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Webrooming

Bing Jing

Most products comprise both digital and inspection attributes. For example, while the style of apparel and shoes can be conveyed online, assessing fit still requires personal inspection. In a discrete model of match, we examine the effects of webrooming in a duopoly. When the match probability of the digital attribute is sufficiently low, a counter-intuitive result is that webrooming increases both firms’ profits. Here the reason is that webrooming induces greater participation. This finding is thus opposite to the general impression from the popular press that webrooming intensifies competition by releasing more information. We then generalize this analysis to a model of continuous match values. Here, webrooming still increases both firms’ profits under broad conditions. The reason is that webrooming informs each consumer about her relative preference over the two firms’ digital attributes and, consequently, her optimal search sequence.

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C.S. Agnes CHENG

 

Biography

Professor Agnes Cheng graduated from National Taiwan University, Taiwan, with a Bachelor of Science degree in Business.  She obtained a Master of Science degree in Accounting from National Chengchi University, Taiwan and a Doctor of Philosophy degree in Accountancy from University of Illinois at Urbana-Champaign in the USA.

Professor Cheng taught at Houston from 1986 to 2007.  During her appointment in University of Houston, she has also served as the Director of the Asian MBA Programme from 2000 to 2002, Visiting Full Professor in University of Arkansas, USA, from 1999 to 2000 and Visiting Academic Scholar in the Office of Economic Analysis of U.S. Securities and Exchange Commission from 2004 to 2005.  Professor Cheng has served as Ourso Distinguished Research Chair in Accounting and Ph.D. Programme Coordinator in Louisiana State University, USA from 2007 to 2013.

Professor Cheng’s current research interest focuses on empirical financial accounting research. Her published work includes a research monograph (Studies in Accounting Research #29, published by American Accounting Association) and numerous articles. She has published articles in journals such as Journal of Accounting Research, The Accounting Review, Journal of Financial Economics, Decision Sciences, Review of Economics and Statistics, Journal of International Business Studies, Journal of Business, Finance and Accounting, Auditing, A Journal of Practice and Theory, Accounting and Business Research, Journal of Management Accounting Research and many others.

Professor Cheng is Co-Editor of Journal of Contemporary Accounting and Economics; and Associate Editor of Journal of International Accounting Research and Journal of Accounting, Auditing & Finance; she also serves on the editorial board of Review of Pacific Basin Financial Markets and Policies (RPBFMP).  In the past, Professor Cheng served as editor of Asia-Pacific Journal of Accounting (APJAE, a SSCI Journal) from 2010-2012; the editorial advisory and review board of The Accounting Review from 1992 to 1995, the editorial board for The Review of Business Studies / International Journal of Business from 1994 to1999, the editorial board of The International Journal of Accounting from 1998 to 2000 and the editorial board of Asian Pacific Journal of Accounting and Economics from 2002 to 2004.

Professor Cheng also held some executive positions in professional organizations.  She is Asia Society Houston’s Advisory Board Member.  She was the President of Chinese Accounting Professors Association of North America (CAPANA) from 1994 to 1995, Vice President, International, of American Accounting Association (AAA) from 1999 to 2001 and Vice President of International Association for Accounting Education and Research (IAAER) from 2002 to 2009. Professor Cheng won the KPMG Research Award in 2010 and Louisiana State University 100 Rainmaker Award in 2009.

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Informational Complementarity

Tony Ke, Lin Song

Many products are correlated because they share some similar or common attributes. We show that when these attributes are uncertain to consumers, a complementarity effect can arise among competing products, in the sense that a lower price of one product may increase the demands of others. The effect occurs when consumers optimally search for information about both common and idiosyncratic product attributes prior to purchase. We characterize the optimal search strategy for the correlated search problem and provide the conditions for the existence of the complementarity effect. We further explore the implications of the effect for firm pricing. When firms compete in price, although product correlation may weaken differentiation between the firms, the complementarity effect due to correlated search may raise equilibrium price and profit.

