Analysis of customers' return behaviour after online shopping in China using SEM

DOIhttps://doi.org/10.1108/IMDS-05-2019-0296
Pages883-902
Publication Date17 Mar 2020
AuthorDanping Lin,Carman Ka Man Lee,M.K. Siu,Henry Lau,King Lun Choy
SubjectInformation & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems
Analysis of customersreturn
behaviour after online shopping in
China using SEM
Danping Lin
Shanghai Maritime University, Pudong, China
Carman Ka Man Lee
Hong Kong Polytechnic University, Kowloon, Hong Kong
M.K. Siu
Department of Industrial and Systems Engineering,
Hong Kong Polytechnic University, Kowloon, Hong Kong
Henry Lau
Western Sydney University, Sydney, Australia, and
King Lun Choy
Hong Kong Polytechnic University, Kowloon, Hong Kong
Abstract
Purpose The purpose ofthis paper is to examine the potential impacts of various variables on product return
activities after online shopping. Previous studies on customer behaviour have been predominantly concerned
with return on used products and other product-quality-related constructs in the model. This study aims to
specially examine the logistics service-related and customer intentionrelated variables for general products
under the e-commerce circumstance.
Design/methodology/approach Structured questionnaire data for this study were collected in the two
southeast cities of China (162 useable responses). Structural equation modelling was used to examine the latent
variables.
Findings The results confirmed that product return intention has the greatest impact on online shopping
returns with a direct effect of 0.63, followed by the flexibility in return (logistics service) with a direct effect
of 0.49.
Originality/value Such a model not only enriches the theoretical understanding of customer behaviour
studies but also offers online shopping storesand platforms a quantitative benchmark and new perspective on
the design of online shopping supply chains by considering product returns so as to improve the customer
satisfaction.
Keywords Online shopping, Product return, Customer behaviour, Structural equation modelling
Paper type Research paper
1. Introduction
The global surge of e-commerce has been an ongoing phenomenon (AI-Qirim, 2006). Despite
efforts to apply multiple approaches to stimulate customer shopping desire, the product
return is a common concern for many countries. Research analysis pointed out that the
worldwide spurt of product/service return after purchasing has been increased due to the
explosive growth of world e-commerce on the one hand and the adversity of consistently
appealing to and satisfying variable customers on the other. World e-commerce trading
volume increased from US$1,336 billion in 2014 to US$2,842 billion in 2018, representing an
average growth of 20 per cent in this five-year period (Statista, 2019). Based on a net increase
Assessing
online shopping
return
behaviour
883
This work is supported by the National Natural Science Foundation of China (grant number 71701126)
and the Shanghai Pujiang Program (grant number 15PJ1402800).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 28 May 2019
Revised 8 September 2019
20 November 2019
4 January 2020
Accepted 18 January 2020
Industrial Management & Data
Systems
Vol. 120 No. 5, 2020
pp. 883-902
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-05-2019-0296
of US$1,506 billion in this period without considering discount and currency inflation, the
e-retail revenues are expected to grow to US$4.88 trillion in 2021 (Statista, 2019). In addition to
the sheer figures, different nations have their own concerns about the return issues. For some
countries, the major concern is the return procedure management (e.g. the United Kingdom,
United States), while for others, the high return portion is the main consideration (e.g. China).
There are three reasons explaining why this research emphasized on the e-commerce
situation in China: (1) Statista (2019) reported Internet shopping accounted for about 19 per
cent of all retail sales in China, and a large volume of the Chinese online shopping and itsrapid
growth represented it as a typical case; (2) the extraordinary increment of the e-commerce
trading volume in the last 10 years and Chinas position in the international online business
share because of the massive size of Chinas market; and (3) the rapidly increasing popularity
of mobile payment in China that has been greatly stimulating consumption and representing
peoples affluent and sophisticated lifestyles in many significant aspects. As stated earlier, a
concise study of the e-commerce situation in China and exploration of the potential reasons
behind it would help to understand the customer behaviour so as to decrease the return
actions as well as evaluating the commercial performance. Comparing the two shopping
holidays (i.e. Black Friday vs 11.11) will benchmark the condition of the retail industry given
their significance in the business and social perspectives. Created in 2009, 11.11 represents a
new e-commerce holiday that boosted sales to US$7.8 million in gross merchandise value
(GMV). In 2012, the sale of 11.11 surpassed that of Black Friday. In 2017, the number of GMV
reached US$25.3 billion versus US$11.62 billion for Black Friday. The number has been
updated to US$31.81 billion. Despite the global bloom of e-commerce, CBRE Group has
released a report that estimated a total US$37 billion for 2018 (Berman, 2019). Moreover,
according to the U.S. Census report (CBRE report), the e-commerce share and e-commerce
sales have increased from 4.5 per cent of the total retail sale and $169.9 billion in year 2010 to
8.9 per cent and $453.5 billion in 2017. Similar booming phenomenon happens in China and
other countries. This calls for a financial and logistics commitment to keep pace with returns.
Online shoppingprovides convenience, variety,price comparison, fewer purchasing costs,
no crowds and more attention (Akroush and Aldebei, 2015). Because of the unique shopping
approach,online vendors are under morepressure to assure a full demonstrationof a products
appearance, flavor, quality, safety, taste and add-on services, particularly logistics service.
There is also pressure from government legislation and consumersconcerns about price
discrimination generated from online shopping (Borgesius and Poort, 2017). The
aforementioned factors influence not only online shopping intention but also post-sales
customersatisfaction, which mayfurther lead to a product return.Past studies examinedwhat
factors thatinfluence the online purchasingaction, whether it is a singleimpact of a particular
factor (Close and Kukarkinney, 2010;Ganesh et al., 2010) or combined action of factors
(Akroushand Aldebei, 2015;Dakduket al.,2017).However, past studiesdid not fully extend the
study to the return action analysis. Therelacks enough evidence on what are thefactors and
how these factors collectively impact consumersreturn behaviour and their relationship
between the onlinepurchasing and return action. This may be due to thatthe electronic trade
volume and e-commerce share at that time are not big enough. Faced with the booming e-
commerce and theincreased online shopping volume, the study of customer return becomesa
crucial problem. Because the large volume of return packages not only represents a waste of
forward logistics resources but alsoarises challenges to the managementof reverse logistics.
Sometimesretailers are left withlittle choice but to getrid of large swaths of inventoryat a cost.
Several impact factors that are used to analyse the online purchasing behaviour can be
interpreted and investigated for return action as well. The existing literature explores the
reasons of return from several angles, such as online product review (Sahoo et al., 2018),
customer value framework (Minnema et al., 2018), buy-online-and-pick-up-in-store strategy
(Shi et al., 2018) and price competition (Zheng et al., 2018). As a result, this paper will
IMDS
120,5
884

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