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Using Fuzzy Set Theory in the Comparison of Customer Satisfaction Levels

Year 2019, Volume: 23 Issue: Special [en], 163 - 168, 01.03.2019

Nur Ayvaz Çavdaroğlu

https://doi.org/10.19113/sdufenbed.511799
Cited By: 1

Abstract

Customer
satisfaction is the key to the survival and profitability of all businesses. In
the ever-changing business world, this concept has gained more importance. In
particular, in industries where new firms based on new business models emerge,
the traditional firms find it more difficult to compete with the new business
models in terms of customer happiness. Sharing economy, which is defined as
“renting non-frequently used resources (e.g. houses, cars, various commodities)
in return of a certain price via digital platforms”, is one of these new
concepts that have inspired new business models. A sharing economy-based
company, Uber, has gained popularity in a short time. In this study, customer
satisfaction levels of Uber and classical taxi firms are compared and the
performance of each firm in various service dimensions is measured. Data is
collected via questionnaires and analyzed using the fuzzy set theory models.
According to the results, Uber performs much higher in all service dimensions
with respect to the classical taxi firms. By evaluating the results from a
managerial perspective, recommendations are developed for Uber and similar
sharing economy-based firms, and the classical firms who want to compete with
these new business models.

Keywords

Sharing economy Customer satisfaction Fuzzy set theory New business model

References

  • [1] Frenken, K., Meelen, T., Arets, M., Van der Glind, P. 2015. Smarter Regularion for the Sharing Economy. http://www.theguradian.com/science/political-science/2015/may/20/smaeter-regulation-for-the-sharing-economy. (Access Date: 07.06.2018).
  • [2] Blystone, D. 2018. The Story of Uber. https://www.investopedia.com/articles/personal-finance/111015/story-uber.asp. (Access Date: 07.06.2018)
  • [3] Zadeh, L. A. 1965. Fuzzy Sets, Information and Control. 8, 338-353.
  • [4] Bellman, R. E., Zadeh, L. A. 1970. Decision-Making in a Fuzzy Environment. Management Science, 17(4), 141-164.
  • [5] Buyukozkan, G., Feyzioglu, O., Nebol, E., 2008. Selection of the strategic alliance + partner in logistics value chain. International Journal of Production Economics, 113, 148–158.
  • [6] Thomassey, S., Happiette, M., Castelain, J.M., 2005. A short and mean-term automatic forecasting system—application textile logistics. European Journal of Operational Research, 161, 275–284.
  • [7] Hwang, H.-S., Yu, J.-C., 1998. R&D project evaluation model based on fuzzy set priority. Computers and Industrial Engineering, 35, 567–570.
  • [8] Bottani, E., Rizzi, A., 2006. Strategic management of logistics service: a fuzzy QFD approach. International Journal of Production Economics 103, 585–599.
  • [9] Celik, E., Bilisik, O. N., Erdogan, M., Gumus, A. T., Baracli, H. 2013. An Integrated Novel Interval Type-2 Fuzzy MCDM Method to Improve Customer Satisfaction in Public Transportation for Istanbul. Transportation Research Part E: Logistics and Transportation Review. 58, 28-51.
  • [10] Tsaur, S. H., Chang, T. Y., Yen, C. H. 2002. The Evaluation of Airline Service Quality by Fuzzy MCDM. Tourism management, 23(2), 107-115.
  • [11] Chou, C. C., Liu, L. J., Huang, S. F., Yih, J. M., Han, T. C. 2011. An Evaluation of Airline Service Quality Using the Fuzzy Weighted SERVQUAL Method. Applied Soft Computing, 11(2), 2117-2128.
  • [12] Aydin, O., Pakdil, F. 2008. Fuzzy SERVQUAL Analysis in Airline Services. Organizacija, 41(3), 108-115.
  • [13] Buckley, J. J. 1985. Ranking Alternatives Using Fuzzy Numbers. Fuzzy Sets and Systems, 15(1), 21-31.
  • [14] Dholakia, U. 2015. Everyone Hates Uber’s Surge Pricing – Here’s How to Fix It. Harvard Business Review. https://hbr.org/2015/12/everyone-hates-ubers-surge-pricing-heres-how-to-fix-it. (Access Date: 07.06.2018)
  • [15] Investopedia, n.d. Network Effect. https://www.investopedia.com/terms/n/network-effect.asp. (Access Date: 07.06.2018)

