I. Introduction
In recent years, the global airline industry has experienced profound transformations, notably with Asia, and specifically China, emerging prominently. Since 2020, China has become the world’s largest aviation market, driven by rapid urbanization, rising disposable income, and an increasing preference for personalized digital services (CAAC, 2022; IATA, 2023). However, as post-pandemic recovery intensifies competition, understanding the determinants of consumer choice is increasingly essential for airlines aiming to differentiate themselves in the Chinese market.
Prior research has extensively examined airline selection attributes in Western contexts, emphasizing service quality, safety, and brand reputation (Han et al., 2008; Sweeney and Soutar, 2001). Yet, the preferences of Chinese consumers have been insufficiently explored, particularly considering their strong digital engagement, price sensitivity, and collectivist decision-making tendencies (Wang et al., 2021; Chai, 2015). Notably, over 70% of Chinese passengers prioritize online booking efficiency and peer recommendations instead of traditional loyalty programs (Tian, 2024). These behavioral patterns reflect a paradigm shift that existing service models may not adequately address.
Moreover, although previous studies have examined the direct influence of service quality on satisfaction or loyalty (Lim et al., 2019), few have explored the mediating effect of perceived value on recommendation intention. The perceived value framework (Zeithaml, 1988) suggests that consumers evaluate services by balancing benefits and sacrifices. However, aviation research has rarely applied this theory. Additionally, current literature seldom investigates how selection attributes vary across cultures. This limitation restricts the applicability of global findings to the Chinese context.
To address these gaps, this study explores the following research questions:
How did airline selection attributes influence perceived value and recommendation intention among Chinese consumers?
Did perceived value mediate the relationship between selection attributes and recommendation intention?
Based on the theory of perceived value and service-dominant logic (Vargo and Lusch, 2004), this study proposes a novel mediation model. The model is empirically tested using data collected from Chinese international students in South Korea. This digitally savvy group represents modern Chinese airline consumers. By integrating cultural insights and statistical evidence, the study refines theoretical understanding of airline consumer behavior. It also provides strategic recommendations for service personalization and market segmentation.
II. Discussion
Air transportation in East Asia has grown rapidly, becoming a key sector within the global aviation economy. The airline industry reflects a nation’s overall economic status and typically outperforms other transportation methods for medium- and long-distance travel as economies develop. Air cargo transportation has also expanded significantly in East Asia, enhancing logistics efficiency and offering considerable future growth potential. The relationship between air transport and economic development is mutually reinforcing, supporting regional economic prosperity and improving living standards. Currently, air transport is the dominant travel mode (Duan, 2023).
Service quality—including safety, customer support, information provision, flight operations, and baggage handling—directly influences the travel experiences of Chinese consumers. Brand communication strengthens consumer recognition and loyalty, thus affecting consumer decisions. For example, Zamen Airlines effectively promotes its brand via social media platforms. Experiential value strongly influences repurchase intentions, prompting full-service airlines to enhance customer satisfaction through innovative services. Digital platforms reduce the costs of accessing information, increase consumer engagement, and fulfill personalized preferences. Moreover, consumers prefer brands that reflect their personal values (Tian, 2024).
Airline selection attributes are the criteria passengers use when choosing airlines (Lee and Yoo, 2007). Passengers have diverse and specific needs depending on personal preferences and circumstances. These range from services that improve comfort and unique travel experiences to providing essential amenities during flights. Therefore, airlines must identify and address passenger needs effectively to enhance satisfaction (Kim et al., 2000).
In this context, airline selection attributes are factors influencing passengers' airline choices based on subjective preferences. Understanding these attributes is critical for airlines aiming to improve competitiveness (Yoon, 2014). Passengers' varied selection criteria can pose marketing challenges but also present opportunities. Segmenting passengers according to their needs and providing tailored services can significantly enhance customer satisfaction (Ahn, 2006).
Passengers invest time and effort to choose the most appropriate airline for their journeys, gathering information through online searches, travel agencies, and reviews. Passengers establish evaluation criteria, considering factors such as services, schedules, prices, and benefits. For example, business travelers may prioritize flight convenience and seat comfort, whereas family travelers emphasize affordability and child-friendly services. By comparing personal criteria with airline offerings, passengers make informed decisions (Cho, 2003).
