Key Determinants of Airline Loyalty Modeling in Thailand

Abstract

The airline industry in Thailand has grown enormously over the past decade. Competition among airline companies to reach market share and profit has been intense, requiring strong strategic abilities. To increase the service quality of such companies, identifying factors related to the context of airlines is important for policymakers. Thus, this study aims to present empirical data on structural factors related to the loyalty of domestic airline passengers. Structural equation modeling was used to confirm the proposed model. The questionnaire was used to survey and collect data from 1600 airline passengers. The results indicate that satisfaction, trust, perceived quality, relationship, and image of airlines positively influenced loyalty with a statistical significance of α = 0.05. Moreover, the study found that expectation and perceived quality indirectly influenced loyalty. The findings provide a reference for airline operators to clearly understand the factors that motivate passenger loyalty, which can be used to develop the sustainability of marketing strategies and support competitiveness.

1. Introduction

Intense competition is the current trend in airline service businesses. The aviation liberalization in Thailand—in accordance with the Open Sky Policy of 2015—has resulted in the steady growth of the aviation industry steadily, as can be seen from the increase in the number of domestic airline passengers from 2017 to 2018, with an average growth rate of 6.1% [1], and the number of foreign tourists visiting Thailand, at a growth rate of 7.54% in 2018 compared to the previous year [2]. The statistical number of passengers reflects the growth of the aviation industry. As such, airline service companies should modify and/or develop new marketing strategies to cope and adjust with the rapid growth of the market, as well as the competition in the domestic airline industry against the competitors from both Low Cost Carriers, and many Full Service carriers.

Customer loyalty is an important motivation that leads passengers to continue using airline services, which influences a high forereach in competition. Therefore, airline market shares are mainly dependent on the airlines’ ability to foster customer loyalty, which leads to reliable profit [3]. This positively affects airline service companies over the lengthy duration of the business cycle. Despite the Covid-19 pandemic, which has had an impact on airline service users, the situation is likely to recover shortly [4]. At that time, the most significant issue will be loyalty. Airline service companies can achieve long-term success by setting a definition of customer loyalty. Previous researchers investigated the factors that influence passenger loyalty to understand its effects. However, studies that comprehensively evaluated such factors were limited and overlooked the connections among commitment, attractiveness of competitors, and passenger loyalty in the airline context. Commitment and attractiveness of competitors have been considered factors correlated to several contexts; for example, Chen [5] found that commitment was a mediator resulting in loyalty in the e-service context. In addition, Wen et al. [6] found that the attractiveness of competitors directly influenced the passenger loyalty in the intercity bus services. Moreover, the majority of previous studies focused on attitudinal loyalty [7]. To address this research niche, the current study aimed to measure behavioral loyalty, which can accurately reflect passenger loyalty.

This study aims to develop a structural equation model of airline passenger loyalty to acknowledge the relationship of structural factors involved with airlines, resulting in passenger loyalty. The finding can provide novel and insightful data in terms of airline passenger behaviors. Furthermore, the results can be utilized as a road map for developing marketing strategies, increasing customers, and efficiently developing new campaigns according to consumer demands.

In Thailand, the factors affecting passenger loyalty in domestic airlines have seldom been addressed, and the samples which were chosen were only at Suvarnabhumi International Airport and Don Mueang International Airport, both which are the main airports in the Central region. For example, Saha Gour [8] studied the factors affecting the behavioral intentions of low-cost carrier (LCC) passengers in Thailand. Vuthisopon and Srinuan [9] studied the factors influencing the passengers’ repurchase intentions in the context of LCC at Don Muang Airport and Suvarnabhumi Airport, Thailand. Here, the areas of Thailand were divided into four regions, namely, northern, northeastern, central, and southern regions. To obtain the passengers’ opinions, this study conducted a survey on samples representing passengers from all four regions.

2. Literature Review and Hypothesis Development

Airline passenger behavior is an important issue and a significant requirement to study for understanding the needs of passengers who select an airline service. However, passenger loyalty in airline service is an indispensably acquired task for airlines. The higher passenger loyalty the airlines obtain, the better stability the airlines have regarding both sales volumes, which potentially increase profits, and decrease marketing expenses for recruiting or attracting new passengers. In fact, passenger loyalty is expressed through behavior involved in the services or airlines, including their intention on service reusability and positive stories shared by word of mouth with others. However, to establish airline loyalty, it is imperative to firstly acknowledge the factors affecting passenger loyalty in the airline so that service providers can manageably administrate the right issues. In this study, the researcher applied all of 11 factors as follows:

2.1. Customer Expectations

Expectation is a human attitude pertaining to the desire for an object or concept that is expected to be received in the future. The expectation of each person is dependent on their social background, experience and environment. It is an important factor for measuring overall service quality from the passenger’s perspective. For this study, service quality expectation was measured using five indicators, namely, airline tangible, flight attendants, airline operation, information, and ground services. Many studies on the airline service industry confirmed that service quality expectation is positively correlated with perceived service quality [10,11,12,13,14], including negatively influenced satisfaction [13,14].

