Consumer Dynamics, Analytical Customer Relationship Management, Internet and Interactive Marketing, Analytical and Empirical Analysis of Signaling Models
Academic Degrees
PhD, Carnegie Mellon University
MSIA, Carnegie Mellon University
MA Honors, Peking University
BA Honors, Peking University
Professional Experience
John R. Gibbs Professor and Professor of Marketing, Marketing Department, Kelley School of Business, Indiana University, August 2017 – present
Professor of Marketing and Weimer Faculty Fellow, Marketing Department, Kelley School of Business, Indiana University, April 2016 - July 2017
Associate Professor of Marketing (with tenure) and Weimer Faculty Fellow, Marketing Department, Kelley School of Business, Indiana University, July 2011 - April 2016
Assistant Professor of Marketing, Marketing Department, Kelley School of Business, Indiana University, July 2005 – June 2011
Assistant Professor of Marketing, Marketing Department, Rutgers Business School, Rutgers University, September 2003 – June 2005
Awards, Honors & Certificates
Journal of Marketing Outstanding Reviewer Award, 2025
Chair 2025 for the Statistics in Marketing Section, American Statistical Association
Finalist, the Paul E. Green Award, Journal of Marketing Research, 2023
Ding, A. W., and Li, S. (2025). Generative AI Lacks the Human Creativity to Achieve Scientific Discovery from Scratch. Scientific Reports, 15, 9587.
Abstract
Scientists are interested in whether generative artificial intelligence (GenAI) can make scientific discoveries similar to those of humans. However, the results are mixed. Here, we examine whether, how and what scientific discovery GenAI can make in terms of the origin of hypotheses and experimental design through the interpretation of results. With the help of a computer-supported molecular genetic laboratory, GenAI assumes the role of a scientist tasked with investigating a Nobel-worthy scientific discovery in the molecular genetics field. We find that current GenAI can make only incremental discoveries but cannot achieve fundamental discoveries from scratch as humans can. Regarding the origin of the hypothesis, it is unable to generate truly original hypotheses and is incapable of having an epiphany to detect anomalies in experimental results. Therefore, current GenAI is good only at discovery tasks involving either a known representation of the domain knowledge or access to the human scientists’ knowledge space. Furthermore, it has the illusion of making a completely successful discovery with overconfidence. We discuss approaches to address the limitations of current GenAI and its ethical concerns and biases in scientific discovery. This research provides insight into the role of GenAI in scientific discovery and general scientific innovation.
Cao, J., Chintagunta, P., and Li, S. (2023). From Free to Paid: Monetizing a Non-Advertising Based App. Journal of Marketing Research, 60(4), 707-727.
Abstract
Non-advertising-based mobile apps face several critical challenges when trying to monetize their free services; among them: the choice of pricing strategies (hard landing vs. soft landing, i.e., a “pay or churn” paywall or continue offering limited free services to existing users after monetization) and aspects of product design (whether to provide exclusive secondary offerings to paying users). We implemented a large-scale randomized field experiment with an app firm to test the causal effects of such pricing and product design strategies. Results show that both soft landing and exclusive secondary offerings decrease existing app users’ willingness to subscribe; but there is a positive interaction between these two strategies on subscriptions. We propose a theoretical framework, discuss potential mechanisms that might be at play, and conduct robustness checks to rule out several alternative explanations. A customer survey by the firm and an experiment on Prolific provide further support for the theoretical mechanism. To assess generalizability, we conducted a second field experiment and obtained consistent results. We also report the results from the actual implementation of the best performing strategy by the firm. Our research provides guidance on possible theoretical underpinnings of users’ responses and important managerial implications for app monetization.
Zhang, X., Zhang, K., Li, S., and Koenitz, D. (2023). Effects of Store Fixture Shape at Retail Checkout: Evidence from Field and Online Studies. Production and Operations Management, 32(10), 3158-3173.
Abstract
Previous research from both marketing and operations management has shown that servicescape design, as an effective retail operations strategy, can significantly influence consumers’ purchases. We extend this literature by investigating how fixture shape, an important environmental factor that has received little attention in previous studies, can influence consumer purchase intentions at retail checkout. Findings from a field study and three online experiments consistently suggest that tower-shaped fixtures, in comparison to other conventional fixtures, can increase checkout purchase intentions. Moreover, the proposed effect is driven by the increased visual attention to products when displayed on tower fixtures. We also identify consumers’ information processing style (i.e., more vs. less reliant on visual processing) as a theoretically relevant moderator. Specifically, customers who rely more heavily on visual information processing are more susceptible to the influence of a tower fixture than those who rely less on visual information processing. Through this research, we contribute novel insights to the understanding of display fixtures and retail operation management. This study augments the design task from product shape to fixture shape for manufacturers, and sheds light on retailer merchandising strategy as well as retail space management at checkout.
