The Impact of Airport Terminal Expansion on Customer Services as a Smart Airport: A Case Study of Incheon International Airport.

The Impact of Airport Terminal Expansion on Customer Services as a Smart Airport: A Case Study of Incheon International Airport
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Table of Figures
TOC h z c “Figure” Figure 1: Sample Quota PAGEREF _Toc514663604 h 30Figure 2: Age Frequencies PAGEREF _Toc514663605 h 30Figure 3: Gender Frequencies PAGEREF _Toc514663606 h 31Figure 4: Purpose vs. Type of Visit PAGEREF _Toc514663607 h 31Figure 5: Travelling through Terminal 2 PAGEREF _Toc514663608 h 32Figure 6: Regression – Convenience Depending on Mobile Facility and Self-Check Ins PAGEREF _Toc514663609 h 32Figure 7: ANOVA for Convenience PAGEREF _Toc514663610 h 33Figure 8: Better Customer Service and Preference of Terminal 2 PAGEREF _Toc514663611 h 33Figure 9: Overall Services, Info and Reduced Time at the Airport PAGEREF _Toc514663612 h 33Figure 10: Customer Satisfaction by Age PAGEREF _Toc514663613 h 34Figure 11: Customer Satisfaction by Gender PAGEREF _Toc514663614 h 34

Table of Contents
TOC o “1-3” h z u Research Aims and Objectives PAGEREF _Toc514663628 h 4Research Strategy/Philosophy PAGEREF _Toc514663629 h 5Sample and Design PAGEREF _Toc514663630 h 6Data Collection Method and Techniques PAGEREF _Toc514663631 h 9Findings PAGEREF _Toc514663632 h 11References PAGEREF _Toc514663633 h 25Appendices PAGEREF _Toc514663634 h 30

The Impact of Airport Terminal Expansion on Customer Services as a Smart Airport: A Case Study of Incheon International Airport.
Methodology
Research Aims and ObjectivesThis study aims to analyze how the expansion of the Incheon International Airport in Seoul, Korea will affect its overall customer satisfaction.

Airports in any economy fall under the hospitality industry and are thus, sensitive to even the smallest changes in their overall structure. For an airport, any small change can bring significant changes in the customer behavior and their satisfaction level with the destination airport (Smit, 2013, 34). That is because these travelers are already going through the hassle of taking the journey from one place to another. They cannot afford being caused any inconvenience as it makes the entire experience even more unpleasant for them (Vreeker et al., 2012, 35). In that context, it is crucial to understand how the changes at an airport will alter customer satisfaction.
For this study, the changes at Incheon International Airport are not as small as just moving the location of the coffee machine and is as severe as turning the entire airport into a smart airport. With that into account, customers can change their preferences and traveler patterns depending on how these changes are influencing them, and that is what this study is focused to scrutinizing CITATION Gri07 l 1033 (Griggs & Howarth, 2007). The results of the study are not only expected help identify specific customer behavior and their expectations from the hospitality industry, but these outcomes can provide enough perspective so that the formation of entire travel and hospitality for a destination can be done. The research objectives of the study are:
To identify the concept of customer services in airport context to evaluate the factors impacting customer services at airports.
To determine the impact of airport expansion as the smart airport in providing excellent customer service at Incheon Airport.
To understand how customers decide the level of quality of customer service at an airport after the expansion practice.
To provide recommendations to airport authorities of Incheon Airport for offering improved customer services through expansion.
To draw an inference from the study of customer service expectation from smart airport expansion and analyze how it can influence hospitality policy it.
Hypotheses:
H1, airport terminal expansion as the smart airport is positively related to passenger satisfaction.
H2, airport terminal expansion is positively related to airport service quality.
H3, airport terminal expansion is positively related to airport image.
H4, airport terminal expansion as the smart airport is positively related to passenger behaviour.
H5, there will be different opinions on airport terminal expansion by gender.
H6, there will be different opinions on airport terminal expansion by age.
Research Strategy/PhilosophyThe research strategy or philosophy for the analysis is based on interpretivism. It is an approach as opposed to positivism which requires scientific evidence and logic for a theory to be proved (May & Hill, 2016, 450). For this analysis, interpretivism is used which provides more space for contextual analysis and makes the study even more useful. Interpretivism is a right way of researching because it makes it possible to take a look at the bigger picture without having to make everything way to specific and conservative (Introna et al., 2016, 223). Something worth noticing here is that the reason this study is constructed around the model of interpretivism is how broad and generalizable the approach makes a study. The approach allows an analysis of the entire situation and considers how things might change or not be replicable for other scenarios (Adcock, 2016, 115).
