ViewpointUnderstanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application
Introduction
In recent years the Internet has been growing and offering many Web-based applications as a new way for organizations to retain customers and offer them new services and products (Tan & Teo, 2000). In order for both parties (customers and organizations) to take advantage of these applications, it is crucial to analyze the genuine perception and main reasons of people's willingness to adopt these technologies (Lee, 2009, Liao and Cheung, 2002).
Internet banking has emerged as one of the most profitable e-commerce applications (Lee, 2009). Most banks have deployed Internet banking systems in an attempt to reduce costs while improving customer service (Xue, Hitt, & Chen, 2011). Despite the potential benefits that Internet banking offers consumers, the adoption of Internet banking has been limited and, in many cases, fallen short of expectations (Bielski, 2003).
While earlier research has focused on the factors influencing the end-user IT adoption, there is limited empirical work which simultaneously captures the success factors (positive) and resistance factors (negative) that drive customers to adopt Internet banking (Lee, 2009). Building upon the premise that purchasing Internet banking services is perceived to be riskier than purchasing traditional banking services (Cunningham, Gerlach, Harper, & Young, 2005), this study introduces the perceived risk factor. Drawing from perceived risk theory, this study couples specific perceived risk facets (Featherman & Pavlou, 2003) – namely performance, financial, time, psychological, social, privacy, and overall risk – with unified theory of acceptance and use of technology (UTAUT) to propose an integrated model to explain customers’ intention to adopt and use Internet banking.
Our research merges an existing and empirically validated theoretical model with a perceived risk factor, which is also an important construct that will be tested on the adoption of Internet banking for the first time. Thus, this study may help banks to understand the determinant factors that influence users and to create the right policies and actions to attract customers to use this service. Additionally, it is in the banks’ and clients’ interest to direct their communication from bank branches to online channels in order to be more productive and cost-effective for both parties.
The structure of the paper is as follows. In the next section the concept of Internet banking, the current theories that explain customers’ acceptance of technology, the definition of perceived risk, and earlier research on this topic are presented. The research model is then conceptualized. The second part of the paper presents the research design, methodology, and results. Finally, the results are discussed, including the implications for theory and practice, and further possible research directions are outlined.
Section snippets
The concept of Internet banking
Concerning the increasing innovation and urgent need of up-to-date, convenient and reliable data, information systems (IS) have gained high importance in the organizational context. Against this background, a great dependency between the organizations’ performance and their IS is emerging. Organizations can now profit from the evolution of new technologies and adapt to the emerging ways of interacting with their clients. The banking sector has been using IS not only to run internal business
Research model
As seen above, the UTAUT model is able to explain 70% of the variance in usage intention, which is a substantial improvement over any of the eight original models used to build it. Thus, it demonstrates that UTAUT is the most complete model to predict information technologies adoption, and it is therefore used in this investigation. According to this model, three constructs are significant direct determinants of intention (performance expectancy, effort expectancy, and social influence).
Measurement instruments
All measurement items were adapted, with slight modifications, from the literature – PE, EE, SI, FC and BI were adopted from Venkatesh et al. (2003) and Davis (1989); UB from Im et al. (2011); perceived risk constructs from Featherman and Pavlou (2003). The items for all constructs are included in the Appendix A.
The questionnaire was initially developed in English, based on the literature, and the final version was independently translated into Portuguese by a professional translator. The
Results
Structural equation modelling (SEM) is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions. Careful researchers acknowledge the possibilities of distinguishing between measurement and structural models and explicitly take measurement error into account (Henseler, Ringleand, & Sinkovics, 2009). There are two families of SEM techniques: (i) covariance-based techniques and (ii) variance-based techniques.
Theoretical implications
Theoretically, our results suggest that perceived risk increases the predictive power of the UTAUT model in explaining intention. While performance expectancy (PE), effort expectancy (EE), and social influence (SI) explain nearly 56% of the variance of behaviour intention (BI), by coupling perceived risk (PCR) to UTAUT, these variables contributed to an increase of 4 p.p. of variance explained, thereby providing a better explanatory power. Furthermore, the proposed joint UTAUT + PCR model
Conclusions
IT adoption is one of the most analyzed fields in IT/IS literature. Adoption models and frameworks are increasingly applied to various individual and organizational contexts to explore factors affecting specific technology's intention to use or to the use itself. However, the influence that risk image may exert on adoption decisions has received limited attention. To address this gap, we contribute to adoption theory by offering a conceptual framework that sheds more light on the influence of
Carolina Martins currently works in the banking industry, with focus in data analysis and credit risk fields. She holds a master degree in Statistics and Information Management, with specialization in business intelligence and knowledge management from Instituto Superior de Estatística e Gestão de Informação of the Universidade Nova de Lisboa (ISEGI-UNL). In 2010 she received an award of best student from the same university. Her current interests are in the areas of technology adoption and
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Carolina Martins currently works in the banking industry, with focus in data analysis and credit risk fields. She holds a master degree in Statistics and Information Management, with specialization in business intelligence and knowledge management from Instituto Superior de Estatística e Gestão de Informação of the Universidade Nova de Lisboa (ISEGI-UNL). In 2010 she received an award of best student from the same university. Her current interests are in the areas of technology adoption and information management.
Tiago Oliveira is Invited Assistant Professor at the Instituto Superior de Estatística e Gestão de Informação of the Universidade Nova de Lisboa (ISEGI-UNL). He holds a Ph.D. from the Universidade Nova de Lisboa in Information Management. His research interests include technology adoption, digital divide and privacy. He has published papers in several academic journals and conferences, including the Information & Management, Decision Support Systems, Journal of Global Information Management, Industrial Management & Data Systems, Applied Economics Letters, Electronic Journal of Information Systems Evaluation, Communications in Statistics - Simulation and Computation, and American Journal of Mathematical and Management Sciences among others. Additional detail can be found in http://www.isegi.unl.pt/toliveira/.
Aleš Popovič is an Assistant Professor of Information Management at the Faculty of Economics at the University of Ljubljana and visiting professor at ISEGI – University Nova in Lisbon. He holds BS, M.Sc. and Ph.D. degrees from the University of Ljubljana. His research focuses on business intelligence, information management, and business process management. He is the (co)author of numerous papers in national and international professional and scientific journals. He has collaborated in many applied projects in the areas of business process modelling, analysis, renovation and informatization and in the area of business intelligence.