Individual characteristics, work perceptions, and affective reactions influences on differentiated absence criteria

JOURNAL OF ORGANIZATIONAL BEHAVIOR, VOL. 14,515-530 (1993)

Individual characteristics, work perceptions, and affective reactions influences on differentiated absence criteria STACEY S . KOHLER St. Paul Fire and Marine Insurance Company, 385 Washington St., St. Paul, MN 55102, U. S. A.

AND JOHN E. MATMEU The Pennsylvania State University, Department of Psychology, 437 Moore Building, Ufiiversity Park, PA 16802, U.S.A.

Summary The present study provides support for the utility of studying absence as a multi-dimensional criterion. Survey responses were collected from 194 bus drivers and paired with categorized archival absences. Seven absence indices were created and linked with three categories of predictors: (1) affective reactions to the work environment; (2) work-related perceptions; and (3) individual resource characteristis. The relationships between the multiple absence criteria and the three sets of predictors were examined both separately and combined using part canonical, and canonical correlation analyses. Affective responses fully mediated the influence of work perceptions on absence, and partially mediated the influence of individual resource variables. Redundancy coefficients and a rotated structure matrix were employed to identify two significant dimensions labeled, nonwork obligations and stress reactions, that linked the combined predictor sets with the set of absence measures. Together these dimensions accounted for 15 per cent of the variance in absence, with predictors differing in their explanatory power. Implications for the management of employee absence programs were discussed.

Introduction Employee absenteeism has been studied extensively over the past three decades. The complexity, variety, and number of variables that are thought to influence attendancelabsence have prompted many disciplines to become involved in its study (Dilts, Reitsch and Paul, 1985). Despite this vast amount of attention, a complete and accurate understanding of absenteeism does not yet exist. From a research perspective, absenteeism represents a complex puzzle, something that researchers seek to understand and solve (Landy, Vasey and Smith, 1984). To organizations, absenteeism represents a serious economic problem, something that they seek to control and limit. It has been estimated that between 3.3 and 3.5 per cent of all scheduled work hours are lost each year to absenteeism (Taylor, 1981) with an estimated cost between 8.0 and 26.4 billion dollars per year (Dilts et af., 1985). Financial costs, however, are not the only consequence of absenteeism. Absence may function to generate positive outcomes for both the individual and the organization. For example, an individual’s absence may serve as a relief from the

We thank three anonymous reviewers for their many helpful comments on an earlier version of this manuscript. Correspondence regarding this article should be sent to either Stacey S. Kohler, St. Paul Fire and Marine Insurance Companies, 385 Washington St., MC 505E, St. Paul, MN 55102, U.S.A. or to John Mathieu, The Pennsylvania State University, Department of Psychology, 437 Moore Building, University Park, PA 16802-3104, U.S.A.

0894-3796/93/060S15-16$13.00 0 1993 by John Wiley & Sons, Ltd.

Received 29 October 1991 Accepted 22 December 1992

516 S. S. KOHLER AND J . E. MATHIEU

stressful nature of a job and allow the individual to return to work with a renewed ability to cope (Staw and Oldham, 1978). Thus, efforts directed at ensuring perfect attendance may have unintended or perhaps even deterimental consequences for the organization such as increased accidents and reduced product quality (Steers and Porter, 1987).

Early investigations of the correlates of employee absenteeism were primarily limited to the examination of bi-variate correlations. Various employee characteristics, affective reactions to the work environment, and perceptions of the work environment were all linked to absence. As a result, a vast quantity of predictors has been identified; in fact Steers and Rhodes (1984) identified 209 such variables. Few of the relationships, however, were found to be consistent across studies. Further, even when significant relationships have been found, they typically account for only a small portion of the variance in the criterion (Hackett and Guion, 1985; Muchinsky, 1977; Porter and Steers, 1973; Scott and Taylor, 1985).

More recent studies of absenteeism have been more firmly based upon various theoretical foundations, and have used multivariate statistical techniques. For example, Steers and Rhodes (1978) proposed a process model of employee attendance (i.e. absence) that has guided many research investigations (e.g. Watson, 1981; Zaccarro, Craig and Quinn, 1991). Brooke (1986; Brooke and Price, 1989) proposed a similar model that includes employees’ affective reactions, perceptions of the work environment, and personal characteristics as interrelated forces influenc- ing absence behavior. Despite these and other recent advances, results continue to be inconsistent across studies and the amount of absence variance accounted for has been small. Steers and Rhodes ( 1984) attributed some discrepancy between findings to uncontrolled statistical artifacts and situational specificity. Another source for the continued discrepant findings in absenteeism studies is related to the criterion itself; that is, researchers have defined, measured, aggregated, and analyzed absence in a myriad of different fashions. Such problems have been called the criterion bias (James, 1973) or the criterion problem (Cascio, 1987). Below, we first consider the causes and consequences of the criterion problem for studies of absenteeism. Second, we outline a framework for testing direct and mediated influences on absenteeism when examined as a multidimensional criterion.