To satisfy the needs of the research mode of the study, something most useful is the data collection method and how the entire process is approached. For interpretivism, analyzing qualitative data is a useful approach for research because of several reasons (Turnbull, 2012, 45). Since the purpose of interpretivism to take a look at the bigger picture and ensure that the entire viewpoint is not missed out on, qualitative analysis paves the way for that. When quantitative analysis gets naïve and cannot get into all the spaces of society, the qualitative analysis makes it easier to understand the facades of a culture that are trickier and not easy to understand (Nelson et al., 2017, 123). For the current study, since the aim is to analyze how airport terminal expansion varies customer expectation into a smart airport, it is crucial to keep the human factor in the study and ensure that the real sentiments of the customers are gauged. Studies conducted with this purpose and framework are neither to naïve to provide a picture of the society nor too broad to make everything look blurry (Denscombe, 2018, 84).
Sample and DesignThe design of the study is based on an experiment research strategy. The reason for choosing this design is that experimental studies are more flexible compared to hard-coded studies that only scrutinize the facts already provided by other analysts and researchers. When the topic of the study is broad and needs to be narrowed down while still analyzing a whole bunch of aspects, experimental study it suitable because it helps the researcher target the desired population (Putra et al., 2015, 8). For any experiment, the entire framework is controlled, and certain limitations are set for the analysis. It is something that makes the entire process structured enough to give a magnified look at the picture. Additionally, it is easier to get rid of outliers in experiments because how narrowed down these are. This idea was kept into account while designing the research strategy for this paper and it was going to impact the overall strength and credibility of the study model (Lee, 2013, 340).
Experiments are structured in a way that the controlled environment can be changed if necessary in the future. For instance, if in the first instance, a group of youngsters is analyzed to understand their perspective on something, the experimental group can be changed the next time to see how the overall results get influenced by changing the sample. In studies where qualitative data is scrutinized, experimental study design and the ability to be able to change the sample and other dimensions of a paper make the analysis more straightforward to understand and also more credible regarding the ways it can replicate to the entire society with variations (Fong, 2016, 260).
The overall design of the analysis is constructed like a cause and effect relationship and see how one thing causes another. This type of research model assists in understanding how one factor in a chosen setting can lead to another one (Mooij et al., 2016, 55). In this way, it becomes easier to observe how a chain reaction set in motion in a scenario specifically when the overall environment of society is under analysis. Cause and effect relationships hold a substantial value in the research domain of social science because of some reasons. Before analyzing how one thing affects another, it is essential to see if something even influences another and then comes the questions about the strength of this relationship (Nadri, 2018, 110). Cause and effect studies pave the way for more refined research methods and can also be used for further analysis of something that has already been provided in the literature which makes the model even more reliable.
For this study, the cause and effect relationship under analysis is regarding airport terminal expansion and customer expectations about the overall service provided at the airport. This method is expected to make it easier and observe the extent of elasticity of travelers regarding the terminal expansion of the airport. Whenever an airport goes through an expansion procedure, customers can react in an entirely unpredicted way (Budd & Ison, 2012, 320). They can either be delighted seeing the long-term growth of the destination and can feel inconvenience because it makes their travel confusing. It depends on the overall preferences of customers. While customers consider it a good stance since it will result in a positive development, others might get uncomfortable because they are introduced to new technologies they are not aware of, and it takes them time and effort to understand the new procedures (Oswald & Louri, 2016, 112). Therefore, the results of the expansion can lead to polarizing results as well depending on the overall scenario.
For the sample of the research, the method of quota sampling will be used, and there are some reasons for choosing this method of sampling. One reason is that quota sample provides a better representation of the overall population. That is so because when a sample is selected through random sampling, although every individual sample unit gets an equal chance of getting selected in the sample pool, not all classes and categories in the population end up getting a representation. Quota sampling is something that can help in that instance and allows the researcher to make the entire sample more generalized that represents all the major categories of the population (Avittathur & Jayaram, 2016, 121). For instance, if the ideal population for a study includes males and females and people from different age groups, it is possible that in a random sample, only the youngsters and mostly women get representation in the sample which can influence the outcomes.
In these instances, quota sample can assist in removing the sampling bias. Since sampling biases are prevalent in experimental studies, using quota sampling method is a good way to avoid its possibility and ensure transparency. Quota sampling is cost-effective as well since it does not need a developed model for the entire population model or reaching out to the maximum amount of people at a time. The researcher, in this case, can directly reach out to the people suitable for the quota, end the search when the quota is filled and move on to the other group. In that case, it becomes easier to understand the sample and final results of the research since the sample demonstrates strong connection back to the population making the linking and stratification efficient.
