“Airport service performance at Abu Dhabi International Airport”

To acquire a significant footing in today’s competitive airport environments, enhancing airport service performance for passengers is essential. This study aims to investigate the relationship between queuing time, prime services, security screening, and service performance at Abu Dhabi International Airport in the United Arab Emirates (UAE). A quantitative methodology was employed. The sample size for the PLS-SEM analysis and the passengers’ airport service performance was determined to be 230 respondents. The results revealed a significant relationship between queuing time, prime services, security screening, and airport service performance. The findings also demonstrated a significant positive relationship between queuing time and airport service performance (β = 0.193, t = 3.564, p ≤ 0.000), a significant positive relationship be-tween prime services and airport service performance (β = 0.478, t = 9.225, p ≤ 0.000), and a significant positive relationship between security screening and airport service performance (β= 0.227, t = 4.196, p ≤ 0.000). The outcomes are anticipated to support Abu Dhabi International Airport management in making efficient processes to augment airport service accomplishment for passengers from UAE and different countries.


INTRODUCTION
Nowadays, air travel is widely accepted as one of the most common ways of transportation. It contributes significantly to the global GDP and has a massive impact on the economy, atmosphere, and people (Karagiannis et al., 2019). Especially in Abu Dhabi, the UAE economy is highly dependent on hydrocarbon and gas revenues. The UAE government aims to reduce the economy's reliance on energy exports by 2030 (Turak, 2018). Many people believe that airports and air travel are critical to the economic development of cities and countries. Every country relies on the aviation business to transport people and goods across national borders (Oxford Economics, 2014). In addition, technological progress and competition increase passenger expectations. To attract more businesses and airlines, airports must ensure high customer service quality (Arif et al., 2013).
Air transportation has had a significant influence on the global economy in the UAE over the past few decades and is a significant contributor to tourist and international trade development. In the 2016 fiscal year, air travelers spent $650 billion, while the average global exchange delivered by air was $5.5 trillion. There are many benefits to the global economy from airline activity, and this is represented in the additional value it generates globally in the form of new jobs in the air transportation sector's supply chain, which has a significant impact on other economic sectors. 67.7 million jobs in the supply chain were expected to be sustained during the 2016 fiscal year, resulting in a $3 trillion value added. As a result, aviation is one of the world's most important industries, with a gross domestic product share of 3.5% in 2014 as a point of comparability (IATA, 2018), and it has tremendous financial and public influence surrounding this sphere.
An essential component of effective airport management is service quality. It illustrates how a company has evolved from having a strong emphasis on infrastructure and operations to having a strong emphasis on providing excellent customer services. The need to gauge airport efficiency from a passenger's perspective, from check-in to baggage claim, prompted this study. The development of Abu Dhabi International Airport has paralleled that of the city itself, which has grown from a modest oasis to a global hub. To put it another way, Abu Dhabi International Airport is one of the world's most efficient and busiest airports, serving more than 150 airlines from its small airstrip. Terminal 3 at Abu Dhabi International Airport opened in 2009, increasing the airport's annual passenger capacity to 21 million (IATA, 2018).
Regarding the demands and expectations of passengers, Abu Dhabi International Airport always attempts to meet them. Passenger experience is a major concern for many research studies. According to Figueiredoa and Castro (2019) and Wattanacharoensil et al. (2017), the airport is the place where visitors or passengers get their first view of a country they are visiting, so making a solid first impression and giving first-rate service is essential. Therefore, this study seeks to determine the factors influencing the service quality of Abu Dhabi International Airport.

