Workforce Analytics and Increased Firm Performance

Amsterdam Business SchoolExecutive Program in Management Studies - Leadership & Management

STUDENT: Mayke den Teuling 11321520SUPERVISOR: mw. dr. C.T. (Corine) Boon

Workforce Analytics and Increased Firm Performance

The influence of Workforce Analytics on the Relationship Between Strategic Human Resource Management and Firm Performance

31 March 2018Final version

Workforce Analytics and Increased Firm Performance i

Statement of Originality This document is written by Student Mayke den Teuling who declares to take full responsibility

for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources

other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion

of the work, not for the contents.

Workforce Analytics and Increased Firm Performance ii

Abstract

Although there is an enormous interest for Workforce Analytics, organizations struggle with

successful implementation. This study examines the role of Workforce Analytics on the

relationship between Strategic Human Resource Management (SHRM) and Firm Performance.

Current literature does not present a view on the role Workforce Analytics has within an

organization and what its effects are; therefore, exploratory research was conducted to

explore the construct Workforce Analytics. Followed by quantitative research among HR

professionals from 107 different organizations, to determine the influence of Workforce

Analytics on the relation between SHRM and Firm Performance. The results showed that

Workforce Analytics is significantly related to SHRM and Firm Performance; however, no

support was found for the moderating effect on this relationship. This paper discusses the

practical & theoretical implications and directions for future research are provided.

Workforce Analytics and Increased Firm Performance iii

Table of Content

1 Introduction 1

1.1 Research question 2

2 Theoretical Framework 4

2.1 Strategic Human Resource Management 4

2.2 Firm Performance 6

2.3 Examining the relation between SHRM and Firm Performance 7

2.4 Workforce Analytics 9

2.4.1 What is Workforce Analytics? 9

2.4.2 Definition of Workforce Analytics 9

2.4.3 Operationalization of Workforce Analytics 10

2.4.4 The role of Workforce Analytics 15

3 Data and method 18

3.1 Research Method 18

3.2 Data collection 18

3.2.1 Qualitative – In-depth interviews 19

3.2.2 Quantitative – Surveys 21

4 Results 26

4.1 Qualitative 26

4.1.1 Definition of Workforce Analytics 26

4.1.2 Organizational benefits of Workforce Analytics implementation 27

4.1.3 Data Quality and availability 29

4.1.4 Workforce Analytics requirements and position in the organization 31

4.2 Quantitative 32

4.2.1 Recoding 32

4.2.2 Missing value 32

4.2.3 Reliability 32

5 Discussion 36

5.1 The construct Workforce Analytics 36

5.2 SHRM related to Firm Performance 38

5.3 Workforce Analytics as a moderator 38

5.4 Implications for research 39

5.5 Implications for practice 41

Workforce Analytics and Increased Firm Performance iv

5.6 Limitations and future research 42

6 Conclusion 43

7 References 44

Appendix 1 – Questionnaire 49

Appendix 2 – HR Analytics Interview Checklist 52

Appendix 3 – Skewness and Kurtosis levels 54

Workforce Analytics and Increased Firm Performance v

List of Figures and Tables

Figures:

Figure 1 HCA as an organizational capability for strategy implementation 2

Figure 2 The LAMP model 13

Figure 3 Conceptual Model (Author's conceptualization, 2017) 17

Figure 4 PROCESS Model 1 35

Figure 5 Normal distribution of the variable Strategic Human Resource Management 54

Figure 6 Normal distribution of the variable Workforce Analytics 55

Figure 7 Normal distribution of the variable Firm Performance 55

Figure 8 Normal distribution of the control variable Organizational size 56

Figure 9 Box-plot of the variable Strategic Human Resource Management 56

Figure 10 Box-plot of the variable Workforce Analytics 57

Figure 11 Box-plot of the variable Firm Performance 57

Tables:

Table 1 Demographic profile of the respondents 23

Table 2 Mean, Standard deviation and Correlations 33

Table 3 Descriptive statistics one-way ANOVA 34

Table 4 Multiple regression table 35

Table 5 Moderator Analysis Hypothesis 3 36

Table 6 Skewness and Kurtosis levels 54

Workforce Analytics and Increased Firm Performance 1

1 Introduction

“Information is the oil of the 21st century and analytics is the combustion engine” a quote by

Peter Sondergaard, Senior Vice President, Gartner Research. A statement which is truer than

ever, with the collection of data by phones, the internet etc. Current technologies have

enabled anyone and everyone – from researchers to housewives to collect and analyse data.

These technical developments have caused a transformation in our thinking and decision

making (Walker, 2014). If it is that uncomplicated to collect data, and data analytics is already

present in many industries. Then how come that data analytics not yet has a dominant place

in HR?

Numerous studies have already stressed the importance of Strategic Human Resource

Management (SHRM) on Firm Performance (Jiang et al., 2012; Becker & Huselid, 2006; Bowen

& Ostroff, 2004; Huselid, 1995; Gerhart, 1996; Buller & McEvoy, 2012). However, the role of

Workforce Analytics is relatively new to this field. Little research has been conducted on how

analytics influences SHRM or Firm Performance.

Seen the recent technological developments, I expect that Workforce Analytics is an

enabler of SHRM. Since the topic is relatively new and there is a lot of scepticism on the future

of HR analytics, I would like to examine if this sceptism is well-founded or a matter of

unknown, unloved. Therefore, I foresee that Workforce Analytics will strengthen the effect of

SHRM on Firm Performance. In the future, Workforce Analytics to my opinion, could

contribute to answering various strategic HR related questions – e.g. Hiring strategies,

Employee performance and Talent Development – these answers on their turn will enhance

Firm Performance.


1.1 Research question

Data analytics has already been introduced to many business domains, you may think of

finance and forecasting etc. When relating to the field of IT, Big Data Analytics is suggested to

by “the next frontier for innovation, competition and productivity” (Manyika, et al., 2011). Big

Data Analytics enables data driven decision making and new opprtunities for organizing,

learning and innovating; leading to operational efficiency and overal Firm Performance

(Wamba, et al., 2017). Building on the positive experiences from other business domains, one

can expect a similar role for Workforce Analytics in the HR domain.

Minbaeva (2017), developed a corresponding model for Data Analytics in HR. Arguing

that the General business strategy supplemented with Workforce Analytics enhances Business

Performance, as shown in figure 1. The model explains Workforce Analytics as an

organizational capability linked to the overall business strategy to achieve superior

performance. According to Minbaeva, “there is a strong need for further theoretical work that

systematically links Workforce Analytics with organizational performance in a strategic

context. Comprehensively identifying and meticulously theorizing the relevant causal

mechanisms and variables involved when proposing that Workforce Analytics, when

developed as organizational capability, can lead to superior organizational performance. To

develop these arguments further, there is a need for explorative, inductive, and process

research in this area.”

Data Quality

Analytical Competencies

Strategic ability to act

General business strategy

HCA as a strategic

business process

HCA as an organizational

capability

Business performance

and SCA

Indiv

idu

als

Pro

ce

sses

Str

uctu

re

P4 P1

P2

P3

Figure 1 HCA as an organizational capability for strategy implementation


This research gap leaded to the following research question:

How does Workforce Analytics influence the relationship between Strategic Human

Resource Management and Firm Performance between firms?

The data to perform this research is collected from HR professionals of medium and large

companies in various industries in The Netherlands. This audience has been selected to

present an insight of the role of Workforce Analytics at Dutch companies and increase the

relevance of the study for a wide audience. This study provides practical implications for HR

practitioners across a medium and large enterprises in all industries, which are interested in

the role Workforce Analytics could play for their organization.

The paper is set out as follows. The next chapter outlines relevant theories on SHRM,

Workforce Analytics and Firm Performance. Followed by chapter 3, the research methodology

and data analysis. Chapter 4 provides an overview of the results, the discussion of the results

follows in chapter 5. Therewith chapter 5 outlines the implications and limitations of the study

and provides suggestions for future research. In the final sections conclusions are drawn and

an answer to the research question is provided.


2 Theoretical Framework

This study investigates the relationships between SHRM, Workforce Analytics and Firm

Performance. SHRM will be analysed using the following leading theories: the 5-P model, Best

Practice, Resource Based View and VRIN-framework (Schuler, 1992; Pfeffer, 1998; Barney,

1991 and Wright, 1994) including the relation with Workforce Analytics. Furthermore, Firm

Performance is examined to explain how Workforce Analytics links to Firm Performance

(Minbaeva, 2017). Hereafter the positive relation between SHRM and Firm Performance will

be explained. Finally, the construct Workforce Analytics is evaluated using the following

theories, HR metrics and Workforce Analytics distinction, LAMP model, Barriers of

implementation and the Abilities Motivation and Interest (Marler & Boudreau, 2017;

Boudreau & Ramstad, 2007; Levenson & Fink, 2017 and Boudreau & Cascio, 2017). Hypothesis

are proposed based on the above-mentioned theories.

