Measuring the Service Quality of E-Commerce and Competitive Strategies

Measuring the Service Quality of E-Commerce and Competitive Strategies

Shu-Fong Chang (Nankai University, China), Jen-Chi Chang (Nankai University, China), Kuo-Hua Lin (Nankai University, China), Bin Yu (Nankai University, China), Yu-Cheng Lee (Chung-Hua University, Taiwan), Sang-Bing Tsai (University of Electronic Science and Technology of China, China & Nankai University, China), Jie Zhou (Nankai University, China), Chao Wu (Nankai University, China) and Zi-Chun Yan (Nankai University, China)
DOI: 10.4018/978-1-4666-9466-8.ch019
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The global online shopping market has exhibited consistent growth, exceeding an annual average growth rate of 10% from 2006 to 2010; however, the online shopping market in Taiwan demonstrated an astounding growth rate, surpassing 35% during the same period. In the current competitive and rapidly expanding market environment, shopping website providers must establish effective methods for measuring and improving service quality to increase customer satisfaction levels. In this study, four service quality dimensions of the e-core service quality scale (E-S-QUAL; efficiency, system availability, fulfillment, and privacy) were used to measure the service quality of shopping websites. Subsequently, the simultaneous importance-performance analysis (SIPA) method and analytical Kano model were integrated to analyze the market competition strategies adopted among members of the shopping website industry. Finally, suggestions are provided regarding potential management methods for the case companies.
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Since the 1990s, the proliferation of the Internet has annually increased the range of available online services, leading to the emergence of new industries such as e-commerce. Zwass (1991) defined e-commerce as “sharing business information, maintaining business relationships, and conducting business transactions by means of telecommunications networks.” Typically, e-commerce comprises activities in the following categories: business to business (B2B), business to consumer (B2C), consumer to consumer (C2C), and consumer to business (C2B). Regarding B2C, online shopping is considered the crucial transaction method (Shan & Hua, 2006; Yang & Shao, 2006; Liu & Guo, 2008; Fred & Thatcher, 2010; Lee, Huang et al., 2011; Mahmood, Siponen et al., 2012; Kim, 2013; Lian, Chen et al., 2013).

Donthu and Garcia (1999) stated that online shopping was a common method for purchasing goods and services. The concept of online shopping was derived from interactive home shopping, in which customers use online stores to order goods and services. Shopping websites are positioned similarly to the various stores in a physical department store; because stores and merchandise are categorized and marked by type, consumers seeking specific products are directed to the appropriate store.

Zeithaml, Pasuraman et al. (2002) contended that the success of an online store was not only influenced by the presentation of a web site or adopting a low-cost strategy; instead, businesses should emphasize the quality of online services. Rust and Kannan (2003) highlighted that the services provided by online stores were no longer limited to completing product and service orders and promptly responding to customer queries, contending that a comprehensive online services should involve providing customers with an interactive experience that enhances consumer satisfaction and loyalty.

In 2011, research data provided by the Marketing Intelligence & Consulting Institute (MIC) of the Institute for Information Industry (III) regarding the continuing growth of the global online shopping market showed that the market grew from US$640.7 billion in 2006 to US$951.4 billion in 2010, and the average annual growth rate exceeded 10%. However, the online shopping market in Taiwan has exhibited a particularly outstanding growth rate compared with the rest of the world, growing from US$3.05 billion in 2006 to US$10.38 billion in 2010 and attaining an average growth rate of more than 35%. Advancements in Internet technology and modern hardware equipment have destroyed traditional business management models because increasing numbers of people elect to shop online. To meet the trends of a competitive and rapidly growing market environment, shopping website providers must establish an effective means of measuring and improving service quality to increase customer satisfaction levels.

Parasuraman, Zeithaml et al. (1985) proposed the five gaps model of service quality to explain the forming of the conceptual framework for the gap model. Subsequently, Parasuraman, Zeithaml et al. (1985) proposed a one-column SERVQUAL scale for measuring service quality by subtracting “expected” from “perceived” level of service obtained. The aforementioned definition of service quality and the SERVQUAL scale are widely accepted and used by researchers in relevant fields (Deklerk & Kroon, 2005; Roses, Hoppen et al., 2009; Yeh & Chen, 2012; Seong & Lee, 2013).

Numerous researchers have used the traditional SERVQUAL scale to measure the service quality of shopping websites (Deklerk & Kroon, 2005). However, Zeithaml, Pasuraman et al. (2002) argued that the traditional service quality dimensions did not apply to online transaction environments because traditional transactions are conducted by people and online transactions occur between customers and websites, yielding disparate customer perceptions of service quality. Parasuraman, Zeithaml et al. (2005) contended that because the quality of online transactions is influenced by the consumer ability to operate the technology, service quality evaluations must be revised according to the online transaction environment. In an empirical study, Parasuraman, Zeithaml et al. (2005) proposed the e-core service quality scale (E-S-QUAL) to measure online service quality. The E-S-QUAL is used to perform empirical tests, using multi-item scales to evaluate the service quality of online shopping websites and to measure consumer perceived service quality (Chen & Chou, 2012; Hsu, Hung et al., 2012; Seong, Lee et al.,2013; Shen, Wang et al., 2013).

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