Review of Integrative Business and Economics Research, Vol. 8, Supplementary Issue 4 401 Copyright 2019 GMP Press and Printing ( ISSN: 2304-1013 (Online); 2304-1269 (CDROM); 2414-6722 (Print) Digitization of Mortgage Banking among Selected Universal Banks in the Philippines: Towards a Model of Acceptance of Digital Mortgage Service Dave T. Morales* The Graduate School, University of Santo Tomas Fernando L. Trinidad The Graduate School, University of Santo Tomas ABSTRACT Digitization of bank services has become a popular focus of many banks around the world that is why banks in Asia transition to digital mortgage service as a means of reducing costs, improving services, and increasing effectiveness and efficiency. This study made use of quantitative research design that investigated the determinants of the behavioral intentions to accept the banks digital mortgage service from the perspective of 250 mortgage clients who are current or prospective users of digital mortgage service. Initial findings showed that the determinants of behavioral intention to accept digital mortgage device among mortgage clients were facilitating conditions, performance expectancy and effort expectancy. Based on the standardized beta coefficients, facilitating conditions (0.405), performance expectancy (0.383) and effort expectancy (0.134) had significant influence on bank mortgage clients behavioral intentions to accept digital mortgage service. It was also found that the age, gender, computer knowledge and internet knowledge of the mortgage clients moderate the impact of the three determinants: performance expectancy, effort expectancy and social influence on clients digital mortgage service acceptance. Moreover, findings revealed that performance expectancy is directly related with internet time, frequency of product purchase using mobile device and use of mobile banking. Keywords: Mortgage Loan, Bank Mortgage Digitization, Digital Mortgage Service, Behavioral Intention 1. INTRODUCTION Today, people live in a technology enabled generation where digital is on everyones agenda. Chen, Durairaj, Vinayak and Lam (2014) reported that around 40 percent of Asian mass affluent customers favor online or mobile banking and among those under 40 years of age, about half prefer digital banking. It was estimated that digital-banking consumers in Asia add up to 670 million and is predicted to expand at 1.7 billion by 2020. In Asia, the financial and banking sector has experienced radical changes and upgrades in the last few years and is in a constant condition of advancement. Digitization has brought the banking industry new plans of action; advancement of ideasReview of Integrative Business and Economics Research, Vol. 8, Supplementary Issue 4 402 Copyright 2019 GMP Press and Printing ( ISSN: 2304-1013 (Online); 2304-1269 (CDROM); 2414-6722 (Print) and areas of changes; and internet banking to monetary exchanges. These new uses in the financial sector require the bank leaders to know about the rapidly changing workplace and the general condition of progress within the mortgage loans division. Today, digitization is changing the banking business. The signal of increasing use of technology is changing consumer behavior is everywhere in Asia, and more specifically in the Philippines. One of the recent changes that is shaping the banking industry is that the consumer decision process has become increasingly multichannel (Chen et al., 2014). There are numerous advantages of mortgage digitization. According to Cognizant Consulting (2018), mortgage digitization would allow lenders to further automate compliance processes and eliminate manual interference. Second, competing on only products and services is no longer sufficient; customers will pay more for a better experience and for mortgage industry, digitization is crucial to that experience. In addition, younger generations often prefer digital technology as a self-service tool to meet their needs. Third, digitization can improve asset quality since a fully digitized mortgage process permits an expanded automation of underwriting, processing, closing, and quality assurance capabilities. While mortgage digitization is happening in many Asian countries, Philippine mortgage industry still seems to be in the wait-and-see mode. It is unclear why Philippine banks are not yet digitizing their current mortgage processes following the footsteps or other bank leaders in Asia despite its numerous advantages. The Philippine mortgage banking industry will need to invest in digital offerings and technologies to improve the loan origination process and the customer experience. In order to plan for such bank mortgage digitization, the acceptance of this technology must be first explored. Currently, there is a knowledge gap in the literature on a comprehensive study on the acceptance of digital mortgage technology since related research studies are almost non-existent. Therefore this study aims to develop a model based on Unified Theory of Acceptance Use of Technology (UTAUT) to provide empirical evidence to support mortgage clients acceptance of mortgage digitization. 