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Bin KE

Biography

Dr. Ke is a Professor of Accounting, Provost’s Chair, and Director of Asia Accounting Research Centre at the NUS Business School since 2015. He is a holder of the prestigious “Chang Jiang Scholar” (“长江学者”) title awarded by China’s Ministry of Education and the Li Ka Shing Foundation (http://www.lksf.org/eng/project/education/ckh_award/main01.shtml). He was the President of the Chinese Accounting Professors Association of North America (www.capana.net), a leading academic organization that promotes high-quality accounting research on China, the Asia Pacific region, and other emerging market economies.

Dr Ke’s primary teaching interests include financial accounting principles, financial statement analysis, and doctoral seminars on empirical financial accounting research. He has also taught U.S. federal income taxation.

Dr. Ke’s primary research interests focus on the economic forces that determine the production and use of accounting information in business decisions. He is interested in using interdisciplinary approaches to tackle today’s complex business problems. Examples of his research include earnings management, insider trading, institutional investors, and financial analysts. Dr. Ke’s recent research focuses on financial reporting, managerial incentives, and investor protection in emerging markets with a particular focus on China. His research has been published in all major accounting journals, including The Accounting Review, Journal of Accounting and Economics, Journal of Accounting Research, Review of Accounting Studies, and Contemporary Accounting Research.

Dr. Ke is a consulting editor of the China Journal of Accounting Research, a current or former editorial board member of the Journal of American Taxation Association, The Accounting Review, and The International Journal of Accounting. He was an advisory board member of the Accounting Research in China published by the Accounting Society of China. He was an editor of The Accounting Review over June 2011-May 2014.

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LIN Song

Song Lin is an Assistant Professor of Marketing at the Department of Marketing of Hong Kong University of Science and Technology. He holds a PhD in Marketing from Massachusetts Institute of Technology. His research interests include product and pricing polices, platform design, consumer learning and search, new products, and advertising.  He has won the 2013 INFORMS Society for Marketing Science (ISMS) Doctoral Dissertation Proposal Competition, and the finalist for the 2015 John Little Award for the best marketing paper published in Marketing Science and Management Science.

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Letting Logos Speak: A Machine Learning Approach to Data-Driven Logo Design

Ryan Dew, Asim Ansari, Olivier Toubia

Logos serve a fundamental role in branding as the visual figurehead of the brand. Yet, due to the difficulty of using unstructured image data, prior research on logo design has been largely limited to non-quantitative studies. In this work, we explore logo design from a data-driven perspective. In particular, we aim to answer several key questions: first, to what degree can logos represent a brand’s personality? Second, what are the key visual elements in logos that elicit brand and firm relevant associations, such as brand personality traits? Finally, given text describing a firm’s brand or function, can we suggest features of a logo that elicit the firm’s desired image? To answer these questions, we develop a novel logo feature extraction algorithm, that uses modern image processing tools to decompose unstructured pixel-level image data into meaningful visual features. We then analyze the links between firm identity, and the features of its logo, through both predictive modelling, and a probabilistic model which links visual features with textual descriptions of firms. We apply our modeling framework on a dataset of hundreds of logos, textual descriptions from firms’ websites, third party descriptions of firms, and consumer evaluations of brand personality to explore these questions.

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Ryan Dew

Ryan Dew is an Assistant Professor of Marketing at the Wharton School of the University of Pennsylvania. He received his B.A. in Mathematics from the University of Pennsylvania, and his M.Phil. and Ph.D. in Marketing from Columbia University. His research explores how machine learning and Bayesian statistical methodologies can solve real world marketing problems, with a particular interest in the domains of customer relationship management, preference dynamics and estimation, and data-driven design. His recent work, Bayesian Nonparametric Customer Base Analysis with Model-based Visualizations, has been published in Marketing Science. His on-going research focuses on understanding and predicting how consumer preferences change over time, through novel hierarchical nonparametric models, and on understanding the underpinnings of effective logo design from a data-driven perspective, utilizing image processing and machine learning techniques. His dissertation, Machine Learning Methods for Data-driven Decisions, was a winner of the ISMS Doctoral Dissertation Proposal Competition, the Marketing Section of the American Statistical Association’s Doctoral Research Award, and was an honorable mention in the Marketing Science Institute’s Alden G. Clayton Doctoral Dissertation Proposal Competition.

