Research Publications

An Across-Store Analysis of Intrinsic and Extrinsic Cross-Category Effects
Venkatesh Shankar , P. K. Kannan
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Omnichannel marketing: Are cross-channel effects symmetric?
Venkatesh Shankar, Tarun Kushwaha
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Channel Blurring: A Study of Cross-Retail Format Shopping among U.S. Households
Venkatesh Shankar
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Are Multichannel Customers Really More Valuable? The Moderating Role of Product Category Characteristics
Venkatesh Shankar
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Transforming the Customer Experience Through New Technologies
Wayne D. Hoyer, Mirja Kroschkeb, Bernd Schmitt, Karsten Kraume, Venkatesh Shankar
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Summary: The paper deals with the implementation of various technologies to improve the way customers experience shopping. The paper starts by explaining the current scenario of customers and then introduces us to the key technologies that will affect customer experiences in the future. These technologies comprise reality technologies such as AR/VR/MR which provide customers an interactive way of shopping, virtual assistants/chatbots which are useful in providing customer service, and finally, the IoT that explains the interconnection of devices to the internet to make the customer life much easier. Then we go into detail about the experience and journey the customer faces in using these technologies along with the impact these technologies have currently on shopping. After this, we delve into the research on what additional areas can these technologies come in hand to improve the customer experiences. We also talk about the interdisciplinary research avenues among the technological component of this research stream comprising of service science, information systems, as well as management and organizational science. The paper talks about the various managerial implications before concluding that the emergence of new technologies will provide a great customer experience and a scope for companies to capitalize on it, but we need to be wary of the potential implications that may also arise with them.

Mobile Shopper Marketing: Key Issues, Current Insights, and Future Research Avenues
Venkatesh Shankar, Mirella Kleijnen, Suresh Ramanathan, Ross Rizley, Steve Holland, Shawn Morrissey
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Mobile Marketing in the Retailing Environment: Current Insights and Future Research Avenues
Venkatesh Shankar, Alladi Venkatesh, Charles Hofacker, Prasad Naik
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Mobile App Introduction and Online and Offline Purchases and Product Returns
Unnati Narang, Venkatesh Shankar
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How Technology is Changing Retail
Venkatesh Shankar
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Driving growth of Mwallets in emerging markets: a retailer’s perspective
V. Kumar, Nandini Nim, Amalesh Sharma
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Summary: The technology growth and evolving customer expectations in emerging markets have led firms to search for strategic tools to engage customers. The digital payment industry has seen the emergence of a new mobile-based service called Mwallet: a digital version of a physical wallet. Although there has been excitement, firms lack guidance regarding leveraging the potential of Mwallets. Relying on the extant literature, industry reports, and a qualitative study, we investigate the strategic potential of Mwallets for retailers. We develop a research framework and provide multiple research propositions linking Mwallet integration to customer engagement (CE) and discussing the moderating effects of market, firm, and customer-related factors. We find that Mwallet integration will have an S-shaped relationship with CE. An initial empirical investigation provides evidence for the effect of Mwallet on CE. This research is the first step towards linking Mwallet to a retailer’s performance; it demonstrates academic and managerial relevance.

Pricing hybrid bundles by understanding the drivers of willingness to pay
Jeffrey Meyer, Venkatesh Shankar,  Leonard L. Berry
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Pricing Strategies for Hybrid Bundles: Analytical Model and Insight
Jeffrey Meyer, Venkatesh Shankar
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Transforming the Customer Experience Through New Technologies
Wayne D. Hoyer, Mirja Kroschkeb, Bernd Schmitt, Karsten Kraume, Venkatesh Shankar
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Summary: The paper deals with the implementation of various technologies to improve the way customers experience shopping. The paper starts by explaining the current scenario of customers and then introduces us to the key technologies that will affect customer experiences in the future. These technologies comprise reality technologies such as AR/VR/MR which provide customers an interactive way of shopping, virtual assistants/chatbots which are useful in providing customer service, and finally, the IoT that explains the interconnection of devices to the internet to make the customer life much easier. Then we go into detail about the experience and journey the customer faces in using these technologies along with the impact these technologies have currently on shopping. After this, we delve into the research on what additional areas can these technologies come in hand to improve the customer experiences. We also talk about the interdisciplinary research avenues among the technological component of this research stream comprising of service science, information systems, as well as management and organizational science. The paper talks about the various managerial implications before concluding that the emergence of new technologies will provide a great customer experience and a scope for companies to capitalize on it, but we need to be wary of the potential implications that may also arise with them.

