Do e-shopping attitudes mediate the effect of the built environment on online shopping frequency of e-shoppers?

Abstract

It is widely acknowledged that e-shopping has considerable effects on e-shoppers’ travel behavior. Therefore, it is valuable to investigate the built environment effects on online shopping, which can help clarify whether land use policy is effective to manage online shopping and further moderate travel demand. However, this issue has not been fully investigated in prior research. In particular, some existing studies fail to identify a significant link between the built environment and online shopping. One of the possible reasons is that the indirect effects of the built environment on e-shopping through e-shopping attitudes are rarely considered. Against this backdrop, considering the mediating role of e-shopping attitudes, this paper aims to explore the influence of the built environment on the frequency of e-shopping for clothes and shoes, food and drinks, cosmetics, and electronics. Data used in this study are acquired from 675 face-to-face interviews with online buyers in Chengdu, China, and the Structural Equation Modeling method is employed. The outcomes show that higher residential density has a positive impact on online shopping frequency. Higher accessibility to metro stations has an indirect and negative influence on e-shopping frequency through pro-e-shopping attitudes. In contrast, mediated by e-shopping attitudes, higher accessibility to bus stations has an indirect and positive impact on online shopping frequency. The mediating role of attitudes provides a possible explanation for the influences of transportation accessibility on e-shopping frequency. Land use policies seem influential in online shopping attitudes and frequency, and thus moderate e-shoppers’ travel demand.

Keywords:

• Built environment
• Chengdu (China)
• e-shopping attitudes
• online shopping
• travel behavior

Acknowledgments

This work was supported by the the Research Foundation – Flanders (FWO) under Grant number 12F2519N; and the China Scholarship Council under Grant numbers 201706180096 and 201706040072. Frank Witlox acknowledges funding from the Estonian Research Council under Grant number PUT PRG306. The authors thank three anonymous referees for their insightful suggestions on the earlier version of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Yuan was around 0.15 U.S. dollars or 0.14 EUR in 2016.

2 The POI data collected from e-maps are commonly used as the indicators of built environment elements in previous studies (e.g., Zhao & Li, 2019; Zhu et al., 2019).