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An integrated Fuzzy AHP and ARAS model to evaluate mobile banking services

    Fatih ECER Affiliation

Abstract

Mobile banking (M-banking) which integrates software, hardware, and human is a new platform for banks. Determining the performance of M-banking services helps bank practitioners identify better policy to improve their positions. The aim of this study is to develop an integrated model for evaluating M-banking services by two methods, namely the Fuzzy Analytic Hierarchy Process (FAHP) with an extent analysis approach and ARAS (Additive Ratio ASsessment). In this study, the priority weights obtained through the FAHP are combined with the ARAS method to as­sess and rank the M-banking services. Moreover, in order to verify the applicability of this proposed model, a case study in Turkey is offered. The findings indicate that facilitating conditions play the most determining role in the adoption of the M-banking, followed by self-efficacy, privacy risk, and security risk. Consequently, the proposed model helps to overcome difficulties in M-banking service evaluation process and increases the efficiency of the M-banking service activities. Besides, the case study validates that the proposed model is an effective and efficient decision making tool for the evaluation of M-banking services under fuzzy environments.

Keyword : M-banking services, M-banking adoption, Fuzzy AHP, ARAS

How to Cite
ECER, F. (2017). An integrated Fuzzy AHP and ARAS model to evaluate mobile banking services. Technological and Economic Development of Economy, 24(2), 670–695. https://doi.org/10.3846/20294913.2016.1255275
  Submitted
Jan 19, 2018
Published
Apr 23, 2017
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References

Aghdaie, M. H.; Hashemkhani Zolfani, S.; Zavadskas, E. K. 2013. Market segment evaluation and selection based on application of fuzzy AHP and COPRAS-G methods, Journal of Business Economics and Management 14(1): 213–233. https://doi.org/10.3846/16111699.2012.721392

Akturan, U.; Tezcan, N. 2012. Mobile banking adoption of the youth market: perceptions and intentions, Marketing Intelligence and Planning 30(4): 444–459. https://doi.org/10.1108/02634501211231928

Aliakbari Nouri, F.; Khalili Esbouei, S.; Antucheviciene, J. 2015. A hybrid MCDM approach based on fuzzy ANP and fuzzy TOPSIS for technology selection, Informatica 26(3): 369–388. https://doi.org/10.15388/Informatica.2015.53

Bakshi, T.; Sarkar, B. 2011. MCA based performance evaluation of project selection, International Journal of Software Engineering and Applications 2(2): 14–22. https://doi.org/10.5121/ijsea.2011.2202

Baležentis, A.; Štreimikienė, D. 2013. Integrated sustainability index: the case study of Lithuania, Intellectual Economics 7(3): 289–303. https://doi.org/10.13165/IE-13-7-3-02

Baležentis, A.; Baležentis, T.; Misiunas, A. 2012. An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods, Technological and Economic Development of Economy 18(1): 34–53. https://doi.org/10.3846/20294913.2012.656151

BAT (Banks Association of Turkey). 2015. Statistical reports [online], [cited 2 October 2015]. Available from Internet: https://www.tbb.org.tr/en/banks-and-banking-sector-information/statistical-reports/20

Bidar, R.; Fard, M. B.; Salman, Y. B.; Tunga, M. A.; Cheng, H. I. 2014. Factors affecting the adoption of mobile banking: sample of Turkey, in 16th International Conference on Advanced Communication Technology (ICACT), 16–19 February 2014, Phoenix Park, PyeongChang, Korea (South), 1278–1282. https://doi.org/10.1109/ICACT.2014.6779165

Bilsel, R. U.; Büyükozkan, G.; Ruan, D. 2006. A fuzzy preference-ranking model for a quality evaluation of hospital web sites, International Journal of Intelligent Systems 21: 1181–1197. https://doi.org/10.1002/int.20177

Bulut, E.; Duru, O.; Keçeci, T.; Yoshida, S. 2012. Use of consistency index, expert prioritization and direct numerical inputs for generic fuzzy-AHP modeling: a process model for shipping asset management, Expert Systems with Applications 39(2): 1911–1923. https://doi.org/10.1016/j.eswa.2011.08.056

Calabrese, A.; Costa, R.; Menichini, T. 2013. Using fuzzy AHP to manage intellectual capital assets: an application to the ICT service industry, Expert Systems with Applications 40(9): 3747–3755. https://doi.org/10.1016/j.eswa.2012.12.081

Calabrese, A.; Costa, R.; Levialdi, N.; Menichini, T. 2016. A fuzzy Analytic Hierarchy Process method to support materiality assessment in sustainability reporting, Journal of Cleaner Production 121: 248–264. https://doi.org/10.1016/j.jclepro.2015.12.005

Canzano, D.; Grimaldi, M. 2012. An integrated framework to implement a knowledge management programme: the role of technological tools and techniques, International Journal of Intelligent Enterprise 1(3/4): 233–247. https://doi.org/10.1504/IJIE.2012.052554

Chan, F. T. S.; Kumar, N. 2007. Global supplier development considering risk factors using fuzzy extended AHP based approach, Omega 35: 417–431. https://doi.org/10.1016/j.omega.2005.08.004

Chang, D. Y. 1992. Extent analysis and synthetic decision, Optimization Techniques and Applica¬tions 1(1): 352–355.

