Academy of Strategic Management Journal (Print ISSN: 1544-1458; Online ISSN: 1939-6104)

Research Article: 2021 Vol: 20 Issue: 5

The Integrated Policy Implementation Modeling for Tourism Administration and Development: The Case of Thailand

Nuttaprachya Nantavisit, National Institute of Development Administration

Kassara Sukpatch, National Institute of Development Administration

Citation: Nantavisit, N., & Sukpatch, K. (2021). The integrated policy implementation modeling for tourism administration and development: The case of Thailand. Academy of Strategic Management Journal, 20(5), 1-14.


This research aims to identify the variables and present a structural model for the integrated tourism policy implementation. The integration of tourism policy implementation was presented by reviewing literature and synthesizing variables from policy-implementation concepts and behavioural science theory. Moreover, advanced statistics were used to prove the structural model by combining two theories and the hypothesis with empirical data from Thailand's tourism. This study is a quantitative research that is populated by policy practitioners of tourism policies in Thailand, using advanced statistics such as Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Behavioural intention and potential facilities variable have a direct positive effect on the integrated policy implementation. The next part is the subjective norm, attitude toward the behavior, and the perceived behavioural control was found to have a positive indirect effect on the implementation of policies in an integrated way through the intention to act. A notable finding in the emergence of an integrated tourism policy implementation is the significant impact of psycho-behavioural variables. The results have shown not only the conventional policy implementation variables should be encouraged, but the psycho-behavioural variables should also be used for the highest implementation.


Integrated Policy Implementation, Tourism Administration and Development, Thailand


The policy's implementation is a significant part of policy implementation, with several involved personals and degrees, from initiatives to implementation. Accordingly, numerous factors can affect accomplishment (Hill & Hupe, 2002). Besides, when it is needed to study in any research context, all factors must be inferred (Bardach, 1977). The previous studies, there have been studied the implementation of policy in multiple dimensions and concepts, such as politics, administrations, collaborations, top-down and bottom-up, or network concepts, etc (Barki & Pinsonneault, 2005; Friedman, 2006). However, most of the studies are based on the thinking of each scholars' ideas. Only a little attention that integrated it with the studies in terms of how it has been consisting when psycho-behavioural brought into in-depth policy implementation and how the structure of the conceptual framework of behavioural concepts for driving policy implementation that has many sectors involved (Evans, 2020). Despite the fact that the implementation of policies is a matter of realizing human behavior (Tummers, 2011), the policy controllers have become an integral part since they are the last person who decides the intensity of the policy implementation through discretion and favorable environment (Hudson et al., 2019; Lipsky, 2010; Tummers et al., 2012). Therefore, it is worth noting that the knowledge of policy implementation studies in an age that requires integration to work through individuals, and are variables in behavioural concepts necessary in order to further enrich the knowledge?

When considered in the context of management and development of tourism, implementing such policies is an intriguing and challenging issue, especially if the country is a developing country where tourism plays a crucial role in economic development. It has been because most of the drive requires the government to be associated with the sectors, multi-agency, and multi-level chains due to their complexity, profoundness, and sensitivity to the economy, social, cultural, environmental, both positively and negatively, and to the tourism sector at the same instant (Anzules-Falcones et al., 2021; Arifin et al., 2019). Hence, Thailand is such an interesting case study as one of the developing countries with the highest economic growth rates in terms of tourism in the Asia-Pacific region (Calderwood & Soshkin, 2019). As can be seen from the tourism revenue in the first half of 2019 accounted for 17% of GDP, including approximately 26.5 million visitors, and an estimated $40 billion in revenue (TAT Intelligence Center, 2019). However, over the same period, there have been challenges in the sustainability of natural and environmental resources, as well as the security and safety that the raking fell to the bottom of the world, according to the World Economic Forum (Calderwood & Soshkin, 2019). It demonstrates the unsuccessful implementation of the National Tourism Development Plan, 2017–2021 to enhance tourism competitiveness based on the World Economic forum's TTCI: Travel & Tourism Competitiveness Index, as well as with previous performance in the 2012 -2016 issue, the report indicated that it was partly due to a lack of integration (National Tourism Policy Committee, 2017).

