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.
Workforce Analytics and Increased Firm Performance 2
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
Workforce Analytics and Increased Firm Performance 3
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.
Workforce Analytics and Increased Firm Performance 4
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. “…,
Workforce Analytics and Increased Firm Performance 5
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
Workforce Analytics and Increased Firm Performance 6
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. –
Workforce Analytics and Increased Firm Performance 7
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
Workforce Analytics and Increased Firm Performance 8
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
Workforce Analytics and Increased Firm Performance 9
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
Workforce Analytics and Increased Firm Performance 10
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
Workforce Analytics and Increased Firm Performance 11
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)
Workforce Analytics and Increased Firm Performance 12
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 […].
Workforce Analytics and Increased Firm Performance 13
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
Workforce Analytics and Increased Firm Performance 14
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
Workforce Analytics and Increased Firm Performance 15
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.
Workforce Analytics and Increased Firm Performance 16
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
Workforce Analytics and Increased Firm Performance 17
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(+)
Workforce Analytics and Increased Firm Performance 18
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).
Workforce Analytics and Increased Firm Performance 19
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.
Workforce Analytics and Increased Firm Performance 20
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;
Workforce Analytics and Increased Firm Performance 21
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
Workforce Analytics and Increased Firm Performance 22
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.
Workforce Analytics and Increased Firm Performance 23
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:
1) Great effort is taken to select the right person
Workforce Analytics and Increased Firm Performance 24
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.
Workforce Analytics and Increased Firm Performance 25
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)
Workforce Analytics and Increased Firm Performance 26
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
Workforce Analytics and Increased Firm Performance 27
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
Workforce Analytics and Increased Firm Performance 28
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
Workforce Analytics and Increased Firm Performance 29
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
Workforce Analytics and Increased Firm Performance 30
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).
Workforce Analytics and Increased Firm Performance 31
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
Workforce Analytics and Increased Firm Performance 32
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
Workforce Analytics and Increased Firm Performance 33
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.
Workforce Analytics and Increased Firm Performance 34
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
Workforce Analytics and Increased Firm Performance 35
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 and Increased Firm Performance 36
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
Workforce Analytics and Increased Firm Performance 37
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.
Workforce Analytics and Increased Firm Performance 38
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
Workforce Analytics and Increased Firm Performance 39
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
Workforce Analytics and Increased Firm Performance 40
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).
Workforce Analytics and Increased Firm Performance 41
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
Workforce Analytics and Increased Firm Performance 42
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
Workforce Analytics and Increased Firm Performance 43
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.
Workforce Analytics and Increased Firm Performance 44
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Workforce Analytics and Increased Firm Performance 49
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)
Workforce Analytics and Increased Firm Performance 50
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
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.
Workforce Analytics and Increased Firm Performance 51
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
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
Workforce Analytics and Increased Firm Performance 52
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?
Workforce Analytics and Increased Firm Performance 53
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?
Workforce Analytics and Increased Firm Performance 54
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
Workforce Analytics and Increased Firm Performance 55
Figure 6 Normal distribution of the variable Workforce Analytics
Figure 7 Normal distribution of the variable Firm Performance
Workforce Analytics and Increased Firm Performance 56
Figure 8 Normal distribution of the control variable Organizational size
Figure 9 Box-plot of the variable Strategic Human Resource Management
Workforce Analytics and Increased Firm Performance 57
Figure 10 Box-plot of the variable Workforce Analytics
Figure 11 Box-plot of the variable Firm Performance
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