Data Collection Method and TechniquesFor the research, a sample of 300 Koreans was selected using quota sampling. Out of the total people involved in the sample, representation was given to both male and female and people of various age groups. All the quota ratios are given in figure 1 in the appendices. The purpose of the allotting these quotas to these groups was to ensure that both genders and each of these age groups get a fair representation in the final sample and all the perspective out there are gathered to provide an overall insight of the society about the customer services of an expanding airport terminal. It is an advantageous technique especially for primary data collection methods because the data has to be as general as possibly applicable to a broader group of people.
The data collection for the analysis will be gathered through primary data collection methods. Primary data is a type of information that is collected first-hand and has not been collected previously. For instance, if people are directly interviewed from a particular perspective, the data is first-hand and is primary. An opposite of primary data is secondary which is something that has already been collected by someone else in the past and is just used to strengthen a new study. Examples of secondary data are anything previously collected in research papers, books, newspaper, already conducted interviews and experiments and empirical analysis conducted by someone else and the results of which are used by someone else for a new analysis. Secondary data is a convenient way to avoid gathering first-hand information and can reduce the amount of time allotted to data collection CITATION Yan151 l 1033 (Yang et al., 2015). However, it primarily depends on the preferences of the researcher whether he wants to keep the data type primary or secondary depending on the aim, objectives, and purpose of the research.
The sample of the study was narrowed down to terminal 2 at Incheon Airport. The purpose of narrowing down the sample to just terminal 2 is to keep the study rationale and realistic and make the data collection process more manageable. That is because if all terminals on the airport were selected as a sample, it would have needed a lot of more resources, time and effort to collect data from all of these terminals. To collect the data from the selected sample, structured questionnaires were used. A structured questionnaire of 30 questions was constructed for this sample and was shared with them on Naver. Naver is an online platform used in Korea for various purposes, search and data collection being two of the most important of them.
The sample of 300 Korea travelers selected for the research was given the link to the online survey which they were asked to fill out during a certain amount of time. Some of these participants did not have access to the online platform, and in those instances, they were handed over a paper version of the same questionnaire so their participation process can be made more accessible. Two aspects that were given strong consideration during the data collection process was anonymity and confidentiality. Whenever human subjects are involved in a study, it is an ethical condition for the study to ensure that the people involved are not affected by the research or its results in any way (Bryman & Bell, 2015, 145). For that, the participants were given full surety that their data will be used only for the research and no other part will be allowed to access this information. Additionally, their data was also assured to be protected throughout and after the research.
FindingsFor the survey, 300 Korean participants were already chosen through quota sampling. The reason for choosing quota sample is that all the subcategories of the population get equal representation. This method is helpful since it reduces the instances of sampling bias because how closely knitted the final version of the sample is to the population (Yang & Banamah, 2014, 26). For this analysis, the purpose was to analyze the smart airport and its influence on customer service and customer perception. Therefore, the sample for the population was supposed to be diverse enough that it represents a significant part of the population and all major points of views gathered in the final results. That is why quote sampling was considered to be the most appropriate for this study.
Figure 1 in the appendices gives the sample quota and provides the age groups, gender ratios, and percentages of the participants. Both male and females had 50% representation in the sample, and these two major groups were further divided into age groups. People from 17-19 age group represented 5% of the population in both genders. Three other age groups were 20-29, 30-39 and 40-49 and each of these groups had a percentage of 10% in each gender. Since a large number of passengers are senior citizens and their needs can be more specific compared to the general public, their perspective about smart airports was also necessary. Therefore, 5% of both genders were also 60 or older.
Most of these 300 participants were given links to the online survey that they were asked to fill out. Since it was not sure if some of them would even bother going back home and using the link to fill out the survey, some extra participants were also reached out just in case to avoid any shortfall of participants. Some participants were also surveyed offline through one-on-one interaction. Most of these participants surveyed offline were older people who either had no access to technology to fill out the online survey or were not well versed in these things. For any study, it is crucial for the researcher to ensure that the participants are given all sort of accommodation they need to participate in the study (Hoffmann & Patel, 2015, 340). Therefore, participants were given the option of online and offline survey depending on their convenience and preference.
