdivide managerial assignments are again
creating slack resources to increase throughput effectiveness.
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Conclusions
This study contributes to the evidence base for designing effective managerial roles and is one of
the first studies to examine managerial time allocation in relation to outcomes. Overall, this study
offers emerging support that the effectiveness of first-line managers in acute care hospitals with
respect to nurse supervision satisfaction and to teamwork is influenced by the many hierarchical,
functional, spatial, and temporal boundaries that managers must negotiate to coordinate and
integrate organizational functioning. Further research is needed to better understand the
complexity of and the outcomes sensitive to managerial work. By enhancing the leadership skills
of first-line managers and by designing first-line management positions to factor in not only
reporting structures, but also other determinants of managerial span, organizations can positively
influence throughputs in production subsystems (i.e., teamwork) and human resource outcomes
(i.e., supervision satisfaction) which are important to efficient health care delivery in acute care
hospitals.
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Appendix A. Studies of Span at the Manager Level and Staff Outcomes Author & reference
Theoretical Framework, Hypotheses
Design, Setting, Sample
Measures Analyses & Results Strengths Limitations
Bohte, J., & Meier, K., J. (2001). Structure & the performance of public organizations: Task difficulty & span of control. Public Organization Review, 1(3), 341-354.
Span is a measure of organizational structure; i.e., how relationships are structured between leaders & subordinates The relationship between span & outcomes may be linear, but as span exceeds capacity of leaders (e.g., because of environmental constraints), it is subject to diminishing returns 1. Is span
conditioned by task difficulty?
Design: Ex post facto Setting: Public education system Sample: 678 Texas school districts >500 students data sets: 1994 – 1997 Control Variables Inputs – minority & low-income students Resources – average teacher salary & per student spending on education Technology – not applicable; assumed to be similar for schools over a certain size
Span Ratios 1. top level mgmt: school
administrator: teachers 2. mid level mgmt: district
administrator: school administrators
3 levels of task difficulty Easy – tasks can be performed based on rules & procedures (3rd grade); decreases to span are not likely to improve performance Moderate – discretion required as to how to apply rules & procedures in particular circumstances; problems are solvable (7th grade); decreases to span are most likely to improve performance Extreme – technology to solve extremely difficult problems is unknown; decreases to span provide few benefits relative to costs (10th grade) Student performance: % of students in each school district who pass standard math & reading tests each year
Negative relationships with performance: inputs Positive relationships with performance: resources Regression Estimation of organization production function (non-linear relationship) Moderate task difficulty – top-level span ratio explained variation in performance Easy task difficulty – top level span ratio explained modest variation in performance Extreme task difficulty – neither span ratio explained variation in performance
Examined the non-linear relationship between span & outcomes
Raw measure of span Single outcome evaluated Class size & school size are not measures of managerial span Hierarchical structure of the data set not accounted for in the analysis
140
Author & reference
Theoretical Framework, Hypotheses
Design, Setting, Sample
Measures Analyses & Results Strengths Limitations
Cathcart, D., Jeska, S., Karnas, J., Miller, S. E., Pechacek, J., & Rheault, L. (2004). Span of control matters. Journal of Advanced Nursing, 34(9), 395-399.
1. What is the relationship between span & employee engagement scores?
2. Does reduction of
span by 30-50% improve employee engagement on 4 units with > 80 employees?
Design: Descriptive, correlational survey Setting: Large integrated health system, Midwest US Sample: 651 work groups
Span: number of direct reports assigned to a manager Employee engagement: Gallup instrument; 12 questions measure employee engagement & strength of workplace; 5 point scale Staff survey variables not specified Control variables: demographics (tenure, employment status, contract status, management/non-management, patient care/non-patient care) Levels of employee engagement by percentiles: 1. bottom 25th 2. 25th – 60th 3. 61st – 90th 4. top 10th
Type of analyses not always specified. Discriminant analysis to differentiate variables influencing 4 levels of employee engagement Relationship between span & employee engagement reported as work group size Improvements in employee engagement scores were observed on the 4 units where span was reduced by 30-50%
Large sample of work groups & employees Health care population
Methodology & analyses inadequately described; hierarchical structure of the data set not accounted for in the analysis Refer to both span & work groups; number of managers for the 651 work groups not explicitly stated Validity & reliability of Gallup instrument & other measures not reported Linear relationship assumed between span & outcome Single outcome evaluated
141
Author & reference
Theoretical Framework, Hypotheses
Design, Setting, Sample
Measures Analyses & Results Strengths Limitations
Gittell, J. H. (2001). Supervisory span, relational coordination & flight departure performance: A reassessment of postbureaucracy theory. Organization Science, 12(4), 468-483.
Flight departure process requires high levels of task interdependence Group process mediates the relationship between span & group performance 1. Wide span
strengthens group process, in turn improving group performance
2. Narrow span
strengthens group process, in turn improving group performance
Design: ex post facto research; prospective cross sectional & longitudinal components Setting: airline industry Sample: convenience sample of 4 airlines for a total of 9 groups to maximize differences in coordination; 354 group members (Response rate = 89%)
Span from administrative records: number of full-time equivalent front-line employees per supervisor on a monthly basis Survey instrument for group process (relational coordination) which considered the respondent’s perception of how other group members currently interact with them in terms of frequency & timeliness of communication, strength of problem solving, helping, mutual respect, shared goals, & shared knowledge among group members; 84 questions on strength of interactions on a 5 point scale; Cronbach’s alpha = 0.84 Performance & control variables from monthly reports over 12 months Performance variables: customer complaints, baggage handling, late arrivals; equally weighted index; Cronbach’s alpha = 0.81 Control variables: number of flights per month, average length of flight, number of passengers per flight, tones of cargo per flight & % passengers connecting Qualitative interviews (n = 48) & non-structured observation (n = 13 days)
ANOVA: cross-airlines & cross-groups differences in relational coordination (p < .001) Hierarchical linear modeling Wide & narrow span associated with lower & higher levels of group performance (p < .05) respectively Regression of relational coordination on performance: Relational coordination Is significantly associated with better performance (p < .01) Relational coordination mediates the influence of span on performance Regression of span on each dimension of relational coordination: Wide span associated with weaker relational coordination: less timely communication, & lower levels of problem solving, shared goals, shared knowledge (p < .01); & with lower levels of helping & mutual respect (p < .05) Qualitative data suggested that narrow spans encouraged feedback & coaching by the supervisor & resulted in better group performance, provided that the supervisor was facilitative, rather than coercive (i.e., did not engage in pressuring & blaming)
Self-report, observational & objective measures; some prospective longitudinal data Reduced common method bias Used hierarchical linear modeling Measured span monthly
Raw measure of span; used full-time equivalents Linear relationship assumed between span & group process
142
Author & reference
Theoretical Framework, Hypotheses
Design, Setting, Sample
Measures Analyses & Results Strengths Limitations
Judge, T. A., & Ferris, G. R. (1993). Social context of performance evaluation decisions. Academy of Management Journal, 36(1), 80-105.