Innovations in Shopper Marketing: Current Insights and Future Research Issues
Venkatesh Shankar, J. Jeffrey Inman, Murali Mantrala, Eileen Kelley, Ross Rizley
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Pricing Strategies for Hybrid Bundles: Analytical Model and Insight
Jeffrey Meyer, Venkatesh Shankar
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How Technology is Changing Retail
Venkatesh Shankar
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New Product Creativity: Understanding Contract Specificity in New Product Introductions
David A. Griffith
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Marketing in Computer-Mediated Environments: Research Synthesis and New Directions
Manjit S. Yadav
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Generating Technology Evolution Prediction Intervals Using a Bootstrap Method
Guanglu Zhang, Douglas Allaire, Daniel A. McAdams, Venkatesh Shankar
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Summary: Technology evolution prediction is critical for designers, business managers, and entrepreneurs to make important decisions during product development planning such as R&D investment and outsourcing. In practice, designers want to supplement point forecasts with prediction intervals to assess future uncertainty and make contingency plans accordingly. However, prediction intervals generation for technology evolution has received scant attention in the literature. In this paper, we develop a generic method that uses bootstrapping to generate prediction intervals for technology evolution. The method we develop can be applied to any model that describes technology performance incremental change. We consider parameter uncertainty and data uncertainty and establish their empirical probability distributions. We determine an appropriate confidence level to generate prediction intervals through a holdout sample analysis rather than specify that the confidence level equals 0.05 as is typically done in the literature. In addition, our method provides the probability distribution of each parameter in a prediction model. The probability distribution is valuable when parameter values are associated with the impact factors of technology evolution. We validate our method to generate prediction intervals through two case studies of central processing units (CPU) and passenger airplanes. These case studies show that the prediction intervals generated by our method cover every actual data point in the holdout sample tests. We outline four steps to generate prediction intervals for technology evolution prediction in practice

From Browsing to Beyond The Needs-Adaptive Shopper Journey Model
Leonard Lee, J. Jeffrey Inman, Jennifer J. Argo, Tim Böttger, Utpal Dholakia, Timothy Gilbride, Koert Van Ittersum, Barbara Kahn, Ajay Kalra, Donald R. Lehmann, Leigh M. Mcalister, Venkatesh Shankar, And Claire I . Tsai
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Summary: In this paper, we propose a needs an adaptive model that complements and contrasts the existing models to identify the psychology behind the shoppers from browsing to purchasing a product. We first review existing customer journey models, discussing their strengths and weaknesses, followed by the key changes that may necessitate a fundamental rethinking of how and why consumers shop? the implications of these factors on the existing models.  Next, we describe in detail our proposed revised conceptual framework: the needs-adaptive shopper journey model, followed by common shopper journey archetypes that characterize the typical paths taken by shoppers. We also present exploratory empirical studies assessing the12 archetypes and mapping the archetypes onto various dimensions that characterize shopping motivations. Finally, drawing upon our proposed framework and the shopper journey archetypes, we identify several key questions and directions for future research. The paper aims for the efforts to push the frontiers of research in retail marketing and shopping behavior.

Mobile Shopper Marketing: Key Issues, Current Insights, and Future Research Avenues
Venkatesh Shankar, Mirella Kleijnen, Suresh Ramanathan, Ross Rizley, Steve Holland, Shawn Morrissey
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Innovations in Shopper Marketing: Current Insights and Future Research Issues
Venkatesh Shankar, J. Jeffrey Inman, Murali Mantrala, Eileen Kelley, Ross Rizley
View PDF

From Browsing to Beyond The Needs-Adaptive Shopper Journey Model
Leonard Lee, J. Jeffrey Inman, Jennifer J. Argo, Tim Böttger, Utpal Dholakia, Timothy Gilbride, Koert Van Ittersum, Barbara Kahn, Ajay Kalra, Donald R. Lehmann, Leigh M. Mcalister, Venkatesh Shankar, And Claire I . Tsai
View PDF
Summary: In this paper, we propose a needs an adaptive model that complements and contrasts the existing models to identify the psychology behind the shoppers from browsing to purchasing a product. We first review existing customer journey models, discussing their strengths and weaknesses, followed by the key changes that may necessitate a fundamental rethinking of how and why consumers shop? the implications of these factors on the existing models.  Next, we describe in detail our proposed revised conceptual framework: the needs-adaptive shopper journey model, followed by common shopper journey archetypes that characterize the typical paths taken by shoppers. We also present exploratory empirical studies assessing the12 archetypes and mapping the archetypes onto various dimensions that characterize shopping motivations. Finally, drawing upon our proposed framework and the shopper journey archetypes, we identify several key questions and directions for future research. The paper aims for the efforts to push the frontiers of research in retail marketing and shopping behavior.