Han et al. (2008) identified marketing, service quality (reservation, airport, cabin, and auxiliary services), airfares, and safety as key airline selection attributes. They further subdivided these attributes into 22 detailed factors, noting variations linked to passenger demographics, income, and age. They emphasized the importance of developing personalized marketing strategies to improve customer satisfaction and competitive advantage.
Chinese airline consumers exhibit distinct consumption patterns. Business travelers prefer direct and punctual flights, whereas vacation travelers value in-flight services. Young, price-sensitive passengers focus heavily on price comparisons and promotional offers. Thus, airlines must adopt targeted marketing strategies aligned with these behavioral characteristics. Business passengers generally prioritize rapid travel, while leisure passengers favor cost-effective connecting options, indicating significant differences in consumer preferences (Chai, 2015).
Perceived value is the overall consumer assessment of the utility of a product or service, based on the costs paid and benefits received (Zeithaml, 1988). It has been examined from both company and consumer viewpoints. From a corporate perspective, perceived value significantly influences customer satisfaction and repurchase intention, attracting extensive research since the 1990s. From the consumer standpoint, perceived value represents a consumer's comprehensive evaluation of product usefulness in relation to cost and benefit received (Ryu, 2018; Kwon, 2020).
Bolton & Drew (1991) considered perceived value a highly abstract concept. According to their research, perceived value encompasses more than simply low prices. It involves benefits obtained through a purchase relative to the costs incurred. Similarly, Dodd et al. (1991) described perceived value as a balance between perceived quality and perceived sacrifices, including monetary and non-monetary costs. They highlighted two roles of price: as a quality indicator and as a cost influencing perceived value.
Perceived value is multidimensional, involving recognition of diverse customer experiences when using shared services. It extends the concept of consumer value. Increased adoption of shared services helps companies maximize profits. Therefore, it is critical to deeply understand and effectively manage consumers' perceived value (Woodruff, 1997).
Sweeney & Soutar (2001) classified perceived value into four dimensions: social value, emotional value, functional (price/value), and functional (performance/quality). These categories are widely adopted in academic studies. Additionally, based on research by Kim(2009), perceived value comprises two principal attributes: emotional and functional value.
Based on the theory of perceived value (Zeithaml, 1988), this study posits that airline attributes, such as reservation services, indirectly affect recommendation intention via passengers' overall evaluation of benefits and sacrifices. This aligns with service-dominant logic (Vargo and Lusch, 2004), which stresses value co-creation through consumer interactions. Previous studies confirm perceived value as a mediator in tourism contexts (Guo et al., 2022), but aviation-specific evidence remains limited.
The concept of recommendation intention, or word-of-mouth (WOM), was first introduced in Fortune magazine in 1954. It describes interpersonal communication among individuals such as customers, salespeople, and reference group members (White, 1954). Recommendation intention is a critical measure reflecting positive experiences related to a visited destination. Thus, enhancing recommendation intention is important for cultural and artistic village managers.
Recommendation intention originates from visitors' experiences at a particular destination and indicates their intention to recommend that location to others (Hossain and Prayag, 2013). It is closely associated with customer satisfaction: higher satisfaction leads to stronger recommendation intentions (Gao, 1998).
Although research focusing exclusively on recommendation intention is limited, the concept has frequently been discussed in consumer behavior literature within the broader context of WOM. Unlike satisfaction, which represents an overall evaluation of experiences, recommendation intention involves informal sharing of positive or negative experiences with others, based on direct or indirect personal experience (Xu, Lee, Lee, 2008).
Building upon Park’s (2010) conceptualization, multiple attributes related to recommendation intention have been identified. Lee, Park, and Park (2003) proposed that individuals tend to trust recommendations from familiar people, such as family and friends. In comfortable, friendly environments, individuals naturally discuss consumer experiences and readily accept suggestions.
Based on this theoretical foundation, the following hypotheses and research model (Fig. 1) are proposed.
Previous research extensively discusses airline selection attributes in travel decision-making contexts. Studies suggest reservation services, in-flight facilities, in-flight services, airport services, online shopping, and marketing significantly enhance passengers' perceived value (Xu, 2012; Ji and Zheng, 2019; Lim et al., 2019; Wu et al., 2021; Guo et al., 2022; Chen and Li, 2024; Oh, 2024). Accordingly, the hypotheses proposed are:
H1: Reservation services positively influence perceived value.