Bulanık Küme Teorisinin Müşteri Memnuniyeti Karşılaştırmasında Kullanılması

Year 2019, Volume: 23 Issue: Special [en], 163 - 168, 01.03.2019

Nur Ayvaz Çavdaroğlu

https://doi.org/10.19113/sdufenbed.511799
Cited By: 1

Abstract

Müşteri
memnuniyeti tüm işletmelerin hayatta kalması ve kâr edebilmesi için elzemdir.
Sürekli değişen iş dünyasında bu konsept daha da önem kazanmaktadır. Özellikle
yeni iş modellerine dayanan firmaların ortaya çıktığı sektörlerde klasik
şirketler müşteri mutluluğu açısından yeni şirketlerle rekabet etmekte zorlanmaktadırlar. Az kullanılan kaynakların (ör: ev,
araba, çeşitli eşyalar vs.) dijital platformlar yardımıyla belli ücretlerle
kiralanması olarak tanımlanabilecek olan paylaşım ekonomisi, bu şekilde yeni iş
modellerie ilham veren konseptlerdendir. Paylaşım ekonomisi tabanlı firmalardan
biri olan Über, kısa zamanda popülerlik kazanmıştır. Bu çalışmada, Über ve
klasik taksi firmaları müşteri memnuniyeti açısından karşılaştırılarak iki tip
firmanın çeşitli hizmet boyutlarında nasıl performans gösterdiği ölçülmektedir.
Veri toplama anket yöntemiyle gerçekleştirilmiş olup veri analizinde ise
bulanık küme teorisi kullanılmıştır. Elde edilen sonuçlara göre Über bütün
hizmet boyutlarında klasik taksi firmalarına göre çok daha yüksek müşteri
memnuniyetine sahiptir. Sonuçlar değerlendirilerek Über ve benzeri paylaşım
ekonomisi tabanlı firmalara ve onlarla rekabet edebilmek isteyen klasik iş
modellerine sahip firmalara çeşitli yönetimsel önerilerde bulunulmuştur.