2.2. Perceived Service Quality

Perceived service quality is the evaluation of overall service quality from the customer’s perspective by comparing the demand or expectation and received services [15]. In the context of the the airline service industry, many studies have found that perceived service quality is directly correlated to satisfaction [8,11,12,13,14,16,17,18,19,20,21,22,23,24]. Moreover, other studies have found that perceived service quality positively influenced perceived value [10,12,13,14,24,25,26]. In the present study, the perceived service quality of airlines was evaluated using five indicators, similarly to customer expectation.

2.3. Perceived Value

Perceived value pertains to a customer’s judgment or evaluation by comparing the pros and cons received from products or services for cost and benefit awareness [27]. The previous literature related to the airline context has posited that perceived value positively influences satisfaction [7,10,11,12,13,24,28]. Moreover, perceived value positively influences behavioral intentions [10,11,13,14,24,25,26,29,30], which is similar to Forgas et al. [7], who found that perceived value positively influences loyalty.

2.4. Customer Satisfaction

Satisfaction is the level of pleasant feeling when customers receive products or services. It is the result of the correlation of a customer’s comparison between perceived service and expected service. If the quality of the products or services that customers receive is less than expected, then dissatisfaction occurs. Equally, satisfaction occurs when the product or service is similar to expectations. That is, the higher the perceived quality of products or services than expected, the higher the satisfaction [31]. Marketing researchers considered satisfaction an emotional response related to knowledge and understanding of the service experience, which are interpreted as pleasant feelings. The extant literature identified a relationship between satisfaction and behavioral intentions (i.e., word of mouth (WOM), repurchase intentions, and recommendations) [8,10,13,19,20,32]. This relationship is the principle of the marketing concept, that is, customer satisfaction is an important and necessary factor for the repeated demand for products or services. Forgas et al. [7], and Akamavi et al. [33] illustrated that satisfaction positively influences trust. Other scholars confirmed the direct positive relationship between satisfaction and behavioral intention [8,9,10,11,13,14,19,20,26,29,32,34,35,36,37]. Moreover, many studies found that satisfaction positively affects loyalty [7,9,12,17,18,21,23,28,33,38].

2.5. Customer Trust

Customer trust is the real circumstance when certain people support the service by exchanging reliability and integrity [39], which are assessed by the confidence of customers. Each service provider receives different levels of trust from customers based on the customers’ perceived quality of service compared with the provider’s definition of such services. Furthermore, trust is important for the relation model definition, which presents the relationship between brand and customers [40]. Previous studies found that trust is directly and positively related to loyalty [7,33,41].

2.6. Customer Commitment

Commitment to products or services indicates the potential of service providers to positively influence the feelings of consumers when using those services, such as the desire to maintain the relationship [40]. Bendapudi and Berry [42] opined that the factors for a positive relationship with customers include environmental factors, maintaining the relationship with customers, meeting customer demands, and interacting with customers. Previous studies highlighted commitment as an important factor for customer loyalty [5].

2.7. Attractiveness of Competitors

The attractiveness of competitors indicates customer awareness about the scope of service of potential competitors. If there are few competitors, then opportunities of customer loyalty to the service increase [6]. The research in the context of transportation has confirmed that the attractiveness of competitors negatively influences customer loyalty [6,26].

2.8. Perceived Risk

Perceived risk pertains to the recognition of the possibility of damage or negative effects of expected events, which frequently occurs with consumers [43]. If the effect of an event is greater than the acceptable level of customers, then this recognition will affect customer behavior. In this study, perceived risk pertains to the recognition of the airlines’ safety and risk measures, which are important factors for passengers in selecting an airline service operator. Cho et al. [44], and Han et al. [45] emphasized that perceived risk negatively influences the intention to travel by airplane.

2.9. Switching Costs

Jones et al. [46] defined switching costs as “the perceived economic and psychological costs associated with changing from one alternative to another”. The factors influencing switching cost differ according to the type of product, business, and customers. The results from studies on transportation showed that when the cost for switching service operators increases, customers remain loyal to the same service operator [6,26,29,47].

2.10. Airline Image

Brand image is defined as “perceptions about a brand as reflected by the brand associations held in consumer memory” [48], including an attitude that reflects the blending of beliefs, impressions, and ideas of customers to products, services, persons, companies, or brands [49]. Organizations with a positive image can attract new customers, retain current customers, and stand out from competitors [50]. Customers evaluate the efficiency and provided services of an airline, which is a factor that positively influences behavioral intentions [11,13,14,22,24,35] and positively influences loyalty to the airline industry [51].

2.11. Customer Loyalty

Customer loyalty indicates that customers hold a promising attitude and behavior, and support products or services. This finding can be reflected in several activities, e.g., recommending the current service operator to other consumers and repurchasing [52]. The study measured customer loyalty using three indicators, namely, WOM, repurchase intention, and identification.

