Investors’ Attention Allocation to Stock Analysis: The Role of Rating Deviation

Stock analysis is important for investors. However, little is known about how investors allocate their attention to different analyses. In the last two decades, online investment communities (OICs) have proliferated. In this study, we use investors’ online activities (i.e., comment and like) and amateur stock analysis in Seeking Alpha to explore how investors allocate their attention among different analyses by examining the effects of stock rating deviation on their attention. We measure the stock rating deviation of one analysis by comparing its stock rating with the previous rating for the same stock. The results show that the analyses with stock ratings that are more deviated from the existing ratings tend to receive more comments and likes from investors, indicating that rating deviation from the consensus positively impacts investor attention to stock analysis. In addition, the deviation’s negativity and the stock volatility strengthen the impact of rating deviation on investor attention. However, analysts’ busyness status negatively moderates this impact

Uncertainty-reduction or reciprocity? Understanding the effects of a platform-initiated reviewer incentive program on regular review generation

To stimulate product reviews, many e-commerce platforms have launched reviewer incentive programs in which free product samples are provided to reviewers in exchange for their ratings of the samples. This study focuses on an unexplored aspect of reviewer incentive programs—the impact of participating in such programs on reviewers’ ratings of products they purchased normally (i.e., regular ratings). We find that after reviewers join the program and receive free product samples, their average regular rating increases by 2.25% (i.e., 0.093 more stars on the five-star scale). Our follow-up analyses indicate that the observed regular-rating increase can be attributed to an uncertainty-reduction effect evoked by the free product samples, as opposed to a reciprocity effect. We further delve into the underlying mechanism by analyzing the reviewers’ regular ratings at a granular, product-category level. Consistent with our theorization of the uncertainty-reduction effect, our findings reveal that reviewers’ regular-rating increase is driven by improved assessment and knowledge about products sharing common attributes with the sampled products, resulting in better post-purchase outcomes. Our results demonstrate that apart from motivating the feedback for the sampled products, free product sampling can reduce reviewers’ product uncertainty and trigger evident change in their regular ratings for the purchased products.First author draf

Hidden Profiles in Corporate Prediction Markets: The Impact of Public Information Precision and Social Interactions

Recently, large companies have been experimenting with corporate prediction markets run among their employees. In the present study, we develop an analytical model to analyze the effects of information precision and social interactions on prediction market performance. We find that increased precision of public information is not always beneficial to the prediction market accuracy because of the hidden profiles effect: the information-aggregation mechanism places a larger-than-efficient weight on existing public information. We show that a socially embedded prediction market with information sharing among participants may help correct such inefficiency and improve prediction market performance. We also identify conditions under which increased precision of public information is detrimental in a nonnetworked prediction market and in a socially embedded prediction market. These results should be of interest to practitioners as the managerial implications highlight the detrimental effect of public information and the role of social networking among employees in a corporate prediction market

The Double-Edged Sword of Expert Reviewer Programs: The Effects of Offering Expert Reviewer Status on Review Generation

As online reviews play an increasingly influential role in consumer decisions in e-commerce, large online retailers such as Amazon recently launched expert reviewer programs to supply high-quality reviews by selected experts. While prior studies examined the impact of online reviews written by experts on the product’s sales performance, our study investigates the effects of soliciting users to join expert reviewer programs and offering free products on their review generation processes and outcomes. Contrary to the common wisdom, our results reveal that, after reviewers participate in expert reviewer programs, they generate longer reviews but offer lower ratings for sponsored free products. However, they produce more reviews and higher ratings for non-sponsored regular products. Additional analyses offer theoretical underpinnings for the asymmetric effects of expert reviewer programs