Teck-Soon Hew

Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach

Abstract The main aim of this study is to determine the factors influencing the adoption of Near Field Communication (NFC)-enabled mobile credit card, an innovation in contactless payment for the future generation. Constructs from psychological science, trust-based and behavioral control theories were incorporated into the parsimonious TAM. Using empirical data and Structural Equation Modeling-Artificial Neural Networks approach together with multi group analysis, the effects of social influence, personal innovativeness in information technology, trust, perceived financial cost, perceived usefulness and perceived ease of use were examined. The significance of indirect effects was examined using the bias-corrected percentile with two-tailed significance through bootstrapping. Gender, age, experience and usage were introduced as the moderator variables with industry being the control variable in the research model. The scarcity in studies regarding the moderating effects of these variables warranted the needs to further investigate their impacts. The mediating effect of perceived usefulness was examined using the Baron–Kenny’s technique. The findings of this study have provided invaluable theoretical, methodological and managerial implications and will contribute to the decision making process by CEOs, managers, manufacturers and policy makers from the mobile manufacturing industry, businesses and financial institutions, mobile commerce, mobile telecommunication providers, mobile marketers, private or government practitioners and etc.

NFC mobile credit card: The next frontier of mobile payment?

With the advancement of mobile devices and the emergence of Near Field Communication (NFC) technology, payment today is a mere wave-of-the-phone. However, the adoption of mobile credit card (MCC) is still not widespread despite its potential as documented. Premised on this, the study extends the Technology Acceptance Model (TAM) with four additional constructs. The moderating effect of gender was also examined. Data collected from 156 respondents were analyzed using Structural Equation Modeling (SEM) and multi group analysis. Cohens f-square statistic for effect size is 0.815. The results revealed that only finance-related risks and the moderating effect of gender are the non-significant factors in this study. The research provides useful theoretical and managerial implications for mobile phone manufacturers, merchants, bank decision makers, software developers, governments and private practitioners when devising their marketing campaigns and business strategies. The study also extends the applicability of TAM in the area of MCC from the perspective of an emerging market.

Understanding and predicting the motivators of mobile music acceptance – A multi-stage MRA-artificial neural network approach

Abstract The adoption level of digital music is still at its formative stage although the adoption renders advantageous to consumers. Therefore, the study develops a model to predict on the motivation leading to consumer’s intention to adopt mobile music services by extending Perceived Cost (PC), Perceived Credibility (PCr), Social Influence (SI), and Personal Innovativeness (INNO) with Technology Acceptance Model (TAM). 160 Respondents were tested using a multi-stage Multiple Regression Analysis (MRA) and Artificial Neural Network (ANN) approach. A non-linear non-compensatory Multi Layer Perceptron (MLP) ANN with feed-forward back-propagation algorithm and ten cross-validation neural networks was deployed in order to capture the motivators of mobile music adoption. All predictor variables were found to have relevance to the output neuron based on the non-zero synaptic weights connected to the hidden neurons. The RMSE values indicated that the ANN models were able to predict the motivators with very high accuracy. The ANN models have out-performed the MRA models as they are able to capture the non-linear relationships between the predictor and criterion variables. While the study found that TAM is a significant predictor, the insignificance linear relationships of PCr and INNO requires further investigation. The music industry can use the findings from this study beneficially to the development of mobile music adoption.

Knowledge management: a key determinant in advancing technological innovation?

Purpose – This paper purports to analyze the relationship between knowledge management (KM) and technological innovation in the Malaysian manufacturing sector. Furthermore, the interrelationships between the KM dimensions will also be investigated. Design/methodology/approach – Survey data from 162 manufacturing firms were obtained. Multiple linear regression and neural network analysis were performed in this study to examine the relationships between KM and technological innovation; as well as the interrelationships between KM practices themselves. Findings – This research study provides empirical evidence and confirms the results of past researchers that KM practices (i.e. knowledge sharing, knowledge application and knowledge storage) are positively and significantly related to technological innovation (i.e. product and process innovation). Moreover, it is also discovered that the interrelationships between the KM dimensions are positive and significant. Research limitations/implications – This researc...

The effects of convenience and speed in m-payment

Purpose – The purpose of this paper is to uncover the effects of perceived transaction convenience (PTC) and perceived transaction speed (PTS) on unified theory of acceptance and use of technology (UTAUT) in the context of m-payment. Design/methodology/approach – A predictive analysis approach was used to examine the PTC and PTS using a two-stage partial least square (PLS) and neural network (NN) analyses. Findings – The findings reveal that only effort expectancy (EE) and facilitating conditions (FC) were discovered to significantly influence BI. More importantly, PTC was found to have positive significant relationship with EE and performance expectancy (PE). Moreover, PTS also supported the positive relationship with BI and EE. Practical implications – The findings of the study provided further insights to mobile payment service providers, online banking industry players, and all decision makers and stakeholders involved. Originality/value – Despite of many attempts devoted to understand m-payment adopt...