Han, Y., Chandukala, S., and Li, S. (2022). Impact of Different Types of In-Store Displays on Consumer Purchase Behavior. Journal of Retailing, 98(3), 432-452.
Abstract
Research on consumer in-store shopping behavior does not account for the existence of different types of display locations (e.g. storefront, store rear, secondary, front end cap, rear end cap, and shelf displays). This article focuses on accounting for and understanding the impact of various displays on consumer purchase behavior based on the Stimulus-Organism-Response (SOR) theory. Specifically, we study how displays closer to and farther from the main location of the focal category influence consumer purchase behavior. Furthermore, within the different types of displays we investigate the impact of specific types of displays on consumer’s category purchase and brand choice and the moderating role of price and discounts. A hierarchical Bayesian model is estimated using scanner panel data for a large U.S. grocery chain that contains unique information on the number of product facings at multiple display locations within a store. We find that displays closer to the focal category have a larger impact, with front end cap displays having the largest impact on category purchase and shelf displays having the largest impact on brand choice. We also demonstrate the synergistic impact of price and discounts in enhancing the impact of displays on consumer purchase behavior and brand choice. Equipped with these findings we propose a display allocation optimization that results in an average increase in revenue of about 11.15% and a strategy to distribute displays across all locations in the store rather than letting one location dominate.
McMullen, J. S., Ding, A. W., and Li, S. (2021). From Cultural Entrepreneurship to Economic Entrepreneurship in Cultural Industries: The Role of Digital Serialization. Journal of Business Venturing, 36(6), 106157.
Abstract
Digitization has provided entrepreneurs direct access to consumers in cultural industries while offering intermediaries an alternative to critics’ reviews when deciding whether to invest in creative products. Using data from the Chinese online self-publishing industry, we examine whether and how intermediaries use popular acclaim when deciding to invest in self-published books. We then flip the script and examine whether cultural entrepreneurs generate intermediary investment through popular acclaim and to what extent they do so through a digital serialization strategy. We find that, by encouraging both popular acclaim and intermediary investment, digital serialization emancipates cultural entrepreneurs from the indirect and uncertain reciprocity historically described by cultural entrepreneurship theory. Instead, digital serialization allows cultural entrepreneurs to generate consumer attention directly through economic entrepreneurship and to alter the power and roles of intermediaries and entrepreneurs in the cultural production process.
Ding, A. W., and Li, S. (2021). National Response Strategies and Marketing Innovations during the COVID-19 Pandemic. Business Horizons, 64(2), 295-306.
Abstract
During the COVID-19 pandemic, different nations have adopted a variety of response strategies to fight and contain the new coronavirus. Such national response strategies can be classified into three categories based on their underlying philosophy: strict control with unlimited resources, relentless contribution with limited resources, and rough rationality with limited resources. We discuss the philosophies, characteristics and performances of the three response strategies and when they should be adopted. We also examine what marketing innovation strategies that enterprises should adopt to survive and grow their businesses in both the short and long term. This study provides important strategic implications for national policymakers and enterprises on the use of response strategies and marketing innovation tactics and strategies both during and after the pandemic.
Ding, A. W., Li, S. (2019), Herding in the Consumption and Purchase of Digital Goods and Moderators of the Herding Bias, Journal of the Academy of Marketing Science, 47(3), 460-478.
Abstract
Digital goods are increasingly produced by average individuals in a serialized fashion. However, it is unclear whether users engage in herding in the consumption and purchase of such digital goods and what the moderators of the herding effect are. Thus, we propose a simultaneous equations model based on herding theory to theoretically examine users’ potential herding behavior through two competing effects: the private signal effect and the sequential actions effect, which refer to the impact of the private signals and observed sequential actions of others on user quality inference and herding, respectively. The model is implemented in a hierarchical Bayes framework, and it is estimated using data from the top Chinese literature site. The empirical results suggest that users engage in rational herding in both digital book consumption and purchase on the focal site and that the herding bias is surprisingly stronger for purchasing. Product features significantly mitigate the herding bias, while the reputation of the producer and competition exacerbate the herding effect. The impact of this rational herding is also quantified. This study offers new insights and important theoretical and managerial implications for marketing researchers, amateur producers, marketing managers, and publishers.
Zhang, X., Li, S., and Burke, R. R. (2018). Modeling the Effects of Dynamic Group Influence on Shopper Zone Choice, Purchase Conversion and Spending. Journal of the Academy of Marketing Science, 46(6), 1089–1107.
