“Understanding the shift from passenger satisfaction to patronage intention” – evidence from Colombo Bandaranaike International Airport Open Access

An international airport connects a nation to the rest of the world and is an essential component of its air transportation infrastructure. Well-developed airports make a substantial economic contribution to their countries. The airport, like Colombo Bandaranaike International Airport (CBIA), has tremendous potential to be a regional hub, given Sri Lanka's strategic location as an island in the Indian Ocean. Patronage intention (PI) is a major part of a successful airport (Imroz et al., 2023). It strengthens the passengers' loyalty and revisiting intention and creates positive word of mouth (Avgan and Özdemir, 2025; Imroz et al., 2023). PI directly affects airport competitiveness, airline attractiveness and long-term revenue generation, making it a strategically significant construct (Imroz et al., 2023); however, CBIA struggles to establish itself as a significant airport, at least in South Asia. Evidence indicates that CBIA is one of the most inefficient airports (Madhushanka et al., 2023; Pathirana, 2024). It experiences a significant increase in passenger complaints (Madhushanka et al., 2023). Despite these challenges, existing empirical evidence on CBIA largely focuses on operational or service-related deficiencies (Madhushanka et al., 2023; Pathirana, 2024), providing limited insight into how such passenger experiences translate into future behavioral outcomes, particularly in terms of passengers' continued usage, recommendations and advocacy. Prior research consistently shows that passenger satisfaction (PS), shaped by multiple service quality (SQ) attributes, is closely associated with PI; however, the mechanism by which satisfaction translates into sustained patronage behavior remains insufficiently examined (Alanazi et al., 2024; Avgan and Özdemir, 2025; Saut and Song, 2022), particularly in emerging airport contexts. When satisfaction is low, complaints increase, thereby reducing PI. In practice, PI is strengthened when PS is consistently positive, as satisfied passengers are more willing to return to the airport, recommend the airport and engage in favorable behaviors. Thus, PI drives repeated passenger behavior, while PS continuously reinforces PI, making satisfaction a central determinant of long-term airport patronage. However, much of the airport SQ literature continues to treat PS as an outcome (Alanazi et al., 2024; Hang et al., 2023) rather than as a mediating construct that describes how specific experience elements drive future PI.

PI is a multidimensional construct comprising aspects such as “positive word of mouth, revisiting intention, desire to recommend and positive behavioral tendency” (Avgan and Özdemir, 2025; Imroz et al., 2023). Although PI is a crucial element in the service business, it remains relatively unexplored within the aviation industry, particularly in understanding how PS contributes to passengers' favorable behavioral responses. In reviewing recent airport-related literature in the Asian context, most of these studies place great emphasis on PS as the terminal outcome of SQ while largely overlooking its mediating role in shaping PI (Alanazi et al., 2024; Hang et al., 2023; Imroz et al., 2023; Usman et al., 2022). On the other hand, unique aspects such as airport facilities and ambience (AFA), health and safety services (HAS), food and beverages and lounge services (FBLS) and security (SEC) and airport access (AA) are significant aspects (Alanazi et al., 2024; Bakır et al., 2022; Pathirana, 2024). In addition, factors such as “baggage retrieval, porter and taxi services” (BRPTS) and airport duty-free services (ADFS) influence PS (Bandara and Fernando, 2019; Naletina et al., 2019); therefore, the major objective of this study is to find how PS influences PI and in what ways SQ attributes impact PS. Thus, our research questions are:

In light of these limitations, our study, therefore, contributes to the body of knowledge by extending PS to measure PI by incorporating the SERVQUAL model with three other behavioral models, such as the theory of planned behavior (TPB), the stimulus-organism-response (SOR) and expectation disconfirmation theory (EDT). Practically, findings would help the management and policymakers in emerging nations' aviation industry to formulate effective strategies and policies, especially for CBIA to reduce the rising customer complaints during recent times and to develop as a regional hub.

This section begins with a discussion of the theoretical foundation, followed by an empirical review, the development of the conceptual framework and the formulation of research hypotheses.

The study analyzes four important and well-established theories to present a comprehensive theoretical foundation: the SERVQUAL model, the TPB, EDT and the SOR.

