People analytics—A scoping review of conceptual boundaries and value propositions

2018, International Journal of Information Management

Edinburgh Research Explorer People analytics-A scoping review of conceptual boundaries and value propositions Citation for published version: Tursunbayeva, A, Di Lauro, S & Pagliari, C 2018, 'People analytics-A scoping review of conceptual boundaries and value propositions', International Journal of Information Management, vol. 43, pp. 224-247. https://doi.org/10.1016/j.ijinfomgt.2018.08.002 Digital Object Identifier (DOI): 10.1016/j.ijinfomgt.2018.08.002 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: International Journal of Information Management General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. 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Dec. 2021 International Journal of Information Management 43 (2018) 224–247 Contents lists available at ScienceDirect International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt People analytics—A scoping review of conceptual boundaries and value propositions T Aizhan Tursunbayevaa,b, , Stefano Di Lauroc, Claudia Pagliaria ⁎ a eHealth Research Group, Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK University of Molise, Via Francesco De Sanctis, 1, 86100, Campobasso, Italy c University of Naples Federico II, Corso Umberto I, 40, 80138, Naples, Italy b A R T I C LE I N FO A B S T R A C T Keywords: People analytics HR analytics Workforce analytics Talent analytics Human resource management HRIS Administrative data analytics Business analytics Business informatics This mixed-method ‘scoping review’ mapped the emergence of the term People Analytics (PA), the value propositions oﬀered by vendors of PA tools and services and the PA skillsets being sought by professionals. Analysis of academic research and online search traﬃc since 2002 revealed changes in the relative trajectory of PA and conceptually related terms over the past ﬁfteen years, indicating both the re-branding of similar innovations and a diﬀerentiation of priorities and communities of practice. The market in commercial PA tools and services is diverse, oﬀering numerous functional and strategic beneﬁts, although published evidence of these outcomes remains sparse. Companies marketing PA systems and services emphasise beneﬁts to employers more than to personnel. Across the sources examined, including specialised online courses, PA was largely aligned with HRM, however its development reﬂects the shifting focus of HR departments from supporting functional to strategic organisational requirements. Consideration of ethical issues was largely absent. 1. Introduction In an increasingly digitised society, interest in the use of so called big (and small) data has never been greater. Data analytic techniques, of varying sophistication, are being used to understand social phenomena, evaluate policies, tailor consumer marketing, predict voting behaviour, enable precision medicine and a host of other real-world applications (Raguseo, 2018). Understanding and optimising the workforce is a key part of this trend (Edwards & Edwards, 2016; Sullivan, 2013). In many ways, this echoes nineteenth century notions of organisations as machines, to be ﬁne-tuned to maximise outputs and minimise waste, with employees seen as components to be stratiﬁed, incentivised, deployed and shed for maximum eﬀectiveness. Although most organisational theorists and leaders now recognise organisations as complex adaptive socio-technical systems (Schneider & Somers, 2006) interest in using data analytics and visualisation tools to render this complexity into a more comprehensible and actionable forms is growing (Gandomi & Haider, 2015). Within this context, the term 'People Analytics' (PA) has been appearing with greater frequency in executive leadership and Human Resources Management (HRM) circles (Deloitte, 2017). PA promises to help organisations understand their workforce as a whole, as departments or work groups, and as individuals, by making data about ⁎ employee attributes, behaviour and performance more accessible, interpretable and actionable (Pape, 2016). This includes the use of information systems, visualisation tools and predictive analytics, underpinned by employee proﬁling and performance data. The association of PA with HRM is obvious, given the emphasis on optimising recruitment, retention, assessment, promotion, remuneration, turnover and other aspects of human capital management. The Information Technology (IT) and cyber-security professions are also stakeholders, since data analytics are essential for red-ﬂagging corporate threats, such as the misuse of organisational information, intellectual property theft or fraud (Guenole, Ferrar, & Feinzig, 2017). While these issues are important for all organisations, the potential value of automated techniques is magniﬁed in those which are large and distributed, since traditional information needs and oversight mechanisms may exceed conventional HRM capabilities. Despite this potential, PA is still not well understood in the business or academic communities (Marler & Boudreau, 2017) beyond HR innovators, or in high-risk sectors such as defence and ﬁnancial services, where such practices are often shrouded in commercial secrecy. This scoping review aimed, through an analysis of online sources and academic literature, to better understand the nature, usage and potential of PA, as well as issues arising in the ﬁeld. The speciﬁc objectives were to examine 1) the emergence of the PA concept over time Corresponding author at: University of Molise, Via Francesco De Sanctis, 1, 86100, Campobasso, Italy. E-mail addresses: aizhan.tursunbayeva@gmail.com (A. Tursunbayeva), stefano.dilauro@gmail.com (S. Di Lauro), Claudia.Pagliari@ed.ac.uk (C. Pagliari). https://doi.org/10.1016/j.ijinfomgt.2018.08.002 Received 21 February 2018; Received in revised form 2 July 2018; Accepted 6 August 2018 0268-4012/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. Human Capital Analytics Group (HCA Group, 2017) of the Copenhagen Business School, encompassing 28 articles dating from 2002 (as of 30/ 07/2017). The disciplinary aﬃliation of journals publishing PA research was assessed with reference to their classiﬁation in the Scimago Journal Ranking Portal (2017), except for the Scopus articles for which this information was available in the database. Where articles speciﬁed keywords these were cross-referenced with our seven search terms to identify those most frequently used. Finally, we analysed the concepts appearing in article titles and abstracts with reference to a framework by Isson and Harriott (2016) which organizes PA into 7 “pillars” according to its potential impact on: 1. Workforce planning; 2. Sourcing; 3. Acquisition/hiring; 4. Onboarding, culture ﬁt, and engagement; 5. Performance assessment and development and employee lifetime value; 6. Churn and retention; and 7. Wellness, health, and safety. and its relationship with other HR-related concepts 2) the contexts in which PA is being used; 3) the value propositions advanced by providers of PA products and services; and 4) training courses currently aimed at PA practitioners. The review was prompted by ﬁndings of a recent systematic literature review on HR Information Systems (HRIS) in healthcare, which highlighted the importance of HR data for eﬀective management and organisational eﬃciency (Tursunbayeva, Bunduchi, Franco, & Pagliari, 2016). It complements a recent review of the academic research literature on HR Analytics (Marler & Boudreau, 2017) by extending the analysis to a wider set of knowledge types. It also addresses calls from within the industry for “independent scientiﬁc research” on PA (e.g. Julia Howes, quoted in Levenson & Pillans, 2017). 2. Methods We undertook a quasi-systematic scoping review, adopting an approach originally proposed by Arksey and O’Malley (2005). Unlike systematic reviews aimed at synthesising evidence from evaluative studies (e.g. Tursunbayeva, Franco, & Pagliari, 2017), scoping reviews are often used to examine emerging topics that are poorly understood, where research is at an early stage, or where pertinent knowledge is being generated outside academia. Scoping reviews thus address broad rather than narrow research questions and seek to proﬁle the literature and understand it holistically, rather than to critically appraise the methodological quality of individual studies (Holeman, Cookson, & Pagliari, 2016). Since PA is an emergent topic it was appropriate to use this broad approach rather than concentrating on a narrow and likely unrepresentative academic research literature and speciﬁc and narrow research questions. Data collection took place in four main phases, which are summarised below, noting the research objectives addressed by each one. 2.3. Scoping commercial PA tools and services (Addresses objectives 2 and 3) To identify venders of PA tools and services, we searched for each of our 7 core PA keywords in Google and analysed the ﬁrst page of results for each one, based on previous studies showing that 91% of searchers check only this page (Van Deursen & van Dijk, 2009). For our analysis we included only the organic results (Ratliﬀ & Rubinfeld, 2014), and omitted paid advertisements. The search was conducted on 30/07/ 2017. Vendors identiﬁed from this search were ﬁrst classiﬁed according to the nature of their business, using a taxonomy, developed by Libert, Beck, and Wind (2016), as Asset Builders; Service Providers; Technology Creators; Network Orchestrators. We then reviewed the narrarative in vendors’ online promotional material, to identify the speciﬁed or implied beneﬁts oﬀered to prospective purchasers (value proposition). These were iteratively coded before settling on a reﬁned list of beneﬁt categories. 2.1. Mapping the use of PA-related terms online (Addresses objectives 1 and 2) 2.4. Scoping online training courses (Addresses objective 4) To inform our literature searches, we ﬁrst created a draft set of ten keywords [HR, Human Resource, People, Workforce, Employee, Human Capital, Manpower, Staﬀ, Personnel, Talent], drawing on the results of the recent systematic evidence review on HRIS in the context of healthcare (Tursunbayeva et al., 2016) and adding the word “Analytics” to each of these. We analysed the prevalence of each of these keyword combinations in online searches, using Google Trends (searched on 10/06/2017), following previous research that uses this free tool to obtain insights on users’ Internet search behaviour (e.g. Nuti et al., 2014). We used additional Google Trends analytics to chart the countries in which each search term has been the most popular, as well as to examine the related terms used alongside PA in online searches in which PA keywords are included. Open coding (Glaser & Strauss, 1967) was applied to the latter to iteratively sort the results into thematic categories. Again, using the 7 keywords reﬁned through Phase 1, we searched the Wikipedia list of massive online open courses (MOOC) by “Notable providers” (Wikipedia, 2017) (Search conducted on 30/07/2017). After examining the openly accessible information describing each course, we extracted those most closely related to PA and attempted to assess their learning objectives, insofar as this was possible without enrolling. These were cross-referenced with the “Proﬁle of a Perfect Data Analyst” developed by the Nesta global innovation foundation (2014), which includes: Core skills (Analytical or Technical); Domain and Business Knowledge (Knowledge of the sector, Awareness of business goals and processes); Soft skills (Storytelling and Team-working) and Competencies (Analytics Mindset, Creativity and Curiosity). Available course content was also classiﬁed according to Isson and Harriot’s 7 PA pillars framework, as described above. Finally, we emailed course developers and asked for the course creation date and attendance statistics. 