Tag Archives: BERT

Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.com

Park, Jaecheol, Joy Wu, Arslan Aziz, Gene Moo Lee. “Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.com”, Working Paper.

  • Presentations: UBC (2021), KrAIS (2021), WISE (2021), PACIS (2022), SCECR (2022), BU Platform (2022), CIST (2022), BIGS (2022)
  • Preliminary version in PACIS 2022 Proceedings
  • RAs: Minsuk Seo, Vibudh Singh

The rapid growth in e-commerce has led to a concomitant increase in consumers’ reliance on digital word-of-mouth to inform their choices. As such, there is an increasing incentive for sellers to solicit reviews for their products. The literature has examined the direct and indirect effects of incentivized reviews on subsequent organic reviews within consumers who received incentives. However, since incentivized reviews and reviewers are often only a small proportion of a review platform (only 1.2% in our sample), it is important to understand whether their presence and absence on the platform affect the organic reviews from other reviewers who have not received incentives, which are often in the majority. We theorize two underlying effects that incentivized reviews can generate on other organic reviews: the herding effect from imitating incentivized reviews and the disclosure effect from the increased trust or skepticism by explicit incentive disclosure statements. Those two effects make organic reviews either follow or deviate from incentivized reviews. Using Bidirectional Encoder Representations from Transformers (BERT) to identify incentivized reviews and a natural experiment caused by a policy change on Amazon.com in October 2016, we conduct difference-in-differences with propensity score matching analyses to identify the effects of banning incentivized reviews on organic reviews. Our results suggest the disclosure effects are salient: banning incentivized reviews has positive effects on organic reviews in terms of frequency, sentiment, length, image, and helpfulness. Moreover, we find that the presence of incentivized reviews has poisoned the well for organic reviews regardless of the incentivized review ratio and that the effect is heterogeneous to product quality uncertainty. Our findings contribute to the literature on online review and platform design and provide insights to platform managers.

Corporate Social Network Analysis: A Deep Learning Approach

Cao, Rui, Gene Moo Lee, Hasan Cavusoglu. “Corporate Social Network Analysis: A Deep Learning Approach,” Working Paper.

Identifying inter-firm relationships is critical in understanding the industry landscape. However, due to the dynamic nature of such relationships, it is challenging to capture corporate social networks in a scalable and timely manner. To address this issue, this research develops a framework to build corporate social network representations by applying natural language processing (NLP) techniques on a corpus of 10-K filings, describing the reporting firms’ perceived relationships with other firms. Our framework uses named-entity recognition (NER) to locate the corporate names in the text, topic modeling to identify types of relationships included, and BERT to predict the type of relationship described in each sentence. To show the value of the network measures created by the proposed framework, we conduct two empirical analyses to see their impacts on firm performance. The first study shows that competition relationship and in-degree measurements on all relationship types have prediction power in estimating future earnings. The second study focuses on the difference between individual perspectives in an inter-firm social network. Such a difference is measured by the direction of mentions and is an indicator of a firm’s success in network governance. Receiving more mentions from other firms is a positive signal to network governance and it shows a significant positive correlation with firm performance next year.

IT Risk and Stock Price Crash Risk

Song, Victor, Hasan Cavusoglu, Jaecheol Park, Mary L. Z. Ma, Gene Moo Lee (2026) “IT Risk and Stock Price Crash Risk,” Under review.

This study examines whether and how firm-level information technology (IT) risk contributes to stock price crash risk. We construct a novel measure of ex-ante IT risk from risk factor disclosures in Item 1A of firms’ 10-K filings using advanced machine learning approaches. We find that higher IT risk is associated with greater stock price crash risk. Mechanism analyses indicate that this effect operates primarily through increased downside operating risk, rather than through heightened exposure to data breach events. We further document heterogeneity in the relationship between IT risk and stock price crash risk: (1) cybersecurity risk has a stronger effect than noncybersecurity IT risk; (2) the effect is stronger for newly disclosed IT risk factors; and (3) higher readability amplifies the crash risk effect. Together, these findings highlight IT risk as a previously underexplored determinant of stock price crash risk and offer new insights into the capital market consequences of firms’ IT-related disclosures.