LITERATURE REVIEW
The airline companies are responsible for offloading aircraft; immigration entrances are the personalized border protection services (Irandu, 2018). All these units socialize in the airport to promote the risk-free, successful, and protected services for passengers (Abeyratne, 2017;Mazhar et al., 2019). This enables airport managers to embrace effective methods to enhance airport service quality and achieve excellent customer satisfaction and engagement (Pitchforth et al., 2015).
Yet another concern is that airport functions create a significant volume of information daily, which is complicated to save lasting (Monterrubio et al., 2020). Big datasets are also complicated to process, frequently needing specialist components to deal with the analytical requirements for filtering many items. The initial efforts focused on queue patterns that emphasized travelers' communication with baggage delivery, security, and the general layout of the airport (Georgios, 2019). In particular, it is a usual kind of likeness model for airport control, related to information from a series of terminals (Wu & Chen, 2019). While this approach offers a viewpoint of the standard behavior of the incurable located upon well-known requirements, it neither offers supervisors an understanding of exactly what passenger top qualities are demonstrated in the files nor provides any evaluation of the changeability around model foresight (Prentice & Kadan, 2019).
Process models are typically utilized to deliver incurable methods concerning refining options and passenger capability (Adacher & Flamini, 2020). The providers connected with the passenger assistance method are asked for passenger security and efficiency well-being (Amro et al., 2020). The performance in connection with specified aims is not constantly analyzed, while queuing time is a strong performance hint. There is currently no straight dimension of when somebody takes part in the queue to become refined in the airport (e.g., simple records that keep track of when a plane has landed and when each passenger has completed immigration procedures) (Brownlee et al., 2020). Apart from being difficult to investigate, it is hard to determine whether passengers are taking a significant opportunity to produce it through immigration since they are progressively honed (Pitchforth et al., 2015). The variables identified in these analyses need evidence of where passengers may be avoided by spotting attributes popular to those sub-processes (Kohli & Muthusamy, 2018).
According to da Rocha et al. (2022), the traveling experience can be enhanced when airport operators surpass passengers' expectations in all facets of service. The objective is to fulfill or surpass the anticipations of passengers. Airport management must be able to identify enhancement opportunities in the service areas of the airport. Airports and airlines must analyze passengers' desires to decide which components are crucial and how to deal with any insufficiency.
Handling and discretionary activities are the two basic categories into which passenger adventure can be divided (Zhao et al., 2020). Handling activities need to be finished by a passenger, which is composed of check-in, security assessment, immigration, and boarding. At the same time, discretionary tasks are unordered and optionally available activities based on passengers' selection (El Najjar, 2018; Nikolova & Garkova, 2022). Queuing time is an essential predictor of airport performance services (Wiredja et al., 2019). Thus, passengers perceive lower value in airport services if they wait longer. Naturally, passengers' satisfaction (with airport service performance) will decrease when longer queuing times are creat-ed. Theoretically, the AIPEX model (Airport Indicators of Passenger Experience) argues that queuing time influences airport performance services (Antwi et al., 2020). Thus, the studies and implementations of the relationship between queuing time and airport performance services were progressively used in Western countries. These research findings become significant orientations for business strategies (IATA, 2019). However, only few studies examined queuing time as a predictor of airport performance services in the UAE. Therefore, studying this critical factor affecting airport performance services at Abu Dhabi International Airport is worthwhile.
Prime services are the second key component influencing airport service performance. Scholars have highlighted prime services, especially in the Western culture, for their potential (Wiredja et al., 2019; Antwi et al., 2020). However, only some studies examined the effect of prime services on airport service performance in the Middle Eastern cultures such as the UAE. According to the AIPEX model, prime services in the primary processing domain form the core of airport performance services (Antwi et al., 2020). Ideally, passengers are known to be interested in such services (Wiredja, 2017).
Another important organizational predictor of airport service performance is a reliable security screening. Applying linear regression analysis, Wiredja et al. (2019) examined the association involving security screening and airport service performance. Accordingly, it was found that security screening is a service factor representing the processing domain variable in measuring airport service performance (Wiredja et al., 2019). Moreover, a literature review on airport services has identified that security screening is one of the critical factors for predicting and enhancing airport performance services, which maximizes travelers' satisfaction (Bogicevic et al., 2013). However, the impact of security screening on airport performance services is yet to be studied (Wiredja et al., 2019). Under the AIPEX model, security screening is essential in enhancing airport performance services (Antwi et al., 2020). Therefore, more empirical research on the direct effect of security screening on airport performance services is required, especially in the UAE setting.  2015) found that passengers prefer shorter lineups at security checkpoints when they view risks to their integrity as significantly higher.
In contrast, those who felt a much better level of professionalism from airport security officials had higher safety sensations.