2.2. Scoping relevant academic research (Addresses objectives 1 and 2) 3. Findings and analysis Using a subset of 7 core keywords reﬁned after Phase 1 ("HR analytics" OR "Human Capital analytics" OR "Human Resource analytics" OR "People analytics" OR "Talent analytics" OR "Workforce analytics" OR “Employee analytics”), we undertook preliminary searches of the academic literature using the Scopus database (30/07/2017). To check the inclusivity of our search results, the titles of articles judged to be relevant were cross-referenced with those appearing in two benchmark literature sources: Firstly, a recent review of academic research on HR Analytics by Marler and Boudreau (2017) which used similar search terms and shortlisted 14 relevant papers dating from 2004. Secondly, a list of relevant articles informally maintained by the 3.1. People analytics in online search trends None of the terms Manpower Analytics, Personnel Analytics or Staﬀ Analytics were found in Google trends since records began in 2004. Although these terms appear in earlier articles included in a recent systematic review of HRIS (Tursunbayeva et al., 2016), their absence post-2004 suggests that they are no longer in common usage and we therefore decided to exclude them from further analysis. Indeed, these were also not found amongst the search terms or results in Marler and Boudreau’s (2017) related review. The relative popularity of online searches for the remaining seven terms is shown in Fig. 1. 225 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. Fig. 1. Keyword utilization in Google Trends*. *Google Trends data starts from 2004. Google Trends description: Numbers represent search interest relative to the highest point on the chart for the given region and time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. Likewise a score of 0 means the term was less than 1% as popular as the peak. Analytics. A further thematic category relates to the PA Profession – including descriptions of PA consultants, job advertisements or levels of remuneration. Other, less dominant, themes included Learning and Development, (e.g. PA courses, training or academic programs), PA Research (both academic and applied), and Conferences (Wharton School Univ. Pennsylvania). Finally, there was a cluster of searches related to PA in a particular Country, speciﬁcally India. As can be seen in Fig. 1, interest in these terms has grown over the last fourteen years, with searches for Human Resource, HR and Workforce Analytics initially being the most popular. Google users began searching for People Analytics in 2005 and this term overtook the latter in 2007. In the last 10 years, the most popular terms have been People Analytics and HR Analytics. Searches for Talent Analytics, Employee Analytics and Workforce Analytics have also taken place, although relatively rarely compared with the former two search terms. Searches for Human Capital Analytics appeared and rose from 2004, although this term and Human Resource Analytics have since become the least popular of the seven reviewed. Searches for PA concepts, recorded in Google Trends, were most popular in the USA, India, the UK, Australia and Canada, with users from the USA searching for the most diverse range of terms. Preferences for diﬀerent search terms varied between countries. For example, the top search terms were, in the USA Human Capital Analytics, in India Talent Analytics and in Australia Workforce Analytics, while searches in the UK were more evenly spread across terms. Where relevant searches were conducted in other countries, these were conﬁned to HR Analytics, People Analytics, or a combination of these two terms. A breakdown can be seen in Appendix A. ‘Related words’ used alongside the seven People Analytics search terms fell into several thematic clusters, based on our open coding (Appendix B). A cluster relating to HR objectives and practices was the most dominant - including aspects of workforce planning, recruitment, talent management and performance/reward. Related words from this cluster were mostly associated with the terms Human Resource, Human Capital and Talent Analytics. The next largest theme concerned Analytics as a class of methodologies, including predictive analytics or statistics. This was followed by a theme concerning the Internet, including searches targeting particular social media platforms (e.g. Twitter), websites (e.g. Wordpress), search engines (e.g. Google search) or analytics engines (e.g. Google Analytics), which could potentially be used as a source of data for informing various HRM requirements. The Internet cluster seems to be unique to the People Analytics keyword, suggesting that the scope of the PA concept extends beyond internal HR processes and practices. The next category concerned Organisations, including references to companies in general or speciﬁc organisations oﬀering PA services or technology, such as Deloitte (authors of the Human Capital Trends report). The PA keywords most closely associated with the Organisations category were Workforce and Talent 3.2. People analytics in published academic research 3.2.1. Comparison of search results with ‘benchmark’ lists Searching the Scopus database using a structured query combining our seven key terms yielded 58 relevant academic articles; almost all published in or after 2012. The table listing the included articles (after removing duplicates, and non-relevant returns: n = 5) is included in Appendix C together with the articles from the two ‘benchmark’ lists. The two ‘benchmark’ lists did not have any article in common. Marler and Boudreau’s review of academic research contains considerably more post-2012 articles than HCA group’s list. Informal communication with the HCA group conﬁrmed that their list is not systematically maintained but rather is a place to record articles of special interest. While our Scopus search identiﬁed more relevant articles than that shortlisted by Marler and Boudreau (58 vs. 14), only three of the same articles are covered in both reviews (by Aral, Brynjolfsson, & Wu, 2012; Bassi, 2011; Rasmussen & Ulrich, 2015). Fig. 2 shows the number of relevant articles appearing in our search and the two benchmark comparators, in the years 2002–2017. 3.2.2. Disciplinary focus As can be seen in Fig. 3, while the majority of publications in all three sources come from the Business, Management and Accounting disciplines, the results from our Scopus search, Marler and Boudreau’s academic literature review and the HCA group’s list vary in several ways. Most signiﬁcantly, technical disciplines such as the computing and mathematical sciences are strongly represented in Scopus and somewhat in Marler and Boudreau’s lists but absent in the HCA group’s list. The list derived from the HCA group, in contrast, shows a strong representation from Psychology. Interpreting these diﬀerences requires a recognition of the diﬀerent scope and timeframes of the sources. Using our targeted search terms in Scopus yielded only studies published after 2008, whilst the HCA group’s list goes back to 2002. Manual 226 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. Fig. 2. Number of relevant articles appearing in search results and two benchmark sources, by publication date. with our Scopus search terms was Human Capital, reﬂecting the focus of the HCA group. Very few articles in Marler and Boudreau’s review of HR Analytics research used keywords. These keywords included: HR Analytics and Human Resource Analytics; Information Systems including Human Resource Information Systems; and generic HR terms such as Human Resource or Human Resource Management. The keywords from all three lists were pooled to produce the word map shown in Fig. 4. The size of the words indicates their relative frequency. Fig. 5 shows the annual frequency with which our core PA search terms, reﬁned via phase 1, were speciﬁed as keywords by authors of academic articles in the combined list. Only ﬁve out of the seven terms are included, as Talent Analytics and Employee Analytics did not appear as keywords amongst these articles. inspection of the pre- and post-2008 publications suggests that PA emerged from organisational psychology and psychometrics but has since been marked by a trend towards the computing and data sciences. Authors publishing most on this topic in the last ﬁve years come from industry, not academia; indeed one ﬁfth of recent articles found in Scopus are aﬃliated with IBM, revealing the company’s strategic shift towards business consulting and analytics. Nevertheless the diﬀerence between these sources partly reﬂects the addition of the word ‘analytics’ to the search terms in our Scopus search and Marler and Boudreau’s review, compared to the HCA group’s list. 3.2.3. Search terms and authors’ keywords represented in published articles The most popular keywords speciﬁed in articles derived from our Scopus search results were: Workforce Analytics; HR Analytics; Human Resource Management; People Analytics; and Human Resource Analytics. Not all articles in the HCA list speciﬁed keywords but where this was the case, these were divided between various HRM practices such as Diversity, Turnover, Engagement, Burnout, and Strategic HRM, and organisational impacts such as Organisational (or departmental) Performance, productivity or strategy. An additional keyword appearing in the this list was ‘meta-analysis’, reﬂecting the inclusion of review papers involving this method. The only keyword overlapping 3.2.4. PA objectives/practices represented in the articles Most of the articles in the combined list were general overviews or discussions of PA as an area of practice or a sub-discipline of HR. This included deﬁning what PA is, its adoption rates in diverse organisations, types of data that may be used for PA analyses, and potential success factors and barriers that could aﬀect PA implementation within organisations. While such generic PA articles appeared in our Scopus results and Fig. 3. Discipline of academic journals publishing articles related to People Analytics. 227 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. Fig. 4. Word map of keywords speciﬁed by authors. biases using data mining and proﬁling tools during candidate screening). Studies that did not belong to any of the aforementioned categories were grouped into separate categories. These included studies that focused on: collaborations (e.g. studying work patterns from employees’ collaboration activities); diversity and inclusion (e.g. understanding how demographic factors and workforce composition can aﬀect individual, team or organisational performance); People Risks (e.g. quantifying human risks and reviewing existing risk measures); and inter-organisational relationships (e.g. investigating how PA can be used in investment processes). in Marler and Boudreau’s list, the HCA group’s list only included studies focusing on the use of PA for speciﬁc HR objectives. Nevertheless, all of the PA objectives/practices from Isson and Harriott’s (2016) 7 pillars framework were present in the studies analysed, as described in Table 1. Many focused on performance assessment and development and employee lifetime values (e.g. remuneration levels for diﬀerent gender groups) and onboarding, culture ﬁt, and engagement (e.g. linking motivational antecedents, strategic implementation and company performance). Another dominant category, primarily in the studies found in Scopus, is workforce planning, including studies concerned with new scheduling models or identifying and estimating employee expertise. Other studies focused on the use of PA for: churn and retention (e.g. employee turnover and their impact); wellness, health, and safety (e.g. strategies for reducing employee burnout); sourcing (e.g. identifying key metrics to evaluate talent acquisition strategies or recruitment channels); and acquisition/hiring (e.g. evaluating potential 3.3. PA vendors represented in Google search results As described in the methods section, we analysed the results appearing on the ﬁrst Google page found when searching for each of our 7 Fig. 5. Frequency of Specﬁed Keywords in PA-Related Journal Articles Over Time. 228 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. tions and the deﬁnitions they follow are provided in Appendix D. Stated value proposition: The following overlapping categories of promised beneﬁt emerged from the qualitative analysis of vendors’ online marketing narratives: (A) Better strategic decision making through access to reliable information and analytical tools; (B) Improved data handling processes using innovative approaches to data collection, combination, analysis and interpretation; (C) Improved people management resulting from greater eﬃciencies and HR decision making; (D) New technological solutions for collecting, storing or analysing HR data, including automating these processes; (E) Direct impacts on HR or strategic business outcomes, such as optimising human resource assets to increase income relevant to payroll; and (F) Employee-oriented beneﬁts such as improved work experience or job satisfaction. Companies that appeared amongst the relevant web pages, but only provide business-tobusiness marketing services to PA vendors (e.g. TechTarget), were not included in this analysis. We mapped the vendors according to their types, terminologies and stated value propositions, to produce the PA landscape map shown in Table 2. Table 1 PA objectives described in studies yielded by the Scopus search, HCA group list, recent reviewa. Study focus Scopus list HCA group list Marler and Boudreau list Workforce planning Sourcing Acquisition/hiring Onboarding, culture ﬁt, and engagement Churn and retention Wellness, health, and safety Performance assessment and development and employee lifetime value Diversity and inclusion Collaboration People Risks Inter-organisational relationships Generic, Technical or too little info to classify 9 2 1 1 – – – 13 – – – 1 1 – 8 6 6 5 – – 1 1 2 1 2 25 6 – – – – – – – – 12 a Some articles focused on more than one HR objective/practice. 