2.1 Strategic Human Resource Management

Becker and Huselid (2006), make a differentiation between traditional HR and SHRM. SHRM

focusses on the organizational performance instead of individual performance and second, it

emphasizes the role of HR management systems as solutions to business problems rather than

individual HR management practices in isolation. The role of HR has changed drastically over

the years, from a transactional, traditional to a transformational function. (Lepak, Bartol, &

Erhardt, 2005)

The HR function as we know it today, finds itself in a tipping point fighting for its

relevance and meeting up to its future demands (Boudreau J. W., 2015). However, the

research by IBM revealed, Workforce Analytics highly contributes to the relevance of HR. “…,


at companies where HR uses data to make decisions consistently, HR’s credibility in the

organization increases considerably. …” (SHRM Foundation, 2016). According to research

conducted by IBM (2016), the need for analytics was amplified by the financial crisis. Where

analytics enhanced productivity and efficiency in a challenging environment.

Schuler (1992), defines SHRM, as “all those activities affecting the behavior of

individuals in their efforts to formulate and implement the strategic needs of the business”.

Human resource activities include (1) Philosophy – defining business values and culture, (2)

Policies – shared values (guidelines for action on people reacted business issues and HR-

programs), (3) Programs – articulated as HR strategies, (4) Practices – for leadership

managerial and operational roles, (5) Processes – for the formulation and implementation of

other activities. Wright & McMahan (1992), add to the definition by stating SHRM is “the

pattern of planned human resource deployments and activities intended to enable an

organization to achieve its goals.”

In almost all theories on SHRM one can see the researcher struggle to relate

organizational theories to a ‘soft’ – i.e. non-numeric – business domain. The most well-known

strategic theory on SHRM is the resource-based view of the firm by Barney (1991). The

resource based view (RBV) focusses on the competitive advantage of the firm rather than the

traditional industry-environment focus. Barney, describes the resource based view as “when

a firm is implementing a value creating strategy not simultaneaously beining implemented by

any current or potential competitors and when these other firms are unable to duplicate the

benefits of this strategy”. Referring to the firm’s unique internal resource configuration as a

source of sustainable competitive advantage. To obtain a sustained a sustainable competitive

advantage it is important that the firm resources are hetrogeneous and immobile. To comply

to this, a firms resource should have four attributes: (a) valuable – it should exploit


opportunities and/or neatralize treaths, (b) Rare – not available at current or potential

competitors, (c) difficult to immiate – by current or potential competitors (d) Non-

substitutable of which the last has been alterted over time to (d) supported by the

organization. According to Wright et al (1994), this relates to human resources as follows; (a)

Valuable – Human capital provides value to the firm, because of the variance between

individuals’ contribution to the firm, general human capital can be a source of competitive

advantage through is unique level. (b) Rare – Every person is different and high quality human

capital is rare, due to its normal distribution. (c) Inimitable – How easy competitors can

identify and duplicate the source of competitive advantage. Human capital is inimitable

because of its unique history, causal ambiguity and social complexity. (d) Non-Substitutable –

Human capital is non-substitutable since it can; learn and develop, not become obsolete as it

is transferable across a variety of technologies, products and markets.

This study will measure the level of SHRM with the seven best practices of Pfeffer,

which identify the organizations system producing profits through people – e.g. Employment

security, Selective hiring, Self-managed teams and decentralization of decision making as the

basic principles of organizational design, High compensation contingent on organizational

performance, Training, Reduction of status differences, and sharing of information.

2.2 Firm Performance

The role of SHRM on Firm Performance has often be examined. There has been a shift from

HRM as a source of competitive advantage rather than a cost which should be minimized.

According to Becker and Huselid (1998), this change is a direct result of the rapidly changing

product markets and the corresponding decline of command and control organizational

structures. Since traditional sources of competitive advantage – e.g. quality, technology etc. –


have become easier to imitate the importance of developing a high-performance workforce

has become significantly more important. Both theoretical and empirical work is consistent in

the conclusion that there is a strong relationship between the quality of a firm's HRM system

and its subsequent financial performance (Becker & Huselid, 1998). Firm Performance could

be defined in three ways; (1) Financial outcomes – e.g. profits sales, market share, (2)

Organisational outcomes – e.g. productivity, quality efficiencies and (3) HR-related outcomes

– e.g. attitudinal and behavioural impacts among employees, such as satisfaction,

commitment and intention to quit (Boselie, Dietz, & Boon, 2005). It can be questioned if the

HRM input and a financial output are directly related since there are so many factors, internal

and external, which might affect the organizational performance. Boselie et al. (2005), argue

that the use of more ‘proximal’ outcome indicators, particularly those over which the

workforce might enjoy some influence, is both theoretically more plausible and

methodologically easier to link. They state that productivity (organizational outcome) is

proven to be the most popular outcome variable overall. This study adopted a specific

measure, retaining financial performance and supplementing it with measures on the drivers

of future potential. It is more useful than intellectual capital or a tangible and intangible

approach because it shows cause and effect links between knowledge components and

organizational strategy (Lee & Choi, 2003).

2.3 Examining the relation between SHRM and Firm Performance

As could be derived from the previous paragraphs, the link between SHRM and Firm

Performance is evaluated as positive (Huselid, 1995; Huselid & Becker, 1996). According to

Becker and Gerhart (1996), it is hard to measure the direct effect of SHRM on Firm

Performance. They describe that all claims for positive relations are based on different


measures. Where some studies – e.g. MacDuffie (1995), Huselid (1995), and Arthur (1992) –

based their findings on the concept of contingent pay, the measures differ in each case – e.g.

proportion of Workforce covered by profit sharing or percentage of employment costs

accounted for by bonus. This research will not further elaborate on the substantive

relationship between SHRM and Firm Performance but build on the idea of a positive

relationship as was derived from the literature. According to the literature SHRM correlates

with higher performance since the employee behaviour has fundamental implications for Firm

Performance. “… human resource practices can affect individual employee performance

through their influence over employee’ skills and motivation and trough organizational

structures that allow employees to improve how their jobs are performed” (Huselid, 1995).

According to Sels, et al. (2006), Firm Performance is not directly influenced by SHRM, but via

various mediating variables. Where performance consists of different levels – e.g. individual

performance which contributes to the organizational performance. The study revealed that

there is a strong and positive total effect of HRM on profitability. Huselid (1995), explains this

positive relationship by the influence of High Performance Work Practices have on employee

turnover and productivity. Wright et al. (2003), add to this when employees are managed with

progressive HR practices, they become more committed to the firm. This on its turn leads to

improved quality and productivity. According to Combs et al. (2006), there is strong evidence

that the High Performance Work System – Firm Performance relation is influenced by the

researchers’ choice for performance measures. Which links to the RBV theory, indication

Human Resources as a source of competitive advantages which is permeated in organizations.

This leads to the following hypothesis;

Hypothesis 1: Strategic Human Resource Management has a positive effect on Firm

Performance


2.4 Workforce Analytics

In the following paragraphs the construct Workforce Analytics is explained by the means of

the available literature.

2.4.1 What is Workforce Analytics?

Workforce Analytics is a relatively new term and is also known under the names of ‘HR

Analytics’, ‘Talent Analytics’, ‘Workforce Analytics’, ‘People Analytics’ or ‘Human Resource

Analytics’. The variety in terms suggests the emerging nature of this topic. This study will use

the term: ‘Workforce Analytics’.

2.4.2 Definition of Workforce Analytics

According to Lawler et al. (2004), a distinction should be made between ‘Workforce Analytics’

and ‘HR Metrics’. Where HR metrics are measures of efficiency, effectiveness or impact,

Workforce Analytics represent statistical techniques and experimental approaches to show

the impact of HR activities. Yet, in the literature this distinction is not always made. There are

various definitions explaining Workforce Analytics, varying from broad – a decision making

process –, to more specific – a list of components or specific practices. Among others,

Mondare, Douthitt, and Carson (2011), define Workforce Analytics as demonstrating the

direct impact of people on important business outcomes. Marler and Boudreau (2017), make

a further distinction between ‘HR Metrics’ and ‘Workforce Analytics’ (1) since the latter

involves a more sophisticated analysis of HR data. (2) Analytics includes data from various

internal functions and external data rather than only HR functional data. (3) in order to analyse

and report data Information Technology (IT) systems are needed. (4) it supports people

related decisions. (5) It involves linking HR decisions to business outcomes and organizational


performance, which creates a direct link to SHRM literature. These five components together

create the following definition: “A HR practice enabled by information technology that uses

descriptive, visual, and statistical analyses of data related to HR processes, human capital,

organizational performance, and external economic benchmarks to establish business impact

and enable data-driven decision-making.”

Workforce Analytics should not, by mistake, be seen as an element of SHRM. According

to Minbaeva (2017), Workforce Analytics is an organizational capability linked to the overall

business strategy to achieve superior performance. This organizational capability uses data

related to HR processes and enables data driven decision making which supports SHRM. It

consists of data quality, analytical competencies and the strategic ability to act (Minbaeva,

2017).

SHRM on the other hand, is a set of theories and HR practices through which one

attempts to understand the role of the firm’s human capital pool and the mechanisms by

which it is acquired in achieving sustained competitive advantage (adapted from Boxall, 1996).