Absencc criteria Hammer and Landau (198 1) identified criterion contamination as one potential source of bias that has contributed to the inconsistency in absence research. They proposed that when frequency of absence (the number of absence occurrences within a specified period) was classified as ‘voluntary withdrawal’ and when time lost (the duration of absence occurrences) was classified as ‘involuntary withdrawal’ the measures might be contaminated such that both contain voluntary as well as involuntary reasons for absence. For example, the frequency measure called voluntary absence may include illnesses of one day or more that are not ‘voluntary’ absences, whereas the duration of involuntary absence measure might contain some voluntary absence (i.e. extending a vacation by two days). The point is that much of the problem associated with absence research is rooted in the very definition and measurement of the criteria. Two of the most important issues related to absence criteria are the notion of dimensionality (Cascio, 1987) and the measure or metric employed. Both concepts are discussed below.

Absence dimensions Absence behavior has been described as an overdetermined behavior. That is, it is a behavior that results from any one, or some combination, of a variety of reasons; it can mean ‘different things to different people at different times’ (Johns and Nicholson, 1982, p. 134). For example,

DIFFERENTIATED ABSENCE CRITERIA 517

Hackett, Bycio and Guion (1989) identified over 30 different reasons for absence from nurses’ responses to an open-ended question concerning why they miss work. The notion of absence as something other than a unitary concept was presented as far back as 1951 (Kerr, Koppelmeier and Sullivan, 1951). Yet, despite the vast amount of research on absenteeism in the past 40 years, an agreed upon definition or set of definitions does not exist (Durand, 1986). Previous researchers have generally classified absence into dichotomous forms or dimensions such as: Voluntary-involuntary, excused-unexcused, or authorized-unauthorized, (Chadwick-Jones, Nicholson and Brown, 1982; Hackett and Guion, 1985; Johns and Nicholson, 1982). The problem that arises, however, is that no universal set of definitions exists for these categories. What is classified in one organization or study as an ‘excused’ absence often is an ‘unexcused’ incident in another (Landy et al., 1984). To further complicate matters, most researchers fail to define explicitly their classification scheme, which makes generalizing the results from empirical studies almost impossible. James (1973) cautioned against using a global criterion measure (or a crude dichotomous distinction) and argued that when personal and situational variables were related to some global behavioral measure, the understanding of that behavior was often deficient. The point is that absenteeism is a multifaceted (i.e. dimensional) behavior that is not likely to be adequately predicted when treated as a unitary variable or when collapsed into simply crude dichotomies such as ‘avoidable - unavoidable’. Thus, a multidimensional classification of absences is warranted.

Blau (1985) illustrated the importance of differentiating absence into various types. He showed that although no significant relationship existed between a set of affective responses and a gIobal measure of absence, when absence was separated into different dimensions, significant relationships were revealed. Fitzgibbons and Moch (1980) also found different relationships between predictors and absence depending upon the type of absence investigated. However, in both studies the authors examined the relationships between a set of correlates and different dimensions of absence taken one at a time. Because different types of absence are likely to be correlated (although distinguishable), the extent to which different aspects of absence were accounted for in these two studies was not clear, The present study overcomes this shortcoming by examining the relationship between various sets of correlates and multiple dimensions of absence using a statistical technique that considers both the correlations among and between independent and dependent variable sets (i.e. canonical correlation). Canonical correlation analysis achieves this by estimating and then relating optimal linear combinations of the independent variables to optimal linear combinations of the dependent variables.

Absence metrics Muchinsky (1977) pointed out, that the ‘single, most vexing problem associated with absenteeism as a meaningful concept involves the metric or measure of absenteeism’ (p. 317). Absence metric refers to the manner in which absences are aggregated over time. Many different metrics have been used in previous research. Chadwick-Jones, Brown and Nicholson (1973) reviewed 85 published absenteeism studies and found that 73 per cent of them employed a time lost measure, or measures of the total time (e.g. hours or days) an employee was absent during a specified period. The next most popular metric was the number of absence events in a given period, whatever the number of consecutive days involved (generally called absence frequency). Although rime lost andfrequency are the two most commonly used metrics (Hackett and Guion, 1985), many others such as single-day events, Monday-Friday occurrences, and pre-post holiday events, have also been examined. The rationale for developing different absence metrics was that they were believed to reflect different underlying motives (Folger and Belew, 1985; Hackett

518 S. S. KOHLER AND J. E. MATHIEU

and Guion, 1985). Considering the information above, one goal of the present study was to examine absence behavior more fully by disentangling it in terms of both metrics (frequency and time lost) and dimensions (illness/injury, personal, family, and transportation problems). Further, we distinguish three sets of influences, or correlates of absence, to examine their unique and joint relationships to the differentiated absence criteria.

