Examining User Switch between Mobile Stores: A Push-Pull-Mooring Perspective

Examining User Switch between Mobile Stores: A Push-Pull-Mooring Perspective

Tao Zhou (School of Management, Hangzhou Dianzi University, Hangzhou, China)
Copyright: © 2016 |Pages: 13
DOI: 10.4018/IRMJ.2016040101
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Retaining users and curbing their switching behavior are critical issues for mobile stores. Drawing on the push-pull-mooring (PPM) model, this research identified the factors affecting user switches between mobile stores. The push factors include dissatisfaction with system quality, information quality and service quality. The pull factor is alternative attractiveness. The mooring factors include switching costs and social influence. The results indicated that user switches receive influences from all three kinds of factors. Hence mobile stores need to be concerned with the effects of push, pull and mooring factors in order to curb users' switching behavior.
Article Preview

Introduction

Mobile internet has been flourishing throughout the world. Especially, the application of the third and fourth generation (3G/4G) mobile technologies has triggered mobile internet development. According to a recent report, the number of mobile internet users in China has exceeded 557 million, accounting for 85.8% of its internet population (649 million) (CNNIC, 2015). Attracted by the great market, service providers have released a variety of services such as mobile instant messaging, mobile purchase and mobile payment. They expect users to widely adopt and use these services. Then they can achieve competitive advantages and make profits. Nevertheless, only acquiring users and facilitating their initial usage is not enough for service providers. Users may discontinue their use of the current service and switch to an alternative one. This may undermine the user base and lead to service providers’ failure as they have invested great effort and resources on releasing these services. Thus, service providers need to understand the factors affecting user switch in order to cub switch behavior and retain users.

With the help of mobile networks and devices, users can conveniently search for product information, place orders and conduct payment on mobile stores. This may help improve user experience and facilitate his or her adoption. Many online companies such as Amazon, JD, and Tmall have developed their mobile stores on the mobile internet. These mobile stores have similar functions and there exists intense competition among them. For users, they can easily switch from a mobile store to an alternative one. This highlights the need to examine user switch and identify the determinants of switch behavior.

Previous research has focused on user adoption of mobile stores (Ko et al., 2009; Kuo et al., 2009; Lu and Su, 2009; Zhou, 2013), and has seldom examined user switch between mobile stores. As noted earlier, it is only the first step for mobile stores to acquire users and facilitate their initial adoption and usage. Mobile stores also need to curb users’ switching behavior in order to retain them and facilitate their post-adoption usage. Thus, it is necessary to identify the factors affecting user switches between mobile stores. This is the purpose of our research. We draw on the push-pull-mooring (PPM) model as the theoretical base. PPM was originally proposed to explain human migration (Lee, 1966), and has recently been used to understand user switch on online services (Hsieh et al., 2012), blogs (Zhang et al., 2012), and social networking sites (Chang et al., 2014; Xu et al., 2014) in the information systems research. Push factors are those factors associated with the original product that compel users to switch, whereas the pull factors are those factors associated with the destination product that attract users to switch. Mooring factors represent those personal and social factors that influence the switch decision (Chang et al., 2014). In our research model, push factors include dissatisfaction with system quality, information quality and service quality, which represent three factors of the information systems success model (DeLone and McLean, 2003). The pull factor is alternative attractiveness. Mooring factors include switching costs and social influence. We propose that these three kinds of factors affect switch intention between mobile stores.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 31: 4 Issues (2018): 1 Released, 3 Forthcoming
Volume 30: 4 Issues (2017)
Volume 29: 4 Issues (2016)
Volume 28: 4 Issues (2015)
Volume 27: 4 Issues (2014)
Volume 26: 4 Issues (2013)
Volume 25: 4 Issues (2012)
Volume 24: 4 Issues (2011)
Volume 23: 4 Issues (2010)
Volume 22: 4 Issues (2009)
Volume 21: 4 Issues (2008)
Volume 20: 4 Issues (2007)
Volume 19: 4 Issues (2006)
Volume 18: 4 Issues (2005)
Volume 17: 4 Issues (2004)
Volume 16: 4 Issues (2003)
Volume 15: 4 Issues (2002)
Volume 14: 4 Issues (2001)
Volume 13: 4 Issues (2000)
Volume 12: 4 Issues (1999)
Volume 11: 4 Issues (1998)
Volume 10: 4 Issues (1997)
Volume 9: 4 Issues (1996)
Volume 8: 4 Issues (1995)
Volume 7: 4 Issues (1994)
Volume 6: 4 Issues (1993)
Volume 5: 4 Issues (1992)
Volume 4: 4 Issues (1991)
Volume 3: 4 Issues (1990)
Volume 2: 4 Issues (1989)
Volume 1: 1 Issue (1988)
View Complete Journal Contents Listing