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.
Based on an industry collaboration with Yahoo! Research
The first MISQ methods article based on machine learning
Presented in WeB (Fort Worth, TX 2015), WITS (Dallas, TX 2015), UT Arlington (2016), Texas FreshAIR (San Antonio, TX 2016), SKKU (2016), Korea Univ. (2016), Hanyang (2016), Kyung Hee (2016), Chung-Ang (2016), Yonsei (2016), Seoul National Univ. (2016), Kyungpook National Univ. (2016), UKC (Dallas, TX 2016), UBC (2016), INFORMS CIST (Nashville, TN 2016), DSI (Austin, TX 2016), Univ. of North Texas (2017), Arizona State (2018), Simon Fraser (2019), Saarland (2021), Kyung Hee (2021), Tennessee Chattanooga (2021), Rochester (2021), KAIST (2021), Yonsei (2021), UBC (2022)
This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model’s power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research.
Presented in Chicago Marketing Analytics (Chicago, IL 2013), WeB (Auckland, New Zealand 2014), Notre Dame (2015), Temple (2015), UC Irvine (2015), Indiana (2015), UT Dallas (2015), Minnesota (2015), UT Arlington (2015), Michigan State (2016), Korea Univ (2021)
Dissertation Paper #3
Research assistant: Raymond Situ
The mobile applications (apps) market is one of the most successful software markets. As the platform grows rapidly, with millions of apps and billions of users, search costs are increasing tremendously. The challenge is how app developers can target the right users with their apps and how consumers can find the apps that fit their needs. Cross-promotion, advertising a mobile app (target app) in another app (source app), is introduced as a new app-promotion framework to alleviate the issue of search costs. In this paper, we model source app user behaviors (downloads and postdownload usages) with respect to different target apps in cross-promotion campaigns. We construct a novel app similarity measure using latent Dirichlet allocation topic modeling on apps’ production descriptions and then analyze how the similarity between the source and target apps influences users’ app download and usage decisions. To estimate the model, we use a unique data set from a large-scale random matching experiment conducted by a major mobile advertising company in Korea. The empirical results show that consumers prefer more diversified apps when they are making download decisions compared with their usage decisions, which is supported by the psychology literature on people’s variety-seeking behavior. Lastly, we propose an app-matching system based on machine-learning models (on app download and usage prediction) and generalized deferred acceptance algorithms. The simulation results show that app analytics capability is essential in building accurate prediction models and in increasing ad effectiveness of cross-promotion campaigns and that, at the expense of privacy, individual user data can further improve the matching performance. This paper has implications on the trade-off between utility and privacy in the growing mobile economy.
Presented at UKC (2017), KISTI (2017), WITS (2017), Rutgers Business School (2018)
There are increasing needs for understanding and fathoming of the business management environment through big data analysis at the industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm-level analyses using publicly available company disclosure data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels.
Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries’ topic trend, software and hardware industries are compared in recent 20 years. Also, the changes in management subject at the firm level are observed with a comparison of two companies in the software industry. The changes in topic trends provide a lens for identifying decreasing and growing management subjects at industrial and firm-level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at the firm level in the software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades.
For suggesting a methodology to develop an analytical model based on public management data at the industrial and corporate level, there may be contributions in terms of making the ground of practical methodology to identifying changes of management subjects. However, there are required further researches to provide a microscopic analytical model with regard to the relation of technology management strategy between management performance in case of related to the various pattern of management topics as of frequent changes of management subject or their momentum. Also, more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.