So, Workforce Analytics is a separate – and possibly supportive – construct of SHRM.

2.4.3 Operationalization of Workforce Analytics

Although all of the above suggest a buzz around Workforce Analytics, a longitudinal study by

Deloitte (2015), found that 75% of the surveyed companies believed using people analytics is

‘important’, yet only 8% believes their organization is ‘strong’ in this area. They however also

state that “Companies that build capabilities in people analytics outperform their peers in

quality of hire, retention, and leadership capabilities, and are generally higher ranked in their

employment brand”. The 2017 survey does not indicate brighter numbers on the

implementation of Workforce Analytics. Seventy-one percent of the companies see people


analytics as a high priority in their organization, yet the number of companies actually

practicing Workforce Analytics has barely changed compared to 2015. Readiness is one of the

core issues, only 8% of the companies reports to have usable data, 9% believes to have a good

understanding of the performance drivers and only 15% has broadly deployed HR and Talent

Scorecards of line managers (Walsh & Volini, 2017). This leads to the question: “how can

Workforce Analytics be measured?”. The following subparagraphs will provide insights from

the literature and will be enhanced with a qualitative study.

2.4.3.1 The barriers of Workforce Analytics implementation

Levenson and Fink (2017), describe six barriers in the implementation of Workforce Analytics.

(1) The tent is too big: there is no focus in analytics. Too much data is available and companies

include anything numerical on HR; therefore, the focus is limited. Underlying this problem, HR

is originally no ‘hard data science’ but more focussed on ‘soft / people’. As a solution

organizations are advised to have a clear HR strategy and specifically target Workforce

Analytics projects. (2) Increased measurement does not guarantee actionable insights. The

provided solution: begin an analytics project with a question in mind, so you gather data

specifically for that question. (3) Incremental versus step-change improvements. There is not

enough prioritization of analytics topics to improve existing HR processes versus the ability to

improve business performance. This could be solved by focussing on identifying an ideal future

state instead of a backward-looking approach. (4) Devotion to searching out needles in

haystacks. Too much time is lost investigating information that does not really matter,

because of the easily accessible data. Therewith too much time is spent on data mining and

less effort is put into on model building and testing. To solve this problem more and better

quantitative and qualitative data should be collected, instead of exploiting existing data. (5)


Lack of basic hygiene. Databases and data are not cleaned. So, more time needs to be invested

in cleaning data. (6) Criticizing the data. The data validity might be questioned – people are

used to ‘objective’ data on business and technical processes, instead of data of people

measurements like performance. Criticizing the data might also be a way to de-legitimize

usefulness of HR based decision making. Therefore, one must clearly explain how the data is

defined and all questions should be answered, considering a firm line in scope and time. So,

companies do agree that analytics and evidence-based HR are the future; however, the

transition from operational HR to analytics remains troublesome.

2.4.3.2 From Operational reporting to Analytics

To better understand why organizations, struggle to make the transition from operational

reporting to analytics, Boudreau and Cascio make a distinction between “push” and “pull”

factors. (Boudreau & Cascio, 2017).

Whereas the push factors – factors necessary to enable Workforce Analytics – are

evaluated using the LAMP model, see figure 2 on the next page. The LAMP – logic, analytics,

measures and processes – model was introduced by Boudreau and Ramstad (2007), describing

the most critical components of a measurement system to disclose evidence-based-

relationships and make decisions based on the analysis. Where ‘logic’ aims at frameworks

describing the relation between human capital and performance. ‘Analytics’ refers to the

“logical depth to clarify these (analyzed) relationships”. The ‘measures’ element alerts to the

pitfall of heavily investing but failure to make progress in analytics. Finally, ‘process’ relates to

the communication mechanisms which ensure adaptation and action from the decision

makers within the organization. Quoting from the measures element “To be sure, data

management remains a significant obstacle to more widespread adoption of HR analytics […].


That said, it is also far too common that the massive data bases available are still built and

structured to reflect early models of HC analytics […]. At best these kinds of data represent

operational or advanced reporting, and not strategic or predictive analytics that incorporate

analyses segmented by employee population and that are tightly integrated with strategic

planning (p. 122).” The management of data is perceived as very difficult by many

organizations. Furthermore, analytics practitioners often have difficulties presenting their

data, resulting in a lack of support by the line management – do senior line managers see the

value of the insights in light of the business strategy?

According to Boudreau and Cascio the pull factors relate to the ability motivation and

opportunities (AMO) of analytics users. The AMO theory suggests that there are three

independent work system components that shape employee characteristics and contribute to

the success of the organization. According to the theory, organizational interests are best

Figure 2 The LAMP model


served by a system that attends to the employee’s knowledge and skills (ability), increase the

motivation of the employee to perform (motivation) and provide employees the opportunity

to perform (opportunity) (Appelbaum et al, 2000 and Bailey et al, 2001). Relating to the AMO,

six competencies that analytics teams should possess are identified by Andersen (2017). (1)

Excellent statistics and numbers skills. (2) Strong data management skills. (3) Captivating

storyteller. (4) Visualization techniques. (5) Strong psychological skills and (6) understanding

the business. As Boudreau and Cascio (2017), state “a fundamental requirement is that HCA

address key strategic issues that affect the ability of senior leaders to achieve their operational

and strategic objectives” (p. 122). They also state that there are 5 conditions for effective ‘pull’

for analytics delivery. (1) Users must receive the analytics. (2) Users must attend to the

analytics: the data must be useful to the users. (3) Users must believe the analytics: do the

users perceive the data as valid and correct. (4) Users must believe that the analytics suggest

effects that are large and compelling: focus on improving decisions or correcting mistakes. (5)

Users must see implications for their actions/ decisions and must have the power, confidence

and understanding to act on them. According to Green (2017), organizations excelling in

Workforce Analytics have a good understanding that data collection will become impossible

when employees do not trust you. Which not only effects the quality of the data but also

makes Workforce Analytics unsustainable in the long run. So, Workforce Analytics teams

should be fully aware of their legal and moral obligations when collecting and working with

employee data. These six competencies relate to the Ability, Motivation and Opportunities of

analytics users, since users must be able to understand the data that they are working with

and be able to make a valuable analysis of the data. Therewith they must be able to

understand how they compute the data, in order to create reliable, and valid analysis. So,

organizations should, amongst other, provide proper training. The motivation to collect and


compute data is another important factor since it mostly involves confidential or sensitive

information, which has to be treated accordingly from an ethical viewpoint. This motivation

could be influenced by internal and external rewards, employment security etc. HR analytics

practitioners which are influenced by questionable motivations could enable negative

consequences for the participants involved. On the other hand, opportunities can be created

when practitioners meet with the above-mentioned criteria.

Workforce Analytics was operationalized by Minbaeva (2017), alongside 47 items. In

three dimensions and ten sub dimensions; (1) Data Quality – e.g. Data Quality, Data Quantity,

Processes and Data Organization. (2) Analytical Competencies – e.g. KSAs of the HCA team,

Boundary-spanning role and HR business partners & performance implications. (3) Strategic

Ability to Act – e.g. Top management attention, Resource investments, Knowledge of strategic

intent, Results are in use and Other stakeholders. Furthermore, differences in organizational

size, industry, and revenue are likely to affect the relations between Workforce Analytics

activities and Firm Performance. Country should be kept constant since differences in

legislation might affect the outcomes.

2.4.4 The role of Workforce Analytics

From the literature no conclusive understanding of the construct Workforce Analytics could

be developed, since the literature first and foremost explains how Workforce Analytics should

be implemented within the organization and what limitations it might have. Little literature

could be found which elaborates on the influence of Workforce Analytics on Firm Performance

or it’s relation to SHRM; however, research has shown that decision making on the bases of

data has a positive impact on the Firm Performance, since it enables us to measure and

therefore manage more accurately and it allows firms become proactive and forward-looking.


It ca be assumed that this also applies to Workforce Analytics (McAfee, Brynjolfsson, &

Davenport, 2012 and SHRM Foundation, 2016). To further investigate this assumption, this

study will first explore the nature of Workforce Analytics before making inferences about the

moderating effect. With the second question for the qualitative research “how does

Workforce Analytics relate to the relationship SHRM and Firm Performance?”. Data will be

collected and analysed on factors related to Workforce Analytics, possible relations and

underlying motivations; in order to be able to measure the construct Workforce Analytics in

the quantitative study.

Hypothesis 2: Workforce Analytics has a positive effect on Firm Performance

Via the quantitative study the moderating effect of Workforce Analytics and the other

hypothesis will investigated. This study assumes that organizations applying a high level of

Workforce Analytics have more well-founded decision making than organizations who do not

or on a low level apply analytics. Workforce Analytics enhances the effectiveness of SHRM by

decision making based on data and supplement intuitive decision making as is very common

within HR (Rasmussen & Ulrich, 2015). Therewith Workforce Analytics, as was described in

the previous paragraphs, combines the data from multiple fields such as finance, operations

etc. to look at human capital elements in the entire value-chain. The combination of this data

enables HR to make more strategic decisions because “Analytics typically only yields truly new

insights when multiple fields and perspectives are combined (investor perspective, customers,

technology, human capital, safety, etc.) …” (Rasmussen & Ulrich, 2015). So, the SHRM – Firm

Performance correlation should be higher among organizations with a High level of Workforce

Analytics applied. This assumption is however not founded in the literature. No studies have


been found that evaluate Workforce Analytics, or analytics in a broader sense, as a moderator

variable. In all literature (workforce) analytics is evaluated as an independent variable.

