“FinTech” or a compound term of Financial Technology refers as a newly emerged industry that utilizes IT-centered technologies which aims to boost the efficiency of the financial ecosystem. Since its inception, FinTech has successfully established its presence in the global financial industry due to the benefits and advantages of the system. However, the research studies that highlight the importance of FinTech are scarce. Specifically, the study pertaining to the consumers’ attitude towards FinTech products and services in the context of Malaysia remains unexplored by most of the studies. This preliminary study proposed the extension of Technology Acceptance Model (TAM) to identify the potential factors that influence consumers’ intention to adopt FinTech products and services in Malaysia. This study reviewed the factors, namely the usefulness, ease of use, competitive advantage, perceived risk, and perceived cost that can potentially influence the attitude of customers towards the product and services of FinTech. This study also proposes the potential mediating effect of attitude towards using FinTech products and the intention to adopt FinTech. This study attempts to create new knowledge geared towards the behavior to utilize FinTech products in Malaysia.
There are approximately 90% of the world natural rubber (NR) supplies are produced by the top 4 NR exporting countries, particularly Thailand, Indonesia, Malaysia and Vietnam. The export challenges and uncertainties of the NR latex in the ASEAN market are influenced by many factors. All these issues have driven the motivation of this study that aims to investigate the factors affecting the export of the NR latex, to analyse the relationship among the export price, production and exchange rate with the export, and to predict the export of the NR latex of the four countries. The data from 1999 to 2016 are utilized as the panel data analysis and granger homogeneously causality test for estimations. Estimation reveals that the independent variables, namely export price and production are exhibiting significant positively and the exchange rate shows a negative relationship with export. To conclude, Indonesia and Malaysia are estimated to have decreasing trends of NR latex export. Inversely, Thailand and Vietnam are predicted to exhibit increasing trends of NR latex export. The novelty of this study concerns effectively on the enhancement and stability of NR production in the ASEAN markets despite of the challenges arising from the global market integration.
Purpose: Natural rubber (NR) production has a long history and has been contributing as one of the most important economic sectors in Malaysia recently. In enhancing the Malaysian rubber economy, it is crucial to find a balance between supply-side and demand-side considerations in order to stabilize the NR price in the worldwide market. This has raised the motivation and objectives of this research is to investigate the critical factors affecting the NR price instability in the world market, and to estimate and predict the NR price instability and to examine the most related factors that influence the price model by using ex-post and ex-ante forecast analysis. Methodology: Number of profound research methods Vector Error Correction Method (VECM) by Gujarati and Porter; cointegration rank test by Dwyer; and ex-post forecast method by Pindyck and Rubinfeld have been utilized in this study. The data used from 2008 January to 2016 December: monthly time series data. Results: The results show that the explanatory variables of NR production, total NR consumption, crude oil price, and Shanghai NR price indicate a significant relationship with Malaysian NR price (SMR20), on the contrary, the exchange rate is not significant. Implications: The outcome of the study is closely related to the current situation of the exchange rate appreciation in the late of 2017 that may benefit the decision-making process of economic planning for the NR production stability, and price in the worldwide NR market as well.
Schooling system must provide high quality learning opportunities to meet the educational needs and ensuring achievement for every student. All teachers monitor their students’ progress throughout the year, includes formative assessment, questioning, providing feedback, etc. This practice helps teachers continually assess students’ academic performance and evaluate the effectiveness of their teaching. In this paper, k-means clustering method with deterministic model is used to analyze the student's overall performance. The results is important for educators to identify students who are at risk academically and areas where teaching strategies may need adjustment to better meet these students' needs.
Schooling systems always offer finest teaching and learning opportunities to reach the educational requirements and ensuring achievement for every student. However, health affects students' academic performance directly. All teachers monitor their students' progress throughout the year, includes formative assessment, attendance rates, involvement in the organization, etc. This practice helps teachers continually assess the conditions of students and their academic performance. Data mining is a process to explore certain style and hidden correlation among massive volume of data. Data mining is applied in multiple disciplinary fields, for example, insurance, education, banking and bioinformatics. Data mining skills such as clustering, classification, regression and prediction are commonly used by educators to measure academic performance. In this paper, method of k-means clustering with deterministic model is applied to analyze the student's overall performance. The students' assessment scores are assigned to k clusters without prior knowledge of the scores. The result is important for educators to further investigate the effect of sickness of students within a cluster that may lead to poor academic performance.
With 68 countries and international organizations signed up to and participating in the Belt and Road Initiative (BRI), the Chinese business community is playing an increasingly important role in the global economy. The number of Chinese-owned businesses within the top 50 of the Fortune Global 500 list has tripled from four in 2012 to 12 in 2017. With a sample of 24 out of the top 25 market capitalization Malaysian construction sector listed companies from 2012 to 2017, the random effects panel data regression model (REM) is applied. Results reveal that only the percentage of Chinese equity ownership (COWN) and the percentage of Chinese directors on corporate boards (CDIR) have significant relationships with companies’ profitability measure of return on assets (ROA). Chinese CEOs (CCEO) can only improve a company's profitability after two years at the top of the management team. One of the main implications of this research is that COWN and CDIR should not be restricted by the government and indigenous protectionist groups because Chinese equity owners and directors can capitalize on their multi-lingual capabilities to build closer interpersonal ties and networking ("guanxi") with business partners from China and other BRI countries.
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