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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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In the citing paper
“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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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
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
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
Research works printed but not linked: 34
- 10.1287/mnsc.1110.1460 · DOI · Paper page
- Bersin, 2014, Talent Management for the Global Workforce: The Market for Talent Management Systems 2014
- Boudreau J., 2011, Transformative HR: How Great Companies Use Evidence Based Change for Sustainable Competitive Advantage
- Boudreau J., 2015, Working paper, Centre for Effective Organisations
- Boudreau J., 2015, Talent analytics measurement and reporting: building a decision science or merely tracking activity? Working paper
- Boudreau J., 2005, Talentship and the evolution of human resource management: from professional practices to strategic decision science, Human Resource Planning, 28, 17
- Boudreau J., 2006, Talentship and HR measurement and analysis: from ROI to strategic human resource planning, Human Resource Planning, 29, 25
- Boudreau J., 2007, Beyond HR: The New Science of Human Capital
- Cascio W., 2011, Investing in People: The Financial Impact of Human Resources Initiatives
- CIPD, 2013, Talent Analytics and Big Data – The Challenge for HR
- Cooper D., 2010, Debating Coal Closures: Economic Calculation in the Coal Dispute 1984–1985
- Davenport T., 2010, Competing on talent analytics, Harvard Business Review, 88, 52
- 10.2307/2095101 · DOI · Paper page
- Douthit S., 2014, Creating a business‐focused HR function with analytics and integrated talent management, People and Strategy, 36, 16
- 10.1093/cje/24.2.211 · DOI
- Fink A., 2010, New trends in human capital research and analytics, People and Strategy, 33, 15
- Fitz Enz J., 2000, The Return on Investment in Human Capital: Measuring the Economic Value of Employee Performance
- Fitz Enz J., 2009, The ROI of Human Capital
- Fitz Enz J., 2010, The New HR Analytics
- 10.5465/amj.2014.4002 · DOI · Paper page
- 10.1111/1748-8583.12030 · DOI
- Grubb A., 2015, SuccessFactorsTM with SAP ERP HCM
- 10.1111/j.1748-8583.2010.00164.x · DOI
- Manyika J., 2011, Big data: the next frontier for innovation, competition and productivity
- 10.1080/09585190802707276 · DOI
- 10.1111/joms.12008 · DOI
- 10.1111/j.1748-8583.2010.00149.x · DOI · Paper page
- Pawson R., 1997, Realistic Evaluation
- 10.1016/j.orgdyn.2015.05.008 · DOI · Paper page
- 10.1177/0038038507080443 · DOI
- Smeyers L., 2015, What We Learned about HR Analytics in 2014 – part 2
- 10.1057/9781137313775 · DOI
- 10.1111/j.1467-8551.2011.00750.x · DOI
- 10.1177/0018726705055032 · DOI
Cited by
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Items 401–497 of 497
Sheng, B., & Islam, M. T. A. (2026) · Global Social Science and Humanities Journal · doi:10.59088/gi.v4i3.31
“The strategic promise of HRA is therefore not simply that it generates more HR metrics, but that it improves the quality and consistency of decisions across the employee lifecycle (Angrave et al., 2016; Van den Heuvel & Bondarouk, 2017).”
Masyhuri, M., Lhutfi, I., Siswanto, S., & Haque, A. (2026) · Journal of Economics, Entrepreneurship, Management Business and Accounting · doi:10.61255/jeemba.v4i4.1255
“This pattern aligns with broader observations that the application of AI in HRM was initially constrained by technological maturity and data availability (Marler & Boudreau, 2017; Angrave et al., 2016).”
AI-Augmented Human Resource Management? Insights from German companies
Kalff, Y., & Simbeck, K. (2026) · arXiv.org · doi:10.48550/arxiv.2607.13839
“Nevertheless, when it comes to analytics capabilities, HRM is often seen as ‘lagging behind’ (Angrave et al. 2016, p. 9; Coolen et al. 2023, p. 14).”
