Airline service quality is an important criterion for determining the service level of airlines. Reference was made in this segment based on two-factor theory. The optimal scale analysis method was used to identify the association characteristics between all domestic and international airline complaints and the time factor, followed by the construction of an airline service quality mea surement (ASQM) model based on the Civil Aviation Passenger Service Evalu ation (CAPSE) index. The ASQM model was used to measure the service quality of 26 full-service airlines and six low-cost service airlines in China. The study found that: the time distribution of airline complaints is uneven, but s hows a relative concentration of annual, seasonal, quarterly and monthly, with the peak of complaints in August each year; there is a high correlation bet ween all airline complaint type variables and time variables, but there are differences in the focus of complaints at different times: summer and autumn not only have a high volume of airline complaints, but also have complex complaint types. The quality of all services provided by China’s full-service airlines is better than that of low-cost service airlines. In general, the quality of service of “in-flight service and ground service” was more stable, while the quality of service of “ticketing service” decreased significantly. The service quality of “flight service” is difficult to maintain. This paper proposes an obje ctive analysis method to evaluate airline service quality by using airline comp laint statistics, and provides solutions and suggestions for airlines to improve their overall service quality.
Airline service quality refers to the extent to which airlines collaborate with relevant civil aviation enterprises to provide airline services that use value to meet the needs of passengers for safety, punctuality, convenience and comfort (Lu et al., 2017), and is the subjective impression of passengers on the efficiency and utility of the service provider’s services. Airline service quality, as the main criterion for passengers to evaluate airline services, is one of the factors limiting the development of the civil aviation service industry. Improving and perfecting airline service quality not only promotes the high-quality development of China’s civil aviation industry and improves airline passenger revenue (Ma, 2021), it can also enhance the world influence of China’s airline services (Zhang, 2019). Objective evaluation of airline service quality is a prerequisite for improving airline service quality and expanding the customer market of airline companies.
Aviation service complaints refer to the act of requesting the Civil Aviation Administration and its authorized agencies to mediate and protect the legitimate rights and interests of consumers after they have received services from public air transport enterprises and other services in order to travel or transport goods (Xu, 2020). Aviation complaints are an important manifestation of poor airline service quality, an act generated by passengers based on dissatisfaction with airline service quality, and an important criterion reflecting service quality (Zhuo, 2015), and handling aviation complaints is an important aspect of airline service quality regulation. According to the Two Factor Theory, the factors influencing service quality are divided into 2 categories: satisfaction and dissatisfaction. In a complete airline service, a complaint item is selected to refer to the dissatisfaction factor, which means that if a complaint occurs, the consumer is dissatisfied with the service; a non-complaint item is selected to refer to the satisfaction factor, which means that if no complaint occurs, the consumer is satisfied with the service. Using airline complaint data from 2015 to 2019, this paper firstly identifies the association characteristics between airline complaints and time factors in China and abroad using optimal scale analysis, and then constructs an airline service quality measurement model based on CAPSE to measure the service quality of full-service airlines and low-cost service airlines in China. Airline complaints are used to study airline service quality, to explore effective methods for researchers and industry practitioners to decipher the objective and comprehensive evaluation of airline service quality (Chen, 2016), and to make active efforts to seek reasonable suggestions and practical countermeasures for improving airline service quality.
Currently, scholars commonly use models such as customer satisfaction, service quality gap and brand asset quality for airline service quality evaluation. Customer Satisfaction Degree (CSD) is a quantitative indicator used to measure the level of consumer satisfaction with a service. “Satisfaction” is the utility obtained by consumers (Sun et al., 2011), airline customer satisfaction includes three levels: product satisfaction, experience satisfaction, spiritual satisfaction. Respectively, it measured the material carrier, service perception, emotional impact of the service (Yu & Li, 2012). Improving service quality is an effective means to improve passenger satisfaction, so most of the research on airline service quality in academia is related to customer satisfaction models. For example, Wang (2014) conducted a study on customer satisfaction of Shandong Airlines in China, which was constructed based on service quality theory. Badama (2015) analyzed the service quality level of Mongolian airlines based on the customer satisfaction index model of China’s civil aviation industry, and concluded that the satisfaction level of ticketing service and the satisfaction level of ground service affect consumers’ airline service experience, which in turn affects customers’ loyalty to the airline. Sun (2018) argued that there is a correlation between different travel patterns of passengers and indicators such as airline service quality, customer satisfaction, actual customer value and customers’ willingness to stay engaged.
The service quality gap evaluation model is mainly based on the difference between the expected and experienced service quality of customers to determine the service quality (Grönroos, 1984), which can be measured by the SERVQUAL (Service Quality) scale (Sun et al., 2011), specifically including five dimensions of reliability, responsiveness, empathy, tangibility and assurance (Li & Xiong, 2014). In the field of airline services, the use of the SERVQUAL scale has been expanded by adding the service process dimension (Cunningham, Young, & Lee, 2002), the elemental dimension (Chen, 2008), the remedial service dimension (Hao & Wu, 2009), and the perception and emotion dimension (Zhou et al., 2019; Du & Chen, 2017). With the update of digital technology, Büyüközkan, Havle, & Feyzioğlu (2020) proposed a 5-dimensional quality service model of digital tangibles, reliability, digital interaction, digital trust and customer-centric to measure the service quality of passengers towards the digital products and services provided by airlines; and the Intuitionistic Fuzzy Cognitive Map(IFCM) approach, based on Group Decision Making (GDM), develops a SERVQUAL model that takes into account the hesitancy, uncertainty and intuition of the consumer decision making process and the opinions of the decision maker (Büyüközkan, Havle, Feyzioğlu, & Göçer, 2020). Some scholars introduced multiple models into the SERVQUAL scale analysis, such as Chen (2016) and Chen & Chen (2010) who used the Fuzzy Analytical Hierarchical Process (FAHP) and Mixed Fuzzy Multiple Criteria Decision Making (MCDM) model to improve the airline service quality evaluation framework model. Gong (2014) combined the Kano two-dimensional quality model with the Decision Experiment Analysis Method (DEMATEL) in order to explore how to develop proper and effective service quality improvement strategies.
