Traditionally, affective reaction consists of three dimensions: arousal, pleasure, and dominance (Russell and Pratt 1980; Mehrabian and Russell 1974; Huang 2003). In this collection of studies, all three are studied to some extent, yet few studies cover all three at the same time except Huang (2003). Pleasure received more attention than the other two dimensions. It has been called per- ceived entertainment value (Chau et al. 2002; O’Keefe et al. 2000) and shopping enjoyment (Koufaris et al. 2001–2002; Koufaris 2002). Empirical studies confirmed that it is associated with personal characteristics (e.g., purpose of Internet use and product involvement) and beliefs about e-stores (e.g., perceived information load and positive challenges of the website) (Chau et al. 2002; O’Keefe et al. 2000; Koufaris et al. 2001–2002; Koufaris 2002; Huang 2003). Huang (2003) observed that arousal (measured as stimulated-relaxed, excited-calm, frenzied-sluggish) experi- enced in a virtual shopping environment is associated with an individual’s arousal-seeking ten- dency (i.e., liking arousal by change or by new stimuli). He also demonstrated that dominance (controlling-controlled, dominant-submissive, autonomous-guided) is significantly related to per- ceived information load of an e-store’s website.
Attitudes Toward Online Shopping Behavior. Three studies discovered antecedents of customers’ attitudes toward using or purchasing at a specific e-store (Chen et al. 2002; Lu and Lin 2002; Suh and Han 2003). All these antecedents fall into the subcategory of beliefs about an e-store/online shopping experience. Chen et al. (2002) observed that perceived usefulness and ease of use of an e-store and the compatibility of its use with existing values, beliefs, and needs positively influ- ence a potential customer’s attitude toward using this store. Lu and Lin (2002) found that a user’s beliefs about the content (referring to the product/service offered by an e-store), context (i.e., effectiveness of the Web site’s interface), and infrastructure (i.e., efficiency of a collection of assets) of an electronic newspaper site positively impact one’s attitude toward using this site. Suh and Han (2003) validated that a customer’s trust in an e-store would predict positive attitude toward using this store.
A Refined Model
The findings of this study support the research framework depicted in Figure 9.1. The foregoing examination of the existing studies provides more details for the research model. A refined model is shown in Figure 9.2.
As discussed in the preceding section, external environment, demographics, personal charac- teristics, and e-store characteristics have been examined as independent variables in most studies. Significant impacts of these factors on individual’s online shopping intentions, behaviors, and satisfaction are confirmed by these studies. These influences are either direct or mediated by (potential) customers’ beliefs, affective reactions, and attitudes. Some studies explore only the impacts of beliefs, affective reactions, and attitudes on intention, behavior, and satisfaction with- out touching those variables in the left box (e.g., Khalifa and Liu 2002–2003). However, this does not deny the external factors’ input roles. In the other direction, outcome variables in the right box, such as satisfaction, could have fundamental impacts on a customer’s beliefs about online
shopping. In addition, significant associations exist between variables within the same box. For example, intention to shop online is a good predictor of shopping at a specific e-store.
Before we discuss the implications of this study and the future directions of this research area, it is important to acknowledge the study’s limitations.
The first limitation is the source of the selected papers, which may introduce bias into the study results. Due to the nature of B2C e-commerce research, relevant studies are published across various journals in multiple disciplines, such as information systems, marketing, manage- ment, advertising, etc. Including all the studies in this area is close to infeasible. To focus on the information systems perspective and still have a good set of representative studies, we used a journal basket approach and did an exhaustive search of nine primary IS journals for publications in most recent years, yielding a total of 44 quantitative empirical papers. Selection of this basket of papers might introduce bias into analysis due to its limited coverage and disciplinary perspec- tives. The second limitation of the current study is the omission of moderating variables and relationships in the analysis. As a first attempt to draw an overview of the research area and to control the scope of the study, we tried to focus on the main factors and their relationships. Moderators are important and give the picture richness that warrants future investigation.
Despite the limitations, we believe that the findings presented in this chapter, representing one of the few studies that synthesize existing work on consumer online shopping behavior, do offer interesting insight into the state of the art of this research stream and have several important implications that may guide future research in this area.
A few studies in the collection explored moderating effects. All significant moderators belong to “external factors” that fall into the left box in Figures 9.1 and 9.2 (Liang and Huang 1998; Luo and Seyedian 2003–2004; Koufaris et al. 2001–2002; Lee and Turban 2001). Shopping experi- ence, online customer tenure (i.e., new vs. repeat), and trust propensity are good examples of such moderators. For example, Koufaris et al. (2001–2002) discovered that customer tenure moderates the positive impact of the perceived control one has experienced in an e-store on his/her intention to return to this e-store. Lee and Turban (2001) revealed that one’s trust propensity positively moderates the relationship between one’s perceived integrity of Internet merchants and his/her trust in online shopping. Though not included in analysis of the current study, moderators serve an important role in understanding the dynamics of online customer behaviors. Therefore we call for future examination in this direction.
The collected studies took different perspectives and utilized different theoretical models. There is little consensus on consistent theoretical models to describe and predict online shopping inten- tion, behavior, satisfaction, and other relevant constructs. This lack of a common theoretical frame- work suggests the need to develop an integrative model of the phenomenon in order to promote systematic investigation of its components and the online shopping process. By identifying com- mon elements and developing our model based on IS literature, we hope to have taken a step toward promoting this type of integration and synthesis of relevant literature.