An SEM-artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline

The study examines the effects of SERVPERF on customer satisfaction and loyalty.A comparison between full service and low cost carriers is conducted.A predictive analytic approach via SEM-artificial-neural-networks is engaged.Multiple group analysis is used to assess the group invariance.63.1% and 55.6% of variance in customer satisfaction and loyalty are explained. There is a dearth of studies pertaining to the influence of SERVPERF on customer satisfaction and customer loyalty among low cost and full service airlines. Prior studies have measured service quality using the GAP-5 model with SERVQUAL; however this study offers a new perspective by using the SERVPERF with an SEM-artificial-neural-networks predictive analytic approach. This is different from the previous studies as it contributes to application of expert systems and intelligent algorithms in the context of low cost and full service airline. The findings revealed significant influences of SERVPERF dimensions on customer satisfaction towards customer loyalty with 63.1% and 55.6% variance explained. The implications from this research may help CEOs and managers of the air travel and tourism industry players to make better decisions in their resource planning stage, at the same time improving customer satisfaction and loyalty.

Predicting Drivers of Mobile Entertainment Adoption: A Two-Stage SEM-Artificial-Neural-Network Analysis

ABSTRACT This study aims to understand users’ motivations to adopt mobile entertainment (m-entertainment). Extending the Technology Acceptance Model (TAM), this study examined the effects of trust, perceived financial cost (PFC), and quality of the service on consumers’ decision in adopting the m-entertainment. Survey data were collected from 524 mobile users and analyzed using both structural equation modeling (SEM) and neural network (NN) . The result showed that perceived usefulness (PU), perceived ease of use (PEOU), and quality of service (QS) are important predictors of m-entertainment adoption. The study contributes to the existing literature by extending the TAM model as well as examining m-entertainment, an important and emerging business model in mobile commerce. A new analytical approach using both SEM and NN was also employed in this study.

Behavioural intention in cloud-based VLE: An extension to Channel Expansion Theory

Abstract Cloud computing has been a phenomenon nowadays. The ability to access, store/retrieve and share information anytime, anywhere on the cloud has contributed to the birth of the cloud-based virtual learning environment (c-VLE). Though there are studies conducted on the grid-based VLEs, however due to the significant differences between the two computing technologies; the findings from prior studies may not be germane in the c-VLE context. Unlike the previous studies which studied TAM, UTAUT and their extensions from the perspective of undergraduates and university instructors, this study examined the role of Channel Expansion Theory (CET), VLE related attributes (i.e. interactivity, content design, trust-in-website), school support and knowledge sharing attitude from the perspective of school teachers. Educational level and teaching experience were introduced as the control variables. Data were gathered in two waves of nation-wide surveys with random sampling. The findings revealed significant effects of CET, all VLE related attributes (except content design), school support and attitude towards knowledge sharing on behavioural intention (BI). BI was found to have direct effect on instructional effectiveness (IE). The model explained 68.9%, 62.5% and 28.9% variances in CET, BI and IE. The study provides theoretical and practical implications to the scholars and practitioners.

Predicting instructional effectiveness of cloud-based virtual learning environment

Purpose Cloud computing technology is advancing and expanding at an explosive rate. These advancements have further extended the capabilities of the virtual learning environment (VLE) to provide accessibility anywhere, anytime where educational resources can be saved, modified, retrieved and shared on the cloud. The purpose of this paper is to examine the predictors of instructional effectiveness of cloud computing VLE by extending the Self Determination and Channel Expansion Theory with external constructs of VLE interactivity, content design, school support, trust in website, knowledge sharing attitude and demographic variables. Design/methodology/approach Random sampling data were collected in two waves of nation-wide survey and analyzed with artificial neural network approach. Findings SDT, CET, content design, interactivity, trust in website, school support and demographics significantly predict instructional effectiveness. Research limitations/implications The study has provided a new paradigm shift from investigating the behavioral intention and continuance intention to the effectiveness of an information system. It advocates that quality of research may be improved by adhering to the basic research methodology starting from rigorous instrument development and validation to future research direction. Practical implications The research provides implications to Ministry of Education, the VLE content and service providers, scholars and practitioners. Social implications The findings of the study may further improve the quality of living of the society when the instructional effectiveness of the cloud-based VLE is further enhanced. Originality/value Existing grid computing VLE studies have focussed on the acceptance of students and teachers and not its instructional effectiveness. Unlike existing studies that examined extrinsic motivational factors (e.g. TAM, UTAUT), this study uses intrinsic motivational factors (e.g. relatedness, competence and autonomy) as well as perceived media richness. Malaysia is the first nation to implement the VLE at a national scale and the findings from this study will provide a new insight on the determinants of instructional effectiveness of the VLE system.

Understanding cloud-based VLE from the SDT and CET perspectives: Development and validation of a measurement instrument

Abstract With the emergence of the cloud computing technology, virtual learning environment (VLE) may play an imperative role in promoting instructional effectiveness of ubiquitous learning. However, the existing literature on VLE has been mostly based on the acceptance of VLE from the perspective of the undergraduate students. There is a dearth in studies on the VLE instructional effectiveness from the K-12 teachers’ perspective, the effects of Self Determination Theory and Channel Expansion Theory. Existing VLE instruments have not been rigorously validated and do not consider the importance of cultural differences. This research aims at creating and rigorously validating an instrument to study the cloud-based Frog VLE’s instructional effectiveness in the Malaysian cultural setting. The robustness of the instrument was validated using structural equation modeling, expert panel, Q-sort, pre-test, pilot-test and fieldwork study. This research may offer a parsimonious instrument to evaluate the instructional effectiveness of the Frog VLE for subsequent studies contributing to theory building in the IS literature.