The long time lag in industrial-organizational (I-O) psychology technology research observed by White etal. (Reference White, Ravid, Siderits and Behrend2022) is in stark contrast to how other scientific fields progress with their research, such as in medicine and the biosciences, which have produced life-changing research with an unexpected speed especially during the COVID-19 pandemic. There is a lot to be learned from approaches in these disciplines, including more systematic collaboration across institutions and research groups, sharing data and ideas. Beyond consideration of these practical strategies, however, Isuggest that it is imperative to review our research process in I-O psychology and specifically the interplay of method and theory, if we want to produce timelier technology research.
White etal. (Reference White, Ravid, Siderits and Behrend2022) point to a central tension in our research approach that contributes to slowing down technology research in I-O psychology:
Some scholars have noted that I-O psychology is distinct from other social science fields in its strong preference to develop theories before observing facts (Hambrick, Reference Hambrick2007). The domain of technology is unique in that it experiences change at an exceptionally fast rate. For this reason, some I-O psychologists have argued that building unifying, long-standing theories of technology is quite challenging (Landers & Behrend, Reference Landers and Behrend2017). (p. X)
There is no doubt that we need theory in our field to organize our knowledge base and develop coherent explanations and better predictions (e.g., Hambrick, Reference Hambrick2007; Suddaby, Reference Suddaby2014). However, scholars have recently criticized the overemphasis on developing/building new theory (Hambrick, Reference Hambrick2007: “too much of a good thing?”), which major journals in the field expect (Edwards, Reference Edwards2010). Papers typically employ a deductive or inductive approach and often present the research process in a linear fashion (e.g. Bamberger, Reference Bamberger2018; Van de Ven & Poole, Reference Van de Ven and Poole2005; Van Maanen etal., Reference Van Maanen, Sørensen and Mitchell2007). This can be challenging for technology research, however, as we might observe a phenomenon that we cannot fully explain with existing theory and for which we do not have new theories (yet), especially given the rate of change in technology. For example, remote/virtual work during the COVID-19 pandemic resulted in many employees reporting so-called Zoom fatigue (e.g., Bennet etal., Reference Bennett, Campion, Keeler and Keener2021; Nesher Shoshan & Wehrt, Reference Nesher Shoshan and Wehrt2021; Shockley etal., Reference Shockley, Gabriel, Robertson, Rosen, Chawla, Ganster and Ezerins2021). Even though research on virtual meetings existed before, this phenomenon appeared to be new, as the frequency and intensity of these meetings increased against the background of a crisis situation.
As an alternative, or complementary approach, to deduction and induction, abduction can help drive the field forward in technology research. Abduction “begins with an unmet expectation and works backward to invent a plausible world or a theory that would make the surprise meaningful” (van Maanen etal. Reference Van Maanen, Sørensen and Mitchell2007, p. 1140). This unmet expectation is also framed in terms of an anomaly, “a novel or unexpected phenomenon that cannot be explained or is poorly understood using existing knowledge” (Sætre & Van de Ven, Reference Sætre and Van de Ven2021, p. 684). Abductive reasoning involves paying attention to “hunches”—sudden insights into a problem or phenomenon—a not yet well-formulated thought process that leads to developing explanations and further investigation (Dane, Reference Dane2020; Sætre & Van de Ven, Reference Sætre and Van de Ven2021; Van de Ven, Reference Van de Ven2007). Abductive reasoning is therefore a generative and iterative process of creating explanations, which are evaluated, to help understand the novel phenomenon (Sætre & Van de Ven, Reference Sætre and Van de Ven2021). Abductive reasoning is often applied in medical research (without making this explicit) and has received growing attention in management and organizational behavior research (e.g., Bamberger, Reference Bamberger2018). An abductive approach can facilitate systematic exploration of how a new technology-related phenomenon, issue, or toolkit affects employees’ attitudes, well-being, and performance.
Sætre and Van de Ven (Reference Sætre and Van de Ven2021) propose a model of abductive reasoning that involves several stages and an iterative process, starting with the observation of an anomaly (for example, Zoom fatigue: Why do we feel more fatigued after virtual meetings compared with in-person meetings?). As a next step, the anomaly is confirmed: By collecting new information, we diagnose its characteristics and the context (for example, do we consistently observe Zoom fatigue in employees who attend video conference calls? When does it occur? Do employees feel fatigued to the same extent when the camera is turned on or off or when they have many calls?). The next stage involves generating hunches (e.g., Do physical aspects such as spatial awareness account for it and/or the lack of human connection?), and in a final stage we evaluate our hunches (e.g., to what extent do employees feel a lack of connection with coworkers and experience Zoom fatigue?) to then select the best plausible explanations.
The process of abductive reasoning lends itself to conducting research on technology by focusing on action, exploration, collaboration, dissemination, and creation as suggested by White etal. (Reference White, Ravid, Siderits and Behrend2022). For example, action emphasizes studying new technologies “as they are created and to imagine possible workplace applications, not wait until they are widely adopted” (White etal., Reference White, Ravid, Siderits and Behrend2022, p. X). This implies being open to studying novel phenomena that, by definition, are not well understood and require exploration. Going through cyclical stages of abduction leads to theory creation, and Sætre and Van De Ven (Reference Sætre and Van de Ven2021) suggest that this process should (and in practice often does) involve collective efforts, which addresses the necessity of having more collaboration and partnerships in technology research in I-O psychology (White etal., Reference White, Ravid, Siderits and Behrend2022). Collectively, dissemination can take place much faster and more iteratively, at different stages of the abductive process—for example, to encourage input from other disciplines and practitioners to collect information on a new technology-driven phenomenon, functionality, or toolkit and receive feedback on hunches in the idea generation phase. Abductive reasoning can be applied at different stages of Gartner’s hype cycle that White etal. (Reference White, Ravid, Siderits and Behrend2022) use as a framework for describing the life span of technologies—for example, when the development of a workplace tool is in early stages (innovation trigger stage: White etal., Reference White, Ravid, Siderits and Behrend2022).
Abduction can help us develop a scientific evidence base in I-O psychology technology research through faster iterations and create new theory, without feeling stifled by it. For further reading and examples of abductive reasoning, please see the references listed in Table 1.
Table 1. Additional Reading
Open access
