CoChat
Sign Up
Cited by 59AI and HR TechnologiesHuman Resource and Talent ManagementEmployer Branding and e-HRM

Help me help you: How HR analytics forecasts foster organizational creativity

Open access
Di Prima, Christian, Cepel, Martin, Kotaskova, Anna, Ferraris, Alberto·2024·Technological Forecasting and Social Change
DOI 10.1016/j.techfore.2024.123540
Open this paper in CoChat — free account

Abstract

Organizational creativity is increasingly deemed a crucial source of organizational effectiveness and competitive advantage. As several previous researches found that some human resource management (HRM) activities act as antecedents of organizational creativity, HR-related decisions are increasingly deemed as high stake decisions, as poor choices may originate large financial loss and may be very costly to reverse. For this reason, organizations are relying more and more on a data-driven approach also when it comes to HRM, for example by increasingly resorting to HR analytics tools to inform and guide the decision-making process. Consequently, the objective of this study is to empirically investigate the effect of an HRM data-driven approach on organizational creativity. Data were collected through online questionnaires addressed to 281 HR managers of heterogeneous companies from Europe and analyzed through a structural equation modeling (SEM) technique. Our results found employee training, organizational knowledge sharing, and recruitment and selection to be positively related to organizational creativity, and that HR analytics positively moderated these relationships. On the contrary, no significant effect was found regarding the relationship between employee rewards and incentives and organizational creativity, and neither regarding the moderating effect of HR analytics. Our study thus contributes to research regarding high stake decisions by empirically demonstrating HR analytics' positive impact on the decision-making process regarding the workforce, also in a peculiar context as organizational creativity. It also contributes to organizational creativity literature by clarifying the mixed findings regarding its antecedents. Finally, it provides further empirical confirmation of the contingency theory applied to HRM.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.techfore.2024.123540

Cites

119 of 123 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • People analytics facilitates increasing human capital value in use through its ability to gain insights into workforce data, providing HRM departments with a unique opportunity to address common good HRM values.

    “In other words; people analytics fa cilitates increasing human capital value in use through its ability to gain insights into workforce data; providing HRM departments with a unique opportunity to address common good HRM values (Di Prima, Kotaskova, et al., 2024).”

    From Reframing people analytics value creation: Balancing organizational performance and common good HRM values (2026) · cites this paper as Di Prima, Kotaskova, et al., 2024

    Section 2.2 (p. 3) says HR analytics provides insights that can help tailor HR practices to employees’ needs and create value for the organization. It does not connect those insights to increasing human capital ‘value in use’ or to addressing common-good HRM values. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 7

  1. Council of the European Union, 2009. COUNCIL RESOLUTION of 27 November 2009 on a Renewed Framework for European Cooperation in the Youth Field (2010 -2018) (Brussels). http://refhub.elsevier.com/S0040-1625(2400336-6/rf0130)
  2. Dewett, T., 2007. Linking intrinsic motivation, risk taking, and employee creativity in an R & D environment. R & D Management 37, 197 -208. https://doi.org/10.1111/ j.1467-9310.2007.00469.x.
  3. Fink, A. (Ed.), 2003. The Survey Kit, 2nd ed. Sage Publications, Thousand Oaks, Calif. George, D., Mallery, P., 2018. Reliability analysis. In: IBM SPSS Statistics 25 Step by Step. Routledge, pp. 249 -260.
  4. Groves, R.M., Fowler, F.J., Couper, M.P., Lepkowski, J.M., Singer, E., Tourangeau, R., 2013. Survey Methodology, 2nd ed. Wiley, Hoboken. http://refhub.elsevier.com/S0040-1625(2400336-6/rf0225)
  5. Harman, H.H., 1976. Modern factor analysis, 3d ed, rev. ed. University of Chicago Press, Chicago.
  6. Lahiri, G., Schwartz, J., 2018. New rewards: Personalized, agile, and holistic, 2018 Global Human Capital Trends. Deloitte. http://refhub.elsevier.com/S0040-1625(2400336-6/rf0345)
  7. Zikmund, W.G., Babin, B.J., 2016. Essentials of marketing research, Sixth, edition. ed. Cengage Learning, Australia. http://refhub.elsevier.com/S0040-1625(2400336-6/rf0660)

Cited by

Sorted by citations

Related

Authors
Di Prima, C., Cepel, M., Kotaskova, A., & Ferraris, A.
Affiliations
Ural Federal University (RU)
Corresponding author
Ferraris, Alberto
ORCID
Ferraris, Alberto
Venue
Technological Forecasting and Social Change
Publisher
Elsevier BV
Published
2024-09
Volume
206
Pages
123540
ISSN
0040-1625 (ISSN-L)
DOI
10.1016/j.techfore.2024.123540
OpenAlex
W4400099168
Cited by
46
References
123
Citations by year
2026: 22 · 2025: 21 · 2024: 3
FWCI
23.12
Type
journal article
Language
en
Open access
Licence
Locations
  • Technological Forecasting and Social Change (publishedVersion, cc-by-nc-nd)
  • Electronic Archive of Ural Federal University (ELAR UrFU) (publishedVersion, cc-by-nc-nd)
  • Institutional Research Information System University of Turin (University of Turin) (submittedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
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 › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
Keywords
HR analytics, organizational creativity, data-driven human resource management, employee training, knowledge sharing, recruitment and selection, employee rewards and incentives, contingency theory, HR managers

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