HR and analytics: Why HR is set to fail the big data challenge
Open accessAbstract
The HR world is abuzz with talk of big data and the transformative potential of HR analytics. This article takes issue with optimistic accounts, which hail HR analytics as a ‘must have’ capability that will ensure HR's future as a strategic management function while transforming organisational performance for the better. It argues that unless the HR profession wises up to both the potential and drawbacks of this emerging field and engages operationally and strategically to develop better methods and approaches, it is unlikely that existing practices of HR analytics will deliver transformational change. Indeed, it is possible that current trends will seal the exclusion of HR from strategic, board‐level influence while doing little to benefit organisations and actively damaging the interests of employees.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI-driven algorithmic monitoring allows the collection and recording of a vast array of new information and metrics about workers.
“AI-driven algorithmic monitoring thus allows to collect and record a vast array of new information and metrics, such as emotions (Chan, 2018;Pinheiro, Ramos, Donizete, Picanço, & De Oliveira, 2017), movements, sleep time, physical and health condition, social media activity, internet browser history (Angrave, Charlwood, Kirkpatrick, Lawrence, & Stuart, 2016;Leicht-Deobald et al., 2019)”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Angrave et al., 2016
In “Defining big data and HR analytics” (pp. 5–6), the paper describes worker data already held in HR systems and the potential to combine it with location, communications and internet-browsing data. This supports the broad point that analytics can draw on many worker metrics, including movement and browsing history. It does not identify sleep time or physical health as monitored metrics, and presents integration of the newer data sources as a possibility rather than an established capability. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDescriptive algorithms are likely to multiply the metrics available to quantify employees' actions, emotions, performance, behaviors, attitudes, physical state, and customer-related information such as satisfaction and traffic.
“Descriptive algorithms are likely to reinvent the world of performance management (PM) in multiplying the metrics available to precisely quantify various facets of employees' actions, emotions, performance, behaviors, attitudes and physical state (Angrave et al., 2016;Tambe et al., 2019), but also customer-related information such as satisfaction and traffic (Evans & Kitchin, 2018;Levy & Barocas,”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Angrave et al., 2016
In ‘Defining big data and HR analytics’ (pp. 5–6), the paper describes existing employee performance and attitude data and the possibility of using digital records to infer mood, morale and activity. It does not establish that descriptive algorithms will precisely quantify employees’ physical state or reinvent performance management; it also says the means of integrating and analysing the larger data sources are not yet well established. The customer-related clause is attached to other citations, not Angrave et al. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPredictive AI algorithms are used to forecast future employee performance based on multiple data sources such as past performance, projects, and training opportunities.
“Moreover, predictive artificial intelligence algorithms, like the famous IBM Watson, are also used to forecast the future performance of employees based on multiple data sources such as past performance, existing and potential projects, and recent and future training opportunities (Angrave et al., 2016;Edwards & Edwards, 2019;Greenfield, 2018;Kirimi & Moturi, 2016;Sajjadiani, Sojourner, Kammeyer-M”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Angrave et al., 2016
The paper lists employee performance and training records among data held in HR systems (Defining big data and HR analytics, p. 5) and discusses predictive analytics as a goal (What are the barriers to successful HR analytics adoption?, p. 11). It does not describe AI algorithms such as IBM Watson forecasting individual employees’ future performance from those records or project opportunities; on p. 12, it says many firms have struggled to develop forward-looking analysis. (LLM, conf=0.84)
Experimental — accuracy not yet measuredForecasted performance is often used as a basis to set future goals or expectations.
“This forecasted performance is often used as the basis to set future goals or expectations (Angrave et al., 2016;Edwards & Edwards, 2019), to estimate the training needs (Horesh, Varshney, & Yi, 2016), to determine pay raise (Greenfield, 2018) or to promote employees to managerial positions (Mallafi & Widyantoro, 2016).”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Angrave et al., 2016
Theorising HR analytics (p. 8) discusses using analysis to improve performance and plan for future talent needs, but does not say that forecasted performance is often used to set future goals or expectations. Searches for forecasting and performance targets found no passage addressing that use. (LLM, conf=0.72)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Understanding HRM–Firm Performance Linkages: The Role of the “Strength” of the HRM System
Bowen, D. E., & Ostroff, C. (2004) · Academy of Management Review · doi:10.5465/amr.2004.12736076 · Cited by 2,996 · see citing works
Operational research from Taylorism to Terabytes: A research agenda for the analytics age
Mortenson, M. J., Doherty, N. F., & Robinson, S. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.08.029 · Cited by 235 · see citing works
An examination of e-HRM as a means to increase the value of the HR function
Parry, E. (2011) · The International Journal of Human Resource Management · doi:10.1080/09585192.2011.556791 · Cited by 193 · see citing works
Flagged first, then by citations
Research works printed but not linked: 14
- Apgar, D. (2013). 'The false promise of big data: can data mining replace hypothesis -driven learning in the identification of predictive performance metrics?' Systems Research and Behavioural Science . Online first publication, doi: 10.1002/sres.2219. · DOI
- Aral , S., Bryjolfsson, E. and Wu, L (2012). 'Three -way complementarities: Performance Pay, Human Resource Analytics, and Information Technology'. Management Science , 58(5): 913 -931.
