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The DOI matches its Crossref record, with a small difference in title or year; 4 of the 4 references it cites were found in a registry or an index.

Big data contributions to human resource management: a systematic review

Garcia-Arroyo, José, Osca, Amparo·2021·The International Journal of Human Resource Management
DOI 10.1080/09585192.2019.1674357
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Abstract

Big Data (BD) has become a valuable resource for companies. Publications on BD have grown enormously but scholarly work is scarce. The present review aims to systematise the academic inputs so far and to clarify what is the novelty of BD, what are its implications and challenges for HRM practices, and in which HR practice systems the main contributions are. In a systematic search of more than 1,500 documents, we found 41 articles studying BD in Human Resource Management (HRM). Results show that BD means a new approach and methodology to manage data on employees and numerous opportunities for HRM, but also important challenges at a technological, methodological and ethical level. Five main clusters of HR practice systems have been identified, concentrating the largest number of papers in the cluster of ​​information, learning and knowledge and in the cluster of ​​strategy, efficiency and performance, suggesting a trend in the studies that is explained by the nature and characteristics of the BD. The steps to carry out BD projects are described. The main contributions of the BD in each area are described and future research avenues are outlined.

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Research works printed but not linked: 64

  1. (Studies with an ‘*’ have been included in the systematic review)
  2. Aguado García D., 2018, Revista Vasca de Gestión de Personas y Organizaciones Públicas, 14, 36
  3. Ashraf T., 2017, Dissertation Abstracts International Section A: Humanities and Social Sciences, 78
  4. Barnes N. D., 2014, Information Management, 48, 34
  5. 10.1177/0149206310391805 · DOI
  6. 10.1111/1748-8583.12123 · DOI
  7. 10.1111/1467-8551.12340 · DOI
  8. 10.1016/j.techfore.2017.07.027 · DOI
  9. 10.1016/j.hrmr.2015.12.001 · DOI
  10. 10.1177/0893318914549952 · DOI
  11. Blau P. M., 1964, Exchange and power in social life
  12. 10.1177/1350507615592113 · DOI
  13. 10.1016/j.ijinfomgt.2018.07.005 · DOI
  14. 10.1016/j.cobeha.2017.04.007 · DOI
  15. 10.1111/j.1744-6570.2006.00045.x · DOI
  16. Davenport T. H., 2013, Enterprise analytics: Optimize performance, process, and decisions through big data
  17. De Mauro A., 2018, Information Processing & Management, 54, 755, 10.1016/j.ipm.2018.02.002 · DOI
  18. Dunn C., 2016, Workforce, 95, 14
  19. 10.1037/h0053870 · DOI
  20. 10.1111/ntwe.12107 · DOI · Paper page
  21. Frank E., 2004, Journal of the American Society for Information Science and Technology, 55, 214, 10.1002/asi.10360 · DOI
  22. 10.1016/j.ijinfomgt.2016.01.006 · DOI
  23. Giacumo L. A., 2016, Quarterly Review of Distance Education, 17, 21
  24. Gibson D., 2013, Managing Information, 20, 4
  25. 10.1002/smj.4250171110 · DOI · Paper page
  26. Guilfoyle S., 2016, Social media in employee selection and recruitment, 127
  27. Halaweh M., 2017, Information Resources Management Journal, 30, 48, 10.4018/IRMJ.2017010104 · DOI
  28. Hausknecht J. P., 2015, Big data at work: The data science revolution and organizational psychology, 250
  29. Hildebrandt, M. (2013). Slaves to big data. Or are we? IDP, Revista de Internet, Derecho y Política, 17. Retrieved from https://www.raco.cat/index.php/IDP/article/viewFile/303366/393038. · DOI
  30. 10.2307/256741 · DOI · Paper page
  31. Illingworth A. J., 2015, Big data at work. The data science revolution and organizational psychology, 368
  32. 10.1108/JKM-07-2015-0293 · DOI
  33. 10.1002/9781119083856 · DOI · Paper page
  34. 10.1146/annurev.ps.46.020195.001321 · DOI
  35. 10.1080/09585192.2017.1384930 · DOI
  36. Jordan S. R., 2015, Legal and regulatory issues in human resources management, 267
  37. 10.1177/1534484316675818 · DOI · Paper page
  38. Kozlowski S. W., 2015, Big data at work. The data science revolution and organizational psychology, 273
  39. 10.1007/978-3-319-29989-1_1 · DOI
  40. *Lazarević, S. & Lukić, J. (2016). The impact of information and communication technology on human resources. In Sinteza 2016 International Scientific Conference on ICT and E-Business Related Research (pp. 369–375). doi:10.15308/Sinteza-2016-369-375 · DOI
  41. 10.1002/smj.4250171109 · DOI
  42. 10.1108/JKM-07-2015-0298 · DOI
  43. Mari F., 2017, IEEE Instrumentation & Measurement Magazine, 20, 13, 10.1109/MIM.2017.8036690 · DOI
  44. 10.1080/15313220.2016.1276874 · DOI
  45. 10.1016/j.cobeha.2017.09.013 · DOI
  46. 10.1371/journal.pmed.1000097 · DOI · Paper page
  47. Morgan W. B., 2015, Big data at work: The data science revolution and organizational psychology, 310
  48. Morrison J. D., 2015, The Industrial–Organizational Psychologist, 52, 134
  49. Nunan D., 2015, Journal of Business Ethics, 14, 481, 10.1007/s10551-015-2904-x · DOI
  50. 10.1080/10630732.2017.1334855 · DOI
  51. 10.1002/9781119040002 · DOI
  52. 10.2307/25148703 · DOI
  53. Roberts B., 2013, HRMagazine; Alexandria, 59, 20
  54. Rotolo C. T., 2015, Industrial and Organizational Psychology, 8, 515, 10.1017/iop.2015.76 · DOI
  55. Ryan J., 2015, Big Data at work: The data science revolution and organizational psychology, 36
  56. Snell S. A., 1996, Research in personnel and human resources management, 61
  57. Shu W., 2017, Revista de la Facultad de Ingeniería UCV, 32, 495
  58. 10.1145/584091.584093 · DOI
  59. *Smith, Jr, T. D. (2018). Perceptions of human resource professionals on using data analytics for talent management (Doctoral Dissertation, St. Thomas University). ProQuest Dissertations Publishing.
  60. 10.4324/9781315780504 · DOI
  61. 10.1177/1094428117718627 · DOI
  62. 10.1108/CCIJ-02-2016-0015 · DOI
  63. 10.1177/014920630102700607 · DOI
  64. 10.1146/annurev-orgpsych-032516-113052 · DOI

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Metadata

Authors
Garcia-Arroyo, J., & Osca, A.
Affiliations
Social and Organizational Psychology, Universidad Nacional de Educacion a Distancia, Madrid, Spain
Venue
The International Journal of Human Resource Management
Publisher
Informa UK Limited
Published
2019-10-09
Volume
32
Issue
20
Pages
4337-4362
ISSN
  • 0958-5192
  • 1466-4399
DOI
10.1080/09585192.2019.1674357
Cited by
118
References
4
Language
en

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