It took a few days to get final results from all 300 participants because of the two mediums used and quota allotted to each category. When some participants did not fill out the survey, additional surveys handed over to volunteers helped fill the quota and ensure that there are no gaps in the results of the study. Whenever primary data is collected through human subjects, gaps in the observations are inevitable since the subjects cannot be forced to fill out the survey and their participation is entirely voluntary (Gentle, et al., 2015, 17781). In that case, getting some extra people on board can be another good way to avoid these gaps to ensure that the least amount of data manipulation is required.
After the data was collected from all 300 participants, it was entered in SPSS software so it could be analyzed in detail. The 30 questions were divided into three major sections; the first section being about some necessary numbers regarding the sample representing the number of visits and other factors. The second section was regarding about the satisfaction level of customers about the expansion of the terminal while the third section talked about age, gender and other basic statistics that represented the diversity of the group being analyzed. Out of the total 300 people, 10% fell within the age group of 17-19. This group was added to ensure that the very young group of passengers is represented in the study so that their perspective on the smart airport is gathered. All other age groups from 20-29, 30-29 and 40-49 had 30% representation each in the total pool of 300 participants and the older group of participants that were 60 or above represented 10% of the total sample.
Regarding gender, the ration was precisely 50/50. Although in some instance, there is a possibility that men travel more than women and their instance of participating in the study is higher than the women, a ratio of 50/50 for both genders was used to ensure that there are no loopholes in the dataset. The fact that these men and women participants were divided into equal age groups was helpful in making the sample get rid of a significant chunk of sampling bias. Out of all the 300 participants, 150 were men, and 150 were women. Most of these participants were in their adulthood while some of them fell in their late teens or were above 60 years.
Out of the 300 participants in the study, a combination of travelers was found where some of them were travelling alone while others were travelling with family. The number of alone travelers was 248 while with family travelers were 52. On the other hand, with some participants travelling for study, other purposes for these travelers included business, pleasure or other miscellaneous reasons. A majority of the travelers were travelling with family while some of them were also travelling solo from one country to another. The most popular reason among these travelers was studied with 206 travelers while second most popular was business with 70 passengers. Some of these people were travelling for pleasure and tourism either solo or with family. These categories made the entire sample pretty diverse and made it possible to gather the point of view of a large pool of travelers. Since their personal preferences about travel vary depending on their personal, professional, physical and other needs, there is a high chance that smart airport terminal has a different meaning for all of them, translating into different results (Sekaran & Bougie, 2016, 250).
Figure 5 in the appendices shows the number of times a passenger was travelling through the terminal. The matrix used for this question was 1-2 years, 3-4, 5-6, and 7 or more times. The purpose of designing the question in this way was to ensure that all types of travelers get a chance to become part of the analysis and get a fair representation in the study. It is crucial because the number of times a traveler has visited the airport can put a massive influence on how he perceives smart airport expansion and a person visiting for the first time can have a different perspective compared to someone who has visited 5 or more times, etc. Hence the question was a part of efforts made to make the sample size diverse, and representative of the broader population that visit the airports in multiple instances ranging from one-time visitor to a frequent one and the ratio between all these options was equal. For instance, the number of people visiting 1-2 times was 75, and then the same number of people said that their visit was between 3-4 times, 5-6 times and 7 or more times.
The next part of the analysis was to run some regression and other tests associated with primary data analysis to understand the entire situation and see how some of these factors influence the overall satisfaction of the travelers. In figure 6 in the appendices, a regression analysis is shown between three variables. The dependent variable is the convenience of travelers while independent variables include a mobile facility at the airport and self-check-in booths. The purpose of this regression analysis was to observe if there is any connection between the level of convenience customers feel when the effect of the mobile facility and check-in booth is involved.
The results of the regression analysis show that there is a positive relationship between the dependent variable and both independent variables representing that the level of convenience that a passenger feel positively dependent on the mobile facility provided at the terminal and self-check-ins. Both coefficients are 0.068 and 0.058 which means that if the mobile facility and self-check-ins are provided, convenience for the traveler improves by this 0.068 and 0.058 units. Both of these two variables have p-values below 0.05 (0.003 and 0.014 respectively) which shows that the results are significant for the analysis. Hence, convenience for the customer improves if these two facilities are provided.
Regarding the same analysis, ANOVA test was run to put light on the relationship between the means of these variables. For this test, the same variables were used so that an overall strength of the model can be judged. Whenever it comes to primary data analysis, the entire model operates utterly different in comparison to a model where secondary data is collected. This is so because when data is collected from sources that already provide a pre-collected data, the robustness of the sample and data depends on some variables and testing the strength of the model is uniform across various theories and types of models. However, when primary data is collected from the population, the data has never been collected before and, therefore, some basic models of econometrics and statistics do not seem to hold.