- Boudreau, J. (2010). Retooling HR: Using proven business tools to make better management decisions . Boston: HBR Press.
- Boudreau, J. and Ramstad, P. (2007). Beyond HR: The new science of human capital . Boston: HBR Press.
- Cascio, W. and Boudreau, J. (2014). Investing in People: The Financial Impact of Human Resources Initiatives (2 nd Edition) . Upper Saddle NJ: Pearson Press.
- Cooper, D. and Hopper, T. (2010). Debating coal closures: Economic calculation in the coal dispute 1984 -1985 . Cambridge: CUP.
- Davenpo rt, T., Harris, J. and Shapiro, J. (2010). 'Competing on talent analytics. Harvard Business Review, 88(10): 52 -58.
- DiMaggio, D. and Powell, W. (1983). 'The iron cage revisited: institutional isomorphism and collective rationality in organizational fields'. American Sociological Review , 48:1, 147 -160.
- Downward, P. (2000). 'A realistic evaluation of postKeynesian pricing theory.' Cambridge Journal of Economics. 24(2): 211 -224.
- George, G. Haas, M. and Pentland, A. (2014). 'Big data and management'. Academy of Management Journal , 57:2, 321 -326.
- Godard, J. (2014). 'The psychologisation of employment relations?' Human Resource Management Journal , 24(1): 1 -18.
- Grubb, A. and Marson, L. (2015). SuccessFactors TM with SAP ERP HCM . Bonn: Galileo Press.
- Guest, D. (2011). 'Human resource management and performance: still searching for some answers'. Human Resource Management Journal , 21, 1:3 -13.
- Lepak, D. and Snell, S. (1999). 'Examining the human resource architecture: the relationships among human capital, employment and resource configurations' . Journal of Management , 28:4, 517 -543.
Cited by
Sorted by citations
Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732 · see citing works
Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020) · Human Resource Management Journal · doi:10.1111/1748-8583.12258 · Cited by 1,050 · see citing works
An evidence-based review of HR Analytics
Marler, J. H., & Boudreau, J. W. (2017) · The International Journal of Human Resource Management · doi:10.1080/09585192.2016.1244699 · Cited by 913 · see citing works
“Finally, analyses and resulting reports are basic and reflect outdated descriptive efficiency-based metrics (Angrave et al., 2016; Falletta, 2014).”
BackgroundMethodInfluentialChowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023) · Human Resource Management Review · doi:10.1016/j.hrmr.2022.100899 · Cited by 927 · see citing works
Newman, D. T., Fast, N. J., & Harmon, D. J. (2020) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2020.03.008 · Cited by 550 · see citing works
Algorithms as work designers: How algorithmic management influences the design of jobs
Parent-Rocheleau, X., & Parker, S. K. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100838 · Cited by 512 · see citing works
Human resources analytics: A systematization of research topics and directions for future research
Margherita, A. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2020.100795 · Cited by 410 · see citing works
“…a ‘must have ’ capability to improve organizational performance, HR professionals should better understand the potential and drawbacks and engage to develop better analytical abilities, methods and approaches able to deliver transformational change (Angrave et al., 2016; Kryscynski et al., 2017).”
InfluentialWhat do we talk about when we talk about dashboards?
Sarikaya, A., Correll, M., Bartram, L., Tory, M., & Fisher, D. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864903 · Cited by 380 · see citing works
“Data choice re-frames problem definition and thus implicitly refocuses the values of possible solutions [3, 24, 29, 31, 45].”