Keywords

Paylaşım ekonomisi Müşteri memnuniyeti Bulanık küme teorisi Yeni iş modeli

References

  • [1] Frenken, K., Meelen, T., Arets, M., Van der Glind, P. 2015. Smarter Regularion for the Sharing Economy. http://www.theguradian.com/science/political-science/2015/may/20/smaeter-regulation-for-the-sharing-economy. (Access Date: 07.06.2018).
  • [2] Blystone, D. 2018. The Story of Uber. https://www.investopedia.com/articles/personal-finance/111015/story-uber.asp. (Access Date: 07.06.2018)
  • [3] Zadeh, L. A. 1965. Fuzzy Sets, Information and Control. 8, 338-353.
  • [4] Bellman, R. E., Zadeh, L. A. 1970. Decision-Making in a Fuzzy Environment. Management Science, 17(4), 141-164.
  • [5] Buyukozkan, G., Feyzioglu, O., Nebol, E., 2008. Selection of the strategic alliance + partner in logistics value chain. International Journal of Production Economics, 113, 148–158.
  • [6] Thomassey, S., Happiette, M., Castelain, J.M., 2005. A short and mean-term automatic forecasting system—application textile logistics. European Journal of Operational Research, 161, 275–284.
  • [7] Hwang, H.-S., Yu, J.-C., 1998. R&D project evaluation model based on fuzzy set priority. Computers and Industrial Engineering, 35, 567–570.
  • [8] Bottani, E., Rizzi, A., 2006. Strategic management of logistics service: a fuzzy QFD approach. International Journal of Production Economics 103, 585–599.
  • [9] Celik, E., Bilisik, O. N., Erdogan, M., Gumus, A. T., Baracli, H. 2013. An Integrated Novel Interval Type-2 Fuzzy MCDM Method to Improve Customer Satisfaction in Public Transportation for Istanbul. Transportation Research Part E: Logistics and Transportation Review. 58, 28-51.
  • [10] Tsaur, S. H., Chang, T. Y., Yen, C. H. 2002. The Evaluation of Airline Service Quality by Fuzzy MCDM. Tourism management, 23(2), 107-115.
  • [11] Chou, C. C., Liu, L. J., Huang, S. F., Yih, J. M., Han, T. C. 2011. An Evaluation of Airline Service Quality Using the Fuzzy Weighted SERVQUAL Method. Applied Soft Computing, 11(2), 2117-2128.
  • [12] Aydin, O., Pakdil, F. 2008. Fuzzy SERVQUAL Analysis in Airline Services. Organizacija, 41(3), 108-115.
  • [13] Buckley, J. J. 1985. Ranking Alternatives Using Fuzzy Numbers. Fuzzy Sets and Systems, 15(1), 21-31.
  • [14] Dholakia, U. 2015. Everyone Hates Uber’s Surge Pricing – Here’s How to Fix It. Harvard Business Review. https://hbr.org/2015/12/everyone-hates-ubers-surge-pricing-heres-how-to-fix-it. (Access Date: 07.06.2018)
  • [15] Investopedia, n.d. Network Effect. https://www.investopedia.com/terms/n/network-effect.asp. (Access Date: 07.06.2018)
There are 15 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Nur Ayvaz Çavdaroğlu 0000-0003-1240-1357

Publication Date March 1, 2019
Published in Issue Year 2019 Volume: 23 Issue: Special [en]

Cite

APA Ayvaz Çavdaroğlu, N. (2019). Using Fuzzy Set Theory in the Comparison of Customer Satisfaction Levels. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23, 163-168. https://doi.org/10.19113/sdufenbed.511799
AMA Ayvaz Çavdaroğlu N. Using Fuzzy Set Theory in the Comparison of Customer Satisfaction Levels. J. Nat. Appl. Sci. March 2019;23:163-168. doi:10.19113/sdufenbed.511799
Chicago Ayvaz Çavdaroğlu, Nur. “Using Fuzzy Set Theory in the Comparison of Customer Satisfaction Levels”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23, March (March 2019): 163-68. https://doi.org/10.19113/sdufenbed.511799.
EndNote Ayvaz Çavdaroğlu N (March 1, 2019) Using Fuzzy Set Theory in the Comparison of Customer Satisfaction Levels. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23 163–168.
IEEE N. Ayvaz Çavdaroğlu, “Using Fuzzy Set Theory in the Comparison of Customer Satisfaction Levels”, J. Nat. Appl. Sci., vol. 23, pp. 163–168, 2019, doi: 10.19113/sdufenbed.511799.
ISNAD Ayvaz Çavdaroğlu, Nur. “Using Fuzzy Set Theory in the Comparison of Customer Satisfaction Levels”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23 (March 2019), 163-168. https://doi.org/10.19113/sdufenbed.511799.
JAMA Ayvaz Çavdaroğlu N. Using Fuzzy Set Theory in the Comparison of Customer Satisfaction Levels. J. Nat. Appl. Sci. 2019;23:163–168.
MLA Ayvaz Çavdaroğlu, Nur. “Using Fuzzy Set Theory in the Comparison of Customer Satisfaction Levels”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 23, 2019, pp. 163-8, doi:10.19113/sdufenbed.511799.
Vancouver Ayvaz Çavdaroğlu N. Using Fuzzy Set Theory in the Comparison of Customer Satisfaction Levels. J. Nat. Appl. Sci. 2019;23:163-8.

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