Explainable AI for employee turnover prediction: a SHAP-based intelligent analytics approach
Jia, H. (2026) · Scientific Reports · doi:10.1038/s41598-026-60901-3
Kumar, S., Chowdhurya, T. R., Guha, S., & Chakraborty, A. (2026) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v7.i1.2026.8951
Using HR Analytics to predict employee turnover in tech startups: an evidence-based HRM approach
Nogueira, P., & Carvalho, A. (2026) · Personnel Review · doi:10.1108/pr-09-2025-1088
Sultan, M., Chaudhary, A. B., & Shehzadi, Y. (2026) · Journal of Political Stability Archive · doi:10.63468/jpsa.4.3.38
“They generate special insights that enable businesses to maintain their competitive edge for longer (Ployhart et al., 2021; Angrave et al., 2022).”
Kiran, S., & Gaur, M. (2026) · The Journal of Theoretical Accounting Research · doi:10.1922/eyx23014
“Moreover, the majority of studies are Western-centric, limiting insights into emerging economies where workforce dynamics differ significantly (Angrave et al., 2016).”
InfluentialAhlawat, J., Thavara, D. S. S., Sharma, T., Khurana, A., Jamwal, D. S., & Kumar, N. (2026) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v7.i14s.2026.8544
HR Analytics: A Bibliometric Analysis using Web of Science (wos) Database and Exploring New Horizons
Srivastava, A., & Singh, R. (2026) · Management Dynamics · doi:10.57198/2583-4932.1367
Kara, A. U. (2026) · İstanbul Ticaret Üniversitesi Girişimcilik Dergisi · doi:10.55830/tje.1895766 · Cited by 1
Impact of Talent Analytics on Organizational HR Decisions
Afreen, R., & Anitha, B. (2026) · International Journal of Science and Social Science Research · doi:10.63671/ijsssr.v4i1.610
Role of Talent Analytics in Strategic Human Resource Decision Making
Afreen, R., & Anitha, B. (2026) · International Journal of Science and Social Science Research · doi:10.63671/ijsssr.v4i1.608
Sanjay., B., Therasa, C., & Suryabalaji, D. (2026) · International Conference on AI, Data Science, Cyber Security and Smart Manufacturing for Sustainable Development (ICADCS) · doi:10.1109/icadcs70036.2026.11583268
“However, algorithmic control is associated with reduced job resources‚ particularly autonomy‚ and elevated job demands which are linked to lower health‚ motivation‚ and performance among employees [16], [17].”
Bakri, M., & Yassir, Y. (2026) · Advances · doi:10.60079/ajeb.v4i3.799
“These findings align with previous studies that emphasize the complexity of implementing AI in organizational contexts and the need for careful management of ethical and governance issues (Angrave et al., 2016; Floridi et al., 2018; Asif et al., 2025; Jarrahi, 2018).”
Analisis Penggunaan Big Data dalam Perencanaan SDM
Fathoni, F. (2026) · Journal of Comprehensive Science (JCS) · doi:10.59188/jcs.v5i5.4167
“…yang memiliki dampak besar adalah pemanfaatan Big Data , yaitu kumpulan data dalam مجح besar, beragam, dan terus berkembang yang dapat diolah menjadi informasi strategis melalui proses analitik yang canggih (Angrave et al., 2016; Bachmann & Kemper, 2021; Institute, 2023; Sangu et al., 2024).”
Hamori, S. (2026) · Businesses · doi:10.3390/businesses6020023
“Angrave et al. (2016) critically examine the limitations of HR analytics practices, noting that data-driven evaluations may yield mis-leading conclusions when underlying assumptions are not carefully considered.”
Maurya, R., Khatri, N. N., Ramchandani, V., & Naz, M. (2026) · 2026 IEEE 1st International Conference on Intelligent Technologies for a Sustainable and Inclusive Future (ICITSIF) · doi:10.1109/icitsif69060.2026.11608407
“However, scholars caution that without operational alignment and strategic engagement, HR analytics may fail to deliver transformational outcomes and even risk excluding HR from board-level influence [30].”
A Comparative Analysis of Interpretable Machine Learning Models for Employee Turnover Prediction
Sivanarulselvan, S., & Srinivasakumar, V. (2026) · 2026 Fourth International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss68683.2026.11526047
“Thus, it is also necessary to consider the interpretable machine learning models ( Angrave, Nilsson, and Taylor 2016) [4].”