Influences on absence Three sets of influences on absence are examined in the present study, as depicted in Figure 1. Afective reactions include variables such as job satisfaction and stress, whereas work-related percepriorrs depict how employees perceive their role@) in the organization and their job environment. Individual resources variables describe personal attributes that employees bring to the work setting including needs, nonwork obligations, and demographics. The ordering of variable sets depicted in Figure 1 reflects the idea of psychological proximity from Field Theory (Lewin, 1943). The general tenets of Field Theory suggest that individuals’ behaviours are influenced most immediately by how they react to the environment as they perceive it. In turn, individuals’ affective reactions are products of both how they perceive the environment and individual characteristics. Individual characteristics and perceptions of the environment may influence individuals’ behaviors directly, particularly if they are highly salient in the situation, but more likely such influence is indirect as mediated by their reactions to the situation (cf. Lewin, 1943, 1951; Mathieu, 1991).

Our approach, while rooted in Field theory, is also consistent with other approaches that have been advanced for studying the influences on absence. For example, both Steers and Rhodes ( 1978) and Brooke (1986) suggested that characteristics of individuals and situations influence individuals’ affective reactions which in turn, in combination with nonwork obligations, have more immediate effects on absence. Previous regression based studies (e.g. Watson, 1981; Zaccarro et al., 1991) and structural equation modeling efforts (e.g. Brooke and Price, 1989) have yielded support for this general framework. However, we should note that we decided not to ernploy such formal modeling techniques here. A prerequisite to the use of structural equation techniques is a well justified theoretical model. Given that previous theory and empirical research have yet to examine absence simultaneously in terms of multiple dimensions and metrics, we feel that it would be premature to suggest such a causal system at this time. Our aims are more modest, and are designed to examine whether absence is a multidimensional construct, and to test the mediational hypotheses outlined below.

Hypo theses We adopt the mediational framework (cf Baron and Kenny, 1986; James and Brett, 1984) depicted in Figure 1 that suggests employees’ absences are most likely to be influenced directly by their work-related reactions (e.g. job satisfaction, organizational commitment). In turn, their affective reactions are expected to be influenced by both their individual resources variables and their perceptions of the work environment. Perceptions of the work environment (e.g. role conflict) and/or individual resource characteristics (e.g. child care obligations) may influence their absences directly, although such influences will primarily be mediated by their affective reactions. In effect, we anticipated that affective reactions would partially mediate the influence of work-related perceptions and individual resource characteristics on employee absence. More specifically, given this approach, we advanced the following hypotheses:


Work Related Perceptions

HY: 2A

Absence

Role Conflict Role Amblgulty

Job Scope

Affective Reactions

HY: 1A Organlsatlonal Commitment HY: 3 lllnerr (FIT)

lntrlnrlc Job Satirfactlon Famlly (FIT) Extrlnrlc Job 8atlafactlon Tranrportrtlon (F)

Job Involvement Perronrl (FIT)

Job Tenrlonr Somatlc Tenrlona

Fatigue

/ Individual Resource Characteristics

Age / HY: 1B

Sex / Race HY: 28

Mrr l t r l 8t8tUr Senlorlty

No. of Children Work Ethic

Note. F- Frequency, T. Time loat

Figure 1. Summary of Study Variables and Mediational Hypotheses

(1) Considered as variable sets, both work-related perceptions and individual resource characteristics will have significant unique effects on individuals’ affective reactions (hypotheses I A and lB, respectively). That is, each set of variables will have a significant influence on affective reactions even after the influence of the other set has been controlled (i.e. partialed out statisti- cally).

(2) Considered as variable sets, both work-related perceptions and individual resource characteristics will have significant unique effects on individuals’ absences even after the influence of affective reactions have been controlled (hypotheses 2A and 2B, respectively).

(3) Affective reactions will have significant effects on individuals’ absences even after the effects of work-related perceptions and individual resource variables have been controlled (hypothesis 3).

Given this framework, evidence of complete mediation would exist if the following four conditions hold: (1) work-related perceptions and individual resource characteristics both have significant influences on absences; (2) work-related perceptions and individual resource characteristics both have significant influences on affective reactions (hypotheses 1A and 1 B); (3) affective reactions exhibit significant influences on absence (hypothesis 3); and (4) the influences of work-related perceptions and individual resource characteristics on absences are not significant when affective reactions are controlled (i.e. hypotheses 2A and 2B are not significant). Evidence of partial mediation would exist if conditions 1-3 hold and the influence of work-related perceptions or individual resource characteristics on absences are still significant when affective reactions are controlled (i.e. hypotheses 2A and 2B are supported). Finally, hypothesis 4 is that when the three sets of antecedent variables are used simultaneously to predict absence more than one significant dimension will be obtained. Support for this hypothesis provides evidence of the multidimensionality of absences.

