Abstract

Customers are skeptical about shopping online because e-commerce environments are typically considered impersonal. To assure product quality and to enhance customer proclivity in such environments, post-sale services (i.e., product returns, exchange, and maintenance) may be considered to alleviate customers’ skepticism. Therefore, this study’s objective is to investigate the role of an online retailer’s post-sale services (i.e., product return, exchange, and maintenance) on customers’ attitudinal (building relationships) and behavioral aspects (developing customers’ repurchase intentions). Structural equation modeling is applied to data collected through an online survey answered by 409 online customers of jd.com (after missing data were removed). Research findings show that product return, exchange, and maintenance services are strongly predictive of online customer satisfaction, and satisfaction significantly impacts customer trust. Both customer satisfaction and trust, as indicators of relationship quality, further mediate the links between product return, exchange, and maintenance services and online customer repurchase intention. In addition, differences between male and female customers were found in various aspects of online retailers’ product return, exchange, and maintenance services. This is the first empirical study that not only examines the influence of all three dimensions of online retailers’ post-sale services on customers’ online shopping perceptions and decisions, but also considers differences between male and female customers. Finally, this research provides theoretical and managerial implications based on conceptual and empirical evidence.

Highlights

  • The online shopping environment is considered complex and competitive (Javed and Wu, 2020)

  • We examine the mediating effect of relationship quality regarding perceived product return, exchange, maintenance services, and customer repurchase intention

  • We analyzed the overall goodness of fit [χ2 (120) = 316.533, P < .000; normed Chi-square χ2/df = 2.637; and the alternative fit indices, i.e., Goodness-of-fit index (GFI) = 0.924; Adjusted goodness-of-fit index (AGFI) = 0.896; Comparative fit index (CFI) = 0.977, Normed fit index (NFI) = 0.959, Root mean square error of approximation (RMSEA) = 0.067, and Parsimony comparative fit index (PCFI) = 0.813] and accepted the model (Table 2)

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Summary

Introduction

The online shopping environment is considered complex and competitive (Javed and Wu, 2020). Online businesses face more negative consequences including online retailers’ credibility, inability to inspect the product before receipt, and the physical distance between buyer and seller (Davari et al, 2016). In this environment, online retailers’ post-sale services may reduce customers’ pre-purchase uncertainty (Heiman et al, 2001; Ramanathan, 2011). Consumers’ psychological concerns will be relieved if they know that they can exchange, return (Hong and Cha, 2013) or repair of any purchased product It is unclear how important post-purchase activities are to e-commerce and its growth (Cao et al, 2018). Marketers and companies need to understand consumers and their relevant behavior in online shopping systems (Hussain et al, 2020)

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