This research aims to identify variables and present structural models in implementing tourism policies in the integrated practices of developing countries with Thailand as a case study. It is presented through a review of the literature and synthesizing variables from relevant research based on the concepts, policy-based theories, and behavioural theory to create a conceptual framework, as well as to prove assumptions with empirical data by analyzing advanced statistics and structural equation models to identify and develop a targeted structural model.

Literature Review

Synthesis of Policy Implementation Frameworks

This study has been conducted and reviewed in journals and a database of the national and international thesis to find theoretical variables that are consistent with empirical contexts, discovering that the success of policy drive can be achieved by the potential of the measurement of eight major factors: policy performance, policy resources, policy stakeholders, policy environment, organizational features, leadership attributes, personnel characteristics, and coordination feature (Bañares-Alcántara, 2010; Lipsky, 2010; Montjoy & O'Toole, 1979; Stoker, 1991), which is in line with Prof. Chandarasorn, who presents the integration theory of policy implementation (Chandarasorn, 2016). This variable is rooted in the integration of two theories: systems theory and organizational theory as follows:

The first theory, the system theory, is a consideration of the mechanisms, management, and the methodical work approach. Operations are divided under the system as a collaborative subunit. System operation occurs when the input factor is taken into the process, which is controlled by various conditions of the environment or the system context, and the response to the results or output obtained from the process can be taken as input factors or components of input factors (Bañares-Alcántara, 2010).

The second theory, the organization theory, is based on the belief that when a policy is established, it must be supported and implemented because the organization will lead the policy to the individual. If a failure occurs, it is necessary to consider which attributes are not conducive to achieving policy goals (Chandarasorn, 2016; Friedman, 2006).

Therefore, the eight factors are an indication of the potential for achieving the goals and situations that can support, promote, or hinder future success.

Integrating Behavioral Theoretical Conceptual Frameworks

Ajzen's planned behavioral theory is considered to be interesting and appropriate to be integrated to create a structural model of integrated policy implementation in the task. Because it is a social psychological theory that explains and predicts human behavior by the intention of acting or not to act in different contexts, depending on attitude towards the behavior, subjective norm and perceived behavioral control, these four variables consist of four indicators of each variable (Ajzen, 1991, 2002).

The planned behavior theory is a concept that has been continuously analyzed and synthesized from the original knowledge base intensively from 1985 until the present, over 35 years. It has been accepted and applied in many contexts and sciences. Besides, this is a conceptual framework that allows other scholars to come together to study an individual’s behavior in more depth by integrating between behavioral sciences with knowledge of the causes of behavior from attitudes and beliefs, with scholars in other fields who have knowledge of the situational causes, such as integrated policy implementation being used in this project (Ajzen, 2011; Ajzen & Fishbein, 2005).

Therefore, variables formed by both concept bases can be linked to increasing the predictive power of the implementation of policies in a fully variable manner. This section's key features can be measured in three factors based on the concepts of Persson (2004) and Basnet (2013), which are Aggregation Relatedness and Holistic.


One of the most important original features of policy implementation is that the policy's potential facilities are confirmed by the integrated theory of policy implementation, that it has a direct influence on policy implementation (Chandarasorn, 2016), in line with Rodriguez et al. (2014), presented in the context of tourism that a group of potential indicators can facilitate or delay the implementation of the policy. Therefore, this study hypothesizes that:

H1            Potential facilities directly affect the integrated policy implementation, attitude toward the behavior, subjective norm, perceived behavioral control, and behavioral intention.

