Amsterdam Business School
Executive Program in Management Studies - Leadership & Management
STUDENT:
Mayke den Teuling
11321520
SUPERVISOR:
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 2018
Final version
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.
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
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
5.6 Limitations and future research 42
6 Conclusion 43
7 References 44
Appendix 1 – Questionnaire 49
Appendix 2 – HR Analytics Interview Checklist 52
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
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
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 In d ivi d u a ls P ro ce sse s S tr u ct u re P4 P1 P2 P3
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
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
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
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
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
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
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
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
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
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,
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
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
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)
Strategic Human
Resource Management Firm Performance
Workforce Analytics H1 (+)
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
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,
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
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).
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 11-20 million euro 14.1 21-50 million euro 20.0 51-100 million euro 15.3 101-250 million euro 11.8 251-500 million euro 10.6 >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:
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’.
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
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
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
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
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
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
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
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
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