An “Essential Services” Workforce Crisis Response
Leonard L. Berry
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Leveraging service recovery strategies to reduce customer churn in an emerging market
Sourav Bikash Borah, Srinivas Prakhya, Amalesh Sharma
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Summary: Building on the properties of emerging markets, we investigate how a firm should align its service recovery strategies with different types of service failure to reduce customer churn in an emerging market. Using resource exchange theory and a multi-method approach, we show that the conventional wisdom related to service recovery needs to be reevaluated in emerging markets. Our results show that process failures lead to a higher likelihood of customer churn compared to outcome failures in emerging markets. Investigating service recovery mechanisms, we find that compensation is more effective in recovering from process failures than in recovering from outcome failures in emerging markets. Similarly, employee behavior has a stronger impact on mitigating the ill effects of process failures than those of outcome failures. The study contributes to the literature on service recovery and resource exchange theory and provides managerial insights for the effective management of customer churn due to service failures in emerging markets.

Service Guarantees Have a Place in Health Care
Leonard L. Berry, P
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Modeling Emerging-Market Firms’ Competitive Retail Distribution Strategies
Amalesh Sharma
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Leveraging service recovery strategies to reduce customer churn in an emerging market
Sourav Bikash Borah, Srinivas Prakhya, Amalesh Sharma
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Summary: Building on the properties of emerging markets, we investigate how a firm should align its service recovery strategies with different types of service failure to reduce customer churn in an emerging market. Using resource exchange theory and a multi-method approach, we show that the conventional wisdom related to service recovery needs to be reevaluated in emerging markets. Our results show that process failures lead to a higher likelihood of customer churn compared to outcome failures in emerging markets. Investigating service recovery mechanisms, we find that compensation is more effective in recovering from process failures than in recovering from outcome failures in emerging markets. Similarly, employee behavior has a stronger impact on mitigating the ill effects of process failures than those of outcome failures. The study contributes to the literature on service recovery and resource exchange theory and provides managerial insights for the effective management of customer churn due to service failures in emerging markets.

Driving growth of Mwallets in emerging markets: a retailer’s perspective
V. Kumar, Nandini Nim, Amalesh Sharma
View PDF
Summary: The technology growth and evolving customer expectations in emerging markets have led firms to search for strategic tools to engage customers. The digital payment industry has seen the emergence of a new mobile-based service called Mwallet: a digital version of a physical wallet. Although there has been excitement, firms lack guidance regarding leveraging the potential of Mwallets. Relying on the extant literature, industry reports, and a qualitative study, we investigate the strategic potential of Mwallets for retailers. We develop a research framework and provide multiple research propositions linking Mwallet integration to customer engagement (CE) and discussing the moderating effects of market, firm, and customer-related factors. We find that Mwallet integration will have an S-shaped relationship with CE. An initial empirical investigation provides evidence for the effect of Mwallet on CE. This research is the first step towards linking Mwallet to a retailer’s performance; it demonstrates academic and managerial relevance.

Transforming the Customer Experience Through New Technologies
Wayne D. Hoyer, Mirja Kroschkeb, Bernd Schmitt, Karsten Kraume, Venkatesh Shankar
View PDF

Summary: The paper deals with the implementation of various technologies to improve the way customers experience shopping. The paper starts by explaining the current scenario of customers and then introduces us to the key technologies that will affect customer experiences in the future. These technologies comprise reality technologies such as AR/VR/MR which provide customers an interactive way of shopping, virtual assistants/chatbots which are useful in providing customer service, and finally, the IoT that explains the interconnection of devices to the internet to make the customer life much easier. Then we go into detail about the experience and journey the customer faces in using these technologies along with the impact these technologies have currently on shopping. After this, we delve into the research on what additional areas can these technologies come in hand to improve the customer experiences. We also talk about the interdisciplinary research avenues among the technological component of this research stream comprising of service science, information systems, as well as management and organizational science. The paper talks about the various managerial implications before concluding that the emergence of new technologies will provide a great customer experience and a scope for companies to capitalize on it, but we need to be wary of the potential implications that may also arise with them.

Mobile Marketing in the Retailing Environment: Current Insights and Future Research Avenues
Venkatesh Shankar, Alladi Venkatesh, Charles Hofacker, Prasad Naik
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Mobile App Introduction and Online and Offline Purchases and Product Returns
Unnati Narang, Venkatesh Shankar
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Understanding Governance Decisions in a Partially Integrated Channel: A Contingent Alignment Framework
David A. Griffith
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Social Comparison in Retailer-Supplier Relationships: Referent Discrepancy Effects
David A. Griffith
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Reciprocal Value Sharing in Manufacturer-Retailer Relationships: The Case of New Product Introductions
David A. Griffith
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Impact of buyer-supplier network complexity on firms’ greenhouse gas (GHG) emissions: An empirical investigation
Anirban Adhikary, Amalesh Sharma , Krishna Sundar Diatha , Jayanth Jayaram
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Channel Blurring: A Study of Cross-Retail Format Shopping among U.S. Households
Venkatesh Shankar
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How Artificial Intelligence (AI) Is Reshaping Retailing
Venkatesh Shankar
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Summary: This paper provides an understanding and evaluation of artificial intelligence methods for enhancing the functioning of retail marketing. After providing an understanding of the working methodology of AI, the paper draws us to the benefits a retailer can use on both the customer and supply sides. On the customer front, retailers can use AI to perform text, voice, and image analysis on the customer's data to understand and anticipate shopper’s behavior, personalize and recommend products based on customer’s preference, manage customer relationship through virtual assistants, etc. While on the supply side, AI allows retailers to store optimum inventory during peak, regular, and infrequent sales periods, providing ideal logistics and delivery management for goods and services and finally designing the layout for best performance. The paper finally concludes with the future scope of AI by analyzing the performance of AI in recent years and suggesting the possible drawbacks along with the various segments where research on AI can be much more useful.

