Lai-Ying Leong

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.

Predicting the drivers of behavioral intention to use mobile learning

We examined the determinants of mobile learning using a hybrid SEM-ANN approach.TAM has significant influence on the intention to adopt.Psychological science constructs are non-significant with the intention to adopt.The moderating effect of age and gender are non-significant in this study.The model is able to explain 53.4% of the variance with effect size of 0.399. This study empirically investigates on the elements that affect the users intention to adopt mobile learning (m-learning) using a hybrid Structural Equation Modeling-Artificial Neural Networks (SEM-ANN) approach. A feed-forward-back-propagation multi-layer perceptron ANN with the significant determinants from SEM as the input units and the Root Mean Square of Errors (RMSE) indicated that the ANN achieved high prediction accuracy. All determinants are relevant and their normalized importance was examined through sensitivity analysis. The explanation on new computer technologies acceptance have been primarily based on the Technology Acceptance Model (TAM). Since TAM omits the psychological science constructs, the study address the weaknesses by incorporating two additional constructs, namely the personal innovativeness in information technology (PIIT) and social influences (SI). Out of the 400 survey distributed to mobile users, 216 usable questionnaires were returned. The results uncovered that the intention to adopt m-learning has significant relationship with TAM. The findings for PIIT, SI and the control variables of age, gender and academic qualifications however show mixed results. The results provide valuable information for mobile manufacturers, service providers, educational institutions and governments when strategizing their adoption strategies. Additionally, from the perspective of an emerging market, the study has successfully extended TAM with psychological constructs.

Modeling the stimulators of the behavioral intention to use mobile entertainment: Does gender really matter?

Abstract This research aims to empirically examine the stimulators that influence consumers’ behavioral intention to use (IU) mobile entertainment (ME) in Malaysia. The proposed stimulators are perceived usefulness (PU), perceived ease of use (PEOU), social influence (SI), perceived self-efficacy (PSE) and perceived enjoyment (PE). PU and PEOU were derived from TAM, SI was taken from the TRA, TPB and DOI model, PE was obtained from the extended-TAM model and PSE was taken from Bandura’s theory. This is among the first study that uses a model consisting of integrated constructs from TAM, extended-TAM, TRA, TPB, DOI and Bandura’s theory in predicting acceptance of ME. Empirical data were analyzed by employing Structural Equation Modeling (SEM) analysis. Gender moderating effect was examined via multiple group analysis (MGA). The findings revealed that PU, PEOU, SI and PE are positively associated with consumer IU of ME. Surprisingly, there were no significant moderating effects of gender. The control variables (i.e. age, marital status, education level, number of mobile phone and experience) were found to have no confounding effects on the ME adoption. The findings have contributed theoretical and managerial implications to ME providers, mobile phone manufacturers, the music, movie and gaming industry players.

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...

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.

Can competitive advantage be achieved through knowledge management? A case study on SMEs

A tripartite model in context of Malaysian SMEs using PLS-SEM-ANN analysis.Knowledge management and competitive advantage mediated by technological innovation.KM and TI spending are directly proportional to each other.KM spending, especially on knowledge dissemination, may lead to stronger CA. Unlike most Knowledge Management (KM) studies which focus on large enterprises, this study focuses on SMEs in Malaysia which represent 99.2% of the total business establishments, the largest percentage of establishments in the country. The tridimensional relationship between KM practices, technological innovation (TI) and competitive advantage (CA) was examined in this case study. Survey approach was conducted to gather data from managers of the manufacturing SMEs and 195 samples were usable for statistical analysis using Partial-Least-Square Structural Equation Modeling (PLS-SEM)-Artificial Neural Network (ANN). The use of the combined PLS-SEM and ANN analysis can provide a significant methodological contribution and substantial impacts to the world of expert and intelligent systems and could be the next methodological research paradigm. Findings validated that KM has a direct positive and significant relation with both TI and CA; while TI positively and significantly affects CA. Most outstandingly, the mediating role of TI that connects KM and CA has been proven to be positive and significant. This paper utilizes samples that were collected from Malaysian SMEs only; therefore the findings cannot be generalized to represent the larger firms. Nevertheless, conclusions garnered from the present research can help both practitioners of the manufacturing SMEs and scholars in implementing the correct KM strategies, so that both TI and CA can be enhanced and improved.

