Description of a Northern California Shopping Survey Data Collection Effort
David T Ory and
Patricia Mokhtarian ()
Institute of Transportation Studies, Working Paper Series from Institute of Transportation Studies, UC Davis
Applications of new information and communication technologies (ICTs) are changing how and where we work, shop, play, travel, and in other ways live our lives. Yet because ICT development and use is in such a volatile state, many of those changes and impacts are poorly understood. This report summarizes the development and deployment of a survey instrument intended to gather information to allow better understanding of the transportation impacts of business-to-consumer (B2C) e-commerce. Although the business-to-business (B2B) segment dominates e-commerce in terms of the dollar value of transactions made, B2C remains important for its potential impacts on urban travel and land use patterns, including potential redistributions of retail land uses, and substantial increases of package delivery trips into residential neighborhoods. We see an analysis of the transportation impacts of B2C e-commerce as having two components: (1) assessing the transportation impacts of a given level or pattern of adoption of B2C e-commerce, and (2) investigating the adoption of B2C e-commerce (who, under what circumstances, in what form). While transportation planners ultimately need to forecast transportation impacts, to do so accurately they need to understand adoption processes and trends. Thus, the data collection described here is intended to lead to modeling the adoption of B2C e-commerce, among other shopping “modes” (specifically store and catalog shopping). Because we take the consumer perspective, we will refer to the use of the internet for B2C e-commerce as “e-shopping”. We consider e-shopping to be a subset of “teleshopping”, which also includes catalog shopping (whether placing the order by phone or mail) and shopping from a television channel (generally by phone). To frame a manageable project, and because most TV shopping appears to be impulsive (Handy and Yantis, 1997), we do not include TV shopping in the survey. Though the survey instrument collects data on the pre-purchase browsing mode(s) as well as the transaction mode choice, our definition of shopping requires a purchase to occur, not just informationgathering or “window shopping”. Because (1) the nature of the shopping process is presumed to be quite heterogeneous depending on the type of good being considered; (2) due to resource constraints our sample will number in the (high) hundreds rather than many thousands; and (3) attempting to survey respondents in-depth with respect to many types of goods would pose too great a burden; we focus on two specific product types. Product classes can be characterized along a number of dimensions, including frequency of purchase, cost, search area (local, regional, broader), tangibility (whether a service, a digital good, or a material good), perishability, “differentiability” (the extent to which retailers can distinguish their offering of the product from others’), product information complexity, and as a “search good” (having features “that can be evaluated from externally provided information”) versus an “experience good” (needing “to be personally inspected or tried”; Peterson, et al., 1997). To yield a sufficient number of purchase occasions in our sample, we focus on low-cost, medium-/highfrequency- of-purchase goods. Specifically, we take the tangible product classes of books/physical CDs/DVDs/videotapes and clothing/shoes to be analyzed in depth. Books/CDs/DVDs/videotapes per se are basically search goods with low intrinsic differentiability, though the bookstore experience (browsing contents, having coffee) may offer some advantages to some customers. Clothing and shoes are basically experience goods, although decades of catalog shopping history show that some customers are willing to buy without trying. The survey also asks a few questions about internet shopping activity of all kinds, especially internet-based purchases. The main research questions to be addressed by this exploratory project build on a number of prior studies: (1) For the product class in question, what are the advantages (motivators and facilitators for choosing) and disadvantages of (constraints on choosing) each shopping mode? A number of authors have identified the potential advantages of e-shopping and/or store shopping (e.g. Brynjolfsson and Smith, 2001; Salomon and Koppelman, 1988; Tauber, 1972). Store shopping is so far still very different from e2 shopping in terms of such attributes as the information provided, the sensual stimulation, the ability to compare prices and to attain immediate ownership. Beyond the functional attributes related to shopping and purchasing, store shopping also offers numerous other experiences, which to varying degrees are less amenable to electronic platforms. These include, for example, the ability to interact with real salespeople and to bargain, the opportunity to be outside the home or work environment, some physical exercise, and so on. Shopping, under many circumstances, is a combined maintenance - leisure activity. Thus, the choice between store shopping and e-shopping is not unambiguous (Handy and Yantis, 1997). (2) Can market segments with different propensities to use alternative modes be identified? A composite of several studies (Cairns, 1996; Koppelman, et al., 1991; Tacken, 1990; Gould and Golob, 1997; Gould et al., 1998; Burke, 1998; Farag et al., 2006; Ferrell, 2005; Ren and Kwan, 2005) identifies four segments of the population that are likely to be early adopters of e-shopping: the mobility-limited, time-starved, technophilic, and shopping-phobic. These four segments may well be generalizable to many e-shopping contexts. In reality, however, they probably represent (and in general we will treat them as) four continuous dimensions, with individuals separately falling somewhere along each of them, rather than four mutually-exclusive and collectively-exhaustive group membership indicators. (3) To what extent do customers perceive there to be viable alternative modes for a given shopping occasion? Previous studies have largely neglected this question, implicitly or explicitly assuming that each shopping activity involves a true choice among competing alternatives. It is obviously important to test that assumption, and identify variables associated with perceived mode captivity versus choice. (4) Are the various shopping modes substitutes, or complements? For example, do people who do a lot of e-shopping tend to do less store shopping, or do high amounts of one tend to be associated with high amounts of the other? Do former catalog shoppers replace the catalog with the internet, or supplement it; conversely, do internet shoppers become new catalog shoppers as retailers engage in cross-channel marketing? Are there different relationships for different segments of the population? For example, shopping modes may be complementary for innate “shopaholics”, but substitutionary for the timepressured. Although to keep the survey at a reasonable length it is not possible to obtain the data for a rigorous analysis of transportation impacts, the answers to these questions will have direct implications for the likely transportation impacts of e-shopping. The remainder of this report focuses on the administration of a survey instrument that will facilitate the research directions discussed above. The organization of the document is as follows. Section 2 discusses the design of the sampling plan for the data collection. The following section then describes the actual sampling process. The fourth section briefly presents the survey design. Section 5 discusses the development of the survey instruments and Section 6 outlines the deployment of the survey. Next, a section is devoted to the steps taken to build the final sample through data cleaning. A concluding section ends the report.
Keywords: UCD-ITS-RR-07-03; Engineering (search for similar items in EconPapers)
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Working Paper: Description of a Northern California Shopping Survey Data Collection Effort (2007)
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