Abstract
Mobile banking (m-banking) is one of the most recent creative banking channels that provide many advantages to both consumers and financial institutions regarding banking services delivery. With an increasing number of mobile phone users, it is one of the fastest-growing banking channels. The breadth of the m-banking industry is anticipated to expand in the near future. However, picking the appropriate one is definitely tough with a rising number of m-banking applications on the market. This research presents a fuzzy decision-making model for selecting appropriate m-banking applications based on the ground that fuzzy-based studies have received increasing attention from decision-makers due to the accuracy and precision with which they gather data. From the literature, a wide range of criteria was extracted for consideration. Further, domain experts' views were sought to finalise the criteria for evaluating m-banking applications. The research employed the fuzzy best–worst method (fuzzy-BWM) to calculate the weight of criteria that affect the adoption of m-banking, taking into account experts' opinions. At the same time, fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (fuzzy-TOPSIS) was employed to evaluate the m-banking applications. The study conducted a real-life case study to exhibit the model's applicability. The weight of the criteria was varied to conduct a sensitivity analysis. According to the research findings, performance quality with a score of 0.221 is the most important factor in choosing an m-banking application, followed by functionality (0.165) and clarity (0.113). The suggested model will assist financial institutions and consumers in overcoming challenges while choosing a suitable m-banking application. The suggested methodology may be used to assess the market's m-banking applications.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aboelmaged M, Gebba TR (2013) Mobile banking adoption: an examination of technology acceptance model and theory of planned behavior. Int J Business Res Dev 2(1):35–50. https://doi.org/10.24102/ijbrd.v2i1.263
Aboutorab H, Saberi M, Asadabadi MR, Hussain O, Chang E (2018) ZBWM: The Z-number extension of Best Worst Method and its application for supplier development. Expert Syst Appl 107:115–125. https://doi.org/10.1016/j.eswa.2018.04.015
Adepoju SA, Oyefolahan IO, Abdullahi MB, Mohammed AA (2020) Multi-criteria decision-making based approaches in website quality and usability evaluation: a systematic review. J Inform Commun Technol 19(3):399–436. https://doi.org/10.32890/jict2020.19.3.5
Afshar A, Mariño MA, Saadatpour M, Afshar A (2011) Fuzzy TOPSIS multi-criteria decision analysis applied to karun reservoirs system. Water Resour Manage 25(2):545–563. https://doi.org/10.1007/s11269-010-9713-x
Akturan U, Tezcan N (2012) Mobile banking adoption of the youth market: perceptions and intentions. Mark Intell Plan 30(4):444–459. https://doi.org/10.1108/02634501211231928
Alalwan AA, Dwivedi YK, Rana NP (2017) Factors influencing adoption of mobile banking by Jordanian bank customers: extending UTAUT2 with trust. Int J Inf Manage 37(3):99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
Alkhaldi NA (2016) Adoption of mobile banking in saudi arabia : an emprical evaluation study. Int J Manag Inform Technol 8(2): 01–14.
