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.
Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “Strategic Competitive Positioning: An Unstructured Structural Hole-based Firm-specific Measure”, Under Review. [Submitted: May 13, 2022]
In this research methods paper, we propose a firm-specific strategic competitive positioning (SCP) measure to capture a firm’s unique competitive and strategic positioning based on annual corporate filings. Using an unsupervised machine learning approach, we use structural holes, a concept in network theory, to develop and operationalize an SCP measure derived from a strategic similarity matrix of all existing U.S. publicly traded firms. This enables us to construct a robust firm-level SCP measure with minimal human intervention. Our measure dynamically captures competitive positioning across different firms and years without using artificially bounded industry classification systems. We illustrate how the measure dynamically captures firm-level, industry-level, and cross-industry strategic changes. Then, we demonstrate the effectiveness of our measure with an empirical demonstration showing the imprinting effect of SCP at the time of initial public offering (IPO) on the subsequent performance of the firm. The results show that our unsupervised SCP measure predicts post-IPO performance. This paper makes a significant methodological contribution to the information systems and strategic management literature by proposing a network theory-based unsupervised approach to dynamically measure firm-level strategic competitive positioning. The measure can be easily applied to firm-specific, industry-level, and cross-industry research questions across a wide variety of fields and contexts.