Ranking airlines' service quality factors using a fuzzy approach: study of the Iranian society

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0265-671X.htm Ranking airlines’ service quality factors using a fuzzy approach: study of the Iranian society Mehran Nejati and Mostafa Nejati School of Management, Universiti Sains Malaysia (USM), Gelugor, Malaysia, and Azadeh Shafaei Ranking airlines’ service quality 247 Received May 2008 Revised September 2008 Accepted September 2008 School of Humanities, Universiti Sains Malaysia (USM), Gelugor, Malaysia Abstract Purpose – This paper seeks to review service quality factors of the airline industry, and to rank these factors in Iranian society. It aims to introduce a fuzzy TOPSIS approach for this purpose. Design/methodology/approach – The research was conducted among graduate students of the University of Tehran, Iran. In order to meet the objectives of the study, Fuzzy TOPSIS approach was used. Required information was gathered through a questionnaire. Findings – The results show that “Flight safety”, “Good appearance of flight crew” and “Offering highest possible quality services to customers 24 hours a day” are the most important airline service quality factors in the eyes of Iranian customers. Interestingly, “the possibility of checking flight schedule via telephone” has been selected as the least important quality factor by respondents. Research findings also announce that the majority of Iranian potential travellers prefer airplane as their transportation means. However, at the moment, they are mostly using buses as their first choice. Practical limitations/implications – The paper considers graduate university students as its sample society. Although university students are a group of airlines’ potential customers, the paper findings might not be generalised to all groups of airline customers and further studies might be essential to see if the same ranking of service quality dimensions will be found within other groups of customers having other career backgrounds. The paper will be helpful in enabling airline industry policy makers to identify the key service quality factors in the eyes of Iranian customers. Originality/value – The concept of ranking airline service quality factors using a Fuzzy TOPSIS is a new approach. The study is the first application of a fuzzy approach to examine and rank customer expectations of airlines’ service quality. Keywords Customer services quality, Airlines, Fuzzy logic, Iran Paper type Research paper Introduction The literature on buyer-seller relationships has stressed the differences between behavioural and attitudinal definitions of the strength of a relationship. Repetition of buying may not indicate any loyalty of a buyer to a seller, but merely the lack of alternatives which are either available to a buyer, or which they are sufficiently motivated to seek out. At an affective level, attention has been paid to: the importance of developing trust between buyer and seller; the customer orientation of front-line sales personnel; the expertise of sales personnel; and the ethics of sales personnel. A number of conceptual and empirical models have been proposed of the processes by which relationships between an organisation and its customers develop. These have International Journal of Quality & Reliability Management Vol. 26 No. 3, 2009 pp. 247-260 q Emerald Group Publishing Limited 0265-671X DOI 10.1108/02656710910936726 IJQRM 26,3 248 drawn on analogies with other forms of human relationships and have identified distinct stages which relationship development goes through. However, most attention has been paid to the initial stages of a buyer-seller relationship during which the relationship is being built. There has been less analysis of the latter stages of the relationship leading to its dissolution. Nowadays no organisation can succeed unless it can attract and retain enough customers. Effective customer management is a vital issue for the success of airline industry. Therefore identifying and prioritising customers’ needs and expectations is of utmost importance for airlines in the current competitive market. Therefore, this study seeks to review the airline industry service quality dimensions and introduces a fuzzy TOPSIS approach for prioritising these dimensions according to customers’ expectations. Defining and measuring quality in an airline context As a concept, service quality has received much attention in the literature because of its sustainability as a source of competitive advantage. Service quality has been defined in different ways by researchers. Kasper et al. (1999) define service quality as “the extent to which the service, the service process and the service organisation can satisfy the expectations of the