However, research has shown that Workforce Analytics is believed to positively influence Firm

Performance (SHRM Foundation, 2016). Therewith, Workforce Analytics is an organizational

capability which supports SHRM, this positive relationship was found in the literature

(Minbaeva, 2017). So, it is expected that Workforce Analytics moderates the relationship

between SHRM and Firm Performance. Where Firm Performance is defined as the position

the organization holds compared to its competitors.

Hypothesis 3: Workforce Analytics moderates the relationship between Strategic

Human Resource Management and Firm Performance.

The hypotheses 1 to 3 are captured in the conceptual model, shown in figure 3.

Figure 3 Conceptual Model (Author's conceptualization, 2017)

StrategicHuman

ResourceManagementFirmPerformance

WorkforceAnalytics

H1(+)

H3(+) H2(+)


3 Data and method

This chapter defines the methods for collection and analysis of the data and identifies the

demographics of the sample and operationalisation of the variables.

3.1 Research Method

The research design on which this study is based is pragmatism, arguing that the most

determinant of research philosophy adopted are the research question and objective. This

study worked with different methods indicating a positivist approach – e.g. the quantitative

survey, alongside a more interpretivist stance, studying the implication of Workforce Analytics

in between firms (Saunders & Lewis, 2012). The research question for the qualitative research

is twofold; (1) how can Workforce Analytics be measured, and (2) how does Workforce

Analytics relate to the relationship SHRM and Firm Performance?

The Research approach in this study is mixed, starting with an exploratory study – e.g.

exploring the construct Workforce Analytics trough qualitative research to form the basis of

the survey design. Based on the insights and understanding obtained from the exploratory

study, a quantitative explanatory study has been designed to look for an explanation beyond

the relationship described in the conceptual model.

3.2 Data collection

The data collected for this study will be of both qualitative and quantitative nature. The

research population for this study includes HR professionals working at firms situated in The

Netherlands. No sampling frame is present, a rough estimate of the population including HR

generalists, HR specialists, HR Directors and Chief Human Resource Officers, in The

Netherlands is 1500 (based on a LinkedIn search on the above-mentioned functions).


3.2.1 Qualitative – In-depth interviews

Methodology and description of the investigation

This paragraph will provide an overview of the qualitative results of the study. The utilization

of Workforce Analytics by the HR departments in relation to Firm Performance was

investigated using a qualitative research design. Which relevance is explained by the lack of

actor-focused research on Workforce Analytics, “to address the issues of description,

interpretation and explanation” (Amalou-Döpke & Süß, 2014). The choice for expert

interviews is twofold; 1. there is no empirically tested knowledge about Workforce Analytics

in the relationship between SHRM and Firm Performance. The research has focused on (the

issues of) implementing Workforce Analytics rather than the added value. 2. Qualitative

interviews give the opportunity to investigate the construct in an open and flexible manner

with room for individual views and context. In order to ensure validity an interview checklist

was developed; starting the interview, the context and significance of this study was

explained. Followed by an introductory question on the background and role of the

interviewee to capture the paradigm and comfort the interviewee. Hereafter, the researchers

quest for factors on HR analytics and Firm Performance started. Questions include among

other; definition of Workforce Analytics, the importance of analytics, data quality and

availability, implications for analytics, impact of analytics on the organisation, performance

measurement of analytics, the level analytics of applied in the organization, basic

requirements for analytics and the position of Workforce Analytics within the organization.

See Appendix 2 – interview Checklist – for reference.

The interviews were concluded with a summary and acknowledgements to thank the

respondent for his attendance. The types of questions included Introductory, specifying,

direct, indirect, structuring and interpreting.


The interviews were conducted in October and November 2017, among HR experts

ranging from HR business partners to HR analytics consultants, with extensive knowledge on

(working with) Workforce Analytics. The interviewees, all from different organizations, were

mainly recruited using work related networks. A total of 7 face-to-face interviews was

conducted whereby the researcher sought in-depth information on the perceived definition

of Workforce Analytics, the extend to what it is used and the roles it could play for the

organization. Three of the interviewees where Workforce Analytics consultants, two HR

business partners and two were members of the HR department. The company size ranged

from 1 to 1,700 employees. The interviews had an average duration of 65 minutes.

Data saturation was reached after six interviews, meaning no new information was

gathered during the interviews anymore.

Description of the analysis

The interviews were analyzed by summarizing qualitative content analysis. At first a category

system was derived from the theoretical framework, next the framework was modified using

a data sample to finalize the categories. The interview outcomes were then coded using the

established framework, thereafter relevant interview excerpts were paraphrased, generalized

and summarized. Minbaeva (2017), provided sub dimensions for coding qualitative analysis,

sub dimensions of this framework were selected and supplemented with the authors

dimensions. The first category (definition of Workforce Analytics) was also defined in the

theoretical framework and is confirmed by the empirical data. The second category relates to

the benefits the organization can gain from implementing Workforce Analytics, and the

priority given to the topic by the organization. This includes the level of Workforce Analytics

applied within the organization – e.g. 1. Reactive; operational reporting, 2. Proactive;


advanced reporting, 3. advanced analysis of 4. predictive analysis. The third main category

addresses data availability & quality and the applications of Workforce Analytics in the

organization. Followed by the requirements to apply Workforce Analytics and the position

within the organization.

The results are discussed in paragraph 4.1 illustrated with quotes from the interviews.

3.2.2 Quantitative – Surveys

The second part of the study, conducted after the interviews, follows a positivistic research

approach, capturing objective reality by survey measures to identify the role of Workforce

Analytics to address the research question. Following this approach literature was explored to

identify the dimensions of Workforce Analytics, the overall impact on Firm Performance and

the moderating role of Workforce Analytics on SHRM and Firm Performance.

Survey, scaling and sampling

The questionnaire-based survey was selected to capture the relationships between the

variables and therefore presents generalizable statements on the research setting (Wamba,

et al., 2017). According to Wamba et al. (2017), surveys precisely depict extreme information

and links between the variables.

This cross-sectional questionnaire adopted previously published multi-item scales, as

will be further discussed in the section ‘measures’. All the variables are measured alongside a

5-point Likert scale (strongly disagree – strongly agree).

The questionnaire is conducted in Dutch but was originally constructed in English.

Conventional translation and back-translation was applied by a Dutch bilingual (Brislin, 1980).

Then the English and Dutch version of the questionnaire were given to yet another Dutch


bilingual to check whether the Dutch questionnaire had achieved the accuracy ‘decentered’

from a literal English language translation.

The data was collected from organizations located in The Netherlands with more than

20 FTE and is targeted at HR professionals in all industries. The responses were collected in

December 2017. The services of a market research firm, with a database with over than

255,000 people, were enlisted to conduct the survey. Organizations are not listed more than

once in the panel to secure a balanced sample. This research firm was selected for its market

knowledge, capacious sample and the professional reputation for quality control. The

questionnaire was distributed to 4,000 respondents using random sampling. All participants

had random identifiers generated by the research firm both to ensure confidentiality and

anonymity, and to permit the subjects to be more candid in their responses. In two weeks the

response of 405 professionals was collected. In the end, 107 useable questionnaires were

collected. Of the respondents, 50.5% has a managing role. The organizations they work at

represent 17 different industries (e.g., with a majority in Industry, 18%, Healthcare, 16% and

financial services 12%). The organizations have on average 51 to 250 employees, with a mode

revenue of 51 to 100 million euro’s.

The demographic characteristics of the respondents and their organization are listed

in Table 1.