3.4. People analytics courses After searching the Wikipedia list of MOOC by “Notable providers” (2017) we identiﬁed three with one or more of our keywords in their title. Two explicitly include the term PA, – both originating from universities, one in Russia and one in the USA. The other, developed by a Canadian consulting ﬁrm, uses the term HR Analytics. All three are introductory-level courses, explaining how PA can be used for diverse HRM practices (see Table 3). Each covers diﬀerent types of HR data and the various ways in which it can be analysed (Knowledge of the Sector) to achieve diverse organisational objectives (Awareness of Business Goals and Processes). Categorizing the curricula according to the 7 PA pillars framework (Isson & Harriott, 2016), revealed that the two courses speciﬁcally focused on PA covered cases related to Performance and development and lifetime value, Onboarding, culture ﬁt, and engagement, Workforce planning, and Acquisition/hiring. Collaboration emerged as a separate theme, and is described in the course syllabus as “principles behind using PA to improve collaboration between employees inside an organisation so they can work together more successfully” (People Analytics course, Massey, Haas, & Bidwell, 2017). core keywords, after removing paid advertisements. Most search results were links to Vendor websites (e.g. IBM’s HR Analytics main page). The remaining results related; in order of frequency; to News (e.g. Forbes), Research (e.g. Harvard Business Review), PA-related Communities (e.g. Kaggle), Organisations/Platforms providing access to educational materials on PA (e.g. HCA Group webpage, Coursera) or speciﬁc training programmes in which PA is a component (e.g. Human Resources MBA), and Conferences (e.g. Wharton people analytics conference). Terms used by vendors: Many vendors’ websites contained several PA-related terms, although these varied in emphasis and frequency. Examples include, “We'll show you how to build your Talent Analytics solutions. Point solutions demonstrate ROI of people analytics to the business” (PWC, 2017), “People analytics, also known as talent analytics or HR analytics, refers to the method of analytics that can help managers and executives make decisions about their employees or workforce” (Cornerstone, 2018). Here, we also identiﬁed an additional PArelated term “Labor Analytics”, used by Kronos, which did not emerge in the previous phases of analysis. Further details of vendors’ descripTable 2 Vendors classiﬁed according to PA-concepts and categories of value proposition. Vendors Value Proposition Workforce Analytics Deloitte Competitive Analytics IBMa McKinsey & Company Accenture PWC A; B; C A; B; C A; B; C; E; F B; C; E A; B; C; D; E; F A; B; E + Technology creators Kronos SAP SuccessFactorsa Talent Analytics Ultimate Software Visier QuestionPRO Talent LMS Cornerstone A; B; C; D; E A, D, E B; D; E A; B; D; E A; C; D D; E; F A; C A; B; C; D; E + + + + + HR Analytics Human Resource Analytics + People Analytics Human Capital Analytics Talent Analytics + + + Labor Analytics + + + + + + Network orchestrators TechTarget a Employee Analytics + + + + + + + + + Both service provider and technology creator. 229 + International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. 4. Discussion 4.3. Objectives of PA The results of our scoping review demonstrate the emergence of the term People Analytics and related concepts over the last 15 years, both within academic research and in online searches. They also delineate the value propositions stated by PA vendors and summarise the core training objectives of PA MOOC. The most popular objectives and practices, based on Isson and Harriot’s 7 pillars model, were performance assessment and development and employee lifetime values; onboarding, culture ﬁt, and engagement, and workforce planning. Our thematic analysis yielded four additional categories – employee collaborations; diversity and inclusion; people risks, and inter-organisational relationships. The emergence of these categories illustrates the rapid development and diversiﬁcation of the ﬁeld, as already discussed with reference to terminologies and disciplines. One example is the shift in emphasis from HR practices focused on individuals, to their interactions, aﬃliations and performance as groups, including the use of data on social and organisational networks. The signiﬁcance of this shift has recently been highlighted in a report by Bersin for Deloitte (2016), who predicts that relationship analytics will soon come to replace traditional organisational design/re-design approaches. An increasing emphasis on measuring people risks (Marsh Risk Consulting, 2017) via analytics is also noteworthy, including not only conventional risks such as staﬀ attrition, organisational reputation or customer safety, but also new forms of cyber-risk for which these new methods are well-suited, such as hack vulnerability or data theft (e.g. Royal & Windsor, 2016). Other recent trends include the use of linked employee data and analytics as a substitute for conventional psychometric testing in the acquisition and management of talent (indeed, the term PA was historically used to describe this sort of test-based employee proﬁling). One market indicator of this emerging trend, is the recent acquisition of psychometric assessment specialists Cut-e by AON, a global professional services ﬁrm that uses data and analytics to help companies manage workforce risks, health and retirement (Consultancy.uk, 2017). 4.1. Terminological trends Our analysis of the PA-associated terms used in academic sources and in Google searches reveals an evolving and diversifying ﬁeld originating in the traditional HRM profession (historically inﬂuenced by industrial psychology), through a critical period of innovation in digital infrastructure, technologies and analytical capabilities, reﬂecting broader trends in the digital economy over the period studied. A range of relevant terms were already being used to search Google when records began in 2004, including Workforce Analytics, Human Resource and HR Analytics, although the latter terms only started appearing as keywords in academic articles from 2012. Human Capital was the ﬁrst of our keywords to appear amongst the academic articles (in 2008). Academic articles speciﬁc to PA emerged only in the last two years and this is now the most popular of the relevant terms according to Google Trends, closely followed by HR Analytics. In practice, many of these terms are being used interchangeably, both by academics and vendors, although some practitioners align HR analytics with conventional HR and PA with a broader range of enterprise-level and strategic analytics (Van Vulpen, 2016). Google searches for one or more of our key terms took place in as many as 13 countries, with preferences for diﬀerent search terms diﬀering between regions (Our analysis of ‘Related words’ in Google queries also revealed a cluster of searches related to PA in India, which may be attributable to online activity surrounding the HR Analytics India Summit 2017) Among academics, the most commonly used terms were Workforce- HR-, People-, and Human Resource- Analytics, while vendor websites show a preference for Workforce-, Employee- and Talent- Analytics. Changes in terminology over time reﬂect the evolution of the HR ﬁeld, from conventional personnel management functions (e.g. payroll), towards the greater use of IT systems and data for strategic purposes such as workforce planning (e.g. Haines & Laﬂeur, 2008). They also suggest a re-branding of similar concepts by successive practitioner cohorts and vendors, as they seek to diﬀerentiate their knowledge, services or products in a competitive market. Elements of both the changing focus and rebranding of concepts can be seen in the recent appearance, both in academic papers and MOOC, of the concept of Collaboration or Relationship Analytics, reﬂecting the growing use of social media or organisational network analysis (see Objectives of PA). 4.4. Business value oﬀered by PA All of the companies identiﬁed through our online searches are either service providers or technology creators. Providers of PA services typically oﬀer strategic and operational consulting aimed at improving the collection, management and use of data for understanding and evaluating work behaviour or outcomes and optimising human resources. PA technology vendors oﬀer IT systems for achieving these aims by making workforce intelligence and predictive analytics more accessible, thus supporting strategic decision making and improving business outcomes, reﬂecting the vision of ‘actionable analytics’ (Dykes, 2016). Cross-referencing vendors’ stated solutions with the coded articles in our review revealed some overlap in vision and intentions, but little evidence of the beneﬁts promised. Thus, while business analysts are promoting new ways of creating measurable organisational impacts through PA, objective academic research is needed to evaluate these. 4.5. PA skills being sought 4.2. Aﬃliation and disciplinary focus An indicator of how companies are actively seeking value from PA can be seen in the MOOC we analysed, including the market leader in PA training (Wharton Business School). The fact that we were able to identify only three such courses, only one of which was explicitly labelled as “People analytics”, suggests that the supply of training is far from suﬃcient to meet the need for relevant skills amongst HR professionals. Nevertheless, the increasing inclusion of PA within university curricula on business and management (e.g. the HR Analytics and Research course from the University of Denver, USA), along with new programmes in data science (e.g. the MSc in Data Science, Technology and Innovation from the University of Edinburgh, UK), and the emergence of other relevant online courses (e.g. People Analytics training with Gene Pease) will go some way towards addressing this deﬁcit. In the meantime, the growth in professional conferences focused on PA (e.g. the Wharton People Analytics Conference or HR Analytics Echoing a recent systematic review on HR Analytics (Marler & Boudreau, 2017), we observed that the authors of most published articles on PA came from consulting or technology companies. This trend has also been reported in previous research on HRIS, in which consultancy ﬁrms feature prominently (Ruel & Bondarouk, 2008). Most of the academic articles and PA courses included in our review approach PA as a sub-ﬁeld of HR, which was also reﬂected in the headline curricula of the three MOOC. Nevertheless, our analysis shows that research in this area is highly interdisciplinary. While most articles come from the Business, Management and Accounting domains, social science has remained prominent, and the importance of the computing and data sciences is increasingly evident, echoing the growth of HRIS and digital innovations for monitoring, evaluating and predicting work. 230 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. 