Evaluation of the Airline Business Strategic Marketing Performance: The Asia-Pacific Region Case

Year 2022, Volume: 6 Issue: 2, 135 - 147, 24.07.2022

Niyazi Cem Gürsoy Furkan Karaman Mert Akınet

https://doi.org/10.30518/jav.1063368

Abstract

Businesses provide various marketing strategies in order to gain a competitive advantage and achieve sustainable profitability in today's globally competitive environment. While some of these strategies are realized through traditional marketing methods, some of them are implemented through digital marketing applications. The continuous and rapid change in information and communication technologies has made it obligatory for businesses to reconsider their marketing strategies and activities. In the literature, there are various studies conducted with multi-criteria decision-making methods in order to measure the marketing performance of businesses. However, there is no study conducted with these criteria specific to airline companies’ marketing performance. The criteria determined as a result of the literature review were analyzed using the fuzzy-AHP and Fuzzy-BWM methods for weight determination, and the TOPSIS method for alternative selection which are among the multi-criteria decision-making techniques. As a result of the study, net profitability, load rate, and total passenger number criteria came to the fore among other criteria, evaluations were made for the 6 airline companies examined, and the best and the worst alternative airline companies were determined, and evaluations were made in terms of marketing strategies. As a result, an exemplary application was introduced to airline companies in order to improve their marketing strategies and performances, and inferences that could contribute to future studies were made in the literature.

Keywords

Aviation, Marketing Strategies, Fuzzy Logic, Analytical Hierarchy Process, TOPSIS, BWM Method

