Cited by 34AI and HR TechnologiesLegal and Labor StudiesEthics and Social Impacts of AI

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Big data: lessons for employers and employees

Open access
Jeske, Debora, Calvard, Thomas·2020·Employee Relations: The International Journal

Abstract

Purpose: The purpose of this paper is to critically reflect on the pros and cons of using employee information in big data projects. Design/methodology/approach: The authors reviewed papers in the area of big data that has immediate repercussions for the experiences of employees and employers. Findings: The review of papers to date suggests that big data lessons based on employee data are still a relatively unknown area of employment literature. Particular attention is paid to discussion of employee rights, ethics, expectations and the implications employer conduct has on employment relationships and prospective benefits of big data analytics at work for work. Originality/value: This viewpoint paper highlights the need for more discussion between employees and employers about the collection, use, storage and ownership of data in the workplace. A number of recommendations are put forward to support future data collection efforts in organisations.

Overview

Integrity signals

Registry

Crossref · 10.1108/er-06-2018-0159

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Show all 3 references

Research works printed but not linked: 58

  1. 2015, The (big) data-security assemblage: knowledge and critique, Big Data & Society, 2, 1
  2. 2012, Elementary, my dear Doctor Watson, Clinical Chemistry, 58, 986, 10.1373/clinchem.2011.180992 · DOI
  3. 2017, Big data analytics and the limits of privacy self-management, New Media & Society, 19, 579, 10.1177/1461444815614001 · DOI
  4. 2011, Ethical breakdowns, Harvard Business Review, 89, 58
  5. 2016, A risk perspective on human resource management: a review and directions for future research, Human Resource Management Review, 26, 149, 10.1016/j.hrmr.2015.12.001 · DOI
  6. 2014, Quantified self: meet the quantified employee, Forbes
  7. 2012, Critical questions for big data. Provocations for a cultural, technological, and scholarly phenomenon, Information, Communication & Society, 15, 662, 10.1080/1369118X.2012.678878 · DOI · Paper page
  8. 2006, Ethical leadership: a review and future directions, Leadership Quarterly, 17, 595, 10.1016/j.leaqua.2006.10.004 · DOI · Paper page
  9. 2014, Do role models matter? An investigation of role modelling as an antecedent of perceived ethical leadership, Journal of Business Ethics, 122, 587, 10.1007/s10551-013-1769-0 · DOI
  10. 2005, Ethical leadership: a social learning perspective for construct development and testing, Organizational Behavior and Human Decision Processes, 97, 117, 10.1016/j.obhdp.2005.03.002 · DOI · Paper page
  11. 2016, Big data, organizational learning, and sensemaking: theorizing interpretive challenges under conditions of dynamic complexity, Management Learning, 47, 65, 10.1177/1350507615592113 · DOI
  12. 2016, Will your ethics hold up under pressure?, Forbes
  13. 2016, Security and privacy in big data lifetime: a review, 3
  14. 2016, Analytics: a catalyst for stagnant science?, Journal of Management Inquiry, 25, 111, 10.1177/1056492615601342 · DOI
  15. 2016, Click here to consent forever: expiry dates for informed consent, Big Data & Society, 3, 1
  16. 2016, Designing and developing analytics-based data products, MIT Sloan Management Review, 58, 83
  17. 2010, Competing on talent analytics, Harvard Business Review, 88, 52
  18. 2018, Algorithmic decision-making based on machine learning from big data: can transparency restore accountability?, Philosophy & Technology, 31, 525, 10.1007/s13347-017-0293-z · DOI · Paper page
  19. 2015, Conducting ethical research with big and small data: key questions for practitioners, Industrial and Organizational Psychology, 8, 563, 10.1017/iop.2015.84 · DOI
  20. 2014, The Formula: How Algorithms Solve All Our Problems – And Create More
  21. 2015, I-Os in the vanguard of big data analytics and privacy, Industrial and Organizational Psychology, 8, 555, 10.1017/iop.2015.83 · DOI
  22. 2014, The game is changing: the business information survey 2014, Business Information Review, 31, 14, 10.1177/0266382114529852 · DOI
  23. 2015, The ethics conversation we’re not having about data, Harvard Business Review
  24. 2013, How Google sold its engineers on management, Harvard Business Review, 91, 74
  25. 2014, Big data and management, Academy of Management Journal, 57, 321, 10.5465/amj.2014.4002 · DOI · Paper page
  26. 2017, Citizenship, social media, and big data: current and future research in the social sciences, Social Science Computer Review, 35, 3, 10.1177/0894439315619589 · DOI
  27. 2018, Rethinking the Silicon Valley myth, Research-Technology Management, 61, 64, 10.1080/08956308.2018.1399028 · DOI
  28. 2015, Big data recommendations for industrial-organizational psychology, Industrial and Organizational Psychology, 8, 491, 10.1017/iop.2015.40 · DOI