Al-Saedi K, Al-Emran M, Ramayah T, Abusham E (2020) Developing a general extended UTAUT model for M-payment adoption. Technol Soc 62(June):101293. https://doi.org/10.1016/j.techsoc.2020.101293
Baabdullah AM, Alalwan AA, Rana NP, Kizgin H, Patil P (2019) Consumer use of mobile banking (M-Banking) in Saudi Arabia: towards an integrated model. Int J Inform Manag 44(July 2018), 38–52. https://doi.org/10.1016/j.ijinfomgt.2018.09.002
Badri Ahmadi H, Kusi-Sarpong S, Rezaei J (2017) Assessing the social sustainability of supply chains using Best Worst Method. Resour Conserv Recycl 126(May):99–106. https://doi.org/10.1016/j.resconrec.2017.07.020
Beauchamp MB, Ponder N (2010) in-Store and Online Shoppers. Mark Manag J 20(1):49–65
Bellman RE, Zadeh LA (1970) Decision-making in a fuzzy environment. Manag Sci 17(4), B-141-B-164. Doi: https://doi.org/10.1287/mnsc.17.4.B141
Brodski S, Desmangles L, Fanfarillo S, Khodabandeh S, Palumbo S, Santinelli M (2019) What Does Personalization in Banking Really Mean? Boston Consulting Group. https://www.bcg.com/en-in/publications/2019/what-does-personalization-banking-really-mean.aspx
Büyüközkan G, Çifçi G (2012) A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Syst Appl 39(3):2341–2354. https://doi.org/10.1016/j.eswa.2011.08.061
Cano MD, Domenech-Asensi G (2011) A secure energy-efficient m-banking application for mobile devices. J Syst Softw 84(11):1899–1909. https://doi.org/10.1016/j.jss.2011.06.024
Chan FTS, Kumar N, Tiwari MK, Lau HCW, Choy KL (2008) Global supplier selection: a fuzzy-AHP approach. Int J Prod Res 46(14):3825–3857. https://doi.org/10.1080/00207540600787200
Chang DY (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95(3):649–655. https://doi.org/10.1016/0377-2217(95)00300-2
Chang IC, Hwang HG, Hung WF, Li YC (2007a) Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Syst Appl 33(2):296–303. https://doi.org/10.1016/j.eswa.2006.05.001
Chang YH, Yeh CH, Wang SY (2007b) A survey and optimization-based evaluation of development strategies for the air cargo industry. Int J Prod Econ 106(2):550–562. https://doi.org/10.1016/j.ijpe.2006.06.016
Chatterjee S, Kar AK, Gupta MP (2018) Success of IoT in smart cities of India: an empirical analysis. Gov Inf Q 35(3):349–361. https://doi.org/10.1016/j.giq.2018.05.002
Chen DN, Hu PJH, Kuo YR, Liang TP (2010) A Web-based personalized recommendation system for mobile phone selection: design, implementation, and evaluation. Expert Syst Appl 37(12):8201–8210. https://doi.org/10.1016/j.eswa.2010.05.066
Chmielarz W, Zborowski M (2018) On analysis of e-banking websites quality—comet application. Procedia Computer Science 126:2137–2152. https://doi.org/10.1016/j.procS.2018.07.238
Chmielarz W, Zborowski M (2020) Science direct sciencedirect the selection and comparison of the methods used to evaluate the the selection and comparison of the methods used of to individual evaluate the quality of e-banking websites: the perspective quality of e-banking websites. Proc Comput Sci 176:1903–1922. https://doi.org/10.1016/j.procs.2020.09.230
Chong AYL, Chan FTS, Ooi KB (2012) Predicting consumer decisions to adopt mobile commerce: cross country empirical examination between China and Malaysia. Decis Support Syst 53(1):34–43. https://doi.org/10.1016/j.dss.2011.12.001
Chou WC, Cheng YP (2012) A hybrid fuzzy MCDM approach for evaluating website quality of professional accounting firms. Expert Syst Appl 39(3):2783–2793. https://doi.org/10.1016/j.eswa.2011.08.138
Chou CH, Liang GS, Chang HC (2013) A fuzzy AHP approach based on the concept of possibility extent. Qual Quant 47(1):1–14. https://doi.org/10.1007/s11135-011-9473-6
