Revisiting HR analytics as a phenomenon-driven research field

EIASM Workshop of People Analytics and Algorithmic Management - PAAM

By Jiarui Yin, Eva Gallardo-Gallardo, Vicenc Fernandez in type:conference theme:business_analytics

June 30, 2022

Abstract

An academic presentation about the need to carry out a new approach to understanding HR analytics.

Date

June 30 – July 1, 2021

Time

12:00 AM

Location

Dublin (Ireland)

Event

1st EIASM Workshop of People Analytics and Algorithmic Management - PAAM

There is little doubt that HR Analytics (HRA) is a hot topic and is evolving quickly (Peeters et al., 2020). In fact, at the time of this writing, a search of the term HR Analytics returns 992,000,000 and 142,000 hits on Google and Google Scholar, respectively, and over 2,000 books on Amazon.com. Likewise, there is an intense debate on the HRA challenges organizations are confronted with on social networking sites. For instance, LinkedIn has over 400 professional groups discussing the ins and outs of HRA. Why is HRA such a hot topic? It is considered critical for HR functions to be strategic in organizations and drive business performance (Marler & Boudreau, 2017; McIver et al., 2018). However, HRA practice is lagging behind in organizations compared with other analytics-driven functions (Angrave et al., 2016). A dilemma exists in the gap between the understanding of HR and the disposal of analytical abilities, where researchers in the area should play an important role in understanding both sides and bridging the gap (King, 2016). However, based on current academic findings on HRA research, the results seem far from satisfactory.

Despite the academic literature on HRA has noticeably expanded during the last decade (Fernandez & Gallardo-Gallardo, 2020), research on HRA has been accused of being far from mature (e.g., Chalutz Ben-Gal, 2019; Marler & Boudreau, 2017). According to Marler & Boudreau (2017), HRA research is still in its infancy, and many issues in the gap between academic research and practical applications require further investigation and clarification. In sync, Qamar and Samad (2021) argue that current HRA research has explored a limited number of aspects of HR practices, and that variables, risks, and challenges in using and adapting HRA remain unclear. Maybe this could be explained by the fact that the majority of the contributors of publications on the topic are practitioners rather than academics (Tursunbayeva et al., 2018). In fact, McIver et al. (2018, p. 402) pointed out that “much of the momentum driving workforce analytics adoption in organizations today focuses solely on a data-driven (inductive) approach without considering a theory-driven approach”. Also, HRA has been considered a management fad (Rasmussen & Ulrich, 2015), and its existing practices have been viewed as unlikely to deliver transformational change (Angrave et al., 2016). HRA can be seen as disruptive rather than incremental, and no existing theories can fully encompass all its facets. However, to the best of our knowledge, these statements have not been objectively verified, nor has HRA’s current state of development been assessed comprehensively. The increasing interest and use of analytics to drive strategy and algorithms that can augment and/or automate HR-related decision-making has been defined as a phenomenon in the overview of this PAAM workshop. In research, a phenomenon is defined as “a perceived fact, change, or event that can be scrutinized or studied, and especially something that is unexpected or in question” (Schwarz & Stensaker, 2016, p. 2). According to Schwarz & Stensaker (2014), a phenomenon is characterized by practice before the existence of relevance theory or literature, and novel emergence in organizations. A phenomenon serves as potential evidence for theories (Apel, 2011) and can be considered as a starting point of research (Moustakas, 1994). Taking the above into account, along with the fact that HRA interest emerged as promoted by consulting firms (such as BCG and KPMG), it would appear that research on HRA can be categorized as phenomenon-driven. Schwarz & Stensake (2016, p. 1) define phenomenondriven research (PDR) as “problem-oriented research that focuses on capturing, documenting, and conceptualizing an observed phenomenon of interest in order to facilitate knowledge creation and advancement”. In short, PDR formulates new knowledge based on phenomena observed in organizations.

