DISC Research Seminar Series

TBD

Retailers are shifting from brick-and-mortar to omnichannel systems. Ship-from-store (SFS) is a prominent yet risky integration because its value depends on product attributes and local shopping frictions: the same program can expand demand for some SKUs while cannibalizing store sales for others. Using transaction and inventory-order data from a large pharmacy chain, we document SKU-level patterns and analytically model how store-only customers and omni-customers choose between visiting the store and ordering online, incorporating shortened online waiting time under SFS and different cross-selling profits by channel. The model’s equilibrium links product characteristics and local geography to demand pooling, in-store fill rates, and profit, yielding testable predictions that separate market expansion from cannibalization and inform SKU selection and inventory targets. Testing these predictions across pre- and post-SFS periods, we find: (i) stores with higher store visit costs realize larger post-SFS profit gains (ii) SKUs with lower baseline in-store availability see larger improvements and (iii) among cannibalization-prone SKUs, order profitability still rises when post-SFS inventory targets are sufficiently high. Together, the model and evidence explain when SFS raises profit and why effects vary across products and locations.

Rising hospitalization rates and costs underscore the need to better understand how health outcomes are shaped by individuals’ social determinants of health, such as lifestyle and socio-economic factors. We propose a novel framework that leverages large-scale, population-level consumer location data to continuously capture granular individual behavior. By combining unsupervised learning with sequential deep learning models, our approach characterizes lifestyle patterns and quantifies their association with future hospitalizations. Applied to 45 million location records from a major U.S. metropolitan area, the framework uncovers diverse lifestyle patterns and their health implications. Our results show that lifestyle choices are stronger predictors of hospitalization risk than socio-economic status, community context, or healthcare accessibility. Importantly, individuals from lower-income communities or with limited access to healthcare can still maintain healthy lifestyles, highlighting opportunities to reduce health disparities through patient self-management. We also find that individuals with busy, varying work routines and limited gym visits are twice more likely to be hospitalized within a year compared to the average. Notably, the regularity of healthy behaviors, rather than their total duration, emerges as a key factor in reducing future hospitalization risk.

The advance of artificial intelligence (AI) technologies has enabled chatbots to be emotionally responsive. In customer service, a critical component of employee emotional responsiveness is to express empathy, which also serves as a popular service recovery strategy. Still, the impact of chatbot-expressed empathy is less clear. In this research, we investigate how and why empathy expressed by a chatbot after service failure may influence customers’ service evaluations differently compared to that expressed by a human. Extending the psychological reactance literature, we propose that an empathic chatbot may trigger customer reactance due to the unique nature of AI chatbots (vs. humans). We further propose that the triggered reactance can undermine perceptions of a chatbot’s competence and warmth, ultimately hurting service evaluations. We conducted three laboratory experiments to test these predictions. Our theoretical framework and findings illuminate the more nuanced and distinct role of empathy expressed by AI chatbots, and they suggest a critical need to be aware of user reactance when AI chatbot developers design empathic chatbots and when companies consider deploying such bots in customer service. Co-authors: Elizabeth Han and Han Zhang

A substantial proportion of purchases in the fashion industry are returned, representing around $890 billion in merchandise annually.  This study investigates how different discount strategies influence net sales, and in particular, return rates. Our analysis focuses on three key factors influencing the effectiveness of discount strategies in enhancing net sales: customers’ uncertainty about a product’s post-purchase value, the post-purchase leverage effect of bundle discounts, and customer inattention to pricing details. Collaborating with one of Turkiye’s largest fashion retailers, we use structural estimation to evaluate bundle discounts in comparison to per-item discounts. Our findings reveal that net sales under bundle discounts can outperform per-item discounts by 15.61% when the post-purchase leverage effect is utilized effectively. However, bundle discounts may underperform when this leverage is absent or poorly implemented. Additionally, we find that customers often exhibit inattention to pricing details, resulting in an overestimation of a product bundle’s value at the time of purchase, which subsequently contributes to higher return rates.

Firms increasingly invest in new technologies to boost productivity, yet little is known about how well workers’ skills align with evolving production systems. We develop a new empirical methodology—leveraging large language models (LLM's) with matched data from worker resumes and firm job postings—to construct novel measures of skill mismatch across firms that utilize different information technologies (IT). We document three sets of findings. First, skill mismatch exhibits a U-shaped pattern as IT evolves: mismatch is high when new technologies are deployed, declines over time as firms and workers adjust, and rises again as technologies become obsolete. Second, mismatch is substantial not only for technical skills, but also for complementary non-technical skills such as managerial ability. Third, skill mismatch is inversely related to firm profitability and total factor productivity over the technology lifecycle. These patterns are consistent with theories of technological change in which skill adjustment lags drive heterogeneity in firm-level productivity. Our results highlight the central role of skill alignment between workers and firms in the diffusion of new technologies. Co-authors: Ashwini Agrawal, London School of Economics Daniel Kim, University of Waterloo

After briefly talking about Information System Research and some recent editorials, I will offer some thoughts about digitalization and its impacts, using the example of digital classrooms in schools. I will highlight the utopian and dystopian narratives on digitalization in the literature and argue that both narratives are incomplete, polarizing, and not particularly helpful. Indeed, Information Systems scholars may be in a unique position to offer a balanced perspective and positively influence digitalization's role in organizations and society. I will end my presentation with some implications for research and practice.

This paper studies a long-term power purchase agreement (PPA) between a firm and a new renewable energy generator. The firm must dynamically satisfy uncertain electricity demand beyond its existing energy sources, while wholesale electricity prices evolve stochastically over time. Upon signing a PPA, a new renewable facility becomes operational, and the firm owns its output for the contract duration. The new facility’s capacity is determined based on PPA terms. The firm dynamically chooses when to initiate the PPA and how much to pay to maximize its expected total discounted benefit. We show that the firm’s optimal timing follows a (time-dependent) threshold policy. Our results offer key insights for policymakers and renewable energy developers. We find that, contrary to common wisdom, reducing investment costs for renewable technologies can lead to smaller renewable capacity, output, and emissions savings when projects are developed under PPAs. This finding calls for caution in applying investment tax credits in such contexts. We also show that total renewable energy generation and emissions savings may decrease with higher site productivity. Therefore, restricting renewable facility development to most productive sites might be counterproductive under PPAs. We establish the robustness of our findings across a broad range of practical scenarios.

This paper examines how firms design their global supply networks in response to perceived trade policy effect uncertainty (TPEU). Drawing on organizational information processing theory and analyzing 33,807 firm-year observations from 2007 to 2022, we document a U-shaped relationship between TPEU and supply network interconnectedness (SNI). This pattern suggests that firms adopt distinct supply network strategies depending on the degree of perceived uncertainty. Specifically, under low TPEU, firms reduce SNI by relying on less connected but more trusted suppliers. In contrast, under high TPEU, firms increase SNI by establishing redundant ties to enhance flexibility. Furthermore, we find that this U-shaped relationship is attenuated for firms with greater digital capability or higher levels of organizational slack. Our results remain robust across a range of tests addressing potential endogeneity concerns.

Artificial Intelligence has seen tremendous successes over the years and is expected to lead to an era of augmented intelligence systems that can leverage both human and AI capabilities in a seamless manner. Yet, this is not without challenges and getting there requires a focus on intentional design. This talk presents an augmented intelligence principle regarding the effectiveness of such systems and uses this principle to highlight key issues including the search for optimal designs and how to do so. Drawing on this perspective we discuss complex systems simulations as a possible alternative and illustrate its value through examples.