Dr Venky Shankar

  • On the Efficiency of Internet Markets for Consumer Goods

    Ratchford_Pan_Shankar_JPPM_2003

    Brian T. Rachford, Xing Pan, and Venkatesh Shankar

    This article was published in Journal of Public Policy and Marketing, 22 (Spring 2003), 4-16.

    Despite claims that electronic commerce lowers search costs dramatically, and therefore makes it easy for consumers to spot the best buy, empirical studies have found a substantial degree of price dispersion in electronic markets for consumer goods. This study investigates the consumer welfare implications of observed price levels and price dispersion in electronic markets. We examine the consumer welfare implications of changes in the structure of electronic commerce markets employing comprehensive data sets on e-tailer prices and services collected from BizRate.com in November 2000 and 2001. We find that price dispersion decreased substantially between these two periods, and that measured differences in e-tailer services bear little relation to e-tailer prices.

  • Price Dispersion on the Internet: A Review and Directions for Future Research

    Pan_Ratchford_Shankar_JIM_2004

    by Xing Pan, Brian T. Ratchford, and Venkatesh Shankar

    This aricle was published in the Journal of Interactive Marketing, 18 (Autumn 2004), 116-135.

    The explosive growth in Internet retailing has sparked a stream of research on online price dispersion, defined as the distribution of prices (such as range and standard deviation) of an item with the same measured characteristics across sellers of the item at a given point in time.  In this paper, we review the empirical and analytical literatures on online price dispersion and outline the future directions in this research stream.  We address the issue of whether price dispersion is greater or smaller online than offline, examine whether price dispersion on the Internet has changed over time, discuss multi-channel retailing and measurement of price dispersion, explore why Internet price dispersion exists, and investigate the drivers of online price dispersion.

     

  • Can Price Dispersion in Online Markets be Explained by Differences in E-Tailer Service Quality?

    Pan_Ratchford_Shankar_JAMS_2002

    by Xing Pan, Brian T. Ratchford, and Venkaesh Shankar

    This article was published in the Journal of the Academy of Marketing Science, 30 (Fall 2002), 433-445.

    It has been hypothesized that the online medium and the Internet lower search costs and that electronic markets are more competitive than conventional markets.  This suggests that price dispersion–the distribution of prices of an item indicated by measures such as range and standard deviation—of an item with the same measured characteristics across sellers of the item at a given point in time for identical products sold by e-tailers online (on the Internet) should be smaller than it is offline, but some recent empirical evidence reveals the opposite.  A study by Smith et al. (2000) speculates that this is due to heterogeneity among e-tailers in such factors as shopping convenience and consumer awareness.  Based on an empirical analysis of 105 e-tailers comprising 6739 price observations for 581 items in eight product categories, we show that online price dispersion is persistent, even after controlling for e-tailer heterogeneity.  Our general conclusion is that the proportion of the price dispersion explained by e-tailer characteristics is small. This evidence is contrary to the hypothesis that search costs in online markets are low, or that online markets are highly competitive.  The results also show that after controlling for differences in e-tailer service quality, prices at pure play e-tailers are equal to or lower than those at bricks-and-clicks e-tailers for all categories except books and computer software.

     

  • Keys Issues in Multichannel Customer Management: Current Knowledge and Future Directions

    Neslin Shankar JIM 2009

    by Scott A. Neslin and Venkatesh Shankar

    This article was published in the Journal of Interactive Marketing, 23 (2009), 70-81.

    Multichannel customer management is “the design, deployment, and evaluation of channels to enhance customer value through effective customer acquisition, retention, and development” (Neslin et al. 2006).  Channels typically include the store, the Web, catalog, sales force, third party agency, call center and the like.  In recent years, multichannel marketing has grown tremendously and is anticipated to grow even further. While we have developed a good understanding certain issues such as the relative value of a multichannel customer over a single channel customer, several research and managerial questions still remain. We offer an overview of these emerging issues, present our future outlook, and suggest important avenues for future research.