Table 1. Summary of structural factors related to airlines.

Authors (Year)	Service Quality	Perceived Value	Satisfaction	Trust	Commitment	Attractiveness of Competitor	Perceived Risk	Switching Cost	Image	Loyalty/Behavioral Intentions
Chen et al. [25]	✓	✓								✓
Cho et al. [44]							✓			✓
Lee et al. [28]	✓	✓	✓							✓
Kos Koklic et al. [20]	✓		✓							✓
Vuthisopon and Srinuan [9]	✓		✓							✓
Rajaguru [36]	✓		✓							✓
Akamavi et al. [33]	✓		✓	✓						✓
Ali et al. [16]	✓		✓						✓
Park et al. [34]	✓		✓							✓
Hussain et al. [12]	✓	✓	✓						✓	✓
Leong et al. [21]	✓		✓							✓
Nesset and Helgesen [47]	✓		✓					✓	✓	✓
Suki [37]	✓		✓							✓
Namukasa [23]	✓		✓							✓
Han [32]	✓		✓							✓
Chiou and Chen [29]	✓	✓	✓					✓		✓
Curry and Gao [38]	✓		✓							✓
Yang et al. [30]	✓	✓							✓	✓
Kim and Lee [19]	✓		✓							✓
Mikulić and Prebežac [51]	✓								✓	✓
Chiou and Chen [11]	✓	✓	✓						✓	✓
Forgas et al. [7]	✓	✓	✓	✓						✓
Saha Gour [8]	✓		✓							✓
An and Noh [17]	✓		✓							✓
Nadiri et al. [22]	✓		✓						✓	✓
Park et al. [13,14,24,35]	✓	✓	✓						✓	✓
This study	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓

and

Table 2. Summary of hypotheses and supporting research.

Hypothesis	Relationship	Previous Studies
H1	Customer expectations→(+) perceived service quality	Chen [10], Chiou and Chen [11], Hussain et al. [12], and Park et al. [13,14]
H2	Customer expectations→(-) customer satisfaction	Park et al. [13,14]
H3	Perceived service quality→(+) perceived value	Chen [10], Hussain et al. [12], Park et al. [13,14,24], Chen et al. [25], and Jen et al. [26]
H4	Perceived service quality→(+) customer satisfaction	Saha Gour [8], Chiou and Chen [11], Hussain et al. [12], Park et al. [13,14], Ali et al. [16], An and Noh [17], Chotivanich [18], Kim and Lee [19], Kos Koklic et al. [20], Leong et al. [21], Nadiri et al. [22], Namukasa [23], and Park et al. [24]
H5	Perceived value→(+) customer satisfaction	Forgas et al. [7], Chen [10], Chiou and Chen [11], Hussain et al. [12], Park et al. [13,24], and Lee et al. [28]
H6	Perceived value→(+) customer loyalty	Forgas et al. [7], Chen [10], Chiou and Chen [11], Park et al. [13,14,24], Chen et al. [25], Jen et al. [26], Chiou and Chen [29], and Yang et al. [30]
H7	Customer satisfaction→(+) customer trust	Forgas et al. [7], and Akamavi et al. [33]
H8	Customer satisfaction→(+) customer loyalty	Forgas et al. [7], Saha Gour [8], Vuthisopon and Srinuan [9], Chen [10], Chiou and Chen [11], Hussain et al. [12], Park et al. [13,14], An and Noh [17], Chotivanich [18], Kim and Lee [19], Kos Koklic et al. [20], Leong et al. [21], Namukasa [23], Jen et al. [26], Lee et al. [28], Chiou and Chen [29], Han [32], Akamavi et al. [33], Park et al. [34], Park et al. [35], Rajaguru [36], Suki [37], and Curry and Gao [38]
H9	Customer trust→(+) customer loyalty	Forgas et al. [7], Akamavi et al. [33], and Forgas et al. [41]
H10	Customer commitment→(+) customer loyalty	Chen [5]
H11	Competitor attractiveness→(-) customer loyalty	Wen et al. [6], and Jen et al. [26]
H12	Perceived risk→(-) customer loyalty	Cho et al. [44], and Han et al. [45]
H13	Switching costs→(+) customer loyalty	Wen et al. [6], Jen et al. [26], Chiou and Chen [29], and Nesset and Helgesen [47]
H14	Airline image→(+) customer loyalty	Chiou and Chen [11], Park et al. [13,14], Nadiri et al. [22], Park et al. [24,35], and Mikulić and Prebežac [51]

illustrate the reviewed literature related to the structural factors in the airline context to identify the limitations of 29 articles from 2004 to 2019. Most articles focused on service quality, perceived value, satisfaction, airline image connected to loyalty, and the behavioral intention of passengers. The results of the related research indicate many limitations in the research on airlines. For example, the influence of commitment and attractiveness of competitors to airline passenger loyalty was overlooked in some studies, but can be found in other research such as the study on the educational tour bus [53]. Due to the limited number of studies exploring trust, perceived risk, and switching cost as factors influencing passenger loyalty, the present study intends to foster a more comprehensive understanding of passenger loyalty in the context of the airline service business to contribute another aspect to the previous literature. We investigated the correlation of factors mentioned by creating a structural model, which is compounded from customer expectation, perceived service quality, perceived value, customer satisfaction, customer trust, customer commitment, attractiveness of competitors, perceived risk, switching costs, airline image, and customer loyalty.

