Richard Ivey Building 2329
- Machine Learning
- Business Analytics
- Behavioral Economics
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Shane Wang is an Assistant Professor of Marketing at the Ivey Business School, Western University, Canada. His research focuses on machine learning with applications in marketing, conjoint analysis, behavioral economics and Bayesian statistics. His work has appeared in Marketing Science, Journal of Consumer Research, International Journal of Research in Marketing, Journal of Management Information System, Customer Needs and Solutions and Journal of Mathematical Psychology. Professor Wang received his Ph.D. in Marketing from the University of Cincinnati and is on the editorial review board of the Journal of Marketing Research.
- Marketing Management
- Social Media Analytics
- Digital Strategy
- Executive Education
Recent Refereed Articles
Aribarg, A.; Otter, T.; Zantedeschi, D.; Allenby, G. M.; Bentley, T.; Curry, D.; Dotson, M.; Henderson, T.; Honka, E.; Kohli, R., et al.,
(Forthcoming), "Advancing Non-Compensatory Choice Models in Marketing", Customer Needs and Solutions.
Abstract: The extant choice literature has proposed different non-compensatory rules as a more realistic description of consumers’ choice than a standard compensatory model. Some research has further suggested a two-stage sequential decision process of non-compensatory consideration and then compensatory choice, where the determinants of each stage may differ. Some aspects of non-compensatory choice modeling are under-studied. In this article, we hope to advance the understanding of non-compensatory choice models with the following aims: (a) providing an overview of existing representations for non-compensatory choice decisions, (b) discussing how such choice decisions can manifest from the economic search theoretical perspective, (c) exploring the empirical identification of non-compensatory decisions using different data, and (d) presenting applications of non-compensatory choice models in novel domains.
Mai, F.; Shan, J.; Bai, Q.; Wang, X.; Chiang, R. H. L.,
2018, "How Does Social Media Impact Bitcoin Value? A Test of the Silent Majority Hypothesis", Journal of Management Information Systems, March 35(1): 19 - 52.
Abstract: As the world's first completely decentralized digital payment system, the emergence of Bitcoin represents a revolutionary phenomenon in financial markets. This study examines the dynamic relationships between social media and bitcoin performance. We consider the distinct effects of different social media platforms and different user groups subdivided by posting volume. The results suggest that more bullish forum posts have a positive effect on bitcoin returns, and the effect is stronger when we only include the posts by users who are less likely to contribute. In addition, messages on Internet forum have stronger impacts on future bitcoin market measures at a daily frequency, but microblogs’ effects are more significant at an hourly frequency.
He, J.; Wang, X.; Curry, D.,
2017, "Mediation Analysis: A New Test When All or Some Variables are Categorical", International Journal of Research in Marketing, December 34(4): 780 - 798.
Abstract: Statistical tests for mediation in consumer research typically use a regression coefficients (RegCoeff)-based framework. We present a new test based on likelihood ratio principles to complement the RegCoeff approach. We compare the new test's performance to conventional methods that use PROCESS, MEDIATION, and extensions thereof to categorical measures. Our tests address situations in which the assumptions of current methods tend not to hold that is when one or more variables may be categorical or relationships among constructs may be nonlinear. In such environments, results significantly favor the new test, which we call LRT for likelihood ratio test. For example, among 72 tests of power, LRT loses just 9 times to PROCESS. It wins outright in 45 cases and ties in 18 others. Differences are non-trivial they are statistically significant at the 0.001 level in 21 of the 45 wins. To assist researchers, we detail how and when the new test can best complement existing methods and offer software that performs the new test for any combination of continuous and categorical variables.
Bendle, N. T.; Wang, X.,
2017, "Marketing Accounts", International Journal of Research in Marketing, September 34(3): 604 - 621.
Abstract: Marketing actions frequently create long-term value yet this is often not recorded in financial accounts. The same records are typically used for internal reporting limiting both recognition of the value created by marketing, and accountability for the misuse of market-based assets. Creating comprehensive marketing accounts can mitigate the problems caused by financial accounting’s omission of market-based assets. We explain current accounting practice, outline the idea of marketing accounts, and contrast this with current accounting practice. Marketing accounts capture the value of market-based assets, applying accounting’s matching concept as consistently as possible to treat marketing as an investment where appropriate. These accounts are based upon expected value, and are feasible within accounting rules given they aim only to aid management, not investor, decision making. Marketing accounts vary between, but not within, firms, and are comprehensive and regular. Finally, they are controlled by marketers with assumptions and models recorded and approved by the chief marketing officer. We conclude by illustrating how to implement marketing accounts.
Bendle, N. T.; Wang, X.; Mai, F.,
2016, "Understanding Co-Authorship among Consumer Behavior Scholars", Journal for Advancement of Marketing Education, June 24(1).
Abstract: Purpose of the Study: To understand co-authorship habits among a set of consumer behavior scholars. We analyze the consumer behavior community and shed light on why these scholars co-author. MethodDesign and Sample: Our data covers all issues of the Journal of Consumer Psychology and the Journal of Consumer Research to December 2014, totaling 2,698 articles with 5,951 author credits. We describe the data and community characteristics. We advance the literature by using modern social network analysis techniques to map a social network and provide social network metrics across two journals using over 40 years of data. Results: We show the distribution of authorship of papers, highlight co-authorship habits, and illustrate rising coauthorship over time. We reveal the most connected scholars, and those with critical connections. The community is surprisingly coherent while most only publish one paper, 72% of scholars are connected by co-authorship. We highlight what we term active collaboration between the hyper-productive scholars, and demonstrate how intergenerational collaboration works through a school’s network. Value to Marketing Educators: Marketing educators will benefit from the descriptive data we provide to aid administrative and career decisions. We show the network of co-authorship and provide benchmarks for marketing academics. We illustrate that consumer behavior is a meaningful community and provide evidence why scholars collaborate.