ПОВЕДІНКОВІ ЦИФРОВІ СЛІДИ ЯК ДЖЕРЕЛО ОЦІНЮВАННЯ ЕФЕКТИВНОСТІ НАВЧАЛЬНИХ ПРОГРАМ ПЕРСОНАЛУ
Любомудрова, Н., & Ямщиков, І. І. (2026) · Економіка та суспільство · doi:10.32782/2524-0072/2026-85-18
Jatobá, M. N., Ferreira, J. J., Fernandes, P. O., & Teixeira, J. P. (2026) · Journal of Organizational Effectiveness People and Performance · doi:10.1108/joepp-09-2025-0797
People Analytics in HRM: A Bibliometric Study
Judijanto, L. (2026) · West Science Interdisciplinary Studies · doi:10.58812/wsis.v4i03.2718
“As a relatively new and evolving field, people analytics requires systematic investigation to identify its key themes, trends, and gaps in the existing literature [3].”
Human Resources Analytics: A Systematization of Research Topics and Research Agenda
Radha, P., & B., M. L. (2026) · International Research Journal of MMC · doi:10.3126/irjmmc.v7i1.93193
“Angrave, Charlwood, Kirkpatrick, Lawrence, and Stuart (2016) critically analyzed the promises and limitations of HR analytics.”
Nuralieva, A. (2026) · American Impact Review · doi:10.66308/air.e2026026
Launberg, A., & Lindell, E. (2026) · Nordic Journal of Working Life Studies · doi:10.18291/njwls.166522
“…sentiments concerning AI among HR professional is still scant, some studies have for instance asserted that HR professionals and HR managers exhibit skep-ticism and ambivalence to quantitative data from HR analytics solutions as a basis for decisions (Angrave et al. 2016; Greasley & Thomas 2020).”
AI-Enabled Human Resource Management in Emerging and Transitional Economies
SALMAN, A. A.-H. (2026) · Journal of Applied Economic Sciences (JAES) · doi:10.57017/jaes.v21.si.1(91).13
“Predictive analytics has been employed more and more for HR management, including job performance prediction, training needs estimation, attrition anticipation and workforce planning (Angrave et al., 2016; Levenson, 2018).”
Relevance of big and thick social media data to businesses: A systematic review
Malik, B., Yeboah, A. N., & Gakpe, B. K. (2026) · African Quarterly Social Science Review · doi:10.51867/aqssr.3.1.23
“Other research has focused on the influence of big data on company operating departments such as marketing (Dekimpe, 2020; Fan et al., 2015), human resources (Angrave et al., 2016; Lindberg, 2020), and manufacturing (Li et al., 2015).”
Siregar, A., Cahyadi, W., Cen, C. C., & Candrasa, L. (2026) · Fundamental and Applied Management Journal · doi:10.66314/famj.v4i1.242
“For example, Angrave et al. (2016) highlight that many organizations fail to realize the full potential of HR analytics due to limited analytical skills and organizational readiness.”
Akther, N., Hamid, Z. A., & Juhdi, N. (2026) · Multidisciplinary Reviews · doi:10.31893/multirev.2026419
Wankhede, V. V., Malkar, V. R., & Bidgar, S. V. (2026) · 2026 5th International Conference on Innovative Practices in Technology and Management (ICIPTM) · doi:10.1109/iciptm69057.2026.11465336
“Studies applying ML in HR contexts have demonstrated improved performance in predicting employee outcomes such as turnover, engagement, and performance using classification and ensemble methods [10], [11].”
Leveraging HR Analytics Capabilities to Drive HR Process Agility: A Strategic Perspective
Chandradasa, A. H. I., & Silva, S. (2025) · Journal of Business Studies · doi:10.4038/jbs.v12i2.119
“This transition from descriptive to predictive and prescriptive analytics demonstrates the increasing importance of advanced analytics techniques to optimize people management (Angrave et al., 2016).”
