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A Model of Consumer Behaviors in Electronic Commerce: Trust, Information Search, and Internet Shopping
Unformatted Document Text:  16 models based on our tentative ideas, the final model including only meaningful and substantively interpretable parameters emerged (see Figure 2). Lisrel 8.3 provides a variety of statistics exist to assess the adequacy of a path model (Bollen, 1989). Most of goodness-of-fit statistics of the proposed final model indicates an exceptionally good fit. The chi-square statistic of this model was 8.06 (degree of freedom: 14, p = .8). The root mean square error of approximation (RMSEA=0.0) and the standardized root mean square residual (SRMS=0.01) were very low. The goodness-of-fit index (GFI = 1.0), the adjusted goodness-of-fit index (AGFI = 1.0), the normed fit index (NFI = .99), the comparative fit index (CFI = 1.0), and the parsimony-normed-fit index (PNFI = .50) also indicated an acceptable fit. The variables included in this model accounted for 8% of the variance in e-commerce trust, 20% in information search on the Internet, and 6% in online shopping. [footnote] As is apparent from the figure1, almost all predicted paths were statistically significant and in the hypothesized direction. Trust in Electronic Commerce As can be seen in table 1 and figure 2, trust in business has a significantly positive effect on Trust in Electronic Commerce ( = .12, p < .001). The more people trust business and companies, the less they were likely to concern privacy problem in the context of e-commerce. Thus, hypothesis 1 was supported. In terms of Internet-related characteristics, respondents who used more Internet ( = .08, p < .001) having more self-confidence in Internet skills ( = .21, p < .001) were more likely to display trust in electronic commerce. Given the results, hypotheses 2- 1 and 2-2 were supported. In sum, the results indicate that both trust in companies and self-

Authors: Keum, Heejo. and Cho, Jaeho.
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models based on our tentative ideas, the final model including only meaningful and
substantively interpretable parameters emerged (see Figure 2).
Lisrel 8.3 provides a variety of statistics exist to assess the adequacy of a path model
(Bollen, 1989). Most of goodness-of-fit statistics of the proposed final model indicates an
exceptionally good fit. The chi-square statistic of this model was 8.06 (degree of freedom: 14, p
= .8). The root mean square error of approximation (RMSEA=0.0) and the standardized root
mean square residual (SRMS=0.01) were very low. The goodness-of-fit index (GFI = 1.0), the
adjusted goodness-of-fit index (AGFI = 1.0), the normed fit index (NFI = .99), the comparative
fit index (CFI = 1.0), and the parsimony-normed-fit index (PNFI = .50) also indicated an
acceptable fit.
The variables included in this model accounted for 8% of the variance in e-commerce
trust, 20% in information search on the Internet, and 6% in online shopping. [footnote] As is
apparent from the figure1, almost all predicted paths were statistically significant and in the
hypothesized direction.
Trust in Electronic Commerce
As can be seen in table 1 and figure 2, trust in business has a significantly positive effect
on Trust in Electronic Commerce ( = .12, p < .001). The more people trust business and
companies, the less they were likely to concern privacy problem in the context of e-commerce.
Thus, hypothesis 1 was supported. In terms of Internet-related characteristics, respondents who
used more Internet ( = .08, p < .001) having more self-confidence in Internet skills ( = .21, p <
.001) were more likely to display trust in electronic commerce. Given the results, hypotheses 2-
1 and 2-2 were supported. In sum, the results indicate that both trust in companies and self-


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