BackgroundInfluentialSmart HR 4.0 – how industry 4.0 is disrupting HR
Sivathanu, B., & Pillai, R. (2018) · Human Resource Management International Digest · doi:10.1108/hrmid-04-2018-0059 · Cited by 378 · see citing works
Shamim, S., Zeng, J., Shariq, S., & Khan, Z. (2019) · Information Manager (The) · doi:10.1016/j.im.2018.12.003 · Cited by 351 · see citing works
Bucher, E. L., Schou, P. K., & Waldkirch, M. (2021) · Organization · doi:10.1177/1350508420961531 · Cited by 349 · see citing works
“Last, as traditional organizations are increasingly making use of algorithms to gain efficiency, for instance in their human resource management (Angrave et al., 2016), our findings point toward the hidden cost of such changes and the implications for organizational power structures (Fleming and…”
BackgroundThe Challenges of Algorithm-Based HR Decision-Making for Personal Integrity
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019) · Journal of Business Ethics · doi:10.1007/s10551-019-04204-w · Cited by 347 · see citing works
“Furthermore, current algorithm-based HR decision-making tools are increasingly able to exploit novel types of data, such as internet browser histories, keystrokes, electronic calendars, and location data from wearable devices, such as fitness wristbands and mobile phones (Angrave et al. 2016; Rosenblat et al. 2014).”
BackgroundInfluentialThe dark sides of people analytics: Reviewing the perils for organisations and employees
Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2021.1927213 · Cited by 331 · see citing works
“One common theme emerging from these studies is that HR professionals often lack the required analytics capabilities (Angrave et al., 2016; Dahlbom et al., 2019; Kryscynski et al., 2018; Levenson, 2018).”
BackgroundInfluentialMeijerink, J., & Bondarouk, T. V. (2021) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100876 · Cited by 374 · see citing works
A critical review of algorithms in HRM: Definition, theory, and practice
Cheng, M. M., & Hackett, R. D. (2021) · Human Resource Management Review · doi:10.1016/j.hrmr.2019.100698 · Cited by 283 · see citing works
Can HR adapt to the paradoxes of artificial intelligence?
Charlwood, A., & Guenole, N. (2022) · Human Resource Management Journal · doi:10.1111/1748-8583.12433 · Cited by 271 · see citing works
“…this information to make or recommend decisions such as which staff to employ, where and when, who to promote into which role, and which staff to award pay rises to in order to optimise motivation and retention (Angrave et al., 2016; Charlwood, 2021; Guenole & Feinzig, 2019; Kellogg et al., 2020).”
BackgroundSelf-citationPeople analytics—A scoping review of conceptual boundaries and value propositions
Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.08.002 · Cited by 263 · see citing works
Shah, N., Irani, Z., & Sharif, A. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.010 · Cited by 251 · see citing works
A review of machine learning applications in human resource management
Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2021) · International Journal of Productivity and Performance Management · doi:10.1108/ijppm-08-2020-0427 · Cited by 401 · see citing works
#Trending topics in careers: a review and future research agenda
Akkermans, J., & Kubasch, S. (2017) · Career Development International · doi:10.1108/cdi-08-2017-0143 · Cited by 234 · see citing works
“Although the study of HR analytics is not without criticism (Angrave et al., 2016) and it would certainly not replace existing ways of examining health and well-being, we argue that it would be an important complementary research avenue that might enhance our understanding of health and well-being related to career de…”
BackgroundOn the shoulders of giants: a meta-review of strategic human resource management
Jiang, K.-F., & Messersmith, J. (2018) · The International Journal of Human Resource Management · doi:10.1080/09585192.2017.1384930 · Cited by 229 · see citing works
“As noted by Angrave et al. (2016), academics will likely have a key part to play in continuing to advance the knowledge of HR analytics, which can be useful for organizations, and also in the actual implementation of the tools and techniques needed to analyze large datasets.”