AIM AND HYPOTHESES
This paper addresses the airport service performance (queuing time, prime services, and security screening) to develop policies, and procedures in Abu Dhabi International Airport. While the study empirically examines the airport environment, the results could benefit a similar domain and various managerial and governance agencies, including policymakers, managers, CEOs, or designers. This study aims to understand airport service performance using a comprehensive analysis of concepts, performance concepts, the AIPEX model (Airport Indicators of Passenger Experience), and relevant literature.
This study proposed a model that examines the effects of airport service performance factors, namely queuing time, prime services, and security screening, on various constructs with unique relationships. The conceptual framework of the study is depicted in Figure 1. Thus, the study elaborates on the following hypotheses: H1: Queuing time positively affects airport service performance.
H2: Prime services positively affect airport service performance.
H3: Security screening positively affects airport service performance.

METHODOLOGY
The methods and procedures directly affect the choice of an analysis design. It contains information on data collection and evaluation to ensure that the analysis is a success. In order to better understand what variables influence the efficiency of airport service performance at Abu Dhabi International Airport, this study employs a quantitative methodological approach. A questionnaire was used since it is the most reliable method for gathering primary data about people's opinions, experiences, and values (Sekaran & Bougie, 2016).
Samples are representatives of the entire population, where the size and selection techniques have to be appropriate and not bias the results. According to Roscoe's (1975) recommendation, the appropriate sample size for most studies falls between 30 and 500. This guideline is called the "rule of thumb" for sample size selection. However, according to some statistical experts, the number of data points should be between 5 and 10 times as many objects as the scale contains (Hair et al., 2009).
For this study, the population is not residents, and the actual sample size could be different from one time to another. Therefore, estimating the sample size based on population size could be messy, and it is better to make the estimation based on other formulas in which population size is separate from the mathematical function. This study uses the rule of thumb stated by Hair et al. (2009), in which the sample size is ten times as many questions as there are respondents. As this analysis has 18 perceptional items and 5 demographic objects, the sample size is estimated between 180 to 230 passengers. So, this study's target-sample size is 230 samples.

Questionnaire and scales
Most social science studies with empirical examination use questionnaires as the main tool for primary data collection. Questionnaires of multiple items and selection polls are the common method to collect the perceptions of participants to assess the opinion level or examine the relational model between different variables (Creswell & Creswell, 2017). The paper examines four major constructs, and selecting the appropriate instrument to measure each one is critical.
Five items (

RESULTS
Constructs are summarized numerically in a descriptive analysis by describing the lowest and highest values (Zikmund et al., 2010). The mean and standard deviation describe the latent constructs (Table 1). The mean value of the variables, as shown in Table  1, varies from 3.5609 to 3.8817. The highest mean value was yielded by prime services (3.8817), and the lowest mean value -by security screening (3.5609). The mean values of the remaining constructs reveal not far from each other, while the standard deviation ranges from 0.53319 to 0.59528.
The PLS-SEM approach is commonly employed in marketing research using Smart PLS, version 3.0 M3 software (Hair et al., 2014). The structural and measurement models are used to measure and understand a PLS-SEM path model (Hair et al., 2014). This analysis followed a two-step method to obtain adequate results from the PLS-SEM path modeling technique. Figure 2 Table 2 and Figure 3 illustrate the results of item loadings and composite reliability (CR). The item loadings range from 0.651 to 0.897, and values of CR range from 0.889 to 0.912, which is also higher than the cut-off value. Table 3 shows that every indicator loading (shown in bold) is over the suggested threshold value of 0.5 and the suggested cut-off point of 0.5. (Hair et al., 2010). This demonstrates that discriminant validity is not a concern in this investigation.
The structural model (or inner model) in PLS-SEM direction simulation explains the association between external and endogenic latent structures. Accordingly, examining the fundamental version makes evaluating an empirical dataset's support for a theory easier (Hair et al., 2014). The valida-  tion of value standards via dimension shape evaluation is required before turning on to fundamental paradigm assessment and hypotheses testing.  Churchill (1979) and Sharma (2000) stated that the importance level of a t-value of 1% is more than or comparable to 2.58, and at 5%, it is greater or similar to 1.96, and at 10%, it is greater or equivalent to 1.645. Based on the t-values and p-values, this study accepts the hypotheses. Table 4 shows that H1 (queuing time positively affects airport service performance) is supported (β = 0.193, t = 3.564, p ≤ 0.000). In the same way, H2 (prime services positively affect airport service performance) is accepted (β = 0.478, t = 9.225, p ≤ 0.000). Finally, H3 (security screening positively affects airport service performance) is also accepted (β= 0.227, t = 4.196, p ≤ 0.000). Thus, the study accepts all the hypotheses.
PLS-SEM path modeling does not consider the goodness of fit because predictive relevance measures the model's accuracy. Henseler (2012) has recently made a case for avoiding empirical and conceptual model fit assessments. Because of this, according to Hair et al. (2017Hair et al. ( , 2016Hair et al. ( , 2014Hair et al. ( , 2013, the goodness of fit cannot discern between a valid and an erroneous model.

