Online Product Opinions: Incidence, Evaluation, and Evolution

Abstract

Whereas recent research has demonstrated the impact of online product ratings and reviews on product sales, we still have a limited understanding of the individual's decision to contribute these opinions. In this research, we empirically model the individual's decision to provide a product rating and investigate factors that influence this decision. Specifically, we consider how previously posted ratings may affect an individual's posting behavior in terms of whether to contribute (incidence) and what to contribute (evaluation), and we identify selection effects that influence the incidence decision and adjustment effects that influence the evaluation decision. Across individuals, our results show that positive ratings environments increase posting incidence, whereas negative ratings environments discourage posting. Our results also indicate important differences across individuals in how they respond to previously posted ratings, with less frequent posters exhibiting bandwagon behavior and more active customers revealing differentiation behavior. These dynamics affect the evolution of online product opinions. Through simulations, we illustrate how the evolution of posted product opinions is shaped by the underlying customer base and show that customer bases with the same median opinion may evolve in substantially different ways because of the presence of a core group of “activists” posting increasingly negative opinions.

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

“…Likewise, our research is related to the burgeoning literature on UGC (Albuquerque et al 2010;Chevalier and Mayzlin 2006;Dellarocas 2006;Duan et al 2008;Shriver et al 2013;Ghose and Han 2011;Moe and Schweidel 2012;Zhang et al 2012) that considers the joint behavior of content consumption and generation. 5 Our research extends this work in two ways.…”

“…An increase in content consumption can also lead to an increase in content generation; those who post content presumably do so, because they are motivated to have their posts read by others (Bughin 2007;Hennig-Thurau et al 2004, Moe andSchweidel 2012;Nardi et al 2004;Nov 2007). There is also a potential direct network effect of content generation on content generation; as more content appears, competition for readers increases.…”

“…As more content is generated by others, the likelihood of finding content of interest grows. Content generation, like posting video game "cheats" and TV show reviews, yields utility from the reputation effect of being influential, knowledgeable or popular, suggesting utility increases as more content is consumed (Bughin 2007;Hennig-Thurau et al 2004;Moe and Schweidel 2012;Nardi et al 2004;Nov 2007). 2 Accordingly, the content generation decision is predicated on beliefs about the number of other people consuming and generating content.…”

A Volunteered Geographic Information Framework to Enable Bottom-Up Disaster Management Platforms

“…However, it is interesting to observe that very few contributions have a credibility level of three or higher. This indicates two things: either the credibility criteria are too demanding, and it may be difficult to develop civic credibility within the context of a crisis because of bandwagon effects [94], or the media's hierarchical network structure (i.e., network centrality and connectivity [95]) does not allow a more equal distribution of contributions among the credibility levels. A problem that we observed, relevant to the total quality score calculation, is that the maximal credibility scores (i.e., not levels) for the geo-reference data subsets reached 0.25 and not 1.0, as would be most desirable.…”

Statistical properties of consideration sets

“…Specifically, we did not find a cite to Heckman's classic 1979 paper on selection bias in any key marketing papers on consideration sets except for Degeratu et al (2000); but their treatment of selection bias involved their respondent sample, not their choice sets. Other marketing papers exploring aspects of selection effects include Winer (1983) who looks at selection bias due to participants dropping out of panels; Krishnamurthi and Raj (1988) on the need to model the discrete choice of whether to purchase a product is linked to the continuous choice of how much to purchase; Moe and Schweidel (2012) who look at section effects in terms of how prior posts influences who posts on product review boards, and Salisbury et al (2013) who show selection effects can occur in market experiments even when random assignment at the initial stage is possible. Many marketing processes involve multiple stages with selection likely to occur at each stage (Wachtel and Otter, 2013).…”