4.6. Working deﬁnition N/A In their recent review, Marler and Boudreau (2017) summarise HR analytics as: “An HR practice enabled by information technology that uses descriptive, visual, and statistical analyses of data related to HR processes, human capital, organisational performance, and external economic benchmarks to establish business impact and enable data-driven decision making”. While our broader scoping analysis reveals similar objectives, we note that vendors such as Accenture, IBM and QuestionPRO have begun to extend this to improvements in employee experience and satisfaction. Indeed, employee experience was identiﬁed as key priority for future HR in the latest Deloitte (2017) report, which states: “The concept of ‘total employee experience’, focused on design thinking and the simpliﬁcation of work, will become a major focus in HR”. Thus, based on our study, we would oﬀer the following deﬁnition of People Analytics “People Analytics is an area of HRM practice, research and innovation concerned with the use of information technologies, descriptive and predictive data analytics and visualisation tools for generating actionable insights about workforce dynamics, human capital, and individual and team performance that can be used strategically to optimise organisational eﬀectiveness, eﬃciency and outcomes, and improve employee experience". 17 short videos covering what HR Analytics is, and success factors and barriers to keep in mind in development and execution of HR Analytics projects Info on learners not available -Performance and development and lifetime value -Onboarding, culture ﬁt, and engagement -Workforce planning -Acquisition/ hiring 7 Weeks: Introduction, Performance, Culture and Assessments, Compensation, Motivation & Engagement, Workforce Planning & Recruitment, Development 4 Weeks: Introduction to People Analytics & Performance Evaluation, Staﬃng, Collaboration, Talent Management & Future Directions -Performance and development and lifetime value -Workforce planning -Collaboration Learners PA Pillars Syllabus 2015: 11,599 2016: 26,312 2017: 49,535 (71% male; 29% female) US = 26%; India = 18; remaining countries all with < 4% Circa 150 students India Summit 2017) indicates the desire, primarily amongst international HR professionals, to ﬁll this gap. The establishment of a new “HR Analytics Academy” in 2016 in the Netherlands, also suggests that these ‘advanced’ approaches will soon be ﬁnding their way into routine HR practices. Practicing HR professionals Diﬀerences in speciﬁcation reﬂect the availability of information online for the three courses. Scoping reviews are exploratory research exercises and the intention here was to provide insights about this emerging area by mapping the terms, concept and practices associated with PA, rather than to provide an in-depth analysis of PA innovations, professions, markets or research. The results nevertheless indicate the direction of developments and the increasing readiness of PA to embrace new innovations and market demands. Given the rapid pace of technological change, this represents only a snapshot of the ﬁeld to date. One purpose of scoping reviews is to inform the design of future systematic reviews by testing and reﬁning literature search strategies. Here, this enabled us to identify a more comprehensive set of search terms than we had previously been aware of and showed that focusing on activities explicitly described as ‘analytics’ is no guarantee of revealing the diversity of the domain. It also demonstrated the importance of widening searches beyond academic databases to uncover relevant grey literature, and of including emerging terms such as “Human Capital Data”, “Human Resource data”, “Talent data”, “Workforce data”, “Analytics in HR”, “HR metrics”, “HR predictive analytics”, “Collaboration Analytics”, “People Intelligence”, “Labor Analytics” and “Relationship Analytics”. The diversity of disciplines represented amongst academic and online sources of PA insights also suggests that scholars and practitioners interested in this topic should look beyond the HR or management literature for relevant studies, including in specialist sectors where PA is being applied, such as banking, security or healthcare, and examine real-world applications in addition to academic research. The ﬁrst page of Google results arising from each key search term provides only a snapshot of the vendor ecosystem. Deeper, iterative web searches, and consultation exercises with PA experts would be valuable for understanding the ‘analytic maturity’ of organizations in diverse sectors, drawing on existing frameworks (e.g. Lismont, Vanthienen, Baesens, & Lemahieu, 2017; Grossman, 2018), as well as for elucidating the changing business demands for PA and identifying PA services not represented online. Finding so few empirical studies leads us to join other reviewers in calling for more academic research in this area, including evaluation a Creelman Research, Toronto, Canada David Creelman, CEO Udemy The Fundamentals of HR Analytics (launched March, 2016) N/A The Wharton School, University of Pennsylvania, USA Cade Massey (Practice Professor), Martine Haas, Matthew Bidwell (Associate Professors of Management) Coursera People Analytics. Part of the Business Analytics specialization. (launched December, 2015) N/A Center of Innovative Educational Technologies, Moscow Institute of Physics and Technology, Russia Alexey Dolinskiy and Ilya Breyman (Adjunct Professors) Coursera Introduction to People Analytics Pre-requisites Aﬃliation Instructors Source Course name Table 3 Massive Online Open Courses focused on People Analyticsa. 5. Conclusions, caveats and recommendations 231 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. of these approaches. Although lawyers, ethicists and management scientists are already addressing some of these issues (e.g. Bodie, Cherry, McCormick, & Tang, 2017; Dagnino, 2017; Pasquale, 2015), our observations suggest that this is a blind spot for the PA profession itself and we recommend further research to understand how practitioners, vendors and employers are reconciling the drive for innovation with requirements for transparency and accountability. Ethics in PA will be the focus of a special Professional Development Workshop at the 2018 British Academy of Management Conference, which also draws on this review (Pagliari, Tursunbayeva, & Antonelli, 2018). studies, case studies of PA implementation and simulation studies exploring the potential outcomes of new PA models before they are implemented, as well as to examine the nascent introduction of machine learning and Artiﬁcial Intelligence in the context of workforce management (Meister, 2017). Amongst the wide variety of sources examined, we noted a marked absence of ethical considerations in relation to PA practices, some of which are covert or reach beyond the boundaries of organisations themselves. Examples include the monitoring of personal social media or email activity, which have implications for privacy, the use of algorithmic decision making for recruitment or promotion, which has potential to introduce bias and discrimination, and the digital abstraction of personality and ability proﬁles from real world data without the need for psychometric testing, which raises issues for transparency and consent (Wiedemann, 2018). With advances in privacy regulations, such as recently enforced European General Data Protection Directive, PA practitioners may soon have to re-think some Funding Claudia Pagliari is an RCUK grant holder and collaborator on the ESRC Administrative Data Research Centre for Scotland (grant number ES/L007487/1), and the EPSRC Science and Practice of Social Machines (grant number EP/J017728/1). Appendix A. Relative popularity of keywords in individual countries (from Google Trends)* Country UK USA India Australia Canada Other countries HR Analytics 20 20 68 21 20 People Analytics 51 44 27 43 50 Workforce Analytics Talent Analytics Human Capital Analytics Employee Analytics Human Resource Analytics 53 49 – – – 83 88 100 – – 46 100 – – – 100 – – – – 75 – – – – Singapore (100) Netherlands (59) Brazil (2) Germany (10) Spain (8) UAE (40) Philippines (30) Singapore (100) Netherlands (40) South Africa (27) Brazil (19) Germany (17) Spain (11) Poland (3) – – – – – * Google Trends description: Values are calculated on a scale from 0 to 100, where 100 is the location with the most popularity as a fraction of total searches in that location, a value of 50 indicates a location which is half as popular, and a value of 0 indicates a location where the term was less than 1% as popular as the peak. Note: A higher value means a higher proportion of all queries, not a higher absolute query count, so a tiny country where 80% of the queries are for "bananas" will get twice the score of a giant country where only 40% of the queries are for "bananas". Appendix B. Related words used in searches for People Analytics terms (Google trends, searched on June 16, 2017)* Related topics HR Objectives/ Practices Employee beneﬁts Employment Human capital Human resource management Human resource management system Human Resources Workforce Workforce management Workforce planning Employee analytics HR Analytics 10 100 5 10 Human Capital Analytics Human resource analytics 100 5 10 5 45 People Analytics Talent Analytics Workforce Analytics 5 5 5 30 10 20 95 5 15 10 15 5 100 5 25 10 100 10 232 5 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. Turnover Recruitment Talent management Management Marketing Measurement Performance metric Strategy Index term Churn rate Analytics Analysis Analytics Big data Data Data analysis Business analytics Predictive analytics Statistics Internet Twitter Website WordPress Blog LinkedIn Google Analytics Google Search Web analytics Advertising AdWords Organisations Deloitte IBM Company Oracle Corporation Organization SAP SE Service Software SuccessFactors Technology Profession Consultant Career Job People Salary 5 5 5 10 5 5 5 15 5 5 5 5 20 25 5 5 15 5 5 5 5 5 5 5 5 5 70 5 5 10 5 5 95 5 5 10 5 5 95 5 5 95 5 5 5 5 10 15 5 5 50 5 5 5 5 5 100 5 5 5 100 10 5 10 5 10 5 10 45 5 5 5 10 5 5 5 5 5 45 5 15 5 5 15 10 25 5 5 5 100 5 5 5 5 5 5 10 5 5 5 5 5 5 5 5 5 5 5 10 5 5 10 10 5 5 15 5 10 5 5 5 10 Learning and Development Course How-to Master's Degree Training Research Research Review User Microsoft PowerPoint Portable Document Format 5 5 5 5 5 5 35 5 5 5 5 10 5 5 10 Conferences Wharton School Univ. Pennsylvania 5 Country 233 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. India 5 5 *Related topics in Google Trends deﬁned as “users searching for your term also searched for these topics”. Includes results for the most popular topics (or Top): Scoring is on a relative scale where a value of 100 is the most commonly searched topic, a value of 50 is a topic searched half as often, and a value of 0 is a topic searched for less than 1% as often as the most popular topic. Appendix C. Analysed articles from Scopus, HCA group, and Marler and Boudreau lists N Reference Scopus List 1. Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1–11. 2. Aral, S., Brynjolfsson, E., & Wu, L. (2012). Three-way complementarities: Performance pay, human resource analytics, and information technology. Management Science, 58(5), 913–931. 3. Baesens, B., De Winne, S., & Sels, L. (2017). Is your company ready for HR analytics? MIT Sloan Management Review, 58(2), 20–21. 4. Bassi, L., & McMurrer, D. (2016). Four Lessons Learned in How to Use Human Resource Analytics to Improve the Eﬀectiveness of Leadership Development. Journal of Leadership Studies, 10(2), 39–43. Chiappinelli, C. (2009). HCM complexity rises in global setups. Managing Automation, 24(11), 35–37. 5. 6. Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard Business Review, 88(10), 52–58, 150. 7. Dessain, N. (2016). Human resources marketing and recruiting: Introduction and overview. In Handbook of Human Resources Management (pp. 3–22). 8. Dexter, F., Ledolter, J., & Hindman, B. J. (2016). Quantifying the Diversity and Similarity of Surgical Procedures among Hospitals and Anesthesia Providers. Anesthesia and Analgesia, 122(1), 251–263. Dong, S., Johar, M., & Kumar, R. (2013). Workforce analytics for knowledge-intensive service delivery using a private service marketplace. In WITS 2013 - 23rd Workshop on 9. Journal Discipline - Business, Management and Accounting - Business, Management and Accounting - Decision Sciences - Business, Management and Accounting - Decision Sciences - Social Sciences - Business, Management and Accounting - Decision Sciences - Engineering - Business, Management and Accounting - Economics, Econometrics and Finance - Business, Management and Accounting - Economics, Econometrics and Finance - Medicine - Computer Science 234 Keywords used - HR analytics - Human resource information systems - Big data - Human Resource Analytics - Incentive systems - Information technology - Performance pay - Production function - Principal-agent model - Complementarity - Enterprise systems - ERP - Productivity - N/A PA objective - Generic - Performance assessment and development and employee lifetime values - Generic - N/A - Performance assessment and development and employee lifetime values - N/A - Generic - N/A - Generic - Recruiting - Recruitment - Recruitment marketing - Staﬃng - Talent acquisition - HR marketing - N/A - Sourcing - Diversity and inclusion - N/A - Generic International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. Information Technology and Systems: Leveraging Big Data Analytics for Societal Beneﬁts. 