Published Work

Ronald S. Burt, Ray E. Reagans, and Hagay C. Volvovsky (2020). Network Brokerage and the Perception of Leadership. Social Networks, 65, 33-50. https://doi.org/10.1016/j.socnet.2020.09.002

We renovate a classic experiment to examine the relationship between randomly assigned network positions and leadership. We find that people are perceived to be leaders when they behave as network brokers, which is to say, when they coordinate information across structural holes. We also find that leadership is ambiguous when multiple people are positioned to be brokers unless one person emerges by his or her network behavior as a monopoly broker. Our summary conclusion is that access to structural holes can be causal to the perception of leadership, a characteristic implicit in many success measures used to document the broker-success association.

Lisa Bernstein and Hagay Volvovsky (2015). Not What You Wanted to Know: The Real Deal and the Paper Deal in Consumer Contracts – Comment on the work of Florencia Marotta-Wurgler. Jerusalem Review of Legal Studies, 12(1), 128-136. https://doi.org/10.1093/jrls/jlv002

We explore the difference between the rights and obligations specified in the legally binding contracts formalizing consumer transactions and how sellers behave in practice. We argue that institutional factors such as the cost and availability of litigation and the spread of reputational information about past performance shape sellers' actions in conjunction with the formal contract. Moreover, the effects of formal contracts, reputation and the availability of litigation on the rights consumers enjoy are interdependent with each other. As a result, researchers and policy makers seeking to protect consumers cannot confine their analysis to the language of legally binding contracts.  

What I'm Working on Right Now

Hagay C. Volvovsky, Revising Economic Action and Social Structure: The Role of Embeddedness in the Age of Amazon

Network scholars posit that networks provide better reputational enforcement than market reputation (i.e. the reputation that would emerge from a sample of market participants). In this view, reputational signals are local to social network neighborhoods. Because it is costly to communicate with strangers, and there is little reason to trust them, reputational signals are stronger in the presence of shared third parties and decay with network distance. Consequentially, reputational sanctions and norm enforcement are also local to the network. Yet, the rise of reputational aggregation platforms (RAP) such as Amazon and eBay call these mechanisms into question. This paper identifies an additional mechanism driving network-based reputational enforcement, one that places a boundary condition on both network and market based reputational enforcement. It argues that the informativeness of a reputational signal from A to B about C increases in relation to B’s belief that A used the same norms B would in making her evaluation of C. The closer A’s norms are to B’s, the more informative the signal. Although some exchanges rely on widely shared norms, many exchanges rely on locally shared understandings. Norms can and do differ across actors, thereby diluting the informativeness of reputational signals. But there are good reasons to believe that norms - and thus reputations and reputational enforcement - are local to social network neighborhoods. The more local the norms, the more local the reputation, which means platforms aggregating market reputation, such as Amazon and eBay won’t be as effective. Those function a lot better when norms are universal.

Ray E. Reagans, Hagay C. Volvovsky and Ronald S. Burt, When Will they (ever) Learn? Identifying the Network Effect on Learning

If the objective is to improve a team’s capacity for learning, how should team members be connected to each other? Existing research provides a variety of answers to this question, with one group of scholars emphasizing the importance of closed networks; another more open networks; and a third group focusing on the importance of combining open and closed network features. Despite the intuitive appeal of each network solution, we believe each one should be met with skepticism. There is little direct evidence linking social networks to learning. Our research objective was to identify the causal network effect on learning. We analyzed learning rates across 45 teams that varied in terms of how team members were allowed to communicate with each other. The communication structures captured the network solutions described in existing research. All teams exhibited evidence for learning but teams in open networks learned faster than teams in closed networks. The best teams, however, combined elements of open and closed network structures. We discuss the implications of our results for research on networks and learning.

Hagay C. Volvovsky and Lisa Bernstein, People as a Contract Provision: How Organizations Manipulate Social Networks to Bond Strategic Alliances

We explore how organizations facilitate trust, cooperation and innovation in strategic alliances via the strategic exchange of personnel. Employees transferring from one partner organization to another become brokers bridging the structural hole between the two. Their unique position allows them to successfully communicate with and understand members of both organizations, thus facilitating knowledge transfer and reducing the possibility that cultural differences will lead to a breakdown in the relationship. Furthermore, transferring employees' personal relationships in organization they depart bond their actions as agents of the organization they join. Employees leaving organization A to join organization B are far less likely to act opportunistically towards their former colleagues in A. We support these propositions using a comprehensive dataset of strategic alliances in the biotechnology industry, from its inception in the 1980's to 2010. 

Hagay C. Volvovsky and Daniel Fehder, Trust in Entrepreneurial Clusters

We ​explore whether differences in norms of trust and trustworthiness can explain variance in the success and failure of entrepreneurial clusters. To this end, we match two pairs of entrepreneurial clusters with similar predicted level of success based on observable characteristics, but different actual outcomes. We then experimentally estimate and compare the levels of trust and trustworthiness in each pair of clusters as well as in Silicon Valley.