Abstract
This chapter deals with the issue of consumer behavior change by taking the case of a digital service such as m-banking and supports the arguments with suitable data analytics. To fulfill this objective, it utilizes the two indicators of awareness and perceived value as the determinants of consumer behavior. The chapter intends to throw light on the relationship between awareness, perceived value, and usage of m-banking, and also helps to find out how the interaction of age affects this relationship. For this purpose, a sample of 524 m-banking users was utilized from the state of Punjab in India. Further, the generated data is analyzed using linear regression and moderated regression analysis. The findings reveal important implications for the banks and other researchers in terms of awareness and perceived value and also depict how the behavior of consumers can change as per their age in this socio-digital era.
TopBackground
To gain some insights into the nature of the current study, a brief background of the topic including the literature review is presented further.
M-Banking
M-banking is an offshoot of the financial industry that is specifically related to banks (Baabdullah et al., 2019). It is a type of electronic service and is also viewed as one of the latest channels of banking. In a broader sense, it is considered as a facility that helps in transacting through mobile phones or other hand-held devices (Vishnuvardhan, Manjula, & Naik, 2020). M-banking is the positive outcome of the collective development of financial services and m-commerce applications (Chung & Kwon, 2009), and allows the consumers to keep a track of their banking operations through mobile phones (Parvin, 2013). It enables the banks to provide different types of personal banking services to customers primarily through a mobile phone. Some of these services may be balance enquiry, cheque book request, funds transfer, bills payment, movie ticket booking, etc. M-banking could be assessed by the customers through different ways such as SMS banking, WAP-based banking, USSD, mobile web banking, and m-banking apps. Out of these, m-banking apps are primarily promoted by the banks as the most efficient medium of accessing the service.
Key Terms in this Chapter
Dummy Variables: These are a special type of variables with values 0 or 1 and are utilized for examining the effect of categorical variables during regression analysis.
Mobile Shopping: An application of mobile commerce that includes the sale and purchase of goods and services using handheld devices such as mobile phones.
Enquiry-Based Service: A type of m-banking service whose sole objective is to provide information to the customers without the involvement of any financial transaction.
Residual Mean: It is widely used as a measure of the accuracy of the fit in the regression model.
Outliers: These are the values that are quite distant from others in the regression model and may significantly influence its predictive capability.
Mobile Ticketing: It provides the ability to purchase tickets for different purposes such as movies, railway journeys, and any event.
Transaction-Based Service: A type of m-banking service based on the purpose and generally involves the transactions such as funds transfer.
Multicollinearity: A situation that shows the higher correlation between variables in the regression analysis.