Shreya Kankanhalli

I am an Assistant Professor of Marketing at Cornell University's SC Johnson College of Business. I graduated from Stanford University's Graduate School of Business with a Ph.D. in Quantitative Marketing in 2021.

I conduct randomized field experiments and econometric analysis to investigate firm strategies and consumer behavior in emerging markets. Substantively, my current research focuses on how traditional small-scale retail firms can modernize and adopt digital technologies. I aim to do policy-relevant and managerially-relevant research that uncovers mechanisms by which emerging market retailers can improve their performance. I create novel datasets in my research by collecting primary field data on understudied marketing variables, algorithmically analyzing images of firms, and obtaining electronic transaction data from traditional firms.



Phone: (650) 285 7436


This paper studies, for the first time, the impact of business modernization on the sales performance of traditional retailers. We define modernization as adopting tangible structures and business practices of organized retail chains (for example, exterior signage with store name and logo, or a database to record product-level information) and adapting these to the practical conditions and constraints of traditional retailers such as small shop size. To address our research question, we implement a randomized field experiment in Mexico City with 1148 traditional retail firms. Our sample is randomized into three groups: 385 firms that we externally modernize in ways that are visible to customers; 383 firms that we internally modernize in ways that are not visible to customers; and 380 firms form a control group. We find a significant and persistent main effect of modernization on sales: firms in both treatment groups increase monthly sales by 15% to 19%, even 24 months after study recruitment. In terms of novel mechanism evidence, we find that externally-modernizing firms improve their store-level branding, while internally-modernizing firms strengthen their product management. These results have important implications for multinational managers who distribute products through traditional retail channels, and for policy stakeholders interested in improving firm performance in the retail sector of emerging markets.
  • Vishal Narayan and Shreya Kankanhalli (Journal of Marketing, 2021)
Households sending members to work away from home often receive information about lifestyles and consumption behaviors in those migration destinations (i.e., social remittances) along with economic remittances. We investigate the effect of having a migrant household member on household brand expenditures in rural India—a market characterized by substantial consumption of unbranded products. We collect and analyze household-level survey data from 434 households across 30 villages using an instrumental variable strategy. Economic remittances result in greater brand expenditure and this level is higher for poorer households. After controlling for economic remittances, the effect of migration on brand expenditures is more positive for households residing in more populous villages, with greater access to mobile phones, lower viewership of television media, and with less recently departed migrants. We demonstrate how marketing resource allocation across villages can be improved by incorporating migration data and provide insights for household targeting in the context of door-to-door selling in villages. Our results are robust to alternate, public policy-based instruments, and can be generalized to expenditure on private schools. Using additional survey data from 300 households in 62 new villages, we replicate our results by comparing within-households brand expenditures before and after migration.


  • With Stephen J. Anderson, Leonardo Iacovone and Sridhar Narayanan (Data collected and analysis in progress)

Measuring customer perceptions of brand equity for small businesses is challenging, especially in the context of developing countries, due to the significant cost and effort required to collect data from random consumers of small businesses. Yet, marketing practitioners and academics alike wish to understand how small businesses build their store-level brand equity, necessitating scalable measurement approaches. In this paper, we propose a novel approach of analyzing photographs of businesses to measure their store-level brand equity. All of this is done in the context of a randomized controlled trial with 1148 retailers in Mexico City. In our study, we obtained photographic data for this sample of businesses which show their exteriors and interiors, and thus contain information similar to those used by consumers to form brand perceptions. We then use a novel AI-based approach to analyze these photographs. First, we take a random sample of these images and ask human subjects to rate these images on various aspects of store-level brand equity. Second, we train a computer vision algorithm using this dataset. Our algorithm utilizes a convolutional neural network (CNN) to extract a high-dimensional set of features. Finally, we assess predictive accuracy of our algorithm on our test set. Once we obtain an algorithm with high predictive accuracy, we aim to convert the full set of photographic images into a dataset containing store-level brand equity measures and link these measures causally to modernization levels of firms as an application. Thus, by exploiting advances in deep learning, we automate the process of measuring store-level brand equity from photographs of small businesses, so that the method is cost-effective to scale to large samples of businesses.
  • With Stephen J. Anderson, Leonardo Iacovone and Sridhar Narayanan (Full funding secured and RCT in progress)

Across developing economies, cash is the conduit for retail transactions. Policymakers, multinational product manufacturers and marketers of electronic payment systems are interested in understanding how to stimulate the growth of electronic payments in emerging markets. In this paper, we investigate what hinders the adoption of e-payment technology by traditional retailers, in particular, whether barriers to adoption are technological, informational or financial in nature. We do this through a rigorous field experiment, where we randomize 1200 small retailers in Guadalajara, Mexico into four experimental groups: i) N = 300 firms receive an e-payment technology kit; ii) N = 300 firms receive the e-payment technology kit and informational materials to market e-payments to customers; iii) N = 300 firms receive the e-payment technology kit, informational materials, and a 4-month transaction fee waiver; and iv) N = 300 firms constitute a control group who receive no intervention. By comparing the adoption rates of the different treatment groups, we are able to cleanly analyze which barriers are critical to technology adoption. We additionally aim to study the impact of these interventions on e-payment adoption by neighboring retailers, and business performance.

Going Cashless in Emerging Market Retail: Behavioral Nudges on a Two-Sided Platform

  • With Stephen J. Anderson, Leonardo Iacovone and Sridhar Narayanan

Transitioning to a cashless retail sector could have substantial social and economic benefits to governments, consumers and small retailers that form the backbone of emerging markets. However, the two-sided nature of electronic payment ecosystems presents unique challenges, and no study to date has focused explicitly on ways to encourage the usage of technologies by firms in such a context. In this paper, we test behavioral interventions that nudge usage of electronic payment systems on two sides of the technology ecosystem. We do so through a rigorous field experiment with 1050 retailers in Mexico City who have already adopted electronic payment technology but do not use it extensively. We randomize the retailers into three groups: i) N = 350 firms receive a behavioral intervention that addresses firm owners’ distrust and unfamiliarity with technology; ii) For N = 350 firms we implement a behavioral intervention with customers in the neighborhood to address customers’ distrust and unfamiliarity with the technology and iii) N = 350 firms constitute a control group. We aim to study the impact of these interventions on e-payment usage by retailers and customers in their neighborhoods, as well as business performance.