Generating Technology Evolution Prediction Intervals Using a Bootstrap Method
Guanglu Zhang, Douglas Allaire, Daniel A. McAdams, Venkatesh Shankar
View PDF
Summary: Technology evolution prediction is critical for designers, business managers, and entrepreneurs to make important decisions during product development planning such as R&D investment and outsourcing. In practice, designers want to supplement point forecasts with prediction intervals to assess future uncertainty and make contingency plans accordingly. However, prediction intervals generation for technology evolution has received scant attention in the literature. In this paper, we develop a generic method that uses bootstrapping to generate prediction intervals for technology evolution. The method we develop can be applied to any model that describes technology performance incremental change. We consider parameter uncertainty and data uncertainty and establish their empirical probability distributions. We determine an appropriate confidence level to generate prediction intervals through a holdout sample analysis rather than specify that the confidence level equals 0.05 as is typically done in the literature. In addition, our method provides the probability distribution of each parameter in a prediction model. The probability distribution is valuable when parameter values are associated with the impact factors of technology evolution. We validate our method to generate prediction intervals through two case studies of central processing units (CPU) and passenger airplanes. These case studies show that the prediction intervals generated by our method cover every actual data point in the holdout sample tests. We outline four steps to generate prediction intervals for technology evolution prediction in practice

System evolution prediction and manipulation using a LotkaeVolterra ecosystem model
Guanglu Zhang, Douglas Allaire, Daniel A. McAdams, Venkatesh Shankar
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Summary: System evolution prediction is critical for designers to make R&D and outsourcing decisions. Many descriptive models are used for this purpose, but they have several limitations. In this paper, we extend the Lotka–Volterra equations as an ecosystem model to predict the performances of the system and its components. This model comprises a set of differential equations that describe symbiosis, commensalism, and amensalism relationships between a system and multiple components. We associate every parameter in the model with its causal factors, develop a three-step application of the model, and illustrate the application through a case study on passenger airplane fuel efficiency. Our model identifies the key components in a system. The identified components help designers generate strategies to boost system performance.

How Artificial Intelligence (AI) Is Reshaping Retailing
Venkatesh Shankar
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Summary: This paper provides an understanding and evaluation of artificial intelligence methods for enhancing the functioning of retail marketing. After providing an understanding of the working methodology of AI, the paper draws us to the benefits a retailer can use on both the customer and supply sides. On the customer front, retailers can use AI to perform text, voice, and image analysis on the customer's data to understand and anticipate shopper’s behavior, personalize and recommend products based on customer’s preference, manage customer relationship through virtual assistants, etc. While on the supply side, AI allows retailers to store optimum inventory during peak, regular, and infrequent sales periods, providing ideal logistics and delivery management for goods and services and finally designing the layout for best performance. The paper finally concludes with the future scope of AI by analyzing the performance of AI in recent years and suggesting the possible drawbacks along with the various segments where research on AI can be much more useful.

How Artificial Intelligence (AI) Is Reshaping Retailing
Venkatesh Shankar
View PDF
Summary: This paper provides an understanding and evaluation of artificial intelligence methods for enhancing the functioning of retail marketing. After providing an understanding of the working methodology of AI, the paper draws us to the benefits a retailer can use on both the customer and supply sides. On the customer front, retailers can use AI to perform text, voice, and image analysis on the customer's data to understand and anticipate shopper’s behavior, personalize and recommend products based on customer’s preference, manage customer relationship through virtual assistants, etc. While on the supply side, AI allows retailers to store optimum inventory during peak, regular, and infrequent sales periods, providing ideal logistics and delivery management for goods and services and finally designing the layout for best performance. The paper finally concludes with the future scope of AI by analyzing the performance of AI in recent years and suggesting the possible drawbacks along with the various segments where research on AI can be much more useful.

How Technology is Changing Retail
Venkatesh Shankar
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