520 S. S. KOHLER AND J. E. MATHEU

Participants Transit operators (N= 194) from a large public authority in a midwestern metropolitan area served as the sample population. This sample is the same as that used in Mathieu and Kohler (1990) and was 88.6 per cent male, 79.4 per cent Caucasian, with an average age of about 45 (see our previous study for sample details). However, our earlier study focused on the influence of two affective responses and only one absence metric. Thus, the current study provides a more eclectic examination of the influences on absence.

Instruments Drivers’ affective reactions, perceptions of their job and work environment, and demographic information were assessed using survey responses. All responses were made on 5-point Likert type scales with anchors particular to each instrument. Scale scores were computed by averaging the items each individual responded to. Higher resulting values mean greater amounts of each variable. A summary of the variables included in this study appears on Figure 1. The specific measures within each category are discussed below.

Mective reactions Seven affective reactions were assessed. All scales exhibited reasonably high reliabilities (i.e. internal consistencies). Organizational commitment was assessed with the 9-item short form (a = 0.88) of a scale described by Mowday, Steers and Porter (1979) that measures the strength of an individual’s involvement in and identification with a particular organization. Job involvement, defined as the importance of one’s job to his or her self-image, was measured using the 6-item short form (a = 0.77) of Lodahl and Kejner’s (1965) scale. Intrinsic and extrinsic satisfaction were assessed using 18 items from the 20-item short form of the Minnesota Satisfac- tion Questionnaire (Weiss, Dawis, England and Lofquist, 1967). Twelve items (a = 0.82) measured Intrinsic satisfaction, defined as the degree of satisfaction derived from the nature of the job itself. Six items (a = 0.70) assessed Extrinsic satisfaction, defined as the degree of satisfaction tangible outcomes associated with the job such as pay.

Jobinduced tension, somatic tension, and general fatigue were measured using scales deve- loped by House and R h o (1972). Job-induced rensions, defined as the extent to which factors specifically related to one’s job affects one’s health and well being, was assessed with seven items (a = 0.84). Somatic tension, defined as the degree of illness experienced by an individual, e.g. ulcers, was measured with five items (a = 0.75), and General fatigue, defined as an overall measure of ill-health of the employee (e.g. tire quickly), was assessed with five items (a = 0.74).

Work-related perceptions The second classification of variables included employees’ perceptions of three aspects of their work environment. Role conflict and role ambiguity were assessed with seven and eight items respectively drawn from House, Schuler and Levanoni (1983). Role conflict, defined as the extent to which there are pressures for conflicting or mutually exclusive behaviors exhibited a scale a = 0.81. Role ambiguity, defined as the extent to which duties or tasks have unclear demands, criteria, or relationships with other duties and tasks had an a = 0.70. Perceptions of five content areas of Job scope, the extent to which the job provides enrichment activities, were assessed. These areas were chosen because they were most applicable to bus drivers’ jobs. Task identity, feedback, and interaction facilitation were measured with scales adapted from

DIFFERENTIATED ABSENCE CRITERlA 521

Stone’s (1974) scale, an3 skill variety and autonomy were assessed using scales drawn from Sims, Szilagyi and Keller (1976). The 17 items from the five scales were summed to yield an overall composite of job scope (a = 0.76).

Individual resource characteristics The third classification contains variables that are specific to each individual. This set included one scale, Protestant work ethic, defined as the extent to which employees feel that personal worth results from self-sacrificing work or occupational achievement, which was measured using the Pro-Protestant aspect of the Blood (1969) Work Ethic Scale (four items, a = 0.50). Other individual resource characteristics were measured using single items as follows: Age; Sex (coded males = 0, females = 1); Race (coded nonwhite = 0, white = 1); Marital status (coded not currently married = 0, married = 1); number of Preschool to senior high children (number of children aged I8 years or younger); and Tenure (number of months with company).

Absence indices Twelve months of absence data were collected from company archives. To provide greater contextual information concerning this sample, a detailed explanation of the transit authority’s absenteeism policy is provided in Appendix A. Two metrics of absence were used: Frequency (number of absence events during the year whatever their duration), and Time lost (the total amount of days absent from the job within the year).

Absence events were recorded by each driver’s assistant supervisor. When drivers were unable to report for work they were required to phone the dispatcher and explain the reason for their absence. In instances when this was not possible, drivers explained the reason for their absence to their assistant manager upon returning to work. It is important to note that the reasons drivers offered for absences are not likely to contain much reporting bias. As noted in Appendix A, in this transit authority, drivers were charged with an absence depending upon whether their run (i.e. bus route) was covered, no matter what their stated reason for absence. Therefore, the motivation to disclaim responsibility for an absence, as compared to other organizations that employ different types of absence policies, should be relatively small.

