Share:


A holistic conception of sustainable banking: adding value with fuzzy cognitive mapping

    Daniela Carlucci Affiliation
    ; Fernando A. F. Ferreira Affiliation
    ; Giovanni Schiuma Affiliation
    ; Marjan S. Jalali Affiliation
    ; Nelson J. S. António Affiliation

Abstract

Integrating sustainability into the banking activity is an increasingly necessary but extremely challenging issue currently facing financial institutions. It is therefore becoming ever more important to understand the key determinants of sustainable banking and how they inter-relate with each other. This research aims to build a cognitive map – a fuzzy cognitive map (FCM) in particular – to model, dynamically analyze and test the reciprocal influence of key factors underlying sustainable banking. FCMs have been shown to be particularly useful for handling complex decision problems characterized by lack of information or unavailable data. They constitute a methodological framework that allows for a reduction of omitted determinants – in this case, with regard to sustainable banking – and are typically able to provide a greater understanding of the cause-and-effect relationships between such determinants. We anticipate implications and practical applications for both bank managers and policymakers aiming to increase the efficiency of their decision making in the context of sustainable banking.

Keyword : sustainable banking, holistic view, problem structuring methods, fuzzy cognitive maps, knowledge management, expert systems

How to Cite
Carlucci, D., Ferreira, F. A. F., Schiuma, G., Jalali, M. S., & António, N. J. S. (2018). A holistic conception of sustainable banking: adding value with fuzzy cognitive mapping. Technological and Economic Development of Economy, 24(4), 1303-1322. https://doi.org/10.3846/20294913.2016.1266412
Published in Issue
Jun 29, 2018
Abstract Views
73
PDF Downloads
66
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Ackermann, F.; Eden, C. 2001. SODA – Journey making and mapping in practice, in J. Rosenhead, J. Mingers (Eds.). Rational analysis for a problematic world revisited: problem structuring methods for complexity, uncertainty and conflict. 2nd ed. Chichester: John Wiley & Sons, 43–60.

Bouma, J.; Klinkers, L.; Jeucken, M. 2001. Sustainable banking: the greening of finance. UK: Greenleaf Publishing.

Canas, S.; Ferreira, F.; Meidutė-Kavaliauskienė, I. 2015. Setting rents in residential real estate: a methodological proposal using multiple criteria decision analysis, International Journal of Strategic Property Management 19(4): 368–380. https://doi.org/10.3846/1648715X.2015.1093562

Carlucci, D.; Ferreira, F.; Schiuma, G.; Jalali, M.; António, N. 2014. A knowledge-based representation of sustainable banking: insights from fuzzy cognitive mapping, in 9th International Forum on Knowledge Asset Dynamics (IFKAD-2014), 12–14 June 2014, Matera, Italy, 1837–1855.

Carlucci, D.; Schiuma, G.; Gavrilova, T.; Linzalone, R. 2013. A fuzzy cognitive map based approach to disclose value creation dynamics of ABIs, in 8th International Forum on Knowledge Asset Dynamics (IFKAD-2013), 12–14 June 2013, Zagreb, Croatia, 207–219.

Carmeli, A. 2004. Assessing core intangible resources, European Management Journal 22(1): 110–122. https://doi.org/10.1016/j.emj.2003.11.010

Carvalho, J. 2013. On the semantics and the use of fuzzy cognitive maps and dynamic cognitive maps in social sciences, Fuzzy Sets and Systems 214: 6–19. https://doi.org/10.1016/j.fss.2011.12.009

Cole, R. 2011. Motivating stakeholders to deliver environmental change, Building Research & Information 39(5): 431–435. https://doi.org/10.1080/09613218.2011.599057

Dias, S.; Hadjileontiadou, S.; Hadjileontiadis, L.; Diniz, J. 2015. Fuzzy cognitive mapping of LMS users’ quality of interaction within higher education blended-learning environment, Expert Systems with Applications 42(21): 7399–7423. https://doi.org/10.1016/j.eswa.2015.05.048

Eden, C.; Ackermann, F. 2001. SODA – The principles, in J. Rosenhead, J. Mingers (Eds.). Rational analysis for a problematic world revisited: problem structuring methods for complexity, uncertainty and conflict. 2nd ed. Chichester: John Wiley & Sons, 21–41.

