Mobile banking adoption: A literature review

Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM

Ease of use and usefulness are believed to be fundamental in determining the acceptance and use of various, corporate ITs. These beliefs, however, may not explain the user’s behavior toward newly emerging ITs, such as the World-Wide-Web (WWW). In this study, we introduce playfulness as a new factor that reflects the user’s intrinsic belief in WWW acceptance. Using it as an intrinsic motivation factor, we extend and empirically validate the Technology Acceptance Model (TAM) for the WWW context. # 2001 Elsevier Science B.V. All rights reserved.

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Beyond the Usefulness and Ease of Use : Extending the TAM for a World - Wide - Web Context

Acceptance of mobile banking framework in Pakistan

Purpose – The purpose of this paper is to identify the factors that differentiate customers with high intentions to adopt mobile banking from others. This study examined the effect of perceived usefulness, ease of use, perceived credibility, trust, normative pressure, self-efficacy, compatibility, and trialability. It also included demographics as control variables. Design/methodology/approach – The data were collected using the snowball approach. The respondents filled in a structured questionnaire in February 2014 in Beirut, Lebanon. In total, 800 responses were received, 776 of which were completed and analysed. Findings – This study showed that perceived compatibility, trialability, perceived usefulness, ease of use, perceived credibility, and trust positively and significantly discriminate high-mobile banking adopters from low adopters. This study also found that perceived self-efficacy separates customers through their willingness to adopt mobile banking. Originality/value – Although a handful of st...

The intentions of Lebanese consumers to adopt mobile banking

M-banking is a channel through which banks interact with customers via mobile devices. M-banking is an emerging mobile commerce application. It is a challenging task for banks to encourage customers to continue using m-banking services, and attract new customers to the service. This study clarifies the differences in the thinking paths of users of m-banking services, and consumers who have not yet used m-banking services, in terms of their involvement. We prove that consumers equipped with more product knowledge tend to pay more attention to the information in relation to product attributes, rather than the peripheral information, which does not consider the advantages and disadvantages of products. These findings can serve as reference for banks in the formulation of different marketing strategies and promotional campaigns targeted at both existing users and consumers who have not adopted m-banking services.

Assessing user experiences and usage intentions of m-banking service

Purpose – The purpose of this paper is to present a model and a supporting approach for effective supplier selection decisions.Design/methodology/approach – Structural equation modeling (SEM) and confirmatory factor analysis are applied to test the evaluation principles and samples. Next, the data tested by SEM is used for artificial neural network (ANN) by Likert and fuzzy scales to structure a classification model, accompanying with canonical discriminate analysis (CANDISC) to diminish variables. After the training and test of the model, multiple discriminate analysis is applied to compare the accuracy of the classification. Last, the CANDISC variable reduction method with ANN classification model utilized in the study is applied.Findings – The supplier selection model designed with ANN classification model and fuzzy scales will be more effective than with the traditional statistics analysis.Research limitations/implications – The new paradigm for decision making includes a combination of several effect...

Supplier evaluation model for computer auditing and decision‐making analysis

The execution and outcome of credit rating policies of banks are highly relevant to banks' decisions in investments, loans, and their measurement of default risks. They also have impacts on capital flows and utilisation of corporate borrowers. Therefore, it is essential to establish a scientific, objective, and accurate model of credit ratings for Customer Relationship Management (CRM) of the banking industry. The correct results of customers' ratings can serve as an important reference in the CRM of banks. This paper applies two classification methods, Multiple Discriminate Analysis (MDA) and Rough Set Theory (RST), to analyse and compare a total of 70 entries of corporate credit data. The result shows that the RST classification model boasts better classifying effects and is suitable for the analysis of credit ratings in the banking industry. This paper also suggests that a decision support system should be established with a scientific classification model in order to assist in the decisions and judgements of decision makers.

Hsu-Feng Hung

Papers

Assessing user experiences and usage intentions of m-banking service

Supplier evaluation model for computer auditing and decision‐making analysis

Construction of classification models for credit policies in banks