BackgroundTackling the HR digitalization challenge: key factors and barriers to HR analytics adoption
Fernandez, V., & Gallardo-Gallardo, E. (2021) · Competitiveness Review: An International Business Journal · doi:10.1108/cr-12-2019-0163 · Cited by 225 · see citing works
Waqas, M., Xue, H.-G., Ahmad, N., Khan, S. A. R., & Iqbal, M. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.128998 · Cited by 203 · see citing works
Shet, S. V., Poddar, T., Wamba, S. F., & Dwivedi, Y. K. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.03.054 · Cited by 232 · see citing works
Hamilton, R., & Sodeman, W. (2020) · Business Horizons · doi:10.1016/j.bushor.2019.10.001 · Cited by 192 · see citing works
Privacy at Work: A Review and a Research Agenda for a Contested Terrain
Bhave, D. P., Teo, L. H., & Dalal, R. (2019) · Journal of Management · doi:10.1177/0149206319878254 · Cited by 184 · see citing works
Minbaeva, D. (2020) · Human Resource Management Review · doi:10.1016/j.hrmr.2020.100820 · Cited by 233 · see citing works
Bridging the gap: why, how and when HR analytics can impact organizational performance
McCartney, S., & Fu, N. (2022) · Management Decision · doi:10.1108/md-12-2020-1581 · Cited by 173 · see citing works
Waldkirch, M., Bucher, E., Schou, P. K., & Grünwald, E. (2021) · The International Journal of Human Resource Management · doi:10.1080/09585192.2021.1914129 · Cited by 173 · see citing works
“This provides an interesting avenue for future research as many traditional organizations integrate algorithms and Big Data analytics into high-performance work systems, creating the foundation for such a hybrid strategy (Angrave et al., 2016).”
BackgroundKaushal, N., Kaurav, R. P. S., Sivathanu, B., & Kaushık, N. (2021) · Management Review Quarterly · doi:10.1007/s11301-021-00249-2 · Cited by 157 · see citing works
“Table 3 discloses that Huselid (1995) has the highest count of CoC, with co-authors; Aral et al. (2012); Becker and Gerhart (1996); Stone et al. (2015); Angrave et al. (2016); and Marler and Boudreau (2017).”
BackgroundKinowska, H., & Sienkiewicz, Ł. J. (2023) · Information Technology & People · doi:10.1108/itp-02-2022-0079 · Cited by 147 · see citing works
Design principles for creating digital transparency in government
Matheus, R., Janssen, M., & Janowski, T. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101550 · Cited by 146 · see citing works
Automation and the future of work: A social shaping of technology approach
Howcroft, D., & Taylor, P. (2022) · New Technology Work and Employment · doi:10.1111/ntwe.12240 · Cited by 156 · see citing works
“Consequently, critical research questions the value of workforce analytics and emphasises the potential damage to employee interests (Angrave et al., 2016).”
BackgroundBig data contributions to human resource management: a systematic review
García-Arroyo, J. A., & Osca, A. (2019) · International journal of human resources management · doi:10.1080/09585192.2019.1674357 · Cited by 137 · see citing works
“But, despite the potential benefits of BD in making strategic decisions about talent management, its use can be challenging for organizations (Angrave et al., 2016) since technical development, skills, and knowledge can be a barrier to implementation.”
BackgroundBig Data and Human Resources Management: The Rise of Talent Analytics
Nocker, M., & Sena, V. (2019) · The social science · doi:10.3390/socsci8100273 · Cited by 132 · see citing works
“These include data availability, the analytical skills among HR professionals (Angrave et al. 2016; Levenson 2011; Mondare et al. 2011; Rasmussen and Ulrich 2015), support from senior management (Rasmussen and Ulrich 2015), and access to core IT capabilities (Angrave et al. 2016; Aral et al. 2012;…”
BackgroundDigital human resource development: where are we? Where should we go and how do we go there?
Thite, M. (2022) · Human Resource Development International · doi:10.1080/13678868.2020.1842982 · Cited by 129 · see citing works
“Angrave et al. (2016) caution that HR professionals are currently not capable of handling big data and unless they develop big data literacy, they may be excluded from strategic decision making.”
BackgroundPeople analytics effectiveness: developing a framework
Peeters, T., Paauwe, J., & Van De Voorde, K. (2020) · Journal of Organizational Effectiveness: People and Performance · doi:10.1108/joepp-04-2020-0071 · Cited by 129 · see citing works
Big data and HR analytics in the digital era
Dahlbom, P., Siikanen, N., Sajasalo, P., & Jarvenpää, M. (2019) · Baltic Journal of Management · doi:10.1108/bjm-11-2018-0393 · Cited by 128 · see citing works
Kumar, M., Raut, R. D., Mangla, S., Ferraris, A., & Choubey, V. K. (2022) · Production Planning & Control · doi:10.1080/09537287.2022.2131620 · Cited by 124 · see citing works
“…stations, such as HRIS (HR Information Systems) is made up of information on employees ’ demographics (employment records, skills, formal professional attainment, and demographic data) as well as softer performance metrics that may be gathered during performance appraisals (Angrave et al. 2016).”
