Article link: https://doi.org/10.1177/0001839219899606
1. It is always fascinating to see how classic behavioral models are continuously extended in new ways. Particularly, the intuition behind combining historical and social aspirations is very interesting. What prompted you to consider the historical performance of competing organizations and attribute the effect back to the focal firm? Did this idea emerge from collective research on relationships between organizations?
One key concept in the paper is “joint component usage,” a label we use for when two or more competing firms have the same supplier for one of their major components. Joint component usage sets up an interesting tension between collaboration and competition—collaboration between the focal firms and their supplier and competition among the focal firms themselves. At the time we started working on this paper, we had been thinking and writing about joint component usage for about a year, focusing on how it impacts firms’ learning curves. At about the same time, David—inspired by a review paper by Gavetti et al. (2012)—was examining the ways in which behavioral theory and network theory interrelate. To draw an analogy with comic book movies, the two theories seem to occupy the same Bounded Rationality Expanded Universe! However, relatively little work explicitly integrates the two. It was thinking about how performance feedback theory might work in the context of joint component usage that led us to consider how the historical performance of competing organizations plays into a focal firm’s decision-making.
2. How or when did you coin the term “vicarious performance feedback”? How much did the paper evolve through the review process – both empirically and theoretically?
From the earliest draft, vicarious performance feedback just seemed like a natural label for the mechanism we developed: an extension of performance feedback theory that incorporates vicarious learning from competing firms. The core of the paper—its Introduction, main hypothesis, and quantitative framework—stayed stable through the review process; much of the rest of the paper changed substantially thanks to the very constructive feedback we received from the associate editor and three reviewers. The review process helped us refine our theorizing, enhance our empirical methods, and overall put forward a broader evidence base for the theory. We feel like the paper is much stronger having been through the review process at ASQ!
3. The idea of investigating the impacts of shared suppliers opens up many opportunities. Do you feel that your theory of tie dissolution generalizes to environments of growth or does it depend, to some extent, on a zero-sum competitive environment? In F1, some driving teams will lose by design but in other environments (perhaps in the high-tech goods including mobile phones), many peer organizations can simultaneously grow. Would you expect similar findings in growing environments?
We agree that the joint component usage phenomenon is widespread in technological industries. For example, competing mobile phone and computer manufacturers share a small set of chip suppliers; video game developers choose between a small number of graphics engines, airlines choose from a small set of airplane suppliers. We expect vicarious performance feedback might play a part in decisions about tie dissolution in those industries too, regardless of whether competition is zero-sum or positive-sum. The beauty of performance feedback theory is that it applies broadly. One simply needs to recognize that the relevant performance dimension on which aspirations are based will vary with the context. In an empirical context characterized by growth, then either firm size or firm growth rate could be the most relevant dimension of managerial aspirations.
The key boundary conditions for the vicarious performance feedback theory are that the shared component has a significant impact on firm performance; there is some causal ambiguity about its contribution to performance; there is consensus around how performance is measured, and historical track records of performance, and of who supplies whom, are known among industry players. These boundary conditions are not too restrictive, and we anticipate that in future we might be able to test the theory in the context of video games (which Henning does research on) or multi-sided platforms (which David studies).
4. Applying a logic of consequences naturally plays an important role in aspiration models, but equally important to causal ambiguity is a logic of appropriateness. Did you consider the implications of social expectations as you developed your theory? To what extent do long-standing ties or reputable suppliers influence tie dissolution in your eyes?
One feature of the paper we hope our readers find valuable is the literature review of tie dissolution that precedes the theory development, encapsulated in Table 1. Both relational embeddedness (i.e. long-standing ties) and structural embeddedness (i.e. shared third parties) have been found in prior work to stabilize ties, partly as they may imply negative consequences to breaking a tie, but partly because they lead to interpersonal bonds between managers. In this paper, our theoretical contribution focuses on calculative decisions about tie dissolution, but we are careful to include controls for indicators of embeddedness such as tie longevity and personnel mobility. In our current and future work, we are looking more closely at how embeddedness and performance feedback interact—watch this space!
5. The use of qualitative data on the paper was very interesting. More and more junior scholars are using qualitative data in their (mostly) quantitative papers. How did you make that decision to add qualitative data to the project? Do you have suggestions for PhD students on mixing these two methods?
The qualitative data was very helpful to us in ensuring that we specify our measures correctly and allowed us to check whether our interpretation of our quantitative findings was valid. Gathering the data was a great experience: we had the chance to talk to senior executives in the industry and to visit multiple manufacturing sites. We would strongly encourage PhD students to collect qualitative data. Even if you do not end up using the qualitative data in your paper, gathering qualitative paper is a great way to understand the setting you are studying and to refine your theoretical idea. While it can be daunting to reach out to people; we were positively surprised by people’s interest in our research and their willingness to support us.
Gavetti, G., Greve, H. R., Levinthal, D. A., & Ocasio, W. (2012). The behavioral theory of the firm: Assessment and prospects. Academy of Management Annals, 6(1), 1-40.
Nur’s Bio: Nur Ahmed is a Ph.D. Candidate at Ivey Business School, Western University. He’s interested in innovation, technology strategy, and computational social science.
Andrew’s Bio: Andrew Sarta is a Ph.D. Candidate at Ivey Business School, Western University. He’s interested in organizational adaptation, behavioral strategy, and technological change.