DISCUSSION
Airport services can simply be looked at as a queuing system, passengers, airplanes, luggage waiting to be claimed, circulation review, and the problems individuals experience (Schmidt, 2017). Generally, the customer satisfaction with the quality of airport services depend on queuing time, prime services, and security screening.  (2017), who discovered a substantial connection between queuing time and service performance. Queuing time is one of the most significant predictors of airport service performance (Wiredja, 2017). Therefore, the literature emphasizes that minimum queuing time boosts the individual perception regarding airport service performance (Wu & Mengersen, 2013). Also, these results are aligned with the AIPEX (Airport Indicators of Passenger Experience). Smooth queuing time significantly impacts the traveler's perception of airport management and its performance (Wiredja et al., 2019).
Prime services are delivered by air providers and airports with a vast array of airplanes, passengers, and security services to ensure their aircrafts are trusted, operations are secure, and the convenience of passengers is achieved (Gupta, 2018). The literature review shows that prime services at airports positively affect service performance. Thus, the impact of prime services on passengers' perception of airport service performance was tested (H2); the results accept this hypothesis. These results are aligned with Gupta (2018) Providing prime services has become a strategic objective for airports looking to improve service delivery. In an environment that is getting more and more competitive, such services have an influential role in the airport performance, signaling the transition from a large emphasis on infrastructure and processes to giving a provision of knowledge that is driven by the demands of passengers (Wyman, 2012;Wiredja et al., 2019). Moreover, the development of prime solutions in the airport transport resources and the resulting advantages to the national economic situation expected to result from any provided project might rouse the regulation of such support for various explanations (Nahvi, 2019). Such services also generate extra income for the airports, and prime services motivate passengers to revisit these airports (Wiredja, 2017 Standard screening processes must be conducted practically and thoughtfully as travelers intend to have a safe trip (Yu & Hyun, 2019). Passengers can be pre-qualified for various levels of assessment using an online risk-based airport security approach (Sakano et al., 2016); after background checks, many of those categorized as low threats will then look at reduced testing at gates while an arbitrary example experiences specified screening. Travelers suspected of posing security risks are taken to checkpoints and subjected to critical physical body inspections (Lee, 2019). However, such a secure atmosphere engages passengers and develops an attraction to such airport fetchers. Security screening at the airport significantly influences the service performance of airports.

CONCLUSION
This study investigated the relationship between queuing time, prime services, security screening, and service operation in the Abu Dhabi International Airport context. A questionnaire was used to gather data for a quantitative research strategy. Analysis was done using Smart PLS 3 in the study. The research model with three hypotheses was developed; all of the proposed hypotheses were supported.
This study found that the relationships between queuing time, prime services, security screening, and service performance are positively significant. The analysis revealed that the R2 value is 0.59, indicating a significant correlation; in-service performance is explained by the queuing time, prime services, and security screening, respectively.
These findings can be implemented in other public sector organizations in Abu Dhabi, Dubai, and in developing countries to create assistance distribution and managerial implementation. As a result, applying this strategy in Abu Dhabi and other developing countries in the Asian region is predicted to accelerate economic growth. In today's corporate environment, fierce competition and rivalry among companies are commonplace. For now, the only option for increasing the service's performance level is to adopt appropriate actions. This model can guide policymakers and practitioners in the evolving context of the Abu Dhabi Airport Industry to ensure high-level organizational performance.