Daǧdeviren M, Yavuz S, Kilinç N (2009) Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Syst Appl 36(4):8143–8151. https://doi.org/10.1016/j.eswa.2008.10.016
Ecer F (2018a) An integrated fuzzy AHP and ARAS model to evaluate mobile banking services. Technol Econ Dev Econ 24(2):670–695. https://doi.org/10.3846/20294913.2016.1255275
Ecer F (2018b) An integrated Fuzzy AHP and ARAS model to evaluate mobile banking services. Technol Econ Dev Econ 24(2):670–695. https://doi.org/10.3846/20294913.2016.1255275
Flint DJ, Blocker CP, Boutin PJ (2011) Customer value anticipation, customer satisfaction and loyalty: an empirical examination. Ind Mark Manage 40(2):219–230. https://doi.org/10.1016/j.indmarman.2010.06.034
Gbongli K, Peng Y, Ackah O (2016) Selection and ranking of perceived risk associated with mobile banking in West Africa: an AHP approach from customers’ perspective. Int J Sci Eng Res 7(1):80–86
Gbongli K, Xu Y, Amedjonekou KM, Kovács L (2020) Evaluation and classification of mobile financial services sustainability using structural equation modeling and multiple criteria decision-making methods. Sustainability (switzerland) 12(4):1–34. https://doi.org/10.3390/su12041288
Gefen D (2000) E-commerce: The role of familiarity and trust. Omega 28(6):725–737. https://doi.org/10.1016/S0305-0483(00)00021-9
Ghaleb AM, Kaid H, Alsamhan A, Mian SH, Hidri L (2020) Assessment and Comparison of Various MCDM Approaches in the Selection of Manufacturing Process. Adv Materials Sci Eng 2020. https://doi.org/10.1155/2020/4039253
Gu JC, Lee SC, Suh YH (2009) Determinants of behavioral intention to mobile banking. Expert Syst Appl 36(9):11605–11616. https://doi.org/10.1016/j.eswa.2009.03.024
Guo S, Zhao H (2017) Fuzzy best-worst multi-criteria decision-making method and its applications. Knowl-Based Syst 121:23–31. https://doi.org/10.1016/j.knosys.2017.01.010
Hanafizadeh P, Behboudi M, Abedini Koshksaray A, Jalilvand Shirkhani Tabar M (2014b) Mobile-banking adoption by Iranian bank clients. Telematics Inform 31(1): 62–78. Doi: https://doi.org/10.1016/j.tele.2012.11.001
Hanafizadeh P, Behboudi M, Abedini Koshksaray A, Jalilvand Shirkhani Tabar M (2014a) Mobile-banking adoption by Iranian bank clients Telematics Inform 31(1):62–78. Doi: https://doi.org/10.1016/j.tele.2012.11.001
Hensmans M, Van Den Bosch FAJ, Volberda HW (2001) Clicks vs. Bricks in the emerging online financial services industry. Long Range Planning 34(2): 231–247. https://doi.org/10.1016/S0024-6301(01)00030-9
Ho JC, Wu CG, Lee CS, Pham TTT (2020) Factors affecting the behavioral intention to adopt mobile banking: an international comparison. Technol Soc 63(December 2019): 101360. https://doi.org/10.1016/j.techsoc.2020.101360
Huang G-HTJ-J (2011) Multiple Attribute Decision Making Methods and applications. In J.-J. H. Gwo-Hshiung Tzeng (Ed.), CRC Press Taylor & Francis Group (Issue 1). https://www.routledge.com/Multiple-Attribute-Decision-Making-Methods-and-Applications/Tzeng-Huang/p/book/9781439861578
Hussain A, Abubakar HI, Hashim NB (2015) Evaluating mobile banking application: Usability dimensions and measurements. In: Conference proceedings—6th international conference on information technology and multimedia at UNITEN: cultivating creativity and enabling technology through the internet of things, ICIMU 2014, 1, 136–140. https://doi.org/10.1109/ICIMU.2014.7066618
Ignatius J, Hatami-Marbini A, Rahman A, Dhamotharan L, Khoshnevis P (2018) A fuzzy decision support system for credit scoring. Neural Comput Appl 29(10):921–937. https://doi.org/10.1007/s00521-016-2592-1
Ishizaka, A., & Nemery, P. (2013). Multi-Criteria Decision Analysis (A. Ishizaka & P. Nemery (eds.); 1st ed., Issue 1). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118644898