Fatemi, A.; Fooladi, I. 2013. Sustainable finance: a new paradigm, Global Finance Journal 24(2): 101–113. https://doi.org/10.1016/j.gfj.2013.07.006

Ferreira, F.; Jalali, M. 2015. Identifying key determinants of housing sales and time-on-the-market (TOM) using fuzzy cognitive mapping, International Journal of Strategic Property Management 19(3): 235–244. https://doi.org/10.3846/1648715X.2015.1052587

Ferreira, F.; Jalali, M.; Ferreira, J. 2016. Integrating qualitative comparative analysis (QCA) and fuzzy cognitive maps (FCM) to enhance the selection of independent variables, Journal of Business Research 69(4): 1471–1478. https://doi.org/10.1016/j.jbusres.2015.10.127

Ferreira, F.; Jalali, M.; Ferreira, J.; Stankevičienė, J.; Marques, C. 2015. Understanding the dynamics behind bank branch service quality in Portugal: pursuing a holistic view using fuzzy cognitive mapping, Service Business 10(3): 469–487. https://doi.org/10.1007/s11628-015-0278-x

Ferreira, F.; Santos, S.; Rodrigues, P. 2011. Adding value to bank branch performance evaluation using cognitive maps and MCDA: a case study, Journal of the Operational Research Society 62(7): 1320–1333. https://doi.org/10.1057/jors.2010.111

Ferreira, F.; Santos, S.; Rodrigues, P.; Spahr, R. 2014. Evaluating retail banking service quality and convenience with MCDA techniques: a case study at the bank branch level, Journal of Business Economics and Management 15(1): 1–21. https://doi.org/10.3846/16111699.2012.673504

Ferreira, F.; Spahr, R.; Santos, S.; Rodrigues, P. 2012. A multiple criteria framework to evaluate bank branch potential attractiveness, International Journal of Strategic Property Management 16(3): 254–276. https://doi.org/10.3846/1648715X.2012.707629

Filipe, M.; Ferreira, F.; Santos, S. 2015. A multiple criteria information system for pedagogical evaluation and professional development of teachers, Journal of the Operational Research Society 66(11): 1769–1782.

Garland, R.; Gendall, P. 2004. Testing Dick and Basu’s customer loyalty model, Australasian Marketing Journal 12(3): 81–87. https://doi.org/10.1016/S1441-3582(04)70108-1

Gavrilova, T.; Carlucci, D.; Schiuma, G. 2013. Art of visual thinking for smart business education, in 8th International Forum on Knowledge Asset Dynamics (IFKAD-2013), 12–14 June 2013, Zagreb, Croatia, 1754–1761.

Glykas, M. 2013. Fuzzy cognitive strategic maps in business process performance measurement, Expert Systems with Applications 40(1): 1–14. https://doi.org/10.1016/j.eswa.2012.01.078

Jalali, M.; Ferreira, F.; Ferreira, J.; Meidutė-Kavaliauskienė, I. 2016. Integrating metacognitive and psychometric decision making approaches for bank customer loyalty measurement, International Journal of Information Technology and Decision Making 15(4): 815–837. https://doi.org/10.1142/S0219622015500236

Jeucken, M.; Bouma, J. 1999. The changing environment of banks, Greener Management International 27: 21–35.

Kang, B.; Deng, Y.; Sadiq, R.; Mahadevan, S. 2012. Evidential cognitive maps, Knowledge-Based Systems 35(15): 77–86. https://doi.org/10.1016/j.knosys.2012.04.007

Kardaras, D.; Mentzas, G. 1997. Using fuzzy cognitive maps to model and analyse business performance assessment, in 2nd Annual International Conference on Industrial Engineering Applications and Practice, 12–15 November 1997, San Diego, California, USA, 63–68.

