Pub Date : 2021-03-30DOI: 10.35609/JFBR.2021.5.4(2)
K. K. Peong, Kwee Peng Peong, Kui Yean Tan
Objective – The objective of this study is to determine the process that takes place in the employment of financial technology in the financial services industry. It is of utmost important that FinTech firms and commercial banks understand the predictors that can influence their consumers’ decision to adopt FinTech services and to increase loyalty toward their services. Methodology/Technique – An online survey was used in the present research to explore factors that can influence commercial bank users’ intention to use FinTech services in Malaysia. The data for the current study was gathered from bank users who aged at least 18 years old and resided in Malacca, Malaysia who accessed FinTech services via smartphone. This research also employed the convenient sampling in distributing online questionnaires to 400 respondents who had successfully completed and returned the questionnaires. Findings – The empirical findings illustrate that trust, social influence, cyber-security risks and privacy risks are the most influential determinants that affect bank customers’ behavioural intention to use FinTech services in Malaysia. Novelty – This research contributes to the theory of TAM, UTAUT and TPB by proposing a direct effect of trust, social influence, cyber-security risks and privacy risks on the adoption of FinTech services. The findings of the current study will be beneficial to policymakers, specifically financial institutions and FinTech firms as they will be informed on workable means to increase the quality of FinTech applications/websites. This can yield greater intentions to adopt FinTech. Stakeholders should play their important role in noticing and considering the influential factors that can impact the consumers’ behavioural intention for using technologies in their policies to fulfil the users’ needs. Type of Paper: Empirical JEL Classification: G02, G21 Keywords: Trust; Social Influence; Cyber-Security Risks; Privacy Risks; Behavioural Intention to Use Reference to this paper should be made as follows: Peong, K.K; Peong, K.P; Tan K.Y. (2021). Behavioural Intention of Commercial Banks’ Customers towards Financial Technology Services, Journal of Finance and Banking Review, 5(4): 10 – 27. https://doi.org/10.35609/jfbr.2021.5.4(2)
{"title":"Behavioural Intention of Commercial Banks’ Customers towards Financial Technology Services","authors":"K. K. Peong, Kwee Peng Peong, Kui Yean Tan","doi":"10.35609/JFBR.2021.5.4(2)","DOIUrl":"https://doi.org/10.35609/JFBR.2021.5.4(2)","url":null,"abstract":"Objective – The objective of this study is to determine the process that takes place in the employment of financial technology in the financial services industry. It is of utmost important that FinTech firms and commercial banks understand the predictors that can influence their consumers’ decision to adopt FinTech services and to increase loyalty toward their services.\u0000Methodology/Technique – An online survey was used in the present research to explore factors that can influence commercial bank users’ intention to use FinTech services in Malaysia. The data for the current study was gathered from bank users who aged at least 18 years old and resided in Malacca, Malaysia who accessed FinTech services via smartphone. This research also employed the convenient sampling in distributing online questionnaires to 400 respondents who had successfully completed and returned the questionnaires. \u0000Findings – The empirical findings illustrate that trust, social influence, cyber-security risks and privacy risks are the most influential determinants that affect bank customers’ behavioural intention to use FinTech services in Malaysia.\u0000Novelty – This research contributes to the theory of TAM, UTAUT and TPB by proposing a direct effect of trust, social influence, cyber-security risks and privacy risks on the adoption of FinTech services. The findings of the current study will be beneficial to policymakers, specifically financial institutions and FinTech firms as they will be informed on workable means to increase the quality of FinTech applications/websites. This can yield greater intentions to adopt FinTech. Stakeholders should play their important role in noticing and considering the influential factors that can impact the consumers’ behavioural intention for using technologies in their policies to fulfil the users’ needs.\u0000Type of Paper: Empirical\u0000JEL Classification: G02, G21\u0000Keywords: Trust; Social Influence; Cyber-Security Risks; Privacy Risks; Behavioural Intention to Use\u0000Reference to this paper should be made as follows: Peong, K.K; Peong, K.P; Tan K.Y. (2021). Behavioural Intention of Commercial Banks’ Customers towards Financial Technology Services, Journal of Finance and Banking Review, 5(4): 10 – 27. https://doi.org/10.35609/jfbr.2021.5.4(2)","PeriodicalId":269671,"journal":{"name":"GATR Journal of Finance and Banking Review VOL. 5 (4) JAN-MAR. 2021","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128582474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-30DOI: 10.35609/JFBR.2021.5.4(4)
