The impact of risk on the technological acceptance of mobile payment services

Abstract

The aim of this paper is to analyse the users' acceptance of mobile payment on social networks. Although social networks and mobile-related technology are widely accepted by society, mobile payment is not among the most widely used mobile services. In order to analyse the level of acceptance of this technology, as well as the factors that determine its use by the consumers, we have introduced a modification of the classical technological acceptance model, including risk as a variable-given its relevance in the field. The empirical results show a particular support for the effects of external influences, of usefulness and, to a lesser extent, of risk. Our research has several implications for companies, by focusing on consumers' intention of using these services.

Consumers’ perceived risk and hold and use of payment instruments

“…This list is built according to payment literature, identifying factors related to the five classical dimensions of perceived risk (financial, physical, performance, psychosocial and time). Even if Roselius (1971) and Jacoby and Kaplan (1972) perceived risk models are rarely used in payment literature, the factors relating to the five classical dimensions of perceived risk are well developed (Dowling 1986;Ho and Ng 1994;Lee 2009;Glover and Benbasat 2010;Schierz, Schilke, and Wirtz 2010;Liébana-Cabanillas, Muñoz-Leiva, and Sánchez-Fernández 2013). Based on payment literature, we develop a list of eight risk factors that may affect consumer choices of a payment instrument.…”

“…Perceived risk is modelled using the eight risk factors presented in Section 2 (see Table 1), and we consider an additive structure, a frequent assumption in perceived risk literature (Roselius 1971;Jacoby and Kaplan 1972;Bettman 1975) and payment studies (Ho and Ng 1994;Liébana-Cabanillas, Muñoz-Leiva, and Sánchez-Fernández 2013).…”

“…Some articles study more precisely card payments (Rysman 2007;Sprenger and Stavins 2008;Kosse and Jansen 2013), others examine cheque payments (Schuh and Stavins 2010;Micheau and Houitte 2012) and a few focus on incentives (Simon, Smith, and West 2010;Arango, Huynh, and Sabetti 2011). More recently, a parallel research stream has highlighted perceived risks associated with payment instruments (Ho and Ng 1994;Kosse 2010;Liébana-Cabanillas, Muñoz-Leiva, and Sánchez-Fernández 2013). This stream uses risk factors, such as fraud, theft or loss, to compare payment instruments (De Mello and Zilberman 2008;Karoubi 2012), focusing on those related to the financial and physical consequences for consumers.…”

An Empirical Examination of User Adoption Mobile Payment

“…The bootstrap is described as "a computer-based method for assigning measures of accuracy to statistical estimates" and the usual measure of accuracy for statistical quantities is the standard error [43]. In traditional methods, "a standard error is usually estimated from equations that are derived from a series of assumptions and mathematical operations" [43].…”

“…In traditional methods, "a standard error is usually estimated from equations that are derived from a series of assumptions and mathematical operations" [43]. For the bootstrap method, it "involves having a computer program generate a series of data sets(bootstrap samples) that are designed to resemble the ones that would be observed if the estimation study were repeated many times.…”