Working Papers under Review and Revision

Discovering and measuring the causal effects of the purchase of one product on the purchases of other products in a shopping basket is an important problem in retailing. It is also extremely challenging because the number of possible interactions among all the products in a retail store can be exponentially large. We present a graph-theoretic causal product network (CPN) representation of consumer basket-shopping behavior and a causal structure learning-based approach to learn the network and recover causal effects among product purchases from basket-shopping data. To address the unique challenge of data sparsity in such datasets, we propose a Euclidean distance-based independence test instead of the partial correlation-based test in the PC algorithm for causal learning. We validate our approach and demonstrate its value by utilizing a large-scale basket-shopping dataset from Numerator. Our results show that CPNs more accurately represents interactions among products than other machine-learning or theory-based models, require significantly fewer parameters, and reveal new insights into consumer behavior such as that the brick-and-mortar channel exhibits denser causal connections among product purchases in a basket than the online channel. Finally, we propose a two-stage graph learning algorithm to improve computational efficiency and demonstrate that the constructed CPN, when applied to a multi-category assortment optimization problem, increases total sales by 8.26%–20.62% compared to a benchmark within-category model, viz., the multinomial logit model. These results make a significant advancement towards utilizing large-scale basket-shopping data for generating new managerial insights and optimizing decisions.

Corporate operational misconduct—also known as operational misconduct—refers to illegal, unethical, or intentionally improper behavior by managers that affects a corporation’s operational systems, which can influence service quality. However, fully understanding the causal relationship and mechanisms is challenging due to (1) much of operational misconduct remaining undetected internally or being concealed from the public, and (2) complex interactions among operational misconduct, service quality, and other factors (e.g., financial and inventory measures), which makes it costly to investigate these pairwise relationships through controlled experimental studies. To address these challenges, we introduce a text-mining approach to detect unobservable misconduct and a causal discovery approach to learn a causal Directed Acyclic Graph (DAG) that reveals the influence pathways from operational misconduct to service performance. We find that misconduct in workforce scheduling can both directly affect service quality and indirectly affect it through inventory levels. In contrast, misconduct in supply chain management has no direct impact but influences service quality indirectly via inventory levels. Additionally, financial measures act as mediators in the influence pathways. We conduct a counterfactual analysis showing that our DAG-based framework can be effectively utilized to enhance service quality. Overall, our study provides new managerial insights through novel measures of operational misconduct and a DAG-based causal framework, enabling companies to effectively enhance service performance by managing misconduct.

Prediction of customer choices is a challenging problem because the choices are influenced by many unobserved factors relevant to customer purchase preferences. Moreover, customer purchase preferences change dynamically as customers make more purchases, further complicating this problem. To address it, we develop a network-based prediction framework comprising three prediction models to forecast the choices of individual customers accurately. The first model is the network regression model of co-purchase probability (NRMCP), which learns about the dynamic evolutions of customer purchase preferences as customers make more purchases. The second model, the Predictive Number Model (PNM), leverages customers' purchase patterns to predict the number of products they will purchase in future orders. The third model, the customer-choice model (CCM), predicts the likelihood of a customer's purchase for each product, enabling the forecast of the customer's choices in future orders. Given the Instacart dataset, our proposed prediction framework outperforms benchmarking models in prediction accuracy by around 4.5%-23%. Based on the NRMCP model, we empirically analyze the dynamic evolution of customer purchase preferences and show its significance in customer decision-making. Besides, our paper introduces a novel approach to estimate the complementary and substitution effects between products from co-purchased product networks whose nodes are products and edges are co-purchase relations. Based on our approach, we validate the asymmetric complementary and substitution effects empirically. To demonstrate the application of our proposed prediction framework in operational problems, we employ it to conduct an assortment optimization task.

As obesity remains one of the most prevalent health concerns in the United States, the government and other public sectors have increasingly invested in pharmaceutical R&D to address it. Beyond the goal of maximizing R&D output, the government has broader responsibilities, such as collecting accurate market information (e.g., private R&D cost) to support the design of effective subsidies and policy interventions for obesity treatment. Our study addresses this challenge by proposing a mechanism that incentivizes firms to truthfully reveal market information (as reflected in their private development costs), while preserving the government's payoff to the greatest extent. We design a funding mechanism in a static setting with multiple firms, in which truthful reporting is theoretically optimal for risk-neutral firms. However, firms do not always behave fully rationally in practice. To account for potential behavioral biases, we conduct laboratory experiments comparing our mechanism to two benchmarks: a threshold-based mechanism and a first-price auction-based mechanism. The experimental results show that firms report private information more truthfully under our proposed mechanism. We also provide behavioral explanations for these findings, shedding new light on behavioral biases in pharmaceutical R&D funding allocation. Overall, our paper proposes an effective mechanism for public sectors to elicit truthful information from pharmaceutical firms while preserving the public sector's expected payoff. The mechanism, supported by both theoretical and experimental evidence, also offers guidance for designing funding policies aligned with public health objectives.

Customer review manipulation is a common strategy employed by sellers of online marketplaces to combat competitors. The impact of this deceptive behavior on the competitive pricing of online marketplaces is intricate. First, buying fake reviews incurs additional costs and alters customer demand in competitive settings by misrepresenting product information. Second, the pricing is determined through internal competitions, where sellers compete with each other within an online marketplace. This is because the winner's price is the default price displayed to customers, representing the price of the online marketplace. Meanwhile, online marketplaces also effectively manage prices in order to stay competitive in external competition against multi-channel retailers, further complicating this problem. To unravel this influence mechanism, we build instrumented econometric models and develop a game-theoretic model to empirically and theoretically analyze this influence mechanism in the context of internal and external competitions, respectively. Our study empirically shows that the level of fake reviews is positively associated with the competitive pricing level of online marketplaces. This empirical finding, combined with our analytical results, suggests that manipulating customer reviews decreases customer demand in the long term. Based on these primary findings, we analytically demonstrate that multi-channel retailers can enhance their profits by incorporating information about fake reviews into their competitive pricing decision-making.