It leads the researcher to a situation where he has to let go of some models of statistics crucial to the significance of a model. It is so because several of these models and tests might not hold for the primary data since the data is collected for the first time, leading to some limitations and also the expansion of some data boundaries. That is the reason these models were run on the data since the criteria of significance for the data are different compared to if the data was of the secondary sort (Campbell & Lele, 2014, 350). ANOVA test has a positive value showing that there is a difference in the means of the variables used in the mode while 0.000 significance level indicates that the results are strongly significant (Schawager & Etzkorn, 2017, 420).
Whenever a customer has a positive experience at the airport and receives a good customer service, there is a good chance that he will prefer coming back to that specific airport terminal just because of how comfortable he was made feel. To test the same thing, a correlation analysis was run between two main variables which were customer service and preference of travelers of terminal 2 at the airport. It was directed to gauge how much interest the travelers will have in coming back to the terminal if they had a good experience with the customer service over there. The results of the correlation showed a definite connection between the two variables representing that if travelers receive better customer service, there is a definite chance that they will prefer coming back to the same terminal. The preference variable also had a significance value of less than 0.05 indicating that the result is significant and will hold in situations.
The final part of the analysis was targeted towards running another test to understand the association between some more variables to close the loop with a broader picture. In figure 9, a regression analysis was run between the customer ranking of the overall services at the airport and information sharing at the terminal and reducing time to be spent. Theoretically speaking, it is pretty evident that whenever passengers have to stay at the airport for a shorter amount of time, it increases their overall satisfaction with the service since less amount of time is required for them to spend at the airport which reduces their hassle (Neufville, 2016, 212). The same results were illustrated in the regression run. It was seen that better methods of sharing information have a positive relation with overall service ranking of customers indicating that they appreciate better information sharing. Secondly, the reduced time had a negative relationship with overall service ranking indicating that reduced time spent at the airport leads to a better level of overall service satisfaction indicating the normal relationship between the variables. Figure 10 and 11 in the appendix show the coefficient correlation between customer satisfaction and age and gender. For age, there is a negative relationship between satisfaction and age, and as the age increases, customer’s satisfaction level declines. For gender, Women were seen to be less satisfied with the service compared to men where all of them said they were satisfied. For women, 132 were satisfied, ten were indifferent, and one was somewhat satisfied.
Analysis and Discussion
The purpose of the study was to analyze what passengers think about the expansion of airport terminals and smart installations at the airport that is supposed to improve the overall service and customer satisfaction. For the analysis, primary data analysis was chosen since it was necessary to gather the real sentiments of travelers without including any data that has already been collected by someone else. For that matter, primary data was collected from 300 Korean passengers on terminal 2 at the Incheon International Airport in Seoul, Korea.
At this point, the gist of the conversation was more about what these travelers think and how they think the service has improved because of the expansion and not if the overall infrastructure of the improved relatively in a national or international perspective. Therefore, interpretive analysis philosophy was the most appropriate one since it allows the researcher to understand and collect the facts, no matter if they are logical or scientifically applicable or not (Yanow, 2014 143). Out of 300 individuals targeted to be selected from the population, the quota was allotted to various sub-categories to ensure their fair representation. At this stage, some researchers prefer random sampling since that gives an equal chance to each to be selected into the final sample. However, one issue with this sampling method is that although every individual gets an equal chance, there is a possibility that it will not be selected which narrows down the sample. How reliable and general a sample is put a profound impact on the study as well and can seem to influence the overall results (Emerson, 2015, 170).
Hence, for this study and its sample, quota sampling was used so that everyone gets a chance to represent some percentage of the total sample size. Out of total 300 individuals, 150 were men, and 150 were women. It was so because at airports, both men and women visit on an equal level and this can be a completely random figure. Therefore, it was safe to give them an equal representation of 50/50 in the final sample. In this way, any chance of gender biases is minimized. Another factor that makes it convenient for the study is that the matter under discussion is not something gender specific and is something that both genders get equally influenced by in all circumstances. If the impact of any change at the airport is affecting both genders equally, they should be able to provide their thoughts on an equal level.
