Herd Behavior and Software Adoption on the Internet: An Empirical Investigation

Online users often need to make adoption decisions without accurate information about the product values. Herding is common in such situations where users infer values from other customers' choices and incorporate that information into their own decision-making process. Herding is often rational for individual decision-making; however, it may lead to adoption of inferior products. The Internet affects the herding phenomenon in adoption decisions in two ways. On the one hand, it provides more information about other users' choices, therefore making herding more feasible. On the other hand, the Internet provides more details about product values, thus making herding less desirable. In this paper, we empirically examine herd behavior in the context of online software adoption. Consistent with the predictions of the informational cascades literature, we find that online users engage in significant herd behavior in choosing software programs, and professional product reviews and user reviews have little impact on the popularity of software programs. We also find that, while product reviews do not directly affect software popularity, their availability mitigates the herd behavior. Our results validate informational cascades as an important driver for decision-making on the Internet.

[1]  W. Powell,et al.  The iron cage revisited institutional isomorphism and collective rationality in organizational fields , 1983 .

[2]  Robert W. Zmud,et al.  Inducing Sensitivity to Deception in Order to Improve Decision Making Performance: A Field Study , 2002, MIS Q..

[3]  C. Chamley.  Rational Herds: Economic Models of Social Learning , 2003 .

[4]  A. Banerjee,et al.  A Simple Model of Herd Behavior , 1992 .

[5]  R. E. Kennedy.  Strategy Fads and Competitive Convergence: An Empirical Test for Herd Behavior in Prime-Time Television Programming , 2002 .

[6]  Yannis Bakos,et al.  A Strategic Analysis of Electronic Marketplaces , 1991, MIS Q..

[7]  Paul A. Pavlou,et al.  Can online reviews reveal a product's true quality?: empirical findings and analytical modeling of Online word-of-mouth communication , 2006, EC '06.

[8]  Lee G. Cooper,et al.  Parameter Estimation for a Multiplicative Competitive Interaction Model—Least Squares Approach , 1974 .

[9]  Chrysanthos Dellarocas,et al.  A Statistical Measure of a Population’s Propensity to Engage in Post-Purchase Online Word-of-Mouth , 2006 .

[10]  Dale A. Stirling,et al.  Information rules , 2003, SGMD.

[11]  Vijay Mahajan,et al.  Chapter 8 New-product diffusion models , 1993, Marketing.

[12]  Alok Gupta,et al.  User heterogeneity and its impact on electronic auction market design: an empirical exploration , 2004 .

[13]  Jeffrey M. Wooldridge,et al.  Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data , 2003 .

[14]  Neil Gandal.  Hedonic price indexes for spreadsheets and an empirical test of the network externalities hypothesis , 1992 .

[15]  Janet S. Netz,et al.  Why do all the flights leave at 8 am?: Competition and departure-time differentiation in airline markets , 1999 .

[16]  Eric Abrahamson.  Managerial Fads and Fashions: The Diffusion and Rejection of Innovations , 1991 .

[17]  Glenn J. Browne,et al.  Information Cascades in the Adoption of New Technology , 2002, ICIS.

[18]  Michael Parent,et al.  Mimetic Isomorphism and Technology Evaluation: Does Imitation Transcend Judgment? , 2002, J. Assoc. Inf. Syst..

[19]  Jonathan W. Palmer,et al.  Web Site Usability, Design, and Performance Metrics , 2002, Inf. Syst. Res..

[20]  D. McFadden.  Econometric Models of Probabilistic Choice , 1981 .

[21]  F. Bass.  A new product growth model for consumer durables , 1976 .

[22]  Pei-Yu Sharon Chen,et al.  The Impact of Online Recommendations and Consumer Feedback on Sales , 2004, ICIS.

[23]  John Gallaugher,et al.  Understanding Network Effects in Software Markets: Evidence from Web Server Pricing , 2002, MIS Q..