10. Dubey, A., Abhinav, K., Taneja, S., Virdi, G., Dwarakanath, A., Kass, A., & Kuriakose, M. S. (2016). Dynamics of software development crowdsourcing. In Proceedings - 11th IEEE International Conference on Global Software Engineering, ICGSE 2016 (pp. 49–58). - Business, Management and Accounting - Computer Science 11. Fang, D., Varshney, K. R., Wang, J., Ramamurthy, K. N., Mojsilovic, A., & Bauer, J. H. (2013). Quantifying and recommending expertise when new skills emerge. In Proceedings - IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013 (pp. 672–679). - Computer Science 12. Fecheyr-Lippens, B., Schaninger, B., & Tanner, K. (2015). Power to the new people analytics. McKinsey Quarterly, (1). - Business, Management and Accounting - Economics, Econometrics and Finance - Social Sciences - Computer Science - Engineering 13. Ghosh, S., Zheng, Y., Lammers, T., Chen, Y. Y., Fitzmaurice, C., Johnston, S., & Li, J. (2016). Deriving public sector workforce insights: A case study using Australian public sector employment proﬁles (Vol. 10086 LNAI). 14. Hausknecht, J. (2013). Workforce Analytics. In Workforce Asset Management Book of Knowledge (pp. 367–392). - Business, Management and Accounting 15. Horesh, R., Varshney, K. R., & Yi, J. (2016). Information retrieval, fusion, completion, and clustering for employee expertise estimation. In Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 (pp. 1385–1393). - Computer Science 16. Kapoor, B., & Kabra, Y. (2014). Current and future trends in human resources analytics adoption. Journal of Cases on Information Technology, 16(1), 50–59. - Business, Management and Accounting - Computer Science - Decision Sciences 17. Khan, S. A., & Tang, J. (2016). The paradox of human resource analytics: Being mindful of employees. Journal of General Management, 42(2), 57–66. 18. King, K. G. (2016). Data Analytics in Human Resources: A Case Study and Critical Review. Human Resource Development Review, 15(4), 487–495. - Business, Management and Accounting - Business, Management and Accounting 19. Lal, P. (2015). Transforming hr in the digital era: Workforce analytics can move people specialists to the center of decision-making. Human Resource Management International Digest, 23(3), 1–4. - Business, Management and Accounting 235 - Workforce analytics - Software development - Tracking - Forecasting - Crowdsourcing - Workforce analytics - Expertise taxonomy - Recommendation systems - Enterprise social networks - Cold-start problem - N/A - Performance assessment and development and employee lifetime values - Performance assessment and development and employee lifetime values - Generic - Workforce analytics - Public sector - Data mining - Generic - Workforce analytics - Benchmarking - Data collection systems - Workforce asset management (WAM) - Workforce management (WFM) - Workforce management professional (WAM‐Pro) - Workforce analytics - Unsupervised learning - Human talent management - Human capital - HR metrics - Return on investment - Business intelligence - Predictive and prescriptive analytics - Analytics Maturity Model - Descriptive - N/A - Generic - - Generic Strategic HRM Retention HRD management HR practices Human resource management - Information management - Workforce planning - Generic - Generic - Generic International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. 20. Lismont, J., Vanthienen, J., Baesens, B., & Lemahieu, W. (2017). Deﬁning analytics maturity indicators: A survey approach. International Journal of Information Management, 37(3), 114–124. - Computer Science - Social Sciences 21. Mankins, M., Brahm, C., & Caimi, G. (2014). Your scarcest resource. Harvard Business Review, 92(5), 74–80, 133. 22. Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. International Journal of Human Resource Management, 28(1), 3–26. - Business, Management and Accounting - Economics, Econometrics and Finance - Business, Management and Accounting 23. Martin-Rios, C., Pougnet, S., & Nogareda, A. M. (2017). Teaching HRM in contemporary hospitality management: a case study drawing on HR analytics and big data analysis. Journal of Teaching in Travel and Tourism, 17(1), 34–54. - Business, Management and Accounting - Social Sciences 24. Mashhadi, A., Acer, U. G., Boran, A., Scholl, P. M., Forlivesi, C., Vanderhulst, G., & Kawsar, F. (2016). Exploring space syntax on entrepreneurial opportunities with Wi-Fi analytics. In UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 658–669). 25. Momin, W. Y. M., & Mishra, K. (2014). Impression of ﬁnancial measures in HR analytics. Journal of Interdisciplinary and Multidisciplinary Research, 2(1), 87–91. - Computer Science - Multidisciplinary 26. Natesan Ramamurthy, K., Varshney, K. R., & Singh, M. (2013). Quantile regression for workforce analytics. In 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings (p. 1134). 27. Nienaber, H., & Sewdass, N. (2016). A reﬂection and integration of workforce conceptualisations and measurements for competitive advantage. Journal of Intelligence Studies in Business, 6(1), 5–20. - Computer Science 28. Ozgur Bayman, E., Dexter, F., & Ledolter, J. (2017). Mixed eﬀects logistic regression modeling of daily evaluations of nurse anesthetists’ work habits adjusting for leniency of the rating anesthesiologists. Perioperative Care and Operating Room Management, 6, 14–19. - Medicine - Nursing 29. Palshikar, G. K., Pawar, S., & Ramrakhiyani, N. (2016). Role models: Mining role transitions data in IT project management. In Proceedings - 3rd IEEE International - Computer Science - Decision Sciences - Business, Management and Accounting - Decision Sciences 236 - Organizational performance - Decision-making - Analytics techniques - Organizational characteristics - Survey research - Analytics maturity - N/A - Generic - Collaboration - Human resource analytics - Talent management - Strategic HRM - HRIS - HR metrics - Workforce analytics - HR analytics - Hospitality education - Human resource management - Teaching guide - Case method - Big data analysis - People analytics - Space syntax - Network sensing - Generic - HR analytics - Human resource management - Predictive analytics - Financial measures - Productivity proﬁle - Quantile regression - Workforce behavior - Attrition proﬁle - Workforce analytics - Predictive analytics - Strategy - Workforce - Organisational performance - Workforce intelligence - Workforce metrics - Competitive advantage - Human resource analytics - Mixed eﬀects logistic regression - Performance evaluation - Anesthesiology - HR Analytics - Project Management - Role-based Teams - Sequence Mining - Generic - Generic - Inter-organisational relationships - Churn and retention - Generic - Performance assessment and development and employee lifetime values - Workforce planning International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. Conference on Data Science and Advanced Analytics, DSAA 2016 (pp. 508–517). 30. Palshikar, G. K., Sahu, K., & Srivastava, R. (2015). After you, who? Data mining for predicting replacements (Vol. 9468). 31. Persson, A. (2016). Implicit Bias in Predictive Data Proﬁling Within Recruitments. In A. Lehmann, D. Whitehouse, S. Fischer-Hübner, L. Fritsch, & C. Raab (Eds.), Privacy and Identity Management. Facing up to Next Steps: 11th IFIP WG 9.2, 9.5, 9.6/11.7, 11.4, 11.6/SIG 9.2.2 International Summer School, Karlstad, Sweden, August 21-26, 2016, Revised Selected Papers (pp. 212–230). Cham: Springer International Publishing. 32. Bassi, L. (2012). Raging debates in HR analytics. Human Resource Management International Digest, 20(2), 74–80. 33. Ramamurthy, K. N., Singh, M., Davis, M., Kevern, J. A., Klein, U., & Peran, M. (2016). Identifying Employees for Re-skilling Using an Analytics-Based Approach. In Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 (pp. 345–354). 34. Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236–242. 35. Royal, C., & O’Donnell, L. (2008). Emerging human capital analytics for investment processes. Journal of Intellectual Capital, 9(3), 367–379. 36. Royal, C., & Windsor, G. S. S. (2016). Sustainable institutional investment models and the human capital analytics approach: A great gap to be ﬁlled. In Routledge Handbook of Social and Sustainable Finance (pp. 431–447). 37. Ryan, J., & Herleman, H. (2016). A big data platform for workforce analytics. In Big Data at Work: The Data Science Revolution and Organizational Psychology (pp. 19–42). 38. Shami, N. S., Muller, M., Pal, A., Masli, M., & Geyer, W. (2015). Inferring employee engagement from social media. In Conference on Human Factors in Computing Systems Proceedings (Vol. 2015-April, pp. 3999–4008). 39. Sharma, A., & Sharma, T. (2017). HR analytics and performance appraisal system: A conceptual framework for employee performance improvement. Management Research Review, 40(6), 684–697. 40. Singer, L., Storey, M.-A., Filho, F. F., Zagalsky, A., & German, D. M. (2017). People analytics in software development (Vol. 10223 LNCS). - Computer Science - Engineering - Decision Sciences - Business, Management and Accounting - Computer Science - Engineering - Business, Management and Accounting - Psychology - Social Sciences - Business, Management and Accounting - Social Sciences - Survival Analysis - Workforce Management - Graph Clustering - Classiﬁcation - N/A - People analytics Discrimination Implicit bias Machine-learning Recruitment Social exclusion Big Data - Workforce planning - Acquisition/Hiring - N/A - Generic - - Performance assessment and development and employee lifetime values Workforce analytics Skill adjacency Skills taxonomy Human resource Employee training N/A Human capital Financial markets Hong Kong Intangible assets Financial institutions - Australia - N/A - Inter-organisational relationships - N/A - Generic - Computer Science - N/A - Onboarding, culture ﬁt, and engagement - Business, Management and Accounting - HR analytics - Perceived accuracy - Performance appraisal - Performance improvement - Employee performance - People analytics - Developer analytics - Feedback - Social network analysis - Computersupported collaborative work - Collaboration - Performance assessment and development and employee lifetime values - Business, Management and Accounting - Economics, Econometrics and Finance - Psychology - Computer Science - Engineering 237 - - Generic - People risks - Collaboration International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. 41. Sinha, V., Subramanian, K. S., Bhattacharya, S., & Chaudhuri, K. (2012). The contemporary framework on social media analytics as an emerging tool for behavior informatics, HR analytics and business process. Management (Croatia), 17(2), 65–84. 42. Ulrich, D., & Dulebohn, J. H. (2015). Are we there yet? What’s next for HR? Human Resource Management Review, 25(2), 188–204. 43. Varshney, K. R., Chenthamarakshan, V., Fancher, S. W., Wang, J., Fang, D., & Mojsilović, A. (2014). Predicting employee expertise for talent management in the enterprise. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1729–1738). 44. Wang, J., Varshney, K. R., Mojsilovic, A., Fang, D., & Bauer, J. H. (2013). Expertise assessment with multi-cue semantic information. In Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2013 (pp. 534–539). 45. Wawer, M., & Muryjas, P. (2017). The utilization of the HR analytics by the high and mid-level managers: Case from Eastern Poland. In Communication, Management and Information Technology - Proceedings of the International Conference on Communication, Management and Information Technology, ICCMIT 2016 (pp. 97–106). 46. Wei, D. (2017). k-quantiles: L1 distance clustering under a sum constraint. Pattern Recognition Letters, 92, 49–55. 