3. Materials and Methods

3.1. Measure Development

The structure of total factors and indicators was comprehensively applied from the literature review of consumer behavior and previous studies related to the airline context. Several modifications were made to suit the perspective of this study. The structure of factors had 11 dimensions, namely (a) customer expectation, (b) perceived service quality (measured by five items from [54,55,56,57]), (c) perceived value (three items from [29,58]), (d) customer satisfaction (four items from [33,58]), (e) customer trust (five items from [33,53]), (f) customer commitment (three items from [53,59]), (g) attractiveness of competitors (two items from [53]), (h) perceived risk (four items from [45,60]), (i) switching costs (two items from [6]), (j) airline image (three items from [58,61]), and (k) customer loyalty (three items from [53]). The total indicators are measured by a 7-point Likert-type scale (1 = strongly disagree; 7 = strongly agree).

For survey reliability, the researchers examined the content validity of the questionnaire by using the value of the index of Item Objective Congruency (IOC), obtained from 5 experts’ scores covering every dimension involved with airline services. The criterion used to consider every question item was whether the IOC value was more than 0.50. Then, the questionnaire was used in a pilot test of 50 samples. To obtain the final version, the researchers assure the respondents’ comprehension by adjusting the question items in the questionnaire according to the experts’ suggestions. In addition, this research was approved by Ethics Committee for Researches Involving Human Subjects, Suranaree University of Technology (COA.30/2562).

3.2. Data Collection Procedure

Data were collected by face-to-face surveys administered to obtain passengers’ information at airports in Thailand. The survey was conducted at the Arrival Terminal building with participants who qualified as passengers with traveling experience by plane. Stratified sampling was carried out by considering the region of a particular airport. The sample was separated by four regions of Thailand, namely central, southern, northern, and northeastern regions. A total of 400 samples were obtained per region. Before the interview, the researchers informed the passengers about the objectives of the research and selected passengers who felt comfortable answering the questions. An average of 15 min was spent on each interview. We collected data from 1600 respondents, of which 739 were male (46.2%) and 861 were female (53.8%). In terms of age, 852 (53.2%) were aged 18–29 years, 595 (37.2%) were aged 30–39 years, and 153 (9.6%) were older than 40 years. In terms of the objective of traveling, 153 (9.6%) of the respondents traveled to visit their hometown, 772 (48.2%) took leisure trips, 457 (28.6%) went on business trips, and 177 (11.1%) visited friend or relatives. In terms of frequency of travel, 33 (2.1%), 61 (3.8%), 193 (12.1%), 534 (33.3%), and 779 (48.7%) traveled every 2 weeks, every month, every 2–3 months, every 4–6 months, and every year, respectively, as shown in

Table 3. Demographics of the participants.

Demographic	Category	N	%
Gender	Male	739	46.2
	Female	861	53.8
Age (yeas old)	18–29	852	53.2
	30–39	595	37.2
	>40	153	9.6
Travel purpose	Visit hometown	194	12.1
	Travel trip	772	48.2
	Business trip	457	28.6
	Visit friends/relatives	177	11.1
Travel frequency	Every 2 weeks	33	2.1
	Every month	61	3.8
	Every 2–3 months	193	12.1
	Every 4–6 months	534	33.3
	Every year	779	48.7

.

3.3. Data Analysis

Structural equation modeling (SEM) was utilized to measure the correlation of variables in the theoretical model. In comparison with SEM, the MCDM techniques contain a consistency measure of responses by examining the intention of respondents and the given scores. This is not a tool in SEM. On the contrary, SEM provides a faster and easier survey process and, by using the latent and observed variable approach, positive and negative impacts can be both considered, which is not possible in MCDM [62,63,64]. Many correlations were observed between latent and observed variables. SEM was utilized for hypothesis testing of the proposed model, which was compounded from two sub-models, namely, measurement and structural models. Structural accuracy can be measured by goodness-of-fit indicators. The literature recommends the following indicators: chi-square/degrees of freedom (χ²/df) < 3 [65], root mean square error of approximation (RMSEA) ≤ 0.06, comparative fit index (CFI) > 0.95, Tucker–Lewis coefficient (TLI) > 0.95, and standardized root mean square residual (SRMR) ≤ 0.08 [66]. SEM was analyzed using Mplus version 7.2 by the maximum likelihood method.

