Browsing by Author "Sin, Tiong Jia"

Now showing 1 - 3 of 3

Continuance Intention to Use Mobile Payments in Malaysia: Integrating Innovation Resistance Theory and Switching Cost
(Department of Marketing Management, Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka., 2021) Sin, Tiong Jia; Shan, Lom Hui; Shollunayagam, Renugala D/O M.; Dyana, Chang Mui Ling
Over recent years, the traditional payment method (i.e. cash) has been gradually replaced by cashless payment. Malaysian mobile payment users are gradually adapting to the new payment method (i.e. cashless payment). This indicates that the usage of cashless payment shows a current popularity trend in the daily routines of people. Furthermore, the Covid19 outbreak has become a catalyst to boost the usage of mobile payments among the mobile users. In Malaysia, mobile payment transactions accounted for a significant proportion in the total amount of annual transactions. According to the Malaysian Communications and Multimedia Commission (2020), the total cashless transactions in 2019 was higher than the past two years which was RM 18.2 billion made from 2094 million transaction. Up to date, Malaysians are using electronic wallets (i.e. Grab, Touch n Go, Boost and others) and Quick Response (QR) payment (i.e. QR code available in mobile banking applications) to conduct their mobile payments. The Innovation Resistance Theory has been applied in this study to provide a better insight of continuance intention of using mobile payments. Moreover, this theory attempts to identify the barriers that are faced by users when they try to adopt a new innovation. The purpose of this study is to examine the relationship between different barriers (i.e. usage barrier, value barrier, risk barrier, tradition barrier and image barrier) and switching cost with respect to the continuance intention of Malaysian mobile payment users to use mobile payment. This study was conducted through a quantitative method. The purposive sampling method has been chosen because the respondents must have usage experience of mobile payment in order to quality to answer the survey. Data collection was collected through self-administrated survey responses. In order to ensure the research instrument was suitable in the Malaysian context, a questionnaire was adopted and adapted which underwent a pilot test to affirm it was feasible for model testing and hypothesis checking. The results of the pilot study reflected that the reliability and discriminant validity of the 34 questions instrument was well established, and this instrument could be applied in the future. Also, the partial least squares-based structural equation modelling was used for analysing the data once the data collection was completed. In short, this research framework is applicable in the future since mobile payment is a common trend in Malaysia. This study contributes to the theory by including switching cost and applying it into the continuance intention to use mobile payment services. It is believed that the study will be able to assist the marketers to remove those barriers which inhibit the mobile users from further use of the cashless payment service. Also, they might benefit from creating a better design mechanism for the applications which can continuously improve their mobile payment service in the future.
Detecting Fraudulent Financial Reporting and Predicting Business Failure Using Probabilistic Neural Network: Malaysia Chapter
(Department of Marketing Management, Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka., 2021) Wen, Chue Kar; Sin, Tiong Jia; Shan, Lom Hui; Dyana, Chang Mui Ling
Methodologies with the integration of machine learning (ML) into fraudulent financial reporting (FFR) and business failure detection have been researched popularly globally however these ML methodologies were not popular researched in Malaysia Specifically. Studies showed that Probabilistic Neural Network (PNN) yielded highest fraudulent detection rate about 98%; and Neural Network achieved overall accuracy of 84% of business failure detection rate. It was also found that there was no specific method mentioned in Malaysia Securities Commission Act 1993 in assessing the financial statements of public listed companies (PLCs), and low expenditure of PLCs in audit functions. Due to the huge impacts resulted from FFR and business failure of public listed companies, there is a need to minimise FFR and business failure incidents with high accuracy detection and prediction tools, which are ML techniques. The applications of ML technique (i.e., PNN) into the research would shorten the analysis time compared to other statistical methods; yield higher accuracy rate that becomes effective layer of screening financial statements; is able to optimise or minimise the loss functions if discrepancies occur in data sets. On the other hand, although the relationship between FFR and business failure has been linked, the two topics have been studied separately in the past. Financially distressed companies may have a higher probability to commit fraudulent financial reporting, and less research that link the two topics although the methodologies and models were found effective in research the two topics. This study aims to firstly determine the accuracy of ML technique, i.e. PNN in the detection of FFR and detection of business failures among the public listed companies in Malaysia. The relationship between business failure and FFR among the PLCs in Malaysia would be identified. This study applies two stage PNN procedures: first stage is to detect FFR among the companies; second stage is to predict business failure of the companies prior to the conduct of FFR. The accuracy of PNN in the applications and the relationship between FFR and business failure will be discovered. Secondary data is to be collected through financial reports from the PLCs that have been identified fraudulent by the Securities Commission Malaysia in the past. A set of identical non-fraudulent and non-failed companies (similar size in same industries) would be as pairs to the fraudulent companies in the study. PNN is expected to yield high accuracy rates in detecting fraudulent companies and predicting business failures. The ML methodology would also be expected to detect the relationship between FFR and business failure (as supported by fraud triangle theory that financial distress is one of the elements in committing frauds). The research should enhance the detectability of frauds and business failures among the PLCs, improves overall corporate governance of the companies and increase public confidences onto PLCs; furthermore, this would also enhance the knowledge of forensic accounting in Malaysia.
Mobile Users Information Privacy Concern in Malaysia: Extended Ducoffe Advertising Value Model
(Department of Marketing Management, Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka., 2021) Shan, Lom Hui; Chin, Thoo Ai; Yeik, Koay Kian; Sin, Tiong Jia
Short message service (SMS) advertisement has emerged as one of the fastest growing advertising mediums in Malaysia. This has prompted the banking industry to use SMS for advertisements to conveniently and effectively disseminate information concerning products and services to their target mobile users. The acceptance of SMS mobile advertisement and the privacy concerns of mobile users is important for the banking industry to reach its target consumers. Furthermore, privacy concerns were investigated using the Mobile Users Information Privacy Concerns (MUIPC) model as a moderator on the relationship between attitude and acceptance behaviour. The research framework and hypotheses were developed based on the Ducoffe advertising value and MUIPC models using gratification theory and the communication privacy management theory in banking industry. This study extended the Ducoffe advertising value model to investigate the impact of user attitude with regard to acceptance behaviour of SMS mobile advertisement. A cross-sectional method was used to collect data via an online survey. Purposive sampling technique was employed to collect 413 usable samples from respondents who had previously received bank advertisement via SMS. SMART partial least square structural equation modelling was used for data analysis. Three major findings were found in this study. Firstly, for the Ducoffe extended model, entertainment and informativeness were proven to have a relationship with advertising value. However, the irritation factor was not significantly related to advertising value as most of the Malaysian mobile users were positive about accepting bank advertisements via SMS. Secondly, advertising value was found to be related to attitude, of which attitude also positively influenced the acceptance behaviour of SMS mobile advertisement from banks. Finally, the moderating effect of MUIPC was proven for the relationship between attitude and acceptance behaviour from banks. Overall, the study contributes to the current mobile advertisement literature by understanding the mechanism that would be useful for banks in Malaysia to include the formation of acceptance behaviour and privacy concerns when designing appropriate bank advertisements via SMS.