Evaluating Mobile Wallet Adoption Barriers Using Fuzzy Mathematical Model
Journal Title: International Journal of Experimental Research and Review - Year 2024, Vol 44, Issue 8
Abstract
A tremendous amount of research has been done on the factors influencing mobile wallet adoption as mobile wallet technology has seen rapid growth. Using expert opinion and Fuzzy PROMETHEE approach, this study investigates the key barriers to mobile wallet adoption. Mobile wallet adoption is constrained by Technological, security and infrastructural barriers, making adoption more challenging when user acceptance is skewed in emerging markets. In this study, we use the F-PROMETHEE to rank these barriers based on expert opinions. A panel of fintech and digital payments experts assessed the key adoption obstacles. Included in the PROMETHEE method were methods for handling variability or uncertainty through fuzzy logic and through subjective expert judgments. The results suggest that the major barriers to the adoption of mobile wallets were identified as risk and usage constraints. Moreover, value barriers are a leading factor. This study found that the risk and value barriers are the two principal risks that must be overcome to raise the client accepted rate of m-wallet services. A step forward in the assessment of such obstacles is the innovative use of a fuzzy mathematical model, which provides a more complex and adaptable approach than traditional methods. This study has learnt a few lessons that can help policy makers and industry players understand how to overcome the main barriers to mobile wallet adoption.
Authors and Affiliations
Archana Kumari, Deepa Kumari, Reema Agarwal
Diagnosing and categorizing of pulmonary diseases using Deep learning conventional Neural network
Lung cancer is one of the major illnesses that contribute to millions of fatalities worldwide. Numerous deaths could be saved through the early identification and categorization of lung cancers. However, with traditional...
A relative study of diversity of endophytic fungi in a Lianas Butea superba from Belpahari and their seasonal variation
Fungal endophytes from Butea superba were studied collecting from Belpahari of Jhargram district of West Bengal during three seasons-winter, summer and monsoon. A total of 159 plant tissue segments were resided by endoph...
Detection of Pleuro Pulmonary Blastoma using Machine Learning Models
Pleura Pulmonary Blastoma (PPB) is a type of lung cancer seen in children. PPB needs to be detected earlier when treating children. The mortality rate of PPB is higher if left untreated. It can be detected from CT images...
Study on segmentation and prediction of lung cancer based on machine learning approaches
Lung cancer is a dangerous disease in human health. At the early stage, lung cancer detection provides a way to save human life. As a result, improvements in Deep Learning (DL), a technique, a branch of Machine Learning...
Spatial variation of valuable bacterial enzymes in soil: A case study from different agro ecological zones of West Bengal, India
The spatial variability of cellulase, amylase, protease and pectinase activities were evaluated from four zones of West Bengal, India. The enzyme production data was plotted on the map of the study areas and spatial vari...