Chang, D. Y. 1996. Applications of the extent analysis method on fuzzy AHP, European Journal of Operational Research 95: 649–655. https://doi.org/10.1016/0377-2217(95)00300-2

Chatterjee, P.; Chakraborty, S. 2013. Gear material selection using complex proportional assessment and additive ratio assessment-based approaches: a comparative study, International Journal of Materials Science and Engineering 1(2): 104–111. https://doi.org/10.12720/ijmse.1.2.104-111

Chen, C. 2013. Perceived risk, usage frequency of mobile banking services, Managing Service Quality 23(5): 410–436. https://doi.org/10.1108/MSQ-10-2012-0137

Chen, L. Y.; Wang, T. C. 2009. Optimizing partners’ choice in IS/IT outsourcing projects: the strategic decision of fuzzy VIKOR, International Journal of Production Economics 120(1): 233–242. https://doi.org/10.1016/j.ijpe.2008.07.022

Chou, W. C.; Cheng, Y. P. 2012. A hybrid fuzzy MCDM approach for evaluating website quality of professional accounting firms, Expert Systems with Applications 39(3): 2783–2793. https://doi.org/10.1016/j.eswa.2011.08.138

Crabbe, M.; Standing, C.; Standing, S.; Karjaluoto, H. 2009. An adoption model for mobile banking in Ghana, International Journal of Mobile Communications 7(5): 515–543. https://doi.org/10.1504/IJMC.2009.024391

Cruz, P.; Neto, L. N. F.; Munoz-Gallego, P.; Laukkanen, T. 2010. Mobile banking rollout in emerging markets: evidence from Brazil, International Journal of Bank Marketing 28(5): 342–371. https://doi.org/10.1108/02652321011064881

Dadelo, S.; Turskis, Z.; Zavadskas, E. K.; Dadeliene, R. 2012. Multiple criteria assessment of elite security personal on the basis of ARAS and expert methods, Economic Computation and Economic Cybernetics Studies and Research 46(4): 65–88.

Das, M. C.; Sarkar, B.; Ray, S. 2012. A framework to measure relative performance of Indian technical institutions using integrated fuzzy AHP and COPRAS methodology, Socio-Economic Planning Sciences 46(3): 230–241. https://doi.org/10.1016/j.seps.2011.12.001

Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly 13(3): 319–340. https://doi.org/10.2307/249008

DeLone, W. H.; McLean, E. R. 2003. The DeLone and McLean model of information systems success: a ten-year update, Journal of Management Information Systems 19(4): 9–30.

Ecer, F. 2014. A hybrid banking websites quality evaluation model using AHP and COPRAS-G: a Turkey case, Technological and Economic Development of Economy 20(4): 758–782. https://doi.org/10.3846/20294913.2014.915596

Ecer, F. 2015. Performance evaluation of internet banking branches via a hybrid MCDM model under fuzzy environment, Economic Computation and Economic Cybernetics Studies and Research 49(2): 211–230.

Ecer, F. 2016. ARAS Yöntemi Kullanılarak Kurumsal Kaynak Planlaması Yazılımı Seçimi, Uluslararası Alanya İşletme Fakültesi Dergisi 8(1): 89–98.

Fishbein, M.; Ajzen, I. 1975. Attitude, intention and behavior: An introduction to theory and research. Washington: Addison-Wesley, 578 p.

Gu, J. C.; Lee, S. C.; Suh, Y. H. 2009. Determinants of behavioral intention to mobile banking, Expert Systems with Applications 36(9): 11605–11616. https://doi.org/10.1016/j.eswa.2009.03.024

Hanafizadeh, P.; Behboudi, M.; Abedini Koshksaray, A.; Jalilvand Shirkhani Tabar, M. 2014. Mobile-banking adoption by Iranian bank clients, Telematics and Informatics 31(1): 62–78. https://doi.org/10.1016/j.tele.2012.11.001

Hoehle, H.; Scornavacca, E.; Huff, S. 2012. Three decades of research on consumer adoption and utilization of electronic banking channels: a literature analysis, Decision Support Systems 54(1): 122–132. https://doi.org/10.1016/j.dss.2012.04.010

