Henning Piezunka

The Matthew Effect as an Unjust Competitive Advantage: Implications for Competition Near Status Boundaries:

Merton often envisioned status growth as a process of stepping across a boundary between one status grade and another more elite status grade. Such boundaries include the border between graduate school and a top academic department that young researchers try to traverse, or the frontier between scientists outside the French Academy and scientists inside the French Academy. As it is now common to measure status continuously using network data, the behavioral ramifications of status boundaries have been understudied in recent research. In this essay, we focus on competitive behaviors that emerge near a status boundary because of the desirability—as well as the “double injustice”—of the Matthew Effect. Offering insights for future research, we discuss how these competitive behaviors are likely to delay, or even derail, status growth for those who are near a status boundary.

Escalation of competition into conflict in competitive networks of Formula One drivers

Significance Competition, while often seen as beneficial, can escalate into destructive conflict. This occurs, for instance, when athletes sabotage each other or when rival executives get caught up in a career-derailing fight. These escalations into conflict are especially likely among status-similar competitors, who are fraught with discordant understandings of who is superior to whom. We examine the link between status similarity and conflict as well as the conditions under which this link holds. We find that status-similar Formula One drivers are more prone to collide, especially when they are age-similar, perform well, are embedded in a stable role structure, and feel safe. Our inquiry deepens our understanding of when violent conflict emerges and can guide conflict prevention efforts. This article investigates the factors that escalate competition into dangerous conflict. Recent sociological theorizing claims that such escalations are particularly likely in dyads of structurally equivalent people (i.e., actors who have the same relations with the same third parties). Using panel data on Formula One races from 1970 through 2014, we model the probability that two drivers collide on the racetrack (an observable trace of conflict) as a function of their structural equivalence in a dynamic network of competitive relationships. Our main hypothesis, that the likelihood of conflict rises with structural equivalence, receives empirical support. Our findings also show that the positive association between structural equivalence and conflict is neither merely a matter of contention for official position nor an artifact of inherently hostile parties spatially exposed to each other. Our analyses further reveal that this positive association is concentrated in a number of theoretically predictable conditions: among age-similar dyads, among stronger performers, in stable competitive networks, and in safe, rather than dangerous, weather conditions. Implications for future research on conflict, networks, and tournaments are discussed.

Deterring the New, Motivating the Established— The Divergent Effect of Platform Competition on Member Contributions in Digital Mapping Communities

While popular platforms developed by knowledge-producing communities such as Wikipedia and Linux co-exist and compete with alternatives such as Encyclopedia Britannica and Microsoft Windows, we understand little about how such competition affects those knowledge-producing communities. Our study examines how community members respond when communities are faced with competition. We examine how community members contributions to OpenStreetMap changed following the competitive entry of Google Maps. We exploit the phased entry of Google Maps in different countries over time. While we find an overall negative effect of competition on contributions, the effect is not uniform. Competition has a divergent effect on community members: it motivates established members to contribute but deters new potential members from joining the community.

Learning-By-Participating in Decision-Making: Broadening Participation, Narrowing Feedback

A central tenet of work in the Carnegie School tradition is the notion of “learning-by-doing”— organizations learn over time through feedback. In this paper we argue that the learning-by-doing account overlooks the fact that an organization’s decision-making structure is often participatory—i.e., organizational decisions often involve multiple individuals aggregating opinions through a process such as voting. In such contexts, individuals in the organization do not themselves learn-by-doing. Rather, when participating in the decision, they may vote for an alternative that is different from the one eventually selected by the organization. A key consequence of this is that these individual participants do not always receive feedback on their own choices; rather, they receive feedback on the choice made by the organization. We call this “learning-by-participating,” and we seek to understand the implications of this form of learning by comparing it to learning-by-doing, where an individual in the organization (such as the CEO) makes decisions on her own. Using a computational model of decision-making under uncertainty, we find that learning-by-participating leads to distinct patterns of individual learning that create trade-offs at the individual and organizational-levels. For example, while learning-by-participating is beneficial with respect to organization-level performance, it causes a minority of individuals within the organization to hold overly-optimistic views of low-payoff alternatives. We discuss the implications of our findings for research on learning and information aggregation.