Mirowska, A., & Mesnet, L. (2021) · Human Resource Management Journal · doi:10.1111/1748-8583.12393 · Cited by 124 · see citing works
Iftikhar, A., Purvis, L., Giannoccaro, I., & Wang, Y.-L. (2022) · Production planning & control (Print) · doi:10.1080/09537287.2022.2032450 · Cited by 123 · see citing works
“…effect of BDA, researchers have identified two significant components which need to be present: the availability of a well-trained workforce (Angrave et al. 2016; Marler and Boudreau 2017) and suitable infrastructure for efficiently handling large amounts of information (Akter et al. 2016;…”
BackgroundBujold, A., Roberge‐Maltais, I., Parent‐Rocheleau, X., Boasen, J., Sénécal, S., & Léger, P. (2023) · AI and Ethics · doi:10.1007/s43681-023-00325-1 · Cited by 183 · see citing works
The role of artificial intelligence in HRM: A systematic review and future research direction
Basu, S., Majumdar, B., Mukherjee, K., Munjal, S., & Palaksha, C. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2022.100893 · Cited by 116 · see citing works
Building supply chain risk resilience
Singh, N. P., & Singh, S. (2019) · Benchmarking : An International Journal · doi:10.1108/bij-10-2018-0346 · Cited by 116 · see citing works
Algorithmic Management in Organizations? From Edge Case to Center Stage
Keegan, A., & Meijerink, J. (2025) · Annual Review of Organizational Psychology and Organizational Behavior · doi:10.1146/annurev-orgpsych-110622-070928 · Cited by 112 · see citing works
Gong, Q., Fan, D., & Bartram, T. (2024) · International journal of human resources management · doi:10.1080/09585192.2024.2440065 · Cited by 110 · see citing works
“As Angrave et al. (2016) stated, HRM is encountering the challenges posed by big data.”
Falletta, S. V., & Combs, W. L. (2021) · Journal of Work-Applied Management · doi:10.1108/jwam-03-2020-0020 · Cited by 110 · see citing works
Individual adoption of HR analytics: a fine grained view of the early stages leading to adoption
Vargas, R., Yurova, Y., Ruppel, C., Tworoger, L. C., & Greenwood, R. (2018) · The International Journal of Human Resource Management · doi:10.1080/09585192.2018.1446181 · Cited by 106 · see citing works
Santana, M., & Díaz-Fernández, M. (2023) · Review of Managerial Science · doi:10.1007/s11846-022-00613-w · Cited by 105 · see citing works
Chang, Y.-L., & Ke, J. (2023) · Human Resource Development Review · doi:10.1177/15344843231200930 · Cited by 105 · see citing works
“However, HRD professionals still need to overcome many challenges on the way of adopting and implementing PA, including safeguarding the interests of its stakeholders (Angrave et al., 2016; Ratnam et al., 2023, Bondarouk et al., 2017).”
BackgroundHuman resource analytics: a review and bibliometric analysis
Qamar, Y., & Samad, T. A. (2021) · Person-centered review · doi:10.1108/pr-04-2020-0247 · Cited by 105 · see citing works
From Big Data to Deep Data to Support People Analytics for Employee Attrition Prediction
Yahia, N. B., Hlel, J., & Colomo-Palacios, R. (2021) · IEEE Access · doi:10.1109/access.2021.3074559 · Cited by 104 · see citing works
Predictive HR analytics and talent management: a conceptual framework
Gurusinghe, R., Arachchige, B., & Dayarathna, D. K. (2021) · Journal of Management Analytics · doi:10.1080/23270012.2021.1899857 · Cited by 102 · see citing works
Data Analytics in Human Resources: A Case Study and Critical Review
King, K. G. (2016) · Human Resource Development Review · doi:10.1177/1534484316675818 · Cited by 98 · see citing works
Critical exploration of AI-driven HRM to build up organizational capabilities
Böhmer, N., & Schinnenburg, H. (2023) · Employee Relations · doi:10.1108/er-04-2022-0202 · Cited by 162 · see citing works
Fostering expatriate success: A meta-analysis of the differential benefits of social support
van der Laken, P. A., van Engen, M. V., van Veldhoven, M., & Paauwe, J. (2019) · Human Resource Management Review · doi:10.1016/j.hrmr.2018.12.003 · Cited by 94 · see citing works
Zhang, Y., Xu, S., Zhang, L., & Yang, M.-X. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.04.019 · Cited by 93 · see citing works
Kuhn, K. M., Meijerink, J., & Keegan, A. (2021) · Research in Personnel and Human Resources Management · doi:10.1108/s0742-730120210000039001 · Cited by 93 · see citing works
Pignot, E. (2021) · Organization · doi:10.1177/1350508420974523 · Cited by 92 · see citing works
“The downsides of ‘algorithmic management’ include surveillance (Zuboff, 2015), people analytics (Bersin et al., 2016; Hansen and Flyverbom, 2015) and behavioural prediction (Angrave et al., 2016; King, 2016).”