Kahraman, C.; Cebeci, U.; Ruan, D. 2004. Multi-attribute comparison of catering service companies using fuzzy AHP: the case of Turkey, International Journal of Production Economics 87: 171–184. https://doi.org/10.1016/S0925-5273(03)00099-9

Kim, G.; Shin, B.; Lee, H. G. 2009. Understanding dynamics between initial trust and usage intentions of mobile banking, Information Systems Journal 19(3): 283–311. https://doi.org/10.1111/j.1365-2575.2007.00269.x

Koenig-Lewis, N.; Palmer, A.; Moll, A. 2010. Predicting young consumers’ take up of mobile banking services, International Journal of Bank Marketing 28 (5): 410–432. https://doi.org/10.1108/02652321011064917

Korsgaard, M. A.; Schweiger, D. M.; Sapienza, H. J. 1995. Building commitment, attachment, and trust in strategic decision-making teams: the role of procedural justice, Academy of Management Journal 38(1): 60–84. https://doi.org/10.2307/256728

Kutut, V.; Zavadskas, E. K.; Lazauskas, M. 2013. Assessment of priority options for preservation of historic city centre buildings using MCDM (ARAS), Procedia Engineering 57: 657–661. https://doi.org/10.1016/j.proeng.2013.04.083

Laarhoven, P. J. M.; Pedrycz, W. 1983. A fuzzy extension of Saaty’s priority theory, Fuzzy Sets and Systems 11: 229–241. https://doi.org/10.1016/S0165-0114(83)80082-7

Laukkanen, T.; Cruz, P. 2008. Barriers to mobile banking adoption: a cross-national study, Proceedings of the International Conference on E-Business – Volume 1: ICE-B, 26–29 July 2008, Porto, Portugal, 300–306.

Laukkanen, T. 2007. Internet vs mobile banking comparing customer value perceptions, Business Process Management Journal 13(6): 788–797. https://doi.org/10.1108/14637150710834550

Lee, K. C.; N. Chung. 2009. Understanding factors affecting trust in and satisfaction with mobile banking in Korea: a modified Delone and Mclean’s Model perspective, Interacting with Computers 21: 385–392. https://doi.org/10.1016/j.intcom.2009.06.004

Lee, K. S.; Lee, H. S.; Kim, S. Y. 2007. Factors influencing the adoption behavior of mobile banking: a South Korean perspective, Journal of Internet Banking and Commerce 12(2): 1–9.

Leung, L. C.; Cao, D. 2000. On consistency and ranking of alternatives in fuzzy AHP, European Journal of Operational Research 124(1): 102–113. https://doi.org/10.1016/S0377-2217(99)00118-6

Lin, H. F. 2011. An empirical investigation of mobile banking adoption: the effect of innovation attributes and knowledge-based trust, International Journal of Information Management 31(3): 252–260. https://doi.org/10.1016/j.ijinfomgt.2010.07.006

Lu, J.; Yu, C. S.; Liu, C.; Yao, J. E. 2003. Technology acceptance model for wireless internet, Internet Research 13(3): 206–222. https://doi.org/10.1108/10662240310478222

Luarn, P.; Lin, H. 2005. Toward an understanding of the behavioral intention to use mobile banking, Computers in Human Behavior 21: 873–891. https://doi.org/10.1016/j.chb.2004.03.003

Medineckiene, M.; Zavadskas, E. K.; Björk, F.; Turskis, Z. 2015. Multi-criteria decision-making system for sustainable building assessment/certification, Archives of Civil and Mechanical Engineering 15(1): 11–18. https://doi.org/10.1016/j.acme.2014.09.001

Mikhailov, L. 2003. Deriving priorities from fuzzy pairwise comparison judgments, Fuzzy Sets and Systems 134: 365–385. https://doi.org/10.1016/S0165-0114(02)00383-4

Negash, S. 2011. Mobile banking adoption by under-banked communities in the United States: adapting mobile banking features from low-income countries, in 11th International Conference on Mobile Business (ICMB), 20–21 June 2011, Como, Italy, 205–209.

Peevers, G.; Douglas, G.; Jack, M. A. 2008. A usability comparison of three alternative message formats for an SMS banking service, International Journal of Human-Computer Studies 66(2): 113–123. https://doi.org/10.1016/j.ijhcs.2007.09.005

Püschel, J.; Mazzon, J. A.; Hernandez, J. M. C. 2010. Mobile banking: proposition of an integrated adoption intention framework, International Journal of Bank Marketing 28(5): 389–409. https://doi.org/10.1108/02652321011064908

Reza, S.; Majid, A. 2013. Ranking financial institutions based on of trust in online banking using ARAS and ANP method, International Research Journal of Applied and Basic Sciences 6(4): 415–423.