The various reasons reported for an absence were recorded on Work History Cards maintained for each employee. These reasons fell into four general dimensions that have been discussed in the absence literature (cJ Blau, 1985; Goodman and Atkin, 1984; Hackett et al., 1989): (1) IZlness/injury (e.g. flu, a d w r i s t injuries); (2) Personal (e.g. overslept, had the wrong scheduled departure time in mind); (3) Family obligations (e.g. funeral attendance, sick child); and (4) Transportation problems (e.g. car would not start, poor road conditions). Two coders reviewed the assistant managers’ notes on the Work History Cards and classified each absence instance into one of the four categories, or an ‘other’ category. Cohen’s (1960) kappa was used to assess intercoder agreement at three approximately 4-month long time intervals, using a random sample of 30 Work History Cards. The intercoder agreement on all three occasions was high: Time 1: 115 instances, K = 0.88, p < 0.001; Time 2: 109 instances, K = 0.96, p < 0.001; and Time 3: 118 instances, K = 0.80, p c 0.001. Thus, drivers’ stated reasons for missing work could be reliably classified.

Inspection of absence instances revealed that, as might be expected, all transportation incidents were one day in duration. The transportation time lost index was, therefore, eliminated from further analyses to avoid redundancy with the transportation frequency index. Thus, the 2 (metric) by 4 (dimension) categories, yielded seven (not eight) indices. The means, standard deviations, and intercorrelations among these seven absence indices are provided in Table 1.

522 S. S KOHLER AND I. E. MATHIEU

Table 1 . Descriptive statistics and correlations between absence criteria

Variables 1 2 3 ~~

Frequency 1 . Illness 2. Personal 3. Family 4. Transportationt

5. Illness 6. Personal

Time lost

7. Family x S. D.

0.9 1 * 0.24 0.23 0.14

0.56 0.08 0.18 1.99 2.18

0.85 0.31 0.31

0.19 0.64 0.35 1.22 2.15

0.90 0.23

0.2 1 0.17 0.88 0.71 1.27

4 5 6 7

0.90

0.12 0.99 0.17 0.06 0.90 0.20 0.15 0.20 0.91 0.19 5.79 1.66 0.83 0.49 10.13 3.56 1.73

N = 194.Comlations z(o.1q.p < 0.05; z ~ . U ) ( , p < 0.01. * Diagonal entries are Spearman-Brown Adjusted Odd/Even Week Stability Coefficients (see text for details). t All transportation absences were oneday occurrences.

A h a 1 issue concerns the period over which absence data are aggregated. Absence researchers have used a variety of aggregation periods such as, one year (Mowday and Spencer, 1981), three one-year periods (Ivancevich, 1985), six consecutive four-month blocks (Clegg, 1983), and two six-month periods (Zaccarro et al., 1991). Roberts, H u h and Rousseau (1978) argued that the main reasons for aggregating absence behavior are to ‘increase the stability in our generalizations about something people do over time’, and to ‘increase the variability that can be assessed in what a single person will do’ (p. 94).

For the present study, absence data were collected for a one-year period, six months prior to and six months after the survey measures were administered. Examination of the post survey administration absence data revealed them to be fairly restricted. On average, drivers were only absent three times in the post six month period (for an average of 5 days total time lost). Therefore, absence data aggregated over the entire year were used as criterion measures. Although this strategy yields a design that is not strictly predictive, because our hypotheses are correlational in nature, this does not present a large methodological confound. Further, the stability of the absence measures were assessed by correlating data summed separately for odd and even weeks over the year period (see Steel, 1990, for a discussion of this procedure). Applying the Spearman-Brown prophecy formula to these values provided a range of stability coefficients from 0.75 to 0.99 (see Table 1). Thus, the absence indices exhibited high levels of stability over the 12-month period.

Becausc the multiple absence measures exhibited an average correIation of 0.27 (p < 0.01), canonical correlation analyses were used to test the hypotheses. Furthermore, unlike the use of a separate multiple regression analysis for each criterion, canonical correlation techniques permit one to consider simultaneously the interrelationships among the different absence indices as related to the various predictor sets (Pedhazur, 1982; Thompson, 1991). A series of full and partial canonical correlations were computed to test the mediational framework outlined in Figure 1. First, affective reactions were considered as the criterion variable set, with both


perceptions of the work environment and individual resource characteristics as predictor sets. Part canonical correlation analyses (a statistical multivariate analog of part correlation, see Cooley and Lohnes, 1971) were then employed to determine the unique influences of work environment perceptions and individual resource characteristics as tests of hypotheses 1 A and 1 B.

The second set of analyses considered the absence indices as a criterion set and related them to affective reactions, work-related perceptions, and individual resources variables to determine the unique influence of each set of predictors both separately and in combinations. The results from the partial analyses test hypotheses 2A, 2B and 3, whereas the combined analysis provides a test of hypothesis 4.

