Behavioural differences between consumers attracted to shopping online versus traditional supermarkets: implications for enterprise design and marketing strategy

Despite the dot.com shakeout, online revenues continue to increase and are projected to impose greater pressure on traditional distribution channels. However, there is a striking absence of published empirical work on how consumers attracted to shopping online behave relative to consumers shopping in a traditional store. Such behavioural differences, if they exist, could guide online enterprise design and marketing strategy. This study uses data from both traditional supermarket scanners and an online supermarket to test expected differences in choice behaviours of such consumers. For two product categories, statistically significant differences are found between consumers attracted to shopping online versus traditional supermarkets with regard to the parameters describing the choice process. Compared to traditional supermarket consumers, online consumers are less price sensitive, prefer larger sizes to smaller sizes (or at least have weaker preferences for small sizes), have stronger size loyalty, do more screening on the basis of brand names but less screening on the basis of sizes, and have stronger choice set effects. Many of these differences are found to be prevalent among the majority of online consumers rather than due to the substantially unique behaviour of a minority. Indeed, 11 to 39% of traditional supermarket consumers (depending on the product category) are found to behave like the majority of online consumers whilst 0 to 31% of online consumers are found to behave like the majority of traditional supermarket consumers. Implications of both sets of results for online enterprise design, marketing, and evolution are outlined.

[1]  Erik Brynjolfsson,et al.  Frictionless Commerce? A Comparison of Internet and Conventional Retailers , 2000 .

[2]  A. Rangaswamy,et al.  Consumer Choice Behavior in Online and Traditional Supermarkets: The Effects of Brand Name, Price, , 2000 .

[3]  Gerald L. Lohse,et al.  Predictors of online buying behavior , 1999, CACM.

[4]  Ajay K. Manrai,et al.  Feature-based elimination: Model and empirical comparison , 1998, Eur. J. Oper. Res..

[5]  John G. Lynch,et al.  Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces , 1997 .

[6]  D. Hoffman,et al.  Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations , 1996 .

[7]  Ronald M. Lee,et al.  InterShop: Enhancing the Vendor/Customer Dialectic in Electronic Shopping , 1995, J. Manag. Inf. Syst..

[8]  Pattie Maes,et al.  Agents that reduce work and information overload , 1994, CACM.

[9]  J. Louviere,et al.  The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models , 1993 .

[10]  Füsun F. Gönül,et al.  Modeling Multiple Sources of Heterogeneity in Multinomial Logit Models: Methodological and Managerial Issues , 1993 .

[11]  Leonard M. Lodish,et al.  Comparing Dynamic Consumer Choice in Real and Computer-simulated Environments , 1992 .

[12]  Pradeep K. Chintagunta,et al.  Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data , 1991 .

[13]  William D. Kalsbeek,et al.  Internet and Web Use in the United States: Baselines for Commercial Development , 2006 .

[14]  E. Clemons,et al.  The Nature of Competition in Electronic Markets: An Empirical Investigation of Online Travel Agent Offerings , 1999 .

[15]  Ho Geun Lee,et al.  Do electronic marketplaces lower the price of goods? , 1998, CACM.

[16]  Joseph P. Bailey,et al.  Intermediation and electronic markets: aggregation and pricing in Internet commerce , 1998 .