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Banking is one of them. Traditional banking operations are gradually changing with the introduction of efficient mobile technologies. Mobile banking (m-banking) has recently emerged as an innovative banking channel that provides continuous real-time customer service. It is expected that the market for m-banking will expand in the near future. There are currently various types of m-banking applications in the market. However, ranking and selecting efficient applications is difficult due to the involvement of multiple factors. As of now, very few studies have reported the m-banking application selection framework, left scope for further research. The current study proposes an m-banking application selection model based on a combined fuzzy best\u2013worst method (fuzzy-BWM) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy-TOPSIS). The research was carried out in several stages, beginning with the identification of potential factors and progressing to pair-wise comparisons and the final ranking of the applications. The fuzzy set theory was applied to handle the ambiguity of the decision maker. In the first stage, fuzzy-BWM was used to determine the weight of the factors. Further, fuzzy-TOPSIS was applied to rank the m-banking applications. The present study has adopted a new fuzzy BWM, which differs significantly from the existing fuzzy-BWM, to solve the nonlinearity problem of optimisation. The applicability of the proposed model has been demonstrated through a real-life case study. The efficacy of the model has been further examined by performing a sensitivity analysis. The study observed application functionality, convenience, and performance expectancy as significant factors in selecting an m-banking application, followed by performance quality, security, and compatibility. The proposed model can assist financial institutions and customers to overcome the challenges of choosing an appropriate m-banking application. The proposed model can be used to benchmark the m-banking applications in the market.<\/jats:p>","DOI":"10.1007\/s40747-021-00502-x","type":"journal-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T03:52:34Z","timestamp":1629777154000},"page":"2017-2038","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["An integrated fuzzy model for evaluation and selection of mobile banking (m-banking) applications using new fuzzy-BWM and fuzzy-TOPSIS"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0929-7563","authenticated-orcid":false,"given":"Pranith Kumar","family":"Roy","sequence":"first","affiliation":[]},{"given":"Krishnendu","family":"Shaw","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,24]]},"reference":[{"key":"502_CR1","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.tele.2014.05.003","volume":"32","author":"AA Shaikh","year":"2015","unstructured":"Shaikh AA, Karjaluoto H (2015) Mobile banking adoption: a literature review. 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