Giriati Giriati
Objectives - This article aims to examine the influence of content dimensions of Organization Change Theory, such as CEO Expertise, Free Assets, Debt to Equity Ratio and Growth of Sales, on a company’s turnaround ability when it is experiencing financial distress. The companies examined are listed on the Indonesian Stock Exchange (IDX). Methodology/Technique - The population used in this study is companies from sectors excluding the finance sector that were listed on the Indonesian Stock Exchange between 2013 and 2018. The sample size was determined using purposive sampling method. From the 109 companies that experienced financial distress, 57 have successfully turned their business around. The research data was collected from the ICMD (Indonesian Capital Market Directory), which was then analysed using multi regression technique analysis, using SPSS software to examine the determinants of company turnaround ability. Finding - The results indicate that CEO Expertise, Debt to Equity Ratio and Growth of Sales have a negative relationship on a company’s turnaround ability. Meanwhile, Free Assets has a positive and significant relationship on a company’s turnaround ability. Novelty - Previous studies have been conducted in many western countries, giving rise to researchers' doubts about the generalizability of research based on previous research findings when applied in developing countries such as Indonesia, particularly due to differences in regulations, conditions of distress, culture, financial systems and strategies used in overcoming distress. Type of Paper: Empirical. JEL Classification: B26, G15, P34. Keywords: Financial Distress; Turnaround Model; CEO Expertise; Free Assets; Debt to Equity Ratio; Growth of Sales Reference to this paper should be made as follows: Giriati, S.E, M.E. (2021). Turnaround Prediction Model with Content Dimension on Financial Distressed Firms, Journal of Finance and Banking Review, 5 (4): 36 – 42. https://doi.org/10.35609/jfbr.2021.5.4(4)
{"title":"Turnaround Prediction Model with Content Dimension on Financial Distressed Firms","authors":"Giriati Giriati","doi":"10.35609/JFBR.2021.5.4(4)","DOIUrl":"https://doi.org/10.35609/JFBR.2021.5.4(4)","url":null,"abstract":"Objectives - This article aims to examine the influence of content dimensions of Organization Change Theory, such as CEO Expertise, Free Assets, Debt to Equity Ratio and Growth of Sales, on a company’s turnaround ability when it is experiencing financial distress. The companies examined are listed on the Indonesian Stock Exchange (IDX).\u0000Methodology/Technique - The population used in this study is companies from sectors excluding the finance sector that were listed on the Indonesian Stock Exchange between 2013 and 2018. The sample size was determined using purposive sampling method. From the 109 companies that experienced financial distress, 57 have successfully turned their business around. The research data was collected from the ICMD (Indonesian Capital Market Directory), which was then analysed using multi regression technique analysis, using SPSS software to examine the determinants of company turnaround ability.\u0000Finding - The results indicate that CEO Expertise, Debt to Equity Ratio and Growth of Sales have a negative relationship on a company’s turnaround ability. Meanwhile, Free Assets has a positive and significant relationship on a company’s turnaround ability.\u0000Novelty - Previous studies have been conducted in many western countries, giving rise to researchers' doubts about the generalizability of research based on previous research findings when applied in developing countries such as Indonesia, particularly due to differences in regulations, conditions of distress, culture, financial systems and strategies used in overcoming distress.\u0000Type of Paper: Empirical.\u0000JEL Classification: B26, G15, P34.\u0000Keywords: Financial Distress; Turnaround Model; CEO Expertise; Free Assets; Debt to Equity Ratio; Growth of Sales\u0000Reference to this paper should be made as follows: Giriati, S.E, M.E. (2021). Turnaround Prediction Model with Content Dimension on Financial Distressed Firms, Journal of Finance and Banking Review, 5 (4): 36 – 42. https://doi.org/10.35609/jfbr.2021.5.4(4)","PeriodicalId":269671,"journal":{"name":"GATR Journal of Finance and Banking Review VOL. 5 (4) JAN-MAR. 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129953543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-09DOI: 10.35609/gcbssproceeding.2020.11(66)