Table 1 Demographic profile of the respondents

Dimension Category Percentage (%)

Job Level Operational 19.6

Managing 50.5

Management 29.9

Industry Administrative and support service activities 2.8

Agriculture, forestry and fishing 0.9

Arts, entertainment and recreation 6.5

Construction 6.5

Education 7.5

Electricity, gas, steam and air conditioning supply 0.9

Financial and insurance activities 12.1

Human health and social work activities 15.9

Industry 17.8

Information and communication 3.7

Professional, scientific and technical activities 7.5

Public administration and defense; compulsory social security 4.7

Real estate activities 0.9

Transportation and storage 4.7

Water supply; sewerage, waste management 0.9

Wholesale and retail trade; repair of motor vehicles and motorcycles

4.7

Other services activities 1.9

Organization size 21 -50 employees 20.6

51- 250 employees 36.4

251-500 employees 16.8

501-1000 employees 11.2

>1000 FTE employees 15.0

Organization revenue < 2 million euro 5.9

3-10 million euro 14.1






>501 million euro 8.2

Measures

Strategic Human Resource Management is measured along the 7 best practices from Pfeffer

(Pfeffer, 1998). Within the questionnaire 22 items are devoted to this variable, all of these

items are adopted from the High Performance Human Resource Scale of Sun et al (2007). All

items are measured on a Likert scale ranging from (1) completely disagree to (5) completely

agree. Example questions are:

1) Great effort is taken to select the right person


2) Extensive training programs are provided for individuals in customer contact or

front-line jobs

3) Employees have few opportunities for upward mobility

4) Employees can be expected to stay with this organization for as long as they wish

5) The duties in my job are clearly defined

Workforce Analytics is measured by 37 questions and covers the topics of data quality,

analytical competencies and strategic ability to act. Five example items are listed below, all

items are measured on a five-point Likert scale ranging from ‘completely disagree’ to

‘completely agree’. The questions are adopted from the suggestions of Workforce Analytics

operationalization of Minbaeva (2017). Ten questions have not been adopted from the

questionnaire in response to the pilot study. The respondents could not appropriately

distinguish between these items.

1) We have reliable human capital data that we trust

2) We have standardized key metrics embedded in our reporting

3) I or my team members have the analytical skills needed to run statistical models (e.g.,

regression analysis)

4) We can document the impact of human capital on business performance

5) We make the findings visible to all relevant stakeholders by means of regular

communication

Firm Performance is measured with 5 questions measured on the degree of overall success,

market share, growth rate profitability, and innovativeness in comparison with major

competitors. These five items are measured on a five-point Likert scale ranging from

‘completely disagree’ to ‘completely agree’.

1) Compared with key competitors, our company is more successful.


2) Compared with key competitors, our company has a greater market share.

3) Compared with key competitors, our company is growing faster.

4) Compared with key competitors, our company is more profitable.

5) Compared with key competitors, our company is more innovative. (Lee & Choi,

2003)

Controls. Several firm characteristics served as control variables, since they are likely to affect

the relations between Workforce Analytics activities and organizational performance

(Minbaeva, 2017). Organization size was included as a control variable because larger

organizations may be more likely to use better developed or more sophisticated HR practices

(Jackson & Schuler, 1995). Furthermore, size is assumed to have a direct effect on financial

performance because of economies of scale and market power (Richard, 2000). Organizational

size was measured as the number of full-time employees. Secondly the study controlled for

revenue, since the financial performance is assumed to have a direct effect on the

implementation of Workforce Analytics. Revenue was measured as the (estimated) total

revenues of the organisation over last year (for non-profit organisations the total operational

budget was indicated)


4 Results

This chapter will report on the data analysis of both the qualitative analysis as the quantitative

survey.

4.1 Qualitative

The interview data is structured alongside 5 categories and is discussed accordingly.

4.1.1 Definition of Workforce Analytics

The definitions of Workforce Analytics provided by the interviewees ranged from “digital

methods to develop a deep understanding on how people and business performance relate, to

make better and more reliable decisions” INT5 to “Use of people data to improve decision

making and enhance performance” INT3 “A conversation starter based on facts and trends”

INT2. From the definitions provided by the respondents, one can conclude that the overall

view on Workforce Analytics is using fact-based data to indicate trends and improve decision

making. From the literature we derived that the definition of HR analytics consists of five

elements (1) analysis of HR data – this is also covered in the explanations of all interviewees.

(2) combined use of HR functional data and HR data - most HR practitioners only addressed

the use of HR functional data, whilst the consultants insist on using more data sources. (3) IT

systems - during all interviews IT systems were mentioned as a source of data for analytics

projects, as previously mentioned part of the interviewees only addressed HR functional IT

systems, consultants tend to include financial and other external IT systems to analyze and

report data. (4) Supporting people related decisions – all interviewees addressed people

related decision making as one of the key characteristics for Workforce Analytics. (5) Link HR

decisions to business outcomes and organizational performance – depending on the level of

Workforce Analytics applied in the organization, this subject was addressed. Companies


applying level 1 or 2 analytics – e.g. operational reporting, advanced reporting – did not

mention organizational performance as a link with business outcomes or organizational

performance in their definitions, whilst companies applying level 4 – predictive analytics –

automatically included it in their definition (see paragraph 2.4.2). Recap; the definitions

provided by the interviewees is in line with the definition provided by the literature, “A HR

practice enabled by information technology that uses descriptive, visual, and statistical

analyses of data related to HR processes, human capital, organizational performance, and

external economic benchmarks to establish business impact and enable data-driven decision-

making.” The operationalization of Workforce Analytics by Minbaeva is comprehensive, the

interviews did not lead to replenishment of any components of Workforce Analytics. The

perception of analytics however, is not in line with the literature. Where researchers see

analytics as building causal models to explain and predict (Minbaeva, 2017); practitioners see

correlation and basic descriptive analysis as true analytics. This indicates a gap in perception

between researchers and practitioners; this also shows the importance for dividing the results

in this study in ‘level of analytics applied’. Comparative analysis have been conducted to

review the discussed topics on level of application.

4.1.2 Organizational benefits of Workforce Analytics implementation

From the interviews was derived that HR analytics leads to several benefits, depending on the

level of application within the organization.

Operational reporting and advanced reporting brought new insights to organizations

on topics such as diversity and inclusiveness. “Generating reports on the division of male/

female in teams, provided insights for our hiring strategy. In order to comply to our diverseness

strategy, we now could define focus in our teaming and recruiting strategy” INT 1. Visualizing


data and combining data from solely different functional HR systems, provides a better

understanding of the workforce and gives the first tangible measures to see whether the

organization is performing in line with its HR strategy. This is not in line with the suggested

practices from the literature. The literature shows that the most persuasive analytics stories

consist of people, operating and financial data along with qualitative analytics (Boudreau &

Cascio, 2017). When organizations evolve to more advanced analytics, they learn how to

combine the data from multiple HR-, operational- and financial systems.

Organizations struggle to reach a more advanced level of analytics, due to scattered

data and lack of basic data hygiene – the HR systems in use do not provide consistent data,

and it is not clear which system is leading. Therewith, HR professionals are often not equipped

with outstanding analytical skills. They find it difficult to define which data entries to use in

performing analytics. Which accounts for the situation sketched by Deloitte (2017), that 71%

of the companies declare Workforce Analytics has a high priority within the organization,

whilst only 8% of the organizations have usable data. Professionals simply struggle to define

the term ‘usable data’. Usable data for many practitioners is data on current status of

employment, compliance with regulations & laws, gender and cost of employees etc. This data

is not the same data as where the literature refers to, since this solely represents operational

reporting (Boudreau & Cascio, 2017). Although operational reporting can be informative, it

also tends to lay focus on the operations of the HR function. This deviates from the intention

to affect human capital decisions and investments to enhance organizational performance.

Seeing the bigger picture is also a burden to move along with analytics. When HR wants

to comply with the organizational vision or strategy, forecasts need to be developed. Where

most CFO’s master the art of forecasting, this procedure is not yet familiarized within HR. From


the interviews was derived that HR professionals struggle with forecasting; they are not used

to defining different scenarios with corresponding solutions for the future.

An insight which contributes to the current literature: the need for forecasting often

emerges when systematic reports start to raise questions. The ‘why question’ is triggered by

the systematic reports but generating answers from existing systems remains troublesome.

To answer Ad hoc ‘why’ questions, one uses an opportunistic approach to generate more

insightful analysis. The transition between the phase of advanced reporting and advanced

analytics is where 60% of the interviewees finds itself.

In this stage, many organizations seek advice from an analytics consultant. The

consultants support in building a mature analytics platform. Predictive Workforce Analytics is

used within organizations to make better business decisions and enhance organizational

performance.

4.1.3 Data Quality and availability

As was described above, the interviews have indicated that the availability of data is often a

major issue for HR practitioners starting with Workforce Analytics. Organizations which are

more experienced with Workforce Analytics have found more creative ways to work with the

available data. Therewith the increase the data entry points by combining data sources apart

from solely functional HR systems. For example, a payroll system already provides an

extensive list of data entry points per month, one can think of Employee name, Manager, Job

title, Date of Birth, Date of hire, Location, Gender, Cost center etc. This data can already

provide a lot of insights on questions such as ‘how big is my failed hire problem?’.

Less experienced analytics users tend to use data availability as an explanation for not

starting with advanced analytics. This trend is also recognized by more experienced analytics


users. According to the experienced users the availability of data should never be the excuse

not to get involved with analytics. Organizations should start with a clear question in mind

from there on, the available information should be gathered. One can better start with little

information and when required start collection additional information through questionnaires

etc. then the other way around. You will realize you dispose of more information than

expected beforehand.