1. Supervisory opportunities to observe job performance will positively influence the performance rating of the subordinate
2. Large span will
negatively influence performance rating of the subordinate
Design: Descriptive, correlational survey Setting: 238 bed hospital in US Sample: 81 registered nurses; 27 supervisors; resulting in 81 nurse-supervisor dyads
Span: number of employees directly reporting to the supervisor Supervisor’s opportunity to observe subordinates’ performance: How often do you think your supervisor regularly has the opportunity to observe your job performance and thus knows how you are doing? ; 5 point scale
Structural equation modeling Opportunity to observe positively & significantly influenced performance ratings Span did not influence performance ratings
Limitation of span measure identified; indirectly accounted for managerial time allocation Nursing population
Hierarchical structure of the data set not accounted for in the analysis Linear relationship assumed between span & outcome Single outcome evaluated
143
Author & reference
Theoretical Framework, Hypotheses
Design, Setting, Sample
Measures Analyses & Results Strengths Limitations
Hechanova-Alampay, R., & Beehr, T. A. (2001). Empowerment, span of control, & safety performance in work teams after workforce reduction. Journal of Occupational Health Psychology, 6(4), 275-282.
Safety performance is influenced by span & empowerment 1. Work group size
is positively correlated to unsafe behaviors & accidents
2. Level of
empowerment is negatively correlated to unsafe behaviors & accidents
3. Unsafe behaviors
are positively correlated to more accidents
Design: Descriptive, correlational survey Setting: Chemical company Sample: 1 company, 3 sites, 24 work teams selected bases on best & worst safety performance Group size: n = 24; M = 47 (SD = 24; range 12-110) Employees: n = 531; 46% response rate Data collected at group level; safety collected by self-report survey
Span: number of employees who report directly to the work group leaders Empowerment: delegation of power & authority to employees; team based assessment of 21 factors (e.g., formulation of team goals, communication outside the team, decision making on team rules, resolving conflict within teams, problem-solving customer interactions, work scheduling, providing feedback to team members) on a 4-point scale (0 - leader centered; 1- shared leadership; 2 - self-directed; 3 - fully empowered) Safety measures: Unsafe behaviors: average team score of the frequency of 18 individual behaviors rated on a 5-point likert scale Safety accidents: % of employees in each work team, in calendar year 1999, with accidents
Empowerment: M = 0.96 (SD = .81; range 0-2) Safety accidents: M = 2.6% (range 0-10%) One-tailed correlations at group level Span correlated positively with unsafe behaviors (r = .43, p < .05) & accidents (r = .44, p < .05) Empowerment negatively correlated with unsafe behaviors (r = -.48, p < .05) & accidents (r = -.51, p < .05) Unsafe behaviors positively correlated with accidents (r = .35, p < .05) Multiple regression analysis Span & empowerment explained 33% of variance in unsafe behavior & 34% of variance in safety accidents
Large sample of employees Self-report & objective measures used
Raw measure of span Data aggregated to resolve levels-of-analysis issues
144
Author & reference
Theoretical Framework, Hypotheses
Design, Setting, Sample
Measures Analyses & Results Strengths Limitations
Lucas, V., Laschinger, H. K. S., & Wong, C. (2008). The impact of emotional intelligent leadership on staff nurse empowerment: The moderating effect of span of control. Journal of Nursing Management, 16, 964-973.
Kanter’s Theory of Structural Empowerment 1. The influence of
managerial leadership on staff structural empowerment is moderated by raw span.
Design: Descriptive, correlational survey Setting: Community hospitals, Ontario Sample: 2 hospitals Staff nurses: n = 203; 68% response rate Managers: n = 16
Span: number of direct reports Emotional intelligent leadership: nurse ratings of their managers’ self-awareness, self-management, social awareness & relationship management as measured by the Emotional Competence Inventory, Version 2 (ECI 2.0) Structural empowerment: nurses’ perceptions of access to opportunity, information, support & resources, & formal & informal power as measured by the Conditions of Work Effectiveness-II (CWEQ-II)
Correlational analyses & moderated regression analyses Span: M = 77.5 (SD 38.56) with a range of 5-151 The moderating influence of span on leadership & empowerment was significant. The interaction term (leadership x span) was ß = -0.711, t = -2.71, p = .007). The final model explained 45% of variance in empowerment.
Nursing population
Raw span measure Linear relationship assumed between span & outcome Single outcome evaluated Hierarchical structure of the data set not accounted for in the analysis
145
Author & reference
Theoretical Framework, Hypotheses
Design, Setting, Sample
Measures Analyses & Results Strengths Limitations
McCutcheon, A. S. (2004). Relationships between leadership style, span of control & outcomes. Unpublished Doctor of Philosophy, University of Toronto, Toronto. Also: Doran, D., McCutcheon, A. S., Evans, M. G., MacMillan, D., McGillis Hall, L., Pringle, D., et al. (2004). Impact of the manager's span of control on leadership & performance. Ottawa, Ontario, Canada: Canadian Health Services Research Foundation. Also: McCutcheon, A. S., Doran, D., Evans, M., McGillis Hall, L., & Pringle, D. (2009). Effects of leadership and span of control on nurses' job satisfaction and patient satisfaction. Canadian Journal of Nursing Leadership, 22(3), 48-67.