Mochammad Ridwan Ristyawan
Objective – The disruption has been occurring in financial services. Thus, rethinking a new strategy for banking is needed to make a sustainable innovation in organizations. Studies mentioned that formulating strategy is a very costly, time-consuming, and comprehensive analysis. The purpose of this study is to present an integrated intelligence algorithm for estimating the bank’s strategy in Indonesia. Methodology – This study used the integration model between two modules. The algorithm has two basic modules, called Artificial Neural Network (ANN) and Analytical Hierarchy Process (AHP). AHP is capable of handling a multi-level decision-making structure with the use of five expert judgments in the pairwise comparison process. Meanwhile, ANN is utilized as an inductive algorithm in discovering the predictive strategy of the bank and used to explain the strategic factors which improved in forward. Findings and Novelty – The empirical results indicate that ANN and AHP integration was proved to predict the business strategy of the bank in five scenarios. Strategy 5 was the best choice for the bank and Innovate Like Fintechs (ILF) is the most factor consideration. The strategy choice was appropriate for the condition of the bank’s factors. This framework can be implemented to help bankers to decide on bank operations. Type of Paper: Empirical JEL Classification: M15, O32. Keywords: Bank’s strategy, ANN, AHP, BSC, Indonesia. Reference to this paper should be made as follows: Ristyawan, M.R. (2021). Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank, Journal of Finance and Banking Review, 5(4): 01 – 09. https://doi.org/10.35609/jfbr.2021.5.4(1)
{"title":"Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank","authors":"Mochammad Ridwan Ristyawan","doi":"10.35609/gcbssproceeding.2020.11(66)","DOIUrl":"https://doi.org/10.35609/gcbssproceeding.2020.11(66)","url":null,"abstract":"Objective – The disruption has been occurring in financial services. Thus, rethinking a new strategy for banking is needed to make a sustainable innovation in organizations. Studies mentioned that formulating strategy is a very costly, time-consuming, and comprehensive analysis. The purpose of this study is to present an integrated intelligence algorithm for estimating the bank’s strategy in Indonesia.\u0000Methodology – This study used the integration model between two modules. The algorithm has two basic modules, called Artificial Neural Network (ANN) and Analytical Hierarchy Process (AHP). AHP is capable of handling a multi-level decision-making structure with the use of five expert judgments in the pairwise comparison process. Meanwhile, ANN is utilized as an inductive algorithm in discovering the predictive strategy of the bank and used to explain the strategic factors which improved in forward. \u0000Findings and Novelty – The empirical results indicate that ANN and AHP integration was proved to predict the business strategy of the bank in five scenarios. Strategy 5 was the best choice for the bank and Innovate Like Fintechs (ILF) is the most factor consideration. The strategy choice was appropriate for the condition of the bank’s factors. This framework can be implemented to help bankers to decide on bank operations.\u0000Type of Paper: Empirical\u0000JEL Classification: M15, O32.\u0000Keywords: Bank’s strategy, ANN, AHP, BSC, Indonesia.\u0000Reference to this paper should be made as follows: Ristyawan, M.R. (2021). Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank, Journal of Finance and Banking Review, 5(4): 01 – 09. https://doi.org/10.35609/jfbr.2021.5.4(1)","PeriodicalId":269671,"journal":{"name":"GATR Journal of Finance and Banking Review VOL. 5 (4) JAN-MAR. 2021","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123329694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：