Exploring Adoption Difficulties in Mobile Banking Services

Canadian Journal of Administrative Sciences, Jun 2009 by Yang, Ann Shawing

Abstract

Factors associated with adopting and resisting mobile banking technologies were investigated among university students in Taiwan. Adoption factors included the belief that mobile banking helps fulfill personal banking needs, provides location-free conveniences, and is cost effective. The primary factors associated with resistance included concerns over system configuration security and basic fees for mobile banking web connections. The theoretical and applied implications of these findings are discussed. Copyright © 2009 ASAC. Published by John Wiley & Sons, Ltd.

JEL Classification: O33, L84

Keywords: mobile banking adoption, Rasch measurement model, item response theory

Résumé

Le présent article examine les facteurs qui poussent les étudiants taïwanais à adopter les technologies de la banque mobile ou à y résister. Parmi les facteurs d'adoption, il y a la croyance selon laquelle les opérations bancaires mobiles permettent de satisfaire les besoins bancaires personnels, sont disponibles partout et coûtent moins cher. Les facteurs de résistance comprennent, entre autres, les inquiétudes liées à la sécurité du système de configuration et les frais de connexion au réseau banque mobile. L'article s'achève par une présentation des implications théoriques et pratiques de l'étude. Copyright © 2009 ASAC. Published by John Wiley & Sons, Ltd.

Mots-clés : adoption des opérations bancaires mobiles, modèle de Rasch, système IRT (item response theory)

Mobile technology is changing the design and deliv- ery of personal financial services (Luarn & Lin, 2005). The introduction of mobile banking services has com- pressed the spatial and temporal constraints associated with traditional banking (Wang & Liao, 2004). However, cognitive and economic barriers pose challenges to the uptake of innovations in financial intermediation tech- nology (Carlsson, Waiden, & Bouwman, 2006). For example, potential barriers to adoption of mobile banking relate to peoples' perceptions of its usefulness (value), its ease of use, its credibility and efficiency, and costs asso- ciated with banking transactions (Lai & Li, 2005; Liao, Shao, Wang, & Chen, 1999; Liu, 2002; Luarn & Lin; Yang, 2005).

The unique contribution of the present study lies in applying the Rasch measurement model (Rasch, 1960) to assessing people's acceptance of new mobile banking technologies.

The Rasch model analyzes the likelihood of a person experiencing difficulty performing a particular mobile banking task with respect to the person's ability to adopt mobile banking technology more generally (i.e., across a variety of tasks). It assumes a respondent with lower ability is more likely to experience difficulties than is a respondent of higher ability (Pallant & Tennant, 2007). That is, the Rasch measurement model expects certain probabilities of responses related to ability level. When observed responses match closely or deviate little from expectations, we say that the "data fit the model"; this is a prescriptive approach (Tesio, 2003). We adopted the Rasch model to identify unexpected responses of study participants of varying levels of ability in their conduct of banking transactions via a mobile phone. Unexpected responses were those reflecting mobile banking tasks for which the majority of respondents experienced litlte difficulty, as well as those reflecting mere "guesses." With the Rasch model, the "unexpected responses" (i.e., nonfitting data) are excluded from further analysis, yielding a better fitting model. With this approach, me administration of our survey to various groups of respondents yielded identical or similar responses (Avery, Russell, Raina, Walter, & Rosenbaum, 2003), allowing for acrosssample stability in response patterns. The Rasch measurement model, drawing on item response theory (IRT) (Massof & Fletcher, 2001), also permits the measurement of latent traits and comparisons of individuals' ability to complete various mobile banking tasks.

A key advantage to the Rasch measurement model is that it provides comparisons among individuals that are independent of the measurement instrument (i.e., "specific objectivity"). Moreover, when sample size is limited, measurement is not affected, thereby ensuring stability. Litde is known about the barriers to determining intentions to adopt mobile banking technology. Therefore applying the Rasch measurement model using IRT represents a novel approach to identifying difficulties in adopting mobile banking technologies. Once these difficulties are identified, actions can then be taken to rectify them. The Rasch measurement model is unlike other approaches to understanding user adoption of mobile banking technologies, which have focused on adoption intentions of potential users, such as with the Technology Acceptance Model (TAM) (Davis, 1989). A key aspect of the current study, then, is its examination of the difficulties faced by mobile banking technology users when conducting financial service transactions via mobile phones. The findings are of potential value to the mobile banking industry in its efforts to build a larger and satisfied clientele.