CRM IN BUSINESS-TO-CUSTOMER COMMERCE

By | February 9, 2018
INTRODUCTION

 

Business-to-consumer (B2C) electronic commerce has emerged in recent years as an important way of doing business. According to ePayments Resource Center (2004), the total B2C e-com- merce revenues for the United States increased from $75 million in 1999 to $750 million in 2003. Similarly, Europe’s B2C revenues grew from U.S. $25 to $60 million and Japan’s from $25 to

$250 million between 1999 and 2003. However, online shopping is far from being a popular act, even among people who are experienced Internet users and spend long hours online. For ex- ample, according to the USC Annenberg School Center for the Digital Future, 75.9 percent of

 

Americans were Internet users in 2003. They spent an average of 12.5 hours/week online. And

96.7 percent of them had more than one year of Internet experience (USC Annenberg School Center for the Digital Future 2004). However, only 43 percent of American adults purchased online in 2003, spending an average of $95.14 per month (USC Annenberg School Center for the Digital Future 2004). While B2C e-commerce has not been widely accepted in the broad sense, there is significant room for its growth, once the B2C shareholders find effective ways to attract and sustain more customers to conduct more transactions online. The question is: What factors lead customers to shop online?

This is a key question to be answered in the e-commerce customer relationship management (e- CRM) area. Abundant studies have been conducted in recent years to investigate customers’ online shopping behaviors. Most of them have attempted to reveal factors influencing or contributing to online shopping beliefs, attitudes, intentions, and behaviors. Romano and Fjermestad (2003) have identified five major perspectives that researchers may adopt to approach various issues surround- ing e-CRM. These include e-CRM markets, e-CRM business models, e-CRM knowledge manage- ment, e-CRM technology, and e-CRM human factors. These areas are not mutually exclusive and may influence one another directly or indirectly. Generally each study of customers’ online shop- ping behaviors takes one or more of the five perspectives. As a result, these studies investigate various factors in diverse ways and reveal different aspects of the phenomenon. For example, Case, Burns, and Dick (2001, p. 873) suggested that “Internet knowledge, income, and education level are especially powerful predictors of Internet purchases among university students” according to an online survey of 425 U.S. undergraduate and MBA students. Ho and Wu (1999) discovered positive relationships between online shopping behavior and five categories of factors: e-stores’ logistical support, product characteristics, Web sites’ technological features, information characters, and home page presentation. Jarvenpaa et al. (2000) empirically revealed positive associations between con- sumer trust in Internet stores and perceived store reputation and size. Higher consumer trust reduces perceived risks associated with Internet shopping and generates more favorable attitudes toward shopping at a particular store, which in turn increases one’s willingness to patronize that store.

These studies have all made important contributions to our understanding of the dynamics of the online shopping phenomenon. However, there is a lack of coherent understanding of the impact of most, if not all, possible factors related to online shopping behaviors. This makes com- parisons of different studies difficult, applications of research findings limited, and the prospect of synthesizing and integrating the empirical literature elusive.

This chapter synthesizes the representative studies of consumer online shopping behavior based on an analytical literature review. To be consistent with the theme of advances in MIS, we focus on the IS literature. To draw validated results, we emphasize empirical studies, especially those using quantita- tive methods. In doing so, we attempt to provide a comprehensive picture of the state of the art of this area and point out limitations and directions for future research. We approach the research question mainly from the e-CRM human factors perspective, since what interests us here is human behavior. Variables related to e-CRM markets, business models, and technology are also investigated, because they influence (potential) customers’ perceptions, attitudes, and behaviors in various ways.

 

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