The quality of the data raises a bigger concern. In order to provide insightful analysis,

the data input should be of high quality. Too often organizations first need to clean their data,

since the consistency between the data is lacking. The reason behind the poor quality was not

yet provided by the literature; however, all interviewees describe the problem of lack in

consistency between multiple systems. Multiple systems keep records of the same data points

yet provide different values. “One of our HR systems provided the number of FTE in our

company, where one of our other HR systems provide a different number which might deviate

2 or 3 FTE”. The interviewees indicated this is the biggest challenge for generating insightful

analytics. Quoted from the interview “crap in leads to crap out” INT6. These findings are in

line with the literature; “Notably, most firms do not know what types of data are already

available to them or in what form. In fact, most firms do not have the answers to some basic

questions: What data do we have? Where do we store it? How was the data collected? What

rules were applied? How can multiple data sets be merged into one? What are the advantages

and disadvantages of each data set? How and when are organizational changes registered?”

(Minbaeva, 2017).


4.1.4 Workforce Analytics requirements and position in the organization

To enlarge the strategic impact of Workforce Analytics, support form top management is

required.

The interest of top management can be grasped by clear and understandable

communication (story telling) and result visualization. The interviews indicated that

organizations who are experts in transforming results into compelling understandable stories

and visual presentations, have great support from the top management. Since they equip the

top management with understandable tools for action for their most pressing problems. Not

only top management should be open to analytics, also the rest of the organization should be

on the same page. It helps when this happens top down – top management acknowledging

the importance of Workforce Analytics for the organization. This can be further enhanced by

transparency on the objectives, processes and results of Workforce Analytics and stimulation

of active participation. This is in line with the findings of Minbaeva (2017), who states that the

development of Workforce Analytics requires (1) a research culture and a habit of evidence-

based decision making and (2) providing tools for action to the management for strategic

discussions.

The interviews show that organizations applying level 1 or 2 of Workforce Analytics are

often insecure on how to apply analytics, or do not have the right knowledge, skills and

abilities. For these organizations the support of an external Workforce Analytics expert can

provide a solution. The interviews do reveal that Workforce Analytics competencies also need

to be developed in-house to preserve a long lasting organizational capability. Therewith,

involving analytics specialists from other departments is argued to be a very comprehensive

idea. “It is easier to learn analytics specialists HR skills, than trying to bring Analytics skills to

HR” INT3. This is also addressed by Rasmussen and Ulrich (2015), in their statement “Take HR


analytics out of HR”. Aiming at intensive corporation with other departments (finance,

operations etc.), to make Workforce Analytics part of end-to-end analytics.

4.2 Quantitative

This section will report on the results of the data analysis.

4.2.1 Recoding

The statistical analysis was performed using the Statistical Software Package for Social

Sciences (SPSS). Counter-indicative items have been recoded, including several items for

Workforce Analytics and SHRM.

4.2.2 Missing value

The data file was checked for missing values. When missing values were identified the missing

value was substituted with the mean of the variable. This could lead to artificial deflation of

variation and has the potential to change the value of the estimates. (Pajic, 2017). The number

of missing date was <10% for all variables, except for organizational revenue (20%).

4.2.3 Reliability

Descriptive statistics, skewness, kurtosis and normality tests have been computed for all

variables. See appendix 3 for the skewness, kurtosis tables.

Checks ensuring reliability of the data were conducted for Workforce Analytics, SHRM,

Firm Performance, Organization Size and Revenue. The Cronbach’s Alpha was tested for all of

the variables. For SHRM one of the items substantially affected the reliability – e.g. ‘promotion

in this organization is based on seniority’ – the item was therefore deleted. Furthermore, the

corrected item-total correlations indicate that two items do not have good correlation with

the total score of the scale (item below .30), these two items were therefore deleted. After


deletion of the necessary items, SHRM has a high reliability (Cronbach’s Alpha of .875). For

Workforce Analytics the Cronbach’s Alpha is .919, no items substantially affected the

reliability. However, 7 items of WFA were deleted since they did not have a good correlation

with the total score of the scale. The Cronbach alpha for all variables is listed in table 2.

The mean was computed for all the items that were used to measure one variable. The

means and standard deviations are exhibited in table 2.

Table 2 Mean, Standard deviation and Correlations

M SD 1 2 3 4

SHRM 3.89 .55 (.919)

Workforce Analytics 3.78 .60 .678** (.875)

Firm Performance 3.71 .72 .613** .705** (.882)

Organization Size 2.06 .68 .164 .026 .056

Revenue 1.85 .72 .110 -.102 .050 .520**

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Upon examining the proposed hypotheses, the relationship between SHRM and Workforce

Analytics were tested. A One-way ANOVA test was computed. Table 3 provides the statistics

of the different groups. There was a non-significant effect of Organization Size on Workforce

Analytics, F(2, 104) = .35, p< 0.05. Tukey post-hoc tests revealed that there was no statistically

significant difference between the perceived level of Workforce Analytics in the large

Organization Size group compared to the Medium Organization Size group (p= .89), and small

Organization group (p=.94). Also, no statistically significant difference of the medium-

Organization Size group with the low-Organization Size group (p = .70) was found.


Table 3 Descriptive statistics one-way ANOVA

SS DF MS F Sig.

Organization Size .255 2 .128 .345 .709

Error 38.49 104 .370

Total 38.75 106

Organization Size M SD N

Small 3.70 .54 22

Medium 3.82 .58 57

Large 3.75 .71 28

Total 3.78 .60 107

Hierarchical multiple regression was performed to investigate the ability of SHRM and

Workforce Analytics to understand the levels of Firm Performance, after controlling for

Organization Size and Revenue.

In the first step of hierarchical multiple regression, two predictors were entered:

Organization Size and Revenue. This model was not statistically significant F (2, 82) = .75; p >

.05. After entry of SHRM and WFA at Step 2 the total variance explained by the model as a

whole was 53% F (4, 80) = 22.71; p < .001. The introduction of SHRM and WFA explained

additional 51% variance in Firm Performance, after controlling for Organization Size and

Revenue (R2 Change = .51; F (2, 80) = 43.88; p < .001). In the final model two out of four

predictor variables were statistically significant, with Workforce Analytics recording a higher

Beta value (β = .54, p < .001) than SHRM (β = .26, p < .05). In other words, if Workforce

Analytics increases for one, their Firm Performance will increase for 0.54. On the other hand,

if organizations SHRM increases for one, the Firm Performance will increase for 0.26. The

results of this regression indicate that both Workforce Analytics and SHRM positively and


significantly relate to Firm Performance, supporting both hypothesis 1 and hypothesis 2. The

details of the multiple regression analysis are exhibited in table 4.

Table 4 Multiple regression table

R R 2 R 2 Change B SE β t

Step 1 .13 .02

Organization Size .15 .14 .15 -.92

Revenue -.03 .13 -.03 1.86

Step 2 .73 .53*** .51**

Organization Size -.03 .10 .09 -2.27

Revenue .10 .10 .23 .40

SHRM .32 .13 .26* 4.40

Workforce Analytics .64 .13 .54*** 8.34

Note. Statistical significance: *p <.05; **p <.01; ***p <.001

To understand whether Workforce Analytics moderates the relationship between SHRM and

Firm Performance, the SPSS macro PROCESS by Andrew F. Hayes was used. The conceptual

and statistical model for simple moderation were applied, see figure 4. M represents the

Moderator, X the independent variable, Y the dependent variable, and XM the product of X

and M. For visualisation see figure 4.

The moderation analysis of the model is displayed in figure 4 significant with F (5, 79) = 24.28,

p<.01. The moderation effect is not significant (P =.63), so there is no sufficient evidence of

moderation. Hypothesis 3 is therefore rejected. Meaning that we cannot verify that

M

X Y

X

Y M

c1

c2

XM

c3

Figure 4 PROCESS Model 1


Workforce Analytics affects the direction and/ or strength of the relationship between SHRM

and Firm Performance. Table 5 provides an overview of the moderation analysis.

Table 5 Moderator Analysis Hypothesis 3

Variable Coeff Std. Error t p LLCI ULCI

Constant 3.60 .25 14.31 .00 -3.10 4.10

Workforce Analytics .64 .15 4.19 .00 .34 .95

SHRM .31 .17 1.77 .08 -.03 .66

Int_1 .07 .15 .47 .63 -.23 .38

Revenue .10 .10 .97 .33 -.10 .30

Organizational size -.03 .09 -.31 .75 -.22 .16

5 Discussion

To answer the research question, ‘How does Workforce Analytics influence the relationship

between Strategic Human Resource Management and Firm Performance between firms?’

Workforce Analytics does not moderate the relationship between SHRM and Firm

Performance, although it does positively influence Firm Performance.

5.1 The construct Workforce Analytics

A number of conclusions can be drawn from the research, both with respect to Workforce

Analytics measurement was well as to the existing literature on Workforce Analytics.

Four contributions can be made to the discussion on Workforce Analytics

measurement: first, the perception of analytics is not in line with the literature. Where

researchers see analytics as building causal models to explain and predict (Minbaeva, 2017);

the practitioners are not yet that far along, also depending on the level of application of

analytics, they see correlation and basic descriptive analysis as true analytics. Second, this gap

in perception identifies the need for distinction in levels of analytics applied. The different


levels of analytics applied to not only address a difference in perception, but also show

different focus areas depending on the level of expertise.