H2: In-flight facilities positively influence perceived value.
H3: In-flight services positively influence perceived value.
H4: Airport services positively influence perceived value.
H5: Online shopping services positively influence perceived value.
H6: Marketing positively influences perceived value.
Rationale for H1-H6: Following Kim & Park (2015), reservation services (H1), in-flight facilities (H2), and online shopping (H5) are hypothesized to enhance perceived value by reducing transactional effort (functional value) and increasing emotional satisfaction (e.g., seat comfort). The rationale for H3, H4, and H6 aligns with the literature cited above.
Prior research indicates that airline selection attributes positively influence passengers' recommendation intentions. Key factors include reservation services, in-flight facilities, in-flight services, airport services, online shopping, and marketing strategies (Li, 2010; Jeong, 2011; Heo and Ham, 2013; Son, 2015; Gandulam and Hyeongyu, 2021; Kim, 2022; Yang et al., 2024). This study therefore proposes the following hypotheses.
H7: Reservation services positively influence recommendation intention.
H8: In-flight facilities positively influence recommendation intention.
H9: In-flight services positively influence recommendation intention.
H10: Airport services positively influence recommendation intention.
H11: Online shopping services positively influence recommendation intention.
H12: Marketing positively influences recommendation intention.
Rationale for H7-H12: Following Jeong (2011), recommendation intention is influenced by satisfaction derived from specific attributes.
Drawing from prior literature, this study further examines the relationship between perceived value and recommendation intention. Because perceived value affects consumer behavior, passengers' perceptions of airlines likely shape their recommendation intentions (Guo et al., 2022; Wu et al., 2021; Wang et al., 2021; Liang et al., 2020; Ji & Zheng, 2019; Sun & Sun, 2017; Xu, 2012; Hu, 2009).
H13: Perceived value positively influences recommendation intention.
Rationale for H13: Perceived value is expected to directly impact recommendation intention (H13). Passengers assess the overall value of services by balancing benefits from selection attributes against costs such as price and time, thereby influencing their willingness to recommend an airline.
III. Conclusion
This study targeted international students in South Korea and utilized a cluster random sampling method in Goyang, Jeonju, Gwangju, and Busan. A total of 545 questionnaires were distributed, and after removing duplicates, incomplete responses, and invalid surveys, 500 valid questionnaires remained, yielding a response rate of 91.74%.
The measurement tool for airline selection behavior was adapted from Lee Jae-gon and Yoo Gi-seok's study, “a study on positioning based on airline selection attributes.” The revised scale included 6 dimensions and 28 items measured on a 7-point Likert scale. Internal consistency was verified, and the reservation services dimension underwent additional review.
For perceived value, the measurement scale was adapted from “consumer perceived value: the development of a multiple item scale” by sweeney and soutar. This adapted scale comprised 1 dimension with 6 items, also using a 7-point Likert scale.
The behavioral intention measurement tool was based on “quality, satisfaction, and behavioral intentions” by Baker and Crompton, modified to fit this study's objectives. It consisted of 1 dimension and 3 items, again employing a 7-point Likert scale.
Smart PLS 4 statistical software was employed to systematically organize and analyze the data. Upon obtaining the results, they were thoroughly compared and validated against the pre-established hypotheses to ensure comprehensive validation of the research propositions. Additionally, the intrinsic relationships among variables were examined carefully, providing strong empirical and theoretical support, thus ensuring the scientific rigor and reliability of the study.
This study focused on Chinese consumers, employing a random sampling approach and questionnaire-based empirical analysis. The questionnaire encompassed six dimensions of airline selection attributes (reservation services, in-flight facilities, in-flight services, airport services, online shopping, and marketing). Additionally, it included two dimensions of perceived value (emotional and functional value) and recommendation intention.
