Asymmetric Social Interactions in Physician Prescription Behavior: The Role of Opinion Leaders

Nair, Harikesh S.; Manchanda, Puneet; Bhatia, Tulikaa

doi:10.2139/ssrn.937021

Cited by 127 publications

(231 citation statements)

References 36 publications

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“…Our work is related to the growing empirical literature in marketing on social networking and interaction (e.g., Ansari et al 2011;Stephen and Toubia 2010;Bulte 2007;Hartmann 2010;Nair et al 2010;Katona et al 2011;Iyengar et al 2011). Our work deviates from the social networking literature inasmuch as we consider user sites with large numbers of agents such that any single agent's participation is not likely to have a sizable effect on aggregate content consumption or generation.…”

Section: Literature Reviewmentioning

confidence: 99%

Managing User-Generated Content: A Dynamic Rational Expectations Equilibrium Approach

2016

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This paper considers the creation and consumption of content on user generated content platforms, e.g., reviews, articles, chat, videos, etc. On these platforms, users' expectations regarding the amount and timing of participation by others becomes germane to their own involvement levels. Accordingly, we develop a dynamic rational expectations equilibrium model of joint consumption and generation of information. We estimate the model on a novel data set from a large Internet forum site and offer recommendations regarding strategies of managing sponsored content and content quality. We find sponsoring content can be effective at creating a self-sustaining network, but once the network tips, sponsored content does little to increase usage.

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Section: Literature Reviewmentioning

confidence: 99%

Managing User-Generated Content: A Dynamic Rational Expectations Equilibrium Approach

2016

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“…On the one hand, some researchers have examined the effects of aggregate word-ofmouth (Chen, Wang and Xie 2011;Chevalier and Mayzlin 2006;Godes and Mayzlin 2004;Zhu and Zhang 2010), online viral marketing activities (De Bruyn and Lilien 2008;Toubia, Freud, and Stephen 2011), and online community participation (Algesheimer et al 2010;Manchanda, Packard, and Pattabhiramaiah 2013;Stephen and Toubia 2010) on aggregate sales. On the other hand, others attempt to establish the casual effects of social interactions on adoption decisions at the individual level (Nair, Manchanda and Bhatia 2010;Nam, Manchanda, and Chintagunta 2010;Zhang 2010). A few researchers examine social interactions in a multiattribute utility framework 1 (Narayan, Rao, and Saunders 2011;Yang and Allenby 2003) or explicitly collect network information (Nair, Manchanda, and Bhatia 2010;Narayan, Rao, and Saunders 2011;Reingen et al 1984).…”

Section: Literature Review and Conceptual Frameworkmentioning

confidence: 99%

“…On the other hand, others attempt to establish the casual effects of social interactions on adoption decisions at the individual level (Nair, Manchanda and Bhatia 2010;Nam, Manchanda, and Chintagunta 2010;Zhang 2010). A few researchers examine social interactions in a multiattribute utility framework 1 (Narayan, Rao, and Saunders 2011;Yang and Allenby 2003) or explicitly collect network information (Nair, Manchanda, and Bhatia 2010;Narayan, Rao, and Saunders 2011;Reingen et al 1984). In studying choice interdependence, we conceptualize individual choices to be driven by multi-attribute utilities and focus on the passive form of social interactions.…”

Section: Literature Review and Conceptual Frameworkmentioning

confidence: 99%

“…The asymmetric effects of social interactions have been documented in the prescription drug (Nair, Manchanda and Bhatia 2010) and video-messaging categories. However, the study contexts primarily involve informational influences.…”

Section: Literature Review and Conceptual Frameworkmentioning

confidence: 99%

“…Although extant research has attempted to measure social interaction effects (e.g., Nair, Manchanda, and Bhatia 2010;Nam, Manchanda, and Chintagunta 2010;, it does not recognize that such effects may arise from different types of social influence. Prior behavioral research (Bearden and Etzel 1982;Burnkrant and Cousineau 1975;Deutsch and Gerard 1955) distinguishes a value-expressive from an informational social influence.…”

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confidence: 99%

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Modeling Choice Interdependence in a Social Network

Wang¹,

Aribarg

Atchadé

2013

Marketing Science

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This paper investigates how individuals' product choices are influenced by the product choices of their connected others and how the influence mechanism may differ for fashion- versus technology-related products. We conduct a novel field experiment to induce and observe choice interdependence in a closed social network. In our experiment, we conceptualize individuals' choices to be driven by multiattribute utilities, and we measure their initial attribute preferences prior to observing their choice interdependence and collecting network information. These design elements help alleviate concerns in identifying social interaction effects from other confounds. Given that we have complete information on choices and their sequence, we use a discrete-time Markov chain model. Nonetheless, we also use a Markov random field (MRF) model as an alternative when the information on choice sequence is missing. We find significant social interaction effects. Our findings show that whereas experts exert asymmetrically greater influence on a technology-related product, popular individuals exert greater influence on a fashion-related product. In addition, we find choices made by early decision makers to be more influential than choices made later for the technology-related product. Finally, using the MRF with snapshot data can also provide good out-of-sample predictions for a technology-related product.

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Endogenous Technology Adoption and Medical Costs

Lamiraud

Lhuillery

2016

Health Economics

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Despite the claim that technology has been one of the most important drivers of healthcare spending growth over the past decades, technology variables are rarely introduced explicitly in cost equations. Furthermore, technology is often considered exogenous. Using 1996-2007 panel data on Swiss geographical areas, we assessed the impact of technology availability on per capita healthcare spending covered by basic health insurance whilst controlling for the endogeneity of health technology availability variables. Our results suggest that medical research, patent intensity and the density of employees working in the medical device industry are influential factors for the adoption of technology and can be used as instruments for technology availability variables in the cost equation. These results are similar to previous findings: CT and PET scanner adoption is associated with increased healthcare spending, whilst increased availability of percutaneous transluminal coronary angioplasty facilities is associated with reductions in per capita spending. However, our results suggest that the magnitude of these relationships is much greater in absolute value than that suggested by previous studies that did not control for the possible endogeneity of the availability of technologies. Copyright © 2016 John Wiley & Sons, Ltd.

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Asymmetric Social Interactions in Physician Prescription Behavior: The Role of Opinion Leaders

Cited by 127 publications

References 36 publications

Managing User-Generated Content: A Dynamic Rational Expectations Equilibrium Approach

Managing User-Generated Content: A Dynamic Rational Expectations Equilibrium Approach

Modeling Choice Interdependence in a Social Network

Endogenous Technology Adoption and Medical Costs

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