In most instances, passengers at the airport are adults while a small number of them are also young or senior citizens. In that instance, it was convenient to give a smaller ratio to people in their late teens 5% for both gender individually, and the same percentage was given to people in or above 60’s. While doing primary research, it can be a challenge to gather enough individuals to participate, and same was the case here as well (Armoogum & Dil, 2015, 12). While some participants were not willing to share their thoughts, other either had time constraints. To cover some of these instances, two steps were taken. Snowball sampling was used in some when already willing participants were asked to get their peers involved if they were willing (Gentle, et al., 2015, 17781). Additionally, they were given an option to fill out an online survey if they had enough time and willingness later. This is something that assisted in reducing the sampling bias and data collection challenges.
Whenever people travel internationally, there can be multiple reasons for them to move from one place to another. All these types of passengers have a certain attitude towards travelling and come to the airport with a certain mindset (Clave et al., 2015, 215). This mindset can either help with a move around at the airport or can also wholly ruin depending on various personal, local, professional, regional, technical and infrastructural factors. Hence, it is always a challenge for airport staff and other professionals working towards providing a good travel services to these passengers since it can vary individual to individual.
When it comes to travelling internationally, it is entirely possible that there is a difference between the expectations that these travelers have and how they react to specific changes being made on the airport terminal depending on whether they are travelling alone or with the family. To ensure that both sides of the coin are covered, travelers were also asked if they were travelling alone or with the family (Yildirimoglu et al., 2015. 344). One thing worth noticing here is that for international passengers, the time required to stay at the airport, how the information is shared, what services are provided, how friendly the customer support is and many other factors might be incredibly important (Zhang & Haghani, 2015, 317). In that instance, the way these passengers respond can vary individual to individual while his experience at the terminal can put a strong influence on the outcome.
When it comes to analyzing the behavior customers have towards airport expansion, nothing can be expected since the attitude these travelers have can vary on a broader scale. On the other hand, there is a possibility of having a large number of outliers since in the hospitality industry, the way people see service can be different due to some minor changes (Thelle & Sonne, 2018, 236). Therefore, it is always necessary for airports and entities working in the hospitality industry to ensure that they have a better understanding of the customer perspective and preferences.
For policy-making and strategic modeling for an airport, it is crucial to take a look at the bigger picture since everything that the terminal does is to accommodate passengers and make their travel journey easier. The analysis of what the customers think can provide profound insights into how the expansion and improvement processes at the airport should work and what are the ways the structure of the program can be designed in a better way to influence the overall productivity and serviceability of the terminal. Another thing that analysts argue about at this point is that anything that the airport administration adopts does not have a stand-alone impact on the productivity and serviceability of the terminal and there are permanent consequences to that (Forsyth, 2003, 30). Therefore, this sensitive issue should be handled with complete care and accuracy. Customers who come to the airport for the first time and end up having a bad experience might decide never to come back no matter how improvements the terminal makes later on and how much it advertises them.
Selecting a larger number of individuals to participate in the survey was helpful in gaining the true of the market and realize what the travelers think about the expansion and what side the policies around the expansion should go to. In that context, something that makes a difference in the results of the study is the number of times a person has been to an airport terminal. This is so because the number of visits of a traveler leads to a battle between his expectations and realities. Someone visiting for the first time can get very overwhelmed with the entire opportunity, to begin with, that he/she can think that everything was amazing. Conversely, it is also possible that since the traveler was already anxious about traveling, who was utterly frustrating on even the smallest things (Forsyth et al., 2017, 222).
To balance out these differences, it is crucial to cover the entire spectrum and discuss the views of people from one to a couple of times. For instance, a first-time traveler can seem to have an awful experience, but an experienced traveler might have a way better time just because of how these two individuals handled a situation. This is something true on a broader scale that how a person handles a situation can matter a lot regarding what they think about a certain situation. On top of that, the situation a person is in can be a driving factor for the overall satisfaction of a person as well CITATION Bez151 l 1033 (Bezerra & Gomes, 2015). An individual who is not a tech pro and does not like to use electronic devices can easily dislike the self-check-in booths because of his prior point of view about technology in the first place. This possibility does not get affected by the number of times a person has visited the airport and anyone from a first-time visitor to a regular one can have the same view. On the other hand, no matter if someone is a first-time visitor or has visited before, he might like these booths if he is a tech-pro and wants to avoid standing in long lines waiting for other people to do his job for him (Jacquillat et al., 2013, 164).
Figure 6 in the appendices shows a linear regression analysis between three variables. The dependent variable is the convenience of these travelers while independent variables are mobile facility provided at the terminal and self-check-in booths. The purpose of both these facilities is to allow the travelers with some space and time so that they be in charge of their travel activities and see what they have to do to get through. For some visitors, it might be a golden opportunity since they will have good control and record of their travel, but for others, it can be a complete hassle that they have to go through to travel through the terminal.
