Tag Archives: doc2vec method

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

Park, Jaecheol, 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

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. Recent studies have examined the direct effect of receiving incentives or introducing incentive policy on review writing behavior. However, since incentivized reviews are often only a small proportion of the overall reviews on a platform, it is important to understand whether their presence on the platform has spillover effects on the unincentivized reviews which are often in the majority. Using the state-of-the-art language model, Bidirectional Encoder Representations from Transformers (BERT) to identify incentivized reviews, a document embedding method, Doc2Vec to create matched pairs of Amazon and non-Amazon branded products, and a natural experiment caused by a policy change on Amazon.com in October 2016, we conduct a difference-in-differences analysis to identify the spillover effects of banning incentivized reviews on unincentivized reviews. Our results suggest that there are positive spillover effects of the ban on the review sentiment, length, helpfulness, and frequency, suggesting that the policy stimulates more reviews in the short-run and more positive, lengthy, and helpful reviews in the long run. Thus, we find that the presence of incentivized reviews on the platform poisons the well of reviews for unincentivized reviews.

Strategic Competitive Positioning: An Unsupervised Structural Hole-based Firm-specific Measure

Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “Strategic Competitive Positioning: Unsupervised Operationalization of a Structural Hole-based Firm-specific Construct”, [Latest version: Aug 15, 2023]

  • doc2vec model of 10-K reports: Link
  • Presented at UBC MIS Seminar 2018, CIST 2019 (Seattle, WA), KrAIS 2019 (Munich, Germany), DS 2021 (online), KrAIS 2021 (Austin, TX), UT Dallas 2022, KAIST 2022, Korea Univ 2022, INFORMS 2022 (Indianapolis, IN)
  • Funded by Sauder Exploratory Grant 2019
  • Research assistants: Raymond Situ, Sahil Jain

In this paper, we build on the network structural hole concept of organizational theory to theorize an individual firm-specific strategic competitive positioning (SCP) construct. We use unsupervised document embedding approaches to operationalize the SCP construct by capturing each firm’s relative competitive and strategic positioning in a strategic similarity matrix of all existing U.S. publicly traded firms’ annual corporate filings. This approach enables us to construct a theoretically driven firm-level SCP measure with minimal human expert intervention. Our construct dynamically captures competitive positioning across different firms and years without using artificially bounded and often outdated industry classification systems. We illustrate how the dynamic measure captures industry-level and cross-industry strategic changes. Then, we demonstrate the effectiveness of our construct with an empirical analysis showing the imprinting and dynamic effects of SCP on firm performance. The results show that our dynamic SCP measure outperforms existing competition measures and successfully predicts post-IPO performance. This paper makes significant contributions to the information systems and organizations literatures by proposing an organizational theory-based unsupervised approach to dynamically conceptualize and measure firm-level strategic competitive positioning. The construct can be easily applied to firm-specific, industry-level, and cross-industry research questions in many contexts across many disciplines.