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I. INTRODUCTION
Mobile usage by travelers is very natural. Travelers use mobile devices in transportation, shopping, and various environments. Since Chinese passengers are the main customers of airports, it is imperative to meet the needs of many Chinese passengers (Lee, Kim, Song, and Choi, 2015). Therefore, the airport tax refund system was developed in 2014 which can be used in Italy, Germany, France, British and South Korea now. The best advantage is to save time and offer convenience to passengers. Yet oddly enough, this innovation system was not yet familiar to most Chinese passengers. Therefore, it is necessary to identify receiving intention factors in order to promote this innovation system successfully. This study will illustrate the effect of airport tax refund system’s innovation characteristics on the receiving intention.
Drawing on the theory of planned behavior (TPB), attitude toward the behavior, subjective norm, and perception of behavioral control lead to the formation of a behavioral intention. As a general rule, the more favorable the attitude and subjective norm, and the greater the perceived control, the stronger should be the person’s intention to perform the behavior in question. Finally, given a sufficient degree of actual control over the behavior, people are expected to carry out their intentions when an opportunity arises. Intention is thus assumed to be the immediate antecedent of behavior. However, because many behaviors pose difficulties of execution that may limit volitional control, perceived behavioral control is thought to have an additional direct effect on behavior (Ajzen, 1991a). Therefore, we employ the theory of planned behavior by analyzing the attitudes of consumers and in order to promote the innovation system’s use of airport tax refund system.
In previous studies should evaluate the existing differences in the adoption stage of the innovation system that was limited addressed spreading. Drawing in the diffusion theory of Rogers (1995) was separated step 5 by increasing the diffusion process. The purpose of this study then was to distinguish between the users of the adoption stage based on innovation diffusion theory of Rogers and look to analyze the differences between those groups. The research contributes to emphasizing the strategic implications and the importance of development of the airport tax refund innovation system spreading. It emphasizes the importance of customer behavior analytics through the analysis of consumer adoption of innovations based on individual beliefs variables and propose a strategic way to gain a competitive advantage for companies operating airport tax refund systems.
II. THEORETICAL BACKGROUND AND MODEL
Innovation system’s characteristics means the technical characteristics of the innovative products or program, according to previous studies composed of relative advantage, conformity, perceived risk, complexity, expectations for better product appearance, practicality, possibility of division, practical utility, communication (Kelly and Kranzberg, 1978). From these elements, different passengers have different reactions to each recognized characteristic. The same can be said of in many studies relating to the characteristics of the innovation system, mostly used the factors of relative advantage, conformity, perceived risk, and complexity (Ram, 1987).
Firstly, relative advantage, which is caused economic income or costs saving by innovation adoption to gain the advantages of the relative value comparison. As the perceived relative advantage increasing, the receiving intention is higher.
Secondly, conformity is the degree of relating to existing values or past experience, which have the compatibility with needs of users. It means user’s consistency for existing value, degrees to meet the traditional, cultural values and the lifestyle of users. Users will be resisting against the innovations if they can’t perceive the conformity for themselves (Laukkanen, Sinkkonen, Kivijarvi, and Laukkanen, 2007).
Thirdly, innovation at airports affects acceptability (Seo, Kim, and Jeon, 2021), but all forms of innovation have the possibility of side effects and the uncertainty which can’t be predicted. Perceived risk is included the social, physical, economic and psychological risk. These elements are largely perceived, the receiving intention of innovation is lower.
Fourthly, complexity is the degree of the users how difficult to understand and execute innovation. As the perceived complexity increasing, the receiving intention is lower (Park and Lee, 2007). Thus, we expect the following:
H1: Airport tax refund system’s innovation characteristics are related to the receiving intention.
H1a: Relative advantage is positively related to receiving intention.
H1b: Conformity is positively related to receiving intention.
H1c: Perceived risk is negatively related to receiving intention.
H1d: Complexity is negatively related to receiving intention.
