<i>Medical Innovation</i> Revisited: Social Contagion versus Marketing Effort

Bulte, Christophe Van den; Lilien, Gary L.

doi:10.1086/320819

Cited by 539 publications

(315 citation statements)

References 46 publications

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“…Because social influence is a theoretical edifice in multiple subfields of the discipline, a growing chorus of authors has critiqued the empirical literature for its inattention to the challenge of establishing evidence of causal social influences in observational data (e.g., Winship and Morgan 1999;Van den Bulte and Lilien 2001;Mouw 2003;Reagans, Zuckerman, and McEvily 2007;Aral, Muchnik, and Sundararajan 2009;Stuart and Sorenson 2009;Shalizi and Thomas 2011). The principal empirical challenge arises because the outcomes that interest researchers often are endogenous to the factors that spur the formation of social ties.…”

Section: Social Influence In Partially Deliberate Matchesmentioning

confidence: 99%

Social Influence Given (Partially) Deliberate Matching: Career Imprints in the Creation of Academic Entrepreneurs

Azoulay¹,

Liu²,

Stuart

2017

American Journal of Sociology

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Actors and associates often match on a few dimensions that matter most for the relationship at hand. In so doing, they are exposed to unanticipated social influences because counterparts have broader attitudes and preferences than would-be contacts considered when they first chose to pair. The authors label as "partially deliberate" social matching that occurs on a small set of attributes, and they present empirical methods for identifying causal social influence effects when relationships follow this generative logic. A data set tracking the training and professional activities of academic biomedical scientists is used to show that young scientists adopt their advisers' orientations toward commercial science as evidenced by adviser-to-advisee transmission of patenting behavior. The authors demonstrate this in twostage models that account for the endogeneity of matching, using both inverse probability of treatment weights and an instrumental variables approach. They also draw on qualitative methods to support a causal interpretation. Overall, they present a theory and a triangulation of methods to establish evidence of social influence when tie formation is partially deliberate.

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Section: Social Influence In Partially Deliberate Matchesmentioning

confidence: 99%

Social Influence Given (Partially) Deliberate Matching: Career Imprints in the Creation of Academic Entrepreneurs

Azoulay¹,

Liu²,

Stuart

2017

American Journal of Sociology

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“…That is, we use the lagged social network variable in accordance with Manski (1993) and other work that has studied the impact of the social network effect on user behavior (Van den Bulte and Lilien 2001, Iyengar et al 2010 Lagging avoids the simultaneity problem, unless (1) people are forward-looking not only about their own behavior but also that of others and (2) social ties over which influence flows are symmetric (Hartmann et al 2008, Iyengar et al 2010. The first condition is quite unlikely in large networks such as ours, and the second condition does not hold in our data.…”

Section: Identification Of the Social Network Endogeneitymentioning

confidence: 99%

An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet

2011

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We quantify how user mobile Internet usage relates to unique characteristics of the mobile Internet. In particular, we focus on examining how the mobile-phone-based content generation behavior of users relates to content usage behavior. The key objective is to analyze whether there is a positive or negative interdependence between the two activities. We use a unique panel data set that consists of individual-level mobile Internet usage data that encompass individual multimedia content generation and usage behavior. We combine this knowledge with data on user calling patterns, such as duration, frequency, and locations from where calls are placed, to construct their social network and to compute their geographical mobility. We build an individuallevel simultaneous equation panel data model that controls for the different sources of endogeneity of the social network. We find that there is a negative and statistically significant temporal interdependence between content generation and usage. This finding implies that an increase in content usage in the previous period has a negative impact on content generation in the current period and vice versa. The marginal effect of this interdependence is stronger on content usage (up to 8.7%) than on content generation (up to 4.3%). The extent of geographical mobility of users has a positive effect on their mobile Internet activities. Users more frequently engage in content usage compared to content generation when they are traveling. In addition, the variance of user mobility has a stronger impact on their mobile Internet activities than does the mean. We also find that the social network has a strong positive effect on user behavior in the mobile Internet. These analyses unpack the mechanisms that stimulate user behavior on the mobile Internet. Implications for shaping user mobile Internet usage behavior are discussed. Sang-Pil HanPostdoctoral Researcher, Stern School of Business, New York University AbstractWe quantify how users' mobile Internet use is related to some unique characteristics of the mobile Internet space like usage-based data pricing, the extent of their geographical mobility and their social network behavior. We use a unique panel dataset consisting of individual-level mobile Internet usage data encompassing their multimedia content uploading and downloading behavior along with detailed data on their voice and text calls, and demographics. We build and estimate an individual-level simultaneous equations panel data model using three-stage least-squares (3SLS) after controlling for the endogeneity of the social network. We find that there exists negative temporal interdependence between the content generation and usage behavior for a given user. While the extent of geographical mobility of users is positively associated with their mobile Internet activities, users more frequently engage in content downloading compared to content uploading when they travel. We find statistically significant social network effects that are robust across model specific...

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“…Thus, Sinha and Chandrashekaran (1992) studied individual adoption behavior using hazard modeling on empirical data. Subsequently, Van den Bulte and Lilien (2001) used hazard modeling to study social contagion with social network data. Bronnenberg and Mela (2004) and Bell and Song (2007) have done this with spatial data.…”

Section: Introductionmentioning

confidence: 99%

Aggregate Diffusion Dynamics in Agent-Based Models with a Spatial Structure

Fibich

Gibori

2010

Operations Research

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We explicitly calculate the aggregate diffusion dynamics in one-dimensional agent-based models of adoption of new products, without using the mean-field approximation. We then introduce a clusters-dynamics approach, and use it to derive an analytic approximation of the aggregate diffusion dynamics in multidimensional agent-based models. The clusters-dynamics approximation shows that the aggregate diffusion dynamics does not depend on the average distance between individuals, but rather on the expansion rate of clusters of adopters. Therefore, the grid dimension has a large effect on the aggregate adoption dynamics, but a small-world structure and heterogeneity among individuals have only a minor effect. Our results suggest that the one-dimensional model and the Bass model provide a lower bound and an upper bound, respectively, for the aggregate diffusion dynamics in agent-based models with "any" spatial structure.

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Medical Innovation Revisited: Social Contagion versus Marketing Effort

Cited by 539 publications

References 46 publications

Social Influence Given (Partially) Deliberate Matching: Career Imprints in the Creation of Academic Entrepreneurs

Social Influence Given (Partially) Deliberate Matching: Career Imprints in the Creation of Academic Entrepreneurs

An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet

Aggregate Diffusion Dynamics in Agent-Based Models with a Spatial Structure

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