1. Span has a main effect on outcomes
2. Span moderates
the relationship between leadership & outcomes
Design: descriptive correlational Setting: hospitals in Ontario, Canada Sample: 717 nurses (Response rate = 88%); 41 nurse managers; 51 patient care units; 7 hospitals
Span: total number of nurses on a unit reporting directly to the manager Leadership: Multifactor Leadership Questionnaire – adapted (Bass & Avolio, 2000) Nursing Job Satisfaction: Mueller & McCloskey (1990) Patient Satisfaction: nursing section of the Patient Judgments of Hospital Quality Questionnaire (Rubin, Ware & Hayes, 1990) Unit turnover rate: % of nurses who left their position during a one-year period Unit labor stability rate: % of nurses who survived the first year in the unit Other variables: nurse & manager demographics, & unit characteristics (number of units per manager, manager roles, number of staff resources reporting to & not reporting to the manager, categories of staff, unit type, & unit unpredictability) Unit level: span, turnover, labor stability Individual level: Mgr leadership style, nurse job satisfaction
Hierarchical linear modeling Multiple regression for each outcome Leadership style, but not span, predicted job satisfaction Span moderated leadership & job satisfaction Span had a main effect on turnover & labor stability Wider span associated with higher turnover & lower stability; for every 10 person increment in span, turnover increased by 1.6%. A span of 100 would be expected to result in a 16% turnover rate Wider span reduced the positive effects of transformational & transactional leadership on job satisfaction Wider span increased the negative effects of management-by-exception & laissez-faire leadership on job satisfaction Wider span reduced patient satisfaction & reduced the positive effects of transformational & transactional leadership on patient satisfaction
Power analysis conducted Nursing population Hierarchical linear modeling used to account for nesting of nurses within units. Multiple outcomes evaluated, including patient outcome
Raw span measure Linear relationship assumed between span & outcome
146
Author & reference
Theoretical Framework, Hypotheses
Design, Setting, Sample
Measures Analyses & Results Strengths Limitations
McGillis Hall et al. (2006). Quality worklife indicators for nursing practice environments in Ontario: Determining the feasibility of collecting indicator data. Toronto, Ontario, Canada: University of Toronto.
1. Does a relationship exist between unit manager span of control and nurses’ perceptions of the work environment?
Design: descriptive correlational Setting: acute (AC), complex continuing (CCC), long-term (LTC) & home (HC) care settings in Ontario, Canada Sample: 451 nurses; 53 nurse managers; 65 units; 20 sites
Span: number of direct reports Work Environment: Revised Nursing Work Index (NWI-R) & Work Quality Index (WQI) Note: High scores on NWI-R indicate lower satisfaction with autonomy, control over the work environment, relationships with physicians & organizational supports High scores on the WQI indicate higher satisfaction with the professional work environment, autonomy, work worth, professional relationships, role enactment & benefits
Span: AC: 81.2% had 40-59 CCC: 86.7% had 30-59 LTC: highly variable Overall, 76% managed more than one unit AC: span correlated positively with NWI-R (rho = .25, p = .01) & negatively WQI (rho = -.19, p = .03) scores CCC: no correlations LTC: span correlated positively with NWI-R (rho = .22, p = .01) HC: no correlations Overall: span correlated positively with NWI-R (rho = .26, p = .01)
Nursing population
Raw span measure Linear relationship assumed between span & outcome Hierarchical structure of the data set not accounted for in the analysis
147
Author & reference
Theoretical Framework, Hypotheses
Design, Setting, Sample
Measures Analyses & Results Strengths Limitations
Meier, K., J., & Bohte, J. (2000). Ode to Luther Gulick: Span of control & organizational performance. Administration & Society, 32(2), 115-137. Note: same data set Bohte & Meier (2001) reported above
Span is a measure of organizational structure The relationship between span & organizational performance is quadratic; as span exceeds capacity of leaders, it is subject to diminishing returns; a linear relationship is expected, except when organizations have incentives to add additional subordinates or supervisors 1. What is an
optimal span for a set of organizations using the same mode of production toward the same goal?
2. Does the effect of
span on performance differ between high- & low-performing districts?
Design: Ex post facto analysis of secondary data Setting: Public education system Sample: 678 Texas school districts with >500 students 2712 pooled cases data set: 1994 – 1997
Span Ratios 1. mid level mgmt: district
administrators: school administrators
2. first level mgmt: school administrators: teachers
Student performance: % of students in each school district who pass standard math & reading tests each year Control Variables Inputs – minority & low-income students Resources – average teacher salary & per student spending on education Technology – not applicable; assumed to be similar for schools over a certain size
Examined linear relationship & estimated organization production function (non-linear relationship) of span ratios & student performance using regression Mid & first level management ratios had a positive linear relationship with performance on average; however this relationship was quadratic for low performing schools (authors speculate that this may be related to the quality of leadership)
Examined non-linear relationship between span & outcome
Class size & school size are not measures of managerial span Hierarchical structure of the data set not accounted for in the analysis Single outcome evaluated
148
Author & reference
Theoretical Framework, Hypotheses
Design, Setting, Sample
Measures Analyses & Results Strengths Limitations
Schriesheim, C. A., Castro, S. L., & Yammarino, F. J. (2000). Investigating contingencies: An examination of the impact of span of supervision and upward controllingness on leader-member exchange using traditional and multivariate within- and between-entities analysis. Journal of Applied Psychology, 85(5), 659-677.
Supervisors in large work units have more demands & fewer opportunities for interaction with subordinates When the work unit is large, subordinates are more likely to value their relationship with their supervisor 1. Wide spans will
positively moderate the relationships between leader-member exchange & outcomes
2. The strongest
span moderator effects exist at the between-groups level
Design: Descriptive correlational surveys Setting: Branch banks Sample: matched data set of 75 managers & 150 staff (one high & one low performing staff per manager)
Span: number of staff full-time equivalents supervised Quality of leader-member exchange: 7 item scale based on 4 point likert; coefficient alpha = .83 (supervisors) & .86 (staff) Staff performance rated by manager: modified Mott’s (1972) scale on quantity & quality of performance over past 6 months; coefficient alpha = .77 Organizational commitment of staff: Porter, Steers, Mowday & Boulian (1974) Organizational Commitment Questionnaire; extent of employee attachment to the organization; 15 item scale; 7 point likert; coefficient alpha = .83
Spans ranged from 5 – 21 full-time equivalents (M = 11.12) Comparison of 2 alternatives to WABA: multiple relationship analysis & multivariate WABA Span did not moderate the relationship between leader-member exchange & performance Wide spans positively moderated ratings of leader-member exchange & organizational commitment of staff
Raw measure of span; used full-time equivalents rather than headcount to study dyadic relationships Managers evaluated the performance of only two staff Small number of subordinates used
Spreitzer, G. M. (1994). Social structural characteristics of psychological empowerment. Academy of Management Journal, 39(2), 483-504.
Compared to supervisors with wider spans, those with narrow spans tend to closely control & monitor subordinates thereby diminishing psychological empowerment (greater feelings of incompetence & reduced meaning). 1. Wide spans will
positively influence staff empowerment
Design: Descriptive correlational surveys Setting: Fortune 50 organization Sample: 393 middle managers; staff M = 4 per manager
Span: number of staff supervised by one middle manager Psychological empowerment: An individual’s orientation to his/her work role in terms of meaning, competence, self-determination & impact; 18 items on 7-point likert scale; coefficient alpha = .81
Spans average 5.36 (SD = 5.92) Correlations: Span correlated weakly with competence (r = 0.12, p < .05) Multiple regression analysis: Wide spans positively associated with staff empowerment (ß = .09, p < .01)
Large sample of managers.
