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Chapter 1 Introduction
With the development of handheld devices such as personal digital assistants (PDAs), tablets, and smart phones, wireless and mobile technologies and their associated applications have become essential tools. According to the International Data Corporation (IDC), in 2009 there were more than 450 million mobile Internet users worldwide, a figure that will exceed the 1 billion mark by 2013. There were 5.3 billion mobile subscribers, representing 77 percent of the world’s population. In 2010, 1388.2 million mobile handsets were sold, up 18.5 percent compared with 2009. Smartphones have shown the strongest growth, with total shipments in 2010 of 302.6 million units, up 74.4 percent from 2009, representing 21.8 percent of all handsets shipped. The fast-growing smartphone market is predicted to grow more than four times the rate of the overall mobile phone market this year. IDC expected smartphone vendors to ship up to 472 million smartphones in 2011 (107 million smartphones were sold worldwide in the second quarter of 2011), and predicted that the figure will nearly double to 982 million by the end of 2015 (IDC, 2011). In contrast, Gartner expected global sales of tablet PCs to reach 7 million units in 2011, and predicted that sales in 2012 would reach 108 million units, but global tablet PC sales in 2010 were only 1.76 million units. This implies a very rapid evolution of Internet access and the mobile market, with more people starting to use handheld devices. Wireless technology has thus become a very important issue.
Advanced and mature wireless communication technology has facilitated the development of various mobile commerce (m-commerce) applications. Many mobile applications have emerged as a result, such as location-based services (LBS), mobile reading services, electronic books, mobile TV, and mobile music. Positioning technology such as the GPS provides localization services for mobile devices (Rao & Minakakis, 2003). Over 300,000 mobile applications (apps) were developed in the three years from 2007 to 2010. IDC indicates that in 2010 alone these 300,000 applications were downloaded 10.9 billion times. They predict that global downloads will reach 76.9 billion times in 2014 and will be worth US$35 billion (IDC,
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2010). The most popular categories of apps are games, news, maps, social networking, and music.
The most used apps across all smartphones in the United States, according to Nielsen (Nielsen, 2010), are Facebook, Google Maps, and the Weather Channel (TWC). New m-commerce applications are developing and m-commerce market is growing.
There are several differences between m-commerce and e-commerce. For example, a particular mobile characteristic that must be considered is that when mobile consumers search for a thing, it is possible that we are near the area relating to the thing. For instance, if we type the keywords “101” and “restaurant”, we may be planning to have a meal in a restaurant around the Taipei 101 shopping mall, and in these mobile times, it is likely that we are close to the mall now and want to have dinner later. The main characteristics of m-commerce that are not found in general e-commerce are the use of the user’s location, non-planned events, and instant business behavior. Positioning technology such as GPS provides localization services for mobile devices, which has allowed the development of location-based applications that connect to a user’s location directly (Rao & Minakakis, 2003). The main LBS applications are those that allow users to navigate with their smart phones if they lose their way. Another use of LBS is the “check-in”
function in applications such as Facebook and Foursquare. As the mobile service applications market grows, companies will inevitably strive to enter the mobile market to search for new opportunities.
A great deal of literature is available on service quality, covering both offline services (traditional services) and online services (e-services). However, there are potential opportunities for further research due to the development of new technologies and service delivery channels.
Service quality describes the gap between consumers’ expectations and performance. If consumers’ initial service expectations are higher than their perceptions after receiving the service, then the total perceived service quality will decrease. Service quality evaluations also involve processes (Sasser et al., 1978), such as the way in which services are delivered. The SERVQUAL service quality scale was developed by Parasuraman, Berry, and Zeithaml in 1985, and further be refined in 1991. For several decades, research on service quality in the field of
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commerce has been strongly influenced by the work of Parasuraman et al. With the development of the Internet, electronic commerce (e-commerce), and services that are provided online, Parasuraman et al. (2005) developed a multiple-item scale for assessing electronic service quality (E-S-QUAL) specifically for the e-commerce context. However, to date there has been limited research on the development of a scale of mobile service quality (m-SQ). The gaps of research are: (1) it is not clear that factors influence of wireless-based applications to be used effectively in m-commerce environment, (2) there is not well-defined measuring and lacks of strict methodology scale development to measure M-S-QUAL, (3) a success of M-S-QUAL cannot be evaluated by a single-item scale, such as global service quality. To fill this gap, this research attempts to develop a new multi-dimensional instrument to measure M-S-QUAL.
The objective of this research is to develop a sound instrument for measuring m-SQ, and to identify the dimensions of m-SQ. To deliver superior mobile service quality, mobile service providers must understand consumers’ perceptions of mobile services. As there are many different types of mobile services, this research attempts to grasps the essential characteristics of mobile services. A core M-S-QUAL measurement scale is then developed that is suitable for assessing consumers’ perceptions of the service quality of mobile applications. The procedure used in generating items, collecting data, and validating the multi-item scale is described in following chapters. This study carefully examines the evidence of reliability, content validity, convergent validity, and discriminant validity by using the statistic software AMOS to analyzing data from sample.
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