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An EFQM-Based Self-Assessment Method for Railway Transportation Service Quality: An Application with Intuitionistic Fuzzy AHP

Yıl 2022, Cilt: 22 Sayı: 4, 371 - 392, 03.10.2022
https://doi.org/10.21121/eab.1008669

Öz

Service quality is one of the most important issues in railway transportation because it is a concept that positively affects customer satisfaction, customer loyalty, corporate image, and intention to repurchase. The European Foundation of Quality Management (EFQM) Excellence Model provides an opportunity to facilitate the service quality-focused self-assessment efforts of the railway companies. This is the first study that integrates intuitionistic fuzzy theory in the application of the EFQM Model of railway industry in Turkey. As the main contribution, it is aimed to find a dedicatedly special weighting schema for the application of EFQM model in railway transportation. For this purpose, Analytic Hierarchy Process (AHP) is utilized with an integration of intuitionistic fuzzy sets that can reveal the decision-makers’ opinions, preferences, and expertise more comprehensively than traditional fuzzy sets can do. Consequently, it is found that the original model should be modified for the railway industry since the weights of all the criteria included in the model are found different than the original ones. The study provides new insights into the long-term benefits of applying the EFQM model as a framework in railway transportation and understanding the associations between the EFQM criteria and railway transportation.

Kaynakça

  • Abdullah, L., & Najib, L. (2016). Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia. International Journal of Sustainable Energy, 35, 360-377.
  • Akyuz, G.A. (2015). Quality excellence in complex supply networks: EFQM excellence model reconsidered. Total Quality Management and Business Excellence, 26 (12), 1282–1297.
  • Anastasiadou, S.D., & Zirinoglou, P.A. (2015). EFQM dimensions in Greek Primary Education System. Procedia Economics and Finance, 33, 411 – 431.
  • Ar, I.M., Erol, I., Peker, I., Özdemir, A., Medeni, T.D., & Medeni, I.T. (2020). Evaluating the feasibility of blockchain in logistics operations: A decision framework. Expert Systems with Applications, 158, 113543.
  • Atanassov, K.T. (1986). INTUITIONISTIC FUZZY SETS, Fuzzy Sets and Systems, 20, 87-96.
  • Babalık-Sutcliffe, E. (2007). Pro-rail policies in Turkey: A policy shift. Transport Reviews, 27(4), 485–498.
  • Banar, M., & Özdemir, A. (2015). An evaluation of railway passenger transport in Turkey assessment and life cycle cost methods. Transportation Research Part D: Transport and Environment, 41, 88–10.
  • Belvedere, V., Grando, A., & Legenvre, H. (2018). Testing the EFQM model as a framework to measure a company’s procurement performance. Total Quality Management and Business Excellence, 29 (6), 633–651.
  • Boran, F.E., Genç, S., Kurt, M., & Akay, D. (2009). A Multi-criteria Intuitionistic Fuzzy Group Decision Making for Supplier Selection with TOPSIS Method. Expert Systems with Application, 36 (8), 11363-11368.
  • Bou-Llusar, J.C., Escrig-Tena, A.B., Roca-Puig, V., & Beltran-Martin, I. (2005). To what extent do enablers explain results in the EFQM excellence model? An empirical study. International Journal of Quality and Reliability Management, 22 (4), 337-353.
  • Brons, M.R.E., & Rietveld, P. (2009). Improving the quality of the door-to-door rail journey: A customer-oriented approach. Built Environment, 35, 30–43.
  • Budak, A., Kaya, İ., Karaşan, A., & Erdoğan, M. (2020). Real-time location systems selection by using a fuzzy MCDM approach: An application in humanitarian relief logistics. Applied Soft Computing Journal, 92, 1-21.
  • Burillo, P., & Bustince, H. (1996). Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets. Fuzzy Sets and Systems, 78, 305-316.
  • Büyüközkan, G., Feyzioğlu, O., & Göçer, F. (2018). Selection of sustainable urban transportation alternatives using an integrated intuitionistic fuzzy Choquet integral approach. Transportation Research Part D: Transport and Environment, 58, 186-207.