Oroojeni Mohammad Javad M, Darvishi M, Oroojeni Mohammad Javad A (2020). Green supplier selection for the steel industry using BWM and fuzzy TOPSIS: a case study of Khouzestan steel company. Sustainable Futures, 2(February), 100012. https://doi.org/10.1016/j.sftr.2020.100012
Jiao Y, Syau YR, Lee ES (2007) Modelling credit rating by fuzzy adaptive network. Math Comput Model 45(5–6):717–731. https://doi.org/10.1016/j.mcm.2005.11.016
Jumaan IA, Hashim NH, Al-Ghazali BM (2020) The role of cognitive absorption in predicting mobile internet users’ continuance intention: an extension of the expectation-confirmation model. Technol Soc 63(August):101355. https://doi.org/10.1016/j.techsoc.2020.101355
Kaya T, Kahraman C (2011) A fuzzy approach to e-banking website quality assessment based on an integrated AHP-ELECTRE method. Technol Econ Dev Econ 17(2):313–334. https://doi.org/10.3846/20294913.2011.583727
Komlan G, Koffi D, Kingsford KM (2016) MCDM technique to evaluating mobile banking adoption in the togolese banking industry based on the perceived value : perceived benefit and perceived sacrifice factors. Int J Data Min Knowl Manag Process 6(3):37–56. https://doi.org/10.5121/ijdkp.2016.6304
Kou G, Peng Y, Wang G (2014) Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Inf Sci 275:1–12. https://doi.org/10.1016/j.ins.2014.02.137
Kujala S, Mugge R, Miron-Shatz T (2017) The role of expectations in service evaluation: a longitudinal study of a proximity mobile payment service. Int J Hum Comput Stud 98(July 2016): 51–61. https://doi.org/10.1016/j.ijhcs.2016.09.011
Kulak O, Kahraman C (2005) Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process. Inf Sci 170(2–4):191–210. https://doi.org/10.1016/j.ins.2004.02.021
Laukkanen T (2007) Internet vs mobile banking: comparing customer value perceptions. Bus Process Manag J 13(6):788–797. https://doi.org/10.1108/14637150710834550
Laukkanen T (2016) Consumer adoption versus rejection decisions in seemingly similar service innovations: the case of the Internet and mobile banking. J Bus Res 69(7):2432–2439. https://doi.org/10.1016/j.jbusres.2016.01.013
Liang D, Zhang Y, Xu Z, Jamaldeen A (2019) Pythagorean fuzzy VIKOR approaches based on TODIM for evaluating internet banking website quality of Ghanaian banking industry. Appl Soft Comput J 78:583–594. https://doi.org/10.1016/j.asoc.2019.03.006
Liébana-Cabanillas F, Molinillo S, Ruiz-Montañez M (2019) To use or not to use, that is the question: analysis of the determining factors for using NFC mobile payment systems in public transportation. Technol Forecasting Social Change 139(August 2018): 266–276. https://doi.org/10.1016/j.techfore.2018.11.012
Liébana-Cabanillas F, Japutra A, Molinillo S, Singh N, Sinha N (2020) Assessment of mobile technology use in the emerging market: analyzing intention to use m-payment services in India. Telecommun Policy 44(9). https://doi.org/10.1016/j.telpol.2020.102009
Lootsma FA (1980) Saaty’s priority theory and the nomination of a senior professor in operations research. Euro J Oper Res 4(6): 380–388. https://doi.org/10.1016/0377-2217(80)90189-7
Mahdiraji HA, Arzaghi S, Stauskis G, Zavadskas EK (2018) A hybrid fuzzy BWM-COPRAS method for analyzing key factors of sustainable architecture. Sustainability (switzerland) 10(5):1–26. https://doi.org/10.3390/su10051626
Malaquias RF, Hwang Y (2019) Mobile banking use: a comparative study with Brazilian and U.S. participants. Int J Inform Manag 44(May 2018): 132–140. https://doi.org/10.1016/j.ijinfomgt.2018.10.004