The theory of planned behavior (TPB) offers a sound theoretical framework to assess innovation system’s receiving. It seeks to explain the reasons of underlying individual behaviors. In psychology, the TPB is a theory about the link between attitudes and behavior. The TPB posits that the most important determinant of behavior is intention. Intention is predicted on attitude, subjective norm, and perceived behavioral control (Ajzen, 1991a).
Attitude is a measure of the degree to which a person evaluates a behavior favorably or unfavorably, subjective norm represents the normative influences or the perceived social pressure to perform or not perform a behavior (Ajzen, 1991b). Thus, we must understand exactly what users know and what their attitudes are. The TPB although were described by many studies and found suitable to provide a theoretical framework, it is not appropriate to describe the influence of specific parameters beliefs. Therefore, many studies have been conducted to study the separation of faith variables which is called decomposed theory of planned behavior (Taylor and Todd, 1995).
As the different characteristics of each person’s social behavior, it is a good claim to reveal that control beliefs is a new variable influencing people's behavior (Fishbein and Ajzen, 1975). Therefore, the existing research behavioral attitude of human is defined by beliefs and behaviors. The TPB holds that human action is guided by three kinds of considerations:
The behavioral beliefs were about the likely outcomes of the behavior and the evaluations of these outcomes, behavioral beliefs produce a favorable or unfavorable attitude toward the behavior (Kim, Park and Park, 2014).
The normative beliefs were about the normative expectations of others and motivation to comply with these expectations, normative beliefs result in perceived social pressure or subjective norm (Ajzen and Fishbein, 1980). Drawing on the theory of the diffusion of innovation, economic factors, adoption of innovation, convenience, social prestige also plays an important role along with the satisfaction (Rogers, 2003). In order to get the enhanced social status or not to suffer alienation from community which belongs to users, they will use information technology services (Kim and Lee, 2001).
The control beliefs were about the presence of factors that may facilitate or impede performance of the behavior and the perceived power of these factors, and control beliefs give rise to perceived behavioral control. Perceived behavioral control is referring to an individual perception of techniques, resources and opportunities based on a particular action (Ajzen and Madden, 1986). Thus, we expect the following:
H2: Chinese passengers’ individual control beliefs are related to the receiving intention.
H2a: Behavioral beliefs are positively related to the receiving intention.
H2b: Normative beliefs are positively related to the receiving intention.
H2c: Controlled beliefs are positively related to the receiving intention.
The decision-making process related to the adoption of the innovation process. When Personal or other decision-making units firstly know about innovation, they will form attitudes and the set of mental processes which can impact the decision-making of adoption.
In addition, the diffusion process of innovation is to be spread to community members for a period of time through a certain channel (Rogers, 1995). Adoption process of innovation and the diffusion process of innovation should be distinguished. Adoption process takes place in the mind of an individual, and it’s a decision process (microscopic processes) that the users or potential users whether want to accept or reject a new product. But the diffusion process is a macroscopic process taking place between the members and the social system (Rogers, Everett and Shoemaker, 1971). Employed to Rogers (1995) users make decisions about innovative products or services separated by a five-step process:
Step 1, the user is exposed to the innovative step and has the knowledge of its function. Step 2 is a process of persuasion, which occurs when consumers form a favorable or unfavorable attitude toward innovation. Step 3 is a process of decision, happens when users are faced with the choice to accept or reject the innovation. Step 4, the implementation happens when the user has used innovation. Step 5, users strengthens the final decision and confirm the innovation decisions. In study of Scheming (1989), the adoption period is separated into cognitive-evaluation period, use-adoption period, the initial users accept in cognitive-evaluation period as soon as possible, whereas the later users accept in use-adoption period as soon as possible. In addition, there were differences in innovation factors.