  • Büyüközkan, G., & Göçer, F. (2018). An extension of ARAS methodology under Interval Valued Intuitionistic Fuzzy environment for Digital Supply Chain. Applied Soft Computing Journal, 69, 634–654.
  • Büyüközkan, G., Göçer, F., & Karabulut, Y. (2019) A new group decision making approach with IF-AHP and IF-VIKOR for selecting hazardous waste carriers. Measurement, 134, 66-82.
  • Büyüközkan, G., Havle, C., & Feyzioglu, O. (2020). A new digital service quality model and its strategic analysis in aviation industry using interval-valued intuitionistic fuzzy AHP. Journal of Air Transport Management, 86, 1-16.
  • Calvo-Mora, A., Dominguez, C.C.M., & Criado, F. (2018). Assessment and improvement of organizational social impact through the EFQM Excellence Model. Total Quality Management, 29 (11), 1259–1278.
  • Calvo-Mora, A., Leal, A., & Roldan, J. L. (2005). Relationships between the EFQM model criteria: A study in Spanish universities. Total Quality Management, 16(6), 741–770.
  • Conti, T. (2007). A history and review of the European Quality Award model. TQM Magazine, 19, 112-28.
  • Dahlgaard-Park, S.M., Bergman, B., & Hellgren, B. (2001). Reflection on TQM for the new millennium. In M. Sinha (Ed.), The best on quality, 12, 279–311. Milwaukee, WI: ASQ Quality Press.
  • Daud, S., & Yusoff, W.F.W. (2011). The influence of soft and hard TQM factors on knowledge management: perspective from Malaysia. International Conference on Management and Service Science, 8, 17–22. IACSIT Press, Singapore.
  • Deveci, M., Öner, S.C., Canıtez, F., & Öner, M. (2019). Evaluation of service quality in public bus transportation using interval valued intuitionistic fuzzy QFD methodology. Research in Transportation Business & Management, 33,1-14.
  • Drea, J.T., & Hanna, J.B. (2000). Niche marketing in intrastate passenger rail transportation. Transportation Journal, 39 (3), 33-43.
  • Dubey, M., & Lakhanpal, P. (2019). EFQM model for overall excellence of Indian thermal power generating sector. TQM Journal, 31(3), 319-339.
  • Ebolia, L., Fub, Y., & Mazzullaa, G. (2016). Multilevel comprehensive evaluation of the railway service quality. Procedia Engineering, 137, 21-30.
  • Ezzabadia, J.H., Saryazdib, M.D., & Mostafaeipour, A. (2015). Implementing Fuzzy Logic and AHP into the EFQM model for performance improvement: A case study. Applied Soft Computing, 36, 165-176.
  • Gomez, J.G., & Costa, M.M. (2011). A critical evaluation of the EFQM model. International Journal of Quality and Reliability Management, 28 (5), 484-502.
  • Gomez-Lopez, R., Serrano-Bedia, A.M., & Lopez-Fernandez, M.C. (2016). Motivations for implementing TQM through the EFQM model in Spain: An empirical investigation. Total Quality Management and Business Excellence, 27 (11), 1224–1245.
  • Govindan, K., & Jepsen, M.B. (2016). Supplier risk assessment based on trapezoidal intuitionistic fuzzy numbers and ELECTRE TRI-C: a case illustration involving service suppliers. Journal of the Operational Research Society, 67, 339-376.
  • Govindan, K., Khodaverdi, R., & Vafadarnikjoo, A. (2015). Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain. Expert Systems with Applications, 42, 7207-7220.
  • Gupta, S., & Datta, R. (2012). Prioritizing service attributes for quality up-gradation of Indian railway stations. TQM Journal, 24 (2), 167-180.
  • Gupta, P., Mehlawat, M.K., & Grover, N. (2016). Intuitionistic fuzzy multi-attribute group decision-making with an application to plant location selection based of a new extended VIKOR method. Information Sciences, 370-371, 184-203.
  • Hanna, J.B., & Drea, J.T. (1998). Understanding and predicting passenger rail travel: An empirical study. Transportation Journal, 38 (1), 38-46.