Raw measure of span Few employees per manager Hierarchical structure of the data set not accounted for in the analysis
149
Author & reference
Theoretical Framework, Hypotheses
Design, Setting, Sample
Measures Analyses & Results Strengths Limitations
Theobald, N. A., & Nicholson-Crotty, S. (2005). The many faces of span of control: Organizational structure across multiple goals. Administration and Society, 36(6), 648-660. Note: same data set used by Meier & Bohte (2000) & Bohte & Meier (2001) reported above
1. Do tests of the functional form of span create conflicting goals for organizations when tested on three outcomes?
Design: Ex post facto analysis of secondary data Setting: Public education system Sample: 678 Texas school districts with >500 students 2712 pooled cases data set: 1994 – 1997
Span Ratios 1. mid level mgmt: district
administrators: school administrators
2. first level mgmt: school administrators: teachers
Performance: % of students in each school district who pass standard math & reading tests each year SAT performance: average score on the SAT in a district Dropout rate: 100 – district dropout number Control Variables Inputs – minority & low-income students Resources – average teacher salary & per student spending on education Technology – not applicable; assumed to be similar for schools over a certain size
Estimated production function (quadratic relationship) of span ratios & three outcomes using regression Goal conflict not evident for mid level management ratio Optimal first-level management ratios conflict when the three outcomes are considered together
Examined non-linear relationship between span Multiple outcomes
Raw measure of span Class & school size are not measures of managerial span Hierarchical structure of the data set not accounted for in the analysis
150
Appendix B Information and Consent Letter and Survey for Managers You are being asked to voluntarily take part in a research study. This is not a quality assurance study. Before agreeing to participate in this research study, it is important that you read & understand the proposed study procedures. The following information describes the purpose, procedures, benefits, discomforts, risks & precautions associated with this study. It also describes your right to refuse to participate or withdraw from the study at any time. In order to decide whether you wish to participate in this research study, you should understand enough about its risks & benefits to be able to make an informed decision. You may consult with anyone you like about your decision of whether or not to participate: family members; friends; human resources; your union; any knowledgeable person may be consulted. This is known as the informed consent process. Please ask the study staff to explain any words you don’t understand before signing this consent form. Make sure all your questions have been answered to your satisfaction before signing this document. Background - Research has studied the work of hospital staff but has not looked closely the work of managers. In a redesigned health care system, first-line managers are faced with managing larger programs & more clinical areas & services. We have little understanding of how the work of managers affects their support to staff & how their span of control & leadership shape staff, patient & system outcomes. This research builds on other studies that have shown that the span & leadership style of managers is related to job satisfaction, patient satisfaction & teamwork. For the purposes of this study, “span” could refer to: a) the number of people supervised by a manager (this is called “raw span”); b) time spent in human resource (HR) activity; c) time spent in contact with staff. Purpose - You are being asked to consider participating in this research study because you hold a first-line management position. A total of 60 managers across at least three hospitals will be sought to participate in the research study. The study will last up to 18 weeks. This research looks at how managers’ spans of control, work & leadership practices shape nurse satisfaction with manager & multidisciplinary teamwork. This research study aims to understand:
1. how span & time spent in human resource activities shape outcomes; 2. how leadership shapes outcomes under different spans; &, 3. which span levels improve outcomes.
Procedures - The table below outlines the three phases of the research study & your role, or the role of a delegate that you assign, in collecting data.
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Manager Manager
(or delegate that you assign) Phase
1 During 2-4 week period • a 30-45 minute survey of work activities,
leadership practices & demographics • 15 minute orientation about work logs
(general session or personal appointment)
During 2-4 week period • assist in collecting & verifying baseline
span data (*see below) on direct report employees from payroll & human resources
• identify budget amounts assigned to you in the previous & current fiscal years
• locate your office in relation to the units, clinics & services managed
Phase 2
10 week period • on a work log day, record at half hourly
intervals the number of minutes spent in HR activity & in contact with staff; each entry will take less than 10 seconds to complete as you gain proficiency. This will take place for 20 work log days (i.e., 2 randomly assigned days per week over 10 weeks)
• interrater reliability; the researcher will shadow you for two half days
• record your daily worked hours
10 week period • record daily worked hours by those in
other supervisory roles (e.g., team leaders)
• fax work logs to researcher weekly • 2 monthly updates of administrative span
data (initially collected during Phase I)
Phase 3
Additional 2-4 week period • 1 final monthly update of administrative
span data (initially collected during Phase I)
* data = number; occupation; clinical areas; year of birth; employment start date; employment status
During Phase 2, you may also be invited to participate in 2 full days (instead of 2 half days) of job shadowing by the researcher. The purpose is to improve the researcher’s understanding of work flow issues experienced by managers. A small sub-set of managers will be invited & a separate consent will be sought. Eligibility - To be eligible, managers need to have been employed in a first-line management position for at least 3 months & manage at least one patient care unit where health care providers directly deliver patient care services. Risks - Beyond the period of time required to complete the survey, orientation & work logs, there may be minimal discomfort associated with being shadowed during Phase 2. There may be employment risks associated with your participation. If you agree to participate, you are allowing the research team to ask:
i. nurses, a peer manager & your director to consider completing a questionnaire about your leadership practices, &
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ii. nurses, other health care providers & physicians in the units, clinics & services that you manage to consider completing questionnaires of nurse satisfaction with manager & teamwork.