4. Results

4.1. Descriptive Analysis

The results of descriptive statistics were derived from the mean ($\overline{X}$) and standard deviation (SD) of all indicators of the structural factors for airline loyalty. The study found that the top average indicators were customer expectation factors, such as flight attendants (EQ2) (M = 6.148, SD = 0.679) and ground services (EQ5) (M = 6.087, SD = 0.750). The lowest average value was assigned to the perceived risk factor as indicated by “My image can be damaged when I travel with this airline again” (PR4) (M = 3.857, SD = 1.779). However, all average values were higher than the median of the 7-point Linkert-type scale meter.

When considering the normal distribution of the collected data, skewness should be lower than 3, and kurtosis should be less than 10 [67].

Table 4. Assessment of measurement models.

Item	Constructs and Indicators	References	Standardized Loading	$\overline{\mathit{X}}$	SD	Sk	Ku
	Customer expectation (Cronbach’s α = 0.944, AVE = 0.872, CR = 0.941)
EQ1	Airline operation	Medina-Muñoz et al. [56]	0.913 **	6.07	0.70	–0.50	–0.37
EQ2	Flight attendants	Kim et al. [55]	0.888 **	6.15	0.68	–0.59	–0.42
EQ3	Ground services	Medina-Muñoz et al. [56]	0.872 **	6.09	0.75	–0.50	–0.55
EQ4	Information	Wu and Cheng [57]	0.851 **	6.03	0.73	–0.43	–0.46
EQ5	Airline tangible	Farooq et al. [54]	0.838 **	6.08	0.72	–0.60	–0.24
	Perceived service quality (Cronbach’s α = 0.927, AVE = 0.843, CR = 0.925)
PQ1	Airline operation	Medina-Muñoz et al. [56]	0.896 **	5.85	0.70	–0.48	0.02
PQ2	Ground services	Medina-Muñoz et al. [56]	0.850 **	5.88	0.78	–0.56	0.63
PQ3	Information	Wu and Cheng [57]	0.849 **	5.82	0.78	–0.44	–0.03
PQ4	Flight attendants	Kim et al. [55]	0.826 **	5.98	0.67	–0.45	–0.04
PQ5	Airline tangible	Farooq et al. [54]	0.793 **	5.80	0.73	–0.39	0.23
	Perceive value (Cronbach’s α = 0.910, AVE = 0.889, CR = 0.919)
PV1	When compared to the received service, I think it is worth the money I paid.	Chiou and Chen [29], and Singh [58]	0.900 **	5.64	0.98	–0.60	0.56
PV2	I accept the provided services. Comparing with the money I paid, it is reasonable.	Chiou and Chen [29]	0.886 **	5.67	0.96	–0.69	0.73
PV3	When I travel with this airline, I think that it worth more than other transportations.	Chiou and Chen [29]	0.880 **	5.65	0.98	–0.72	0.72
	Customer satisfaction (Cronbach’s α = 0.924, AVE = 0.863, CR = 0.921)
CS1	I am very happy to use services form this airline.	Akamavi et al. [33]	0.888 **	5.63	1.00	–0.56	0.33
CS2	Overall, I am satisfied with services of this airline.	Akamavi et al. [33], and Singh [58]	0.877 **	5.70	0.96	–0.62	0.42
CS3	The quality of service that I receive is higher than I expect.	Singh [58]	0.857 **	5.63	1.03	–0.62	0.44
CS4	The quality of service that I receive is the services in my dream.	Jomnonkwao et al. [53]	0.831 **	5.55	1.11	–0.86	1.35
	Customer trust (Cronbach’s α = 0.937, AVE = 0.853, CR = 0.930)
CT1	This airline is the airline that I always trust.	Jomnonkwao et al. [53]	0.876 **	5.59	1.07	–0.81	1.24
CT2	This airline is the airline that knows how to make passengers feel satisfied.	Jomnonkwao et al. [53]	0.874 **	5.62	1.06	–0.73	0.87
CT3	This airline is a very reliable airline.	Akamavi et al. [33], and Jomnonkwao et al. [53]	0.857 **	5.64	1.03	–0.70	0.87
CT4	I trust that traveling with this airline was the best service airline.	Jomnonkwao et al. [53]	0.833 **	5.50	1.10	–0.81	1.27
CT5	This airline is a stable and reliable airline.	Jomnonkwao et al. [53]	0.824 **	5.62	1.05	–0.75	0.92
	Customer commitment (Cronbach’s α = 0.908, AVE = 0.850, CR = 0.887)
CC1	I think that traveling by this airline make my image better	Jomnonkwao et al. [53]	0.892 **	5.32	1.17	–0.52	0.09
CC2	I concerned about the long-term-success of this airline.	Jomnonkwao et al. [53]	0.845 **	5.29	1.16	–0.43	–0.06
CC3	I feel so proud to use the services of this airline.	Jomnonkwao et al. [53], and Chen and Kao [59]	0.814 **	5.25	1.17	–0.32	–0.21
	Attractiveness of competitors (Cronbach’s α = 0.911, AVE = 0.915, CR = 0.912)
AC1	I think that I might be happier if I travel by other airlines instead of this airline.	Jomnonkwao et al. [53]	0.928 **	4.67	1.38	–0.35	0.10
AC2	In comparison, traveling by other airlines make me feel more satisfied than this airline.	Jomnonkwao et al. [53]	0.902 **	4.69	1.32	–0.38	0.31
	Perceived risk (Cronbach’s α = 0.961, AVE = 0.919, CR = 0.957)
PR1	There is a very high chance to make me uncomfortable when I travel by this airline	Han et al. [45]	0.966 **	4.03	1.68	–0.28	–0.73
PR2	There is a very high chance to make me worried when I travel by this airline again.	Han et al. [45]	0.938 **	4.09	1.64	–0.34	–0.58
PR3	There is a very high chance to make me injured when I travel by this airline again.	González Mieres et al. [60]	0.909 **	3.87	1.74	–0.16	–0.85
PR4	There is a chance to damage my image when I travel by this airline again.	González Mieres et al. [60]	0.864 **	3.86	1.78	–0.18	–0.95
	Switching costs (Cronbach’s α = 0.878, AVE = 0.890, CR = 0.887)
SC1	I can pay more charge to use other airlines if there are better services.	Wen et al. [6]	0.975 **	4.86	1.34	–0.37	–0.05
SC2	I can spend more time to search for information of other airlines who provide better services for my next travel.	Wen et al. [6]	0.804 **	4.98	1.29	–0.37	–0.09
	Airline image (Cronbach’s α = 0.914, AVE = 0.885, CR = 0.916)
AI1	In my opinion, this airline has a good image in passengers’ minds.	Singh [58], and Chien-Chang [61]	0.935 **	5.78	0.95	–0.69	0.19
AI2	I always well impress with this airline	Singh [58], and Chien-Chang [61]	0.883 **	5.75	0.96	–0.56	–0.14
AI3	I trust that this airline has a better image than other competitors.	Singh [58], and Chien-Chang [61]	0.836 **	5.63	1.02	–0.84	1.10
	Customer loyalty (Cronbach’s α = 0.910, AVE = 0.869, CR = 0.902)
CL1	WOM	Jomnonkwao et al. [53]	0.901 **	5.70	0.88	–0.43	–0.20
CL2	Identification	Jomnonkwao et al. [53]	0.875 **	5.50	1.02	–0.79	1.33
CL3	Repurchase	Jomnonkwao et al. [53]	0.830 **	5.70	0.91	–0.60	0.40