As noted in the study of Ajzen (1991, 2011), that human behavior is more likely to occur high or low only if the intention is high or low. According to Ajzen's planned behavior theory, the intention is high or low, depending on attitude toward the behavior, subjective norm, and perceived behavioral control. For this reason, this work hypothesizes that:

H2            Behavioral intention directly affects the integrated policy implementation.

H3            Perceived behavioral control indirectly affects integrated policy implementation through behavioral intentions.

H4           The subjective norm has an indirect effect on the integrated policy implementation through behavioral intention.

H5            Attitude toward the behavior has an indirect effect on integrated policy implementation through behavioral intention.

Planned behavioral theory is a theory that allows for the integration of other science-based situationism to help in predicting human behavior in different contexts (Ajzen, 1991; Tett & Burnett, 2003). Therefore, the theory is appropriate to be combined with variables from the integrative theory of policy implementation that require variables to explain the integrated policy implementation, which acts as a response to the policy implementation of organization plans (Figure 1). Consequently, this work makes a final hypothesis to prove the idea that:

Figure 1: Structural Model Adopting Integrated Policy Implementation

H6            The structural models of integrated policy implementation are consistent with empirical data.

Research Methodology

Research Design and Data Analysis

This study is a quantitative research that is populated by policy practitioners in developing countries like Thailand, using advanced statistics such as Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). These statistics are developed to confirm the structure of synthetic theory as to whether it can be applied to empirical data, including being a statistical technique that eases the early boredom agreement, which allows data tolerances obtained from each observable variable measurement to be correlated. This is to make the results of the data analysis more accurate (Byrne, 2016).

Data Collection

Purposive sampling was utilized in this study, which can provide accurate information, namely individuals or officials under the relevant government agencies at the operating level, and supervisors in Thailand who are responsible for managing and developing tourism, responding to, and acting according to the action plan or project from the National Tourism Development Plan No. 2, 2017 – 2021 or policies related to tourism management and development into practice.

The sample size in this research was based on the analysis technique of structural equation models, according to the proposal of Hair Jr et al. (2014); Kline (2015) says that analysis of structural equation models, the sample size should be between 10-20 people per observable variable. Accordingly, in this research, the sample size was ten persons per observable variable, and in the analysis model there were 27 observable variables, 270 samples were obtained, corresponding to the number of acceptable samples of the structural correlation model analysis. Therefore, the sample is equal to 270 people (Hair Jr et al., 2014).

Research Instruments

The study developed a questionnaire based on all variables according to conceptual frameworks and research obtained from document processing and related literature synthesis to create two scales: the first part is the main scale: the 116 main variables in data collection, adapted to the context of Thailand. The questionnaire used was characterized using 5 points Likert-scales, measuring a level of understanding, beliefs, opinions, and other related information depending on the context being considered in the scoring process and interprets. The second part is to be characteristic data of the respondents, totaling 121 questions.

This research examined the quality of the instruments through two processes. The first process was to examine content validity from expert scholars, professors, and associate professors of political science and public administration in Thailand. The audit results showed that 111 questions pass the criteria, with a score value between 0.50-1.00 points from 121 questions. 10 questions were scored less than 0.5 points; therefore, the questions have been revised according to the experts' recommendations and the advisor to reconsider before being tested for the reliability of the data (Turner & Carlson, 2003). Moreover, the second process, the reliability of the data, was found that a total of 116 questions had a value of .977. The observable variables were found to be between .785 - .961, which were above 0.70, and the instruments in this study were considered reliable and able to continue to collect data (Cortina, 1993; Nunnally et al., 1967).


Analysis Results of Personal Attributes of Samples

According to the sample data, the majority of 180 females accounted for 66.70 percent more than men with 90 or 33.30 percent. The majority of respondents were 62 in the 26-30 years old, representing 23 percent, and 155 students at the highest education level of the master's degree, equivalent to 57.40 percent. When considering the respondent's affiliation, it was found that most of them were from the Tourism Authority of Thailand of 103 people or 38.10 percent. It is followed by the Department of Tourism, Permanent Secretary Office, Ministry of Tourism and Sports, Designated Areas for Sustainable Tourism (Public Organization), and from the local government organizations are the top five organizations the respondents were from, and are mostly positioned at the operating level, 202 persons, equivalent to 74.80%.