Afective reactions as the criterion set Examined separately, both sets of variables yielded two significant (p c 0.05) dimensions (work perceptions: R1 = 0.78, R2 = 0.33; individual resource characteristics: R1 = 0.53, R2 = 0.42), as related to affective reactions. Redundancy coeflcients (RC) were computed to determine the percentage of variance in affective responses accounted for by the predictor sets. Redundancy coefficients refer to the proportion of variance accounted for in the original observed dependent variables by a given linear combination of predictor variables. Because the dimensions (i.e. variates) that link the two sets of variables in canonical analysis are orthogonal, the sum of the predictor redundancy coefficients equals the total variance accounted for in the criterion set given the predictor set (Pedhazur, 1982). Thompson (1984) argued that redundancy coefficients are not truly multivariate because they ‘can only equal 1 when the synthetic variables for the function represent all the variance of every variable in the [criterion] set, and the squared multiple correlation [between the two sets of variables] also exactly equals 1’ (p. 89). He recom- mended the use of adequacy coeflcients (AC) which ‘indicate how “adequately”, on the average, a given set of canonical variate scores perform with respect to representing all the variance in the original, unweighted [dependent] variables in the set’ (Thompson, 1984, p. 25). Accord- ingly, we report both RCs and ACs below, but we relied more heavily on the RCs because they represent more conservative estimates of the percentage of criteria variance accounted for.

Taken separately, work perceptions accounted for RC = 29 per cent of variance (AC = 0.55) of the affective responses and individual resource variables accounted for RC = X per cent (AC = 0.37). The part canonical correlation analysis between work perceptions and affective responses, holding the influence of individual resource characteristics constant, also yielded two significant dimensions (R1 = 0.78, R = 0.30) that together accounted for RC = 28 per cent of the variance (AC = 0.53) among affective responses. The part canonical analysis calcu- lated between individual resource characteristics and affective reactions holding the influence of work perceptions constant also yielded two significant dimensions (p < 0.05) with canonical correlations of R = 0.54 and R = 0.38, that together accounted for RC = 8 per cent of variance (AC = 0.33) in affective reactions. Thus, hypotheses 1A and 1B are both supported.

While it is informative to consider the unique influence of the two predictor sets on affective reactions, many authors have suggested that such a separation may be unrealistic (Roberts, H u h and Rousseau, 1978). In reality, people exist within situations and consequently behavior is thought of as a function of both resources of the individual and of the situation (Lewin, 1943, 195 1; Schneider, 1983). A combined analysis of work-related perceptions and individual resource characteristics in relation to affective reactions yielded three significant dimensions @ < 0.05) with canonical corrrelations of R = 0.82, R = 0.52, and R = 0.44, respectively


that collectively accounted for RC = 36 per cent of the variance (AC = 0.63) of the affective reactions.

Absence indices as the criterion set Partial analyses The second set of analyses considered the various absence indices as the criteria set. First, the relationships between the three predictor sets, taken separately, and absence were examined. As summarized in Table 2, the analyses considering each set of predictors yielded one significant dimension in each instance (work perceptions, R = 0.37; individual resource characteristics, R = 0.55; and affective reactions, R = 0.50) that accounted for RCs = 5 per cent, 10 per cent, and 6 per cent of variance in absence (ACs = 0.38, 0.34, and 0.25), respectively. The second step entailed computing the partial canonical correlations for each predictor set in relation to absence, holding the other two predictor sets constant. The results of these analyses are also summarized in Table 2. Employees’ work-related perceptions did nor relate significantly to the absence set after partialing out the influences of individual resource characteristics and affective reactions. Thus, hypothesis 2A was not supported. Individual resource characteristics, however. uniquely explained RC = 7 per cent of the variance (AC = 0.28) in absence (one dimension, R = 0.50, p c 0.001) while holding the influence of work perceptions and affective reactions constant. Thus, hypothesis 2B was supported. Finally, hypothesis 3 was supported as the aff‘ective reaction set uniquely contributed RC = 3 per cent absence variance (one dimension, R = 0.41, p c 0.05, AC = 0.18) after the influence of work-related perceptions and individual resource characteristics were partialed from the absence set. Taken with the results of the first set of analyses, these findings show that employees’ affective reactions fully mediated the influence of work perceptions on absence, and partially mediated the influence of individual resource characteristics on absence (cf. Baron and Kenny, 1986; James and Brett, 1984).

Combined analysis The final analysis concerned the integration of the three sets of predictors in relation to the absence set. As illustrated in Table 2, there were two significant dimensions (p < 0.05) with canonical correlations of R = 0.64 and R = 0.43, respectively. The redundancy analysis showed that 15 per cent of the variance in the absence indices were accounted for by the three sets of predictors combined (AC = 0.46). To interpret the nature of the relationships between the combined predictor set and the absence indices, structure coefficients (correlations between the original variables and their corresponding canonical variate scores, see Pedhazur, 1982; Thompson, 1991) were used. To clarify the interpretation of the canonical variates the structure coefficient matrix was rotated to a VARIMAX solution which is presented in Table 3 (Krus, Reynolds and Krus, 1976). Coefficients p 2 0.30 (absolute value) are underlined and, as recom- mended by Pedhazur (1982, p. 732), are the only ones used for interpretation.

