Discussion on the Behavior Intention Model of Consumer Online Shopping
Chih-Chung, Chen, Chang, Su-Chao, Journal of Business and Management
The objective of this study was to test the sufficiency of the Theory of Planned Behavior (TPB) and extended TPB models by looking at the added variables of past experience and channel knowledge in predicting consumer online shopping. Data was collected by questionnaire from employed students in junior college and university (n=334), and by web-based survey from Internet users (n=92). Using Multi-regression analysis, the results of the study demonstrate that the TPB is applicable to measuring behavioral intentions in online shopping. Furthermore, adding past experience and channel knowledge to the TPB model improves the prediction of online shopping behavior.
People's shopping habits have changed. According to the forecasting of Forrester Research, by 2010, online sales will reach $331 billion in America. The growing numberol online shopping households combined with retailer innovations and website improvements will drive e-commerce to account for 13% of total retail sales in 2010, up from 7% in 2004. Between 2004 and 2010, online sales will grow at a 15% compound annual growth rate (Johnson, 2004). In addition, MIC of the Institute for Information Industry estimated online shopping in 2003 was $0.6 billion (NT$20.4 billion), and would grow by 30% to $0.81 billion (NT$26.6 billion), and increase by 88% in 2005 to $1.52 billion (NT$50.1 billion) in Taiwan. Currently, online shoppers in Taiwan only represented 20% of the Internet population. Compared with America in which B2C e-business accounted for 1.5% of total retailing sales, B2C in Taiwan(0.39% of total retailing sales) still had a large growth space (Wu, 2004). As the Internet becomes an increasingly common medium for consumer transactions throughout the world, it becomes increasingly important to identify the factors affecting consumer adoption of c-commerce.
Several studies (James & Cunningham, 1987; Scansaroli, 1997; Li, Kuo, & Russell, 1999; Chen, 2000; Abend, 2001) explore the factors affecting consumer online shopping. But, Goldsmith (2001) claims that a lot of research concerning online consumer behavior is rather descriptive in nature and not based on consumer theory. One aim of this study, therefore, is to use consumer theory to predict consumer online shopping behavior intention.
According to the research of Bagozzi (1981) and Shimp and Kavas (1984), the TPB in attitude and behavior could be applied to research in different fields, and it could also help to clearly understand the effects of every dimension of the behavior intention. For example, TPB has been applied in a number of areas: decision making (Venkatesh, Morris, & Ackerman, 2000), environment protection (Cordano & Frieze, 2000), science and technology (Morris & Venkatesh, 2000; Gary, Franklin, Alan, & Mohammed, 1995), marketing (Huff & Alden, 2000; Shaoyi, Yuan, Huaiqing, & Ada, 1999; Shirley & Peter, 1995), and health behavior (Armitage & Conner, 2001). However, applications toward online shopping are rare.
In addition, some prior studies have challenged the assumption that the three variables in the TPB model are sufficient in order to predict behavioral intentions. Some argue that additional variables could further enhance the model's predictive utility and significantly improve its predictive power (Conner & Armitage, 1998; Norman, Conner, & Bell, 1999). For this reason, the study adds online purchase experience (Steven, Gerald, &r Eric, 1999; Gerald et al., 2000) and Channel Knowledge (Li, et al., 1999) to further enhance its predictive power. Therefore, the purpose of the study is two-fold: to apply TBP to the online shopping area and to extend TPB models in predicting consumer online shopping behavior.
We will first provide a detailed discussion of the TPB and hypotheses, develop the research methodology, present the results, and finally discuss the implications of the study and provide suggestions for further research. …