Multi-criteria decision analysis towards robust service quality measurement

Abstract Importance The role of airports is critical for a region in which it is viewed as an engine for the economic development. Facilities, infrastructure, information and in general the services offered by an airport represent the fuel for this engine. Evidently, customers and travelers expect standard-quality services that need to be framed and measured. Therefore, services in airports should be quantified and maintained, accordingly. Objectives This article reports a case study for evaluating quality of services offered by five main airports located in Spain. Quality of service was modelled based on a number of factors such as convenience, comfort, courtesy of staffs, information visibility, prices, security, and transportation facilities. The grey based multi-criteria decision analysis (MCDA) was employed towards a reliable evaluation process by airport experts and to accommodate the several qualitative and conflicting evaluation factors with distinct definitions. To this end, Grey Step-wise Weight Assessment Ratio Analysis (SWARA-G) and grey Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS-G) methods were applied for quantifying relative weights of decision factors and rating airports, respectively. Several sensitivity analysis, simulations, and comparisons were conducted for verifying the preciseness of the revealed results. Findings Research findings demonstrate that the proposed SWARA-G-MARCOS-G-based methodology (i) enables decision makers to express their preferences clearly; and (ii) attenuates the embedded subjectivity and uncertainty within the decision-making process. In addition, they revealed that access to the parking and Wi-Fi connection are amongst the critical factors in evaluating service quality of airport. Contribution This paper contributes to related literature in presenting a novel decision-making approach for measuring service quality of airports, validated via a real-case study. The employed interval and linguistic grey variables allow experts, in airport operations, to express their opinions with higher flexibility and comfortability. The presented model could be re-applied for other studies or practical cases as a user-friendly decision support system.

[1]  Mirosław Siergiejczyk,et al.  Some issues of data quality analysis of automatic surveillance at the airport , 2014 .

[2]  Ender Özcan,et al.  Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey , 2018 .

[3]  Francesco Palmieri,et al.  Distributed classification of multiple moving targets with binary wireless sensor networks , 2011, 14th International Conference on Information Fusion.

[4]  M. Vanderschuren,et al.  Analytic Hierarchy Process assessment for potential multi-airport systems – The case of Cape Town , 2014 .

[5]  Arnoldina Pabedinskaitė,et al.  Evaluation of the Airport Service Quality , 2014 .

[6]  Rong-Ho Lin,et al.  Using a modified grey relation method for improving airline service quality. , 2011 .

[7]  Sarfaraz Hashemkhani Zolfani,et al.  A VIKOR and TOPSIS focused reanalysis of the MADM methods based on logarithmic normalization , 2020, ArXiv.

[8]  Prasenjit Chatterjee,et al.  Development of a decision support framework for sustainable freight transport system evaluation using rough numbers , 2020, Int. J. Prod. Res..

[9]  Edmundas Kazimieras Zavadskas,et al.  Contractor selection for construction works by applying saw‐g and topsis grey techniques , 2010 .

[10]  Laura Eboli,et al.  Latent factors on the assessment of service quality in an Italian peripheral airport , 2020, Transportation Research Procedia.

[11]  A. Ghobadian,et al.  Service Quality: Concepts and Models , 1994 .

[12]  M. Sadiq Sohail,et al.  Measuring Service Quality at King Fahd International Airport , 2005, Int. J. Serv. Stand..

[13]  A. Pantouvakis,et al.  Exploring different nationality perceptions of airport service quality , 2016 .

[14]  Stelios Tsafarakis,et al.  A multiple criteria approach for airline passenger satisfaction measurement and service quality improvement , 2017 .

[15]  Mary Jo Bitner,et al.  The Service Encounter: Diagnosing Favorable and Unfavorable Incidents , 1990 .

[16]  Panos E. Kourouthanassis,et al.  Measuring Service Quality From Unstructured Data: A Topic Modeling Application on Airline Passengers’ Online Reviews , 2018, Expert Syst. Appl..

[17]  Nihal Erginel,et al.  Designing the airport service with fuzzy QFD based on SERVQUAL integrated with a fuzzy multi-objective decision model , 2019 .

[18]  I-Shuo Chen,et al.  A hybrid MCDM model encompassing AHP and COPRAS-G methods for selecting company supplier in Iran , 2012 .

[19]  Anneli Douglas,et al.  An application of the airport service quality model in South Africa , 2011 .

[20]  Toni Lupo,et al.  Fuzzy ServPerf model combined with ELECTRE III to comparatively evaluate service quality of international airports in Sicily , 2015 .

