The Impact of Geographic Proximity on What to Buy, How to Buy, and Where to Buy: Evidence from High-Tech Durable Goods Market*

Abstract

Social contagion effects due to geographical proximity refer to the social effects wherein the behavior of an individual varies with the behavior of other individuals who are geographically close. Although the influence of such effects on consumer choices has been established in several contexts, much of the extant studies have focused on its effect on consumers' decision of whether to buy a new product or adopt a new innovation. There has been no systematic examination of the influence of geographic proximity on other aspects of consumers' product buying process such as what to buy (i.e., brand choice), how to buy (i.e., the channel), and where to buy (i.e., retailers). Such effects can matter significantly in high-technology and durable goods markets and therefore, it is critical to understand the scope of these on consumers' choice of retailers and channel as well. Drawing on literatures from word of mouth effects, ecommerce, and consumers' perception of risk in their purchase process, we develop a set of hypotheses on the effect of geographic proximity on consumers' choices of what to buy, how to buy, and where to buy. Leveraging a microlevel dataset of purchases of personal computers, we develop brand-, retailer-, and channel-related measures of proximity effects at the individual consumer level and estimate a joint disaggregate model of the three choices that make up a product purchase process to test these hypotheses. Our results indicate a significant contagion effect on each of the three choices. Furthermore, we find evidence of a greater effect of geographic proximity on inexperienced consumers-those who are new to the product category. Our results thus help develop a holistic understanding of the influence of social contagion effects on consumers' decision making. * We thank the anonymous reviewers and the seminar participants at the following conferences for their comments and suggestions:

Conceptualising and measuring consumer-based brand–retailer–channel equity

“…Verbal stimulus forces the consumer to add pros and cons to determine the highest value (Chen, 2010). This research is underpinned by the assumption that decision makers are 'rationally bounded' (Simon, 1972), and that rationality is expected as 'consumers need to find out about brands, channels, and place before buying a product' (Janakiraman and Niraj, 2011). Consumers develop choice criteria before making a purchase decision (Yasin et al, 2007).…”

Linking Sustainability with Geographical Proximity in Food Supply Chains. An Indicator Selection Framework

“…Moreover, the definition of the boundaries of the system under study is crucial. For example, the geographical proximity of food consumption depends of on product distribution channels (e.g., farmers' markets, direct online sales, retailers) and on consumer preferred channels [46].…”

Multichannel Commerce

“…Channel choice has been studied in surveys (e.g., Gupta et al 2004a;Konuş et al 2008;Verhagen and van Dolen 2009), using secondary data (Avery et al 2012;Chintagunta et al 2012;Forman et al 2009;Janakiraman and Niraj 2011;Yang et al 2013) or using experiments (Keen et al 2004). To structure the knowledge about consumers' channel choice, the plentitude of factors is classified into four groups that emerged from the analysis: channel determinants, purchase specifics, external influences and individual differences.…”

“…Three studies find an influence on marketing communication on channel choice and thereby confirm that channel choice is also prone to the effects of marketing (Ansari et al 2008;Chintagunta et al 2012;Valentini et al 2011). The social influence was studied in terms of the three processes of attitude changes (compliance, identification or internalization) (Datta 2011), social contagion effects due to geographical proximity (Janakiraman and Niraj 2011) or social norms (Johnson 2008;Keen et al 2004;Verhoef et al 2007). …”

“…In contrast, other studies explicitly state that demographics are irrelevant for the channel choice decision (Konuş et al 2008). Several studies find an influence of the geographic proximity to a store to drive offline channel choice (Chintagunta et al 2012;Forman et al 2009;Janakiraman and Niraj 2011). Lastly, it is unquestioned that previous experiences with certain channels (Ansari et al 2008;Valentini et al 2011) as well as internet or IT skills (e.g., Frambach et al 2007) make a difference for channel outcomes.…”