Third, resource dependency of Top Management. The Workforce Analytics

practitioners need to understand which are the top managements’ most pressing questions.

Only when the gains from the HR data analysis are greater than it can be received from other

sources, the top management is resource dependent. It is not only the data that is a critical

resource, but the understandable interpretation of the data, as well as the measures and

advice that arise from it (Amalou-Döpke & Süß, 2014). Nevertheless, compared to the finance

department, as the most established analytics department in most organizations, the HR

department still needs to measure more proactively (Amalou-Döpke & Süß, 2014). Currently

more than half of the interviewees find themselves on operational/ advanced reporting

justifying itself and to communicate its performance to the organization. In the coming years

organizations should proceed to the next – e.g. advanced and predictive – levels of analytics

to support and influence decisions and positively influence Firm Performance.

Fourthly, the Workforce Analytics – Firm Performance link. The performance

enhancing role of Workforce Analytics was immediately mentioned and highly present at the

interviewed organizations applying level 3 or 4 analytics. However, organizations applying 1

or 2 analytics did not mention organizational performance as a link with business outcomes

or organizational performance in their definitions. Workforce Analytics is, regardless of the

level of analytics applied, seen as a separate but supportive construct of SHRM. According to

the interviewed organizations, Workforce Analytics uses data to enable data driven decision

making which supports the strategic HRM activities of the company.


5.2 SHRM related to Firm Performance

In this study the relationship between SHRM and Firm Performance was examined. The results

show that SHRM is significantly related to Firm Performance. SHRM correlated significantly

with Firm Performance (r=.61). This correlation is much higher than the correlation Chang and

Huang (2005) found (r=.14). The regression analysis shows that SHRM is significantly positively

related to Firm Performance. This means that when an organization has a high level of SHRM

the Firm Performance will increase. These findings are in line with Huselid (1995) and Becker

and Huselid (1998). Huselid concluded that the use of HPWS reflects in lower turnover, greater

productivity and higher financial performance (Huselid, 1995).So, Hypothesis 1: Strategic

Human Resource Management has a positive effect on Firm Performance is accepted.

5.3 Workforce Analytics as a moderator

It was expected that Workforce Analytics would influence the relationship between SHRM and

Firm Performance. Minbaeva (2017), shows that Workforce Analytics presumably positively

affects Firm Performance when seen as an organizational capability. Furthermore McAfee et

al. (2012), explain that decision making based on data leads to higher Firm Performance.

Unexpectedly the regression analysis showed that Workforce Analytics does not have a

moderation effect on the relationship between SHRM and Firm Performance. A possible

explanation for this unexpected result is the fairly high correlation between the constructs,

where Workforce Analytics correlated significantly strong with SHRM (r=.67). Meaning that

when a higher level of Workforce Analytics is measured, SHRM also increases. It might be that

quite small differences in level of Workforce Analytics led to minimal statistical effects. The

research sample was fairly homogeneous with relatively small standard deviations of

Workforce Analytics (M = 3.78, SD = .60). Therefore, it could be that the level of homogeneity


of the sample resulted into smaller statistical effects, whereby the effect of Workforce

Analytics could not be found. Although Workforce Analytics does not have a moderating effect

on the relation between SHRM and Firm Performance. As expected Workforce Analytics has

a positive main effect on Firm Performance (β=.54) and is strongly correlated with Firm

Performance (r=.70, p<.01). The findings of the main effect are in line with McAfee et al.

(2012).

To conclude, by testing the hypothesis no sufficient evidence was provided that

Workforce Analytics positively influences the relationship between SHRM and Firm

Performance, so hypothesis 3 was rejected. Hypothesis testing for hypothesis 1, provides

evidence that Firm Performance is positively influenced by a higher level of SHRM. Workforce

Analytics has a positive main effect on Firm Performance (hypothesis 2), but it does not

moderate the effect of SHRM on Firm Performance (hypothesis 3).

5.4 Implications for research

From this research several important theoretical contributions can be derived. First numerous

theoretical perspectives on SHRM have been adopted to extend previous models of the SHRM

and Firm Performance relationship Huselid (1995), Huselid & Becker (1996), and Becker &

Gerhart (1996). Based on these theories, this study shows that SHRM positively relates to Firm

Performance. Moreover, this study added the moderated effect to the SHRM – Firm

Performance relationship. Recently researchers called for explorative, inductive and process

research to determine how workforce analytics as an organizational capability can lead to

superior performance and ultimately competitive advantage (Minbaeva, 2017). With the help

of several analytics techniques the moderating model was tested, providing support for the

theoretical propositions that Workforce Analytics has a positive main effect on Firm


Performance, and unexpectedly revealed that the SHRM – Firm Performance was not

moderated by Workforce Analytics. The findings of this current study suggest for future

research to explore the moderating effect of Workforce Analytics.

One major contribution of the study to the strategic SHRM literature is the importance

of differentiating between the different levels of Workforce Analytics application within

organizations. The field is not as far in the perception of analytics as the literature is. The

majority of the organization finds itself performing analytics on the operational reporting

level. This finding is important in both theory and methodology of measuring Workforce

Analytics. For theory, these findings challenge previous work which assumes that Workforce

Analytics in organizations functions in the same pattern. The findings of this study show that

different levels of Workforce Analytics require different approaches and request for different

methodologies to move up the analytics ladder. For example, organizations performing

Workforce Analytics on the level of organizational reporting request for information on how

to apply analytics, whilst organizations performing advanced analytics require additional

literature on building analytics as an organizational capability to enhance performance. This

result is consistent with Minbaeva (2017), who suggest organizations to understand the needs

and requirements to continue to the next stage of analytics. Therefore, I encourage additional

research to explore the different needs and applications for the different levels of analytics.

The methodological implications imply that the different levels of Workforce Analytics

have unique effects on Firm Performance, not including these dimensions may compromise

the overall impact of Workforce Analytics on Firm Performance or lead to inaccurate results.

As an alternative, researchers might categorize Workforce Analytics into four dimensions and

explore their main effects on Firm Performance (Gardner, Wright, & Moynihan, 2011).


5.5 Implications for practice

Quite a lot of literature on Workforce Analytics discusses the HR function for not being

prepared to meet with the high demands of analytics. The results of this study show that HR

is struggling to move from operational reporting towards advanced analytics. To resolve this

issue, I suggest to involve other departments such as finance who have credibility in building

statistical models, they have a better understanding on the data and know how to use it.

Furthermore, to strengthen the analytical competencies, effort has to be put in to build

technical knowledge, business knowledge and relational knowledge related to Workforce

Analytics. Which is a little less drastic measure than “Take HR analytics out of HR” as was

suggested by Rasmussen and Ulrich (2015). This study indicates that Workforce Analytics

should be developed as an organizational capability to ensure a sustainable future for the

Analytics function, as was also discussed by Minbaeva (2017).

One of the most important findings of this study is the importance of differentiating

between the different levels of application of Workforce Analytics. Since all levels require a

different approach, as Minbavae (2017), rightly remarks it might even be more important for

an organization to fully understand the needs and requirements to evolve to the next level of

Workforce Analytics in their organization.

The buy in from the top of the organization can be an important driver for the analytics

journey, to ensure adoption and create momentum in the organization. Furthermore, the

results of this study show that the impact of the analytics is enhanced when the results are

presented in an understandable manner and include a conveying story.

A final remark to HR practitioners: be curious! When asking the ‘why question’ one

uses an opportunistic approach to generate more insightful analysis, which helps you to move

from operational reporting to advanced analytics. Do not hide behind excuses of data


availability or the quality of the data, look around and seek advice from the organizations

around you to find more creative ways to work with the available data.

The positive main effect of Workforce Analytics on Firm Performance clearly shows

how Workforce Analytics can be leveraged as a source of sustainable competitive advantage

to enhance Firm Performance.

5.6 Limitations and future research

Although I believe that the research model of this study is firmly grounded and is tested with

reliable instruments and data, this study encounters some limitations

First, this study was conducted within The Netherlands, although SHRM and Workforce

Analytics are by its nature are context specific, imitating this study within other western

countries would enhance the generalizability. Secondly, the data was tested with cross-

sectional data, retesting the data using panel data is suggested to investigate the stability.

Thirdly, perceptual measures for Firm Performance were used, these could be replaced by

objective measures to concretize the impact of Workforce Analytics on Firm Performance.

Fourthly, some items of the variables SHRM and Workforce Analytics were counter indicative.

Jak and Evers (2010), depict that reverse scored items could have a negative effect on the

reliability and validity of the scales. According to Jak and Evers (2010), there is a difference in

measurement between positively and negatively formulated items. During the analysis of the

results it showed that indeed the counter indicative items (11 in total) caused problems with

reliability (1 item) and correlation (9 items) and were therefore deleted. Fourthly, the analysis

showed a high correlation between the variables. Indicating that possibly two scales might

have measured the same construct (i.e. respondents could not appropriately distinguish

between the attributes). Finally, organizational culture was not taken into account in this


study, this might be added as a second moderator variable in future research. Further research

with large-N empirical studies with longitudinal data on the positive effect of Workforce

Analytics on Firm Performance are recommended, to provide further insights in this

relationship.