Abstract
In many retail contexts, social interaction plays an important role in the shopping process. We propose a three-stage dynamic linear model that captures the influence of group discussion on shopper behavior within a hierarchical Bayes framework. The model is tested using a video tracking and transaction dataset from a specialty apparel store. The research reveals that group conversations have a significant impact on the shopper’s department or Bzone choice, purchase likelihood, and spending over time. This group influence is magnified by the size of the group (particularly for zone penetration and purchase conversion), and is also moderated by group composition and cohesiveness. The conversations of mixed-age groups and groups who stay together while shopping have a significant influence on shopper behavior across all three stages, while discussions by adult groups exhibit a marginal carryover effect for purchase conversion. When shoppers have repeated discussions in a specific department, they are more likely to return to and buy from this department, while the cumulative number of discussions in the store drives higher spending levels. We also observe that group shoppers visit more departments than their solo counterparts; and mixed-age groups and solo shoppers are more likely to buy than adults-only or teen groups. This study has important implications for how retailers manage shopper engagement and group interaction in their stores.
Li, S., Sivadas, E., and Johnson, M. (2015). Explaining Article Influence: Capturing Article Citability and Its Dynamic Effects. Journal of the Academy of Marketing Science,43(1), 52-72.
Ding, A. W., Li, S., and Chatterjee, P. (2015). Learning User Real-Time Intent for Optimal Dynamic Webpage Transformation. Information Systems Research, 26(2), 339-359.
Sun, Y., Li, S., and Sun, B. (2015). An Empirical Analysis of Consumer Purchase Decisions under Bucket-Based Price Discrimination. Marketing Science, 34(5), 646-668.
Zhang, X., Li, S., Burke, R. R., and Leykin, A. (2014). An Examination of Social Influence on Shopper Behavior Using Video Tracking Data. Journal of Marketing, 78(5), 24-41.
Li, S., Sun, B., and Montgomery, A. L. (2011). Cross-Selling the Right Product to the Right Customer at the Right Time. Journal of Marketing Research, 48(4), 683-700.
Abstract
Firm are challenged to improve the effectiveness of cross-selling campaigns. In this research, we propose a customer-response model that recognizes the evolvement of customer demand for various products, the possible multi-faceted roles of cross-selling solicitations for promotion, advertising, and education, and customer heterogeneous preference for communication channels. We formulate cross-selling campaigns as solutions to a stochastic dynamic programming problem in which the firm’s goal is to maximize the long-term profit of its existing customers while taking into account the development of customer demand over time and the multi-stage role of cross-selling promotion. The model yields optimal cross-selling strategies about how to introduce the right product to the right customer at the right time using the right communication channel. Applying the model to panel data with cross-selling solicitations provided by a national bank, we demonstrate that households have different preferences and responsiveness to cross-selling solicitations. Other than generating immediate sales, cross-selling solicitations also help households move faster along the financial continuum (educational role) and build up good will (advertising role). We show that the suggested cross-selling solicitations are more customized and dynamic and significantly improve over the currently adopted campaign-centric solicitations.
Sun, B., and Li, S. (2011). Learning and Acting on Customer Information: A Simulation-Based Demonstration on Service Allocations with Offshore Centers. Journal of Marketing Research, 48(1), 72-86.
Abstract
As service centers become crucial corporate assets for increasing customer relationships and profits, it is imperative to understand customer reactions to service allocations. Using customer call history from a DSL service, the authors empirically investigate how customers’ onshore and offshore experiences affect service duration and customer retention. They formulate service channel allocation decisions as solutions to a dynamic programming problem in which the firm learns about heterogeneous customer preferences, balances short-term service costs with long-term customer retention, and optimally matches customers with their preferred centers to maximize long-term profit. They demonstrate through simulations that learning enables a firm to make more customized allocations and that acting on long-term customer responses prompts the firm to make proactive decisions that prevent customers from leaving. As a result, the firm can improve customer retention and profit. The proposed framework also mirrors the recent trend of companies seeking solutions that transform customer information into customized and dynamic marketing decisions to improve long-term profit.
Kalra, A., Li, S., and Zhang, W. (2011). Understanding Responses to Contradictory Information about Products. Marketing Science, 30(6), 1098-1114.
Abstract
While prior literature has examined reactions to drastic negative news, we examine the situation how decision makers receive contradictory information about products where they have to decide whether to persist or abandon product usage. We investigate physician reactions to conflicting information concerning cardiovascular risk of Avandia, a diabetes drug. We examine how beliefs both about drug effectiveness and drug safety are updated speculating that experience, expertise and self-efficacy impacts how such information is integrated with current quality beliefs. Unlike previous Bayesian learning models, we consider that some signals like positive and negative news releases and the firm’s marketing effort may be biased in that they provide an opinionated point of view. The results show interesting differences in how physician types (specialists, hospital-based PCPs, heavy and light prescribers) update their beliefs and information sources they use to do so. We find evidence that safety issue about Avandia resulted in spillover concern to close competitor Actos. The results have implication for determining who should be targeted and what vehicles should be used if a firm is faced with a situation where consumers are in a quandary due to receiving conflicting messages.