Sadagopalaramanujam, G., Allada, S., Punithavathi, I. S. H., Nagarajan.S, Chauhan, V., & Gupta, A. (2025) · 2025 Modern Electronics Devices and Intelligent Communication Systems (MEDCOM) · doi:10.1109/medcom67532.2025.11404951
Marshall, E., Bennett, S., Carter, L., Reynolds, O., & Kulkarni, A. (2025) · International Journal of Scientific Research in Science Engineering and Technology · doi:10.32628/ijsrset25138555
Mercer, D., Bennett, O., Caldwell, E., Whitaker, S., & Kulkarni, A. (2025) · International Journal of Scientific Research in Science Engineering and Technology · doi:10.32628/ijsrset25138554 · Cited by 1
Endüstri 4.0 Çağında İnsan Kaynakları Yönetimi Lisans Eğitimi: Türkiye ve ABD Karşılaştırması
Özyurt, A. Y. (2025) · Yönetim bilimleri dergisi · doi:10.35408/comuybd.1585404
malekinezhad, M. K., Rahimnia, F., Eslami, G., & Farahi, M. M. (2025) · Roshd -e- Fanavari · doi:10.61882/jstpi.44758.21.83.79
Wibowo, T., & Haryati, E. (2025) · Jurnal Akuntansi Dan Keuangan West Science · doi:10.58812/jakws.v4i03.2687
S, S. H. S. M., Pooja, H. J., V, & Rutika, R. K. (2025) · Economic Sciences · doi:10.69889/dvad4z72
Leveraging Advanced Data Techniques in HR Analytics: Revolutionizing Workforce Management
Adil, K. (2025) · Journal of Social Research Dynamics · doi:10.67265/jsrd.01
“Advanced analytics, including predictive workforce planning, enables organizations to anticipate skill requirements and future workforce needs (Angrave et al., 2020).”
Deep Learning Based Recommendation System for Employee Retention Using Bipartite Link Prediction
Siregar, I. M., Othman, Z., & Bakar, A. A. (2025) · Jurnal INTECH Teknik Industri Universitas Serang Raya · doi:10.30656/intech.v11i1.10069
“Organizations leveraged statistical methods to identify patterns, predict turnover, and improve workforce planning (Davenport & Prusak, 1998; Angrave et al ., 2016).”
Dissanayake, D., & Kumari, A. (2025) · Journal of Human Resource Management Perspectives · doi:10.4038/jhrmp.v10i1.35
“If HR practitioners seem that the required information is widely available and reliable, they are inclined to embrace and utilize HR analytics (Angrave, et al., 2016).”
InfluentialDigital Dexterity: The New Competitive Advantage in Modern Work Place
Kanetkar, M. (2025) · Communications on Applied Nonlinear Analysis · doi:10.52783/cana.v32.6669
Big Data Analytics in HRM and Its Impact on Internal Learning and Competitive Advantage
Cao, G. (2025) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd64986.2025.11082066 · Cited by 1
“Angrave et al. [19] argue that HR faces significant barriers in leveraging big data, including technical, organizational, and cultural hurdles.”
Reducing Customer Complaints through Algorithm-Generated Feedback: Evidence from a Field Experiment
Deng, H., Zhuang, X., Du, M., Wang, L., & Wu, D. (2025) · Journal of Systems Science and Systems Engineering · doi:10.1007/s11518-025-5659-7
“…can collect and record a vast array of information, such as employees’ emotions (Chan 2018, Pinheiro et al. 2017), internet browsing history (Angrave et al. 2016, Leicht-Deobald et al. 2019), calendars (Chen 2019), stress levels (Horton 2020), cognitive or physical workload (Heard et al.…”
HR Analytics Adoption in the Indian Hospitality Industry: A Path to Data-Driven Decision Making
Chauhan, V. (2025) · PUSA Journal of Hospitality and Applied Sciences · doi:10.48165/pjhas.2025.11.1.7
“Levenson (2005) differentiated between ROI, cost-benefit analysis and impact analysis in HR analytics.”
InfluentialGiran, A. H. A., Tamyez, P. F. M., Fauzi, M. A. F. @., Idris, N. F. M., & Madhavkumar, V. (2024) · International Journal of Industrial Management · doi:10.15282/ijim.18.4.2024.10330
“As noted by Angrave et al. (2016), HR professionals must be willing to engage strategically and operationally to enhance current practices and take full advantage of the potential benefits of HR analytics (Bulsari & Pandya, 2023).”