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The Role of the Physical Store: Developing Customer Value through ‘Fit Product’ Purchases

Chun-wei Chang, Jonathan Z. Zhang, Scott Neslin

Recent trends suggest retailers are ambivalent regarding the contribution of the physical retail store.  Ironically, several traditionally offline retailers are closing stores, while some traditionally online retailers are opening them.  This raises the question, what is the role of the physical retail store in today’s multichannel environment?  We posit that the type of product purchased, “fit” or “non-fit”, impacts subsequent customer value, and that purchasing fit products offline is especially effective at creating high value customers. We formulate a multivariate hidden Markov model (HMM) to investigate how customers make product and channel decisions. The HMM identifies two dynamic states – low-value and high-value. We hypothesize and find that fit-product purchases accelerate customer migration to the high-value state, especially if those purchases are made in the physical store. We theorize this occurs because buying fit products requires customer engagement, the physical store excels at providing this engagement, and engagement leads to higher customer satisfaction and hence value.  In addition, we find that offline marketing communication, specifically direct mail, enhances the likelihood the customer buys fit products offline and hence migrates customers to the high-value state, or keeps them at high value if they are already there. Our findings identify a strategic role that fit products and retail stores play in customer development, and show that marketing can help implement this strategy.

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Scott Neslin

Scott A. Neslin is the Albert Wesley Frey Professor of Marketing at the Tuck School of Business, Dartmouth College.  He has been a visiting scholar at the Yale School of Management, the Fuqua School of Business, and Columbia Business School. Professor Neslin’s expertise is in the fields of customer relationship management, measurement of marketing effectiveness, sales promotion, and advertising.  He has published several papers on these topics in leading academic journals.  He is co-author with Robert C. Blattberg and Byung-Do Kim of the book, Database Marketing:  Analyzing and Managing Customers, co-authors with Robert C. Blattberg of the book, Sales Promotion: Concepts, Methods, and Strategies, and author of the Marketing Science Institute monograph, Sales Promotion. Professor Neslin has served as President of the INFORMS Society for Marketing Science (ISMS) and is an ISMS Fellow.

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The Impact of Soda Taxes on Nutritional Intake and Welfare

Stephan Seiler, Anna Tuchman, Song Yao

Price-based interventions are widely considered by policy makers as a tool to shift customers’ behavior. This paper investigates one such policy intervention where a local government imposed a tax on sweetened beverages in order to discourage unhealthy food consumption and fight obesity and diet-related diseases. Through an extensive set of analyses, we document the effect of the tax on retailers’ pricing decisions and market demand for taxed products and substitutes. We show that the tax on sweetened beverages has had limited effects in reducing total consumption or leading to a shift in consumption towards healthier products. Furthermore, the financial burden is the highest for low income households, while higher income households avoid the tax by driving to stores outside the taxed zone.

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YAO Song

Song Yao is an Associate Professor of Marketing at the Carlson School of Management, University of Minnesota. Professor Yao has won the 2012  Paul Green Best Paper Award and the 2009 John Howard Dissertation Award, both of which are sponsored by the American Marketing Association. He was the finalist for INFORMS Long Term Impact Award in 2017, the Frank Bass Outstanding Dissertation Award in 2011 and 2012, the John Little Best Paper Award in 2009 and 2011. He has also been selected by the Marketing Science Institute (MSI) as one of the MSI Young Scholars of 2017. He serves on the Editorial Boards of the Journal of Marketing Research, Marketing Science, and Quantitative Marketing and Economics. Professor Yao’s research interests include quantitative marketing, online marketing, advertising, pricing, and customer management. His publications appear in leading academic journals, including Management Science, Marketing Science, the Journal of Marketing Research, and Quantitative Marketing and Economics. Professor Yao received his Ph.D. in Business Administration from Duke University, M.A. in Economics from the University of California, Los Angeles, and B.A. in Economics from the Renmin University of China.