  • Challenges and Opportunities in Multichannel Customer Management

    Neslin et al. JSR 2006

    by Scott A. Neslin, Dhruv Grewal, Robert Leghorn, Venkatesh Shankar, Marije L. Teerling, Jacquelyn S. Thomas, and Peter C. Verhoef

    This article was published in Journal of Service Research, 9 (November 2006), 95-112.

    Multichannel customer management is the design, deployment, coordination, and evaluation of channels through which firms and customers interact, with the goal of enhancing customer value through effective customer acquisition, retention, and development.  The authors identify five major challenges practitioners must address to manage the multichannel environment more effectively: (1) data integration, (2) understanding consumer behavior, (3) channel evaluation, (4) allocation of resources across channels, and (5) coordination of channel strategies. The authors also propose a framework that shows the linkages among these challenges, and provides a means to conceptualize the field of multichannel customer management.  A review of academic research reveals that this field has experienced significant research growth, but the growth has not been distributed evenly across the five major challenges.  The authors discuss what has been learned to date, and identify emerging generalizations as appropriate.  They conclude with a summary of where the research-generated knowledge base stands on several issues pertaining to the five challenges.

  • Customer Value, Satisfaction, Loyalty, and Switching Costs: An Illustration from a Business-to-Business Service Context

    Lam_Shankar_Erramilli_Murthy_JAMS_2004

    by Shun Yin Lam, Venkatesh Shankar, Krishna Erramilli, and Bvsn Murthy

    This article was published in Journal of Academy of Marketing Science, 32 (Summer 2004), 293-311.

    Although researchers and managers pay increasing attention to customer value, satisfaction, loyalty and switching costs, not much is known about their interrelationships, in particular, in the business-to-business (B2B) context.  Prior research has examined the relationships within subsets of these constructs, mainly in business-to-consumer (B2C) environment.  We extend prior research by developing a conceptual framework linking all of these constructs in a B2B service setting.  We also advance the notion that customer loyalty is best conceptualized as a two-dimensional construct comprising repeat patronage and word-of-mouth recommendation.  Based on the cognition-affect-behavior model, we hypothesize that customer satisfaction mediates the relationship between customer value and customer loyalty and that customer satisfaction and loyalty have significant reciprocal effects on each other.  We also examine the relative strengths of the drivers of customer loyalty, and explore potential interaction effect of satisfaction and switching costs and the quadratic effect of satisfaction, on loyalty.  We test the hypotheses using structural equation modeling on data obtained from a courier service provider in a B2B context.  The results support most of our hypotheses and in particular, confirm the mediating role of customer satisfaction.  We discuss how the results can help managers enhance customer loyalty.

  • Asymmetric New Product Development Alliances: Win-Win or Win-Lose Partnerships?

    Kalaignanam_Shankar_Varadarajan_ MgtS_2007

    by Karthik Kalaignanam, Venkatesh Shankar, and Rajan Varadarajan

    This article was published in Management Science, 53 (March, 2007), 357-374.

    Inter-organizational alliances are widely recognized as critical to product innovation, particularly in high technology markets. Many new product development (NPD) alliances tend to be asymmetric, that is, they are formed between a larger firm and a smaller firm. As is the case with alliances in general, asymmetric alliances also typically result in changes in the shareholder values of the partner firms. Are the changes in shareholder values of partner firms significant? Are asymmetric NPD alliances win-win or win-lose partnerships? Are the gains or losses symmetric for the larger and smaller partner firms? What factors drive the changes in shareholder values of the partner firms? These important questions remain largely unexplored as evidenced by the dearth of empirical research on the effect of asymmetric NPD alliances on shareholder value and on the apportionment of this value between the partner firms. We develop and empirically test a model of short-term changes in shareholder values of larger and smaller firms involved in NPD alliances, using the event study methodology on data covering 167 asymmetric alliances in the information technology and communication industries. In this model, we examine alliance, firm, and partner characteristics as potential determinants of the changes in shareholder values of the partner firms due to a NPD alliance announcement. Our model accounts for selection correction, potential cross-correlation across the residuals from the models of firm value changes for the larger and smaller firms, and unobserved heterogeneity. The results suggest that both the partners experience significant short-term financial gains, but there are considerable asymmetries between the larger and smaller firms with regard to the effects of alliance, partner and firm characteristics on the gains of the partner firms. The results relating to alliance characteristics suggest that while a broad scope alliance enhances the financial gains for the larger firm, a scale R&D alliance (relative to a link alliance) contributes positively to the financial gains for the smaller firm. With regard to partner characteristics, while partner alliance experience positively influences the financial gains for the larger firm, it has no significant effect on the financial returns for the smaller firm. Further, partner innovativeness is positively associated with the financial gains for the larger firm, but partner reputation is unrelated to the financial gains of the smaller firm. As regard firm characteristics, the magnitude of the financial gains accruing from a firm’s own alliance experience is considerably higher for the smaller firm than it is for the larger firm. We outline the implications of the research findings for future research and management practice.