user”. Parasuraman et al. (1988) define service quality as “a function of the difference between service expected and customers’ perceptions of the actual service delivered”. Grönroos (1978) suggests that service quality is made of two components – technical quality and functional quality. Technical quality refers to what the service provider delivers during the service provision while functional quality is how the service employee provides the service. In the services marketing literature, the quality construct can be summarised as providing customer value (Feigenbaum, 1951), conformance to requirements (Crosby, 1979), fitness for use (Juran et al., 1974) and meeting customers’ expectations (Parasuraman et al., 1985). Service quality is therefore an enduring construct that encompasses quality performance in all activities undertaken by management and employees. Customers are the sole judges of service quality. If they perceive it to be bad service, then it is like that. They assess service quality by comparing what they want or expect with what they perceive they are getting. Few airlines have been able over the years to establish a reputation of high service quality. This is because of rapid changes in the industry both in terms of changing needs of customers and definitions of what constitutes the industry itself (Rhoades et al., 1998). Singapore Airlines (SIA), British Airways (BA) and American Airlines (AA) are among the few airlines that have successfully positioned themselves globally as offering excellent service quality (Chan, 2000b). Delivering consistent service quality is difficult for both large and small airline companies. Mega carriers and small airlines are working together rather than competing with one another to maintain and enhance quality standards. Forms of cooperation include sub contracting, code sharing, franchising and the formation of global marketing networks. Such alliances allow firms to focus on their respective core competencies, while drawing the benefits of scale economies (Dana and Vignali, 1999). Firms enter alliances for competitive reasons, which help them increase flight availability and yield from passengers. However, such an alliance is dependent on both airlines offering similar service levels and having similar market positioning for its success. Image of the two cooperating airlines has to be consistent to avoid negative perceptions of service levels. As rightly pointed by Wirtz and Johnston (2003), customers adjust their expectations according to brand image of the airline company. Service quality contributes significantly towards service differentiation, positioning and branding. SIA and BA have long been widely acknowledged within the airline industry as the industry’s strategic benchmark airlines, as well as the industry leaders and innovators of service branding as a source of strategic competitive advantage (Chan, 2000a). Companies that search for the most effective ways to incorporate the best service methods and processes tend to be winners in the long term in terms of favourable customer perceptions. Such companies excel in relation to their competitors and are able to build a solid foundation for customer loyalty based on segmented service. Service, both poor and outstanding, has a strong emotional impact on the customer, creating intense feelings about the organisation, its staff and its service, and influencing the loyalty to it (Wirtz and Johnston, 2003). Several authors have shown empirically that there is a positive link between customer service improvements and customer satisfaction, customer loyalty and profitability (Buzzell and Gale, 1987; Boulding et al., 1993; Rust and Oliver, 1994). Services are more subject to social, cultural and national boundaries influence, which predetermine customers’ evaluation of service quality (Philip and Hazlett, 1997). Few studies have focused on the relationship between a passenger’s cultural background and perceptions of service quality (Ling et al., 2005). Sultan and Simpson (2000) indicated that customer expectations and perceptions varied by nationality in an international environment. Service quality ratings of European passengers were significantly lower than those of US passengers. Cunningham et al. (2002), Furrer et al. (2000), Herbig and Genestre (1996) found that there were some significant relationships between culture and perception of service quality. Cross cultural comparison between US and Mexican consumers revealed that Mexicans had poorer perceptions of service quality compared to their US counterparts on the evaluation of products and services in general. Service quality has been shown to lead to different behavioural intentions with respect to customers from different cultures (Liu et al., 2001). Therefore, the cultural