CCE Theses and Dissertations

Date of Award

2009

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Information Systems (DISS)

Department

Graduate School of Computer and Information Sciences

Advisor

Yair Levy

Committee Member

Timothy J Ellis

Committee Member

Ling Wang

Keywords

apparel, e-commerce, gender, online shopping

Abstract

E-commerce has experienced exponential growth within the last few years. The rapid growth of e-commerce has created a need to improve consumer acceptance and the consumer's intention to engage in e-commerce. Female consumers have yet to embrace e-commerce as readily as male consumers. Differences between male and female consumer shopping behavior were examined.

This study developed and empirically tested a model to predict the consumer's intention to engage in apparel e-commerce shopping based on the constructs of gender, shopping orientation, online experience, and Website's interactive features. Male and female U.S. consumers age 18 and older were surveyed to determine their intention to engage in apparel e-commerce shopping. A total of 240 responses were received. After the pre-analysis data screening, a total of 216 responses were available for further analyses. Factor analysis was conducted by using principal component analysis (PCA) with VARIMAX rotation. The PCA resulted in four new factors including consumer shopping preference (CSP), personalization Website features (PWF), shopping environment (SE), and social interaction (SI). The statistical method Ordinal Logistic Regression (OLR) was used to predict whether gender (G1), CSP, PWF, SE, and SI have a significant influence on the consumer's intention to engage in apparel e-commerce shopping. Results of the OLR indicated that CSP was the only significant predictor of INT. A second OLR model was developed to determine the interaction effect of G1, CSP, PWF, SE, and SI used to predict the probability of INT. Results indicated the interactions of G1 and CSP, CSP and PWF, G1 and PWF, as well as SE and SI were significant predictors of INT.

Two important contributions of this study include 1) an investigation of the key constructs that contribute to the consumer's intention to engage in apparel e-commerce shopping, and 2) an investigation of the interaction effect between the key constructs used to predict the consumer's intention to engage in apparel e-commerce shopping. The investigation results provide online retailers with the knowledge of how to increase e-commerce acceptance through understanding differences in male and female consumer shopping behaviors.

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