We labeled the first canonical variate extracted as nonwork obligations. It was defined from the criterion set by frequency of personal absences (0.85), time lost personal absences (0.56), frequency of transportation absences (0.70), along with family-related time lost absence (0.39) and frequency illness (0.34). Extrinsically dissatisfied employees (-0.43) and individuals express- ing role conflict (0.42), role ambiguity (0.35) and somatic tension (0.32) significantly contributed to this dimension from the affective reaction variable set. In addition, race (-0.68, i.e. noncauca- sian), younger employees (-0.39) and those with young children (0.34) exhibited significant contributions to this dimension from the individual resource characteristics set.

We defined the second rotated canonical dimension as stress reactions. This dimension was


Table 2. Summary of partial and full canonical correlations predicting absence

Number of Canonical significant correlation variates* R F RC t ACS

Separate analyses 1. Work perceptions 2. Individual resource

characteristics 3. Affective reactions

4. Work perceptions partialing Partial analyses

affective reactions and individual resource characteristics

5. Individual resource characteristics partialing work perceptions and affective reactions

6. Affective reactions partialing work perceptions and individual resource characteristics

Combined analysis 7. Work perceptions, individual

resource characteristics, and affective reactions

1

1 1

0

1

1

2 First Second

0.37

0.55 0.50

NAB

0.50

0.41

0.63 0.44

1.73 0.05 0.38

2.25 0.10 0 34 2.00 0.06 0 25

NA NA kA

1.88 0.07 0.28

1.64 0.03 0.18

1 .so 0.15 0.46

* p < 0.05. t RC = Redundancy coefficients. * AC = Adequacy coefficients. 0 NA = Not applicable.

identified by frequency and time lost illness (0.73 and 0.78, respectively) from the criterion set. The affective reactions of intrinsic job dissatisfaction (-0.50), extrinsic job dissatisfaction (-OM), and fatigue (0.38), as well as gender (0.66, i.e. women) were significant variates on this dimension from the predictor set.

Discussion

The current study had two primary purposes. First, we were interested in whether separating absence into different types (i.e. dimensions) and metrics (i.e. time lost and frequency) would help to illustrate the influence of different types of predictors on employees’ absenteeism. Second, we tested the hypothesis that employees’ affective responses would partially mediate the influence of their work perceptions and individual resource characteristics on absence. In terms of the first purpose, when the absence criteria set was linked with the combined set of predictors two significant dimensions were identified. Thus, as hypothesized, absence was found to be a multidimensional criterion.


Table 3. VARIMAX rotated structure coefficients linking affective reactions, work-related perceptions, and individual resource characteristics with absences

Canonical variates Nonwork Stress

Variables obligations reactions

Predictor set Organizational commitment -0.12 -0.19 Job involvement -0.1 1 -0.11 Intrinsic satisfaction 0.07 -0.50 Extrinsic satisfaction -0.43 -0.44 Job tensions 0.B 0.23 Somatic tensions 0.31 0.23 Fatigue 0.n 0.38 Role conflict 0.42 020 Role ambiguity 0.33 0.19 Job scope 0.i-i -0.18 Age -0.39 -0.05 Gender (+ women) 0.a 0.66 Caucasian -0.68 -0.V Married 0.m -0.26 Children 0.34 -0.10 Seniority - 0 3 -0.14 Protestant work ethic -0.17 -0.15

Frequency illness 0.34 0.73 Time lost illness 0.n 0.B Frequency personal 0.85 0.04

Frequency family 0.B 0.20

Frequency transportation -

Criterion set

Time lost personal 0.36 0.23

Time lost family 0.39 0.23 0.70 -0.17

Underlined codficients (those z (301) were used for interpretation.

Close inspection of the nature of these two dimensions, that we labeled nonwork obligations and strtw reactions, revealed that many relationships would have been obscured had a single omnibus absence criterion been employed. Further, an examination of the findings on the predictor side of the canonical equations illustrates the importance of separating the absence criteria. Taken as three separate sets, individual resource variables, work perceptions, and affective reactions accounted for 10 per cent, 5 per cent and 6 per cent of absence variance, respectively. Looking at the contributions of the predictor variables to the underlying dimensions from the combined analysis (see Table 3) reveals that, of the 10 that exhibited structure coefficients z (0.301, nine influenced only one of the underlying dimensions. Only extrinsic satisfaction contributed to both canonical dimensions.

In terms of the second purpose of this study, drivers’ affective responses partially mediated the influence of individual resource characteristics on absenteeism. Taken alone, the individual resource variables accounted for 10 per cent of the absence criteria set, although this dropped by 3 per cent when affective reactions and work perceptions were controlled. Clearly, however, most 01’ the influence of individual resource variables on absence were not mediated by affective reactions. Alternatively, work perceptions accounted for a significant amount of absence criteria


variance (5 per cent) when considered alone, but did not relate significantly when affective reactions and individual resource variables were controlled. Thus, affective reactions fully mediated the influence of work perceptions on absence. It should be noted, however, that the high degree of shared variance between the drivers’ work perceptions and their affective responses is also likely to be attributable, at least in part, to common method variance. Although the three sets of predictors were all assessed by survey, potential artifacts such as response biases are likely to infuence the observed relationships between work perceptions and affective reactions more than they would the relationship between individual resource characteristics and affective reactions (see Mathieu and Zajac, 1990).

