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Housing finance: borrowers’ awareness, information disclosure according to fair practices code and service quality of providers : an empirical investigation
(University of North Bengal, 2023) Bhowmik, Dipika; Dhar, Samirendra Nath
Housing Finance is a long-term institutional finance for owning a property by way of purchase, construction, improvement or extension; generally against the same property pledged as security. It enhances affordability of individuals who otherwise cannot afford to own a housing unit. Also, home-loans are a good means to avail tax benefits and useful tool to have long-term savings in addition to possess a roof over head. As housing is an important component of quality living, government has always accorded due priority to the issue by acting as provider for the vulnerable section and facilitator for the rest by taking measures to develop the housing finance market. In India at present the two major housing finance providers are the Scheduled Commercial Banks (SCB) and Housing Finance Companies (HFC). Though HFCs are specialised Housing Finance Institution (HFI) and initially used to hold a major share in the Indian housing finance market, SCBs reach and huge customer base has given them a competitive edge over the HFCs. And thus, SCBs continues to be the major lender in Indian housing finance market since 2004. With the aim to find out the trend in growth of housing finance and share of SCB & HFC, it has been found that the outstanding housing loan has collectively grown to Rs. 24,61,083 crores in 2021 from Rs. 65,481 crores in 2001; and SCB holds almost 63.46% share of the total outstanding housing loan in the country against HFC holding almost 35.99% as on 31st March, 2021. This study is majorly based on primary data collected with the help of three separate set of structured questionnaires, based on five-point bi-polar Likert scale, self-administered to home-loan borrowers to analyse their awareness level, service quality delivered by the lenders and compliance of Fair Practices Code (FPC), as perceived by the borrowers. Awareness items were based on Measuring Awareness of Financial Skills (MAFS), Financial Competency Assessment Inventory (FCAI) & Decision Making Competence Assessment Tool (DMCAT) scales. Evidence of low awareness level has been found regarding various items, especially regarding loan-tenure, EMI, tax aspects, financial charges and purpose & size of loan. Further, evidence of significant differences has been found among the home-loan borrowers with respect to their gender, age, loan-amount availed and type of lending institution. Service quality expectation of borrowers and perception of borrowers regarding service quality of the lenders has been analysed using SERVQUAL.The perceived service quality was found not to be satisfactory against the expectations of the borrowers. Both bank and HFC were found to be deficient in meeting expectation of their customers. Bank and HFC found to differ significantly in quality of services delivered by them, as perceived by their customers regarding twelve attributes out of twenty-two. HFC’s home loan customers perceived the service quality as higher than the home loan service rendered by banks, as perceived by their customers. Reserve Bank of India (RBI) issued guidelines for banks and other financial institutions to develop their own respective FPC in 2003 and in 2016 NHB came up with a general FPC to be complied by all HFCs to ensure fair and transparent lending practices. Since, there is non-uniformity in FPCs of different lenders, attempt has been made to standardise the FPC. Thirty-two variables were identified from NHB master circular and through observation and interviews. Exploratory Factor Analysis (EFA) was used to extract factors essential for FPC. Five factor solution emerged with 18 variables, which cumulatively explains 84.216% variance. The five constructs identified are named as, Online Communication Facilities, Loan Application Services, Grievance Handling, Documentation Services and Financial Charges. This implies that the 18 variables considered important by the borrowers majorly relates to Online Communication Facilities, Loan Application Services, Grievance Handling, Documentation Services and Financial Charges. Confirmatory Factor Analysis (CFA) was done to confirm the factor structure identified. The convergent validity and discriminant validity were satisfied by the hypothesized model. All fitness indices examined have met the required criteria. One variable was eliminated in the process for improving the results. The final standardised model consists of five factors, reflected by 17 observed variables. Thus, the FPC can be revised for implementing a standardised FPC for all housing finance providers taking into account the factors which have been perceived to be important by the borrowers.