  • Kafetzopoulos, D., Gotzamani, K., & Skalkos, D. (2019). The relationship between EFQM enablers and business performance. Journal of Manufacturing Technology Management, 30 (4), 684-706.
  • Kang, G.D., & James, J. (2004). Service quality dimensions: an examination of Grönroos’s service quality model. Managing Service Quality, 14 (4), 266-277.
  • Koksalmis, E., & Kabak, Ö. (2019). Deriving decision makers’ weights in group decision making: An overview of objective methods. Information Fusion, 49, 146-160.
  • Liu, P., Yang, L., Gao, Z., Li, S., & Gao, Y. (2015). Fault tree analysis combined with quantitative analysis for high-speed railway accidents. Safety Science, 79, 344–357.
  • Liu, Y.L., & Ko, P.F. (2018). A modified EFQM Excellence Model for effective evaluation in the hotel industry. Total Quality Management and Business Excellence, 29 (13-14), 1580–1593.
  • Macmillan, H., & Tampoe, M. (2000). Strategic management. Great Britain: Oxford University Press.
  • Madrigal, A.I., & Lara, J.A.S. (2017). Applying the EFQM model to golf course management. Journal of Sport Tourism, 21 (3), 223–241.
  • Maskeliūnaite, L., Sivilevičius, H., & Podvezko, V. (2009). Research on the quality of passenger transportation by railway. Transport, 24 (2), 100–112.
  • Memari, A., Dargi, A., Jokar, M.R.A., Ahmad, R., & Rahim. A.R.A., (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, 50, 9-24.
  • Mesgari, I., Miab, A.K., & Sadeghi, M.J. (2017). Causal structure of the EFQM excellence model among healthcare sector: a case study in Iran. Total Quality Management and Business Excellence, 28 (6), 663–677.
  • Mirandaa, S., Tavaresa, P., & Queiró, R. (2018). Perceived service quality and customer satisfaction: A fuzzy set QCA approach in the railway sector. Journal of Business Research, 89, 371–377.
  • Moreno-Rodriguez, J.M., Cabrerizo, F.J., Pérez, I.J., & Martinez, M.A. (2013). A consensus support model based on linguistic information for the initial-self assessment of the EFQM in health care organizations. Expert Systems with Applications, 40, 2792–279.
  • Nedeliaková, E., Sekulová, J., Nedeliak, I., & Ľoch, M. (2014). Methodics of identification level of service quality in railway transport. Procedia - Social and Behavioral Sciences, 110, 320-329.
  • Niroomand, S., Garg, H., & Mahmoodirad, A. (2020). An intuitionistic fuzzy two stage supply chain network design problem with multi-mode demand and multi-mode transportation, ISA Transactions, 1-17.
  • Para-González, L., Jiménez-Jiménez, D., & Martínez-Lorente, A.R. (2018). The link between people and performance under the EFQM excellence model umbrella. Total Quality Management and Business Excellence. DOI: 10.1080/14783363.2018.1552516.
  • Paraschi, E.P., Georgopoulosa, A., & Kaldis, P. (2019). Airport Business Excellence Model: A holistic performance management system. Tourism Management, 72, 352-372.
  • Parasuraman, A., Zeithaml, V.A., & Berry, L.L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49 (1), 41-50.
  • Ruiz-Carrillo, J.I.C., & Fernández-Ortiz, R. (2005). Theoretical foundation of the EFQM model: the resource-based view. Total Quality Management, 16 (1), 31–55.
  • Rusjan, B. (2005). Usefulness of the EFQM Excellence Model: Theoretical Explanation of Some Conceptual and Methodological Issues. Total Quality Management, 16 (3), 363–380.
  • Saaty, T.L. (1980). The analytical hierarchy process: Planning priority setting. New York: McGraw Hill. Sadeh, E., & Arumugan, V. (2010). Interrelationships among EFQM excellence criteria in Iranian industrial SMEs. European Journal of Economics, Finance and Administrative Sciences, 19, 155–167.