The SERVQUAL model describes the most influential factors relating to SQ (Parasuraman et al., 2017). The theory proposes five general dimensions: tangibility, reliability, responsiveness, assurance and empathy. Tangibility refers to what the person sees physically in the service offer, represented by appearance, equipment and physical facilities. Reliability highlights the service providers' ability to perform promised services dependably and accurately. Responsiveness describes a service provider's willingness to help customers and provide prompt service. The assurance of the service denotes elements such as knowledge, courtesy, trust and confidence shown by the service provider when delivering the service. Empathy includes factors such as caring and personalized attention. Factors like AFA and ADFS in the airport context reflect tangibility aspects of SQ. SEC and HAS more closely relate to assurance and reliability aspects of the airport. FBLS more likely describes tangibility and responsiveness. AA, on the other hand, explains the reliability and tangibility elements in the SERVQUAL model. In the airport environment, dimensions such as AFA, SEC, HAS, FBLS, ADFS, AA and BRPTS are significant in assessing overall SQ. This theory effectively supports integrating them into SQ dynamics. In line with this, TPB, introduced by Ajzen (1991), posits that a particular action (behavior) is triggered by behavioral intention, which is shaped by three exogenous factors: attitude, subjective norms and perceived behavioral control. This emphasis is that when a passenger experiences high-quality service at the airport, it fosters a positive attitude and triggers PI behavior. EDT, on the other hand, explains how a person evaluates satisfaction by comparing expected and perceived service performance (Oliver, 1980). This means that if the service received at the airport exceeds the expected level, the positive disconfirmation occurs, leading to higher PS and PI. In contrast, the received service level falls below the expectation; negative disconfirmation results in disconfirmation and a decline in PI. The significance of this theory is that it underpins the mediating role of PS in associating SQ perception with PI. In addition, the SOR model draws the relationship between external stimuli, the organism and the response (Mehrabian and Russell, 1974). In the context of an airport, the stimuli are SQ attributes that the passenger is exposed to. The passenger represents the organism, and the response involves the person's behavior. In the airport, the level of SQ attributes impacts the passenger's emotional state - PS, which in turn drives the response behavior – PI. For instance, a well-designed airport environment, high-quality luggage services and efficient baggage handling enhance the passengers' emotional experiences, increasing PI.

1. Airport facilities and ambiences (AFA)

AFA encompasses several dimensions such as airport infrastructure, cleanliness, comfort and aesthetic appeal. The studies claim that these dimensions significantly influence PS (Bakır et al., 2022; Hang et al., 2023), which in turn affects PI. The infrastructure includes capacity expansion, seating arrangements, signage and terminal designs. The physical infrastructure is a key part of the SQ dimensions that affect PS (Bakır et al., 2022). Well-developed physical infrastructure enables enhanced passenger comfort and operational efficiency and provides a seamless experience for passengers. However, a lack of cleanliness and comfort makes passengers unsatisfied. Lau et al. (2022) claim that cleanliness and seating comfort in the airport are significant matters in determining PS. The other important aspect of AFA is aesthetic appeal – factors such as ambiance, lighting and design. They may shape the passengers' mood and satisfaction (Bakır et al., 2022; Hang et al., 2023). This evidence collectively suggests that AFA is a crucial determinant of PS (Pathirana, 2024).

1. Security (SEC)

SEC of an airport involves ensuring the safety of passengers. The SEC includes measures like passenger safety and surveillance and trained SEC personnel to ensure a secure airport environment. Any doubt about SEC affects PS, which in turn affects PI (Ceccato and Masci, 2017; Güres et al., 2017; Hang et al., 2023). This emphasizes that the SEC at the airport is an important variable by any standard. Discomfort regarding the SEC level reduces passengers' willingness to reuse the airport (Al-Saad et al., 2019; Hang et al., 2023). The efficient and courteous SEC mechanisms enhance passengers' overall experience (Ceccato and Masci, 2017; Güres et al., 2017). It's an important aspect of PS, recommendation intention, and loyalty (Pathirana, 2024).