This study has based on Rogers adoption process model and explains that there are different factors impacting the step of adoption. In particular, the user can bring a change in the adoption and resistance after knowledge-determining step. In this study, the user adoption stage was separated into the early majority stage (knowledge-persuasion-decision) and the late majority stage (implementation-confirmation) used as a control variable. Thus, we expect the following:
H3a: According to the user adoption stage, the degree of innovation receiving intention will be different. (Early majority group < Late majority group)
H3b: According to the user adoption stage, innovation receiving intention factors will be different. (Early majority group ≠ Late majority group)
According to integration of the above hypothesis, this study can be established in following research model (see Fig. 1):
III. METHODOLOGY
This study targets general Chinese passengers who have experience using the airport tax refund system. Seoul was selected because it is the most important Korean consumer market, reflecting urban consumption patterns using the latest technology. The samples were collected from diverse attractions and shopping malls in Seoul. Before conducting a survey, we explained to the subject purpose, contents of survey and received oral consent. The passengers who did not want to participate in this survey were excluded. All respondents were selected through non-probability sampling through the link to the online survey. In the period from September 5 to October 3, 2022, the final number of respondents was 370 individuals and 27 incomplete responses were deleted, and thus 343 responses were used in data analysis. Statistical analysis of the sample (see Table 1) as follows:
Demographic characteristics and consuming patterns were analyzed using SPSS 22.0. It was subjected to factor analysis and reliability analysis to verify the validity and reliability of the measurement items. The hypothesis testing was carried out T-test and multiple regression analysis. T-test was used to explain the differences between the two groups, regression analysis was also conducted to determine what factors influenced the receiving intention.
The survey addressed innovation system’s characteristics using 12 items were developed based on previous studies (Ram, 1987; Rogers, 1995; Rogers, Everett and Shoemaker, 1971), assessed individual control beliefs using 12 items modified from (Koufaris, 2002; Webster, Trevino and Ryan, 1993; Novak, Hoffman and Yung, 2000; Davis, 1989; Limayem, Khalifa and Frini, 2000; Goldsmith, 2000; Hoffman, Novak and Peralta, 1999), and assessed receiving intention 4 items modified from (Limayem, Khalifa and Frini, 2000; Jung and Yoon, 2013). We used five-point Likert scales anchored by 1 (“strongly disagree”) and 5 (“strongly agree”) for each self-determination need. Drawing on Nunnally (1978) if Cronbach's α is 0.6 or more can be said sufficiently. As shown in Table 2, measurements of the parameters used in this study are shown in Cronbach's α more than 0.8, so there is a good internal consistency and a high reliability.
IV. ANALYSIS AND RESULTS
All by the receiving intention as a dependent variable, multiple regression analysis was performed (see Table 3).
We ran the first model tapped the first-order innovation system’s characteristics including of relative advantage, conformity, perceived risk, and complexity. R2=.550, F=103.164, sig.=.000, it shows the fit of the model significantly higher. As shown in Table 3, relative advantage is positively related to receiving intention (β=.394, p<.001), in support of H1a. Conformity is positively related to receiving intention (β=.373, p<.001), in support of H1b. Perceived risk is show a negative relationship to receiving intention (β= -.116, p<.05), if the perceived risk reduction it will increase the receiving intention, in support of H1c. In contrast of H1d is a positive relationship between complexity to receiving intention (β=.116, p<.05), offering support for H1d. The second model tapped the second-order individual control beliefs including of behavioral beliefs, normative beliefs, and controlled beliefs. R2=.633, F=194.853, sig.=.000, it also shows the fit of the model significantly higher. The results suggest that behavioral beliefs is positively related to receiving intention (β=.450, p<.001), Normative beliefs is positively related to receiving intention (β=.150, p<.01), and controlled beliefs is positively related to receiving intention (β=.280, p<.01), so in support of all H2a, H2b, H2c.