  29. Hashimoto, E., Ichino, M., Kuboyama, T., Echizen, I. and Yoshiura, H. (2016), “Breaking anonymity of social network accounts by using coordinated and extensible classifiers based on machine learning”, in Dwivedi, Y., Mäntymäki, M., Ravishankar, M.N., Janssen, M., Clement, M., Slade, E.L., Rana, N.P., Al-Sharhan, S. and Simintiras, A.C. (Eds), Social Media: The Good, the Bad, and the Ugly, I3E 2016. Lecture Notes in Computer Science, Vol. 9844, Springer, New York, NY, pp. 455-470. · DOI
  30. 2009, Pathologies of big data, Communications of the ACM, 52, 36, 10.1145/1536616.1536632 · DOI
  31. 2017, Factors influencing big data decision-making quality, Journal of Business Research, 70, 338
  32. 2016, Using social media content for screening in recruitment and selection: pros and cons, Work, Employment and Society, 30, 535, 10.1177/0950017015613746 · DOI
  33. 2018, Advances in the treatment of context in organizational research, Annual Review of Organizational Psychology and Organizational Behavior, 5, 21, 10.1146/annurev-orgpsych-032117-104406 · DOI
  34. 2015, Big data, little individual: considering the human side of big data, Industrial and Organizational Psychology, 8, 527, 10.1017/iop.2015.78 · DOI
  35. 2014, Big data, new epistemologies and paradigm shifts, Big Data & Society, 1, 1
  36. 2013, Private traits and attributes are predictable from digital records of human behavior, Proceedings of the National Academy of Sciences of the United States of America, 110, 5802, 10.1073/pnas.1218772110 · DOI · Paper page
  37. 2016, Social media dilemmas in the employment context, Employee Relations, 38, 420, 10.1108/ER-04-2015-0072 · DOI
  38. 2018, Better people analytics, Harvard Business Review, 96, 70
  39. 2016, The Internet of Things: a security point of view, Internet Research, 26, 337, 10.1108/IntR-07-2014-0173 · DOI · Paper page
  40. 2015, Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else
  41. 2016, Algorithms need managers, too. Know how to get the most out of your predictive tools, Harvard Business Review, 104, 96
  42. 2016, Help wanted: HR analysts, HRMagazine, 61, 32
  43. 2017, Inductive reasoning: the promise of big data, Human Resource Management Review, 27, 277, 10.1016/j.hrmr.2016.08.005 · DOI
  44. 2012, Big data: The management revolution, Harvard Business Review, 90, 60
  45. 2013, Big Data: A Revolution that will Transform How We Live, Work, and Think
  46. 2016, Taking ethics seriously: by setting up board committees?, Forbes, 9
  47. 2017, Regulating wellbeing in the brave new quantified workplace, Employee Relations, 39, 308, 10.1108/ER-06-2016-0126 · DOI · Paper page
  48. 2013, Market research and the ethics of big data, International Journal of Market Research, 55, 505, 10.2501/IJMR-2013-015 · DOI
  49. 2017, Big data: a normal accident waiting to happen?, Journal of Business Ethics, 145, 481, 10.1007/s10551-015-2904-x · DOI
  50. 2014, How web tracking changes user agency in the age of big data: the used user, Big Data & Society, 1, 1
  51. 2015, Learning from practice: how HR analytics avoids being a management fad, Organizational Dynamics, 44, 236, 10.1016/j.orgdyn.2015.05.008 · DOI · Paper page
  52. Roberts, B. (2013), “The benefits of big data”, SHRM HR Magazine, 10 January, Vol. 58 No. 10, pp. 20-27, available at: www.shrm.org/publications/hrmagazine/editorialcontent/2013/1013/pages/1013-big-data.aspx (accessed 23 May 2016).
  53. 2015, The algorithm that tells the boss who might quit, Wall Street Journal
  54. Stone, C.B., Neely, A.R. and Lengnick-Hall, M.L. (2018), “Human resource management in the digital age: big data, HR analytics and artificial intelligence”, in Novo Melo, P. and Machado, C. (Eds), Management and Technological Challenges in the Digital Age, CRC Press, Boca Raton, FL, pp. 13-42.
  55. 2015, Big Data at Work: The Data Science Revolution and Organizational Psychology
  56. 2014, Make sure big data adds up: the science of data collection is meaningless without the art of interpretation, Training, 51, 14
  57. 2017, The jobs that artificial intelligence will create, MIT Sloan Management Review, 58, 14
  58. 2012, Understanding big data: Analytics for Enterprise Class Hadoop and Streaming Data

Cited by

Sorted by citations

Metadata

Authors
Jeske, D., & Calvard, T.
Affiliations
  • University College Cork (IE)
  • University of Edinburgh (GB)
Venue
Employee Relations: The International Journal
Publisher
Emerald
Published
2020-01-06
Volume
42
Issue
1
Pages
248-261
ISSN
0142-5455 (ISSN-L)
OpenAlex
W2980250686
Cited by
34
References
3
Citations by year
2025: 13 · 2024: 5 · 2023: 4 · 2022: 8 · 2021: 2 · 2020: 2
FWCI
4.32
Type
journal article
Language
en
Open access
Locations
  • Employee Relations (publishedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (publishedVersion, other-oa)
  • Arrow@dit (Dublin Institute of Technology) (acceptedVersion)
  • Cork Open Research Archive (University College Cork, Ireland) (submittedVersion, cc-by-nc)
Topics
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Keywords
big data, employee data, workers' rights, data ethics, employer conduct, employment relationship, data ownership, data storage, employee expectations

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