Kauko, T. 2010. Value stability in local real estate markets, International Journal of Strategic Property Management 14(3): 191–199. https://doi.org/10.3846/ijspm.2010.14

Keršulienė, V.; Turskis, Z. 2011. Integrated fuzzy multiple criteria decision making model for architect selection, Technological and Economic Development of Economy 17(4): 645–666. https://doi.org/10.3846/20294913.2011.635718

Kim, H.; Lee, K. 1998. Fuzzy implications of fuzzy cognitive map with emphasis on fuzzy causal relationship and fuzzy partially causal relationship, Fuzzy Sets and Systems 97(3): 303–313. https://doi.org/10.1016/S0165-0114(96)00349-1

Kok, K. 2009. The potential of fuzzy cognitive maps for semi-quantitative scenario development, with an example from Brazil, Global Environmental Change 19(1): 122–133. https://doi.org/10.1016/j.gloenvcha.2008.08.003

Kosko, B. 1986. Fuzzy cognitive maps, International Journal of Man-Machine Studies 24(1): 65–75. https://doi.org/10.1016/S0020-7373(86)80040-2

Kosko, B. 1992. Neural networks and fuzzy systems. New Jersey: Prentice-Hall.

Lopez, C.; Salmeron, J. 2013. Dynamic risks modelling in ERP maintenance projects with FCM, Information Sciences 256: 25–45. https://doi.org/10.1016/j.ins.2012.05.026

Mardani, A.; Jusoh, A.; Zavadskas, E. 2015. Fuzzy multiple criteria decision-making techniques and applications: two decades review from 1994 to 2014, Expert Systems with Applications 42(8): 4126–4148. https://doi.org/10.1016/j.eswa.2015.01.003

Martins, V.; Filipe, M.; Ferreira, F.; Jalali, M.; António, N. 2015. For sale… but for how long? a methodological proposal for estimating time-on-the-market, International Journal of Strategic Property Management 19(4): 309–324. https://doi.org/10.3846/1648715X.2015.1072746

Mazlack, L. 2009. Representing causality using fuzzy cognitive maps, in Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS-2009), 14–17 June 2009, Cincinnati, Ohio, USA, 1–6. https://doi.org/10.1109/nafips.2009.5156434

Pan, Y.; Sheng, S.; Xie, F. 2012. Antecedents of customer loyalty: an empirical synthesis and reexamination, Journal of Retailing and Consumer Services 19(9): 150–158. https://doi.org/10.1016/j.jretconser.2011.11.004

Papageorgiou, E.; Roo, J.; Huszka, C.; Colaert, D. 2012. Formalization of treatment guidelines using fuzzy cognitive maps and semantic web tools, Journal of Biomedical Informatics 45(1): 45–60. https://doi.org/10.1016/j.jbi.2011.08.018

Papageorgiou, E.; Salmeron, J. 2013. A review of fuzzy cognitive maps research during the last decade, IEEE Transactions on Fuzzy Systems 21(1): 66–79. https://doi.org/10.1109/TFUZZ.2012.2201727

Peña, A.; Sossa, H.; Gutiérrez, A. 2008. Causal knowledge and reasoning by cognitive maps: pursuing a holistic approach, Expert Systems with Applications 35(1/2): 2–8.

Peng, Z.; Peng, J.; Zhao, W.; Chen, Z. 2015. Research on FCM and NHL based high order mining driven by big data, Mathematical Problems in Engineering 2015: 1–7. https://doi.org/10.1155/2015/802505

Peng, Z.; Wu, I.; Chen, Z. 2016. Research on steady states of fuzzy cognitive map and its application in three-rivers ecosystem, Sustainability 8: 1–10. https://doi.org/10.3390/su8010040

Ramos, J.; Ferreira, F.; Monteiro Barata, J. 2011. Banking services in Portugal: a preliminary analysis to the perception and expectations of front office employees, International Journal of Management and Enterprise Development 10(2/3): 188–207. https://doi.org/10.1504/IJMED.2011.041549

Rebai, S.; Azaiez, B.; Saidane, D. 2012. Sustainable performance evaluation of banks using a multiattribute utility model: an application to French banks, Procedia Economics and Finance 2: 363–372. https://doi.org/10.1016/S2212-5671(12)00098-6

Reis, J.; Ferreira, F.; Monteiro Barata, J. 2013. Technological innovation in banking services: an exploratory analysis to perceptions of the front office employee, Problems and Perspectives in Management 11(1): 34–49.