The Role of Big Data in Transforming HR Analytics and Talent Management Practices
Anwar, U. A. A., Senen, S. H., Rofaida, R., & Suwarsi, S. (2024) · JURNAL NUSANTARA APLIKASI MANAJEMEN BISNIS · doi:10.29407/nusamba.v9i2.19964 · Cited by 1
Harlianto, J., Rudi, R., & Hapsari, I. A. (2024) · Asian Conference on Communication and Networks (ASIANComNet) · doi:10.1109/asiancomnet63184.2024.10811096
ANALYSING THE ROLE OF HR ANALYTICS IN STRATEGIC DECISION MAKING
Thomas, L., Leo, R. S., Javeria, N., & Taj, N. (2024) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v5.i7.2024.2921 · Cited by 3
Digitalization of talent planning in IT sector: Mediating role of HR policies and practices
Yadav, S., Kapoor, S., & Gupta, S. K. (2024) · Problems and Perspectives in Management · doi:10.21511/ppm.22(3).2024.19 · Cited by 4
“Organizations may anticipate their talent demands and the possible effects of external changes on workforce dynamics with the use of predictive analytics, which is a crucial component of sophisticated HR platforms (Angrave et al., 2016).”
RESPONSES TO DIGITAL HUMAN RESOURCE MANAGEMENT MODEL: SUGGESTIONS FROM DISRUPTIVE INNOVATION THEORY
Vu, T. (2024) · RMUTT GLOBAL BUSINESS ACCOUNTING AND FINANCE REVIEW · doi:10.60101/gbafr.2024.271771
“Similarly, despite the availability of analytics tools, HR analytic programs are still mostly used for historical reporting (Angrave et al., 2016), and many managerial decisions are still intuitive rather than data driven (Jewell, 2017).”
BackgroundAlkhayyat, R., Ertürk, A., Safi, M., & Abdallah, F. (2024) · International Scientific Conference on Digitalization, Innovations & Sustainable Development: Trends and Business Perspectives 2023 · doi:10.3390/proceedings2024101022
“Business intelligence and analytics have been important and very critical tools for organizations along with a significant increase in the usage of data in almost all HR functions [1–3].”
Abdali, M., Ho, M. W. Y., & Morrow, J. (2024) · New Zealand journal of employment relations · doi:10.24135/nzjer.v48ix.144 · Cited by 1
“Wang et al. (2002) suggest that, to be effective, DLS should match HRM strategy while, others argue that such technological investments may damage employee interests and needs (Angrave et al., 2016).”
Exploring the Impact of Technology Factors on HR Analytics Adoption in Manufacturing Organizations
Mahendran, C., & Krithika, M. (2024) · Journées Francophones d'Ingénierie des Connaissances · doi:10.1109/ic457434.2024.10486334
“In order to acquire a comprehensive understanding of the strategic influence of human capital, the use of HR analytics has progressed from descriptive models to forecasting models [1-2] Previously, the use of HR analytics consisted of descriptive models.”
MethodA Bibliometric Analysis and Visualization of Human Resource Big Data Research
Liu, G., Zhu, R.-Q., Li, B., & Pan, L. (2023) · BDEIM · doi:10.1145/3659211.3659348
“Angrave underscores the pivotal role of HR analytics in driving strategic HR operations and elevating organizational performance [15].”
BackgroundSridar, S. P. (2023) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v4.i1.2023.5763
Masta, K. (2023) · International Journal For Multidisciplinary Research · doi:10.4324/9781315464695-13
“Angrave et al. (2016) HR Analytics should facilitate experimentation to identify the reason for performance improvement and quantify the return on investment that such efforts may provide.”
BackgroundLacroux, A. (2023) · doi:10.48550/arxiv.2305.08889
“Parmi les défis à relever pour les promoteurs de l’analytique RH, certains concernent l’accès, l’utilisation et la protection des données (Angrave et al., 2016).”
Data Analytics for HR Students: Using RapidMiner to Develop Systems Thinking Skills Web Appendix
Czegledi, A., & Dafoe, K. (2023) · The BRC Academy Journal of Business · doi:10.15239/j.brcacadjb.2023.13.01.wa02
“However, to get the full benefits of data analytics in HRA, it should be integrated with business requirements (Levenson, 2005, 2011; Angrave et al., 2016; Lawler et al., 2004; Boudreau & Ramstad, 2007).”
