The Effect of Income Level on E-Commerce Adoption: A Multigroup Analysis

The Effect of Income Level on E-Commerce Adoption: A Multigroup Analysis

Ángel F. Agudo-Peregrina, Ángel Hernández-García, Emiliano Acquila-Natale
DOI: 10.4018/978-1-4666-9787-4.ch161
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In the past years, profiles of the average e-shopper have gradually changed as Internet expanded and new segments of consumers have started using electronic commerce. Socio-demographic characteristics of online shoppers are more heterogeneous: while a few years ago the average e-shopper was a middle-aged male with high income and high educational level, it is difficult to define such a precise average profile nowadays. As a consequence of this, there is renewed interest in the study of demographic variables for segmentation, such as gender and age. However, despite this interest, other socio-demographic segmentation variables have been left apart. One of the most noteworthy omissions in this regard is the study of the effect of income level in the adoption of electronic commerce, a topic where the scarcity of research is alarming. This research investigates and explores the influence of income level in e-shopping behavior. In order to explore how e-shoppers’ income level might affect e-commerce adoption––or, more precisely, how it moderates the effect of variables influencing e-commerce acceptance––the research proposes a model based on UTAUT2 that includes seven antecedent variables influencing purchase intention and purchasing behavior in e-commerce. The study also presents an empirical study to validate this model.

Internet shopping is becoming more relevant and integrated with everyday life. Electronic commerce maintains a constant growth rate (eMarketer, 2013) and it is ubiquitous (e.g. mobile commerce), leading to more heterogeneous groups of e-shoppers with very different lifestyles.

Regarding the heterogeneity of e-shoppers, there is a shift from the first days of e-commerce, when the average e-shopper was essentially a male shopper, of middle age, and with high purchasing power and high educational level (Donthu & García, 1999), toward a profile of the average Internet shopper that is more difficult to define due to the current high heterogeneity among e-shoppers.

For example, in Spain the ratio of male e-shoppers (52.6%) is currently approximately the same as female e-shoppers (47.4%) (Urueña, Valdecasa, Ballestero, Antón, Castro, & Cadenas, 2013). Furthermore, despite the fact that middle-aged people still are the main group of e-shoppers (66.2%), younger and older––less than 25 and more than 50 years old, respectively––online shoppers are increasingly using electronic commerce in more recent years. In the same way, the number of Internet shoppers from lower social classes is also expanding (22.3% of the total in 2013, 8.3% more than in 2009) (Urueña et al., 2013).

Changes in differences among online shopping behaviors based on socio-demographic variables for segmentation have raised the interest of scholars and practitioners. Thus, prior research has explored the influence of differences of online shopping behaviors between groups of shoppers depending on their gender (Rodgers & Harris, 2003; Hasan, 2010; Pascual-Miguel, Agudo-Peregrina & Chaparro-Peláez, 2015) or age (Joines, Scherer, & Scheufele, 2003; Doolin, Dillon, Thompson, & Corner, 2005). However, literature on differences based on other demographic variables, such as income level, is still scant.

The objective of this study is to bridge that research gap. Thereby, this research focuses on the analysis of how income level may affect the factors that predict purchase intention and purchasing behavior in Internet shopping. In order to do so, the structure of this article is as follows: the next section presents a background of the research, introduces the concepts of electronic commerce acceptance and use, and covers prior research on the effect that demographic variables have on e-commerce adoption and purchasing behaviors; after presentation of the background, the subsequent section describes the study methodology, which is followed by a presentation of the results of the analysis from the empirical study; the final sections cover a discussion of the findings, avenues of future research and a summary of the conclusions from this study.

Key Terms in this Chapter

Multigroup Comparison: The comparison of group-specific effects between different population groups considering a categorical moderator variable which affects the direction and/or strength of the relation between an independent or predictor variable and a dependent or criterion variable.

Facilitating conditions: The degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system, and the extent to which he or she has the appropriate knowledge and resources to make use of said system.

Social Influence: The degree to which an individual perceives that important others believe he or she should use the new system.

Perceived risk: Inherent uncertainty associated with transactions between buyers and sellers and related to the technology used and the lack of physical interaction with the buyer and the product.

Effort Expectancy: The degree of ease associated with the use of the system.

Electronic Commerce: The process of buying and selling products or services using electronic data transmission via the Internet and the World Wide Web.

Hedonic Motivation: Fun or pleasure from using a technology, regardless of the final result.

Income Level: Amount of money earned by people over a given period of time (typically, a month).

Performance Expectancy: The degree to which an individual believes that using the system will help him or her to attain gains in job performance.

Trust: Voluntary will from the buyer –trusting party– to depend upon the seller –trusted party– based on the belief that the seller will have four characteristics: competence, benevolence, integrity and predictability.

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