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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.
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“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
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In the citing paper
“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
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“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
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“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
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“Research on People Analytics has been extensively carried out for past 15 years, however availability of Big Data has enabled HR departments to gather better insights on employee processes for making important business decisions [5] [6].”
BackgroundHarnessing generative AI in nursing education: A bibliometric review
Borromeo, A. S., Manaloto, A. M., Santos, M. J. M. D., Antonio, R. P., Soyosa, M. D., & Wider, W. (2025) · Teaching and learning in nursing · doi:10.1016/j.teln.2025.04.014 · Cited by 21
Jain, D., & Sharma, H. (2024) · Business Process Management Journal · doi:10.1108/bpmj-06-2023-0458 · Cited by 33
Creating accountability through HR analytics – An audit society perspective
Loscher, G. J., & Bader, V. (2023) · Human Resource Management Review · doi:10.1016/j.hrmr.2023.100974 · Cited by 17
(How) do advanced data and analyses enable HR analytics success? A neo-configurational analysis
Strohmeier, S., Collet, J., & Kabst, R. (2022) · Baltic Journal of Management · doi:10.1108/bjm-05-2021-0188 · Cited by 28
Jordan, P. J., Troth, A. C., & Yan, H. (2024) · Australian Journal of Management · doi:10.1177/03128962241286258 · Cited by 18
“…data in these three disciplines has become more rigorous (Harrison et al., 2020), there has also been an uptick in objective data collection with attempts to “objectify” measurement in these fields (Angrave et al., 2016; Lamers et al., 2024; Melin et al., 1999) in the last couple of decades.”
Revolutionizing HRD through digitalization
McLean, G. N., & de Zárate, A. G. O. (2024) · Human Resource Development International · doi:10.1080/13678868.2024.2399492 · Cited by 14
“The removal of the more interpersonal and empathetic aspects of people management is unlikely to be effective, at least in the short term (Angrave et al., 2016).”
Roul, J., Mohapatra, L. M., Pradhan, A. K., & Kamesh, A. V. S. (2024) · Kybernetes · doi:10.1108/k-11-2023-2512 · Cited by 31
Human Resource Analytics using Power Bi Visualization Tool
Ameer, M., Rahul, S., & Manne, S. (2020) · International Conference Intelligent Computing and Control Systems · doi:10.1109/iciccs48265.2020.9120897 · Cited by 14
Bottesch, S., Schwenke, C., Förster, M., & Klier, M. (2025) · Electronic Markets · doi:10.1007/s12525-025-00842-3 · Cited by 19
“…analytics remain unsolved, including ethical concerns (Gal et al., 2020; Klöpper, 2023; Köchling et al., 2021), a gap of skills and knowledge (Angrave et al., 2016; Jiang & Akdere, 2022; Ongena et al., 2024), and a lack of suitable organizational processes (Füller et al., 2022; McIver et…”
InfluentialRana, S., & Kaur, D. (2023) · International journal of organizational analysis · doi:10.1108/ijoa-05-2023-3745 · Cited by 22
Dhiman, N., Kumar, S., & Nagpal, T. (2023) · Vision The Journal of Business Perspective · doi:10.1177/09722629231183540 · Cited by 19
Employees' adoption of HR analytics – a theoretical framework based on career construction theory
Dhankhar, K., & Singh, A. (2022) · Evidence-based HRM a Global Forum for Empirical Scholarship · doi:10.1108/ebhrm-02-2022-0053 · Cited by 23
Ekka, S. (2021) · Journal of Contemporary Issues in Business and Government · doi:10.47750/cibg.2021.27.02.238 · Cited by 13
Anthun, K. S., Anthun, K. S., Håland, E., & Lillefjell, M. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-11610-y · Cited by 22
“Related to this, the importance of data exchange and opportunities and potential for integrating data have also been emphasized [36], together with issues such as communication and collaboration between units within the organization, and a supportive culture for HRA [29].”
BackgroundArora, S., Chaudhary, P., & Singh, R. K. (2024) · International journal of organizational analysis · doi:10.1108/ijoa-03-2024-4392 · Cited by 23
Moulaï, K., Islam, G., Manning, S., & Terlinden, L. (2022) · The International Journal of Human Resource Management · doi:10.1080/09585192.2022.2066982 · Cited by 12
“This complements a growing body of scholarship that examines how automation trends and recent technological developments impact work processes and workers (Angrave et al., 2016; Lindsay et al., 2014; Park, 2018).”
BackgroundThe Effect of Positive Management Practices on Firm Profitability – Evidence from Text Mining
Becker, M. (2022) · The Journal of Applied Behavioral Science · doi:10.1177/00218863221120827 · Cited by 15
“Scholars agree that the relationship between HR measures and business performance needs to be further explored in quantitative and large-scale studies in order for HR analytics to gain more attention in practice (Angrave et al., 2016; Huselid, 2018; Marler & Boudreau, 2017).”
BackgroundBIBLIOMETRIC ANALYSIS OF HR ANALYTICS LITERATURE
Allaham, M. V. (2022) · Elektronik Sosyal Bilimler Dergisi · doi:10.17755/esosder.950426 · Cited by 17
Metadata
- 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
- 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
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