47. Wei, D., & Varshney, K. R. (2015). Robust binary hypothesis testing under contaminated likelihoods. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 2015-August, pp. 3407–3411). 48. Wei, D., Varshney, K. R., & Wagman, M. (2015). Optigrow: People Analytics for Job Transfers. In Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015 (pp. 535–542). - Business, Management and Accounting - N/A - Generic - Business, Management and Accounting - Psychology - Outside inside approach - Strategy - Transformation - Future human resource management - N/A - Generic - Workforce planning - Business, Management and Accounting - Computer Science - Computer Science - N/A - Workforce planning - N/A - Performance assessment and development and employee lifetime values - Computer Science - Generic - Computer Science - Engineering - - Computer Science - - Computer Science - 49. Weiss, C. (2016). Human resources strategy and change: Essentials of workforce planning and controlling. In Handbook of Human Resources Management (pp. 1343–1373). - Business, Management and Accounting - Economics, Econometrics and Finance - 50. 238 Workforce analytics k-means++ Proportional data Compositional data Centroid Workforce analytics Minimax Signal detection theory Label noise Linear programming Workforce analytics Enterprise transformation Expertise analytics Human capital management Total variation distance HR analytics HR controlling HR KPIs HR planning HR strategy Job family Job model Resource planning Scenarios Simulation Strategic capabilities Strategic talent management Strategic workforce planning Critical jobs Workforce analytics Scheduling models - Generic - Workforce planning - Workforce planning - Workforce planning International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. Williams, J. C., Lambert, S., Pitt-Catsouphes, M., James, J., Sweet, S., Cahill, K., … Disselkamp, L. (2013). New Scheduling Models for the Workforce. In Workforce Asset Management Book of Knowledge (pp. 309–344). 51. Wroe, N. (2012). Innovations in talent analytics. T and D, 66(8), 30–31. 52. Xu, H., Yu, Z., Yang, J., Xiong, H., & Zhu, H. (2016). Talent circle detection in job transition networks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. 13–17-August-2016, pp. 655–664). 53. Zhao, M., Javed, F., Jacob, F., & McNair, M. (2015). SKILL: A system for skill identiﬁcation and normalization. In Proceedings of the National Conference on Artiﬁcial Intelligence (Vol. 5, pp. 4012–4017). 1. 2. Marler And Boudreau List Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1–11. Aral, S., Brynjolfsson, E., & Wu, L. (2012). Three-way complementarities: Performance Pay, human resource analytics, and information technology. Management Science, 58, 913–931. 3. Bassi, L. (2011). Raging debates in HR Analytics. People & Strategy, 34, 14–18. 4. Coco, C. T., Jamison, F., & Black, H. (2011). Connecting people investments and business outcomes at Lowe’s: Using value linkage analytics to link employee engagement to business performance. People & Strategy, 34, 28–33. DiBernardino, F. (2011). The missing link: Measuring and managing ﬁnancial performance of the human capital investment. People & Strategy, 34, 44–49. Douthitt, S., & Mondore, S. (2014). Creating a businessfocused HR Function with Analytics and Integrated Talent Management, People & Strategy, 36(4), 16–21. Falletta, S. (2014). In search of HR intelligence: Evidencebased HR Analytics practices in high performing companies. People & Strategy, 36, 28–37. Giuﬀrida, M. (2014). Unleashing the power of talent analytics in federal government. Public Manager, 43, 7–10 Harris, J. G., Craig, E., & Light, D. A. (2011). Talent and analytics: New approaches, higher ROI. Journal of Business Strategy, 32, 4–13. 5. 6. 7. 8. 9. 10. Lawler III, E. E., Levenson, A., & Boudreau, J. W. (2004). HR metrics and analytics: Use and Impact. Human Resource Planning, 27, 27–35. 11. - Business, Management and Accounting - Business, Management and Accounting - Computer Science - Work‐life balance - Workforce asset management professional (WAM‐Pro) - Workplace ﬂexibility - N/A - Generic - People analytics - Talent circle detection - Sourcing - Computer Science - N/A - Workforce planning - Business, Management and Accounting - HR analytics - Human resource information systems - Big data - Human Resource Analytics - Incentive systems - Information technology - Performance pay - Production function - Principal-agent model - Complementarity - Enterprise systems - ERP - Productivity - N/A - Generic - Business, Management and Accounting - Decision Sciences - Performance assessment and development and employee lifetime values - Generic - Business, Management and Accounting - N/A - N/A - Onboarding, culture ﬁt, and engagement - N/A - N/A - Generic - N/A - N/A - Generic - N/A - N/A - Generic - N/A - N/A - Generic - Business, Management and Accounting - - Generic - N/A Analytics Talent management Human resources Decision making Human resource management - N/A - Generic - N/A - N/A - Generic 239 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. Levenson, A. (2011). Using targeted analytics to improve talent decisions. People & Strategy, 34, 34–43. 12. Mondare, S., Douthitt, S., & Carson, M. (2011). Maximizing the impact and eﬀectiveness of HR Analytics to drive business outcomes. People & Strategy, 34, 20–27. 13. Pape, T. (2016). Prioritising data items for business analytics: Framework and application to human resources. European Journal of Operational Research, 252, 687–698. 14. Rasmussen, T., & Ulrich, D. (2015). Learning from practice: how HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236–242. 1. 2. 3. 4. 5. 6. HCA Group List Barrick, M. R., Thurgood, G. R., Smith, T. A., & Courtright, S. H. (2015). Collective Organizational Engagement: Linking Motivational Antecedents, Strategic Implementation, and Firm Performance. Academy of Management Journal, 58(1), 111–135. Bell, S. T., Villado, A. J., Lukasik, M. A., Belau, L., & Briggs, A. L. (2011). Getting Speciﬁc about Demographic Diversity Variable and Team Performance Relationships: A MetaAnalysis. Journal of Management, 37(3), 709–743. Bowen, D. E., & Ostroﬀ, C. (2004). Understanding HRM–Firm Performance Linkages: The Role of the “Strength” of the HRM System. Academy of Management Review, 29(2), 203–221. Call, M. L., Nyberg, A. J., Ployhart, R. E., & Weekley, J. (2015). The dynamic nature of collective turnover and unit performance: The impact of time, quality, and replacements. Academy of Management Journal, 58(4), 1208–1232. Christian M. S., Garza A. S., & Slaughter J. E. (2011). Work engagement: a quantitative review and test of its relations with task and contextual performance. Personnel psychology, 64(1), 89–136. Cole, M. S., Walter, F., Bedeian, A. G., & O’Boyle, E. H. (2012). Job Burnout and Employee Engagement: A MetaAnalytic Examination of Construct Proliferation. Journal of Management, 38(5), 1550–1581. - N/A - N/A - Generic - Decision Sciences - Mathematics - - Generic - Business, Management and Accounting - Psychology - Social Sciences Business analytics Business intelligence Data requirements Human resources Multi-criteria decision analysis - N/A - Generic - Business, Management and Accounting - N/A - Onboarding, culture ﬁt, and engagement - Business, Management and Accounting - Economics, Econometrics and Finance - Business, Management and Accounting - Teams diversity - Demographic diversity - Team diversity - Meta-analysis - Diversity and inclusion - N/A - Onboarding, culture ﬁt, and engagement - Business, Management and Accounting - N/A - Churn and retention - Business, Management and Accounting - Psychology - Business, Management and Accounting - Economics, Econometrics and Finance - N/A - Onboarding, culture ﬁt, and engagement - Burnout - Construct redundancy - Construct proliferation - Construct validity - Engagement - Team work engagement - Team dynamics - Emergent states - Collective constructs - Employee engagement - Burnout - Job demands and resources - Challenge and hindrance stress - Meta-analysis - Strategic HRM - Perceived HRM - Communication - Satisfaction - Unit performance - Onboarding, culture ﬁt, and engagement - Wellness, health, and safety 7. Costa P. L., Passos A. M., & Bakker, A. B. (2014). Team work engagement: A model of emergence. Journal of Occupational and Organizational Psychology, 87(2), 414–436. - Business, Management and Accounting - Psychology 8. Crawford, E. R., LePine, J. A., & Rich, B. L. (2010). Linking job demands and resources to employee engagement and burnout: A theoretical extension and meta-analytic test. Journal of Applied Psychology, 95(5), 834–848. - Psychology 9. Den Hartog, D. N., Boon, C., Verburg, R. M., & Croon, M. A. (2013). HRM, Communication, Satisfaction, and Perceived Performance: A Cross-Level Test. Journal of Management, 39(6), 1637–1665. - Business, Management and Accounting 240 - Onboarding, culture ﬁt, and engagement - Onboarding, culture ﬁt, and engagement - Wellness, health, and safety - Wellness, health, and safety - Performance assessment and development and employee lifetime values International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. 10. Dobrow Riza, S., Ganzach, Y., & Liu, Y. (2016). Time and Job Satisfaction: A Longitudinal Study of the Diﬀerential Roles of Age and Tenure. Journal of Management. 11. Fang, R., Landis, B., Zhang, Z., Anderson, M. H., Shaw, J. D., & Kilduﬀ, M. (2015). Integrating Personality and Social Networks: A Meta-Analysis of Personality, Network Position, and Work Outcomes in Organizations. Organization Science, 26(4), 1243–1260. 12. Harter, J. K., Schmidt, F. L., & Hayes, T. L. (2002). Businessunit-level relationship between employee satisfaction, employee engagement, and business outcomes: A metaanalysis. Journal of Applied Psychology, 87(2), 268–279. 13. Heavey, A. L., Holwerda, J. A., & Hausknecht, J. P. (2013). Causes and consequences of collective turnover: A metaanalytic review. Journal of Applied Psychology, 98(3), 412–453. 14. Jiang, K., Lepak, D. P., Hu, J., & Baer, J. C. (2012). How Does Human Resource Management Inﬂuence Organizational Outcomes? A Meta-analytic Investigation of Mediating Mechanisms. Academy of Management Journal, 55(6), 1264–1294. 15. Kehoe, R. R., & Wright, P. M. (2013). The Impact of HighPerformance Human Resource Practices on Employees’ Attitudes and Behaviors. Journal of Management, 39(2), 366–391. 16. Kunze, F., Boehm, S., & Bruch, H. (2013). Organizational Performance Consequences of Age Diversity: Inspecting the Role of Diversity‐Friendly HR Policies and Top Managers’ Negative Age Stereotypes. Journal of Management Studies, 50(3), 413–442. 17. Leslie, L. M., Manchester, C. F., & Dahm, P. C. (2017). Why and When Does the Gender Gap Reverse? Diversity Goals and the Pay Premium for High Potential Women. Academy of Management Journal, 60(2), 402–432. 18. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717. 19. Murphy, K. R. (2008). Explaining the Weak Relationship Between Job Performance and Ratings of Job Performance. Industrial and Organizational Psychology, 1(2). 20. Nahrgang, J. D., Morgeson, F. P., & Hofmann, D. A. (2011). Safety at work: A meta-analytic investigation of the link between job demands, job resources, burnout, engagement, and safety outcomes. Journal of Applied Psychology, 96(1), 71–94. 21. Nishii, L. H. (2013). The Beneﬁts of Climate for Inclusion for Gender-Diverse Groups. Academy of Management Journal, 56(6), 1754–1774. - Economics, Econometrics and Finance - Business, Management and Accounting - Economics, Econometrics and Finance - Business, Management and Accounting - Psychology - Psychology - Business, Management and Accounting - Business, Management and Accounting - Economics, Econometrics and Finance - Business, Management and Accounting - Business, Management and Accounting - Job satisfaction Time Age Tenure Pay Longitudinal study Social networks Network position Structural holes Brokerage Indegree centrality Personality Self-monitoring Big Five personality traits - Meta-analysis - N/A - Onboarding, culture ﬁt, and engagement - Churn and retention - Strategic HRM - Commitment - Onboarding, culture ﬁt, and engagement - Churn and retention - - Diversity and inclusion Age discrimination Age diversity Age stereotypes Diversity-friendly HR-practices - Social identity theory - Structural equation modelling - N/A - N/A - Psychology - N/A - Psychology - 241 - Performance assessment and development and employee lifetime values - Collective turnover - Organizational performance - Retention - Meta-analysis - N/A - Medicine - Psychology - Business, Management and Accounting - Wellness, health, and safety Workplace safety Safety climate Meta-analysis Job demands Job resources N/A - Churn and retention - Performance assessment and development and employee lifetime values - Diversity and Inclusion - Performance assessment and development and employee lifetime values - Performance assessment and development and employee lifetime values - Onboarding, culture ﬁt, and engagement - Wellness, health, and safety - Diversity and inclusion International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. 