Drivers and Inhibitors of Mobile-Payment Adoption by Smartphone Users

Drivers and Inhibitors of Mobile-Payment Adoption by Smartphone Users

Pavel Andreev (University of Ottawa, Canada), Nava Pliskin (Ben-Gurion University of the Negev, Israel) and Sheizaf Rafaeli (University of Haifa, Israel)
Copyright: © 2012 |Pages: 18
DOI: 10.4018/jebr.2012070104
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Abstract

The widespread penetration of smart mobile devices has facilitated rapid growth of mobile location-based services (LBS), which provide users with a variety of benefits and are attractive from a marketing perspective. However, mobile-payment (M-Payment) adoption by users has been below expectations. For better understanding of drivers and inhibitors of the willingness to M-Pay for mobile LBS, this study contributes by conceptual modeling and empirical assessment of user willingness to M-Pay. To test the proposed conceptual research model, data from 122 valid responses were analyzed by employing the Partial Least Squares (PLS) technique. The findings show that Perceived Risk is the main inhibitor of user willingness to M-Pay for LBS and that the magnitude of this inhibitor’s negative impact is at least twice the magnitude of any driver’s positive impact.
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Introduction

The smartphone, now a valuable and critical business tool for mobile delivery of products and services, has been investigated by academics, professionals, and the media (Bauer, Barnes, Reichardt, & Neumann, 2005; Gao & Küpper, 2006; Hsu & Kulviwat, 2006; Leppäniemi & Karjaluoto, 2005; Varshney & Vetter, 2002). Industry experts predict that the range and extent of mobile products and services available through smart mobile devices will increase exponentially in the coming months and years, as more and more commercial entities realize their profit potential.

The widespread penetration of smart mobile devices facilitates the rapid growth of mobile location-based services. A location-based service (LBS) provides services based on the user's geographical location. It is possible to classify LBSs based on the target market: business-to-customer (B2C) and business-to-business (B2B), the service type: infotainment, navigation, information provision, games, emergency response, supply chain management and tracking (Giaglis, Kourouthanassis, & Tsamakos, 2003) or, as classified in this study, the delivery mode: pull and push (Paavilainen, 2002). The Pull services are sent to the user upon request while the Push services are non-request based (Unni & Harmon, 2007). According to industry analyses of the current mobile LBS market, the main drivers of this market's rapid growth include success of new mobile business models, expansion of mobile advertising, expanding of network coverage and increasing of high speed mobile Internet (Pyramid Research, 2011).

Another factor related to this growth is mobile payments (M-Payments). Leading players in the mobile market provides a variety of solutions facilitating of M-Payments. Google proposes, for instance, smartphones with built-in NFC-powered digital wallets (https://squareup.com/ retrieved on November 24th, 2011).

Despite visible M-Payment advantages and regardless of the noticeable agiotage around expectations for M-Payment boom, the status quo shows that there are still many factors inhibiting user willingness to M-Pay. Indeed, a study by the Portio Research (2010) demonstrated that, in 2009, 81.3 million people worldwide M-Paid (2% of mobile subscribers) and forecasted the rise to nearly 490 million (8% of mobile subscribers), by the end of 2014, raising interest in investigating factors driving and inhibiting the willingness to M-Pay.

The objective of this study is thus to increase understanding of M-Payment drivers and inhibitors, through modeling and empirically assessing the willingness of users to M-Pay. The study's scope is limited to Push-LBS for which users exercise less control over their interaction with the service provider (Xu, Hock-Hai, Tan, & Agarwal, 2010) and since behavioral attitudes and intentions regarding Push-LBS remain blurred and hardly addressed by literature. Moreover, while adoption of advanced mobile devices facilitates new business opportunities for mobile commerce sector, the future of the Push-LBS boom depends on user willingness to M-Pay.

The next section explores the theoretical grounding for the development of conceptual model described in the third section. Then we outline the methods of data collection and analysis, followed by description of the results obtained via empirical assessment using the partial least square (PLS) approach to structural equation modeling (SEM). The last section is devoted to the discussion and the conclusions.

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