Merhi M, Hone K, Tarhini A (2019) A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with file:///E:/IIT ISM/Mbanking/Consumer Acceptance and Use of Information venkatesh2012.pdfsecurity, privacy and trust. Technol Soc 59(January): 101151. https://doi.org/10.1016/j.techsoc.2019.101151
Mi X, Tang M, Liao H, Shen W, Lev B (2019) The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega (united Kingdom) 87:205–225. https://doi.org/10.1016/j.omega.2019.01.009
Mishra V, Singh V (2015) Selection of appropriate electronic banking channel alternative: critical analysis using analytical hierarchy process. Int J Bank Marketing 33(3):223–242. https://doi.org/10.1108/IJBM-09-2013-0099
Mishra V, Singh Bisht S (2013) Mobile banking in a developing economy: a customer-centric model for policy formulation. Telecommun Policy 37(6–7):503–514. https://doi.org/10.1016/j.telpol.2012.10.004
Mou Q, Xu Z, Liao H (2016) An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Inf Sci 374:224–239. https://doi.org/10.1016/j.ins.2016.08.074
Nysveen H, Pedersen PE, Thorbjørnsen H (2005) Intentions to use mobile services: antecedents and cross-service comparisons. J Acad Mark Sci 33(3):330–346. https://doi.org/10.1177/0092070305276149
Oliveira T, Faria M, Thomas MA, Popovič A (2014) Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. Int J Inf Manage 34(5):689–703. https://doi.org/10.1016/j.ijinfomgt.2014.06.004
Pal D, Vanijja V, Papasratorn B (2015) An empirical analysis towards the adoption of NFC mobile payment system by the end user. Proc Comput Sci 69:13–25. https://doi.org/10.1016/j.procs.2015.10.002
Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57. https://doi.org/10.1016/j.omega.2014.11.009
Rezaei J, Wang J, Tavasszy L (2015) Linking supplier development to supplier segmentation using best worst method. Expert Syst Appl 42(23):9152–9164. https://doi.org/10.1016/j.eswa.2015.07.073
Roy, P. K., & Shaw, K. (2021b). An integrated fuzzy model for evaluation and selection of mobile banking (m-banking) applications using new fuzzy-BWM and fuzzy-TOPSIS. Complex Intell Syst 0123456789. https://doi.org/10.1007/s40747-021-00502-x
Roy PK, Shaw K (2021a) A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS. Financial Innovation 7(77):1–27. https://doi.org/10.1002/ijfe.2425
Roy PK, Shaw K (2021c) Modelling a sustainable credit score system (SCSS) using BWM and fuzzy TOPSIS. Int J Sust Dev World 00(00):1–14. https://doi.org/10.1080/13504509.2021.1935360
Saka D, Putra K, Sadikin MA (2017) S-Mbank : secure mobile banking authentication scheme using signcryption, pair based text authentication, and contactless smart card. In: 15th international conference on quality in research (QiR) : international symposium on electrical and computer engineering, 230–234. https://doi.org/10.1109/QIR.2017.8168487
Salimi N, Rezaei J (2018) Evaluating firms’ R&D performance using best worst method. Evaluation and Program Planning, 66(May 2017): 147–155. https://doi.org/10.1016/j.evalprogplan.2017.10.002
Salisbury WD, Pearson RA, Pearson AW, Miller DW (2001) Perceived security and world wide web purchase intention. Ind Manag Data Syst 101(4):165–176. https://doi.org/10.1108/02635570110390071
Saracevic, M. H., Adamovic, S. Z., Miškovic, V. A., Elhoseny, M., Maˇcek, N. D., Selim, M. M., & Shankar, K. (2020). Data Encryption for Internet of Things Applications Based on Catalan Objects and Two Combinatorial Structures. IEEE Transactions on Reliability, 1–12. https://doi.org/10.1109/TR.2020.3010973
Shaikh AA, Karjaluoto H (2015) Mobile banking adoption: a literature review. Telematics Inform 32(1):129–142. https://doi.org/10.1016/j.tele.2014.05.003
Shankar A, Rishi B (2020) Convenience matter in mobile banking adoption intention? Australas Mark J 8(4):273–285. https://doi.org/10.1016/j.ausmj.2020.06.008