  • The Roles of Channel-Category Associations and Geodemographics in Channel Patronage

    Inman_Shankar_Ferraro_JM_2004

    by J. Jeffrey Inman, Venkatesh Shankar, and Rosellina Ferraro

    This article was published in the Journal of Marketing, 68 (April 2004),  51-71.

    Consumers purchase goods from a variety of channels or retail formats such as grocery stores, drug stores, mass merchandisers, club stores and convenience stores. To identify the most appropriate channels and to efficiently allocate the distribution of products among channels, managers need a better understanding of consumer behavior with respect to these channels. We examine the moderating role of  “channel-category associations” in consumer channel patronage by extending the literature on brand associations to the context of channels and estimate a model linking channel-category associations with consumer geodemographics and channel share of volume. We identify the product categories associated with particular channels through a correspondence analysis of a field intercept survey. We then use these channel-category associations, along with geodemographic factors to estimate their direct and interactive effects on channel share of volume. These channel-category associations have significant main effects and interaction effects with channel type and geodemographic factors on channel share of volume and account for the majority of the explained variance (72%) in channel share of volume. Overall, the findings provide several conceptual and managerial insights into consumer channel perceptions and patronage behavior.

  • Inferring Market Structure fom Customer Response to Competing and Complentary Products

    Elrod_Shocker__- Shankar_MLetters_2002

    by Terry Elrod, Gary Russell, Allan D. Shocker, Rick L. Andrews, Lynd Bacon, Barry L. Bayus, J. Douglas Carroll, Richard M. Johnson, Wagner A. Kamakura, Peter Lenk, Josef A. Mazanec, Vitala R. Rao, and Venkatesh Shankar

    This article was published in Marketing Letters, 13 (3), 219-230, 2002.

    We consider influences on market structure, arguing that market strucure should explain the extent to which any given set of market offerings are substitutes or complements. We describe recent additions to the market structure analysis literature and identify promising directions for new research in market structure analysis. Impressive advances in data collection, statistical methodology and information technology provide unique opportunities for researchers to build market structure tools that can assist “realtime” marketing decision-making.

  • An Empirically Derived Taxonomy of Retailer Pricing and Promotion Strategies

    Bolton_Shankar_JR_2003

    by Ruth N. Bolton and Venkatesh Shankar

    This article was published in the Journal of Retailing, 79 (2003), 213-224.

    Most research categorizes grocery retailers as following either an EDLP or a HiLo pricing strategy at a store or chain level, whereas this paper studies retailer pricing and promotions at a brand-store level. It empirically examines 1,364 brand-store combinations from 17 chains, 212 stores and six categories of consumer package goods in five U.S. markets.  Retailer pricing and promotion strategies are found to be based on combinations of four underlying dimensions:  relative price, price variation, deal intensity and deal support.  At the brand-store level, retailers practice five pricing strategies, labeled exclusive, moderately promotional, HiLo, EDLP, and aggressive pricing.  Surprisingly, the most prevalent pricing strategy is characterized by average relative brand price, low price variation, medium deal intensity, and medium deal support.  The findings provide some initial benchmarks and suggest that retailers should closely monitor their competitors’ price decisions at the brand level.