Research

I am broadly interested in understanding how consumers and organizations (businesses or government) interact in the new technology-mediated environment. My research inquiries fall into three areas.

Multichannel customer behavior

The rapid growth in new technology-enabled channels (such as telephone, web, and mobile-based channels) offers firms tremendous opportunities to connect with their customers. However, the availability of multiple channels has made customers omnichannel (customers use multiple channels to complete their interaction with the firm), thus making managing its multichannel operations extremely challenging. In my research, I have conducted several field studies to answer questions, such as: How customers fulfill different needs at different stages (pre-sales, sales, and post-sales) of their interaction via different channels of the firm? How do customers’ needs vary with the types of products/services (such as a higher need for physical evaluation for high-touch products)? How the capabilities of different channels are better suited for fulfilling the specific needs of customers?

  • Kumar, A., R. Telang. (2011) “Product Customization and Customer Service Costs: An Empirical Analysis.” Manufacturing and Service Operations Management, 13(3), Summer 2011, 347-360.
  • Kumar, A., R. Telang. (2012) “Does Web Reduce Customer Service Cost? Empirical Evidence from a Call Center, “Information Systems Research, 23(3), 721-737.
  • Kumar, A., R. Telang, M. D. Smith (2014). “Information Discovery and the Long Tail of Motion Picture Content” Management Information Systems Quarterly, 38(4), 1057-1078.
  • Jerath, K., A. Kumar, S. Netessine (2015). “An Information Stock Model of Customer Behavior in Multichannel Customer Support Services.” Manufacturing and Service Operations Management. 17(3), 368-383.
  • Kumar, A., A. Mehra, S. Kumar (2019). “Why do Stores Drive Online Sales? Evidence of Underlying Mechanisms from a Multichannel Retailer” Information Systems Research,30(1), March 2019, 319-338.
  • Kitchens, B, A. Kumar, P. Pathak (2017). “Electronic Markets and Geographic Competition among Small, Local Firms.” Information Systems Research, 29(4), December 2018, 928-946.

Online product recommendation network

Recommendation engines are extensively used to hyperlink related products on the focal products’ pages. Such hyperlinking of related products’ pages results in a network of interconnected products – called product recommendations network – on the websites. How do these networks create economic value? Do these networks help users learn the interrelationship between products or they simply provide higher exposure to products? Through these mechanisms, the recommendation networks can reduce users’ search costs and thus increase their demands on the website. However, measuring the causal value of recommendation networks is challenging because such networks are endogenously formed based on product sales/popularity. In my research, I examine the underlying mechanisms through which recommendation networks create economic value and estimate its causal value in different field settings.

  • Kumar, A., T, Yinliang (2015). “Demand Effects of Joint Product Advertising in Online Product Videos” Management Science, 61(8), 1921-1937.
  • Kumar, A., K. Hosanagar (2019). “Measuring the Value of Recommendation Links on Product Demand.” Information Systems Research. 30(3), 819-838.
  • Wan(Shawn). X., A. Kumar, X. Li (2023).”How do Recommendations Help Consumers Search Products? Evidence from a Field Experiment,” Accepted for publication in Management Science.
  • Wan (Shawn), X,, A. Kumar. X. Li (2023).“Retargeted Versus Generic Product Recommendations: When is it Valuable to Give Retargeted Recommendations.” Accepted for publication in Information Systems Research.
  • Wan (Shawn), X., A. Kumar. H. Aytug. “Estimating Optimal Recommendation Policy under Heterogeneous Treatment Effect of Product Recommendations.” Under review
  • Wan (Shawn), X., A. Kumar. “Is it Beneficial to Recommend Differently Priced Products? Experimental Evidence from an Online Product Recommendation System.” Under review.

Societal Impact of IT

Different functionalities of information technology have the potential to augment and/or substitute for traditional inputs in meeting societal needs. For instance, IT-enabled personalization can augment the educational infrastructure to improve the delivery of education in resource constraint educational systems in developing countries. I have conducted a field study to examine how computer-generated adaptive homework can improve learning among students in resource-constrained schools in India.

  • Kumar, A., A. Mehra. “Personalized Education at Scale: Evidence from Randomized Field Experiment in India,” Working paper.
  • Hasan S, and A. Kumar. “Digitization and Divergence: Online School Ratings and Segregation in America.” Working paper.
  • Bano, S., S. Hasan. A. Kumar, A. Kumar. “Educational Inclusion and Behavioral Spillovers at Home.”
  • Ananthakrishnan, M. U., S. Hasan, A. Kumar. “Gentrification and Racial Distrust in Communities: Evidence from 911 Calls” Accepted for publication in Management Science.