Brand asset evaluation is mainly based on consumers’ perception of the brand to evaluate airline service quality, and its formula is: brand asset value = f (consumer utility − expectation value). The graded value assessment of brand equity can be used for airline service quality inspection (Lu et al., 2017), which mainly includes: firstly, determining the content and extent to which the brand understands customer needs, such as highly efficient products or services, delivery of promises, sincere service of staff, comfortable environment, timely service, right to know, personalized service, good experience, pleasant memories, trust and expectation, etc. Secondly, it is determined whether the brand is adapted to customer needs. Airlines give full consideration to the practical features of product and service functions, meet the material and spiritual needs of customers, products should be tasteful and unique, pay attention to the artistic and visual perception of the brand, and give consumers the perception of enjoyment from the inside out. Keller (1993) proposed the Consumer Based on Brand Equity (CBBD) model, which defines the value of brand equity based on the differential effect of consumer responses to brand marketing. Chinese brand scholars have independently studied the brand value development theory (Zhou et al., 2019), which organically combines brand strength evaluation with brand equity value measurement, and then evaluates the service quality of brand products (Du & Chen, 2017).
The evaluation methods of aviation service quality are gradually diversified. To improve the airline service quality evaluation model, Liu et al (2017) constructed an international airline ticket review system and found that service is the core element, price affects customers’ psychological expectations, equipment and facility conditions are valued by customers, punctuality is a necessary condition, and management factors affect the image of airlines. Ma (2021) constructed a model of air passenger service quality under non-cooperative game between airlines and airports through differential game method, which was used to evaluate the relationship between service quality and passenger revenue. Lyu (2019) determined the service quality level and customer satisfaction of domestic and international airlines through the measurement of basic services such as in-flight, ground and ticketing, based on the CAPSE 2019 first quarter airline service measurement report. Li et al. (2017) incorporated total transport turnover and passenger traffic into the airline service quality evaluation system and used factor and cluster analysis models for analysis. Ma & Chen (2020) used network text analysis to analyze Xiamen airline service quality satisfaction and its influencing factors.
In summary, researches have evaluated airline service quality from multiple perspectives, but there are limitations, such as the strong subjectivity of customer satisfaction evaluation, the solidity of SERVQUAL scale assessment, which makes it difficult to fully present consumers’ true feelings; brand equity evaluation mostly uses questionnaires, with limited sample size and non-representative; diversified studies are based on online review data, but the data complexity and ambiguity are strong. Therefore, in order to break through the limitations, this paper uses airline complaint data as the basis for a comprehensive evaluation of airline service quality, which not only ensures the total amount of data and its typical characteristics, but also fully reflects the real feelings of customers.
Optimal scale analysis visually reveals the correlation between different categories of variables by means of dimensionality reduction graphs, and is often used to analyze the correlation between 2 or more variables in low-dimensional space, and allows any variable type, specifically including multiple correspondence analysis, principal component analysis and non-linear typical correlation analysis. Based on the multiple nominal and pooled quantitative characteristics of the research data, this paper focuses on multiple correspondence analysis to determine the correlation characteristics of aviation complaints with different temporal factors. SPSS was used to map the variables to a two-dimensional space and to output a discriminant measure plot and a joint category point plot. The discriminant plot shows the correlation between the dimensional scores and the quantified values of the variables, with the typical correlation between the variables being the sum of the first dimensional eigenvalues and the second dimensional eigenvalues, and if there is an extremely close acute relationship between the variables, this indicates a high correlation. If the closer the perpendicular distance from each type of point to the ray and its reverse extension, the stronger the correlation between the variables, which can be calculated using Equation (1).
ρ i = [ ( k × E i ) − 1 ] k − 1 (1)
In Equation (1): ρi is the typical correlation value, which is the interpretation of the correlation between variables, the closer to 1, the better the correlation; k is the number of variable sets, which is used to specify the number of groups of variables to be compared with other groups of variables; Ei is the characteristic value, which is used to explain the degree of variation generated by the variables in the first and second dimensions.
Drawing on the approach of Lyu (2019), an airline service quality measurement model was constructed based on the CAPSE method to calculate the airline service quality index, which is used to analyze the service quality level of airlines, calculated using Equation (2).
P j = ∑ i 1 − N i j C i 2 (2)
In Equation (2): P is the airline service quality measurement index, and the index is divided into three levels (Chen, 2016): lower level (0 - 3), medium level (3 - 4) and very high level (4 - 5); N is the number of airline complaints (pieces); C is the air traffic volume (thousand people); i represents each airline; j indicates the type of airline service measured, including comprehensive service quality, ticketing service quality, in-flight service quality, ground service quality and flight service quality (amount of irregular flights).
The datasets used in this paper is derived from the monthly data of the Air Transport Consumer Complaint Bulletin issued by the Civil Aviation Administration of China, which was collated and selected from a total of 79,773 Chinese domestic and international airline complaint data for 60 months over five years from January 2015 to December 2019, including 8217 foreign airline complaint data and 71,556 domestic airline complaint data, involving 5 types of complaints (
From 2015 to 2019, the temporal distribution of domestic aviation complaints was uneven, which in turn manifested itself in the relative concentration of annual, seasonal, quarterly and monthly (