This study supports previous work revealing absence to be a multifaceted behavior. The specific classifications used here (i.e. illness, personal, family, and transportation) may not be directly generalizable to other industries or investigations (cf. Hackett et al., 1989). Nevertheless, such classifications should continue to be pursued. The relatively low ratio of subjects to variables examined in this study, as well as the use of canonical correlation analyses, further suggests that these results need to be replicated. As future research continues to disentangle absence criteria and begins to identify more specific relationships, we would anticipate more significant dimensions to emerge. Because the absence rates observed in the present study were somewhat restricted, the number of potentially significant dimensions and the percentage of variance accounted for were limited. Further, the criteria distributions were positively skewed (as is common for absenteeism) which also limited the extent to which canonical techniques could account for substantial amounts of criteria variance. Nevertheless, the 15 per cent of absence variance accounted for in this study is roughly comparable to that commonly found in previous studies (e.g. 16 per cent - Fitzgibbons and Moch, 1980; 13 per cent - Johns, 1978; 12 per cent - Watson, 1981; and 22 per cent - Brooke and Price, 1989).

This study also serves as a necessary precursor to more comprehensive structural modeling efforts that should include multiple absence criteria. Future studies are needed to examine whether different structural models are predictive of different types of absence. The insights gained from differentiated absence-predictor relationships also should aid organizations’ ability to understand the nature of their workforce’s absence. Current omnibus absence control programs may be less effective than more targeted organizational interventions. For example, if absence is primarily due to family responsibilities, and further probing shows that most of the cases involve employees caring for sick children, it appears appropriate for the organization to target interventions toward child sick care. One possible organizational intervention might be to contract a professional nursing care program that would dispatch a nurse to the employee’s home to care for the child. Alternatively, provisions could be made so that the sick child could be taken to a healthcare facility versus the more costly in-home care. Not understanding the dimensionality of absence, however, would prevent organizations from targeting such interventions.

Most organizations are unlikely to have only one type of absence occurrence. The implication that follows is that multifaceted interventions are needed to deal with multifaceted absences. For example, rather than offering a uniform incentive program in hopes of reducing absence among all employees, organizations might be better served by introducing a ‘cafeteria benefits plan’ tied in part to absence rates. Such plans could permit employees to align rewards that they value with their attendance/absence behavior. One option could be for employees to cash in non-used ‘absence’ days for health care, child care days, or even as a cash lump sum, and thereby better meet the needs of all employees. In summary, absence is a complex phenomenon that needs to be investigated using multiple criterion measures. The results of such investigations, then, can help target more appropriate interventions for the management of employee absence.


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Appendix A. Detailed description of the authority’s absence policy

The transit authority initiated a new absenteeism policy a year before this study was conducted with the intention of reducing excessive absenteeism system wide and to assist employees in improving their attendance record through counseling and warnings. Assistant managers were responsible for ensuring that an absence was properly recorded on each operator’s Work History Card and that records could substantiate any disciplinary action. Operators were required to initial all entries that were noted on their record.

Policy definition The transit authority’s absence policy defined an occurrence of absence as part of a work day, or a single work day, or consecutive work days in which an employee was not present. It is important to note that for purposes of this study, lateness was differentiated from absence. If an employee reported for work, heJshe was not counted as absent for this study. All occurrences were documented on the driver’s record and some were chargeable and some were not charged. All full-time drivers were allowed nine paid holiday days per year, in addition to two ‘floating’ holiday days (two days of driver’s choosing). Drivers also received between one and six weeks of vacation days a year, depending on the length of their employment. The only stipulation on taking day@) off was the authority’s ability to cover the work, so that no mileage would be lost, arid that no runs were late. Thus, employees’ stated reasons for absences did not determine whether they were ‘charged’ for the instance. For example, requesting time off for coaching a softball game would not be different from requesting time off for elective surgery. Either incident would be charged if the authority denied the request because it was unable to secure a replacement driver. By contract, drivers were allowed 10 paid sick days a year, although a driver must have been out at least three days before receiving back pay for one day sick. Thus, a driver who was sick with the flu and missed five days of work, received two days sick pay although he/she was held accountable (charged) with one occurrence of five days duration.

Discipline procedure Discipline for excessive absence was administered in a progressive fashion and ample opportuni- ties were provided for employees to show improved attendance. The policy consisted of four stages from regular absenteeism status to discharge and included such proactive steps as an optional one day with pay to consider one’s own behavior and write an improvement contract.

Documents

Individual characteristics, work perceptions, and affective reactions influences on differentiated absence criteria