BackgroundDo big data-driven HR practices improve HR service quality and innovation competency of SMEs
Verma, S., Singh, V., & Bhattacharyya, S. S. (2021) · International Journal of Organizational Analysis · doi:10.1108/ijoa-04-2020-2128 · Cited by 91 · see citing works
Norlander, P., Jukic, N., Varma, A., & Nestorov, S. (2021) · The International Journal of Human Resource Management · doi:10.1080/09585192.2020.1867614 · Cited by 88 · see citing works
“The rise of big data and information processing systems to handle HRM can undermine the role of traditional HR departments (Angrave et al., 2016).”
BackgroundZehir, C., Karaboğa, T., & Başar, D. (2019) · Contributions to Management Science · doi:10.1007/978-3-030-29739-8_12 · Cited by 87 · see citing works
Shamim, S., Yang, Y., Zia, N. U., & Shah, M. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106777 · Cited by 84 · see citing works
21st century HR: a competency model for the emerging role of HR Analysts
McCartney, S., Murphy, C., & McCarthy, J. (2020) · Person-centered review · doi:10.5465/ambpp.2020.11928abstract · Cited by 84 · see citing works
Technologically mediated human resource management in the gig economy
McDonnell, A., Carbery, R., Burgess, J., & Sherman, U. (2021) · The International Journal of Human Resource Management · doi:10.1080/09585192.2021.1986109 · Cited by 82 · see citing works
“This removes many of the costs of HRM and passes employment risks to the individual (Snyder, 2016), further eradicating the more interpersonal and empathetic aspects of people management (Angrave et al., 2016).”
BackgroundDeveloping human resource data risk management in the age of big data
Calvard, T. S., & Jeske, D. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.07.011 · Cited by 79 · see citing works
Samson, K., & Bhanugopan, R. (2022) · Journal of business research · doi:10.1016/j.jbusres.2022.01.044 · Cited by 79 · see citing works
Exploring the Evolution of Human Resource Analytics: A Bibliometric Study
Bonilla-Chaves, E. F., & Palos-Sánchez, P. (2023) · Behavioral Science · doi:10.3390/bs13030244 · Cited by 77 · see citing works
The impact of HRM digitalization on firm performance: investigating three‐way interactions
Zhou, Y., Liu, G., Chang, X., & Wang, L. (2020) · Asia Pacific Journal of Human Resources · doi:10.1111/1744-7941.12258 · Cited by 76 · see citing works
Korherr, P., & Kanbach, D. K. (2021) · Review of Managerial Science · doi:10.1007/s11846-021-00506-4 · Cited by 78 · see citing works
“There is likewise a risk that these transformational changes will affect human-related capabilities needed in the context of BDA (Angrave et al. 2016).”
BackgroundReiche, B. S., Lee, Y.-T., & Allen, D. (2018) · Journal of Management · doi:10.1177/0149206318808885 · Cited by 70 · see citing works
Do, H., Chu, L.-X., & Shipton, H. (2025) · Journal of business research · doi:10.1016/j.jbusres.2025.115279 · Cited by 69 · see citing works
Fan, D., Zhu, C., Timming, A., Su, Y.-Y., Huang, X.-L., & Lu, Y. (2020) · International journal of human resources management · doi:10.1080/09585192.2019.1657167 · Cited by 67 · see citing works
Srivastava, P., & Eachempati, P. (2021) · Journal of Global Information Management · doi:10.4018/jgim.20211101.oa23 · Cited by 66 · see citing works
“The open-source data analytics tool R (Angrave et al .,2016; Frisk and Bannister,2017; Huang et al., 2017; Kaya et al .,2019; Singh and Giudice,2019) was adopted to build the above three machine learning models and for simulating the Fuzzy AHP (Singh and Prasher,2019) process while Excel tool was…”
MethodEdwards, M. R., Charlwood, A., Guenole, N., & Marler, J. (2024) · Human Resource Management Journal · doi:10.1111/1748-8583.12435 · Cited by 66 · see citing works
Self-citationHuman resources analytics: A legitimacy process
Belizón, M., & Kieran, S. (2021) · Human Resource Management Journal · doi:10.1111/1748-8583.12417 · Cited by 65 · see citing works
“…(Marler & Boudreau, 2017), the added value it can bring to the heart of HR, from its very strategic position and contribution through to its operational deliverables across the organisation is, for the most part, still unclear (Angrave et al., 2016; Huselid, 2018; Marler & Boudreau, 2017).”