Note: ** Significant at α = 0.001, $\overline{X}$ = Mean, SD = standard deviation, Sk = skewness, and Ku = Kurtosis.

shows that all indicators had skewness values between −0.865 and −0.163 and kurtosis values between −0.950 and 1.353, indicating that the statistical values were within the acceptable range. These results confirm that the collected data were normally distributed.

4.2. Reliability and Validity

Cronbach’s alpha value was used to test data reliability; if reliable, this value should be higher than 0.7 [68]. The tested reliability values of each factor ranged between 0.878 and 0.961, as shown in Table 4, which confirms the consistency of the collected data. For convergent and discriminant validity, the standardized factor loading value of each indicator was considered; this should be higher than 0.7 [69]. Indicators for each factor should have a high covariance to explain the same factor when using AVE; this value should be higher than 0.5 [69]. In addition, construct reliability should be equal to or higher than 0.7 [69]. Table 4 presents the standardized factor loading values of each indicator as ranging between 0.793 and 0.975, average variance extracted in the range between 0.843 and 0.919, and construct reliability in the range of between 0.887 and 0.957. These findings indicate that the results were within the recommended ranges. Therefore, the measurement structure of the model was suitable and reliable.

4.3. Measurement Model

The measurement model for the 39 observed indicators was divided into 11 perspectives. Table 4 shows the results. When evaluating the importance of the factor of each observed indicator, statistical significance at the 0.001 level was reached with the weighted component of the observed indicators as follows:

Customer expectation. This aspect was measured using five observed variables, namely, airline operation (EQ1), flight attendants (EQ2), ground services (EQ3), information (EQ4), and airline tangible (EQ5). The finding shows that airline operation had the highest factor weighted value at γ = 0.913, followed by flight attendants (γ = 0.888) and ground services (γ = 0.872).

Perceived service quality. This was measured using five observed variables (PQ1–PQ5). Airline operation (PQ1) had the highest weighted component value (γ = 0.896), followed by ground services (PQ2) and information (PQ3) with γ = 0.850 and γ = 0.849, respectively.

Perceived value. The measurement model for perceived value used three observed variables (PV1–PV3). The findings show the highest weighted component value of PV1 at γ = 0.900, followed by PV2 and PV3 at γ = 0.886 and γ = 0.880, respectively.

Customer satisfaction. The measurement model for this factor was measured using four observed variables (CS1–CS4). The results show that CS1 had the highest weighted component value at γ = 0.888, whereas CS4 had the lowest weighted component value at γ = 0.831.