6 Conclusion

The study investigated the influence of Workforce Analytics on the relationships between

SHRM and Firm Performance. The results showed that SHRM is significantly positively related

to Firm Performance. This means that practice of SHRM within an organization leads to an

increased Firm Performance. Based on the literature, I hypothesized that Workforce Analytics

moderates this relationship. However, this expectation was not supported by the results.

Workforce Analytics did not moderate the relationship between SHRM and the performance

of the firm. Furthermore, I hypothesized that the Workforce Analytics would have a positive

effect on Firm Performance. I found that Workforce Analytics has a significant influence on

Firm Performance. When a company decides to implement Workforce Analytics the

performance of the firm will increase.

These findings will contribute to the literature that Workforce Analytics could lead to

increased organizational performance, however not as a moderator. It also indicated that

organizational size and revenue do not affect Workforce Analytics, eliminating the chicken –

egg discussion – e.g. higher revenues lead to Workforce Analytics implementation vs

Workforce Analytics implementation leads to higher revenue.


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Appendix 1 – Questionnaire

Dimensions Sub dimensions

Questions Overall: To what extent do the following statements correspond to/ describe your company’s current situation? Scale: Strongly Agree; Agree; Neutral; Disagree; Strongly Disagree; Do not know/does not apply

Selective staffing Great effort is taken to select the right person

Long-term employee potential is emphasized

Considerable importance is placed on the staffing process

Extensive training Extensive training programs are provided for individuals in customer contact or front-line jobs

Employees in customer contact jobs will normally go through training programs every few years

There are formal training programs to teach new hires the skills they need to perform their job

Formal training programs are offered to employees in order to increase their promotability in this organization

Internal mobility Employees have few opportunities for upward mobility

Employees do not have any future in this organization

Promotion in this organization is based on seniority

Employees have clear career paths in this organization

Employees in customer contact jobs who desire promotion have more than one potential position they could be promoted to

Employment security Employees can be expected to stay with this organization for as long as they wish

Clear job description My job has an up-to-date description

Results oriented appraisal

Performance is more often measured with objective quantifiable results

Performance appraisals are based on objective quantifiable results

Employee appraisals emphasize long term and group-based achievement

Incentive reward Individuals receive bonuses based on the profit of the organization

Close tie or matching of pay to individual/group performance

Participation Individuals are allowed to make decisions

Employees are provided the opportunity to suggest improvements in the way things are done

Data quality

In this section, you will be asked to evaluate the quality, availability, and quantity of human capital data in your company. Human capital data refers to all kinds of people-related data, including demographic data, data about

Data quality We have reliable human capital data that we trust

The human capital data that we have available is mainly unstructured/unorganized

We have many incorrect entries in our human capital data

Our human capital data is difficult to integrate (e.g., because it is stored in different places)




HR practices (e.g., compensation, talent, and development), and soft performance data (e.g., engagement, satisfaction and turnover).

In our human capital data, we can trace an individual's movements within the organization

Data quantity We have a large amount of human capital data

We have human capital data that has been collected over several years

Processes We have implemented an enterprise resource planning (ERP) system that we use to collect, store, and manage human capital data

We have standardized key metrics embedded in our reporting

We are able to blend internal corporate data with data from external sources (e.g., public information, market growth, and trends)

We have processes in place to ensure the quality of our data (e.g., training and handbook/guidelines for data entry)

Data Organization We only have individual-level human capital data on our managers

We use our human capital data for regular operational reporting

Analytical competencies

In this section, you will be asked to evaluate your company’s analytics capabilities. These statements refer to your statistical skills and those of your team, your abilities and those of your team to visualize and communicate the results, and to your more general abilities.

KSAs of the HCA team I or my team members have the analytical skills needed to run statistical models (e.g., regression analysis)

We use Microsoft Excel to analyze our human capital data

I or my team members have all of the analytical skills needed to satisfy business demands for human capital analytics

We can easily “tell a story” from our results

I or my team members are capable of using different statistical software packages (e.g., SPSS, SAS, R, or Stata)

We are unable to derive analytical models that can help us answer business questions

We lack the skills needed to produce standardized key metrics

We are good at visualizing our results for communication purposes

Boundary-spanning role

If I need analytical skills, I know who to contact in my organization (beyond my team)

We attend conventions, courses, and seminars in order to stay up to date on current trends

We integrate academic research and external statistics into our work

HR business partners and performance implications

HR business partners are “consumers of analytics” (e.g., they generate hypotheses, evaluate results, and develop recommendations)

We can document the impact of human capital on business performance

HR business partners are able to easily draw managerial implications from the results of analytics projects

Strategic ability to act

In this section, you will be asked to evaluate your company’s strategic ability to act. This refers to whether top management supports human capital analytics projects and whether

Top management attention

The insights that we produce from our data are taken seriously by top management.

We regularly communicate insights gained from human capital analytics projects to top management.

Resource investments We have top management's support for human capital analytics projects.




the results of such projects are used for change management.

Our company makes human capital analytics a priority by investing in them.

Knowledge of strategic intent

We are aware of the key business challenges that our business will face in the next few years.

We proactively search for interesting new questions that can be investigated through the use of human capital analytics.

Results are in use Organizational politics prevent the implementation of the evidence-based decisions that we suggest

We inspire relevant organizational stakeholders (e.g., line managers and HR business partners) to take action on the basis of our findings

We have success stories in which our human capital analytics projects have been used for action

Firm Performance Compared with key competitors, our company is more successful.

Compared with key competitors, our company has a greater market share.

Compared with key competitors, our company is growing faster.

Compared with key competitors, our company is more profitable.

Compared with key competitors, our company is more innovative


Appendix 2 – HR Analytics Interview Checklist

1. Zou u om te beginnen kort iets kunnen vertellen over wie u bent, uw functie en waar

u werkzaam bent?

HR ANALYTICS GLOBAAL

2. Er zijn vele definities van HR analytics in omloop, wat is uw definitie van HR analytics?

De definitie die ik gebruik is: het gebruik van data en analyse middelen om inzichten te

verkrijgen in medewerkers welke het mogelijk maken om sneller, preciezer en met meer

zekerheid bedrijfsbeslissingen te kunnen nemen.

3. Wat is volgens u het belang van HR analytics?

Volgens een grootschalige studie van Deloitte (2017) geeft 71% van de bedrijven aan dat HR

analytics grote prioriteit heeft binnen hun organisaties, terwijl slechts 8 procent bruikbare data

heeft.

4. Hoe zou u dit verklaren?

5. Welke toepassingsmogelijkheden voor HR analytics ziet u?

6. Hoe ziet u HR analytics toegepast worden binnen organisaties?

7. Welke impact heeft HR analytics volgens u op organisaties?

8. Hoe zou dit gemeten kunnen worden?


INZOOMEN OP UW BEDRIJF/ ORGANISATIES WAAR U VOOR WERKT

9. Wanneer we dit onderverdelen in stadia, in welk stadium bevindt HR analytics zich

dan binnen de organisaties waar u werkzaamheden voor verricht?

Level 1: Reactief – Operationele rapporten

- Ad-hoc operationele rapporten

- Reactief op bedrijfsbehoeften; data is geïsoleerd en lastig om te analyseren

Level 2: Proactief – geavanceerde rapportage

- Operationele rapporten voor benchmarken en besluitvorming

- Multidimensionale analyse en dashboarden; data dictionary

Level 3: Geavanceerde analyse

- Segmentatie; statistische analyse; ontwikkeling van 'mensenmodellen'

- Analyse van dimensies om de oorzaak, correlatie, en dimensies te begrijpen

Level 4: Voorspellende Analytics

- Ontwikkeling van voorspellende modellen, scenario planning

- Risicoanalyse; integratie met personeelsplanning

10. Wat zijn volgens u de basis benodigdheden om HR analytics toe te kunnen passen

binnen een organisatie?

11. Welke data is er volgens u nodig?

12. Waar binnen de organisatie zou het op de agenda dienen te staan?

13. Wie (welke functie/ rol) zou er verantwoordelijk dienen te zijn voor de uitvoering?

14. Over welke vaardigheden zou deze persoon dienen te beschikken?


Appendix 3 – Skewness and Kurtosis levels

Table 6 Skewness and Kurtosis levels

Skewness Kurtosis

SHRM -,373 ,381

Workforce Analytics -,673 ,949

Firm Performance -,330 -,066

Organizational size -,071 -,831

Revenue ,234 -1,002

Figure 5 Normal distribution of the variable Strategic Human Resource Management


Figure 6 Normal distribution of the variable Workforce Analytics

Figure 7 Normal distribution of the variable Firm Performance


Figure 8 Normal distribution of the control variable Organizational size

Figure 9 Box-plot of the variable Strategic Human Resource Management


Figure 10 Box-plot of the variable Workforce Analytics

Figure 11 Box-plot of the variable Firm Performance

Documents

Workforce Analytics and Increased Firm Performance