Borders and Boundaries in Markets: A Sociocognitive Approach for Market Definition and Implications for Antitrust

Categorical distinctions are foundational to firm competition and regulation. Yet, market categories are notoriously difficult to define. The question of how to delineate markets is well-worn in the antitrust literature but is now the focus of a growing sociocognitive literature in strategy and organizational sociology.1 Historically, there has been little cross-pollination between these research areas. More integration, however, may be increasingly important in modern markets, where change is rapid, new technologies are key differentiators in many traditional industries, and platform competition is on the rise. In this paper, I introduce recent theoretical and empirical advances in sociocognitive research on categories in markets. I describe a theoretical model that incorporates the probabilistic nature of how people categorize, ambiguity in category boundaries, and that multiple audiences are relevant in most markets. Empirically, researchers employ a range of approaches to represent these aspects of market definition, from qualitative studies, to surveys, to computational approaches that leverage recent advances in machine learning applied to large corpora of text. I discuss key implications from this theoretical model and how they might inform market definition in antitrust

Borders and Boundaries in Markets: A Sociocognitive Approach for Market Definition and Implications for Antitrust

Categorical distinctions are foundational to firm competition and regulation. Yet, market categories are notoriously difficult to define. The question of how to delineate markets is well-worn in the antitrust literature but is now the focus of a growing sociocognitive literature in strategy and organizational sociology.1 Historically, there has been little cross-pollination between these research areas. More integration, however, may be increasingly important in modern markets, where change is rapid, new technologies are key differentiators in many traditional industries, and platform competition is on the rise. In this paper, I introduce recent theoretical and empirical advances in sociocognitive research on categories in markets. I describe a theoretical model that incorporates the probabilistic nature of how people categorize, ambiguity in category boundaries, and that multiple audiences are relevant in most markets. Empirically, researchers employ a range of approaches to represent these aspects of market definition, from qualitative studies, to surveys, to computational approaches that leverage recent advances in machine learning applied to large corpora of text. I discuss key implications from this theoretical model and how they might inform market definition in antitrust

The Persistence of Lenient Market Labels

Booth for their support. 1 Research across disciplines presumes that markets will have strong boundaries. Markets without well-defined boundaries typically are not useful and do not become institutionalized, so are expected to fade away. In contrast, we suggest that in many contexts lenient markets, or market labels with porous boundaries, persist and become important. This can be explained by looking at both market entry and market exit. Consistent with prior research, we suggest that lenient market labels offer less credibility than labels with strong boundaries, and so organizations will be more likely to exit these markets. At the same time, lenient market labels are more accepting of many different types of organizations. As a result, we expect lenient labels to also have high rates of entry. When entry rates are higher than exit rates, lenient markets will endure over time. We also predict that organizations exiting lenient labels will enter other lenient labels, which further fuels their persistence. Finally, this trend is exaggerated when influential external agents favor leniency. We find support for these ideas in a longitudinal analysis of organizational entry into and exit from market labels in the software industry