The purpose of linear regression or this coefficient analysis is to measure the strength and direction of the relationship between two instruments. The relation can be either positive or negative while the significance of the relationship results is also looked at to gauge the authenticity, generalizability, and credibility of these results. In this test, there was a positive relationship between convenience and mobile facility and self-check-in booths. The value of the coefficient is something that shows if the relationship is positive or negative. In this instance, the relationship is upwards since both coefficients are positive.
It indicates that when a mobile facility service is provided by the airport to the traveler, his overall level of convenience improves making it useful for the airport. Additionally, installing self-check-in booths also make the entire experience better for the customers (Abdelaziz et al., 2010, 45). A good level of significance for any study is less than 0.05. In some instances, it is also as competitive as 0.01 which means that variables and sample size have to be almost perfect. In some instances, the value can go up to 0.1, and these instances vary depending on the instrument selected in a study, how the analysis was conducted and what was the purpose of the research. In this case, the significance value of both variables was less than 0.05 which shows that the data is highly significant to do this study analyze the selected population (Schawager & Etzkorn, 2017, 339).
As mentioned above, primary data analysis is something entirely different than secondary one and the rules, conditions, and models applied to secondary data are not very relevant for primary data. It is so because primary data is something that is getting directly collected from the population for the first so there are no boundaries and filters to the data. It exempts the data from some statistical models. However, there are still ways to run sanity checks on primary data to see the good of a representative it is of the population and if the results are significant. Along with the significance value for the coefficients in the linear regression, another method that can be used is the ANOVA test.
The purpose of the test is mainly to tell if the study is good enough to represent the market. While telling about the overall significance of the study, it also helps in identifying the strength between the dependent and independent variable and might even provide insights for future analysis (Glantz et al., 2016, 232). Figure 7 in the appendices shows the ANOVA test of the mode. It can be seen that the significance value of the test is 0.000 which shows that the results are highly significant. Hence, it can be said that the data was good enough to represent the sample which was a representative of the entire population.
When customers have a good experience, they always come back and recommend it to others as well. It is not something specifically relevant to airports and is something that can be applied on all instances as well (Bao et al., 2018, 6). For instance, a good experience at a restaurant might drag the person to come back again while he might also recommend the place to his friends and family because of the food, service and an overall encounter CITATION Asc18 l 1033 (Ascarza et al., 2018). Same goes for airports when a good and interactive encounter of a passenger increases his probability of coming back while he might recommend the place to others who are expected to travel from that region.
To check the strength of this contextual idea, a correlation coefficient was run between two variables; one of them was the better service experience while the other was a preference that the passenger will give to the terminal. With customer service experience being the independent variable, it was analyzed how it would influence the preference of these passengers to revisit the terminal. It was seen that a better customer service experience could lead to a positive change in the way people prefer the terminal.
The idea gets retained according to the theories of marketing and ideas around retaining customers. One of the most important rules of the market and gaining space in the industry is that customer service provided to the client should be so good that they are forced to stay there and always keep coming back. The reason customer service is essential because people need a customer service representative to help them when they are stuck and need some help. In that instance, the individual is in a soft spot and needs someone to save him (Sama et al., 2015, 322). When excellent customer support is provided, there is a very bright chance that he will come back and recommend the place, other people, as well. Regarding this customer service, the idea of marketing is strongly associated, and the 4 Ps of marketing can play a role in determining how well space is being advertised to the customers CITATION Bud12 l 1033 (Budd & Ison, 2012). For instance, price, product, promotion, and place related to the terminal can play a significant role in its popularity. When a traveler is already happy with these four instances, better customer service can play the role of a cherry on top that can exalt the overall experience of the customer.
Recommendations
Depending on the results of the study, various recommendations can be provided to the airport administration, and some of them are mentioned below.
Since there is a negative relationship between age and customer satisfaction, there should be more facilities for the elderly at the airport to make their experience better and journey easier.
Women should be given some specific convenience services so that their satisfaction level can increase.
Customers’ biggest concern is time to be spent at the airport waiting in the lines. More self-check booths should be installed to reduce the turnaround time.
More facilities should be given to accommodate the family and solo travels.