Since, as mentioned before, academic research has failed to comprehensively examine and understand the phenomenon of HRA, a phenomenon-driven approach may provide a solution to facilitate such understanding. Thus, the current review takes a phenomenon-driven approach to reviewing the HRA literature, applying methods from bibliometrics (such as science mapping) and content analysis to come to an objective and quantifiable assessment of the development stage of the HRA research field at the present time without making a-priori assumptions. To do so, we ground on von Krogh et al.’s (2012) phases of the scientific study of a phenomenon (i.e., embryonic, growth, and mature), and we will complement this framework with detailed measurable metrics that can help with the assessment of each stage in a quantifiable way. To do so, we will base on the works of Keathley-Herring et al. (2016) and Edmonson & McManus (2007) that provide specific frameworks to evaluate the maturity of a research field or the methodological fit in a research field. Such proposed metrics will be used to assess the current development of HRA research and envision its future path. A comprehensive and objective assessment of the stage of development of HRA research is timely and needed. It will offer evidence and provide new avenues for future research to advance the knowledge and theory of the phenomenon, which can drive the maturity of HRA research. Keywords:

HR Analytics, Phenomenon-driven Research, Research Maturity, Literature Review

Reference List:

  • Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1–11. https://doi.org/10.1111/1748-8583.12090
  • Apel, J. (2011). On the meaning and the epistemological relevance of the notion of a scientific phenomenon. Synthese, 182(1), 23–38. https://doi.org/10.1007/s11229-0099620-y
  • Chalutz Ben-Gal, H. (2019). An ROI-based review of HR analytics: Practical implementation tools. Personnel Review, 48(6), 1429–1448. https://doi.org/10.1108/PR-11-2017-0362 Edmondson, * A. C., & Mcmanus, S. E. (2007). Methodological fit in management field research. Academy of Management Review, 32(4), 1246–1264. https://doi.org/10.5465/amr.2007.26586086 Fernandez, V., &
  • Gallardo-Gallardo, E. (2020). Tackling the HR digitalization challenge: Key factors and barriers to HR analytics adoption. Competitiveness Review: An International Business Journal, 31(1), 162–187. https://doi.org/10.1108/CR-12-20190163
  • Huselid, M. A. (2018). The science and practice of workforce analytics: Introduction to the HRM special issue. Human Resource Management, 57(3), 679–684. https://doi.org/10.1002/hrm.21916
  • Keathley-Herring, H., Van Aken, E., Gonzalez-Aleu, F., Deschamps, F., Letens, G., & Orlandini, P. C. (2016). Assessing the maturity of a research area: Bibliometric review and proposed framework. Scientometrics, 109(2), 927–951. https://doi.org/10.1007/s11192-016-2096-x
  • King, K. G. (2016). Data Analytics in Human Resources: A Case Study and Critical Review. Human Resource Development Review, 15(4), 487–495. https://doi.org/10.1177/1534484316675818
  • Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699
  • McIver, D., Lengnick-Hall, M. L., & Lengnick-Hall, C. A. (2018). A strategic approach to workforce analytics: Integrating science and agility. Business Horizons, 61(3), 397407. https://doi.org/10.1016/j.bushor.2018.01.005
  • Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. El Profesional de La Información, 29(1). https://doi.org/10.3145/epi.2020.ene.03
  • Moustakas, C. E. (1994). Phenomenological research methods (Nachdr.). Sage.
  • Peeters, T., Paauwe, J., & Van De Voorde, K. (2020). People analytics effectiveness: Developing a framework. Journal of Organizational Effectiveness: People and Performance, 7(2), 203–219. https://doi.org/10.1108/JOEPP-04-2020-0071
  • Qamar, Y., & Samad, T. A. (2021). Human resource analytics: A review and bibliometric analysis. Personnel Review, ahead-of-print(ahead-of-print). https://doi.org/10.1108/PR-04-2020-0247
  • Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236–242. https://doi.org/10.1016/j.orgdyn.2015.05.008
  • Schwarz, G. M., & Stensaker, I. G. (2016). Showcasing phenomenon-driven research on organizational change. Journal of Change Management, 16(4), 245–264. https://doi.org/10.1080/14697017.2016.1230931
  • Schwarz, G., & Stensaker, I. (2014). Time to Take Off the Theoretical Straightjacket and (Re-)Introduce Phenomenon-Driven Research. The Journal of Applied Behavioral Science, 50(4), 478–501. https://doi.org/10.1177/0021886314549919
  • Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics—A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43, 224–247. https://doi.org/10.1016/j.ijinfomgt.2018.08.002
  • Van de Ven, A. H. (2016). Grounding the research phenomenon. Journal of Change Management, 16(4), 265–270. https://doi.org/10.1080/14697017.2016.1230336
  • von Krogh, G., Rossi-Lamastra, C., & Haefliger, S. (2012). Phenomenon-based Research in Management and Organisation Science: When is it Rigorous and Does it Matter? Long Range Planning, 45(4), 277–298. https://doi.org/10.1016/j.lrp.2012.05.001
Posted on:
June 30, 2022
Length:
7 minute read, 1334 words
Categories:
type:conference theme:business_analytics
See Also: