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• W4384823916 abstract "Free AccessBusiness and Science – Not So Different After All? A Commentary on Ontrup et al. (2023)Markus LangerMarkus LangerProf. Dr. Markus Langer, Fachbereich Psychologie, Digitalisierung in psychologischen Handlungsfeldern, Philipps-Universität Marburg, Gutenbergstraße 18, 35032 Marburg, Germany, [email protected]Fachbereich Psychologie, Digitalisierung in psychologischen Handlungsfeldern, Philipps-Universität Marburg, GermanySearch for more papers by this authorPublished Online:July 20, 2023https://doi.org/10.1026/0932-4089/a000419PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit SectionsMoreIntroductionIn their essay, Ontrup et al. (2023) consider that the field of human resource analytics (HR-A) operates under a tension between satisfying business needs and applying sufficient scientific rigor. I understood this essay as trying to provide arguments for bringing additional scientific rigor into HR-A practice and thereby moving the worlds of practice and research closer together. For instance, the authors highlight that HR-A practice could benefit from a better theoretical foundation when defining research questions or when interpreting results of analyses. However, Ontrup et al. also emphasize that there may be some unresolvable distinctions. For instance, the authors see these distinctions in the focus of research on novelty or on increments on theory when identifying a problem worth examining and they argue that this “academic mindset” could sometimes hinder business needs.As an initial side note, while reading the essay I wondered whether it is possible to replace the term “HR-A” with various other areas of HR management and the arguments would remain fairly similar. In areas such as personnel selection or development, there are aspects where business and research appear rather distinct and others where they seem rather similar; there are parts of daily business where there could be more scientific rigor and others where business needs and scientific standards seem to contradict each other. Additionally, some of the implications derived by the authors are similarly loud in other areas of research, for instance, the call for more collaboration between practice and research (something that has also been implicitly or explicitly been called for by the author of this commentary in the area of artificial intelligence for management purposes; Langer & König, 2022; Langer & Landers, 2021)What I wish to focus on in this commentary is that even in the points in which the authors describe seemingly unresolvable distinctions, business and science may not be so different after all. Even in these points, practice could learn from research but, importantly, the academic world could also sometimes benefit from a business mindset. I first describe this assessment along the lines of the simplified process of HR-A and research projects that the authors use. Taking on the authors’ quest of deriving implications for the competencies for successful HR-A and for successful science-practice collaborations, I then outline the implications of assuming that business and science may not be so different after all.On the Comparison Between HR-A and Research ProjectsThe authors use a simplified project process to describe distinctions and similarities in HR-A and research projects along five project phases: Identification of a problem, derivation of research questions, data collection and analysis, results, as well as implantation and evaluation.Identification of the ProblemThe authors describe that research is all about increments on theory and about novelty, whereas HR-A is phenomenon-driven and does not need to be about novel questions. Acknowledging that the authors describe a simplified process, I would argue that not all research needs to focus on novelty and that not all research aims (or should aim) at increments on theory. Arguably, both of these issues have been successful strategies to publish in “high-impact” journals, and combining “novelty” and “theoretical contributions” may still reflect an effective strategy to increase the likelihood of getting published (Highhouse et al., 2020; Romero, 2017). However, the eagerness to identify novelty has been identified as a possible factor that has contributed to the replicability crisis across scientific disciplines (Nosek et al., 2012). Furthermore, the overemphasis on theoretical contributions by some of the most prestigious journals in the fields of industrial and organizational psychology as well as management has faced criticism in recent years (Highhouse et al., 2020). Specifically, it may have alienated research from business needs with practitioners lamenting that what is published in the most important outlets of management research mostly has no relevance for them (Highhouse et al., 2020; Ones et al., 2017). Perhaps this is what the authors mean when stating that practice and research are distinct in the “identifying problems phase” – that business and research just have very different ideas of what problems are important.Yet, other important problems in research are to examine whether what we know reflects robust knowledge and whether interventions for which we think they are effective also work under various conditions. Those aspects seem to be relevant to both practice and science, making the identifying problems phase more similar than described by Ontrup et al. (2023). Moreover, research can perhaps even learn from practice when identifying relevant problems. Potentially, research could become a bit more phenomenon-driven without losing sight of theoretical contributions or generalizability, and could study phenomena that actually address business needs, all of which would be developments that could bring practice and research closer together.Derivation of Research QuestionsThere are possible similarities in this phase as it can make much sense to build on theory (or at least on existing research) to derive research questions and to come up with relevant variables to pay attention to. Yet, Ontrup et al. again seem to mostly refer to theory-based research approaches. Exploratory research is also valuable, for instance, to generate hypotheses. Sometimes it may add value to play around with existing data, identify correlations, and then try to more closely examine them in follow-up projects. This seems to be true for practice and for research.Data Collection and AnalysisThe authors argue that this phase is similar in research and practice because in both worlds it is advisable to invest time to make this phase as rigorous as possible. I support the call for a more psychometric view of data analytics. Examining whether data are objective, reliable, and valid is crucial in research and in HR-A practice. Especially if HR-A tries to infer things about people from data, it seems necessary to ask whether the data that are associated with those people really allow for conclusions that may affect their fate at work. For instance, a psychometric perspective could let HR-A examine whether it even makes sense to use a combination of variables such as distance to the working place, family duties, pay raise, and supervisor ratings to predict turnover for single individuals.Interpretation of ResultsThe authors claim that one of the main differences between research and practice is that generalizability of findings does not matter in practice. This may be true for generalizability beyond the boundaries of organizations but there is also possible generalizability within organizations. For instance, findings from one subsidiary of an organization may translate to others, and HR-A teams could explicitly focus on finding such generalizable implications. One part of generalizability is that it prevents us from reinventing the wheel in every project. In an extreme case of having no focus on generalizability, this could mean that an HR-A team has to conduct the same set of analyses over and over again (e. g., for every subsidiary) – this does not seem to be very business-oriented.Another point the authors make for the interpretations of the results phase is that careless data mining and overinterpreting correlations can undermine finding good solutions for practice and can overlook confounding effects – issues that could be prevented with more scientific rigor. Again, the authors highlight the value of theory, specifically, tying results to theory. However, also without any theory at your workbench, an academic mindset can be beneficial in this phase. This can mean to consider alternative explanations, easier explanations, to critically challenge interpretations for alternative ones. Arguably, being informed about theory can help to come up with such alternative interpretations, but a critical mindset alone can already advance this phase.Implementation and EvaluationThe authors state that this phase is crucial for practice but less relevant for research. In research it is most likely state of the art to come up with findings that are relevant for theory but that may never be tested for their impact in the real world. However, this state of the art also seems to be a reason why some practitioners may find research not useful for their daily business (Highhouse et al., 2020). Thus, this may also point to factors where there is potential for both worlds to move closer together. It may not be the case that research sees no value in testing their concepts in the real world but that there is simply no opportunity to do so because of a lack of access to resources that would allow for such research. Nevertheless, research could be inspired by HR-A practice, or HR-A practice could reach out to research more often to try to also implement research findings and evaluate them in practice. Research that explicitly does this may be highly valued by editors and reviewers as well as practitioners (see, e. g., Luo et al., 2021, for an example of an HR-A-related study that benefits from also implementing and evaluating findings in the real world).Implications of Being Not So Different After AllOverall, it seemed to me that Ontrup et al. (2023) predominantly describe a perspective that shows where an academic mindset could help or hinder business needs, where this respective academic mind operates mostly on a basis of strategies for getting published in prestigious journals (i. e., focusing on novelty and theory). I argue that with a broader understanding of the academic mindset, research and practice may not be so different after all and that learnings from science and practice could flow in both directions. For example, practice could learn not only from the theory-focused but also from the hypothesis-generating exploratory part of the academic mind (the part that may currently be less successful in publishing in prestigious outlets but that may still spark joy). At the same time, research could be more phenomenon-driven and could try to see practice as a challenging field in which to implement and validate its insights. All of this also implies that there is much potential for both worlds to move closer together.After the comparison of the different project phases, Ontrup et al. derive implications for competencies for successful HR-A and for science–practice collaborations. If we assume that business and research may not be so different after all, that learnings flow both ways, and that the areas have potential to move closer together, this may have implications with respect to the proposed competencies for successful HR-A and with respect to science–practice collaboration.Two of the proposed implications by the authors for competencies regarding successful HR-A are more rigorous statistical training for people in HR-A roles and storytelling. Rigorous statistical training can definitely be beneficial for HR-A. More generally (and perhaps already implied by the authors), this may also involve rigorous methods training, meaning that people in HR-A roles learn not only about statistical methodology but also about things such as psychometrics or about the large variety of possible research designs. Such training for people in HR-A roles would again contribute toward moving business and research closer together.With respect to storytelling competencies, I have some concerns over the implications of focusing on this competency in training HR-A people. Yes, it makes sense to learn how to adequately communicate findings to other stakeholders. And yes, this may be a competency that research has to work on, also to be better able to communicate their findings to non-researchers. However, focusing too strongly on storytelling may prioritize the “impression management” part of solving business problems that Ontrup et al. mention early in their paper. For instance, being overly focused on telling a story could involve trying to find such a story in all project phases. In the identification of the problem, one could prioritize novelty over business needs; in formulating the research question, one may optimize research questions to sound appealing instead of being testable; in the interpretation of the results, one might be biased toward finding an interesting story instead of adequately interpreting the results. Overall, overemphasizing storytelling and selling the results to other stakeholders could be dangerous if it comes with a focus on making an impression in exchange for rigor (notably, some of the issues that may come with focusing too strongly on storytelling seem to be factors that may have contributed to replicability issues – so perhaps research and practice were, again, not so different after all).With respect to science–practice collaborations, Ontrup et al. call for fostering such collaborations in future. This call is and has been loud in different areas of research (Burke et al., 2004; Langer & Landers, 2021). However, there still seem to be not many successful collaborations or else this call may have become quieter over time. Some of the points described by the authors may cause or may have been caused by the lack of such collaborations. For instance, it may not be the best basis for a collaboration that research wants to find generalizable increments on theory to publish in one of the high-impact journals whereas HR-A practice wants to solve a specific problem in one of their subsidiaries to keep business going. An underlying issue seems to be that there are different goals in research and practice. As the authors conclude: “Reaching a shared goal for HR-A and a research project can be challenging, due to significant differences in problem definition, implementation, and evaluation of the project.”However, as I have outlined in this commentary, there may be more similarities than been described by Ontrup et al. Moreover, even if the concrete goals of business and research differ (e. g., business wants solutions for practical problems, research wants to generate knowledge), certain underlying incentive structures may be comparable. Both research and practice are incentivized by some form of output. To put it simply: HR-A may want to produce actionable recommendations in line with business needs, and research wants to communicate their insights via publications. In line with what the authors state, such incentive structures may lead to a focus on quick outputs instead of rigor in practice – but they have also led to not-very-rigorous, quick-output focused research (Nosek et al., 2012). Thus, even for underlying incentive structures it may be possible to conclude that research and business are not so different after all. Arguably, some consequences of such incentive structures in the past have not been positive for business or for research. Therefore, the conclusion should not be to realize that both worlds are similar because they occasionally tend to overemphasize outputs rather than rigorous processes. The conclusion should instead be to realize that both business and research need to have some outputs out of science–practice collaborations to benefit from those collaborations and that both research and practice have methods and approaches for successful processes that may be beneficial for each other. If business and research communicate early on what they can get out of collaborations, it may be possible to find aspects that are valuable for both, and it may be more likely to find a common ground for collaborations. In such collaborations, the learnings about beneficial processes could flow from research into practice (e. g., rigorous analytical approaches, theory-informed reasoning) and from practice into research (e. g., methods to identify real-world problems, ways to evaluate interventions in practice). This could move the two worlds closer together, benefit both worlds, and contribute to “balancing scientific rigor and satisfying business needs” in HR-A and beyond.LiteraturBurke, M. J., Drasgow, F., & Edwards, J. E. (2004). Closing science-practice knowledge gaps: Contributions of psychological research to human resource management. Human Resource Management, 43 (4), 299 – 304. https://doi.org/10.1002/hrm.20025 First citation in articleCrossref, Google ScholarHighhouse, S., Zickar, M. J., & Melick, S. R. (2020). Prestige and relevance of the scholarly journals: Impressions of SIOP members. Industrial and Organizational Psychology, 13 (3), 273 – 290. https://doi.org/10.1017/iop.2020.2 First citation in articleCrossref, Google ScholarLanger, M., & König, C. J. (2022). Applied Explainable Artificial Intelligence (XAI) in human resource management: How to address issues of AI opacity and understandability in HRM. In S. Strohmeier (Ed.), Handbook of research on human resource management and artificial intelligence (pp. 285 – 302). Edward Elgar Publishing. First citation in articleGoogle ScholarLanger, M., & Landers, R. N. (2021). The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers. Computers in Human Behavior, 123, 106878. https://doi.org/10.1016/j.chb.2021.106878 First citation in articleGoogle ScholarLuo, X., Qin, M. S., Fang, Z., & Qu, Z. (2021). Artificial intelligence coaches for sales agents: Caveats and solutions. Journal of Marketing, 85 (2), 14 – 32. https://doi.org/10.1177/0022242920956676 First citation in articleCrossref, Google ScholarNosek, B. A., Spies, J. R., & Motyl, M. (2012). Scientific utopia: II. Restructuring incentives and practices to promote truth over publishability. Perspectives on Psychological Science, 7 (6), 615 – 631. https://doi.org/10.1177/1745691612459058 First citation in articleCrossref, Google ScholarOnes, D., Kaiser, R. B., Chamorro-Premuzic, T., & Svensson, C. (2017). Has industrial-organizational psychology lost its way? The Industrial-Organizational Psychologist, 54 (4), 67 – 74. First citation in articleGoogle ScholarOntrup, G., Moeschke, J., Buechsenschuss, R., & Biemann, T. (2023). When to think like a scientist: Balancing scientific rigor and satisfying business needs in HR analytics. Zeitschrift für Arbeits- und Organisationspsychologie. Adance online publication https://doi.org/10.1026/0932-4089/a000418 First citation in articleGoogle ScholarRomero, F. (2017). Novelty versus replicability: Virtues and vices in the reward system of science. Philosophy of Science, 84 (5), 1031 – 1043. https://doi.org/10.1086/694005 First citation in articleCrossref, Google ScholarFiguresReferencesRelatedDetails Volume 0Issue 0ISSN: 0932-4089eISSN: 2190-6270 InformationZeitschrift für Arbeits- und Organisationspsychologie A&O (2023), 0,https://doi.org/10.1026/0932-4089/a000419.© 2023Hogrefe VerlagPDF download" @default.
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