background of passengers cannot be ignored in assessing service quality as it contributes to building long-term brand recognition (Ling et al., 2005). Service quality in Iranian airlines The Iranian airline industry is facing various problems and challenges and the strategic solution for these problems has seldom been noticed. This industry lacks a clear strategic and flexible policy, productivity, and a balance between expenses and incomes (Nik Amal, 2004, p. 3). There is an apparent lack of any research studies regarding service quality in Iranian airlines. The only noticeable research, studied the satisfaction level of the Iranian passengers of Iran Air. In this research, using SERVQUAL technique, the importance of various factors on passenger satisfaction and the level of their present contentment with services rendered in domestic flights of Iran Air have been identified and measured. Results indicate that with regard to reliability of services, responsiveness, and empathy, there is a significant difference with the optimal situation. Furthermore, with the exception of tangible factors, other variables have negatively affected the satisfaction of passengers of Iran Air domestic flights (Venus and Madadi Yekta, 2005). Ranking airlines’ service quality 249 IJQRM 26,3 250 Identifying and ranking service quality dimensions The Gaps model proposed by Parasuraman et al. (1985) has been the most comprehensive and widely used model to understand service quality. Its operationalisation through SERVQUAL, using a battery of 22 statements, has been proven to be reliable and valid across many service industries. The SERVQUAL scale has been applied to airlines (Nel et al., 1997; Sultan and Simpson, 2000), hotels (Ingram and Daskalakis, 1999; Juwaheer, 2004), travel agencies (Luk, 1997; Johns et al., 2004), financial services (Kagis and Passa, 1997; Lassar et al., 2000; Newman, 2001), health care (Desombre and Eccles, 1998; Kilbourne et al., 2004) and the public sector (Donnelly et al., 1995; Wisniewski, 2001; Brysland and Curry, 2001). At the heart of the SERVQUAL model is an understanding of the nature and determinants of customer expectations and perceptions of service quality. Consumers’ expectations and perceptions are measured to identify any shortfall in service levels, better known as the disconfirmation paradigm in the services marketing literature. A customer will perceive quality in a positive way only when the service provider meets or exceeds his/her expectations (Parasuraman et al., 1985, 1988; Bitner, 1990; Robledo, 2001). In the airline industry, customers’ expectations are shaped at the “moment-of-truth” by reservations department of the airline, telephone sales, ticketing, cabin crew, cabin services, baggage handling, flight delays and others (Albrecht, 1992). SERVQUAL uses a concise 22-item scale to measure expectations and perceptions. The model suggests the existence of five dimensions namely: tangibles, reliability, responsiveness, assurance and empathy that can discriminate well across customers with differing quality perceptions. The last two dimensions contain items representing seven of the original dimensions namely: communication, credibility, security, competence, courtesy, understanding/knowing customers, and access (Parasuraman et al., 1985). Various researchers such as Carman (1990), Cronin and Taylor (1992), Babakus and Boller (1992), Boulding et al. (1993), Teas (1993, 1994), Buttle (1996), Asubonteng et al. (1996), Llosa et al. (1998), Sureshchandar et al. (2001) and Coulthard (2004) have criticised the model. Carman (1990, p. 44) suggests that “it is better to collect data in terms of the perception/expectation difference directly rather than to asks about each separately. It is also important to take into account the level of experience of the customer with the service.” Cronin and Taylor (1992) through the SERVPERF model argue that service quality should be measured as an attitude and support the use of perception statements only in the measurement of service quality. Numerous studies have been undertaken to assess the superiority of the two scales but consensus continues to elude as to which one is better (Jain and Gupta, 2004). One of the main criticisms of SERVPERF has been the way it measures customer satisfaction. Parasuraman et al. (1988) argues that quality is an enduring global attitude towards a service while SERVPERF measures satisfaction related to a specific transaction. Methodology There are various models for prioritising factors in research. The most important models are multiple criteria decision making (MCDM) models such as analytic hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), etc. In this paper, we try to apply Fuzzy TOPSIS model, introduced by Chen (1997) for