I am an Assistant Professor of Organisational Behaviour at INSEAD. I'm an organizational theorist studying categorization, strategic decision-making, and social networks. I do that through formal models and computational techniques. I like coding up new ways to solve problems.
At INSEAD, I teach Organisational Behaviour 2, a core class focused on organization-scale social dynamics, including organizational culture, social networks and networking, organizational design, and change.
To help teach students about networks, I designed an easy-to-use class network survey tool and presentation dashboard that illustrates concepts of network position, centrality, and clustering with a live view of the students' own network. You should take a look: code and dashboard.
My work has focused on theoretical questions in market categorization, innovation, and social networks. Literature in organizational theory has a tendency to fragment into fiefdoms that struggle to communicate not just at the level of findings, but at the level of basic assumptions about the settings they describe. My work has aimed to bridge these gaps: between work on market categorizations and competitive strategy, between inter-organizational coordination problems and the behavioral theory of the firm, between research on personal status and work on intertemporal decision-making. I have focused on identifying theoretical problems and gaps existing within these streams of work and on providing formal approaches that help interpret, extend, and unify existing findings. Where possible, I have attempted to apply these theoretical extensions to empirical settings, especially in my work on social networks.
The Natural Emergence of Category Effects on Rugged Landscapes | pdf
forthcoming, Organization Science
Category theory finds that markets partition producers into categories and that producers who do not fit one specific category—or who span multiple categories—perform worse than their single-category peers. The dominant thread of category theory argues that categorizations stem from the bounded rationality of market audiences, who are forced to impose categorizations and ignore miscategorized producers to efficiently interact with the market. I present an alternative model in which producers in a market segregate into categories and experience an apparent miscategorization penalty not driven by a market audience: In a complex environment, producers imitate successful predecessors. Ex-post rationalization identifies clusters as categories. Categories reflect, but do not cause, producer success. This model of exploration of a complex environment accounts for the basic findings of category theory, and it predicts the dynamics of category emergence and change over time. I establish these results in a formal model and simulation.
Does the Middle Conform or Compete? Quality Thresholds Predict the Locus of Innovation | pdf
Organization Science — Vol 30, No 1 (2019)
Where does innovation come from? This research models producer incentives to innovate with a focus on the role of audiences in constructing quality thresholds within markets. Market audiences create mechanisms for identifying the highest quality producers in a market. I highlight a key distinction between fixed quality thresholds (such as accreditations) and quality thresholds that respond to producer quality (such as rankings or best-of-breed awards). Producers evaluate how the inherently risky nature of innovation interacts with these thresholds. The model predicts conditions under which innovation emerges from the best producers in a market, from producers near the threshold in a market, from both, or from nowhere. Such predictions generalize and simplify several existing organizational theories of innovation.
Dynamic Models of Communication in an Online Friendship Network
Brooke Foucault Welles, Anthony Vashevko, Nick Bennett, Noshir Contractor
Communication Methods and Measures — Vol 8, No 4 (2014)
In this article, we argue for the usefulness of relational event network analysis to study online communication networks. Unlike other network analytic techniques that require online communication data to be summarized prior to analysis, relational event network analysis uses un-summarized time-stamped data to track the dynamic evolution of communication networks. To illustrate, we use relational event network analysis to analyze the evolution of a communication network within the virtual world Second Life. Results suggest that there are different patterns of communication among nonfriends and friends within the network. Nonfriends tend to communicate with those they have communicated with in the past, reciprocate communication, and close communication triads. Friends tend not to communicate with those they have communicated with in the past, instead preferring to reciprocate communication and close triads. We discuss implications for the study of online communication and identify directions for future research using relational event network analysis.
Visualizing hierarchical social networks
Kurtulus Gemici, Anthony Vashevko
Socius — Vol 4 (2018)
The authors propose a novel technique for the visualization of networks that contain a hierarchical structure: networks in which certain nodes and groups of nodes can be classified through a relation of precedence. Networks with a hierarchical structure frequently arise in sociology and various other disciplines, but the existing methods for visualizing such networks leave much to be desired. The method developed in this work builds on the tradition of visualization in social network analysis; it aims to simultaneously represent the positions of different nodes and the relationships between groups containing the nodes in the network. As such, the proposed visualization method facilitates theoretical and empirical analysis of social structures by algorithmically combining information from the underlying network with the information from the hierarchical structure of the network. The authors illustrate the proposed method with social networks examined through cohesive blocking and k-core decomposition.
The Matthew Effect as Skill and Strategy | pdf
Literature on Matthew effects and cumulative advantage typically conceives of the Matthew effect as a property of the field in which actors operate. This paper proposes a reinterpretation of the Matthew effect as a property of the individual—as a capability an actor may possess or as a strategy an actor may pursue. This paper discusses the consequences of a strategic interpretation of Matthew effects, including the factors required to incentivize actors to pursue them and the consequences of such pursuit. In addition, the paper highlights how strategic pursuit of cumulative advantage can generate paradoxical results, including the reduction of inequality in response to tournament-based competitive settings.
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