  • Sadeh, E., Arumugam, V.C., & Malarvizhi, C.A. (2013). Integration of EFQM framework and quality information systems. Total Quality Management and Business Excellence, 24 (2), 188–209.
  • Safari, H., Abdollahi, B., & Ghasemi, R. (2012). Canonical correlation analysis between people criterion and people results criterion in EFQM model. Total Quality Management and Business Excellence, 23 (5), 541–555.
  • Sila, I. (2007). Examining the effects of contextual factors on TQM and performance through the lens of organizational theories: An empirical study. Journal of Operations Management, 25(1), 83–109.
  • Sivilevičius, H., & Maskeliūnaite, L. (2010). The criteria for identifying the quality of passengers’ transportation by railway and their ranking using AHP method. Transport, 25(4), 368–381.
  • Şahin, B., & Soylu, A. (2020). Intuitionistic fuzzy analytical network process models for maritime supply chain. Applied Soft Computing Journal, 96, 106614.
  • Tan, K.C. (2002). A comparative study of 16 national quality awards. TQM Magazine, 14, 165-71.
  • Tavana, M., Zareinejad, M., Capriod, D., & Kaviani, M.A. (2016). An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics. Applied Soft Computing, 40, 544-557.
  • Tavana, M., Zareinejad, M., & Santos-Arteaga, F. (2018). An intuitionistic fuzzy-grey superiority and inferiority ranking method for third-party reverse logistics provider selection. Journal of Systems Science: Operations & Logistics, 5(2), 174-194.
  • Trébucq, S., & Magnaghi, E. (2017). Using the EFQM excellence model for integrated reporting: A qualitative exploration and evaluation. Research in International Business and Finance, 42, 522–531.
  • Tutuncu, O., & Kucukusta, D. (2009). Canonical correlation between job satisfaction and EFQM business excellence model. Quality and Quantity, 44(6), 1227–1238.
  • Wan, S., Wang, F., & Dong, J. (2016). A novel group decision making method with intuitionistic fuzzy preference relations for RFID technology selection. Applied Soft Computing, 38, 405-422.
  • Weske, M. (2007). Business Process Management—Concepts, Languages, Architectures. Springer-Verlag, Berlin Heidelberg.
  • Wu, Y., Zhang, B., Xu, C., & Li, L. (2018). Site selection decision framework using fuzzy ANP-VIKOR for large commercial rooftop PV system based on sustainability perspective. Sustainable Cities and Society, 40, 454-470.
  • Xu, Z. (2007). Multi-person Multi-attribute Decision Making Models under Intuitionistic Fuzzy Environment. Fuzzy Optimization and Decision Making, 6(3), 221-236.
  • Yousefie, S., Mohammadi, M., & Monfared, J.H. (2011). Selection effective management tools on setting European Foundation for Quality Management (EFQM) model by a quality function deployment (QFD) approach. Expert Systems with Applications, 38, 9633- 9649.
  • Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
  • Zhang, S., Xu, S., Zhang, W., Yu, D., & Chen, K. (2018). A hybrid approach combining an extended BBO algorithm with an intuitionistic fuzzy entropy weight method for QoS-aware manufacturing service supply chain optimization. Neurocomputing, 272, 439–452.
Yıl 2022, Cilt: 22 Sayı: 4, 371 - 392, 03.10.2022
https://doi.org/10.21121/eab.1008669

Öz

Kaynakça

  • Abdullah, L., & Najib, L. (2016). Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia. International Journal of Sustainable Energy, 35, 360-377.
  • Akyuz, G.A. (2015). Quality excellence in complex supply networks: EFQM excellence model reconsidered. Total Quality Management and Business Excellence, 26 (12), 1282–1297.
  • Anastasiadou, S.D., & Zirinoglou, P.A. (2015). EFQM dimensions in Greek Primary Education System. Procedia Economics and Finance, 33, 411 – 431.
  • Ar, I.M., Erol, I., Peker, I., Özdemir, A., Medeni, T.D., & Medeni, I.T. (2020). Evaluating the feasibility of blockchain in logistics operations: A decision framework. Expert Systems with Applications, 158, 113543.
