Soft Computing

, Volume 23, Issue 23, pp 12295–12304 | Cite as

A fuzzy scenario-based approach to analyzing neuromarketing technology evaluation factors

  • Nazli Goker
  • Mehtap DursunEmail author
Methodologies and Application


Neuromarketing, which uses neuroimaging techniques for marketing goals, is the application of neuroscientific methods to analyze and understand consumer behavior according to marketing initiatives. Medical diagnostic devices for brain imaging are utilized by marketers as neuromarketing technologies. This paper proposes a fuzzy scenario-based approach to analyzing neuromarketing technology evaluation criteria. Fuzzy cognitive map methodology, which is originated from the combination of fuzzy logic and neural networks, is employed due to the cause-and-effect relationships between pair of factors, presence of conflicting criteria, positive as well as negative relationships among factors, and lack of crisp data. Importance degrees of each evaluation factor are computed, and then, four different scenario analyses are provided to understand the influence of an increase or a decrease in the power of specific factor(s) on other factors. Throughout the literature, this is the first study that considers multiple and conflicting criteria framework of neuromarketing technology evaluation problem.


Neuromarketing Technology selection Fuzzy cognitive map Scenario analyses Cause-and-effect relations 


Compliance with ethical standards

Conflict of interest

Nazli Goker and Mehtap Dursun declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Abu Aarqob O, Shawagfeh NT, AbuGhneim OA (2008) Functions defined on fuzzy real numbers according to Zadeh’s extension. Int Math Forum 3(16):763–776MathSciNetzbMATHGoogle Scholar
  2. Abu Arqub O (2017) Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm-Volterra integrodifferential equations. Neural Comput Appl 28:1591–1610CrossRefGoogle Scholar
  3. Abu Arqub O, Al-Smadi M, Momani S, Hayat T (2016) Numerical solutions of fuzzy differential equations using reproducing kernel Hilbert space method. Soft Comput 20:3283–3302CrossRefGoogle Scholar
  4. Abu Arqub O, Al-Smadi M, Momani S, Hayat T (2017) Application of reproducing kernel algorithm for solving second-order, two-point fuzzy boundary value problems. Soft Comput 21:7191–7206CrossRefGoogle Scholar
  5. Ahmadi S, Yeh CH, Martin R, Papageorgiou E (2015a) Optimizing ERP readiness improvements under budgetary constraints. Int J Prod Econ 161:105–115CrossRefGoogle Scholar
  6. Ahmadi S, Yeh CH, Papageorgiou EI, Martin R (2015b) An FCM-FAHP approach for managing readiness-relevant activities for ERP implementation. Comput Ind Eng 88:501–517CrossRefGoogle Scholar
  7. Alipour M, Hafezi R, Amer M, Akhavan AN (2017) A new hybrid fuzzy cognitive map-based scenario planning approach for Iran’s oil production pathways in the post-sanction period. Energy 135:851–864CrossRefGoogle Scholar
  8. Ariely D, Berns GS (2010) Neuromarketing: the hope and hype of neuroimaging in business. Nat Rev Neurosci 11:284–292CrossRefGoogle Scholar
  9. Azadeh A, Zarrin M, Abdollahi M, Noury S, Farahmand S (2015) Leanness assessment and optimization by fuzzy cognitive map and multivariate analysis. Expert Syst Appl 42:6050–6064CrossRefGoogle Scholar
  10. Bağdatlı MEC, Akbıyıklı R, Papageorgiou EI (2017) A fuzzy cognitive map approach applied in cost–benefit analysis for highway projects. Int J Fuzzy Syst 19(5):1512–1527CrossRefGoogle Scholar
  11. Bastiaansen M, Straatman S, Driessen E, Mitas O, Stekelenburg J, Wang L (2018) My destination in your brain: a novel neuromarketing approach for evaluating the effectiveness of destination marketing. J Destin Mark Manag 7:76–88Google Scholar
  12. Baykasoğlu A, Gölcük İ (2015) Development of a novel multiple-attribute decision making model via fuzzy cognitive maps and hierarchical fuzzy TOPSIS. Inf Sci 301:75–98CrossRefGoogle Scholar
  13. Bevilacqua M, Ciarapica FE, Mazzuto G (2018) Fuzzy cognitive maps for adverse drug event risk management. Safety Sci 102:194–210CrossRefGoogle Scholar
  14. Burgos-Campero AA, Vargas-Hernandez JG (2013) Analytical approach to neuromarketing as a business strategy. Proc Soc Behav Sci 99:517–525CrossRefGoogle Scholar
  15. Büyükavcu A, Albayrak YE, Göker N (2016) A fuzzy information-based approach for breast cancer risk factors assessment. Appl Soft Comput 38:437–452CrossRefGoogle Scholar