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AppendicesFigure SEQ Figure * ARABIC 1: Sample Quota
Figure SEQ Figure * ARABIC 2: Age FrequenciesAge
Frequency Percent Valid Percent Cumulative Percent
Valid 17-19 30 10.0 10.0 10.0
20-29 60 20.0 20.0 30.0
30-39 60 20.0 20.0 50.0
40-49 60 20.0 20.0 70.0
50-59 60 20.0 20.0 90.0
>=60 30 10.0 10.0 100.0
Total 300 100.0 100.0 Total 300 100.0
Figure SEQ Figure * ARABIC 3: Gender FrequenciesGender
Frequency Percent Valid Percent Cumulative Percent
Valid M 150 50.0 50.0 50.0
F 150 50.0 50.0 100.0
Total 300 100.0 100.0 Total 300 100.0 Figure SEQ Figure * ARABIC 4: Purpose vs. Type of VisitWithfamilyoralone * Purpose CrosstabulationPurpose Total
Study Business Pleasure Other WithfamilyoraloneWith family 162 64 14 8 248
Alone 44 6 0 2 52
Total 206 70 14 10 300
Figure SEQ Figure * ARABIC 5: Travelling through Terminal 2
Figure SEQ Figure * ARABIC 6: Regression – Convenience Depending on Mobile Facility and Self-Check InsCoefficients
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta 1 (Constant) 4.339 .144 30.221 .000
Mobilefacility .068 .022 .172 3.032 .003
Likeselfcheckin .058 .023 .141 2.484 .014
a. Dependent Variable: Convenient

Figure SEQ Figure * ARABIC 7: ANOVA for Convenience
ANOVA
Model Sum of Squares df Mean Square F Sig.
1 Regression 1.258 2 .629 8.305 .000b
Residual 22.489 297 .076 Total 23.747 299 a. Dependent Variable: Convenient
b. Predictors: (Constant), Likeselfcheckin, Mobile facility
Figure SEQ Figure * ARABIC 8: Better Customer Service and Preference of Terminal 2Correlations
Bettercustomerservice PerferT2
Bettercustomerservice Pearson Correlation 1 .049
Sig. (1-tailed) .200
N 300 300
PerferT2 Pearson Correlation .049 1
Sig. (1-tailed) .200 N 300 300
Figure SEQ Figure * ARABIC 9: Overall Services, Info and Reduced Time at the AirportCoefficients
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta 1 (Constant) 3.610 .846 4.268 .000
Info .206 .172 .069 1.199 .232
Reducedtime -.051 .100 -.030 -.514 .607
a. Dependent Variable: Overallservices
Figure SEQ Figure * ARABIC 10: Customer Satisfaction by AgeCoefficientsaModel Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta 1 (Constant) 4.621 .066 70.510 .000
Age -.126 .017 -.390 -7.317 .000
a. Dependent Variable: OverallservicesFigure SEQ Figure * ARABIC 11: Customer Satisfaction by GenderGender * Overallservices CrosstabulationOverallservicesTotal
Somewhat satisfied Indifferent Satisfied Very satisfied Gender M 0 0 91 59 150
F 1 10 132 7 150
Total 1 10 223 66 300
Questionnaire
1. Age 2. Gender
3. Traveling with family or alone?
4. Purpose of visiting?
a. Study b. Business c. Pleasure d. Other
5. Number of travel through the airport
a. 1-2 b. 3-4 c. 5-6 d. 7 or more
6. Number of travel through terminal 2
a. 1-2 b. 3-4 c. 5-6 d. 7 or more
7. Do you like smart airports?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
8. Do you find smart airports convenient?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
9. Does terminal 2 have a good mobile facility?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
10. Do you like the self-check-in booths?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
11. Have the self-check-in booths reduced the amount of time required at the airport?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
12. Do you appreciate robots moving around at the airport?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
13. Are these robots helpful?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
14. Does a smart airport work better than others?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
15. Do smart terminals seem to have a better customer service?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
16. Would you prefer terminal 2 only because it is ‘smart’?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
17. Before finalizing your travel itinerary, do you consider how smart an airport is in your layovers?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
18. Is terminal 2 cleaner than before?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
19. Has the serviceableness of the terminal has improved after the shift?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
20. Would you agree that information visibility of the new terminal is better than how it was before?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
21. Does being a smart terminal seem to affect the level of security of the terminal?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
22. Has your overall processing and waiting time at the airport improved?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
23. Do smart airports look more fashionable and up to date?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
24. Are you satisfied with the place available for waiting at the terminal?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
25. Are you satisfied with the overall infrastructure of the terminal?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
26. Is necessary equipment available at the airport?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
27. Is all the necessary information available?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
28. Would you recommend the terminal to others?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
29. Would you revisit the airport?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied
30. How would you rank the overall services at the terminal?
a. Not satisfied b. Somewhat satisfied c. Indifferent d. Satisfied e. Very satisfied