22. Nishii, L. H., & Mayer, D. M. (2009). Do inclusive leaders help to reduce turnover in diverse groups? The moderating role of leader–member exchange in the diversity to turnover relationship. Journal of Applied Psychology, 94(6), 1412–1426. - Psychology 23. Park, T.Y., & Shaw, J. D. (2013). Turnover rates and organizational performance: a meta-analysis. The Journal of Applied Psychology, 98(2), 268—309. - Psychology 24. Rich, B. L., Lepine, J. A., & Crawford, E. R. (2010). Job Engagement: Antecedents and Eﬀects on Job Performance. Academy of Management Journal, 53(3), 617–635. 25. Saks, A. M., & Gruman, J. A. (2014). What Do We Really Know About Employee Engagement? Human Resource Development Quarterly, 25(2), 155–182. - Business, Management and Accounting - Arts and Humanities - Business, Management and Accounting 26. Schaufeli, W.B., & Bakker, A. B. (2003). Utrecht Work Engagement Scale. Utrecht: Occupational Health Psychology Unit, Utrecht University. 27. Schaufeli, W. B., & Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: a multi‐sample study. Journal of Organizational Behavior, 25(3), 293–315. - N/A 28. Seong, J. Y., Kristof-Brown, A. L., Park, W.W., Hong, D.S., & Shin, Y. (2012). Person-Group Fit: Diversity Antecedents, Proximal Outcomes, and Performance at the Group Level. Journal of Management, 41(4), 1184–1213. - Business, Management and Accounting - Psychology - Social Sciences - Business, Management and Accounting - Economics, Econometrics and Finance - Leader–member exchange - Work groups - Diversity - Inclusion - Turnover - Meta-analysis - Organizational performance - Turnover rates - N/A - Employee engagement - Burnout - Job resources - Job demands - Personal resources - N/A - Churn and retention - Diversity and inclusion - Churn and retention - Onboarding, culture ﬁt, and engagement - Onboarding, culture ﬁt, and engagement - Onboarding, culture ﬁt, and engagement - N/A - Onboarding, culture ﬁt, and engagement - Wellness, health, and safety - Group-level - Person-group (PG) ﬁt - Categorizationelaboration model (CEM) - Social cohesion - Transactive memory system - Group performance - Diversity and inclusion Appendix D. Vendors’ descriptions and the deﬁnitions they follow Company Description Deﬁnition Web Link Deloitte Smarter insights with workforce data analytics. Analytics perspectives and solutions Can you predict which high performers are at risk of leaving months before they resign? Merge external data with your own business metrics to project next year’s workforce demand? And can you triage resumes to predict employee success and tenure – before you hire? Here are just a few examples of how companies can gain smarter insights and stronger outcomes with workforce data analytics and cognitive computing. We are a trusted information hub for all the essential human resources (HR) technology you rely on to do your job. We know that HR leaders have many diﬃcult, strategic technology decisions to make when trying to ﬁnd the best employees, keep them happy, and get them paid. Not speciﬁed https://www2.deloitte.com/us/en/ pages/deloitte-analytics/solutions/ analytics-in-action-workforce.html Workforce analytics is a combination of software and methodology that applies statistical models to worker-related data, allowing enterprise leaders to optimize human resource management (HRM). http://searchhrsoftware.techtarget. com/about http://searchhrsoftware.techtarget. com/deﬁnition/workforce-analytics TechTarget 242 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. Competitive Analytics McKinsey & Company Accenture IBM PWC Competitive Analytics [CA] helps organizations integrate all the necessary analytics and develop a strategy to truly get the best from your team. We help you understand who your top performers are, where ineﬃciencies may occur, and how you can recruit, empower, and motivate other employees. CA helps you ensure that the data being used is timely, clean, and accurate. Because organizations often have employee related data spread throughout departments, in myriad ﬁle formats, and within multiple systems, Competitive Analytics blends data sources to develop a complete workforce database, which can then be easily used to develop your employee analytics. Competitive Analytics displays our comprehensive labor analyses in easy-tointerpret, interactive dashboards so executives can make employee related decisions immediately. Identify and enable talent-driven performance Top employees outperform average employees by up to eight times. To stay ahead, leading companies identify, attract, develop, and retain these top employees. How do they do it? By developing a people strategy that is based on data and analytics. Imagine all the questions you’ve ever had about your workforce answered: which people are needed for speciﬁc projects? Do you have the resources you need to successfully grow your organization? How are individuals performing against KPIs? Analytics reveals the answers, giving you solid, reliable insights so you can plan, recruit and manage with conﬁdence. We’ll help you analyze HR trends, set workforce capacity and capability goals, track progress, monitor performance and automate services. By equipping you with integrated, strategic insights we can help you speed up decisions, reduce risk and empower your management team. HR analytics enable organizations to use their wealth of employee data to make better decisions about their workforces and improve operational performance. From attracting top talent, to accurately forecasting future staﬃng needs or improving employee satisfaction, HR analytics tools empower organizations to align HR metrics with strategic business goals. Our tactical and predictive analytics programs will help you address speciﬁc workforce trouble spots so you can manage issues before they appear. We'll show you how to build your Talent Analytics solutions. Point solutions demonstrate ROI of people analytics to the business Gartner deﬁnes Employee Analytics, also known as Workforce Analytics, as, advanced set of data analysis tools and metrics for comprehensive workforce performance measurement and improvement. It analyzes recruitment, staﬃng, training and development, personnel, and compensation and beneﬁts, as well as standard ratios that consist of time to ﬁll, cost per hire, accession rate, retention rate, add rate, replacement rate, time to start and oﬀer acceptance rate. http://competitiveanalytics.com/ employee-analytics/ People Analytics-the application of https://www.mckinsey.com/businessadvanced analytics and large data sets to functions/organization/how-we-helptalent management clients/people-analytics https://www.mckinsey.com/businessfunctions/mckinsey-analytics/ourinsights/using-people-analytics-todrive-business-performance-a-casestudy Human Capital Analytics help lead to better focus, employee experience and ROI https://www.accenture.com/us-en/ service-accenture-analytics-talent-andhr-analytics https://www.accenture.com/nz-en/ service-fei-digital-hr-transformationinform-analytics HR Analytics-a data-driven approach to building a smarter workforce https://www-01.ibm.com/software/ analytics/solutions/operationalanalytics/hr-analytics/ Not speciﬁed https://www.pwc.com/us/en/hrmanagement/people-analytics/ predictive-analytics.html 243 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. We have a wide range of experience assisting clients with targeted analytic, modeling, and forecasting requirements to address the top workforce concerns today. We have pre-built talent analytics services to cover the full HR/employee lifecycle – from talent acquisition to performance, safety & learning management, to retention and retirement – all designed to assist your organization solve critical workforce and business objectives. We oﬀer analytics around total rewards that will enable your organization to optimize across diﬀerent beneﬁts and compensation options. Every talent analytics and predictive service we oﬀer has one goal in common: apply advanced analytic techniques to solve a critical workforce problem and thereby, demonstrate value of talent analytics to the entire organization. Kronos Transform your workforce data into actionable insights. Organizations need to identify, predict, and manage opportunities for cost savings and productivity gains — all while improving the quality of their products and services. But how can they achieve productivity gains and stay within budget when they lack a reliable way to measure and analyze workforce performance? Improve business decisions with trusted SAP SuccessFact- intelligence from SAP SuccessFactors Workforce Analytics. Beneﬁt from the ors technology and expertise of a global leader in people analytics and business intelligence solutions to accelerate your organization’s understanding of Big Data in HR and use workforce data strategically to drive business impact. SAP makes workforce analytics simple and accessible for HR professionals, analysts, and business partners so they can quickly and accurately answer key questions about your workforce and inﬂuence talent and business decisions being considered by your managers and executives. Talent Analytics Why Talent Analytics? 8 Compelling Reasons to Engage with Us: 1. Self Funding Projects – Our Projects Pay for Themselves; 2. High Volume, High Turnover Roles; 3. Predictions are both Pre-Hire and Post-Hire; 4. Business Results (not HR Results); 5. No Distractions; 6. Globally Recognized Leaders; 7. Cloud Deployment Platform, Advisor; 8. Machine Learning – Models Continuously Recalculate, Learn and Update Ultimate HR leaders use workforce analytics in software various forms, such as predictive and prescriptive analytics. Predictive Workforce Analytics helps managers comb through mounds data to determine which employees have the Not speciﬁed https://www.kronos.com/products/ workforce-central-suite/workforceanalytics Not speciﬁed https://www.successfactors.com/en_ us/solutions/workforce-planning-andanalytics/workforce-analytics.html Not speciﬁed http://www.talentanalytics.com/whytalent-analytics/ Workforce analytics describes a set of tools https://www.ultimatesoftware.com/ that measure, characterize and organize what-are-workforce-analytics sophisticated employee data. These tools are used to present detailed employee performance to provide a better 244 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. Visier QuestionPRO TalentLMS Cornerstone greatest potential of success with the organization. The data also predicts which employees are most likely to leave in the near future. Prescriptive Workforce Analytics prescribes actions HR leaders can take to help develop and retain key members of the workforce, based on data uncovered from predictive analytics. Both are an essential part of workforce analytics and when used together, these tools can make all the diﬀerence! Why Do Companies Invest in Workforce Analytics? All great companies are made up of the right people in the right positions. Workforce analytics allows HR leaders to determine who the right people are, which tasks suit them best and how to ensure they remain satisﬁed in their roles. Ultimate Software has designed workforce analytics tools that are easy to understand and utilize eﬀectively. Analytics aren’t just for statisticians anymore. A growing number of organizations are using analytics to examine and act upon data about their people in the workplace. Known as workforce analytics, these sophisticated tech tools are changing the HR game, and those companies willing and able to harness the power of Big Data for analytics are seeing great gains and meaningful advantages over their competition. UltiPro® At Ultimate Software, our award-winning solution for HR, payroll, and talent management, UltiPro®, oﬀers a