Basic Statistical Analysis Result

Primary statistical analysis results showed that the variable of potential facilities, behavioral intention, perceived behavioral control, subjective norm, attitude toward the behavior, and integrated policy implementation, the result showed the mean values were 2.802, 3.111, 2.792, 3.215, and 3.140 respectively. The observed variables of each latent variable had a mean of 2.426 to 3.244. When considering the standard deviation (S.D.), the value is in the range of 0.373 to 0.725 and when considering the skewness and the kurtosis, the skewness value (KS) was in the range of -0.567 to 0.282 and the kurtosis (KU) was in the range of -0.859 to 0.865, consistent with the acceptable normal criteria of skewness and kurtosis with the skewness value that does not exceed the perfect at value 3.00 and Kurtosis value that does not exceed the excellent value at 8.00, indicating the acceptable data of variable with the normal distribution of skewed and kurtosis (Kline, 2015).

The Results of the Correlation Coefficient Test and the Collinearity Test

When considering the correlation coefficients between all 27 variables, it was found that the correlation coefficients of all variables were all positive in the range of .806 to .177 (Significant at the 0.01 level) and the value of not more than .90 which shown that all variables are suitable for effect analysis (Aroian & Norris, 2005).

Besides, when considering the correlation test results using the Tolerance and Variance Inflation Factor (VIF) values, it was found that the two independent variables were greater than 0.1 in the Tolerance Value (= .172) and not more than 10 in the VIF Value (= 5.804) of the attitude toward the behavior variable (AB). Thus, it can be concluded that this study's independent variables are appropriate to analyze the model because there is no issue of high correlation and multicollinearity (Hair Jr et al., 2014).

The Results of the Analysis of the Confirmation Elements of the Measurement Model

All analyses had Bartlett's Test of Sphericity statistically different from zero at the .01 level, a Kaiser- Mayer- Olkin (KMO) index greater than 0.5, and an approach value of 1 (= .691 -. .887) and Measures of Sampling Adequacy (MSA) is greater than 0.5 (= .654 - .734). It showed that the observable correlation matrix is not an identity matrix and there is enough correlation between the variables to be able to analyse for confirmation elements to verify their structural validity (Kerlinger & Lee, 2000). Also, there is a sufficient P-value not to reject the hypothesis.

It denoted that the result of χ2 value was significantly different from zero statistically; that is, the measurement model has construct validity. This is consistent with the results of the confirmation composition analysis of all variable measurement models in as shown in Table 1.

Table 1
Results of Confirmatory Factor Analysis on Measurement Model
Consistency index Criteria Last Model Test Results
χ2 P > 0.05 .227 0.899 0.798 0.997 0.663 0.802 Pass
χ2/df < 2 1.220 0.016 0.065 0.000 0.190 0.063 Pass
GFI > 0.95 0.990 1.000 1.000 1.000 1.000 1.000 Pass
NFI > 0.95 0.991 1.000 1.000 1.000 1.000 1.000 Pass
TLI > 0.95 0.995 1.010 1.011 1.014 1.006 1.012 Pass
CFI > 0.95 0.998 1.000 1.000 1.000 1.000 1.000 Pass
RMSEA < 0.05 0.029 0.000 0.000 0.000 0.000 0.000 Pass
RMR < 0.05 0.010 0.000 0.001 0.000 0.001 0.001 Pass