Riivari, J. 2005. Mobile banking: a powerful new marketing and CRM tool for financial services companies all over Europe?, Journal of Financial Services Marketing 10(1): 11–20. https://doi.org/10.1057/palgrave.fsm.4770170

Riquelme, H. E.; Rios, R. E. 2010. The moderating effect of gender in the adoption of mobile banking, International Journal of Bank Marketing 28(5): 328–341. https://doi.org/10.1108/02652321011064872

Saaty, T. L. 1980. The analytic hierarchy process. New York: McGraw Hill, 287 p

Singh, S.; Srivastava, V.; Srivastava, R. K. 2010. Customer acceptance of mobile banking: a conceptual framework, SIES Journal of Management 7(1): 55–64.

Sliogeriene, J.; Turskis, Z.; Streimikiene, D. 2013. Analysis and choice of energy generation technologies: the multiple criteria assessment on the case study of Lithuania, Energy Procedia 32: 11–20. https://doi.org/10.1016/j.egypro.2013.05.003

Sušinskas, S.; Zavadskas, E. K.; Turskis, Z. 2011. Multiple criteria assessment of pile-columns alternatives, The Baltic Journal of Road and Bridge Engineering 6(3): 77–83. https://doi.org/10.3846/bjrbe.2011.19

Tavana, M.; Momeni, E.; Rezaeiniya, N.; Mirhedayatian, S. M.; Rezaeiniya, H. 2013. A novel hybrid social media platform selection model using fuzzy ANP and COPRAS-G, Expert Systems with Applications 40(14): 5694–5702. https://doi.org/10.1016/j.eswa.2013.05.015

Tupenaite, L.; Zavadskas, E. K.; Kaklauskas, A.; Turskis, Z.; Seniut, M. 2010. Multiple criteria assessment of alternatives for built and human environment renovation, Journal of Civil Engineering and Management 16(2): 257–266. https://doi.org/10.3846/jcem.2010.30

Venkatesh, V. 2000. Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model, Information System Research 11(4): 342–365. https://doi.org/10.1287/isre.11.4.342.11872

Wang, Y. M.; Luo, Y.; Hua, Z. 2008. On the extent analysis method for fuzzy AHP and its applications, European Journal of Operational Research 186(2): 735–747. https://doi.org/10.1016/j.ejor.2007.01.050

Wang, Y. M.; Chin, K. S. 2011. Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology, International Journal of Approximate Reasoning 52: 541–553. https://doi.org/10.1016/j.ijar.2010.12.004

Wessels, L.; Drennan, J. 2010. An investigation of consumer acceptance of M-banking, International Journal of Bank Marketing 28(7): 547–568. https://doi.org/10.1108/02652321011085194

Zadeh, L. A. 1965. Fuzzy set, Information Control 18(2): 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

Zamani, M.; Rabbani, A.; Yazdani-Chamzini, A.; Turskis, Z. 2014. An integrated model for extending brand based on fuzzy ARAS and ANP methods, Journal of Business Economics and Management 15(3): 403–423. https://doi.org/10.3846/16111699.2014.923929

Zavadskas E. K.; Turskis Z. 2010. A new additive ratio assessment (ARAS) method in multicriteria decision‐making, Technological and Economic Development of Economy 16(2): 159–172. https://doi.org/10.3846/tede.2010.10

Zavadskas, E. K.; Turskis, Z.; Vilutiene, T. 2010. Multiple criteria analysis of foundation instalment alternatives by applying Additive Ratio Assessment (ARAS) method, Archives of Civil and Mechanical Engineering 10(3): 123–141. https://doi.org/10.1016/S1644-9665(12)60141-1

Zavadskas, E. K.; Vainiūnas, P.; Turskis, Z.; Tamošaitienė, J. 2012. Multiple criteria decision support system for assessment of projects managers in construction, International Journal of Information Technology and Decision Making 11(2): 501–520. https://doi.org/10.1142/S0219622012400135

Zavadskas, E. K.; Vilutiene, T.; Turskis, Z.; Saparauskas, J. 2014. Multi-criteria analysis of projects’ performance in construction, Archives of Civil and Mechanical Engineering 14(1): 114–121. https://doi.org/10.1016/j.acme.2013.07.006

Zhou, T. 2012. Examining mobile banking user adoption from the perspectives of trust and flow experience, Information Technology and Management 13(1): 27–37. https://doi.org/10.1007/s10799-011-0111-8

Zhou, T.; Lu, Y.; Wang, B. 2010. Integrating TTF and UTAUT to explain mobile banking user adoption, Computers in Human Behavior 26(4): 760–767. https://doi.org/10.1016/j.chb.2010.01.013

Zhu, K. J.; Jing, Y.; Chang, D. Y. 1999. A discussion on extent analysis method and applications of fuzzy AHP, European Journal of Operational Research 116(2): 450–456. https://doi.org/10.1016/S0377-2217(98)00331-2

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