The questionnaires about leadership behaviours, nurse satisfaction with the manager & teamwork may lead the respondents to reflect on issues they might not have otherwise. This also means that nursing staff, health care providers & physicians on the units, clinics & services that you manage & manager peers & your director will know that you have agreed to participate in the research & that they will be asked to consider filling in questionnaires about your leadership practices. The research team will do everything possible to maintain the confidentiality of all surveys. No participant will be given access to the surveys of other participants (e.g., directors will not have access to the surveys of other staff). All findings will be rolled up to group, hospital or study levels to protect against identification. For example, if only 3 palliative care units participated, the findings from these units would be rolled up into the results for medical units. Benefits - There are no direct benefits to you. However, your participation will assist in understanding how organizations & policy makers can optimize the work of managers in the hospital system. Participation & Withdrawal - Participation in research is voluntary. If you choose to participate in this research study, you can withdraw from the study at any time. You may also refuse to answer any question(s) or choose to stop responding to the survey or filling in the work logs at any time. Withdrawal from the research study does not necessarily include withdrawal of any data complied up to that point. Cumulative research findings will be available to participants. Confidentiality - All information obtained during the study will be held in strict confidence. A 6 digit study number will be assigned to you to enable data collection & analyses & to protect your confidentiality. This study number will be linked to your name & assigned clinical areas in a confidential code book which will only be accessible to the research staff & which will be destroyed at the completion of the thesis. Your study number will be imprinted in small font on your work logs. As you will be self-recording work log data, you are asked to carry & store the work logs securely to ensure your confidentiality. Your submitted survey & work logs are completely confidential. Your name will not be written on the questionnaires given to other study participants (i.e., nurses, peer manager, director, health care providers & physicians). After participants return their questionnaires to the researcher, the manager study number & unit study number will be written onto the questionnaires by the researcher & the questionnaires will be stored. Only the research team will have access to the raw data. No names or identifying information will be used in any publication or presentations. Hospital level findings will be provided to the University Health Network on the condition that a minimum of 10 managers from the University Health Network participate in order to ensure confidentiality of the participating managers. All forms will be stored for 7 years in the Nursing Health Services Research Unit’s locked data storage unit & then destroyed. No information identifying you will be transferred outside the investigators of this research study.
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Reimbursement - During Phase 2, for each week that the work logs are completed & returned, you will receive a small, weekly thank you token (e.g., coffee coupon). There is no other reimbursement for your participation in this research study. Compensation - In no way does signing this consent form waive your legal rights nor does it relieve the investigators, sponsors or involved institutions from their legal & professional responsibilities. Questions - If you have concerns or general questions about the research study, please call the study personnel in charge of this study, Raquel Meyer at 416-946-7154. If you have any questions about your rights as a research participant, please call the Chair of the Research Ethics Board. This person is not involved with the research project in any way & calling him/her will not affect your participation in the study. Consent I have had the opportunity to discuss this research study & my questions have been answered to my satisfaction. I consent to take part in the study with the understanding I may withdraw at any time. I have received a signed copy of this consent form. I voluntarily consent to participate in this research study. ________________________ ________________________ ________________________ Study Subject’s Name Study Subject’s Signature Date I confirm that I have explained the nature & purpose of the research study to the subject named above. I have answered all questions. ________________________ ________________________ ________________________ Name of Person Obtaining Signature Date Consent
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Manager Survey 1. What is your profession?
Business/Management Nursing Occupational Therapy Physiotherapy Social Work Other, specify your profession: __________________________________
2. How long have you worked in your profession?
Years:
3. How long have you worked as a first-line manager? Years:
4. How long have you worked in your current position?
Years:
5. Please indicate your highest educational credential: college diploma, specify qualification: _______________________________ undergraduate degree, specify qualification: ___________________________ masters degree, specify qualification: ________________________________ doctoral degree, specify qualification: ________________________________
6. Do you have any other management related credentials? Please spell out acronyms.
___________________________________________________________________________________________________________________________________________________________________________________________________
7. Please list the professional management associations to which you belong. Please spell out
acronyms. ___________________________________________________________________________________________________________________________________________________________________________________________________
8. What is your gender?
Female Male
9. What year were you born? Year: 19
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10. List the units you manage and how long you have managed each one:
Unit ID Unit Name Number of years & months you have managed this unit:
11. List the clinics you manage and how long you have managed each one:
Clinic ID Clinic Name Number of years & months you have managed this clinic:
12. Do you manage other departments or services distinct from the units and clinics you listed
in 10 & 11? No, skip to 13 Yes, please list: _______________________________________________________
Service ID
Service Name Number of years & months you have managed this service:
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Appendix C. Information and Consent Letter and Survey for Employees
Informed Consent to Participate in a Research Study TITLE: Relationships between span, time allocation & leadership of first-line
managers & nurse & team outcomes (Short Title: Manager Study) INVESTIGATORS: Raquel Meyer, RN, PhD(c)
Dr. Linda O’Brien-Pallas, RN, PhD, FCAHS SPONSOR: Canadian Institutes of Health Research & the Nursing Health Services
Research Unit. You are being asked to voluntarily take part in a research study. This is not a quality assurance study. Before agreeing to participate in this research study, it is important that you read & understand the proposed study procedures. The following information describes the purpose, procedures, benefits, discomforts, risks & precautions associated with this study. It also describes your right to refuse to participate or withdraw from the study at any time. In order to decide whether you wish to participate in this research study, you should understand enough about its risks & benefits to be able to make an informed decision. You may consult with anyone you like about your decision of whether or not to participate: family members; friends; human resources; your union; any knowledgeable person may be consulted. This is known as the informed consent process. Please ask the study staff to explain any words you don’t understand before signing this consent form. Make sure all your questions have been answered to your satisfaction before signing this document. Background - Research has studied the work of hospital staff but has not looked closely at the work of managers. In a redesigned health care system, first-line managers are faced with managing larger programs & more clinical areas & services. We know little about how the work of managers affects their support to staff & how their span of control & leadership shape staff, patient & system outcomes. This research builds on other studies that have shown that the span & leadership style of managers is related to job satisfaction, patient satisfaction & teamwork. For the purposes of this study, “span” could refer to: a) the number of people supervised by a manager (this is called “raw span”); b) time spent in administrative human resources activities; c) time spent in relational human resource activities. Purpose - You have been asked to consider participating in this research study by completing a one-time survey of nurse satisfaction with manager & multidisciplinary teamwork. Approximately 800 nurses across at least three hospitals will be sought to participate in the study. This research looks at how managers’ spans of control, work & leadership practices shape nurse satisfaction with manager & multidisciplinary teamwork. This research study aims to understand:
4. how span & time spent in human resource activities shape outcomes;
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5. how leadership shapes outcomes under different spans; &, 6. which span levels improve outcomes.