Wang, X.; Xie, Y.; Jagpal, H.; Yeniyurt, S.,
2016, "Coordinating R&D, Product Positioning, and Pricing Strategy: A Duopoly Model", Customer Needs and Solutions, June 3(2): 104 - 114.
Abstract: This paper develops an integrated duopoly model for coordinating R&D, product positioning, and pricing strategy. The model can be applied to a broad spectrum of market structures and market conditions since it allows for bidirectional technology transfers (i.e., firms can learn from each other), partial or complete technology transfer, differential quality-adjusted production costs across firms, preference heterogeneity across consumers for different product quality levels, and different behavioral modes of competitive reaction (e.g., sequential or simultaneous decision-making). We show that the managerial implications differ sharply for information goods and physical goods and vary depending on the behavioral modes chosen by the firms and on whether the technology transfers are unidirectional or bidirectional. Interestingly, contrary to common belief, for certain scenarios, product differentiation can increase when technology transfers are bidirectional.
Link(s) to publication:
Bendle, N. T.; Wang, X.,
2016, "Uncovering the Message from the Mess of Big Data", Business Horizons, January 59(1): 115 - 124.
Abstract: User-generated content, such as online product reviews, is a valuable source of consumer insight. Such unstructured big data is generated in real-time, easily accessed, and contains messages consumers want managers to hear. Analyzing such data has potential to revolutionize market research and competitive analysis but how to extract the messages? How to condense the vast amount of data into insights to help steer businesses strategy? We describe a non-proprietary technique that can be applied by anyone with statistical training. Latent Dirichlet Allocation (LDA) can analyze huge amounts of text and describe the content as focusing on unseen attributes in a specific weighting. For example, a review of a graphic novel might be analyzed to focus 70% on the storyline and 30% on the graphics. Aggregating the content from numerous consumers allows us to understand what is, collectively, on consumers’ minds and from this we can infer what consumers care about. We can even highlight which attributes are seen positively or negatively. The value of this technique goes well beyond the CMO’s office as LDA can map the relative strategic positions of competitors where they matter most -- in the minds of consumers.
Link(s) to publication:
Wang, X.; Bendle, N. T.; Mai, F.; Cotte, J.,
2015, "The Journal of Consumer Research at Forty: A Historical Analysis", Journal of Consumer Research, June 42(1): 5 - 18.
Abstract: This article reviews forty years of the Journal of Consumer Research. Using text-mining, the authors uncover the key phrases associated with consumer research. The authors use a topic modeling procedure to uncover 16 topics that have featured in the journal since its inception, and to show the trends in topics over time. For example, the authors highlight the decline in Family Decision Making research and the flourishing of Social Identity and Influence research since the journal’s inception. A citation analysis shows which JCR articles have had the most impact, and compares the topics in top cited articles with all JCR journal articles. The authors show that Methodological and Consumer Culture papers tend to be heavily cited. Finally, the authors conclude by investigating the scholars that have been the top contributors to the journal across the four decades of its existence. And to better understand which schools have contributed most to the knowledge of consumer research over this history, the authors provide an analysis of where these top performing scholars were trained. Our approach shows that the JCR archives can be an excellent source of data for scholars trying to understand the complicated, challenging, and dynamic field of consumer research.
Link(s) to publication:
Wang, X.; Mai, F.; Chiang, R. H. L.,
2014, "Database: Market Dynamics and User--Generated Content about Tablet Computers", Marketing Science, June 33(3): 449 - 458.
Abstract: Our Tablet Computer data set, collected from various websites, contains market dynamics related to 2,163 products, characteristics of 794 products, more than 40,000 consumer-generated product reviews, and information about 39,278 reviewers. The market dynamic information was collected weekly for 24 weeks starting February 1, 2012. Our Tablet Computer data set comprises four tables: the Market Dynamics of Products, Product Characteristic Information, Consumer-Generated Product Reviews, and Reviewer Information tables. In turn, it offers three unique properties. First, it contains both structured product information and unstructured product reviews. Second, it comprises product characteristic information and market dynamic information. Third, this data set integrates user-generated content with manufacturer-provided content. This integrated data set is valuable for both academics and practitioners who conduct research related to marketing, information systems, computer science, and other fields using digital data readily available through the Internet.
Link(s) to publication:
Wang, X.; Dugan, R.; Sojka, J.,
2013, "CRM Systems as a Form of Social Media for Business: The Value of Incorporating CRM Experiential Learning in SalesMarketing Education", Marketing Education Review, October 23(3): 241 - 250.
Abstract: Implementation of a customer relationship management (CRM) 2.0 system can provide both a valuable pedagogical tool and a needed skill set in a marketing and sales curriculum. A CRM 2.0 system incorporated in the sales and marketing curriculum can help manage relationships between students, practitioners, and faculty while teaching students a valuable skill set. Preliminary survey results suggest that students perceive value in incorporating the proposed CRM 2.0 system into the marketing and sales curriculum.
Honours & Awards
- David G. Burgoyne Teaching Award, 2017
- Research Merit Award, Ivey Business School, 2016
- Western University Teaching Honor Roll Award of Excellence, 2015, 2016
- Lindner College of Business Outstanding Doctoral Student Research Award, University of Cincinnati, 2014
- Best Paper in the Marketing Education Track, American Marketing Association Winter Marketing Educator's Conference, 2013