The airline selection attributes scale comprised 38 items: 5 for reservation services, 5 for in-flight facilities, 8 for in-flight services, 8 for airport services, 8 for online shopping, and 4 for marketing. Perceived value had 8 items, equally divided between emotional value (4 items) and functional value (4 items). Recommendation intention included 3 items, and demographic variables were captured through 4 items. The detailed structure of the questionnaire is provided in Table 1.
| Variables | Measurement items | Question items | Source | |
|---|---|---|---|---|
| Airline selection attributes | Reservation services | Convenient schedule, easy booking changes, diverse channels, friendly staff, direct flights. | 5 | Kim and Park (2015) |
| In-flight facilities | Room cleanliness, seat convenience, luggage storage, aisle space, in-flight amenities. | 5 | Kim and Park (2015) | |
| In-flight services | In-flight dining, special meals, newspaper/magazine service, in-flight entertainment, broadcast service, crew appearance, cabin crew reception, duty-free products. | 8 | Kim and Park (2015) | |
| Airport services | Fast check-in, diverse boarding options, convenient and accurate procedures, preferred seat arrangement, friendly staff service, free baggage allowance, easy lost baggage handling. | 8 | Kim and Park (2015) | |
| Online shopping services | More choices, better service, lower risk, higher seller credibility, less time and effort, proficient online ticket purchase, improved ticket purchasing efficiency. | 8 | Feng (2009) | |
| Marketing | Discounts & rewards, advertising & promotion, brand image, partnership services. | 4 | Kim and Park (2015) | |
| Perceived value | Perceived value | Brings joy, attractive, safe and reliable, likable. | 4 | Kim (2009) |
| Functional value | Reasonable pricing, offered discounts, great value for money. | 4 | Kim (2009) | |
| Recommendation intention | Highly recommend, willing to share, eager to recommend. | 3 | Park (2010) | |
Data were collected from Chinese international students in South Korea (N=500), representing a digitally oriented, frequent-flyer demographic from China (CAAC, 2022). To mitigate potential biases, age, gender, and flight frequency were controlled (Table 2). Although this sample provides insights into tech-savvy preferences, future research should expand sampling to include general Chinese consumers.
IV. Analysis Results
Table 2 presents respondents' demographic characteristics. Regarding gender, 259 (51.8%) were female and 241 (48.2%) were male, with females slightly outnumbering males.
The largest age group was 26–30 years (37.2%). In terms of education, 35.0% of respondents held a master’s degree. Regarding annual flight frequency, the majority (52.8%) traveled 3-4 times per year.
This section evaluates the reliability and discriminant validity of the constructs (X1, X2, X3, X4, X5, X6, M, and Y). Cronbach’s α (CR) and Average Variance Extracted (AVE) were computed for each construct.
The Cronbach’s α (CR) values were: X1=0.832, X2=0.796, X3=0.722, X4=0.829, X5=0.866, X6=0.795, M=0.868, and Y=0.829. All values exceed the recommended threshold of 0.7, indicating high reliability.
The AVE values were: X1=0.598, X2=0.620, X3=0.642, X4 =0.594, X5=0.552, X6=0.618, M=0.600, and Y=0.743. All values are above the acceptable level of 0.5, confirming validity.
Discriminant validity was assessed using the Fornell-Larcker criterion. The square roots of AVE (diagonal values) exceeded correlations with other constructs (off-diagonal values). The diagonal values were: X1=0.775, X2=0.773, X3=0.788, X4=0.801, X5=0.770, X6= 0.743, M=0.786, and Y=0.862. These results confirm adequate discriminant validity among constructs.
In conclusion, the measurement scales used in this study demonstrate high reliability and validity, providing robust support for the research.
Regarding airline selection attributes, reservation services (X1) significantly influenced perceived value (M) (β=0.125, p<0.05). In-flight facilities (X2) also had a significant positive effect (β=0.131, p<0.05). Similarly, in-flight services (X3) (β=0.130, p<0.05), airport services (X4) (β=0.208, p<0.05), online shopping services (X5) (β=0.178, p<0.05), and marketing (X6) (β=0.202, p<0.05) all showed significant impacts on perceived value.
In examining recommendation intention, reservation services (X1) had a significant direct impact (β=0.124, p<0.05). Similarly, in-flight Facilities (X2) significantly affected recommendation intention (β=0.099, p<0.05). However, in-flight services (X3) showed no significant effect (β=-0.036, p>0.05). Airport services (X4) (β=0.089, p>0.05) and marketing (X6) (β=0.083, p>0.05) also did not significantly influence recommendation intention. Conversely, online shopping services (X5) demonstrated the strongest significant influence (β=0.221, p<0.05).