[21]  Chieh-Yuan Tsai,et al.  A decision rules approach for improvement of airport service quality , 2011, Expert Syst. Appl..

[22]  Theodor J. Stewart,et al.  Integrating multicriteria decision analysis and scenario planning—Review and extension , 2013 .

[23]  Ibrahim Badi,et al.  Supplier selection for steelmaking company by using combined Grey-Marcos methods , 2020 .

[24]  Prasenjit Chatterjee,et al.  Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS) , 2020, Comput. Ind. Eng..

[25]  Ahmed Mohammed,et al.  Towards ‘gresilient’ supply chain management: A quantitative study , 2020 .

[26]  Ahmed Mohammed,et al.  Gresilient supplier assessment and order allocation planning , 2020, Annals of Operations Research.

[27]  Toni Lupo,et al.  DINESERV along with fuzzy hierarchical TOPSIS to support the best practices observation and service quality improvement in the restaurant context , 2019, Comput. Ind. Eng..

[28]  Dragana Nenadić,et al.  Ranking dangerous sections of the road using MCDM model , 2019, Decision Making: Applications in Management and Engineering.

[29]  Herzegovina,et al.  A Novel Integrated Fuzzy PIPRECIA–Interval Rough Saw Model: Green Supplier Selection , 2020 .

[30]  Carlo Bonferroni Sulle medie multiple di potenze , 1950 .

[31]  Bryan T Wilson,et al.  Decision-Making Guideline for Preservation of Flexible Pavements in General Aviation Airport Management , 2017 .

[32]  B. Murray,et al.  Passengers' expectations of airport service quality , 2007 .

[33]  Xuejiao Zhao,et al.  Service quality driven approach for innovative retail service system design and evaluation: A case study , 2019, Comput. Ind. Eng..

[34]  Ivan Petrovic,et al.  Modification of the Best-Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers , 2018, Expert Syst. Appl..

[35]  Thomas L. Saaty,et al.  When is a Decision-Making Method Trustworthy? Criteria for Evaluating Multi-Criteria Decision-Making Methods , 2015, Int. J. Inf. Technol. Decis. Mak..

[36]  Wen-Hsien Tsai,et al.  A gap analysis model for improving airport service quality , 2011 .

[37]  Helder Gomes Costa,et al.  Analysis of the operational performance of Brazilian airport terminals: A multicriteria approach with De Borda-AHP integration , 2016 .

[38]  Dragan Pamučar,et al.  A Sensitivity analysis in MCDM problems: A statistical approach , 2018, Decision Making: Applications in Management and Engineering.

[39]  L. Ustinovicius,et al.  A New Approach to Assessing the Biases of Decisions based on Multiple Attribute Decision making Methods , 2012 .

[40]  Mukesh Mohan Pandey,et al.  Evaluating the service quality of airports in Thailand using fuzzy multi-criteria decision making method , 2016 .

[41]  Darko Bozanic,et al.  Multi-criteria FUCOM – Fuzzy MABAC model for the selection of location for construction of single-span bailey bridge , 2019, Decision Making: Applications in Management and Engineering.

[42]  Abolfazl Kazemi,et al.  Evaluating service quality of airports with integrating TOPSIS and VIKOR under fuzzy environment , 2016 .

[43]  Anil Bilgihan,et al.  Airport service quality drivers of passenger satisfaction. , 2013 .

[44]  Himanshu Gupta,et al.  Evaluating service quality of airline industry using hybrid best worst method and VIKOR , 2017 .

[45]  Carlos F. Gomes,et al.  The effects of service quality dimensions and passenger characteristics on passenger's overall satisfaction with an airport , 2015 .

[46]  Anestis Papanikolaou,et al.  Users’ perceptions and willingness to pay in interurban toll roads: identifying differences across regions from a nationwide survey in Spain , 2017 .

[47]  Chandra Prakash,et al.  A Robust Multi-Criteria Decision-Making Framework for Evaluation of the Airport Service Quality Enablers for Ranking the Airports , 2016 .

[48]  T. Oum,et al.  Air Transport Liberalization and its Effects on Airline Competition and Traffic Growth – An Overview , 2014 .

[49]  Chia-Hao Chang,et al.  Data flow model of a total service quality management system , 1991 .

[50]  S. Sebhatu,et al.  Passengers’ Perspective Toward Airport Service Quality (ASQ) (Case Study at Soekarno-Hatta International Airport) , 2017 .

[51]  Mukesh Mohan Pandey,et al.  Evaluating the strategic design parameters of airports in Thailand to meet service expectations of Low-Cost Airlines using the Fuzzy-based QFD method , 2020, Journal of Air Transport Management.