1. Health and safety services (HAS)

HAS encompasses factors such as medical facilities, adherence to health protocols, particularly in emergencies and sanitation. These dimensions shape customer perception and behavior and play an important role in influencing PI through PS. Hang et al. (2023) highlight how important HAS metrics are in determining PS. Additionally, Bauer et al. (2020) indicate that specific designs, such as ultra-long-haul flights, enable the management of health hazards and enhance customer satisfaction. Safety level is a significant aspect in determining the level of PS (Pathirana, 2024). Similarly, Imroz et al. (2023) emphasize the significance of HAS in PS. These facts collectively emphasize that the HAS is critical in reaching a high level of PS.

1. Food and beverages and lounge services (FBLS)

FBLS is also a key determinant of the overall satisfaction of the clients. FBLS includes various high-quality dining options, including their availability. Also, the amenities of airport lounges and the comfort level of the passenger experience are key aspects of increased PS. Food and beverages are the most important aspects influencing PS (Bakır et al., 2022). The study conducted by Batra (2024) found that the choice of credit cards is influenced by the value-added lounge services offered. Evidence also indicates that if the reward points offered on using cards for travel-related expenses can be converted into lounge benefits, passengers increase their card expenses because more use increases the point accrual and redemption (Sorensen, 2024). This emphasizes that lounge services are a significant aspect of PS. Pathirana (2024) emphasize the significance of FBLS as part of SQ factors relating to the airport. These facts together highlight the significance of FBLS for a higher level of PS and PI (Hang et al., 2023).

1. Airport duty-free services (ADFS)

ADFS is measured based on product selection, pricing and the level of SQ received by the passengers at duty-free outlets. They are significant aspects of PS (Hang et al., 2023; Naletina et al., 2019). Service delivery and product satisfaction at duty-free shops are key elements in forming passenger perception (Oliveira et al., 2023). Maintaining product variety offers better value for money (Bandara and Fernando, 2019; Massaretto and Oliveira, 2024; Naletina et al., 2019). CBIA requires more improvement in the service offered at duty-free shops (Pathirana, 2024). SQ factors, including those related to ADFS, significantly improve PS (Imroz et al., 2023). These facts emphasize the significance of the ADFS in achieving PS influencing PI.

1. Airport access (AA)

AA consists of several sub-elements such as ease of transportation, connectivity with public and private transport and adequate parking facilities. The study found that access-related factors such as transportation and signage in the airport environment influence PI (Zhou et al., 2021). Particularly, signage assists passengers in navigating with convenience. Transport, on the other hand, helps customers to reach the service point easily. Enhancing these elements improves PS. Pathirana (2024) found that efficient access to and from the airport is a significant aspect influencing PS. This evidence suggests that AA dimensions are key determinants in shaping PS and PI.

1. Baggage retrieval, porter and taxi services (BRPTS)

BRPTS is measured based on dimensions such as the efficiency and reliability of baggage handling, porter assistance and availability of taxi or ride-share services. These factors are highly influential on PS and then PI; for example, the quality of baggage handling affects passengers' overall satisfaction (Ruminda et al., 2025). The efficient porter services give passengers a sense of personalized attention and a speedier process, leading to higher satisfaction. This significantly improves the PI (Malinda et al., 2023). The airport can deliver a seamless experience for passengers if ride-sharing and taxi services are reliable and always available. These facts emphasize the significance of BRPTS in determining PS and PI; therefore, the hypothesis between BRPTS and PS is drawn as follows.

1. Passenger satisfaction (PS)

PS is the mediator variable that highlights passengers' overall evaluations of the airport, affecting PI. Various SQ factors influence PS. Factors such as a peaceful environment, tolerable lighting, cleanliness and the attitude and professionalism of airport employees play a major role (Usman et al., 2022). However, Massaretto and Oliveira (2024) highlight that efficient queuing and SEC processes, quality dining and retail options, clear signage and wayfinding systems, as well as better-designed seating areas and lounges are critical for PS. PS is influenced by several attributes unique to a particular airport (Fakfare et al., 2021). Poor infrastructure, inadequate capacity, long queues at check-in and immigration and delays in baggage claims may lead to dissatisfaction, stress and misconnections. Lack of cleanliness and poor hygiene result in passenger frustration and a negative perception of service culture (Bae and Chi, 2022). Also, the influence of these factors on PS is positive and significant (Gari, 2024; Rahnama et al., 2024). Furthermore, empirical evidence supports the fact that PS mediates the passenger loyalty behavior (Ali et al., 2021). In addition, prior research demonstrates that airports with superior SQ enjoy enhanced recommendation and loyalty (Ali et al., 2021; Pholsook et al., 2023). Improving these services encourages passengers to remain loyal to the airport (Hang et al., 2023). Hence, the hypothesis between PS and PI can be formed as follows.

