How Pay it Forward Happens

Generalized exchange is a common human practice with wide-ranging implications, involved in behavior such as helping a stranded motorist, advising a colleague, contributing to Wikipedia or engaging in kidney exchange. Yet it remains a puzzle for theories of rational choice, social exchange, and evolutionary biology.  We present and test an integrated model of generalized exchange that combines strategic reputation building, fairness-based selective-giving, the obligation to pay it forward, and heterogeneous social preferences (values).  Using laboratory experiments, we show that each mechanism is robust:  each has significant effects even when we include all four mechanisms in the model and control for other factors. However, some mechanisms have more influence than others, and different combinations of mechanisms yield a wide range of generalized exchange. Overall, generalized exchange is a collective result of people who strive to increase their resources, make decisions contingent on the visibility of their actions and on the behavior of others with whom they interact, reciprocate to a system from which they benefited, and behave consistently with their values.  These values moderate how people respond to the presence or absence of reputational incentives, as well as the extent to which they reward generous behavior and punish stingy behavior.

Why Do We Share? Generalized exchange online
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How Knowledge Transfer Impacts Performance: Benefits and Liabilities

Does knowledge transfer necessarily lead to better performance? Building on field data, we model how efforts to supplement one’s knowledge from co-workers interact with individual, organizational and environmental characteristics to impact organizational performance. Accounting for the cost of transfer and variations in exchange patterns, the impact of knowledge transfer is highly contingent: it can better performance or matter little, depending on the specific characteristics. Three illustrative computational studies propose boundary conditions. At the individual level, better organizational support of learning diminishes the value of knowledge transfer. At the organizational level, broader access to organizational memory makes global knowledge transfer less beneficial to performance. When the organizational environment becomes more turbulent, the benefits of knowledge transfer fall. The studies demonstrate that the contingency is affected by parameters at multiple levels and may explain seeming contractions in prior work. We discuss implications for research on knowledge transfer, for theory development, and for managers.

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Where and When Can Open Source Thrive?

Open collaborative innovation has emerged as an important economic and social phenomenon. Loosely-bounded groups of individuals, collaborating to create goods and services, have created alternatives to commercial software, upended traditional repositories of knowledge and replaced reviewers and critics. While it is understood that open innovation is different from firm-based innovation, it remains a puzzle: How does it survive despite massive free-riding/non-contributing users? To what extent it can expand beyond software? In which environments can it thrive? How to design open innovation systems? How to facilitate contributions?

Where and When Does Open Innovation Thrive?
From a presentation at the University of Maryland, 2010

Where Do Price Bubbles Come From?

Price bubbles remain a puzzle for economic theory, particularly given their appearance in experimental markets with high efficiency and minimized uncertainty and noise. We propose that bubbles are caused by the institutionalization of social norms, when individuals observe and adopt the behavior of others. Explanations of bounded rationality or individual bias appear insufficient as we show experimentally that (1) participants’ pricing skills are better ex-ante than ex-post and (2) that individual discrepancies between intrinsic values and market prices become increasingly serially correlated during trading. We also find no support for the Greater Fool explanation.

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Explaining Clustering in Social Networks

Individual and organizational actors enter into a large number of relationships that include benefiting others without ensuring the equality of reciprocal benefits. We suggest that actors have evolved mechanisms that guide them in the choice of exchange partners, even without conscious calculation or bookkeeping of gain and loss. One such mechanism directs actors to membership in clusters, which are homogenous groups of actors densely connected among themselves and only loosely connected to other groups. We suggest that clusters offer network externalities, which are not possible in sparse networks, thus conferring cascading benefits on the actors contained in those clusters. Using this logic, one can understand the omnipresence of clustering in social networks of individuals and firms. We review the benefits and challenges associated with clustering and use the logic of cascading benefits to derive empirical predictions.

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