  • Atanassov, K.T. (1986). INTUITIONISTIC FUZZY SETS, Fuzzy Sets and Systems, 20, 87-96.
  • Babalık-Sutcliffe, E. (2007). Pro-rail policies in Turkey: A policy shift. Transport Reviews, 27(4), 485–498.
  • Banar, M., & Özdemir, A. (2015). An evaluation of railway passenger transport in Turkey assessment and life cycle cost methods. Transportation Research Part D: Transport and Environment, 41, 88–10.
  • Belvedere, V., Grando, A., & Legenvre, H. (2018). Testing the EFQM model as a framework to measure a company’s procurement performance. Total Quality Management and Business Excellence, 29 (6), 633–651.
  • Boran, F.E., Genç, S., Kurt, M., & Akay, D. (2009). A Multi-criteria Intuitionistic Fuzzy Group Decision Making for Supplier Selection with TOPSIS Method. Expert Systems with Application, 36 (8), 11363-11368.
  • Bou-Llusar, J.C., Escrig-Tena, A.B., Roca-Puig, V., & Beltran-Martin, I. (2005). To what extent do enablers explain results in the EFQM excellence model? An empirical study. International Journal of Quality and Reliability Management, 22 (4), 337-353.
  • Brons, M.R.E., & Rietveld, P. (2009). Improving the quality of the door-to-door rail journey: A customer-oriented approach. Built Environment, 35, 30–43.
  • Budak, A., Kaya, İ., Karaşan, A., & Erdoğan, M. (2020). Real-time location systems selection by using a fuzzy MCDM approach: An application in humanitarian relief logistics. Applied Soft Computing Journal, 92, 1-21.
  • Burillo, P., & Bustince, H. (1996). Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets. Fuzzy Sets and Systems, 78, 305-316.
  • Büyüközkan, G., Feyzioğlu, O., & Göçer, F. (2018). Selection of sustainable urban transportation alternatives using an integrated intuitionistic fuzzy Choquet integral approach. Transportation Research Part D: Transport and Environment, 58, 186-207.
  • Büyüközkan, G., & Göçer, F. (2018). An extension of ARAS methodology under Interval Valued Intuitionistic Fuzzy environment for Digital Supply Chain. Applied Soft Computing Journal, 69, 634–654.
  • Büyüközkan, G., Göçer, F., & Karabulut, Y. (2019) A new group decision making approach with IF-AHP and IF-VIKOR for selecting hazardous waste carriers. Measurement, 134, 66-82.
  • Büyüközkan, G., Havle, C., & Feyzioglu, O. (2020). A new digital service quality model and its strategic analysis in aviation industry using interval-valued intuitionistic fuzzy AHP. Journal of Air Transport Management, 86, 1-16.
  • Calvo-Mora, A., Dominguez, C.C.M., & Criado, F. (2018). Assessment and improvement of organizational social impact through the EFQM Excellence Model. Total Quality Management, 29 (11), 1259–1278.
  • Calvo-Mora, A., Leal, A., & Roldan, J. L. (2005). Relationships between the EFQM model criteria: A study in Spanish universities. Total Quality Management, 16(6), 741–770.
  • Conti, T. (2007). A history and review of the European Quality Award model. TQM Magazine, 19, 112-28.
  • Dahlgaard-Park, S.M., Bergman, B., & Hellgren, B. (2001). Reflection on TQM for the new millennium. In M. Sinha (Ed.), The best on quality, 12, 279–311. Milwaukee, WI: ASQ Quality Press.
  • Daud, S., & Yusoff, W.F.W. (2011). The influence of soft and hard TQM factors on knowledge management: perspective from Malaysia. International Conference on Management and Service Science, 8, 17–22. IACSIT Press, Singapore.
  • Deveci, M., Öner, S.C., Canıtez, F., & Öner, M. (2019). Evaluation of service quality in public bus transportation using interval valued intuitionistic fuzzy QFD methodology. Research in Transportation Business & Management, 33,1-14.