2. RESEARCH PROBLEM This study explored the determinants of the acceptance of mortgage digitization in selected universal banks in the Philippines from the perspectives of mortgage clients. It is crucial to explore such factors, because findings of this study can be valuable to banking industry and bank leaders to plan and effectively deliver mortgage services using digital technologies. 3. REVIEW OF RELATED LITERATURE 2.1.1 Banking Banking study has considerable interest at the macroeconomic and microeconomicReview of Integrative Business and Economics Research, Vol. 8, Supplementary Issue 4 403 Copyright 2019 GMP Press and Printing ( ISSN: 2304-1013 (Online); 2304-1269 (CDROM); 2414-6722 (Print) levels. From the macroeconomic point of view banking one of the financial intermediation types plays special role in the movement and distribution of country financial resources in market conditions. Hence, banking inefficiency leads to borrowers financial resources shortage when populations financial resources excess and, consequently, low rates of economic growth and the common weal deterioration, for services consumers it means overpriced banking services and their unavailability. Loan approval organizations depend on their clients and therefore should understand their current and future needs, they should meet their requirements and should be concerned with exceeding their expectations. This means understanding the fact that profitability or avoiding losses comes from customer satisfaction, which implies organizing all the processes and directing the entire staff towards the client (Dragolea, Achim, 2304-1269 (CDROM); 2414-6722 (Print) estate property; from the economic point of view, it is a type of loan where the repayment of which is secured through the establishment of a pledge on a specific real estate property in favor of a creditor mortgagee. Banks all over the world over have been continuously deregulated; fiscal arrangements have experienced changes from an attention on the cash supply to an emphasis on loan costs; money related frameworks have been subjected to a scope of developments and the monetary condition of numerous countries has changed after the development of emerging countries, for example, China and India (Lim, Tsiaplias, 2304-1269 (CDROM); 2414-6722 (Print) to be custom fitted to the banks particular plan of action, in light of the fact that there is no innovation arrangement that is a general fit for all organizations. Speculators that are thinking about cooperation in the home loan managing an account industry will discover two boundlessly extraordinary states of mind toward innovation. 4. THE THEORETICAL FRAMEWORK Figure 1 The Theoretical Framework Unified Theory of Acceptance and Use of Technology (UTAUT Model) by Venkatesh, V ., Morris, M., Davis, G., 2304-1269 (CDROM); 2414-6722 (Print) 5. THE CONCEPTUAL FRAMEWORK Figure 2 The Conceptual Framework The research model shows the hypothesized relationships between the independent and dependent variables. The independent variables are the four main constructs of UTAUT model such as: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC), while the dependent variable is the behavioral intention (BI) to accept the banks digital mortgage service. The independent variables have arrows that point to the dependent variable. From the conceptual framework, the following hypotheses were tested: Ha1: Performance expectancy will positively affect behavioral intention of the respondents to accept digital mortgage service. Ha2: Effort expectancy will positively affect behavioral intention of the respondents to accept digital mortgage service. Ha3: Social influence will positively affect behavioral intention of the respondents to accept digital mortgage service. Ha4: Facilitating conditions will positively affect behavioral intention of the respondents to accept digital mortgage service. Ha5: Gender, age and technology experience will affect behavioral intention of the respondents to accept digital mortgage service. 6. METHODOLOGY This research used quantitative research design to achieve the aims of the study. This study used the UTAUT model as the theoretical basis which was evaluated using a sequence of quantitative data and analysis in order to produce a final model that bestReview of Integrative Business and Economics Research, Vol. 8, Supplementary Issue 4 407 Copyright 2019 GMP Press and Printing ( ISSN: 2304-1013 (Online); 2304-1269 (CDROM); 2414-6722 (Print) explains the predominant phenomena of the data that were collected. A survey questionnaire, which was subjected for validity and reliability tests, was used to gather the data from 250 mortgage clients who are current or prospective users of digital mortgage service. 