  16. Chen Y, Mazlack LJ, Minai AA, Lu LJ (2015a) Inferring causal networks using fuzzy cognitive maps and evolutionary algorithms with application to gene regulatory network reconstruction. Appl Soft Comput 37:667–679CrossRefGoogle Scholar
  17. Chen TC, Lee AC, Huang SH (2015b) FCM based hybrid evolutionary computation approach for optimization power consumption by varying cars in EGCS. Appl Math Model 39:5917–5924CrossRefGoogle Scholar
  18. Dias SB, Hadjileontiadou SJ, Hadjileontiadou LJ, Diniz JA (2015) Fuzzy cognitive mapping of LMS users’ quality of interaction within higher education blended-learning environment. Expert Syst Appl 42:7399–7423CrossRefGoogle Scholar
  19. Ducange P, Pecori R, Mezzina P (2018) A glimpse on big data analytics in the framework of marketing strategies. Soft Comput 22:325–342CrossRefGoogle Scholar
  20. Fisher CE, Chin L, Klitzman R (2010) Defining neuromarketing: practices and professional challenges. Harvard Rev Psychiatry 18:230–237CrossRefGoogle Scholar
  21. Froelich W (2017) Towards improving the efficiency of the fuzzy cognitive map classifier. Neurocomputing 232:83–93CrossRefGoogle Scholar
  22. Hao S, Yu B (2011) The impact of technology selection on innovation success and organizational performance. iBusiness 3:366–371CrossRefGoogle Scholar
  23. Hsieh YH, Chen IH, Yuan ST (2014) FCM-based customer expectation-driven service dispatch system. Soft Comput 18:359–378CrossRefGoogle Scholar
  24. Irani Z, Sharif A, Kamal MM, Love PED (2014) Visualising a knowledge mapping of information systems investment evaluation. Expert Syst Appl 41:105–125CrossRefGoogle Scholar
  25. Jayashree LS, Palakkal N, Papageorgiou EI, Papageorgioui K (2015) Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region. Neural Comput Appl 26:1963–1978CrossRefGoogle Scholar
  26. Kardaras DK, Karakostas B, Mamakou XJ (2013) Content presentation personalization and media adaptation in tourism web sites using Fuzzy Delphi method and Fuzzy cognitive maps. Expert Syst Appl 40:2331–2342CrossRefGoogle Scholar
  27. Kayikci Y, Stix V (2014) Causal mechanism in transport collaboration. Expert Syst Appl 41:1561–1575CrossRefGoogle Scholar
  28. Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24:65–75CrossRefGoogle Scholar
  29. Kyriakarakos G, Patlitzianas K, Damasiotis M, Papastefanakis D (2014) A fuzzy cognitive maps decision support system for renewables local planning. Renew Sustain Energy Rev 39:209–222CrossRefGoogle Scholar
  30. Lee DH, Lee H (2015) Construction of holistic Fuzzy Cognitive Maps using ontology matching method. Expert Syst Appl 42:5954–5962CrossRefGoogle Scholar
  31. Lee N, Broderick AJ, Chamberlain L (2007) What is ‘neuromarketing’? a discussion and agenda for future research. Int J Psychophysiol 63:199–204CrossRefGoogle Scholar
  32. Leon M, Mkrtchyan L, Depaire B, Ruan D, Vanhoof K (2014) Learning and clustering of fuzzy cognitive maps for travel behavior analysis. Knowl Inf Syst 39:435–462CrossRefGoogle Scholar
  33. Natarajan R, Subramanian J, Papageorgiou EI (2016) Hybrid learning of fuzzy cognitive maps for sugarcane yield classification. Comput Electron Agric 127:147–157CrossRefGoogle Scholar
  34. Papageorgiou EI, Huszka C, Roo De, Douali N, Jaulent MC, Colaert D (2013a) Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision. Comput Methods Programs Biomed 112(3):580–598CrossRefGoogle Scholar
  35. Papageorgiou EI, Aggelopoulou KD, Gemtos TA, Nanos GD (2013b) Yield prediction in apples using Fuzzy Cognitive Map learning approach. Comput Electron Agric 91:19–29CrossRefGoogle Scholar
  36. Ross TJ (2010) Fuzzy logic with engineering applications, 3rd edn. Wiley, HobokenCrossRefGoogle Scholar
  37. Ruanguttamanun C (2014) Neuromarketing: i put myself into a fMRI scanner and realized that I love Louis Vuitton ads. Proc Soc Behav Sci 148:211–218CrossRefGoogle Scholar
  38. Vidal R, Salmeron JL, Mena A, Chulvi V (2015) Fuzzy cognitive map-based selection of TRIZ (theory of inventive problem solving) trends for eco-innovation of ceramic industry. J Clean Prod 107:202–214CrossRefGoogle Scholar
  39. Zhao ZY, Zhu J, Zuo J (2014) Sustainable development of the wind power industry in a complex environment: a flexibility study. Energy Policy 75:392–397CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Industrial Engineering DepartmentGalatasaray UniversityIstanbulTurkey

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