Customer trust. This factor was measured using five observed variables (CT1–CT5), where CT1 and CT5 obtained the highest and lowest weighted component values at γ = 0.876 and γ = 0.824, respectively.

Customer commitment. The factor was measured using three observed variables (CC1–CC3), where CC1 and CC3 gained the highest and lowest weighted component values at γ = 0.892 and γ = 0.814, respectively.

Attractiveness of competitors. The measurement model of attractiveness of competitors was measured using two observed variables (AC1–AC2). AC1 obtained a higher weighted component value (γ = 0.928) than AC2 (γ = 0.902).

Perceived risk. This factor was measured using four observed variables (PR1–PR4), where PR1 and PR4 achieved the highest and lowest weighted component values at γ = 0.966 and γ = 0.864, respectively.

Switching costs. Two observed variables (SC1–SC2) were used to measure switching costs. SC1 obtained a higher weighted component value at γ = 0.975 than SC2 at γ = 0.804.

Airline image. Using three observed variables (AI1–AI3), the model of airline image was measured. The weighted component values were ranked as follows: AI3 (γ = 0.935), AI1 (γ = 0.883), and AI2 (γ = 0.836).

Customer loyalty. The weighted component values for customer loyalty were ranked in the following order: WOM (γ = 0.901), identification (γ = 0.875), and repurchase (γ = 0.830).

4.4. Structural Model

Figure 1 presents the structural model in terms of accuracy and indicates that it had a sufficient suitability index compared with the data in Section 3.3 (χ² = 1763.023, df = 627, p < 0.001; χ²/df = 2.812, RMSEA = 0.034, CFI = 0.982, TLI = 0.979, SRMR = 0.035). Therefore, the collected data were in agreement with empirical data.

SEM results were utilized for hypothesis testing, as shown in

Table 5. The results of structural model.

Hypothesis Path	Standardized Coefficients	Standard Error	t-Value	Result
H1: Customer Expectation→Perceived service quality	0.585	0.018	32.271 **	Supported
H2: Customer Expectation→Customer satisfaction	−0.442	0.084	−5.235 **	Supported
H3: Perceived service quality→Perceived value	0.954	0.050	18.919 **	Supported
H4: Perceived service quality→Customer satisfaction	0.634	0.060	10.623 **	Supported
H5: Perceived value→Customer satisfaction	0.800	0.029	27.879 **	Supported
H6: Perceived value→Customer loyalty	0.225	0.029	7.714 **	Supported
H7: Customer satisfaction→Customer Trust	0.989	0.010	97.885 **	Supported
H8: Customer satisfaction→Customer loyalty	0.407	0.047	8.655 **	Supported
H9: Customer Trust→Customer loyalty	0.233	0.048	4.855 **	Supported
H10: Customer Commitment→Customer loyalty	0.097	0.025	3.895 **	Supported
H11: Attractiveness of competitors→Customer loyalty	−0.013	0.021	−0.618	Not Supported
H12: Perceived risk→Customer loyalty	−0.022	0.020	−1.107	Not Supported
H13: Switching costs→Customer loyalty	−0.011	0.019	−0.541	Not Supported
H14: Airline image→Customer loyalty	0.035	0.018	1.999 *	Supported

Note: → regression. * Significant at α = 0.05; ** significant at α = 0.001.

. The findings indicate that customer expectation directly influenced perceived service quality (β = 0.585, p < 0.001), which supported H1. Furthermore, customer expectation was negatively correlated with satisfaction (β = −0.442, p < 0.001), which supported H2. Perceived quality was positively correlated with perceived value and satisfaction with a statistical significance level of 0.001 (β = 0.954 and β = 0.634, respectively), which accordingly supported H3 and H4. Perceived value was positively correlated with satisfaction (β = 0.800) and customer loyalty (β = 0.225) with a statistical significance level of 0.001, which supported H5 and H6, respectively. In addition, customer satisfaction was positively correlated with trust (β = 0.989) and customer loyalty (β = 0.407) at a statistical significance level of 0.001, which supported H7 and H8, respectively. Furthermore, customer loyalty was influenced by trust, commitment, and airline image by positive effect coefficients at β = 0.233 (p < 0.001), β = 0.097 (p < 0.001), and β = 0.035 (p < 0.05), which support H9, H10, and H14, respectively. Finally, attractiveness of competitors (β = −0.013), perceived risk (β = −0.022), and switching costs (β = −0.011) negatively influenced customer loyalty and were statistically non-significant, which did not support H11, H12, and H13, respectively. Figure 1 presents the structural model related to the airline context and the results of hypothesis testing.

The results can be considered consistent only by using the measuring tools of SEM, the Cronbach alpha, average variance extracted (AVE), construct reliability (CR) and variance explanation. This is acceptable but the real intentions of respondents cannot be measured directly in SEM.