Li, S., Srinivasan, K., and Sun, B. (2009). Internet Auction Features as Quality Signals. Journal of Marketing, 73(1), 75-92.
Abstract
Internet auction companies have developed innovative tools that enable sellers to reveal more information about their credibility and product quality to avoid the “lemons” problem. On the basis of signaling and auction theories, the authors propose a typology of Internet auction quality and credibility indicators, adopt and modify Park and Bradlow's (2005) model, and use eBay as an example to examine empirically how different types of indicators help alleviate uncertainty. This empirical evidence demonstrates how Internet auction features affect consumer participation and bidding decisions, what modifies the credibility of quality indicators, and how different buyers react to indicators. The signaling-based hypotheses provide coherent explanations of consumers' bidding behavior. As the first empirical study to evaluate the signaling role of comprehensive Internet auction institutional features in mitigating the adverse selection problem, this research provides evidence to clarify the economic foundation behind innovative Internet auction designs.
Kalra, A. and Li, S. (2008). Signaling Quality through Specialization. Marketing Science, 27(2), 168-184.
Abstract
Firms frequently position themselves as specialists. An implication of specialization is that the firm has forgone alternative opportunities. In the context of effort-intensive categories, we show that a firm can signal quality to consumers by specializing. In the model, a firm must decide to provide one service offering or to market two services. By entering a single category, the firm incurs an opportunity cost of not entering the secondary profitable category, but may attain reduced costs. The net cost is the signaling cost that a high-quality type firm incurs to signal quality over a low-quality type firm. We show that in homogenous markets, a high-quality type firm signals its high-quality type by specializing in one category. When consumers are heterogeneous, the firm can signal its high-quality type by using prices alone in both the primary and secondary categories. However, specialization can be used as a secondary signal of quality in heterogeneous markets because of lower signaling costs. We also find that signaling using specialization is more likely in the presence of competition.
Sun, B., Li, S., and Zhou, C. (2006). 'Adaptive' Learning and 'Proactive' Customer Relationship Management. Journal of Interactive Marketing, 20(3-4), 82-96.
Abstract
Customer Relationship Management (CRM) is about introducing the right product to the right customer at the right time through the right channel to satisfy the customer's evolving demands; however, most existing CRM practice and academic research focuses on methods to select the most profitable customers for a scheduled CRM intervention. In this article, we discuss a two-step procedure comprising "adaptive learning" and "proactive" CRM decisions. We also discuss three key components for customer-centric CRM: adaptive learning, forward-looking, and optimization. We then formulate CRM interventions as solutions to a stochastic dynamic programming problem under demand uncertainty in which the company learns about the evolution of customer demand as well as the dynamic effect of its marketing interventions, and make optimal CRM decisions to balance the cost of interventions and the long-term payoff. Finally, we choose two examples to demonstrate the input, output, and benefit of "adaptive" learning and "proactive" CRM.
Li, S., Sun, B., and Wilcox, R. T. (2005). Cross-Selling Sequentially Ordered Products: An Application to Consumer Banking Services. Journal of Marketing Research, 42(2), 233-239.
Abstract
Customers have predictable life cycles. As a result of these life cycles, firms that sell multiple products or services frequently observe that, in general, certain items are purchased before others. This predictable phenomenon provides opportunities for firms to cross-sell additional products and services to existing customers. This article presents a structural multivariate probit model to investigate how customer demand for multiple products evolves over time and its implications for the sequential acquisition patterns of naturally ordered products. The authors investigate customer purchase patterns for products that are marketed by a large midwestern bank. Among the substantive findings are that women and older customers are more sensitive to their overall satisfaction with the bank than are men and younger customers when determining whether to purchase additional financial services, and households whose head has a greater level of education or is male move more quickly along the financial maturity continuum than do households whose head has less education or is female.
Montgomery, A. L., Li, S., Srinivasan, K. and Liechty, J. C. (2004). Modeling Online Browsing and Path Analysis Using Clickstream Data. Marketing Science, 234), 579-595.
Abstract
Clickstream data provide information about the sequence of pages or the path viewed by users as they navigate a website. We show how path information can be categorized and modeled using a dynamic multinomial probit model of Web browsing. We estimate this model using data from a major online bookseller. Our results show that the memory component of the model is crucial in accurately predicting a path. In comparison, traditional multinomial probit and first-order Markov models predict paths poorly. These results suggest that paths may reflect a user's goals, which could be helpful in predicting future movements at a website. One potential application of our model is to predict purchase conversion. We find that after only six viewings purchases can be predicted with more than 40% accuracy, which is much better than the benchmark 7% purchase conversion prediction rate made without path information. This technique could be used to personalize Web designs and product offerings based upon a user's path.