1. Patronage intention (PI)

PI is a crucial variable measured based on passengers' desire to recommend, positive word of mouth and revisiting intention (Massaretto and Oliveira, 2024; Shah et al., 2020). PI is significant in the airport context, especially where the airport operates in a globally competitive market. The passenger forms a positive attitude if they receive better service (Suranta and Sekali, 2024). PI depends on the satisfaction level and SQ (Bezerra and Gomes, 2020; Syah and Olivia, 2022). A recent study conducted reveals that passenger experience at the airport hotel influences return PI (Rahnama et al., 2024). This expresses that better experience influences PI. The higher level of SQ makes the passenger revisit the airport (Kour et al., 2020). SQ factors enhance PS and loyalty (Alanazi et al., 2024; Becker and Boettcher, 2024). Positive statements, willingness to revisit the airport and recommending it to others represent the passengers' overall satisfaction with the airport services.

Figure 1 below depicts the comprehensive research framework that integrates variables identified based on the research gap.

The study uses a deductive paradigm within the constructivist philosophy, followed by a quantitative method for data gathering and analysis. The data were gathered using simple random sampling from 694 passengers departing and arriving using a specifically constructed quick-response (QR) technique at the CBIA and the corporate website. The sample size was determined using the model proposed by Krejcie and Morgan (1970), and the researcher also used Cochran's formula (Cochran, 1977). Both approaches produce 384 as the best size when the sample size is unknown and surpasses one million; nevertheless, the researcher increased the sample size further to improve the accuracy and generalizability of the results. The sample included 392 males and 302 female passengers. The sample frame includes all passengers arriving at or departing the airport. The data were collected during two years, from January 2022 to March 2024.

The study used a questionnaire designed to measure passenger experience attributes, PS, and PI in an airport context. A five-point Likert scale (from strongly disagree to strongly agreed) was used to measure the items. Before full deployment, the questionnaire was reviewed for clarity and consistency to ensure its suitability for airport passengers. The data entry process was automated to help the data entry mechanism feel more at ease while filling out the data. Any passenger who prefers to answer can input details by scanning the QR code provided at the main touch points.

The data collected were filtered to eliminate missing entries and outliers to improve the quality of the dataset (Hair et al., 2021). Any responder who did not complete a questionnaire by at least 20% was eliminated (Hair et al., 2021). The others were imputed using the SPSS data imputing method. In addition, the researchers used the SPSS box plot to detect outliers. Following the screening, the dataset was examined for skewness and kurtosis. The standard deviations that are more than |1.5| but less than |0.25| were deleted.

After improving the data set for its normality, the exploratory factor analysis was performed to ensure that most of the items load under the construct intended to measure. Table 1 below depicts the rotated component matrix obtained after three iterations using the principal component matrix and varimax rotation. Some items, which were cross-loaded with other components, were removed to improve clarity and interpretability. Only items with eigenvalues of one and above were chosen for confirmatory factor analysis (CFA). The total variance explained by the nine variables in the model is 85.5%.

The reliability of constructs is tested using Cronbach's alpha. Cronbach's alpha values of 0.7 typically imply a high level of construct reliability (Basu, 2021; Taber, 2018). Table 2 below indicates that all constructions exceed this criterion, demonstrating high internal consistency.

Table 3 below shows the Kaiser–Meyer–Olkin (KMO) value of 0.818, indicating sample adequacy. Barlett's test of sphericity yields χ²(595) = 21993.11, p < 0.001, confirming that the correlation between variables is sufficiently high for factor analysis.

Figure 2 below indicates the measurement model (MM) obtained at a normed chi-square value of 1.881 and a P-value less than 0.05. The confirmatory fit index (CFI) is 0.979, which is above the threshold of 0.9. The MM shows root mean square error of approximation (RMSEA) of 0.043, less than its threshold of 0.08. The shown model was obtained after covariation error terms e1 and e2, e12 and e13, e15 and e16 and e17 and e18 based on the modification indices set for the threshold of 20. Covariating the error terms further improved the model's fitness. The original MM was also subjected to the Mahalanobis distance test to identify multivariate outliers based on p1 and p2 distances.