References

• Ambler, T., Kokkinaki, F., and Puntoni, S. (2004). Assessing Marketing Perormance: Reasons for Metrics Selection. Journal of Marketing Management, 475-498.
• Bakır, M., Akan, Ş., and Durmaz, E. (2019). Exploring service quality of low-cost airlines in Europe: An integrated MCDM approach. Economics and Business Review, 5(19), 109-130.
• Ballı, S., and Korukoğlu, S. (2009). Operating system selection using fuzzy AHP and TOPSIS methods. Mathematical and Computational Applications, 14(2), 119-130.
• Brookes, R., Brodie, R., Coviello, N., and Palmer, R. (2004). How Managers Perceive The Impacts of Information Technologies on Contemporary Marketing Practice. Journal of Relationship Marketing, 7-26.
• Bruni, A., Cassia, F., and Magno, F. (2017). Marketing Performance Measurement in Hotels, Travel Agencies and Tour Operators: A Study of Current Practices. Current Issues in Tourism, 339-345.
• Buckley, J. J. (1985), Fuzzy hierarchical analysis, Fuzzy Sets and Systems, 17(3), 233–247.
• Chuang, M.-L., Chen, W.-M., and Liou, J. J. (2007). A Fuzzy MCDM Approach for Evaluating Corporate Image and Reputation in the Airline Market. NN Aan, and F. Farry.
• Clark, Bruce H. (1999), "Marketing Performance Measures: History and Interrelationships”, Journal of Marketing Management, Vol.15, pp.711-732
• Dayı, F. (2019). Faaliyet Kaldıraç Derecesinin Satış Gelirleri Üzerindeki Etkisi: Havayolu Şirketlerinde Bir Uygulama. Afyon Kocatepe University Journal of Social Sciences, 21(3).
• Doganis, R. (2009). Flying off course IV: airline economics and marketing. Routledge.
• Dožić, S. (2019). Multi-criteria decision making methods: Application in the aviation industry. Journal of Air Transport Management, 79, 101683.
• Emrouznejad, A., and Ho, W. (2017). Analytic Hierarchy Process and Fuzzy Set Theory. In Fuzzy Analytic Hierarchy Process (pp. 23-32). Chapman and Hall/CRC.
• Erdoğan, D. and Kaya. E. (2014). Understanding Performance Indicators of Organizational Achievement in Turkish Airline Companies. Journal of Management Research, 6(4), 109-123.
• Ertuğrul, İ., and Karakaşoğlu, N. (2009). Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Systems with Applications, 36(1), 702-715.
• Feng, C. and Wang, R. (2000). Performance evaluation for airlines including the consideration of "financial ratios. Journal of Air Transport Management: 6, 133-142.
• Feng, H., Morgan, N. A., and Rego, L. L. (2015). Marketing department power and firm performance. Journal of Marketing, 79(5), 1-20.
• Francis, G., Humphreys, I., and Jackie, F. (2005). The nature and prevalence of the use of performance measurement techniques by airlines. Journal of Air Transportation Management, 11(4), 207–217.
• Grønholdt, L., and Martensen, A. (2006). Key Marketing Performance Measures. The Marketing Review, 3, 243-252.
• Gumus, A. T. (2009). Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert systems with applications, 36(2), 4067-4074.
• Guo, S., and Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31.
• Gupta, H. (2018). Assessing organizations performance on the basis of GHRM practices using BWM and Fuzzy TOPSIS. Journal of environmental management, 226, 201-216.
• ICAO (2021) Effects of Novel Coronavirus (COVID‐19) on Civil Aviation: Economic Impact Analysis. Available at: https://www.icao.int/sustainability/Pages/Economic-Impacts-of-COVID-19.aspx (Accessed: April 21, 2022).
• Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., and Diabat, A. (2013). Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain. Journal of Cleaner production, 47, 355-367.
• Keh, H. T., Chu, S., and Xu, J. (2006). Efficiency, effectiveness and productivity of marketing in services. European Journal of Operational Research, 170(1), 265-276.
• Khim, L. S., Chang, C. S., and Larry, N. K. (2010). Service quality, service recovery, and financial performance: an analysis of the US airline industry. Advances in Management Accounting, 18, 27 - 53.
• Kotler, Philip (2015), Marketing Management, (11th edition), Upper Saddle River, NJ: Prentice Hall
• Kuo, M.-S. (2011). A novel interval-valued fuzzy MCDM method for improving airlines’ service quality in Chinese cross-strait airlines. Transportation Research Part E, 47(6), 1177-1193.
• Leong, C.C. (2008). An importance-performance analysis to evaluate airline service quality: the case study of a budget airline in Asia. Journal of Quality Assurance in Hospitality and Tourism, 8(3), 39-59.
• Liang, X., and Gao, Y. (2020). Marketing Performance Measurement Systems and Firm Performance. European Journal of Marketing, 885-907.
• Liedtka, S. L. (2002). The information content of nonfinancial performance measures in the airline industry. Journal of Business Finance and Accounting, 29(7), 1105 – 1121.
• Liou, J. J., and Chuang, M.-L. (2008). A hybrid MCDM model for evaluating the corporate image of the airline industry. Int. J. Applied Management Science, 1(1), 41-54.