  • Drea, J.T., & Hanna, J.B. (2000). Niche marketing in intrastate passenger rail transportation. Transportation Journal, 39 (3), 33-43.
  • Dubey, M., & Lakhanpal, P. (2019). EFQM model for overall excellence of Indian thermal power generating sector. TQM Journal, 31(3), 319-339.
  • Ebolia, L., Fub, Y., & Mazzullaa, G. (2016). Multilevel comprehensive evaluation of the railway service quality. Procedia Engineering, 137, 21-30.
  • Ezzabadia, J.H., Saryazdib, M.D., & Mostafaeipour, A. (2015). Implementing Fuzzy Logic and AHP into the EFQM model for performance improvement: A case study. Applied Soft Computing, 36, 165-176.
  • Gomez, J.G., & Costa, M.M. (2011). A critical evaluation of the EFQM model. International Journal of Quality and Reliability Management, 28 (5), 484-502.
  • Gomez-Lopez, R., Serrano-Bedia, A.M., & Lopez-Fernandez, M.C. (2016). Motivations for implementing TQM through the EFQM model in Spain: An empirical investigation. Total Quality Management and Business Excellence, 27 (11), 1224–1245.
  • Govindan, K., & Jepsen, M.B. (2016). Supplier risk assessment based on trapezoidal intuitionistic fuzzy numbers and ELECTRE TRI-C: a case illustration involving service suppliers. Journal of the Operational Research Society, 67, 339-376.
  • Govindan, K., Khodaverdi, R., & Vafadarnikjoo, A. (2015). Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain. Expert Systems with Applications, 42, 7207-7220.
  • Gupta, S., & Datta, R. (2012). Prioritizing service attributes for quality up-gradation of Indian railway stations. TQM Journal, 24 (2), 167-180.
  • Gupta, P., Mehlawat, M.K., & Grover, N. (2016). Intuitionistic fuzzy multi-attribute group decision-making with an application to plant location selection based of a new extended VIKOR method. Information Sciences, 370-371, 184-203.
  • Hanna, J.B., & Drea, J.T. (1998). Understanding and predicting passenger rail travel: An empirical study. Transportation Journal, 38 (1), 38-46.
  • Kafetzopoulos, D., Gotzamani, K., & Skalkos, D. (2019). The relationship between EFQM enablers and business performance. Journal of Manufacturing Technology Management, 30 (4), 684-706.
  • Kang, G.D., & James, J. (2004). Service quality dimensions: an examination of Grönroos’s service quality model. Managing Service Quality, 14 (4), 266-277.
  • Koksalmis, E., & Kabak, Ö. (2019). Deriving decision makers’ weights in group decision making: An overview of objective methods. Information Fusion, 49, 146-160.
  • Liu, P., Yang, L., Gao, Z., Li, S., & Gao, Y. (2015). Fault tree analysis combined with quantitative analysis for high-speed railway accidents. Safety Science, 79, 344–357.
  • Liu, Y.L., & Ko, P.F. (2018). A modified EFQM Excellence Model for effective evaluation in the hotel industry. Total Quality Management and Business Excellence, 29 (13-14), 1580–1593.
  • Macmillan, H., & Tampoe, M. (2000). Strategic management. Great Britain: Oxford University Press.
  • Madrigal, A.I., & Lara, J.A.S. (2017). Applying the EFQM model to golf course management. Journal of Sport Tourism, 21 (3), 223–241.
  • Maskeliūnaite, L., Sivilevičius, H., & Podvezko, V. (2009). Research on the quality of passenger transportation by railway. Transport, 24 (2), 100–112.
  • Memari, A., Dargi, A., Jokar, M.R.A., Ahmad, R., & Rahim. A.R.A., (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, 50, 9-24.
  • Mesgari, I., Miab, A.K., & Sadeghi, M.J. (2017). Causal structure of the EFQM excellence model among healthcare sector: a case study in Iran. Total Quality Management and Business Excellence, 28 (6), 663–677.
  • Mirandaa, S., Tavaresa, P., & Queiró, R. (2018). Perceived service quality and customer satisfaction: A fuzzy set QCA approach in the railway sector. Journal of Business Research, 89, 371–377.