Direct Effects: online shopping services had the greatest impact on recommendation intention (β=0.221, p<0.05), in contrast to in-flight services (β=-0.036, n.s.), differing from findings in Western contexts (Han et al., 2008) (Table 4, 5).
| SRMR | d_ULS | d_G | Chi-square | NFI |
|---|---|---|---|---|
| 0.045 | 1.426 | 0.454 | 1,331.152 | 0.842 |
Additionally, perceived value (M) significantly influenced recommendation intention (Y) (β=0.287, p<0.05).
Data analysis was conducted using SmartPLS 4 software. The model incorporated three key variables: airline selection attributes (six dimensions), perceived value, and recommendation intention.
Generally, an NFI≥0.9 indicates a strong model fit, while values≥0.8 are considered acceptable. The NFI measures how effectively the proposed model fits the observed data, validating the model’s overall quality.
Model fit evaluation revealed the following indices: SRMR= 0.045, D_ULS=1.426, D_G=0.454, Chi-Square=1331.152, and NFI=0.842.
These values demonstrate that the model aligns well across various dimensions. Integrating future research directions with practical applications can further enhance the model’s explanatory power and practical effectiveness (Fig. 2).
V. Conclusion
This study examines airline selection attributes and explores their relationships with perceived value and recommendation intention. Using a model comprising 13 hypotheses, it empirically identifies how various airline service dimensions influence consumer perceptions and behaviors among Chinese international students in South Korea.
The results indicate that reservation services, in-flight facilities, and online shopping services significantly affect both perceived value and recommendation intention. However, airport services, in-flight services, and marketing strategies influence only perceived value, without directly impacting recommendation intention. This result underscores the mediating role of perceived value.
Notably, while in-flight services, airport services, and marketing activities exhibit statistically significant positive effects on perceived value (p<0.05), their direct impact on recommendation intention proves statistically insignificant. This suggests that perceived value serves as the primary mediator in this relationship. The standardized nature of airline services likely limits their ability to create differentiated customer experiences, thereby reducing their direct influence on behavioral intentions. Concurrently, passenger skepticism toward marketing messages, exacerbated by industry-wide promotional saturation, appears to undermine the effectiveness of direct marketing efforts.
Empirical findings refine theoretical understandings of consumer behavior in aviation. Specifically, rejection of hypotheses H9, H10, and H12 suggests traditional emphasis on in-flight services, airport experiences, and marketing may be overvalued, especially for Chinese consumers prioritizing digital convenience and functional benefits.
This study provides three primary theoretical contributions. First, it expands the conceptual relationship between airline selection attributes and recommendation intention, challenging traditional assumptions about service elements. Second, it confirms perceived value as a complete mediator, extending Zeithaml's (1988) framework into aviation contexts. Third, it contributes insights into marketing theory, highlighting gaps between conventional strategies and actual consumer selection criteria in culturally distinct markets.
These findings highlight perceived value's critical mediating role in converting service attributes into customer advocacy. Airlines should therefore prioritize enhancing service distinctiveness and perceived quality to strengthen perceived value, thereby potentially increasing recommendation intentions.
Practically, findings advocate shifting toward data-driven and personalized service strategies. Airlines should adopt AI-enhanced booking platforms, tailored in-flight services, and digital engagement tools. Airports need to offer fast, seamless, and personalized passenger experiences. Marketing should transition from general campaigns to targeted communications based on consumer data, utilizing platforms such as WeChat or biometric systems.
Nevertheless, this study has several limitations. The sample consisted solely of Chinese students in South Korea, limiting generalizability. Future studies should include diverse demographics, such as domestic travelers and passengers from various cultural backgrounds. Furthermore, this study focused exclusively on perceived value as a mediator; future research should examine additional mediators or moderators, including trust, satisfaction, or emotional engagement.
While some demographic controls were included, contextual factors like travel purpose, flight distance, or cabin class were not considered. Finally, this research used self-reported data, making longitudinal or behavior-based studies necessary to enhance causal validity and practical relevance.
Addressing these limitations will allow future studies to deepen theoretical understanding and strengthen strategic applicability in airline consumer behavior, especially in dynamic markets like China.