prioritising quality dimensions of airline services. TOPSIS is an operational design approach that helps select the optimal levels of service quality attributes that would facilitate the delivery of customer satisfaction. This technique can be extremely useful for service design. Similarly, loss function is better suited to highlight the future long-term damage caused by not delivering on customer-defined service standards (Mukherjee and Nath, 2005). TOPSIS views a multi-attribute decision-making problem with m alternatives as a geometric system with m points in the n-dimensional space. The method is based on the concept that the chosen alternative should have the shortest distance from the positive-ideal solution and the longest distance from the negative-ideal solution. In the meanwhile, since the judgments from experts and humans are usually vague rather than crisp, therefore a judgment should be expressed by using fuzzy sets which have the capability of representing vague data (Kahraman et al., 2007). Fuzzy TOPSIS is a methodology that extends TOPSIS for decision making to cases conducted in uncertain and fuzzy environment; thus, providing the ability to deal with the uncertainty of human judgments in evaluating the quality factors in airline industry. The study sample society was randomly selected from the graduate students of the University of Tehran. The reason for this selection was that graduate students, as future managers and job staff of the society, are considered as an important potential customer group of airline industries. Questionnaires were distributed among a sample size of 250 students, among which 231 questionnaires were returned and proper for use (return rate of 92.4 per cent). Table I shows the characteristics of the sample society. Ranking airlines’ service quality 251 Questionnaire development, validity and reliability In order to prioritise the airline service quality in an Iranian context, the service quality factors were driven from the SERVQUAL model. The required data were gathered in the form of a questionnaire asking the respondents to choose the importance of the mentioned service quality factors based on Likert scale, with rankings of: 1 very low; 2 low; 3 relatively low; 4 fair; 5 relatively high; 6 high; and 7 very high. Prioritising the factors was done using the Fuzzy TOPSIS. Factor Frequency Percentage Age (years) 20 to 22 22 to 24 24 to 26 Over 26 26 78 102 25 11 34 44 11 Gender Male Female 124 107 54 46 Previous number of flights None 1 to 3 4 to 6 Over 6 31 127 62 11 13 55 27 5 Table I. Sample characteristics IJQRM 26,3 252 The numerical value of each linguistic term used in the questionnaire, was determined based on Table II (Lin et al., 2005), using a fuzzy approach. Fuzzy sets theory acts as a very powerful tool to address the uncertainty and imprecision issue which affects the airline potential customers’ selection. Fuzzy logic ensures a mathematical precise approach to deal with the vagueness that may feature the importance of a criterion or relative judgment of people. As it can be seen in Table II, the solid 7 scale linguistic term has been transformed to equal fuzzy intervals. Because the questionnaire used in this research had already been used in previous studies (Nel et al., 1997; Sultan and Simpson, 2000), its validity is confirmed. In order to test the reliability of the questionnaire, Cronbach’s alpha was found to be 0.819, which indicated that the questionnaire has high internal reliability. In the last section of the questionnaire, the respondents were also asked several questions regarding the transportation means they most often use, and the one which they prefer. Besides, they had been asked to determine whether in case of flight travel, they would prefer an Iranian airline or a similar international counterpart. Measurement with fuzzy set The subject of service quality is burdened by fuzzy terms or buzzwords (e.g. attitude, taste, atmosphere), and respondents may fill out the questionnaire subjectively based on their unique experience or personal characteristics. This subjective assessment is intrinsically imprecise and ambiguous (Williams and Zigli, 1987). To reflect the subjectivity and imprecision in the survey, the assessment made by the respondents can be represented as fuzzy sets (Yeh and Kuo, 2003). Fuzzy set theory, initially introduced by Zadeh (1965), is used to manage the vagueness of human thought, since it can represent vague expressions such as “usually,” “fair” and “satisfied,” which are regarded as the natural representation of respondents’ preference and judgment. The theory also enables the application of the fuzzy domain in mathematics and programming. A fuzzy set is a class of objects with a continuum of membership degrees, characterised by a membership