If p2 is less than 0.0001, they were treated as multivariate outliers and removed to improve the data normalcy. The modified MM depicted below shows Tucker–Lewis Index (TLI) as 0.976 and the comparative fit index (CFI) as 0.979. These values are above the threshold of 0.900; however, the goodness-of-fit index (GFI) is 0.898, indicating that it is slightly less than 0.900. These values for indices show that the observed model fits with the hypothesized model, even though the GFI is slightly less than 0.900. Given this large sample, the slightly lower value for GFI does not impact the goodness of the good-fit model (Idris et al., 2010).

Table 4 below, derived using the Gaskin tools, displays the MM reliability and validity requirements. The table shows that the composite reliability (CR) for all variables is more than 0.7. Additionally, the model will attain discriminant and convergent validity. To ensure discriminant validity, the average variance extracted (AVE) should be more than 0.5 (Fornell and Larcker, 1981). For convergent validity, the square root of AVE should be greater than the maximum shared variance (√AVE > MSV) (Henseler et al., 2015). The numbers shown for MSV are fewer than the square root of AVE, demonstrating the model's convergent validity. Because the modified model has no reliability or validity concerns, the modified MM is well-suited for developing the structural model (SM) for testing the hypothesis. Figure 3 below depicts the SM derived from the AMOS interface.

The SM obtained does not indicate any significant deviations in model fit indices such as GFI, TLI and CFI obtained for MM (Figure 2), except RMSEA with a value of 0.042, indicating a good-fit model for hypothesis evaluation. Tables 5 and 6 below present the beta coefficient values for the direct and indirect relationships obtained for SM on the AMOS interface. The indirect relationship describes the mediation impact obtained through the 5,000 bootstrapping samples at a 95% confidence interval. Bootstrapping measures the strength of mediation.

Table 5 shows that the impact of AFA on PS is insignificant (p > 0.05); as a result, the alternative hypothesis (H1) is rejected, demonstrating that AFA has no direct influence on PS. Furthermore, its mediator impact is also insignificant, as confidence intervals include zero (the lower bound value (LBV) = −0.055 and upper bound value (UBV) = 0.163) (Coenen, 2022). Additionally, the P-value in the two-tailed test is greater than 0.05; however, the direct impact of AFA on PI is positive and significant (β = 0.188, p = 0.16, and CR = 2.418), implying that rather than being a determinant of PS, it is a direct determinant of PI. The direct effect of all other IVs (SEC, FBSL, ADFS, BRPTS, HAS and AA) on PS was significant and positive (P-values <0.05 and CR> |1.96|), as indicated in Table 5. Also, the direct influence of PS on PI was positive and significant (β = 0.755, p < 0.05 and CR> |1.96|). The alternative hypotheses of H2, H3, H5, H6, H7 and H8 are therefore accepted. Table 6 indicates the mediation impact of IVs on PI through PS. Except for AFA, the mediation impact of all IVs was positive and significant (LBV and UBV do not include zero, p < 0.05).

Findings emphasize that PS is the primary driver that drives long-term loyalty and positive word-of-mouth recommendation (Usman et al., 2023). The satisfied passenger has a higher likelihood of recommending the airport and revisiting it in future travels (Pholsook et al., 2023; Rahnama et al., 2024). Even though several dimensions influence PS, it is PS itself that influences the passengers' revisiting intention, desire to recommend and positive behavioral tendency. This finding is consistent with previous studies that emphasize satisfaction as a key variable linking SQ to behavioral outcome (Hang et al., 2023; Usman et al., 2023); therefore, CBIA and airports in developing countries require effective marketing and branding to leverage PS scores to enhance PI and attract more airlines.

The favorable impact of the SEC suggests that it is crucial for PS (Hang et al., 2023). To secure the safety of travelers, the airport must implement a sophisticated SEC system. Passengers should have no doubts about the degree of protection given. To increase customer satisfaction, the SEC system must be more efficient and polite (Hang et al., 2023; Usman et al., 2023). CBIA should implement strong SEC procedures while maintaining a high level of courtesy (Pathirana, 2024). An artificial intelligence (AI)-based monitoring system, a pre-smart SEC zone and biometric verification at checkpoints are significant aspects of a robust SEC system. It can also include an integrated risk profiling system that employs AI to assess passenger travel history and indicate high-risk profiles. As technology advances, continuous improvement is required.

