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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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“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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Simón, C., & Ferreiro, E. (2018) · Human Resource Management · doi:10.1002/hrm.21853 · Cited by 65
Li-jun, W., Zhou, Y., & Zheng, G. (2022) · Sustainability · doi:10.3390/su14021003 · Cited by 71
“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).”
MethodLi-jun, W., Zhou, Y., Sanders, K., Marler, J. H., & Zou, Y. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114312 · Cited by 97
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
“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 81
Tan, S. Y., Taeihagh, A., & Tripathi, A. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120686 · Cited by 65
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 71
“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
“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 77
“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 People and Performance · doi:10.1108/joepp-01-2021-0013 · Cited by 106
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 89
Wang, L., & Cotton, R. (2017) · Human Resource Management · doi:10.1002/hrm.21856 · Cited by 52
Avrahami, D., Pessach, D., Singer, G., & Gal, H. C. (2022) · International Journal of Manpower · doi:10.1108/ijm-12-2020-0548 · Cited by 97
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
This (AI)n’t fair? Employee reactions to artificial intelligence (AI) in career development systems
Köchling, A., Wehner, M., & Ruhle, S. A. (2024) · Review of Managerial Science · doi:10.1007/s11846-024-00789-3 · Cited by 63
“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
“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) · Review of Managerial Science · doi:10.1007/s11846-022-00574-0 · Cited by 81
“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 56
“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
Félix, B., Dourado, D., & Nossa, V. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1088183 · Cited by 66
“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
Ellmer, M., & Reichel, A. (2021) · The International Journal of Human Resource Management · doi:10.1080/09585192.2021.1886148 · Cited by 63
“…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
“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).”
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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