function which assigns a membership grade ranging between zero and one to each object (Kahraman et al., 2000). In classical set theory, an object is either a member of a set or excluded from it. Thus, in conventional dual logic, a statement can only be either true or false. In reality, however, human cognition, perception and judgment involve approximate and vague reasoning, and cannot be modelled adequately by classical set theory. Fuzzy sets were introduced by Zadeh (1965) as a method of handling vagueness or uncertainty; particularly linguistic variables. Fuzzy sets consider the grey area of data, rather than Table II. Fuzzy range and numbers Linguistic term Fuzzy number 1 2 3 4 5 6 7 (0, 0.05, 0.15) (0.1, 0.2, 0.3) (0.2, 0.35, 0.5) (0.3, 0.5, 0.7) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.85, 0.95, 1) considering membership of a set to be simply true or false. In other words, fuzzy sets allow partial membership of a set. There are two main characteristics of fuzzy systems that give them better performance for specific applications (Kahraman et al., 2007): (1) fuzzy systems are suitable for uncertain or approximate reasoning, especially for the system with a mathematical model that is difficult to derive; and (2) fuzzy logic allows decision-making with estimated values under incomplete or uncertain information That is why fuzzy logic has been combined and used along with TOPSIS, and has resulted in a Fuzzy TOPSIS methodology for reviewing service quality factors of airline industry in Iran. The following seven steps, based on the technique introduced by Chen (1997), are used for this research purpose in ranking airlines service quality factors. Step one Consider a fuzzy decision matrix of respondents’ ideas as follows, where i stands for the number of factors (quality factors) and j stands for the number of respondents. Also, X~ ij stands for the score assigned by respondent number i for factor j. On the other ~ ij is the importance (weight) of each respondent’s ideas. It must be added that, hand, W ~ ij will be defined because all the respondents are considered to have the same weight, W ~ j ¼ ð1; 1; 1Þ;j [ n: as W x~ 11 x~ 12 ::: 6~ 6 x21 6 6 : 6 D~ ¼ 6 6 : 6 6 6 : 4 x~ m1 x~ 22 ::: 2 : : ::: : x~ m2 ::: x~ 1n 3 7 x~ 2n 7 7 : 7 7 7 : 7 7 7 : 7 5 x~ mn
 X~ ¼ aij ; bij ; cij   ~ ¼ w~ 1 ; w~ 2 ; :::; w~ n : W Step two This step includes neutralising the weight of decision matrix and generating fuzzy ~ To generate R, ~ either of the following relations can be applied. un-weighted matrix (R). Ranking airlines’ service quality 253 IJQRM 26,3 254 Relation 1 where: ! aij bij cij ; ; ; c*j c*j c*j   R~ ¼ r~ij m£n r~ij ¼ * cj ¼ max c i Relation 2  2 2  aj aj ca2 j r~ij ¼ ; ; ; cij bij cij where: a2 j ¼ min aij i Step three ~ while having W ~ ij as an This step includes generating fuzzy un-weighted matrix (V), input for the algorithm:   V~ ¼ v~ ij m£n i ¼ 1; 2; . . . ; m; j ¼ 1; 2; . . . ; n; v~ ij ¼ r~ij :w~ j : Step four Determine positive ideal (ðFPIS; A þ Þ) and negative ideal (ðFNIS; A 2 Þ) for the factors:   A þ ¼ v~*1 ; v~*2 ; :::; v~*n
 ~2 ~2 A 2 ¼ v~ 2 1 ; v 2 ; :::; v n : In this research, the positive and negative ideas introduced by Chen (1997) are used. Therefore:
 v~*j ¼ 1; 1; 1
 v~ 2 j ¼ 0; 0; 0 : Step five In this step, we calculate the sum of distances from positive and negative ideas for each factor. For fuzzy numbers such as A and B, the difference between A and B shown as D(A, B), is determined using the following formula:

 ~ ¼ a1 ; b1 ; c1 B~ ¼ a2 ; b2 ; c2 A rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi i 1h D A; B ¼ ða2 2 a1 Þ2 þðb2 2 b1 Þ2 þðc2 2 c1 Þ2 : 3
 Therefore, the difference of each factor from positive and negative ideals is calculated: d*i ¼ n   X d v~ ij 2 v~*j i ¼ 1; 2; :::; m d2 i ¼ n   X d v~ ij 2 v~ 2 j i ¼ 1; 2; :::; m: j¼1 n j¼1 n Step six The adjacency of each factor to positive ideal is calculated as the following: CCi ¼ d2 i d*i þ d2 i i ¼ 1; 2; :::; m: Step seven This is the final step where we rank factors in a descending order of CCi. Therefore the higher CCi go to top. Findings The findings of this research shows that “Flight safety”, “Good appearance of flight crew” and “Offering highest possible quality services to customers 24 hours a day” are considered as the most important quality factors for airlines in the perspective of Iranian customers (Table III). Interestingly, “the possibility of checking flight schedule via telephone” has been selected as the least important quality factor by respondents, while instead most of the respondents stated the importance of “the possibility of booking and buying a ticket through internet”. This finding was expectable considering the rapid development of ICT in the country and the increasing number of internet users in