7. FINDINGS The respondents behavioral intention to accept the banks digital mortgage service are shown in Table 1 and Table 2. Half of the respondents agreed while 38.8 percent agreed on the statements that measured their acceptance to banks digital mortgage service technology in processing and approval of home loans. Only few of the respondents had an overall response of disagree (9.6%) and strongly disagree (1.6%) on the indicators of behavioral intention to accept the banks digital mortgage service. Table 1 Perceptions of the respondents on the four factors of behavioral intention to accept digital mortgage service Factor of Behavioral Intention Frequency (n=250) Percent Performance expectancy strongly disagree 4 1.6 disagree 20 8.0 agree 125 50.0 strongly agree 101 40.4 Effort expectancy strongly disagree 2 .8 disagree 25 10.0 agree 122 48.8 strongly agree 101 40.4 Social influence strongly disagree 14 5.6 disagree 30 12.0 agree 117 46.8 strongly agree 89 35.6 Facilitation conditions strongly disagree 1 .4 disagree 28 11.2 agree 133 53.2 strongly agree 88 35.2Review of Integrative Business and Economics Research, Vol. 8, Supplementary Issue 4 408 Copyright 2019 GMP Press and Printing ( ISSN: 2304-1013 (Online); 2304-1269 (CDROM); 2414-6722 (Print) Table 2 Behavioral intention of the respondents to banks digital mortgage service acceptance Behavioral Intention Frequency (n=250) Percent strongly disagree 4 1.6 disagree 24 9.6 agree 125 50.0 strongly agree 97 38.8 The results presented in Table 3 revealed that the overall model accounts for 75.2% of the variance in behavioral intention to accept digital mortgage service. The full model is significant (F= 186.173, p= .001) and all the variables contribute significantly to the multiple regression (at p = 0.05) except the variable social influence (SI) which does not relate significantly to the dependent variable(p =.575) when controlling for the other three predictors. Table 3 Determinants of behavioral intention to accept digital mortgage service Unstandardized Coefficients Standardized Coefficients Sig. (p) B Beta VIF (Constant) .011 .926 performance expectancy .400 .393 .001 3.061 effort expectancy .150 .145 .010 3.089 social influence .021 .025 .575 2.030 facilitating conditions .425 .400 .001 2.289 Dependent Variable: behavioral intention R 2 = .752; F= 186.173, p=.001 The results of regression analysis showed that performance expectancy (PE), effort expectancy (EF) and facilitating conditions (FC) were significant predictors of behavioral intention (BI) at 5% level of significance. The positive values of the b- coefficients of these three independent variables (PEB = 0.400, EFB = 0.150, and FC B=0.425) indicated that as the perception of the respondents increases, the independent variable likewise increases. In other words, the more positive the perceptions of the respondents on PE, EF, and FC, the higher their behavioral intention to accept the banks digital mortgage service. Moreover, since the variance inflation factor (VIF) for all variables are much less than the critical value of 5, it indicates that no violation on multicollinearity or redundancy of the factors of behavioral intention were committed in the multiple regression.Review of Integrative Business and Economics Research, Vol. 8, Supplementary Issue 4 409 Copyright 2019 GMP Press and Printing ( ISSN: 2304-1013 (Online); 2304-1269 (CDROM); 2414-6722 (Print) The model summary and ANOVA for behavioral intention to accept digital mortgage service are illustrated in Table 4. The combined relationship of the influence of the four determinants of behavioral intention showed an 86.7% relationship. The adjusted R-square is 74.8% signifying that the determinants moderately explained the variation in behavioral intention and about 25.2% was left unexplained due to other determinants not included in the model. The ANOVA, as explained by the F-value of 186.17, represents that more than 10% has been explained by the determinants of behavioral intention at a significant level of 0.001. Thus, the regression model was accepted and an appropriate estimate of behavioral intention. The results led to the acceptance of the following hypotheses: 1) PE positively affects behavioral intention of the respondents to accept digital mortgage service; EE positively affects behavioral intention of the respondents to accept digital mortgage service; and FC positively affects behavioral intention of the respondents to accept digital mortgage service. Meanwhile, the hypothesis that SI positively affects behavioral intention of the respondents to accept digital mortgage service was rejected. The results concur with the study of Alshehri (2012) who examined the factors affecting acceptance and use of E-government services in the Kingdom of Saudi Arabia by adopting the UTAUT model revealed that trust (TR), effort expectancy (EE), performance expectancy (PE), and website quality (WQ) contribute sign