Prioritising data items for business analytics: Framework and application to human resources
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PA promises to help organisations understand their workforce as a whole, as departments or work groups, and as individuals, by making data about employee attributes, behaviour and performance more accessible, interpretable and actionable.
“PA promises to help organisations understand their workforce as a whole, as departments or work groups, and as individuals, by making data about employee attributes, behaviour and performance more accessible, interpretable and actionable (Pape, 2016).”
From People analytics—A scoping review of conceptual boundaries and value propositions (2018) · cites this paper as Pape, 2016
Section 4.1 (p. 9) describes analyses of employee performance and headcount, while section 4.2 (p. 10) says analyses can be broken down by location, role, department and manager. Appendix A (p. 13) includes decisions about individual employees. These passages support using HR analytics to understand the workforce at several levels, but they do not establish the claim that it makes employee data more accessible and interpretable. (LLM, conf=0.82)
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Cites
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250 · see citing works
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 · see citing works
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An evidence-based review of HR Analytics
Marler, J. H., & Boudreau, J. W. (2017) · The International Journal of Human Resource Management · doi:10.1080/09585192.2016.1244699 · Cited by 913 · see citing works
Management challenges in creating value from business analytics
Vidgen, R., Shaw, S., & Grant, D. B. (2017) · European Journal of Operational Research · doi:10.1016/j.ejor.2017.02.023 · Cited by 493 · see citing works
A bibliometric-based survey on AHP and TOPSIS techniques
Zyoud, S. H., & Fuchs-Hanusch, D. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.016 · Cited by 492 · see citing works
Business analytics and firm performance: the mediating role of business process performance
Aydiner, A. S., Tatoglu, E., Bayraktar, E., Zaim, S., & Delen, D. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2018.11.028 · Cited by 321 · see citing works
People analytics—A scoping review of conceptual boundaries and value propositions
Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.08.002 · Cited by 263 · see citing works
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- Authors
- Pape, T.
- Affiliations
- University College London (GB)
- Corresponding author
- Pape, Tom
- ORCID
- Pape, Tom
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 252
- Issue
- 2
- Pages
- 687-698
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W2264142365
- Cited by
- 106
- References
- 2
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 8 · 2023: 16 · 2022: 13 · 2021: 21 · 2020: 8 · 2019: 8 · 2018: 10 · 2017: 7 · 2016: 3
- FWCI
- 15.16
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- European Journal of Operational Research (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Università Bocconi
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Management Information Systems › Decision Support System Applications
- Keywords
- business analytics, business intelligence, human resources, data storage costs, process mapping, multi-criteria decision making
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