• Mahmoodzadeh, S., Shahrabi, J., Pariazar, M., and Zaeri, M. S. (2007). Project selection by using fuzzy AHP and TOPSIS technique. World Academy of Science, Engineering and Technology, 30, 333-338.
• Mandic, K., Delibasic, B., Knezevic, S., and Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, 43, 30-37.
• Mehrjerdi, Y. Z. (2012). Developing fuzzy TOPSIS method based on interval valued fuzzy sets. International Journal of Computer Applications, 42(14), 7-18.
• Meyer, Marshall W. (1998), “Finding Performance: The new discipline in management”, Performance Measurement- Theory and Practice, Vol. 1, Cambridge, UK, Centre for Business Performance, xiv-xxi.
• Mikhailov, L. (2002), Fuzzy analytical approach to partnership selection in formation of virtual enterprises, Omega, 30, 393–401.
• Mikhailov, L. (2003), Deriving priorities from fuzzy pairwise comparison judgements, Fuzzy Sets and Systems, 134, 365–385.
• Norouzi, A., and Namin, H. G. (2019). A hybrid fuzzy TOPSIS–best worst method for risk prioritization in megaprojects. Civil Engineering Journal, 5(6), 1257-1272.
• Omrani, H., Alizadeh, A., and Emrouznejad, A. (2018). Finding the optimal combination of power plants alternatives: A multi response Taguchi-neural network using TOPSIS and fuzzy best-worst method. Journal of cleaner production, 203, 210-223.
• Patterson, L. (2007). Taking on the Metrics Challenge. Journal of Targeting, Measurement and Analysis for Marketing, 270–276.
• Pehlivan, N. Y., Paksoy, T., and Çalik, A. (2017). Comparison of Methods in FAHP with Application in Supplier Selection. Ali Emrouznejad and William Ho, 45-76.
• Pineda, P. J., Liou, J. J., Hsu, C.-C., and Chuang, Y.-C. (2018). An integrated MCDM model for improving airline operational and financial performance. Journal of Air Transport Management, 68, 103-117.
• Riley, R. A., Pearson, T. A., and Trompeter, G. (2003). The value relevance of non-financial performance variables and accounting information: the case of the airline industry. Journal of Accounting and Public Policy, 22(3), 231–254.
• Saaty, T.L., (1986). Axiomatic Foundation of the Analytic Hierarchy Process. Management Scienece, 32(7).
• Saaty, T.L., 1980. The Analytical Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York.
• Sagnak, M., Berberoglu, Y., Memis, İ., and Yazgan, O. (2021). Sustainable collection center location selection in emerging economy for electronic waste with fuzzy Best-Worst and fuzzy TOPSIS. Waste Management, 127, 37-47.
• Saranga, H., and Nagpal, R. (2016). Drivers of operational efficiency and its impact on market performance in the Indian Airline industry. Journal of Air Transport Management, 53, 165-176.
• Samanlioglu, F., Burnaz, A. N., Diş, B., Tabaş, M. D., and Adıgüzel, M. (2020). An Integrated Fuzzy Best-Worst-TOPSIS Method for Evaluation of Hotel Website and Digital Solutions Provider Firms. Advances in Fuzzy Systems, 2020.
• Schefczyk, M. (1993). Operational performance of airlines: an extension of traditional measurement paradigms. Strategic Management Journal, 14(4), 301–317. Retrieved from http://www.jstor.org/stable/2486797
• Singh, R., Shankar, R., Kumar, P., and Singh, R. K. (2012). A fuzzy AHP and TOPSIS methodology to evaluate 3PL in a supply chain. Journal of Modelling in Management.
• Surovitskikh, S., and Lubbe, B. (2008). Positioning of selected Middle Eastern airlines in the South African business and leisure travel environment. Journal of Air Transport Management, 14(2), 75–81.
• Tian, Z. P., Zhang, H. Y., Wang, J. Q., and Wang, T. L. (2018). Green supplier selection using improved TOPSIS and best-worst method under intuitionistic fuzzy environment. Informatica, 29(4), 773-800.
• Thomas, J., and Gupta, R. (2005). Marketing Theort and Practice: Evolving Through Turbilent Times. Global Business Review, 95-114.
• Torlak, Ö., and Altunışık, R. (2018). Pazarlama Stratejileri Yönetsel Bir Yaklaşım (3. b.). İstanbul: Beta.
• Tsaur, S.-H., Chang, T.-Y., and Yen, C.-H. (2002). The evaluation of airline service quality by fuzzy MCDM. Tourism Management, 23(2), 107-115.
• Tzeng, G. H., and Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
• Van Laarhoven, P. J. M., Pedrycz, W. (1983), A fuzzy extension of Saaty’s priority theory, Fuzzy Sets and Systems, 11(1–3), 229–241.
• Yucesan, M., Mete, S., Serin, F., Celik, E., and Gul, M. (2019). An integrated best-worst and interval type-2 fuzzy TOPSIS methodology for green supplier selection. Mathematics, 7(2), 182.
• Zadeh, L.A., 1965. Fuzzy sets. Journal of Information and Control 8, 338e353. Zadeh, L.A., 1976. A fuzzy algorithmic approach to the definition of complex or imprecise concepts. International Journal of Man-Machine Studies 8, 249e291.
• Zimmermann, H. J. (1996). Fuzzy set theory—and its applications. Kluwer Academic Publishers,
• Zimmermann, H.J. 1978. Fuzzy programming and linear programming with several objective functions, Fuzzy Sets and Systems 1(1), 45–55.