BackgroundHR Analytics: Concept, Application, and Impact on Talent Management, Branding, and Challenges
Prabhakar, S., & Jayaprakash, S. (2023) · European Journal of Business and Management · doi:10.7176/ejbm/15-8-06 · Cited by 1
“Tesco and McDonald's are two instances of companies that were able to determine how employee attitudes, management style, and staff demographics interacted to improve restaurant performance (Sparrow, P., Hird, M. and Cooper, C. 2015; David Angrave et al., 2016).”
Gupta, S., & Sharma, R. R. (2022) · IEEE International Conference on Industrial Engineering and Engineering Management · doi:10.1109/ieem55944.2022.9989675
İNSAN KAYNAKLARI ANALİTİĞİ OLGUNLUĞU ÖLÇEĞİ (İKAOÖ): BİR ÖLÇEK GELİŞTİRME ÇALIŞMASI
Özkanlı, F. M., & Gök, S. (2022) · Journal of Research in Business · doi:10.54452/jrb.1107177
Pan, M., & Wang, M. (2022) · International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR) · doi:10.1109/icbar58199.2022.00015
“…statistical and econometric techniques for empirical analysis that go beyond correlation analysis between variables to build causal statistical models based big data to identify factors that drive performance, thereby help to improve human resource management practices and enhance performance [5] .”
BackgroundUma Proposta de Framework para a Execução e Estruturação de HR Analytics
Peres, A., & Laurindo, F. J. B. (2022) · Anais do Encontro Nacional de Engenharia de Produção · doi:10.14488/enegep2022_tn_st_387_1921_43797
Engl, F., Trubjansky, P., & Herrmann, F. (2022) · Anwendungen und Konzepte der Wirtschaftsinformatik · doi:10.26034/lu.akwi.2022.3337
Hajam, A. A., & John, A. S. (2022) · MORFAI JOURNAL · doi:10.54443/morfai.v1i2.744 · Cited by 3
Whitaker, D., Montgomery, A., Brooks, N., Bennett, C., Harrington, E., & Kulkarni, A. (2022) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2281225
Lee, Z., Tanaka, H., Schneider, L., Martinez, S., & Kulkarni, A. (2021) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2281224
Den analytiske etikk og taylorismens ånd? HR-analyse som ledelseskonsept
Stensli, H. O. (2020) · Samfundslederskab i Skandinavien · doi:10.22439/sis.v35i6.6109 · Cited by 3
“Dette gjør, at enkelte forskere advarer mot en utvikling, der HR-A blir enda mer preget av et styringsperspektiv enn av lederskap med mennesker i sentrum (Angrave et al., 2016).”
BackgroundInfluentialParasa, M. (2020) · SAMRIDDHI A Journal of Physical Sciences Engineering and Technology · doi:10.18090/samriddhi.v12i02.15 · Cited by 8
Coron, C. (2020) · Wiley eBooks · doi:10.1002/9781119721765.refs
Netten, N., Choenni, S., & Bargh, M. S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29374-1_16 · Cited by 2
“As concluded in [11], unfortunately HR professionals and big data analytics professionals do not understand each other well.”
InfluentialMaDaScA: Instruction of Data Science to Managers
Golan, S., & Bouhnik, D. (2019) · Proceedings of the 2019 InSITE Conference · doi:10.28945/4271
“Here, too, it is important to employ data - driven processes to get an efficient and unbiased assessment (Angrave, Charlwood, Kirkpatrick, Lawrence, & Stuart, 2016) .”
Yahiaoui, N. E. H. (2026) · Journal of Engineering Management and Competitiveness · doi:10.5937/jemc2600008y
“Fourth, organizational resistance from HR professionals, line managers, and employees themselves may limit adoption; the literature on HR analytics implementation identifies human and cultural resistance as a more common cause of analytics failure than technical inadequacy (Angrave et al., 2016).”
Online Platforms as Labour Market IntermediariesPreprint
Ratti, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5087834 · Cited by 1
THE THREE PILLARS OF BUSINESS: A GUIDE TO INTEGRATED MARKETING, FINANCE & HR PRACTICESPreprint
Kaakandikar, R. (2025) · Social Science Research Network · doi:10.2139/ssrn.5161493
Predicting Employee Attrition in the Saudi Private Sector Using Machine Learning
Alqahtani, H., Almagrabi, H., & Alharbi, A. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0161097
“However, in HR analytics, the focus should shift from simply collecting large volumes of data to ensuring the data provides meaningful insights and is used effectively to create organizational value [10].”