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Ramkumar Mukhopadhyayer kathasahitya (2020 parjanta) : prantik manusher jibancharcha
(University of North Bengal, 2023) Bera, Samiran; Roy, Ashis
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Natyakarmi Badal Sarkar : ekti mulyayan
(University of North Bengal, 2023) Roy, Amar Chandra; Paul, Bikash chandra
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Online shopping attributes and its influence on customers satisfaction, trust and behavioural intention : an empirical study
(University of North Bengal, 2023) Prasad, Narayan; Bhattacharya, Debasis
Shopping is an integral part of one’s daily life routine. In online shopping, buying and selling goods and services is done on a virtual platform (here, e-stores of retailers) with the help of the Internet. Online shopping helps retailers to get connected with their customers 24x7. Similarly, it allows buyers to place desired orders on the retailers’ e-stores (or web stores) anywhere and anytime. However, customers cannot interact face-to-face with retailers in online shopping. Moreover, they can only physically evaluate goods and services (such as touch, smell or test) once they receive and use them. Therefore, it is challenging to satisfy and build trust among online shoppers towards online shopping compared to traditional shopping. Consequently, it is necessary to evaluate (or a research gap) how e-retailers are satisfying customers and building trust among customers to adopt online shopping platforms. Moreover, it also needs to be checked that if the customers are satisfied with the online shopping platform and the online sellers successfully build trust, do they adopt online shopping into their behavioural intention? This study conducts empirical research to answer the above questions and bridge this gap in the body of knowledge. Based on previous research and theories in the areas of online shopping, the study identified the relevant online shopping dimensions (e.g., product reviews, perceived risk, website interface quality, perceived security, customer trust, customer satisfaction, and customer purchase behavioural intention) that influence customers’ buying behavioural intention on online shopping platforms. The study developed a structured questionnaire to measure the identified online shopping dimensions and analyze the online buyers’ demographic profiles. The study uses a five-point Likert scale to measure the items/questions of various online shopping parameters. In this five-point Likert scale, five represents “strongly agree”, four represents “agree”, three represents “undecided/neutral”, two represents “disagree”, and one represents “strongly disagree”. The target population of this study is college and university-going students who buy goods and services online. The study applied convenience sampling {as suggested by Gopinath (2021), Sunitha & Gnanadhas (2014), and Dani (2017)} to select institutions, departments, and centres. After that, the study used systematic random sampling techniques {as suggested by Alwan & Alshurideh (2022), Farzin et al. (2022), and Ariansyah et al. (2021)} to collect the responses from online shoppers. The study takes the help of Cochran’s formula (1977) to determine the sample size for an infinite population. The study took a sample of 576 online shoppers to explain the purchase behaviour intention of customers in online shopping platforms. The study considers parameters such as gender (Slyke et al., 2002), age (Khare et al., 2012), educational qualification (Susskind, 2004) and income (Mahmood et al., 2004) to analyse online customer demographic profiles. In addition, the study takes two new variables, called “payment method” and “time spent on the Internet” (suggested by Brown et al., 2003), to gauge consumers’ payment method preferences and Internet experience. The study used Exploratory Factor Analysis (EFA) with Principal Component Analysis (PCA) method to extract the underlying dimensions of online shopping that influence customer buying behavioural intention on online shopping platforms. Furthermore, the study used varimax rotation with Kaiser normalisation to obtain the Rotated Component Matrix (RCM). The univariate normality of the data is checked with descriptive statistics, such as mean, standard deviation, skewness, and kurtosis. The study takes the help of Mardia’s coefficient test (1970) to test the multivariate normality of the data of online shopping parameters. The study examined the internal consistency in scale items or reliability of the online shopping construct with the help of “Cronbach alpha (α)” and “Composite reliability (CR)”. The validity of an online shopping construct is examined with the help of “discriminant validity” and “convergent validity”. The study established discriminant validity by average variance extracted (AVE) and convergent validity by the Fornell-Larcker test. The study develops an online shopping behaviour intention measurement model, structural model and respecified structural model of customers with the help of a rotated component matrix using statistical software (AMOS). The study established the fit indices of these models with the help of various specified model fit indices, such as the overall fit index (i.e., CMIN), absolute fit index (i.e., GFI, RMSEA, RMR, SRMR, and Normed chi-square), incremental fit index (i.e., NFI, CFI, and RFI) and parsimony fit index (i.e., AGFI and PNFI). The study found that women (57.5 per cent) are more inclined towards online shopping than men (42.5 per cent). Compared to shoppers in other age groups (such as up to 20, 26 to 30 and over 30), shoppers aged 20 to 25 are more interested in online shopping (40.8 per cent). Of the 576 online shoppers, 53.5 per cent are pursuing graduate programs. The study reveals that 33.9 per cent of online shoppers have a household income between Rs 2.5 lakh to Rs 5 lakh. Furthermore, the study shows that 46.5 per cent of online shoppers prefer the cash-on-delivery (COD) option payment method, and 50.2 per cent surf the Internet for 2 to 4 hours per day. The statistical results of this study show that perceived security (PSEC), product review (PRV), and perceived risk (PRK) affect both customer satisfaction (CSAT) and trust (CTRT). Web interface quality (WIQ) affects customer trust (CTRT) but does not affect customer satisfaction (SAT). Customer satisfaction (CSAT) is influenced by customer trust (CTRT) in the online shopping platform. Furthermore, the study shows that the behavioural intention (BI) of customers in online shopping platforms is directly influenced by customer satisfaction (CSAT) and trust (CTRT) and, indirectly, by perceived security (PSEC), product reviews (PRV), perceived risk. (PRK), and Web Interface Quality (WIQ). The square multiple correlations (R2) of customer satisfaction (CSAT) and customer trust (CTRT) in the online shopping platform are 0.43 and 0.38, respectively. This means that the proposed model (i.e., a model for estimating customer’s purchase behaviour intention in online shopping platforms) explains 49 and 38 per cent variation in customer satisfaction (CSAT) and customer trust (CTRT), respectively, with the help of taking online shopping factors in this study. The square multiple correlations (R2) of customer behavioural intention (BI) in the online shopping platform is 0.32. This means that the proposed model explains a 32 per cent variation in customer purchase behaviour intention (BI) with the help of taking online shopping factors in this study. There are some research limitations of this study. This study does not consider the responses of other online shoppers (such as housewives, senior citizens and professional online shoppers). This study proposed a customer purchase behaviour model on online shopping platforms considering relevant dimensions (such as product reviews, perceived risk, perceived security, website interface quality, customer trust, customer satisfaction and customer behavioural intentions). However, these online shopping dimensions are only indicative lists and not exhaustive lists of online shopping dimensions. Since online shopping uses technology and the Internet, it can be a new dimension if any technological innovation is adopted to make online shopping convenient. Hence, online shopping dimensions are dynamic as technological innovations are dynamic. Thus, assessing customers’ buying behaviour on online shopping platforms is dynamic and continuous, and the research on online shopping is considered a never-ending process.
