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 (2021) “Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.comWorking Paper.

  • Presentations: UBC (2021), WISE (2021)

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 nd that the presence of incentivized reviews on the platform poisons the well of reviews for unincentivized reviews.

Structural Hole-based Measures of Firm’s Strategic Competitive Positioning

Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “Structural Hole-based Measures of Firm’s Strategic Competitive Positioning”, Working Paper.

This research methods paper proposes structural hole-based strategic competitive positioning (SCP) measures to capture a firm’s competitive and strategic positioning. Drawing on the network theory and structural holes concept, we operationalize SCP measures using an unsupervised machine learning approach called doc2vec, creating a similarity matrix of all existing U.S. publicly traded firms. This enables us to construct firm-level measures of strategic competitive positioning with minimal human intervention. To show the effectiveness of the proposed measures, we provide illustrative examples and conduct a case study of the imprinting effect of SCP at the time of initial public offerings on the subsequent performance of those IPOs. This paper makes a significant methodological contribution to the information systems and strategic management literature by proposing a network theory-based approach to measure firm-level competition and strategic positioning.