Evaluating the lending channel of monetary transmission in Qatar

This study uses OLS, fixed effect and random effect models to evaluate the lending channel of the monetary policy transmission in Qatar over the 2000-2013 period. The growth in the loans extended by the Qatari banks is regressed on the current and lagged values of monetary policy stance, bank liquid assets, deposits, GDP and the lagged values of change in bank lending. The results show that bank lending responds positively but insignificantly to changes in the monetary policy stance. In contrast, the results reveal that bank lending responds to changes in the values of cash and due with banks and customer deposits. These results may indicate lack of a lending channel in Qatar though it may concur with the implicit objective of the Qatari monetary policy of changing the structure of banking system assets and liabilities but not on the account of the target credit in the market. Copyright 2017 Inderscience Enterprises Ltd.Scopu

Continued intention to use of m-banking in Jordan by integrating UTAUT, TPB, TAM and service quality with ML

Mobile banking is a service provided by a bank that allows full remote control of customers' financial data and transactions with a variety of options to serve their needs. With m-banking, the banks can cut down on operational costs whilst maintaining client satisfaction. This research examined the most crucial factors that could predict the Jordanian customer's continued intention toward the use of m-banking. Following the proposed model, the research was conducted by using a self-conducted questionnaire and the responses were collected electronically from a convenience sample of 403 Jordanian customers of m-banking through social networks. The suggested model was adapted from the theory of planned behavior (TPB), the unified theory of acceptance and use of technology (UTAUT), and the technology acceptance model (TAM). The research model was further expanded by considering the factors of service quality and moderating factors (age, gender, educational level, and Internet experience). The collected data of customers were analyzed, validated, and verified by using a structural equation modeling (SME) approach including a confirmatory factor analysis (CFA), in addition to machine learning (ML) methods, artificial neural network (ANN), support vector machine (SMO), bagging reduced error pruning tree (RepTree), and random forest. Results showed that effort expectancy, performance expectancy, perceived risk, perceived trust, social influence, and service quality impacted behavioral intention, whereas facilitating conditions did not. Furthermore, behavioral intention impacted upon word of mouth and facilitating conditions (the latter regarding the continued intention to use m-banking), and had the highest coefficient value. Results also confirmed that all moderating factors affect the behavioral intention to continue using m-banking applications