BackgroundWorkforce analytics: A case study of scholar–practitioner collaboration
Simón, C., & Ferreiro, E. (2018) · Human Resource Management · doi:10.1002/hrm.21853 · Cited by 65 · see citing works
Wang, L., Zhou, Y., & Zheng, G. (2022) · Sustainability · doi:10.3390/su14021003 · Cited by 63 · see citing works
“There is a “productivity paradox” in practice [13]; many organizations, including large multinational organizations, have spent billions of dollars in support of digital HRM practices, but the financial benefits have not yet been realized [14,15].”
BackgroundStrategic HRM in the logistics and shipping sector: Challenges and opportunities
Ogedengbe, D. E., Oladapo, J. O., Elufioye, O. A., Ejairu, E., & Ezeafulukwe, C. (2024) · Magna Scientia Advanced Research and Reviews · doi:10.30574/msarr.2024.10.1.0032 · Cited by 61
“Utilizing HR analytics for workforce planning has been recognized as a crucial aspect of this approach (Angrave et al., 2016).”
MethodWang, L.-J., Zhou, Y., Sanders, K., Marler, J. H., & Zou, Y. (2024) · Journal of business research · doi:10.1016/j.jbusres.2023.114312 · Cited by 61 · see citing works
Negotiating limits on algorithmic management in digitalised services: cases from Germany and Norway
Doellgast, V., Wagner, I., & O'Brady, S. (2022) · Transfer - European Review of Labour and Research · doi:10.1177/10242589221143044 · Cited by 61 · see citing works
“Workflow algorithms can be used to match workloads with employee schedules (Angrave et al., 2016).”
BackgroundMethodHR analytics: The onto‐epistemology and politics of metricised HRM
Greasley, K., & Thomas, P. (2020) · Human Resource Management Journal · doi:10.1111/1748-8583.12283 · Cited by 59 · see citing works
Tan, S.-Y., Taeihagh, A., & Tripathi, A. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120686 · Cited by 58 · see citing works
Hickman, L., Saef, R. M., Ng, V., Woo, S. E., Tay, L., & Bosch, N. (2021) · Human Resource Management Journal · doi:10.1111/1748-8583.12356 · Cited by 65 · see citing works
“Such algorithms automatically infer applicant knowledge, skills, abilities, and other characteristics (KSAOs; Angrave et al., 2016), such as personality traits (Rotolo et al., 2018).”
BackgroundStrategic Human Resource Management
Storey, J., Wright, P. M., & Ulrich, D. (2019) · Routledge eBooks · doi:10.4324/9780429490217 · Cited by 55 · see citing works
“As Angrave et al (2016) put it: ‘Much of the value of HR data is realised by using it to answer strategic questions about how people create value for the organisation, so that value can be captured and leveraged.’”
BackgroundPoulose, S., Bhattacharjee, B., & Chakravorty, A. (2024) · Management Review Quarterly · doi:10.1007/s11301-024-00423-2 · Cited by 54 · see citing works
“Following the realization of organizational strategies, an organization’s ability to derive and formulate appropriate HR strategies to attain digital-competence is critical for the effective operation of the HR systems (Angrave et al. 2016).”
Promise versus reality: a systematic review of the ongoing debates in people analytics
McCartney, S., & Fu, N. (2022) · Journal of Organizational Effectiveness · doi:10.1108/joepp-01-2021-0013 · Cited by 54 · see citing works
The ethical implications of big data in human resource management
Manroop, L., Malik, A., & Milner, M. (2024) · Human Resource Management Review · doi:10.1016/j.hrmr.2024.101012 · Cited by 51 · see citing works
Wang, L., & Cotton, R. D. (2018) · Human Resource Management · doi:10.1002/hrm.21856 · Cited by 51 · see citing works
Avrahami, D., Pessach, D., Singer, G., & Ben-Gal, H. C. (2022) · International journal of manpower · doi:10.1108/ijm-12-2020-0548 · Cited by 50 · see citing works
Sustainable food supply chains: overcoming key challenges through digital technologies
Mahroof, K., Omar, A., & Kucukaltan, B. (2021) · International Journal of Productivity and Performance Management · doi:10.1108/ijppm-12-2020-0687 · Cited by 50 · see citing works
This (AI)n’t fair? Employee reactions to artificial intelligence (AI) in career development systems
Köchling, A., Wehner, M., & Ruhle, S. (2024) · Reviews of Management Sciences · doi:10.1007/s11846-024-00789-3 · Cited by 48 · see citing works
“Some companies also consider the employee’s digital footprints (e.g., professional profiles, such as LinkedIn, and nonprofessional profiles, such as Facebook) (Angrave et al. 2016).”