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Measurement of efficiency among life insurance companies in India during the post reform period
(University of North Bengal, 2022) Sarda, Madhu; Roy, Indrajit
An economy is built upon financial intermediation. The efficacy of financial intermediation ripples through distant economic boundaries and benefits all the stakeholders. Insurance has emerged as one of the significant constituents of the Indian financial intermediation; thus, the question of its efficiency should not be lost sight of. Time and again, the Indian insurance industry was found at crossroads. Each time, whenever structural changes took place, the insurance industry got transmuted. The latest structural change took place in 2000 when the Insurance Regulatory and Development Authority (IRDA) Act, 1999 was enacted. It marked the ending of state exclusivity and the beginning of market-driven competition in the Indian insurance sector. Then policymakers believed that the liberalisation-led competition would persuade both the public insurer and private insurers to perform efficiently and to grab higher shares of the uninsured market. They prophesied that the reform would bring a positive impact on key insurance parameters. The potential insurance market of India appeals for unabated scale expansion. However, scale expansion policy should be subject to scale efficiency analysis. The present study primarily focuses on the measurement of scale efficiency scores of the life insurance companies operating in India during the post-reform period. This study measures and analyses scope efficiency also. The scale efficiency relates to the volume of production and the scope efficiency relates to the variety in production. The efficiency aspect of the Indian insurance industry has not been researched as substantially as compared to foreign insurance industries. The Econometric Frontier Approach (EFA) and the Data Envelopment Approach (DEA) are popular techniques in the realm of efficiency studies. The present study chooses the EFA over the DEA. In the Indian context, the econometric path of the efficiency measurement is almost not travelled. The study undertakes the Ordinary Least Square (OLS) method of estimation to measure the scale efficiency of the life insurance companies operating in India during the period from 2003-04 to 2018-19. It measures the scope efficiency of the entire industry, as well as the public sector and the private sector individually. It adopts the translog cost function. Separate output sets are adopted to measure the scale efficiency and the scope efficiency. Benefits paid (QR) is one of the output variables to measure scale efficiency. The other output variable is investment (policyholders’) and income from investment along with assets held to cover linked liabilities (QI). Three output variables, namely, life fund (Q1), pension and group fund (Q2) and ULIP fund (Q3) are adopted for the measurement of scope efficiency. Three input variables are chosen, namely, labour (L), capital (K) and technical provision (TP). Cost (C) is the single dependent variable. Operating expenses related to the insurance business along with payment to suppliers of labour, capital and technical provision is used to surrogate the cost. Labour (L) is priced with commission per agent (W). Insofar as the price of shareholders’ fund (R) and that of technical provision (P) are concerned, income from investments per rupee of shareholders’ fund and prime lending rate are adopted respectively. It is found that the reform has caused a structural break in the life insurance premium series. It has driven the average premium upward. However, the slope, i.e., growth rate per year, has reduced by 5.9 per cent. Around 73 per cent of life insurance companies enjoy scale economies throughout the study period. No life insurance company is scale-neutral, i.e., scale efficiency equals unity. Two life insurance companies, namely, TATA-AIA Life Insurance Company Limited (TALIC) and Shriram Life Insurance Company Limited (SHLICL), experience scale diseconomies. Around 18 per cent of life insurance companies experience both scale economies and scale diseconomies during the study period. Insofar as the entire life insurance industry is concerned, scale economies prevail. The public life insurance sector enjoys scale economies, whereas the private life insurance sector experiences scale diseconomies, except in two financial years. Hence, the entire life insurance industry should expand its scale of production, and such expansion should be contributed by the public sector. The yearly scale efficiency scores of the entire life insurance industry stay in between the public and the private life insurance sector except in the financial year 2009-10. It is due to the combined impact of scale economies and scale diseconomies as prevailed in the public sector and the private sector respectively. It is also found that with the increase in asset size and with the expansion of scale (output), the value of scale economies, though remaining less than unity, increases. Insofar as the scope efficiency is concerned, this empirical study indicates that a higher cost is involved in specialised production than in joint production. In other words, joint production is better as dropping any two of three outputs would lead to higher costs. Hence, the entire life insurance industry, as also individually the public life insurance sector and the private life insurance sector, are scope efficient, as they are currently pursuing joint production.