5. Discussion

This study had a total of 14 hypotheses, and the correlations between the mutual initial factors and initial factors and customer loyalty were tested using SEM. The findings of this research are as follows: From SEM, customer loyalty was influenced by customer satisfaction, trust, perceived value, commitment, and airline image. Satisfaction was considered the most important variable. In other words, customer loyalty largely reflected satisfaction. This notion was observed when customers were satisfied with a particular service, which led to the decision of repurchasing, which supported Hussain et al. [12], Leong et al. [21], Lee et al. [28], and Akamavi et al. [33]. Trust was deemed important to establish the reliability of the airline. If passengers have a high level of trust, it has been shown that the level of loyalty is also high, which supported Forgas et al. [7], and Akamavi et al. [33]. Perceived value is a result of a customer’s realization that the airline offers reasonable prices, which may influence the intent to repurchase. This notion is beneficial for gaining a competitive advantage, which supported Forgas et al. [7]. Commitment was one of the factors that created a long-term relationship between airlines and customers. When an airline service company continuously delivers high-standard services, customers are more likely to remain loyal to the airline, which supported Chen [5]. Airline image plays an important role in creating an outstanding airline. If the airline is well known, it can preserve its current customers and attract customers from other airlines, which supported Mikulić and Prebežac [51]. Therefore, establishing a positive airline image by delivering attractive and impressive services to promote the airline is an essential step.

The model indicated that several factors influenced satisfaction, namely perceived value, perceived service quality, and customer expectation. Perceived value has the largest influence, which indicates that airlines should create good value and show customers that the airline fees are suitable and reasonable for the services provided, which supported Hussain et al. [12], and Lee et al. [28]. When considering the measurement models of expectation and perceived service quality, five indicators influenced customers’ perceived value and satisfaction: Airline tangible, flight attendants, airline operation, information, and ground services. Airline operation had the highest weighted value. Therefore, airline service operators should focus on quality service operation, e.g., punctuality of flight, safety, and reliability of airline Medina-Muñoz et al. [56]. In other words, to elicit satisfaction from customers, airlines should be knowledgeable about such indicators of direct specific development.

Three hypotheses were not statistically significant in the SEM in terms of loyalty: H12 (perceived risk), H13 (attractiveness of competitors), and H14 (switching costs). For H12 (Perceived risk), this was because passengers trusted that traveling by air achieved greater convenience and safety compared weighted against the risk to body and mind. For H13 (Attractiveness of competitors), this was because there were few service airlines, leading to low competition. In addition, the majority of domestic airlines in Thailand are low-cost, which may have led passengers to state that the price was reasonable compared with domestic low-cost airlines. In relation to H14 (switching costs), airline service business provides low-frequency flights. As such, passengers are hesitant to change to other service operators, which may become a complex process, take up time, and become more expensive. When comparing between the results and the study of Wen et al. [6], and Jen et al. [26], it was found that switching costs had dissimilar directions of correlation with loyalty. This possibly results from their studies on different transport types including intercity bus services and the coach industry.

6. Conclusions, Limitations, and Future Work

The aviation industry in Thailand has experienced enormous recent growth. A high level of competition was notable among airline companies to reach intended market shares and profits, which clearly required strong strategic abilities. To cope with this development, the study aimed to investigate the correlation between structural factors related to passenger loyalty in the airline context. The study tested correlations among 11 factors, namely, customer expectation, perceived service quality, perceived value, satisfaction, trust, commitment, attractiveness of competitors, perceived risk, switching costs, airline image, and customer loyalty. The findings can be used to enhance the knowledge and understanding of the airline service market by presenting empirical evidence for fostering loyalty toward airlines.

In this study, the researcher found significant results which concluded that customer satisfaction is the main factor resulting in customer loyalty, and customer satisfaction is influenced by customer expectation, perceived service quality, and perceived value. To satisfy service users, service providers need to develop strategies consistent with passengers’ needs by prioritizing the most factor loading value. For example, when considering the results of the model measuring the expectation, it was found that airline operation had the highest factor loading. Thus, service providers need to pay special attention to this factor, followed by flight attendants, ground services, information and airline tangible, respectively. However, improving service quality cannot be carried out without limits: the cost-benefit analysis is required before deciding on the development of certain service quality attributes. In addition, from the structural equation model it was found that the subsequent factors affecting airline loyalty were trust, perceived value, commitment, and airline image. These factors have to be taken into consideration. Thus, it can be seen that Commitment is a new correlated factor. Therefore, airline service providers have to make passengers feel positive about airline services, for example, passengers feel proud and have a good image when using the airline services and also concerned about the airline long-term success.

As previously mentioned, customer loyalty is imperative in the airline context. When customers receive reasonable value, they are impressed with services, and display trust, commitment and maintain a good image of the airline. Their repurchasing behavior can be influenced, and the current customer base retained through WOM. Therefore, understanding these factors can provide the airlines with market share and profit advantages. However, the study presented an overview of airlines in Thailand without specifically scrutinizing each airline service company. As such, future studies should investigate individual airline service companies to create a suitable road map and develop strategies for airline service companies.