Iran. Also, the research findings show that while the majority of respondents use bus as their primary means of transportation for travel, it is one of the least preferred transportation means according to respondents. Findings of this study show that, in spite of relatively low service quality offered by Iranian airlines (Venus and Madadi Yekta, 2005), still the Iranian customers prefer airplane as the best transportation means for travel and after that the majority prefer train for travel. Table IV summarises the result of this section. Ranking airlines’ service quality 255 IJQRM 26,3 Rank Factor 1 2 3 256 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Table III. Ranking service quality factors of airline services 21 22 Flight safety Good appearance of flight crew Offering highest possible quality services to customers 24 hours a day Friendly and helping behaviour of flight crew toward passengers Proper transfer and delivery of luggage and cargo Availability of enough flight staffs and crew Comfortable chairs with sufficient space for sitting Proper food services during the flight Availability of an up-to-date internet web site for responding to customers’ questions and requests Clean rest-rooms in the airplanes The possibility of booking and buying a ticket through internet The speed of offering services by flight crew Without delay flights Providing sufficient flight information during flight Sufficient number of information and service offices in the cities Seriousness in solving passengers’ problems and facilitating the process of meeting their needs Avoiding flight cancellation Avoiding flight cancellation Providing up-to-date newspapers, magazines, and video films during the flight Quick announcement of flight schedules and the availability of alternative flights in case of delay or cancellation Quick response to passengers’ needs and requests during the flight by flight crew The possibility of checking flight schedule via telephone d*i d2 i Ci 0.198 0.206 0.598 0.589 0.752 0.741 0.208 0.208 0.214 0.216 0.220 0.234 0.585 0.583 0.579 0.578 0.574 0.558 0.738 0.737 0.730 0.728 0.723 0.704 0.237 0.247 0.247 0.247 0.252 0.255 0.269 0.556 0.556 0.547 0.543 0.540 0.539 0.521 0.701 0.692 0.689 0.687 0.682 0.679 0.659 0.271 0.272 0.273 0.520 0.521 0.522 0.658 0.657 0.656 0.273 0.519 0.655 0.278 0.519 0.651 0.278 0.312 0.515 0.481 0.650 0.606 Conclusion and suggestions This research used a questionnaire driven from SERVQUAL model and analyzed by Fuzzy TOPSIS methodology to review and rank the quality service factors in the airline industry in Iran. The result of this study shows that “Flight safety”, “Good appearance of flight crew” and “Offering highest possible quality services to customers 24 hours a day” are the most important airline service quality factors in the eyes of Iranian customers. Research findings also announce that the majority of Iranian potential travellers prefer airplane as their transportation means. However at the moment, they are mostly using bus as their first choice. This might be due to the relatively higher price of airplane tickets and un-availability of flights in many routes not departing from or landing to Tehran, Iran capital city. The lower quality of Iranian airline services in comparison to foreign airlines cannot also be ignored. This is clearly observed in respondents’ preference (88 per cent) for choosing foreign airlines instead of an Iranian airline in International flights. 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(2001), “Assessing customer satisfaction with local authority services using SERVQUAL”, Total Quality Management, Vol. 12 No. 7 and 8, pp. 995-1002. Yeh, C.H. and Kuo, Y.L. (2003), “Validating fuzzy multicriteria analysis using fuzzy clustering”, International Journal of Operations & Quantitative Management, Vol. 9 No. 3, pp. 161-75. Zadeh, L.A. (1965), “Fuzzy set”, Information and Control, Vol. 8 No. 3, pp. 338-53. About the author Mehran Nejati holds BSc. of Industrial Engineering and MSc. of Executive MBA from Iran and is currently pursing his PhD in Malaysia. He has several papers in international conference proceedings and journals. Mehran Nejati is the corresponding author and can be contacted at: mehran.nejati@gmail.com Mostafa Nejati has a Master of Management and currently works in a Conglomerate Group Company. He has various publications in international journals. Azadeh Shafaei is an English Lecturer currently pursing her MA at Universiti Sains Malaysia (USM). As the managing director of a consulting company in Iran, she has shown great interest and abilities in managerial systems and approaches. To purchase reprints of this article please e-mail: reprints@emeraldinsight.com Or visit our web site for further details: www.emeraldinsight.com/reprints