  • Moreno-Rodriguez, J.M., Cabrerizo, F.J., Pérez, I.J., & Martinez, M.A. (2013). A consensus support model based on linguistic information for the initial-self assessment of the EFQM in health care organizations. Expert Systems with Applications, 40, 2792–279.
  • Nedeliaková, E., Sekulová, J., Nedeliak, I., & Ľoch, M. (2014). Methodics of identification level of service quality in railway transport. Procedia - Social and Behavioral Sciences, 110, 320-329.
  • Niroomand, S., Garg, H., & Mahmoodirad, A. (2020). An intuitionistic fuzzy two stage supply chain network design problem with multi-mode demand and multi-mode transportation, ISA Transactions, 1-17.
  • Para-González, L., Jiménez-Jiménez, D., & Martínez-Lorente, A.R. (2018). The link between people and performance under the EFQM excellence model umbrella. Total Quality Management and Business Excellence. DOI: 10.1080/14783363.2018.1552516.
  • Paraschi, E.P., Georgopoulosa, A., & Kaldis, P. (2019). Airport Business Excellence Model: A holistic performance management system. Tourism Management, 72, 352-372.
  • Parasuraman, A., Zeithaml, V.A., & Berry, L.L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49 (1), 41-50.
  • Ruiz-Carrillo, J.I.C., & Fernández-Ortiz, R. (2005). Theoretical foundation of the EFQM model: the resource-based view. Total Quality Management, 16 (1), 31–55.
  • Rusjan, B. (2005). Usefulness of the EFQM Excellence Model: Theoretical Explanation of Some Conceptual and Methodological Issues. Total Quality Management, 16 (3), 363–380.
  • Saaty, T.L. (1980). The analytical hierarchy process: Planning priority setting. New York: McGraw Hill. Sadeh, E., & Arumugan, V. (2010). Interrelationships among EFQM excellence criteria in Iranian industrial SMEs. European Journal of Economics, Finance and Administrative Sciences, 19, 155–167.
  • Sadeh, E., Arumugam, V.C., & Malarvizhi, C.A. (2013). Integration of EFQM framework and quality information systems. Total Quality Management and Business Excellence, 24 (2), 188–209.
  • Safari, H., Abdollahi, B., & Ghasemi, R. (2012). Canonical correlation analysis between people criterion and people results criterion in EFQM model. Total Quality Management and Business Excellence, 23 (5), 541–555.
  • Sila, I. (2007). Examining the effects of contextual factors on TQM and performance through the lens of organizational theories: An empirical study. Journal of Operations Management, 25(1), 83–109.
  • Sivilevičius, H., & Maskeliūnaite, L. (2010). The criteria for identifying the quality of passengers’ transportation by railway and their ranking using AHP method. Transport, 25(4), 368–381.
  • Şahin, B., & Soylu, A. (2020). Intuitionistic fuzzy analytical network process models for maritime supply chain. Applied Soft Computing Journal, 96, 106614.
  • Tan, K.C. (2002). A comparative study of 16 national quality awards. TQM Magazine, 14, 165-71.
  • Tavana, M., Zareinejad, M., Capriod, D., & Kaviani, M.A. (2016). An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics. Applied Soft Computing, 40, 544-557.
  • Tavana, M., Zareinejad, M., & Santos-Arteaga, F. (2018). An intuitionistic fuzzy-grey superiority and inferiority ranking method for third-party reverse logistics provider selection. Journal of Systems Science: Operations & Logistics, 5(2), 174-194.
  • Trébucq, S., & Magnaghi, E. (2017). Using the EFQM excellence model for integrated reporting: A qualitative exploration and evaluation. Research in International Business and Finance, 42, 522–531.
  • Tutuncu, O., & Kucukusta, D. (2009). Canonical correlation between job satisfaction and EFQM business excellence model. Quality and Quantity, 44(6), 1227–1238.
  • Wan, S., Wang, F., & Dong, J. (2016). A novel group decision making method with intuitionistic fuzzy preference relations for RFID technology selection. Applied Soft Computing, 38, 405-422.