Salmeron, J. 2009. Augmented fuzzy cognitive maps for modelling LMS critical success factors, Knowledge-Based Systems 22(4): 275–278. https://doi.org/10.1016/j.knosys.2009.01.002

Salmeron, J. 2012. Fuzzy cognitive maps for artificial emotions forecasting, Applied Soft Computing 12(12): 3704–3710. https://doi.org/10.1016/j.asoc.2012.01.015

Salmeron, J.; Gutierrez, E. 2012. Fuzzy grey cognitive maps in reliability engineering, Applied Soft Computing 12(12): 3818–3824. https://doi.org/10.1016/j.asoc.2012.02.003

Salmeron, J.; Lopez, C. 2012. Forecasting risk impact on ERP maintenance with augmented fuzzy cognitive maps, IEEE Transactions on Software Engineering 38(2): 439–452. https://doi.org/10.1109/TSE.2011.8

Salmeron, J.; Vidal, R.; Mena, A. 2012. Ranking fuzzy cognitive maps based scenarios with TOPSIS, Expert Systems with Applications 39(3): 2443–2450. https://doi.org/10.1016/j.eswa.2011.08.094

Stach, W.; Kurgan, L.; Pedrycz, W.; Reformat, M. 2005. Genetic learning of fuzzy cognitive maps, Fuzzy Sets and Systems 153(3): 371–401. https://doi.org/10.1016/j.fss.2005.01.009

Stankevičienė, J.; Nikonorova. 2014. Sustainable value creation in commercial banks during financial crisis, Procedia – Social and Behavioral Sciences 110(1): 1197–1208. https://doi.org/10.1016/j.sbspro.2013.12.966

Stephens, C.; Skinner, C. 2013. Banks for a better planet? The challenge of sustainable social and environmental development and the emerging response of the banking sector, Environmental Development 5: 175–179. https://doi.org/10.1016/j.envdev.2012.11.011

Stylios, C.; Groumpos, P. 1999. Fuzzy cognitive maps: a model for intelligent supervisory control systems, Computers in Industry 39(3): 229–238. https://doi.org/10.1016/S0166-3615(98)00139-0

Tsadiras, A.; Kouskouvelis, I.; Margaritis, K. 2003. Using fuzzy cognitive maps as a decision support system for political decisions, in 8th Panhellenic Conference on Informatics (PCI-2001), 8–10 November 2003, Nicosia, Cyprus, 172–182. https://doi.org/10.1007/3-540-38076-0_12

Vidal, R.; Salmeron, J.; Mena, A.; Chulvi, V. 2015. Fuzzy cognitive map-based selection of TRIZ trends for eco-innovation of ceramic industry products, Journal of Cleaner Production 107: 202–214. https://doi.org/10.1016/j.jclepro.2015.04.131

Wu, H. 2012. Constructing a strategy map for banking institutions with key performance indicators of the balanced scorecard, Evaluation and Program Planning 35(3): 303–320. https://doi.org/10.1016/j.evalprogplan.2011.11.009

Xu, B.; Ouenniche, J. 2012. Performance evaluation of competing forecasting models: a multidimensional framework based on MCDA, Expert Systems with Applications 39(9): 8312–8324. https://doi.org/10.1016/j.eswa.2012.01.167

Yaman, D.; Polat, S. 2009. A fuzzy cognitive map approach for effect-based operations: an illustrative case, Information Sciences 179(4): 382–403. https://doi.org/10.1016/j.ins.2008.10.013

Yesil, E.; Ozturk, C.; Dodurka, M.; Sakalli, A. 2013. Fuzzy cognitive maps learning using artificial bee colony optimization, in IEEE International Conference on Fuzzy Systems (Fuzz-IEEE), 07–10 July 2013, Hyderabad, India, 1–8.

Zavadskas, E.; Turskis, Z. 2011. Multiple criteria decision making (MCDM) methods in economics: an overview, Technological and Economic Development of Economy 17(2): 397–427. https://doi.org/10.3846/20294913.2011.593291

Zavadskas, E.; Turskis, Z.; Kildienė, S. 2014. State of art surveys of overviews on MCDM/MADM methods, Technological and Economic Development of the Economy 20(1): 165–179. https://doi.org/10.3846/20294913.2014.892037