Mittal, Vikas, Kyuhong Han, Carly M. Frennea, Markus Blut, Muzeeb Shaik, Narendra Bosukonda, and Shrihari Sridhar (2023), “Customer Satisfaction, Loyalty Behaviors, and Firm-Financial Performance: What 40 Years of Research Tells Us,” Marketing Letters. Link
Singal, Amit G., Yixing Chen, Shrihari Sridhar, Vikas Mittal, Hannah Fullington, Muzeeb Shaik, Akbar K. Waljee and Jasmin Tiro (2022), “Novel Application of Predictive Modeling: Identifying Patients for a Tailored Approach to Promoting HCC Surveillance in Patients with Cirrhosis,” Clinical Gastroenterology and Hepatology, 20 (8), 1795–1802. Link
Shaik, Muzeeb, Narendra Bosukonda, Vikas Mittal, and Shrihari Sridhar (2022), “Price Sensitivity and Customer Perceived Switching Costs in Business-to-Business Markets: Joint Effect on Customer Repurchase Intentions,” Journal of Service Management Research, 6(1), 64-79. SSRN, Link
“How Fatal School Shootings Impact a Community’s Consumption,” with John P. Costello, Mike Palazzolo, Adithya Pattabhiramaiah , and Shrihari Sridhar, revise and resumit at Journal of Marketing Research. Link Abstract
School shootings are a disturbingly regular occurrence in the US. While the direct impacts on those involved are well-researched, the broader effects on communities are less understood. We focus on the under-researched question of how such a traumatic incident affects community consumption. Using data from various sources, we find that fatal school shootings decrease grocery purchases by 1.35% in affected communities, lasting up to 6 months. This economic impact is felt more in liberal than conservativeleaning counties. Three experimental studies provide evidence that this decrease is driven by heightened anxiety around consumption in public spaces, particularly for political liberals. Our work suggests the violence has far-reaching consequences, harming the local economy. The decline in spending highlights the need for community leaders to develop supportive community responses to such horrific incidents.
“Opportunity Management for B2B Service Organizations: A Framework, Evidence and Application,” with Shrihari Sridhar, Chelliah Sriskandarajah and Vikas Mittal, revise and resumit at Production and Operations Management. LinkAbstract
To meet sales targets with finite resources, business-to-business (B2B) service organizations need to prioritize promising sales opportunities from a large pipeline of possibilities. Extant evidence in theory and practice suggests that B2B service organizations use arbitrary or gut-based decision rules to prioritize sales opportunities, which leads to sub-optimal sales opportunity management and operations planning. We draw from the relationship management and organizational buying literature to build a new sales-operations framework that relates buyer-class typology (e.g., new bid vs. modified rebid), and opportunity characteristics (e.g., opportunity size) to a service organizations' decision to bid, and the bid outcome. We test the framework using archival data from a major B2B on-site services provider. The data span sales outcomes (did not bid, bid and won, bid and lost) for 4,564 sales opportunities, spanning 10 years, and 23 countries. Model-free evidence indicates an ongoing tension in sales pursuit: while the best projects to bid on are ones with the best relationship typology (e.g., direct relationship) and lowest risk (e.g., small opportunity size), such opportunities are not enough to maximize profits in each region, leading to sub-optimal operations planning. Accordingly, we first develop an ensemble machine learning framework to predict the focal seller’s propensity to win the opportunity, as a function of complex non-linear interplay among the sales opportunity characteristics. Subsequently, we propose a combinatorial optimization approach that allows a company to take the right level of risk (i.e., some new bids, some large opportunities) so that each region can maximize its sales potential given its finite capacity. We demonstrate that using the ensemble approach enables the focal company to improve its predictive validity over simple models by 11%. Moreover, by using a combinatorial optimization approach in conjunction with the predictive ensemble model, we retrospectively demonstrate that the focal firm could have increased its sales by 22% while bidding on 38% fewer projects.
“Designing New Studies Using Meta-Analysis for Estimate Precision: The Case of Customer
Satisfaction and Customer Retention,” with Han, Kyuhong, Vikas Mittal, and Shrihari Sridhar, reject and resubmit at Journal of Consumer Research. Abstract
Scholars often use a descriptive meta-analysis to quantitatively synthesize extant empirical research on the relationship between a focal construct and an outcome. A meta-analysis can provide more generalizable conclusions than those obtained from a single study. Yet, a typical descriptive meta-analysis only provides a snapshot of extant research at the time the meta-analysis is conducted. Further, a descriptive meta-analysis does not provide guidance on how new studies may improve the estimate precision of the focal relationship (i.e., decrease its variance and confidence interval) and, thus, further our understanding of the focal relationship. Against this background, the current study proposes an approach that assesses the estimate precision of the focal relationship from a meta-analysis. By illustrating the approach using the customer satisfaction–retention relationship, the authors address three questions: (1) how well have extant empirical studies collectively improved the estimate precision of the association between CS and retention, (2) how has the estimate precision evolved over time, and (3) what should be the best-next study to improve the estimate precision of the CS–retention relationship?