This finding indicates that HAS is a significant positive determinant of PS (Hang et al., 2023). It is a crucial element influencing PS (Usman et al., 2023). This emphasizes that the availability of excellent medical facilities with well-equipped medical staff, emergency response readiness and hygiene standards is crucial. The quality of these services is measured when actual situations arise. This finding also indicates that its indirect impact on PI is significant and positive, suggesting that HAS attributes are significant metrics for PI. Passengers may think that they are essential aspects of the airport. The CBIA should ensure that it provides high health standards, complying with those recommended by the World Health Organization and the International Air Transport Association guidelines. The CBIA should continue deploying well-trained health teams with 24/7 availability, in addition to improving sanitation standards, particularly in high-traffic areas, specifically such as waiting lounges and restrooms.

FBLS is also a significant aspect of PS (Bakır et al., 2022; Hang et al., 2023). The airport needs to ensure accessibility in dining and relaxation areas and availability of high-quality food to cater to the diverse preferences of travelers from different parts of the world. This is significant, especially in terms of transit passengers (Rahnama et al., 2024). A variety of food, including local cuisine, targeted for different passenger profiles, would be crucial in enhancing PS.

The significance of ADFS emphasizes that duty-free shopping plays an essential part in PS, especially in terms of products, price, selection and service received by passengers (Hang et al., 2023; Naletina et al., 2019). The CBIA should consider expanding the range of products available to passengers. The attractive design of the outlets and competitive pricing (Rahnama et al., 2024) often are important factors for customers arriving at duty-free shops. In addition, trained and well-skilled personnel need to be stationed at these points (Usman et al., 2023).

The importance of AA shows that factors related to access, such as transportation and signage within the airport that improve efficient access, affect PS and PI (Pathirana, 2024; Zhou et al., 2021); therefore, these areas need improvement. The combination of public and private sector transportation with an improved ride-hailing system within the airport could enhance PS. The investment to improve road connectivity, public transport integration and shuttle service makes it more convenient to reach their destinations without any hassle. This mitigates the access-related challenges, leading to a better passenger experience.

Airports need to focus on these aspects as well. The quality of baggage handling affects passengers' overall satisfaction, reduces passenger waiting time (Ruminda et al., 2025) and enhances confidence. Mishandling or any delay may annoy and frustrate the passenger and negatively affect PI (Malinda et al., 2023). Providing porter service with multilingual capabilities to support international travelers and a regulated taxi system with transparent pricing are significant establishments that improve the passengers' perception positively (Malinda et al., 2023). CBIA needs to train porters, language handlers and transport coordinators in time efficiency, handling customer grievances and politeness. These implementations minimize passenger complaints and improve the operational efficiency of the airport.

The direct impact of AFA on PS is insignificant, emphasizing that it is not a direct contributor to PS. This is slightly different from what is claimed by scholars (Hang et al., 2023; Lau et al., 2022); however, its direct impact on PI is significant, suggesting that factors such as airport physical infrastructure designed to emphasize beauty, aesthetics and cleanliness are direct influencers on PI (Rahnama et al., 2024). Areas where congestion could occur require more space for passenger handling; for example, a spacious terminal reduces the inconveniences felt by passengers (Pathirana, 2024). This further suggests the dire need for commencing terminal-2 construction immediately, as envisaged by the management of CBIA, with a particular emphasis on other critical infrastructure improvements. Implementing digital wayfinding systems, proper seating arrangements and lighting makes a good first impression. The passengers form their perceptions based on these. The other thing is modern travelers' desire for a photo-worthy travel experience by sharing their experience on social media. Travelers want to show off that they are in the most beautiful place. A well-designed airport that integrates AFA elements reduces stress and encourages repeat travel. CBIA should invest in improving visually appealing interior designs with cultural themes that emphasize Sri Lankan heritage-inspired architecture, a digital wayfinding system with interactive navigation tools, virtual and augmented realities, etc. Furthermore, improving seating arrangements and cleanliness are necessary factors that significantly affect PS.

