Product Category vs Demographics: Comparison of Past and Future Purchase Intentions of E-Shoppers

Product Category vs Demographics: Comparison of Past and Future Purchase Intentions of E-Shoppers

Prateek Kalia (Ludhiana College of Engineering & Technology, Punjab, India)
Copyright: © 2018 |Pages: 17
DOI: 10.4018/IJEA.2018070102
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To remain profitable, managers and researchers want to gain insights about products bought by e-shoppers in past and their future shopping interests. They also want to know, “what factors are creating difference in shopping behavior of these buyers.” This article addresses above situation by presenting product category-wise demographic comparison of past and future e-purchase intentions of e-shoppers. Results revealed significant differences in past e-purchases within gender, marital status, age, city of residence and occupational categories with respect to different product categories, surprisingly no such differences were observed in educational and family income categories. For future e-purchases intentions, significant differences were found within gender, city of residence, marital status, age and education categories. Here differences within occupational and family income groups were not observed. Maximum demographic differences were observed in product categories like clothing, books and auto parts.
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Online retailers carry variety of product categories and they need to understand demand effects within and across categories for planning and execution of successful marketing mix strategies (Gelper et al., 2016). The demand function relates with price and sales within the category, and retailers want to set prices which can maximize the category's profitability by increasing retail margins and demand (González-benito et al., 2010). Customer may restrain themselves from buying in a high-effort product category (e.g. size or weight of product) due to significant return effort involved in it (Jeng, 2017), or they can rely heavily on general product-category attribute beliefs to distort brand evaluations (Cleveland et al., 2011). In terms of typicality, consumer link stronger brand associations to products with higher typicality of product category and learn them faster as compared to less typical ones (Hsu et al., 2014). Researchers have reported that categories have asymmetric roles in the product category network. Destination categories are most influential, whereas occasional and convenience categories are most responsive (Gelper et al., 2016).

Researchers have argued market segmentation on the basis of group characteristics, because household demographics factors have significant relationship with buying behavior towards various product categories (Bass et al., 1968; Trinh et al., 2009; Utsey & Cook, 1984). Khare (2013) also reported that income, marital status, age and education can affect compulsive behavior. She mentioned that income and education can affect consumers’ propensity to purchase products to exhibit their social status. Bhatnagar and Ghose (2004) considered “site stickiness” to propose that consumer demographics can determine consumer search patterns in a product category. Cleveland et al. (2011) suggested demographic segmentation complimented with psychographic segmentation to harmonize product attributes with customer attitudes. Zhang and Prybutok (2005) advocated customization of new technologies as per requirement of customer segments and product categories to enhance satisfaction levels.

Companies should design their marketing strategies to attract right customers because consumers are heterogeneous with respect to their personal characteristics, needs and preferences. If companies can differentiate consumers based on their product category knowledge and preference (Fu & Elliott, 2013), they can use this information to identify most attractive product category relative to a particular segment of consumers to increase their sales performance (Arabadzhieva, 2016). In addition, past studies have indicated that consumer's decisions are dependent on earlier decisions i.e. motivation and purpose of earlier purchases affect future purchases. This phenomenon is popularly known as licensing effect (Hui et al., 2009; Khan & Dhar, 2006). Considering the above discussion regarding importance of product category research and effectiveness of demographic customer segmentation, an analysis of product categories versus demographics has been undertaken vis-à-vis past and future purchase intentions of online shoppers with following specific objectives:

  • To check any significant difference within each demographic factor with respect to various product categories during past e-purchase and future purchase intentions.

  • To specify demographic categories in which these differences exist, through post hoc testing.

  • To identify product categories in which maximum demographic differences exist.

To achieve these objectives this article starts with literature review of relevant studies followed by research methodology, data analysis and conclusion.

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