The effects of Facebook browsing and usage intensity on impulse purchase in f-commerce

Due to the rapid advancements in Web 2.0 and social media, a novel class of online social business called Facebook commerce (f-commerce) has emerged. Even though there are studies on the factors that influence Facebook browsing and usage intensity, however, there is dearth in research that examine the impacts of f-commerce browsing and usage intensity on consumers urge to purchase and impulse purchase. Unlike previous studies, this study examined the moderating effect of income. Since Facebook has become a phenomenon, there is a necessity to explore whether the level of f-commerce browsing and usage intensity can trigger urge to purchase and impulse purchase. Following the Stimulus-Organism-Response framework, data was collected using mall-intercept technique and analyzed with SmartPLS 3. Majority of the suggested hypotheses have been empirically validated and the research framework can explain 33.0% of variance in urge to purchase and 61.7% variance in impulse purchase. Interestingly, the finding showed no moderating effect of income. However, marital status and Internet hours were found to have moderating effects. The research findings can contribute to the online retailers, marketers and other f-commerce stakeholders in formulating their marketing strategies and policies while providing novel insight in understanding the impulse purchase behavior. The effects of Facebook browsing & usage intensity on impulse purchase were studied.Stimulus-Organism-Response framework was used with income as the moderator variable.A total of 808 f-commerce shoppers were gathered using mall-intercept technique.The model explained 33% variance in urge to purchase and 61.7% in impulse purchase.Interestingly, no moderating effects of income on all the relationships were found.

Understanding impulse purchase in Facebook commerce: does Big Five matter?

Purpose The purpose of this paper is to examine the effects of the Big Five Model (BFM), the urge to purchase (UP) and urgency (UR) on impulse purchase (IP) in Facebook commerce (F-commerce), with the F-commerce purchase as control variable. It also investigates the influence of BFM and UR on UP and the effects of BFM on UR. Design/methodology/approach The survey instrument was rigorously validated via content validity index by expert panel, Q-sort procedure for construct validity by practitioners in pre-test, followed by evaluation of construct reliability in the pilot test. Data gathered from 808 usable questionnaires were analyzed using SmartPLS 3. Findings The study showed that BFM, UP, UR and F-commerce purchase are significant predictors of the F-commerce IP. UP is influenced by BFM and UR. BFM has a significant positive relationship with UR. F-commerce experience has insignificant moderating effect. Practical implications This study provides some useful practical implications for the F-commerce administrators, advertisers, dealers and promoters. Originality/value Existing studies focus on the antecedents of IP in conventional stores and online businesses; however, IP in F-commerce has been largely overlooked. The study investigates the impacts of personality traits on IP and its effects on UR and UP. The mediating effects of UR and UP were also examined. The study is able to predict 64.4, 68.0 and 49.0 percent variance in IP, UP and UR, respectively.

Do Electronic Word-of-Mouth and Elaboration Likelihood Model Influence Hotel Booking?

ABSTRACT The emergences of Web 2.0 and cloud computing have contributed greatly to the prevalence of electronic Word-of-Mouth (eWoM). Unlike most of the existing studies which have used linear models, nonlinear relationships were discovered in hotel booking intention. So far, the effects of elaboration-likelihood model (ELM) and demographics have been largely overlooked, though studies have shown that ELM may explain consumers’ perception, behavior, and IS acceptance. Data were gathered from 497 patrons of 10 hotels in Golden Triangle, Kuala Lumpur, Malaysia. Using artificial neural network (ANN), we found that user involvement, positive eWoM, user expertise, perceived credibility, education, negative eWoM, and income are among the important predictors explaining 81% of variance in booking intention. The theoretical implications may further advance eWoM and ELM literatures, while the managerial implications may provide novel understandings to hotel operators, advertisers, and relevant hospitality policy makers in formulating effective decision making.