Procedures - If you agree to participate, you will be asked to 1) identify your main clinical area & your first-line nurse manager to the researcher & 2) complete a 15-25 minute survey. You are invited to complete this survey only once, even if you work in more than one clinical area. If you are asked again, please let us know if you have already participated. Eligibility - To be eligible, health care providers need to have worked on the participating unit for at least 3 months. Risks - Beyond the period of time required to complete the survey, there may be employment risks associated with your participation if the results of the surveys became known. For this reason the research team will do everything possible to maintain the confidentiality of all surveys. No names will be collected with surveys. No participant will be given access to the surveys of other participants (e.g., managers will not have access to the surveys of other staff). All findings will be rolled up to group, hospital or study levels to protect against identification. For example, if only 3 palliative care units participated, the findings from these units would be rolled up into the results for medical units. Benefits - There are no direct benefits to you. However, your participation will assist in understanding how organizations & policy makers can optimize the work of managers in the hospital system. Participation & Withdrawal - Participation in research is voluntary. If you choose to participate in this research study, you can withdraw from the study at any time. You may also refuse to answer any question(s) or choose to stop responding to the survey at any time. Withdrawal from the research study does not necessarily include withdrawal of any data complied up to that point. Cumulative research findings will be available to participants. Confidentiality - Your name will not be recorded. Your submitted survey is completely confidential. Only the research team will have access to your survey & the raw data. All forms will be stored for 7 years in the Nursing Health Services Research Unit’s locked data storage unit & then destroyed. Reimbursement - There is no reimbursement for your participation in this research study. Compensation - In no way does participating in this research study waive your legal rights nor does it relieve the investigators, sponsors or involved institutions from their legal & professional responsibilities. Questions - If you have concerns or general questions about the research study, please call the study personnel in charge of this study, Raquel Meyer at 416-946-7154.
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If you have any questions about your rights as a research participant, please call the Chair of Research Ethics Board. This person is not involved with the research project in any way & calling him/her will not affect your participation in the study. Consent I have had the opportunity to discuss this research study & my questions have been answered to my satisfaction. I consent to take part in the study with the understanding I may withdraw at any time. By returning my filled-in survey to the researcher, I hereby voluntarily consent to participate & have been given a copy of this consent form.
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Employee Survey For each question, please check the box that best describes you or enter the information asked. 1. What is your occupation?
Clerical staff (e.g., unit clerk, secretary) Health Care Aide, Personal Support Worker Occupational Therapist Physiotherapist Recreational Therapist Registered Nurse Registered Practical Nurse Respiratory Therapist Social Worker Other, specify: ___________________________
2. How long have you worked in this occupation? (Including years worked for other
employers) Years: Months:
3. How long have you worked in this hospital?
Years: Months: 4. How long have you worked on this unit?
Years: Months: 5. What is your highest educational credential?
High school diploma College certificate College diploma Undergraduate degree Graduate degree
6. What is your highest nursing credential?
Not applicable. I am not a nurse. College practical nursing certificate/diploma College nursing diploma Undergraduate nursing degree Masters of Nursing degree
7. What is your gender?
Female Male
8. What year were you born?
Year: 19
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Appendix D. Pilot Work
This appendix presents the pilot work undertaken to pre-test the work log procedures and work
log classification system for time allocation as well as the metrics related to time allocation.
Consistent with Prescott and Soeken (1989), this pilot work assessed the adequacy of the
instrument as well as problems in data collection strategies and methods and developed
operational definitions in the study setting. The work log methodology was pre-tested to
establish the feasibility of the work log procedures and to clarify the classification scheme for
time allocation. A convenience sample of managers (n = 3) was recruited via third party email
from one hospital participating in the study. The information and consent form are presented
near the end of this appendix. The work log pretest consisted of four days, with one manager
participating twice. A pilot sample size of less than 10 is considered acceptable for purposes such
as assessing the ease of administration, wording, or acceptability (Hertzog, 2008).
Managers were asked to complete the log for a work day in the presence of the investigator in
order to assess any difficulties in completing the work diaries. The investigator asked managers
the extent to which the work activities were easily classified and mutually exclusive and
exhaustive as recommended by Ross et al. (1994). Difficulties in classifying work activities,
reasons for the reported difficulties, omissions in logging, and clarity of the wording and
instructions were noted. These difficulties are described below. The investigator revised the work
log procedure and categories accordingly (Washington & Moss, 1988). Inter-observer agreement
was assessed.
Work Log Procedure
For each log entry, managers recorded the number of minutes spent in the specified categories.
Twenty and thirty minute work log entry intervals were trialed. Half hourly intervals were
deemed acceptable to the managers as shorter intervals were more disruptive, especially during
meetings. Managers were provided with a pager that automatically alarmed (silent vibration or
auditory alarm) at 30 minute intervals. Instructions for the use of the pager and the work logging
procedure were clarified.
Classification System
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As discussed in Chapter 1, Ouchi and Dowling (1974) proposed that measures of span as
closeness of contact that factor in time allocation would be more sensitive to staff outcomes than
measures of span as reporting structure (i.e., raw span). This thesis addressed this argument by
determining the extent to which alternative measures of managerial span explained variation in
outcomes. Ouchi and Dowling (1974) further specified that it matters how much time managers
allocate to the supervision and support of staff. This suggested two facets reflecting
administrative responsibilities and interpersonal relationships. Using the employee subject
dimensions of management performance delineated by Mahoney et al. (1963), the initial
proposed classification scheme used 2 categories to reflect these facets: administrative human
resource activity and relational human resource activity. Activities in the employee subject
dimension were classified as either administrative or relational in nature. Administrative human
resource activities included: staffing, scheduling, evaluating and observing performance, wage
and salary administration, collective bargaining, and dispute resolution. Relational human
resource activities included: training, counseling, coaching, providing informal feedback,
recognition, and rewards, and social exchange at work.
The first pre-test day for the work logs revealed considerable subjectivity by the manager in
interpreting the initial classification scheme (i.e., administrative versus relational human resource
activity). For example, speaking to a charge nurse about staffing and scheduling for the next shift
was classified by the manager as a relational activity because the purpose from the manager’s
perspective was to support and coach the charge nurse. However, the purpose inferred by the
researcher, as an independent observer, was that the activity was administrative given the focus
on staffing and scheduling. The researcher was unable to infer the manager’s intention and thus
these classification categories were problematic.
In a second iteration of the classification scheme, the categories were redefined as staff contact
and indirect human resource activity. Time in staff contact consisted of verbal and written
communication and person-to-person interaction with direct report and non-direct report
employees working in the area(s) assigned to managers. Manager feedback was that substantial
communication also occurs with staff through email as most managers now carry a personal
digital assistant device (PDA). Staff contact through email was subsequently added to the
definition for staff contact. As well, managers indicated that contact with physicians was an
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important aspect of their responsibilities in managing their assigned area(s). Physician contact
was subsequently included. Indirect human resource activity included any of the following
activities as long as there was no direct contact with staff: staffing, scheduling, evaluating and
observing performance, wage and salary administration, collective bargaining, dispute
resolution, and recognition and rewards. The 2 categories were mutually exclusive. Pre-testing
with 2 managers revealed that this second classification scheme was confusing, and that the
required concentration and time by the manager to classify the activities exceeded the managers’
resources as they were operating under pressure and juggling multiple activities.