InfluentialStrategic human resource management in the context of environmental crises: A COVID‐19 test
Minbaeva, D. B., & Navrbjerg, S. E. (2023) · Human Resource Management · doi:10.1002/hrm.22162 · Cited by 48 · see citing works
“However, the progress was slow (Angrave et al., 2016; Minbaeva, 2021).”
BackgroundTowards a process-oriented understanding of HR analytics: implementation and application
Wirges, F., & Neyer, A. (2022) · Reviews of Management Sciences · doi:10.1007/s11846-022-00574-0 · Cited by 48 · see citing works
“It is generally agreed that one of the main reasons for the low use of HR analytics is the lack of analytical skills (Angrave et al. 2016; Mar-ler and Boudreau 2017).”
BackgroundInfluentialWehrle, M., Lechler, S., von der Gracht, H. A., & Hartmann, E. (2020) · Journal of Business Logistics · doi:10.1111/jbl.12259 · Cited by 48 · see citing works
“However, there are also critical studies that consider the topic more of a hype due to the modest progress made in recent years (Angrave et al. 2016; Leonardi and Contractor 2018).”
BackgroundCayrat, C., & Boxall, P. (2022) · Journal of Organizational Effectiveness: People and Performance · doi:10.1108/joepp-08-2021-0238 · Cited by 41 · see citing works
Felix, B., Dourado, D., & Nossa, V. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1088183 · Cited by 41 · see citing works
“Because algorithms are operated without the visible interference of humans and are based on objective and mathematical criteria, they tend to be seen as correct and reliable (Lee et al., 2015; Angrave et al., 2016).”
BackgroundQuantifying human resource management: a literature review
Coron, C. (2022) · Personnel Review · doi:10.1108/pr-05-2020-0322 · Cited by 40 · see citing works
Ellmer, M., & Reichel, A. (2021) · The International Journal of Human Resource Management · doi:10.1080/09585192.2021.1886148 · Cited by 49 · see citing works
“…in that past (Bondarouk et al., 2017), one central question arising from such technological shifts is how they affect the work and perception of HRM in organizations (Angrave et al., 2016; Bondarouk & Ruël, 2009; Marler & Boudreau, 2017; Strohmeier & Parry, 2014; van der Laken, 2018).”
BackgroundResultMastering digital transformation in workforce management
Akter, S., Biswas, K., Vrontis, D., Cooper, C. L., & Tarba, S. Y. (2023) · Production Planning & Control · doi:10.1080/09537287.2023.2270465 · Cited by 48 · see citing works
“Digitally enabled workforce management systems provide HR professionals with real-time reporting and analytics on performance trends, training effectiveness, and workforce planning (Angrave et al. 2016).”
- Authors
- Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M.
- Affiliations
- University of Sheffield (GB)
- Loughborough University (GB)
- University of Leeds (GB)
- Independent Strategic HR Analytics Consultant
- Corresponding author
- Charlwood, Andy
- Venue
- Human Resource Management Journal
- Publisher
- Wiley
- Published
- 2016-01
- Volume
- 26
- Issue
- 1
- Pages
- 1-11
- ISSN
- 0954-5395 (ISSN-L)
- 1748-8583
- OpenAlex
- W2222612072
- Cited by
- 633
- References
- 0
- Citations by year
- 2026: 71 · 2025: 74 · 2024: 77 · 2023: 87 · 2022: 77 · 2021: 79 · 2020: 61 · 2019: 48 · 2018: 24 · 2017: 19 · 2016: 14 · 2014: 1
- FWCI
- 68.00
- Type
- journal article
- Language
- en
- Open access
- green
- dspace.lboro.ac.uk
- Licence
- Locations
- Human Resource Management Journal (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Loughborough University Institutional Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
- Loughborough University Research Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Human Resource and Talent Management
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- HR analytics, big data, human resource management, strategic management, organizational performance, transformative change, employee interests
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