range of predictive and prescriptive analytics designed to combine managers’ expertise with proven algorithms. The winning result? Knowledge without bias. Visier Workforce Intelligence is a cloudbased people strategy platform that provides answers to hundreds of pre-built, best practice questions, across a range of HR and business topics. Unlike the reports oﬀered by your HR management systems, Visier delivers insights. Insights that reduce resignations. Sharpen recruiting eﬀectiveness. Optimize learning. And drive business outcomes. Employee analytics tool providing 360 reviews, online surveys, weekly pulse to increase employee retention, improve morale, and create workforce eﬃciency. Simple and comprehensible analytics about everything that happens inside your elearning environment. What Are the Beneﬁts of People Analytics? People analytics helps organizations to make smarter, more strategic and more informed talent decisions. With people analytic, organizations can ﬁnd better understanding and assist in overall management. Not speciﬁed https://www.visier.com/solutions/ Not speciﬁed https://www.questionpro.com/ workforce/ Not speciﬁed https://www.talentlms.com/ People analytics, also known as talent analytics or HR analytics, refers to the method of analytics that can help managers and executives make decisions about their employees or workforce. People analytics applies statistics, https://www.cornerstoneondemand. com/glossary/people-analytics 245 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. applicants, make smarter hiring decisions, and increase employee performance and retention. Cornerstone’s suite of people analytics products apply sophisticated data science and machine learning to help organizations more eﬃciently and eﬀectively manage their people. Cornerstone’s analytics suite give organizations options for viewing, understanding and acting on talent data across the entire employee lifecycle. This includes Cornerstone View, an interactive data visualization application that gives business leaders deeper intelligence about their people, Cornerstone Planning, an intuitive workforce planning application that helps organizations easily create, manage and execute accurate hiring plans over multiple time horizons, as well as Cornerstone Insights, its predictive and prescriptive analytics solution that equips business leaders with the intelligence to better recruit, train, manage and develop their people. technology and expertise to large sets of talent data, which results in making better management and business decisions for an organization. People analytics is a new domain for most HR departments. Companies are looking to better drive the return on their investments in people. The old approaches of gut feel is no longer suﬃcient. Isson, J. P., & Harriott, J. S. (2016). People analytics in the era of big data: Changing the way you attract, acquire, develop, and retain talent. John Wiley & Sons Inc. Levenson, A., & Pillans, G. (2017). Strategic workforce analyticsRetrieved fromhttps:// www.orgvue.com/sites/default/ﬁles/strategic-workforce-analytics-report.pdf. Libert, B., Beck, M., & Wind, Y. J. (2016). Why are we still classifying companies by industry? Harvard Business Review. Retrieved from https://hbr.org/2016/08/whyare-we-still-classifying-companies-by-industry?. Lismont, J., Vanthienen, J., Baesens, B., & Lemahieu, W. (2017). Deﬁning analytics maturity indicators: A survey approach. International Journal of Information Management, 37(3), 114–124. https://doi.org/10.1016/j.ijinfomgt.2016.12.003. Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR analytics. International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10. 1080/09585192.2016.1244699. Marsh Risk Consulting (2017). Workforce and people risks. Retrieved fromhttps://www. marsh.com/uk/services/marsh-risk-consulting/workforce-and-people-risks.html. Massey, C., Haas, M., & Bidwell, M. (2017). People Analytics. Coursera, 2017. Meister, J. (2017). The future of work: The intersection of artiﬁcial intelligence and human resources. Retrieved fromhttps://www.forbes.com/sites/jeannemeister/2017/03/01/ the-future-of-work-the-intersection-of-artiﬁcial-intelligence-and-human-resources/ 2/#517bbd5c67ee. Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen, S. I., et al. (2014). The use of google trends in health care research: A systematic review. PloS One, 9(10), e109583. https://doi.org/10.1371/journal.pone.0109583. Pagliari, C., Tursunbayeva, A., & Antonelli, G. (2018). People analytics – pathway to organisational enlightenment or ethical mineﬁeld? Professional development workshop. British Academy of Management Conference. Pape, T. (2016). Prioritising data items for business analytics: Framework and application to human resources. European Journal of Operational Research, 252(2), 687–698. https://doi.org/10.1016/j.ejor.2016.01.052. Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Cambridge, USA: Harvard University Press. PWC (2017). Talent analytics and predictive. Retrieved fromhttps://www.pwc.com/us/en/ hr-management/people-analytics/predictive-analytics.html. Raguseo, E. (2018). Big data technologies: An empirical investigation on their adoption, beneﬁts and risks for companies. International Journal of Information Management, 38(1), 187–195. https://doi.org/10.1016/j.ijinfomgt.2017.07.008. Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236–242. https://doi.org/ 10.1016/j.orgdyn.2015.05.008. Ratliﬀ, J. D., & Rubinfeld, D. L. (2014). Is there a market for organic search engine results and can their manipulation give rise to antitrust liability? Journal of Competition Law and Economics, 10(3), 517–541. https://doi.org/10.1093/joclec/nhu013. Royal, C., & Windsor, G. S. S. (2016). Sustainable institutional investment models and the human capital analytics approach. Routledge handbook of social and sustainable ﬁnance. Ruel, H. J. M., & Bondarouk, T. V. (2008). Exploring the relationship between e-HRM and HRM eﬀectiveness: Lessons learned from three companies. In G. Martin, M. Reddington, & H. Alexander (Eds.). Technology, outsourcing and HR transformation (pp. 161–192). Oxford. Schneider, M., & Somers, M. (2006). Organizations as complex adaptive systems: Implications of complexity theory for leadership research. The Leadership Quarterly, References Aral, S., Brynjolfsson, E., & Wu, L. (2012). Three-way complementarities: Performance pay, human resource analytics, and information technology. Management Science, 58(5), 913–931. https://doi.org/10.1287/mnsc.1110.1460. Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. https://doi.org/10. 1080/1364557032000119616. Bakhshi, H. (2014). Model workers. How leading companies are recruiting and managing their data talent. Nesta. Retrieved from http://www.rss.org.uk/Images/PDF/ inﬂuencing-change/rss-nesta-model-workers-data-talent-recruitment-2014.pdf. Bassi, L. (2011). Raging debates in HR analytics. People & Strategy, 34(2), 14–18. Bersin, J. (2016). Predictions for 2017: Everything is becoming digital. Bersin by Deloitte. Bodie, M. T., Cherry, M. A., McCormick, M. L., & Tang, J. (2017). The law and policy of people analytics. University of Colorado Law Review, 88(4), 961–1042. Retrived from https://ssrn.com/abstract=2769980. Consultancy.uk (2017). Aon acquires psychometric and advisory ﬁrm cut-e. Retrieved fromhttps://www.consultancy.uk/news/13493/aon-acquires-psychometric-andadvisory-ﬁrm-cut-e. Cornerstone (2018). People analytics. Retrieved fromhttps://www.cornerstoneondemand. com/glossary/people-analytics. Dagnino, E. (2017). People analytics: Work and labour protection in the era of HRM through big data. Labour & Law Issues, 3(1), 1–31. Deloitte (2017). 2017 Deloitte Global Human Capital Trends report: Rewriting the rules for the digital age. Dykes, B. (2016). Actionable insights: The missing link between data and business value. Retrieved fromhttps://www.forbes.com/sites/brentdykes/2016/04/26/actionableinsights-the-missing-link-between-data-and-business-value/#571f0651e57c. Edwards, M. R., & Edwards, K. (2016). Predictive HR analytics: Mastering the HR metric. Kogan Page. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Human Resource Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007. Glaser, B., & Strauss, A. (1967). The discovery of grounded theory. Hawthorne, NY: Aldine Publishing Company. Grossman, R. L. (2018). A framework for evaluating the analytic maturity of an organization. International Journal of Information Management, 38(1), 45–51. https://doi. org/10.1016/j.ijinfomgt.2017.08.005. Guenole, N., Ferrar, J., & Feinzig, S. (2017). The power of people: Learn how successful organizations use workforce analytics to improve business performance. Pearson FT Press. Haines, V. Y., & Laﬂeur, G. (2008). Information technology usage and human resource roles and eﬀectiveness. Human Resource Management, 47(3), 525–540. https://doi. org/10.1002/hrm.20230. HCA Group (2017). Research insights. Retrieved fromhttps://www.cbs.dk/en/research/ departments-and-centres/department-of-strategic-management-and-globalization/ human-capital-analytics/publications/research-insights. Holeman, I., Cookson, T. P., & Pagliari, C. (2016). Digital technology for health sector governance in low and middle income countries: A scoping review. Journal of Global Health, 6(2), https://doi.org/10.7189/jogh.06.020408. 246 International Journal of Information Management 43 (2018) 224–247 A. Tursunbayeva et al. 17(4), 351–365. https://doi.org/10.1016/j.leaqua.2006.04.006. Scimago Journal Ranking Portal, 2017. Scimago Journal & Country Rank. Sullivan, J. (2013). How google is using people analytics to completely reinvent HR (2013, February 26). Retrieved fromhttps://www.tlnt.com/how-google-is-using-peopleanalytics-to-completely-reinvent-hr/. Tursunbayeva, A., Bunduchi, R., Franco, M., & Pagliari, C. (2016). Human resource information systems in health care: A systematic evidence review. Journal of the American Medical Informatics Association, 633–654. https://doi.org/10.1093/jamia/ ocw141. Tursunbayeva, A., Franco, M., & Pagliari, C. (2017). Use of social media for e-Government in the public health sector: A systematic review of published studies. Government Information Quarterly, 34(2), 270–282. https://doi.org/10.1016/j.giq.2017.04.001. Van Deursen, A. J. A. M., & van Dijk, J. A. G. M. (2009). Using the internet: Skill related problems in users’ online behavior. Interacting With Computers, 21(5–6), 393–402. https://doi.org/10.1016/j.intcom.2009.06.005. Van Vulpen, E. (2016). The diﬀerence between people analytics and HR analytics. 2016, November 24) Retrieved fromhttps://www.analyticsinhr.com/blog/diﬀerencebetween-people-analytics-and-hr-analytics/. Wiedemann, K. (2018). Automated processing of personal data for the evaluation of personality traits: Legal and ethical issues. Max Planck Institute for Innovation & Competition Research Paper No. 18-04https://doi.org/10.2139/ssrn.3102933. Wikipedia (2017). Massive open online course. Retrieved fromhttps://en.wikipedia.org/ wiki/Massive_open_online_course. Aizhan Tursunbayeva (M. Eng, MIS, PhD, Doctor Europaeus, GRP) recently completed a PhD focused on HRIS implementations within complex organisations. She now works as a Senior Project Consultant at KPMG Advisory S.p.A., and tutors the Managing Change course on the MSc in Global eHealth at the University of Edinburgh and Human Resources course at the University of Molise. Her previous professional roles include Global Organizational Development Manager at Giunti Psychometrics, and HR Manager with HSBC Bank Kazakhstan, amongst other managerial positions in Canada, Poland and the UK. Stefano Di Lauro (MSc) is a PhD Candidate in Management at the University of Naples Federico II (Italy), studying Organisational Identity and Social media. He has an extensive international work experience in communication, marketing and social media marketing ﬁelds. Claudia Pagliari (BSc, PhD, FRCPE) is Director of Global eHealth at the University of Edinburgh, where she leads a programme of interdisciplinary research on digital and data innovations and their organisational, societal and policy implications. She is a member of the Administrative Data Research Centre for Scotland, the Farr Institute of Health Informatics Research, the Institute for Science, Technology and Innovation, the NHS Digital Academy and the Global Interprofessional Workforce Council. 247

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