According to Rogers (1995), the user adoption stage groups of samples in the survey were from Step 1: knowledge (36.1%), Step 2: persuasion (6.7%), Step 3: decision (15.2%), Step 4: implementation (30.3%), Step 5: confirmation (11.7%) (see Table 4). The final sample consists of 199 persons (58.0%) in initial stage group (knowledge-persuasion-decision), 144 persons (42.0%) in late stage group (implementation-confirmation). As T-test results depending on the adoption stage of innovation, there is a difference in the degree of the receiving intention such as Table 5. The early majority group 1’s receiving intention was less than the late majority group 2, so H3a was supported.
| Type | Frequency | % | Group | Frequency | % |
|---|---|---|---|---|---|
| Knowledge | 124 | 36.1 | Group1 | 199 | 58.0 |
| Persuasion | 23 | 6.7 | |||
| Decision | 52 | 15.2 | |||
| Implementation | 104 | 30.3 | Group2 | 144 | 42.0 |
| Confirmation | 40 | 11.7 |
| User adoption stage | N | Average | Standard deviation | t | Sig. |
|---|---|---|---|---|---|
| Group 1 | 199 | 4.0063 | .6344 | 11.401 | .000 |
| Group 2 | 144 | 3.1476 | .7569 |
To verify the H3b dividing into the two groups performed by multiple regression analysis. As shown in Table 6, users belonging to the early majority group, innovation system’s conformity characteristic is positively related to receiving intention (β=.213, p<.01), so as the conformity higher, it will increase the degree of adoption. Individual control beliefs include of behavioral beliefs (β=.305, p<.001), normative beliefs (β=.152, p<.05), and controlled beliefs (β=.178, p<.01) were all positively related to receiving intention. Among them, the behavioral beliefs have the greatest positively effect to the receiving intention. The other side, users belonging to the late majority group, innovation system’s perceived risk characteristic is negatively related to the receiving intention (β=-.154, p<.05), so as the perceived risk higher, it will reduce the degree of adoption. Individual control beliefs including of behavioral beliefs (β=.313, p<.01), controlled beliefs (β=.196, p<.05) were positively related to receiving intention, normative beliefs does not have a direct impact on the receiving intention.
V. CONCLUSION
Proceeding from what has been said above, it should be concluded that based on the results of a survey conducted by Chinese passenger, Airport tax refund system’s innovation characteristics and individual control beliefs of users have some relations to the receiving intention identified through the verification of the hypothesis. User adoption stage was divided in order to determine the differences between group 1 (early majority) and group 2 (late majority). Based on the empirical results, presenting the strategic implications and innovative ways to spread the Airport tax refund system can help to activate the electronic payment industry by providing materials. Summing up, the present paper shows that:
Firstly, as the innovation system’s characteristics include of relative advantage, conformity, and complexity higher besides the perceived risk was lower it showed a high degree of users’ receiving intention. Secondly, as the individual control beliefs include of behavioral beliefs, normative beliefs, and controlled beliefs higher it showed a high degree of users’ receiving intention.
Thirdly, depending on the analysis of the difference between the receiving intentions, the early majority group was less than the late majority group. In the early majority group conformity was an important variable on the degree of adoption, and in the late majority group perceived risk was an important variable on the degree of adoption. Interestingly, both individual control beliefs affected the degree of adoption except normative beliefs in the late majority group. For all the reasons given previously, we convinced that: Firstly, in order to efficiently accommodate the proliferation of Airport tax refund system, depending on the system characteristics construction system that can be fully reflected in the various properties is required. Secondly, making the differentiation strategy in services is to improve work efficiency by conforming to the innovation and providing useful information to the electronic payment provider who is expected to be able to increase users’ confidence and satisfaction. Thirdly, through compared the differences between two groups, we can know providing more conformity to the early majority group and reduce the perceived risk to the late majority group is required. For example, in the early majority group emphasized the need for a strategy of the necessity of use innovation system services. In the late majority group normative beliefs does not have a direct impact on the receiving intention but it should be stressed that ensure the safety of the innovation system. Each of the different specific strategies is suggested to be carried out.
Finally, limitations of the study and future research directions are as follows:
Firstly, Airport tax refund system’s innovation characteristics need to be further excavated. Secondly, just separated two groups of user adoption stage have been conducted. In future research will need to obtain sample with more and different regional characteristics and determine the innovation adoption factors in greater detail.