Subsequently in a third iteration, the classification categories were redefined as staff contact and
human resource activity. Staff contact, emphasized the manager’s interactions with others.
Human resource activity focused on the content of the manager’s work. These categories were
consistent with the traditional types of categories used in structured observations of managerial
work (Martinko & Gardner, 1985). The categories were not mutually exclusive. Staff contact
consisted of verbal and written communication, email, and person-to-person interaction with
direct and non-direct report employees and physicians working in the area(s) assigned to
managers. Human resource activity included: updating staff on initiatives, policies, protocols;
staffing; scheduling; assigning or delegating work; administering wages, salaries, benefits;
evaluating staff performance; disciplining; handling staff complaints and disputes; developing or
giving orientation, training, and in-services; promoting, transferring, and terminating; recruiting,
interviewing, and hiring; and collective bargaining. Time spent in a human resource activity that
also involved interaction with staff (e.g., staff meeting) was counted in both categories. Inter-
observer agreement was assessed with one manager. The manager and the researcher rated the
presence or absence of time in staff contact at half hourly intervals. Disagreement on any one
activity or interaction during the half hour period resulted in the interval being coded as
disagreement (i.e., agree/disagree or disagree/agree). Based on Cicchetti’s (1981) guidelines, for
kappa sample sizes with two categories (e.g., agree/disagree) approximately 16 observations are
sufficient to estimate this parameter. Cohen’s Kappa was 0.75 for human resource activity and
0.86 for staff contact (n = 16 half hourly observations). The final work log is presented at the end
of this appendix.
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Two additional issues arose regarding the metrics used in the operational definitions of the time
allocation measures as well as the overlap between classification categories. In terms of metrics,
the adjusted span measures initially proposed were operationally defined as the average daily
amount of time per employee. This metric adjusted the manager’s time allocation per direct
report and served to standardize the time values across managers. Values for time adjusted span
for human resource activity and for time adjusted span for staff contact are presented in Table 1
(rows 2 and 3). Table 1. Alternative Span Measures Metric Mean SD Min Max 1. Raw Span number of direct reports 86.6 36.2 29 174.3 2. Time Adjusted Span for Human Resource Activity minutes per direct report 2.0 1.2 .51 5.6 3. Time Adjusted Span for Staff Contact minutes per direct report 2.6 1.5 .78 6.5 4. Time in Human Resource Activity minutes per weekday 150 84 36 420 5. Time in Staff Contact minutes per weekday 192 84 84 426
However, the ‘time adjusted span’ metrics emphasized how much support and supervision are
received per direct report, rather than the manager’s capacity to support and supervise staff on a
typical weekday and led to non-meaningful values in this sample. For example, using the mean
time value for time adjusted span for staff contact (2.6 minutes per direct report; Table 1, row 3),
a manager with an average raw span of 87 would spend a minimum of 3.8 hours to a maximum
of 9.4 hours in staff contact daily. A manager with a wide raw span of 175 would spend a
minimum of 7.6 hours to a maximum of 19 hours in staff contact daily. These values are
nonsensical because the manager cannot engage solely in these aspects of his/her assigned work.
As well, when a manager covers an area with extended hours of operation, he/she cannot
interface with all staff during his/her workday and therefore a metric of daily time per employee
is not meaningful.
Because the raw spans observed in this study were very wide and because the phenomenon of
interest was the manager’s capacity to supervise and support staff (not the amount of supervision
and support received by each staff member), this metric was redefined as: (a) “the average daily
amount of time spent by the manager in human resource activity” (i.e., time in human resource
activity); and (b) “the average daily amount of time spent by the manager in staff contact” (i.e.,
time in staff contact).
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However, the categories with the revised metrics were highly correlated (r = .78, p < .01; Table
2) indicating significant overlap. During observations of managers, the researcher noted that
managers spent substantial amounts of time in human resource activities that involved only a few
staff (e.g., union grievances or disciplinary issues) or that were not likely to influence the
supervision satisfaction or teamwork of current staff (e.g., seeking new hires). Time in staff
contact was the more fundamental concept of interest in terms of closeness of contact between
the manager and his/her staff. Therefore time in human resource activity was excluded from the
analysis.
Table 2. Pearson Correlations of Revised Alternative Span Measures Metric 1 2 1. Raw Span number of direct reports 2. Time in Human Resource Activity minutes per weekday 0.25 3. Time in Staff Contact minutes per weekday 0.26 0.78** * p < .05, ** p < .01
The final alternative measures of managerial span are presented in Table 3. To ease the
interpretation of time allocation relative to the manager’s workday, time in staff contact was
defined using hours instead of minutes.
Table 3. Final Alternative Measures of Managerial Span Metric Mean SD Min Max 1. Raw Span number of direct reports 86.6 36.2 29 174.3 2. Time in Staff Contact hours per weekday 3.2 1.4 1.4 7.1
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Information and Consent Form for Managers for Pre-Test of Work Logs You are invited to participate in a pre-test for the research study entitled: Relationships between span, time allocation & leadership of first-line managers & nurse & team outcomes. Purpose of the Research - The pre-test of the work log & interrater reliability methods will help make sure that ‘time spent in human resource activities’ is well measured. The pre-test objectives are to get manager feedback about:
A. the work log instructions; B. the list for sorting work activities; C. the work log forms; D. the binders used to carry the work logs; E. the frequency of the work log entries F. the use of silent pagers; & G. ways that the researcher can stay out of the manager’s way when job shadowing the
manager while still understanding the general purpose of the manager’s activity. The goal of the overall research study is to understand how managers’ spans of control, time spent in management activities & leadership practices shape nurse satisfaction & multidisciplinary teamwork. To be eligible, you need to be a first-line manager who has worked in this position for at least 3 months. Research Funding – The study is being done for research purposes & is funded through a personnel award from the Canadian Institutes of Health Research & the Nursing Health Services Research Unit. This study is part of the requirement for doctoral student Raquel Meyer to complete a Doctor of Philosophy degree at the Faculty of Nursing, University of Toronto under the supervision of Dr. Linda O’Brien-Pallas. Description of the Research - Up to 3 managers will be sought to participate. The pre-test will last up to one work day for each manager. The researcher will job shadow you for the day. To fill in the work log, every 30 minutes you will record on paper the number of minutes spent in human resource activities & the percentage of those minutes spent in administrative or relational human resource activities. A silent pager to remind you to work log every 30 minutes will be tested. You can provide feedback at any time to the researcher. When the purpose of your work activities is unclear to the researcher (e.g., doing paperwork or email), the researcher will ask you to briefly explain the subject of your activity in general terms (e.g., budgeting, updating a clinical protocol). The researcher will remain as unobtrusive as possible by remaining at a distance or walking away when you require privacy or are approached by staff. After the interaction, the researcher will ask you to describe in general terms the subject of the activity (e.g., to discuss patient care, equipment issues, procedures or a personal issue). The researcher will discretely ask you questions about any observed difficulties & will ask to compare work log entries & to discuss reasons for differences between the manager’s & the researcher’s entries to improve the work log method.
