ABSTRACT
Purpose The purpose of this paper is to examine the antecedents of customer satisfaction during mortgage purchases. Mortgage demand in the USA has reached an all-time high because of an increase in housing demand after COVID-19. Nonetheless, several customers are dissatisfied with their service providers. Customers who actively search the market gain more information about mortgage providers and use this information to define expectations for lenders. The only way there will be customer satisfaction is if lenders meet these expectations. Therefore, it is economically significant for mortgage lenders to discover the antecedents of mortgage satisfaction. Design/methodology/approach In this study, the partial least squares approach was used to test the hypothesis that satisfaction was influenced by objective knowledge, familiarity and search intensity among a sample of customers (n = 4,512) from the National Survey of Mortgage Originations who had purchased a mortgage in the USA between 2019 and 2020. Findings The results of structural modelling showed that familiarity (beta = 0.23 and p = 0.01) with and knowledge (beta = 0.16 and p = 0.01) of mortgages significantly affected consumer satisfaction during mortgage purchase. Search intensity (p = 0.01) mediated the relationship between knowledge, familiarity and satisfaction. Research limitations/implications The primary implication is that mortgage service providers should prioritise educating customers about the mortgage buying process on their websites and in person. So managers must actively assist clients in having realistic expectations. Second, mortgage companies should establish a presence on third-party mortgage comparison websites to ensure that customers actively consider alternatives, thereby increasing customer satisfaction. Originality/value This study is unique in being an exploratory study to examine the antecedents of mortgage satisfaction using a public data set. This study uniquely examines the National Survey of Mortgage Originations data set with partial least squares approach to examine underlying customer attitudes.
ABSTRACT
In the current scenario, people are more attracted to buy groceries from Supermarket/Hypermarket. In such a case, finding the essential need of any customer in supermarket consumes more time and after all findings the customer need to wait in the billing queue to complete billing process of the selected product. Currently, due to the covid-19 pandemic, the customers are strictly instructed to maintain social distance but practically it is not possible especially in the billing process. To overcome this significant challenge, this research work proposes a smart trolley based on Internet of Things [IoT] with an advanced billing system that makes shopping easier and secured and also avoids standing in long queue. The proposed system consists of a smart trolley attached with LCD display, barcode scanner and a raspberry-pi. This exploratory model is intended to completely eradicate the tedious shopping interaction and administration-related issues. The proposed framework can be undoubtedly implemented at a business scale under the genuine situation. © 2021 IEEE.