  • Weske, M. (2007). Business Process Management—Concepts, Languages, Architectures. Springer-Verlag, Berlin Heidelberg.
  • Wu, Y., Zhang, B., Xu, C., & Li, L. (2018). Site selection decision framework using fuzzy ANP-VIKOR for large commercial rooftop PV system based on sustainability perspective. Sustainable Cities and Society, 40, 454-470.
  • Xu, Z. (2007). Multi-person Multi-attribute Decision Making Models under Intuitionistic Fuzzy Environment. Fuzzy Optimization and Decision Making, 6(3), 221-236.
  • Yousefie, S., Mohammadi, M., & Monfared, J.H. (2011). Selection effective management tools on setting European Foundation for Quality Management (EFQM) model by a quality function deployment (QFD) approach. Expert Systems with Applications, 38, 9633- 9649.
  • Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
  • Zhang, S., Xu, S., Zhang, W., Yu, D., & Chen, K. (2018). A hybrid approach combining an extended BBO algorithm with an intuitionistic fuzzy entropy weight method for QoS-aware manufacturing service supply chain optimization. Neurocomputing, 272, 439–452.
Toplam 71 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Makaleler
Yazarlar

Gözde Yangınlar 0000-0002-3814-2982

Sait Gül 0000-0002-6011-0848

Erken Görünüm Tarihi 27 Haziran 2022
Yayımlanma Tarihi 3 Ekim 2022
Kabul Tarihi 6 Mayıs 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 22 Sayı: 4

Kaynak Göster

APA Yangınlar, G., & Gül, S. (2022). An EFQM-Based Self-Assessment Method for Railway Transportation Service Quality: An Application with Intuitionistic Fuzzy AHP. Ege Academic Review, 22(4), 371-392. https://doi.org/10.21121/eab.1008669
AMA Yangınlar G, Gül S. An EFQM-Based Self-Assessment Method for Railway Transportation Service Quality: An Application with Intuitionistic Fuzzy AHP. eab. Ekim 2022;22(4):371-392. doi:10.21121/eab.1008669
Chicago Yangınlar, Gözde, ve Sait Gül. “An EFQM-Based Self-Assessment Method for Railway Transportation Service Quality: An Application With Intuitionistic Fuzzy AHP”. Ege Academic Review 22, sy. 4 (Ekim 2022): 371-92. https://doi.org/10.21121/eab.1008669.
EndNote Yangınlar G, Gül S (01 Ekim 2022) An EFQM-Based Self-Assessment Method for Railway Transportation Service Quality: An Application with Intuitionistic Fuzzy AHP. Ege Academic Review 22 4 371–392.
IEEE G. Yangınlar ve S. Gül, “An EFQM-Based Self-Assessment Method for Railway Transportation Service Quality: An Application with Intuitionistic Fuzzy AHP”, eab, c. 22, sy. 4, ss. 371–392, 2022, doi: 10.21121/eab.1008669.
ISNAD Yangınlar, Gözde - Gül, Sait. “An EFQM-Based Self-Assessment Method for Railway Transportation Service Quality: An Application With Intuitionistic Fuzzy AHP”. Ege Academic Review 22/4 (Ekim 2022), 371-392. https://doi.org/10.21121/eab.1008669.
JAMA Yangınlar G, Gül S. An EFQM-Based Self-Assessment Method for Railway Transportation Service Quality: An Application with Intuitionistic Fuzzy AHP. eab. 2022;22:371–392.
MLA Yangınlar, Gözde ve Sait Gül. “An EFQM-Based Self-Assessment Method for Railway Transportation Service Quality: An Application With Intuitionistic Fuzzy AHP”. Ege Academic Review, c. 22, sy. 4, 2022, ss. 371-92, doi:10.21121/eab.1008669.
Vancouver Yangınlar G, Gül S. An EFQM-Based Self-Assessment Method for Railway Transportation Service Quality: An Application with Intuitionistic Fuzzy AHP. eab. 2022;22(4):371-92.