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Potential Harms, Risks & Inconveniences – There may be minimal discomfort associated with being shadowed by the researcher. Because the researcher will be shadowing you during your daily work activities, this means that other people at the Toronto East General Hospital may know that you have agreed to participate in the research. No individual manager will be identifiable in the study results. Potential Benefits – There are no direct benefits to you. However, your participation will assist in understanding the work flow issues faced by managers. Confidentiality & Privacy – All information obtained during job shadowing will be held in strict confidence. Your name will not be attached to the field notes. Only the research team will have access to the raw data. No names or identifying information will be used in any publication or presentations. All forms will be stored for 7 years in the Nursing Health Services Research Unit’s locked data storage unit & then destroyed. No information identifying you will be transferred outside the investigators of this research study. Publication of Results – Information obtained from the pre-test will be rolled up to the group level when reported. The pre-test process will be described in publications & presentations. No individual will be identifiable. Reimbursement – There is no reimbursement for your participation in this study. However all participation will occur during working hours & time away from your duties will be covered as necessary. Compensation for Injury – There is no risk for injury associated with participation in this study. Participation & Termination – Participation in research is voluntary. If you chose not to participate, it will not affect your job in any way. If you choose to participate in the pre-test for this study, you can withdraw from the pre-test at any time without any effect on your job. You may also refuse to answer any question(s) or choose to stop filling in the work logs at any time. Withdrawal from the pre-test does not necessarily include withdrawal of any data complied up to that point. Research findings from the overall study will be made available to participants. Questions Regarding Participation & Contact Information – If you have any questions as a research participant, you may contact the Chair of the Research Ethics Committee. If you have any questions about the study, you may contact the Student Investigator or Thesis Supervisor.
Faculty of Nursing, University of Toronto (Monday to Friday 9:00 – 5:00)
Toronto East General Hospital(Monday to Friday 9:00 – 5:00)
Doctoral Student Investigator: Raquel Meyer, RN, PhD(c)
Chair, Research Ethics Committee: Dr. Donald Borrett
Phone: 416-946-7154 Phone: 416-469-6580 x6639Thesis Supervisor: Linda O’Brien-Pallas, RN, PhD, FCAHS
Phone: 416-978-1967
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If you choose to participate, please sign the consent form & return it to the investigator. Thank you. Raquel Meyer, RN, PhD(c) Faculty of Nursing, University of Toronto Phone: 416-946-7154 [email protected]
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Date: Start time: Finish time: Worked time (exclude breaks):
Time HR Activity Notes & Tally
HR Min.
Staff Contact Notes & Tally
Staff Min.
Time HR Activity Notes & Tally
HR Min.
Staff Contact Notes & Tally
Staff Min.
7:00- 7:30 1:00-
1:30
7:30- 8:00
1:30- 2:00
8:00- 8:30
2:00- 2:30
8:30- 9:00
2:30- 3:00
9:00- 9:30
3:00- 3:30
9:30- 10:00
3:30- 4:00
10:00- 10:30
4:00- 4:30
10:30- 11:00
4:30- 5:00
11:00- 11:30
11:30- 12:00
12:00- 12:30
12:30- 1:00
HR Activity What HR work did I do? • Staff meetings & updates to staff on initiatives, policies, protocols • Staffing, scheduling; assign/delegate work • Administer wages, salaries, benefits • Evaluate staff performance; discipline • Handle staff complaints & disputes • Develop/give orientation, training, in-services • Promote, transfer, terminate; recruit, interview, hire • Collective bargaining
Staff Contact Who did I have contact with? Who: • Staff who report to you • Staff who work in your areas but may not report to you • Physicians who work in your areas Contact: • Verbal, written & email communication with staff (exclude brief greetings) • Person-to-person interaction with staff
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Appendix E. Pearson Correlations of Study Variables
Table 1. Pearson Correlations of Level-2 Variables & Aggregated Level-2 Outcomes 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 Satisfaction (Aggregated) 2 Teamwork (Aggregated) .38* 3 Raw Span -.02 .06 4 Time in Staff Contact .07 -.05 .26 5 Leadership Practices - Other .54** .29 .18 .10 6 Hours of Operation .04 .02 .08 .15 -.02 7 Education -.09 .13 -.10 -.05 -.28 -.14 8 Experience -.14 -.38* .02 -.19 -.07 -.15 .27 9 Position Tenure .04 .17 -.05 -.17 -.14 .10 .06 .17 10 Worked Hours .23 .04 .36* .58** .26 -.08 .10 -.08 -.24 11 Administrative Support Roles -.29 -.25 .42* .25 -.04 .16 .10 .24 -.33 .19 12 Clinical Support Roles -.17 .19 .60** .27 -.14 .07 .30 -.03 .20 .23 .06 13 Total Areas -.15 -.27 .31 .14 -.08 .63** -.04 .25 .28 -.02 .43* .10 14 Occupational Diversity -.03 -.05 .16 .21 -.03 .30 -.10 .23 .11 -.01 .34 .07 .48** 15 Employee Tenure -.33 -.06 .08 .28 -.25 .19 -.04 -.30 .08 .24 .08 .20 .28 -.11 16 Full-time Employment -.13 -.28 .13 .15 -.18 .19 -.11 .15 -.15 .28 .42* .02 .21 .34 .26 17 Non-Direct Reports -.16 -.22 .54** .12 .19 .11 -.12 -.10 -.17 .09 .34 .34 .08 -.11 .14 .32 * p < .05, ** p < .01
Table 2. Pearson Correlations of Level-1 Variables & Level-1 Satisfaction Outcome 1 2 3 4 1 Satisfaction 2 Nurse Age -.01 3 Nurse Day Shift -.02 .10* 4 Nurse Education .01 -.32** .06 5 Nurse Registration -.08 -.08 .12** .22** * p < .05, ** p < .01 Table 3. Pearson Correlations of Level-1 Variables & Level-1 Teamwork Outcome 1 2 1 Teamwork 2 Occupational Group -.21** 3 Full-time Status -.08* -.06 * p < .05, ** p < .01