Sharma SK, Govindaluri SM, Muharrami SM, Tarhini A (2017) A multi-analytical model for mobile banking adoption: a developing country perspective. Rev Int Business Strategy 27(1):133–148. https://doi.org/10.1108/RIBS-11-2016-0074
Sharma SK, Sharma M (2019) Examining the role of trust and quality dimensions in the actual usage of mobile banking services: an empirical investigation. Int J Inform Manag 44(September 2018): 65–75. https://doi.org/10.1016/j.ijinfomgt.2018.09.013
Singh S, Srivastava R (2018) Predicting the Intention to Use Mobile Banking in India Introduction. Int J Bank Marketing 36(2): 357–378. https://doi.org/10.1108/IJBM-12-2016-0186
Sinigaglia F, Carbone R, Costa G, Zannone N (2020) A survey on multi-factor authentication for online banking in the wild. Comput Security, 95. https://doi.org/10.1016/j.cose.2020.101745
Sofuoğlu MA (2020) Fuzzy applications of Best-Worst method in manufacturing environment. Soft Comput 24(1):647–659. https://doi.org/10.1007/s00500-019-04491-5
Sujeet Kumar S, Saeed A-M (2018) Mobile Banking Adoption: Key Challenges and Opportunities and Implications for a Developing Country. In Y. Dwivedi (Ed.), Emerging Markets from a Multidisciplinary Perspective. Advances in Theory and Practice of Emerging Markets. (1st ed., pp 75–86). Springer. https://doi.org/10.1007/978-3-319-75013-2_7
Syau Y-R, Hsieh HT, Stanley Lee E (2001) Fuzzy numbers in the credit rating of enterprise financial condition. Rev Quant Financ Acc 17(4):351–360. https://doi.org/10.1023/A:1012783613875
Szopiński TS (2016) Factors affecting the adoption of online banking in Poland. J Bus Res 69(11):4763–4768. https://doi.org/10.1016/j.jbusres.2016.04.027
Tam C, Oliveira T (2016) Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Comput Hum Behav 61:233–244. https://doi.org/10.1016/j.chb.2016.03.016
Tam C, Oliveira T (2017) Literature review of mobile banking and individual performance. Int J Bank Marketing 35(7):1042–1065. https://doi.org/10.1108/IJBM-09-2015-0143
Thakur R (2014) What keeps mobile banking customers loyal? Int J Bank Marketing 32(7):628–646. https://doi.org/10.1108/IJBM-07-2013-0062
Tian Zp, Wang Jq, Zhang Hy (2018) An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Appl Soft Comput J 72: 636–646. Doi: https://doi.org/10.1016/j.asoc.2018.03.037
Van Laarhoven PJM, Pedrycz W (1983) A fuzzy extension of Saaty. Fuzzy Sets Syst 11(1–3):229–241
Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540
Viswanath Venkatesh JYLT, XX (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q (March 2012): 36(1): 157–178. https://doi.org/10.1109/MWSYM.2015.7167037
Westin AF (1968) Privacy and Freedom. Washington Lee Law Rev 25(1):166–170. https://doi.org/10.2307/3479272
Wu J, Wang S (2005) What drives mobile commerce ? An empirical evaluation of the revised technology acceptance model. 42: 719–729. Doi: https://doi.org/10.1016/j.im.2004.07.001
Zarifopoulos M, Economides AA (2009) Evaluating mobile banking portals. Int J Mobile Commun 7(1):66–90. https://doi.org/10.1504/IJMC.2009.021673
Acknowledgements
The author is immensely thankful to Mrs. Kamalika Halder (Roy), Aishi Roy and Ariya Roy for their continuous supports and encouragements.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
There are no competing/conflicts of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Roy, P., Shaw, K. A fuzzy MCDM decision-making model for m-banking evaluations: comparing several m-banking applications. J Ambient Intell Human Comput 14, 11873–11895 (2023). https://doi.org/10.1007/s12652-022-03743-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-022-03743-x