The study draws a significant relationship between PS and PI in the airport setting. PS explains 75.5% of variance in PI, indicating PS is a key determinant of PI. Also, most of the independent variables (SEC, FBSL, ADFS, BRPTS, HAS and AA) significantly influence PS and PI directly and indirectly with various proportions. This indicates the relevance of this model in the airport and aviation industry, emphasizing a set of unique factors influencing PS and PI behavior. Thus, this model significantly fills a theoretical gap. It also brought our comprehension that the classical SERVQUAL model introduced by Parasuraman et al. (2017) has limitations. Its extension by integrating with behavioral models such as TPB, EDT and SOR allows the counting of context-specific factors in research focusing on service-oriented industries. Such integration enables researchers to understand how PS influences the PI better.

Because AFA has a direct impact on PI, upgrading infrastructure is critical while focusing on functionality. Capacity expansion and appealing architectural designs with improved facilities are important factors in this regard. Buildings and other large structures that are combined with appealing architectural characteristics in a green environment have a strong PI appeal. Furthermore, modernized airport aesthetics that incorporate cultural and passenger-friendly design values passenger feedback. Expand the inexpensive shuttle service system and public transportation accessibility to ensure that passengers have easy access. The other important thing is that implementing a real-time tracking system and better baggage handling with advanced technology (by incorporating AI technology). Essentially, the airport requires developing a robust SEC system incorporating advanced technologies such as an AI-based monitoring system, a pre-smart SEC zone, biometric verification at checkpoints and an integrated risk profiling system while paying more attention to passenger service.

Essentially, position the airport as a passenger-centric airport with an effective strategic marketing campaign. This could be done by developing a passenger-oriented service culture. Additionally, passenger feedback systems can be used to enhance vulnerable service areas. Real-time passenger feedback systems will help to identify and rectify the service issues timely. Innovative loyalty-building programs, such as exclusive duty-free offers, would encourage passengers' revisiting intention. Investing in human capital is another area, and airport management needs to focus on a constant training program for the airport employees to sharpen their skills in HAS, FBSL, AA BRPTS and ADFS for delivering world-class service. Upskilling the frontline to be world-class in service delivery will be critical in achieving a high level of PS. Implementation of performance evaluation based on a rewarding system and service employee recognition programs for frontline airport employees will further enhance the service delivery. Introduce multilingual assistance teams with a high level of cultural competencies to cater to international travelers.

The cross-sectional nature of the study is one of the significant limitations since data collection was focused on one point in time. Passengers' mood, perception, stress and experience may influence the responses. In addition, data collection is predominantly considered airport service satisfaction and PI. SQ aspects of the airline also influence the passengers' responses.

In line with future research recommendations, researchers can consider other theories like the expectation confirmation theory to broaden the theoretical framework to gain a better understanding of passenger behavior, particularly to understand how emotional and psychological factors influence shaping PS and PI. Future researchers can incorporate demographic variables as moderators to study how they affect the SQ attributes and the PS in determining PI. In addition, researchers can consider the impact of technology and the digitization of airport services in the context of developing countries' airports; features such as biometric authentication, AI-powered assistance and self-service kiosks have become prominent in shaping PS and behavior. Future studies can examine how concepts such as green airports and energy-efficient facilities influence overall satisfaction and airport experience in shaping passenger behavior.

The study was undertaken to explore how distinctive airport SQ factors influence PS and, in turn, influence patronage. The study emphasizes that overall, passengers' satisfaction strongly relates to PI. Findings reveal that factors such as SEC, “facilities and ambience” and AA play crucial roles in shaping PS and PI. In addition, ADFS, “FBLS” and “BRPTS” emerged as decisive predictors of passengers' evaluations. By strategically incorporating these factors, CBIA can heighten its position in the global airport industry.

The study offers a significant set of priorities for enhancing service delivery and passenger experience. CBIA and other airports in emerging nations can adopt these strategies to provide a seamless experience and enhance PIs. These initiatives help in becoming a regional hub, elevate their global standing, attract more airlines and increase their revenue potential.

We appreciate Professor Ferdous Azam from MSU in Malaysia for his valuable advice to enhance the content in the article.