A Data Analytics Module Introducing Principles of Social Enterprise and Humanistic Management
Ariyachandra, T. (2024) · Information systems education journal · doi:10.62273/odio6282
Determining Our Future: How Artificial Intelligence Creates a Deterministic WorldPreprint
Goldfus, Y., & Eder, N. (2023) · Social Science Research Network · doi:10.2139/ssrn.4534217
Application of Statistics in Human Resource Management
Puri, R., & Sengupta, P. (2022) · Research Anthology on Human Resource Practices for the Modern Workforce · doi:10.4018/978-1-5225-4947-5.ch002
Artificial Intelligence Inroads Into HR
Bansal, S., Agarwal, M., Bansal, D., & Narayanan, S. (2022) · Handbook of Research on Innovative Management Using AI in Industry 5.0 · doi:10.4018/978-1-7998-8497-2.ch016
Loscher, G. (2021) · Quick Guide People Analytics · doi:10.1007/978-3-658-34731-4_1
Chancen und Risiken für das Personalmanagement durch People Analytics
Loscher, G. J. (2021) · Quick Guide · doi:10.1007/978-3-658-34731-4_2
Optimization of employee turnover through predictive analysis
Stachová, K., Baroková, A., & Stacho, Z. (2021) · Management Trends in the Context of Industry 4.0 · doi:10.4108/eai.4-12-2020.2303446
“Angrave & Charlwood (2016) describe the importance of understanding the data sample, which was also addressed in the first part of the research.”
BackgroundDEFINE TO MANAGE IT: MANAGEMENT IMPLICATIONS FOR HR ANALYSIS ACCORDING TO SYSTEMS THEORY
Peres, A. R., Riccio, É. L., & LAURINDO, F. J. B. (2022) · CONTECSI - International Conference on Information Systems and Technology Management · doi:10.5748/18contecsi/pse/ism/6856
Smart HRM: Wohin geht die Reise?
Gärtner, C. (2020) · Springer eBooks · doi:10.1007/978-3-658-29431-1_7 · Cited by 1
Positive Integration of ICT to Reshape the Learning Process in Large Corporates
Faruq, Q. O. (2020) · Encyclopedia of Education and Information Technologies · doi:10.1007/978-3-319-60013-0_256-1
Is Employee Turnover Related to Higher Education Institutional Performance? An Empirical Analysis
Glazer, R. S. (2019) · ProQuest Demo Repository · doi:10.7916/d8-nn66-qh43
“One reason for this is the assumption that the data contained in current Human Resources Information Systems (HRIS) are not adequate to answer the strategic questions that such an analysis might accomplish (Angrave et al., 2016).”
BackgroundMethodInfluentialLe « Big Data RH » : vers une nouvelle convention de quantification ?
Coron, C. (2019) · Annales des Mines - Gérer et comprendre · doi:10.3917/geco1.137.0027
“…par ces travaux, notamment la définition d’indicateurs de suivi qui permettent d’identifier des grandes tendances et l’utilisation de méthodes statistiques plus sophistiquées (Davenport, Harris et Shapiro, 2010 ; Rasmussen et Ulrich, 2015 ; Angrave et al. , 2016 ; Madsen et Slåtten, 2017).”
Gestaltung der Personalbedarfsplanung
Stock-Homburg, R., & Groß, M. (2019) · Personalmanagement · doi:10.1007/978-3-658-26081-1_3
Big Data and People Management
Sarti, D., & Torre, T. (2019) · Big Data Governance and Perspectives in Knowledge Management · doi:10.4018/978-1-5225-7077-6.ch006
The Call to Teach Human Capital Analytics
Trusson, C. R. (2019) · Human Performance Technology · doi:10.4018/978-1-5225-2820-3.ch006
Personalmanagement: Einleitung
Stock-Homburg, R., & Groß, M. (2019) · Personalmanagement · doi:10.1007/978-3-658-26081-1_1
Wawer, M., & Muryjas, P. (2016) · Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu · doi:10.15611/pn.2016.429.27
malekinezhad, M. K., Rahimnia, F., Eslami, G., & Farahi, M. M. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124880
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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