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A model of consumer information search and online network externalities
Institution:1. Department of Psychology, Concordia University, Montréal, Canada;2. Center for Clinical Research in Health, Concordia University, Montréal, Canada;3. Department of Psychiatry, University of NC at Chapel Hill, USA;1. Polytechnic Institute of Tomar, Tomar, Portugal;2. LIAAD/INESC TEC – INESC Technology and Science, Portugal\n;3. DCC – FCUP, University of Porto, Portugal;4. HULTECH/GREYC, University of Caen Basse-Normandie, Caen, France;5. Department of Mathematics, University of Beira Interior, Covilhã, Portugal;6. Center of Mathematics, University of Beira Interior, Covilhã, Portugal;2. MIEIC, SAPO Labs, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal;3. LIACC, SAPO Labs, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
Abstract:This article looks at a consumer who faces a choice between two sources of information: conventional and online. The online source is different from the conventional one because consumers care about how many people are using it along with them. Generally, the more users the better (positive network externality), but, on the other hand, the presence of more users implies a reduction of available capacity, about which consumers also care. We assume that consumers maximize their utility, which is modeled as a function of all other consumption, knowledge, and network externalities. Knowledge is conceptualized as a function of the amount of information spent both on the online network and on conventional sources. Since consumer information searches may differ according to demographic differences, this may result in consumers’ differences in their usage of online networks and conventional sources. We include these externalities in our model to better understand consumer choice behavior for the online network. The consumer utility function is estimated by using the model estimation method developed in this article, since the interactions implied by the consumer utility function are more complex than one could hope to estimate econometrically. We use survey data to regress the demand for online information depending on income, education, the number of users, and remaining capacity. We introduce a methodology to simulate how consumers’ representative utilities will behave in different network environments and derive implications for online network suppliers.
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