The analysis of the elemental weight matrix of the measurement model of the latent variables separately found that the standard component weights (λ) of the 27 observable variables of the 6 latent variables were positive. First of all, the potential facilities' observable variables are between .909 and .458, and the variance can be described at 82.6% to 21.1%. Second, the behavioral intention variable's observable variable ranged from .959 to .741, and the percentage can explain the variance at 91.9 to 54.9. Third, the observable variables of the perceived behavioral control latent variable ranged from .860 to .723 and the variance could be described at 73.9% to 52.3%. Fourth, the subjective norm's observable variable is between .807 and .684, and the variance can be described at 65.1% to 46.8%. Fifth, the observable variable of attitude toward the behavior is between .930 and .824, and the variance can be described at 86.6% to 67.9%. Lastly, the observable variable of the latent variable integrated policy implementation, the values ranged from .837 to .662 and the variance could be explained from 70.1 to 43.9%, with all of the variables being statistically significant at the .01 level. Therefore, it can be concluded that the observed variables or indicators of all variables in this work theoretically correspond to the empirical data.

The Results of the Analysis of a Structural Equation Model

Once all the variables were confirmed with the composition, this work was analyzed a structural model developed from theory with empirical data, resulting in the analysis of Figure 2, Table 2, and the following details.

Figure 2: The Results of the Integrated Policy Implementation Model

Table 2
Analysis of Direct and Indirect of Variable
Dependent Variable BI IM
Causal Variables TE DE IE TE DE IE
PF       0.263** 0.263**  
BI       0.333** 0.333**  
BC 0.284** 0.284**   0.095**   0.095**
SN 0.365** 0.365**   0.122**   0.122**
AB 0.329** 0.329**   0.110**   0.110**
R2   0.742     0.296  
Model-fit χ2 = 202.528, df = 181, χ2/df = 1.119, P (p-value) = 0.130, GFI = 0.951, NFI = 0.965, TLI = 0.992, CFI = 0.996, RMSEA = 0.021, and RMR = 0.015

Figure 2 illustrates the findings of the study of the structural equation model of integrated policy implementation., it was found that the model after adjusting the recommendations of M.I (Modification Indices) values is consistent with empirical data determined by value χ2 = .202.528, df = 181, χ2 /df = 1.119, P (p-value) = .181, GFI = .951, NFI = .965, TLI = .992, CFI = .996, RMSEA = .021, and RMR = .015. Furthermore, P-value is sufficient not to reject the hypothesis, revealed that χ2 value differs not statistically significant from zero, which is the measurement model is structural validity.

Table 2 found that the behavioral intention (Direct Effect=0.333) and the potential facilities (Direct Effect=0.263) were the causal variables that had a direct influence on the implementation of the policy to be the most integrated, respectively. This was consistent with assumptions 2 and 1 and the subjective norm (Indirect Effect=0.122), attitude toward the behavior (Indirect Effect=0.110), and the perceived behavioral control (Indirect Effect=0.095), which is the causal variable indirectly influencing through the intention of behavior and the implementation of the policy to be integrated respectively, consistent with the hypotheses 4, 5, and 3. Therefore, from the coherence analysis of the model and the results testing of effect, it will find that the structured model developed from the theory to integrate policy is consistent with the empirical data, as in hypothesis 6.


In this study, the study results were included in the concepts, theories, and related research. Accordingly, the study results could be discussed in the division by individual assumptions as follows:

Dimension of Potential Facilities

Based on the results of the study, it found that potential facilities have a positive direct effect on the integrated policy implementation (IPIM) with an effect size of .263 (p <0.01) can be inferred that if the organization intends to increase IPIM, at first, the organization should promote its potential facilities. In line with the findings of Giacchino & Kakabadse (2003) and Rodriguez et al. (2014) that agencies must have the potential in various areas of increased policy action to enable the implementation of the policy as expected, same as works in Thailand which indicated that the influence of leaders, officers, organizations or coordination affects increasing policy implementation (Koson, 2011; Promraksa, 2015). Also, the results of the hypothesis are in line with the integrative theory of policy implementation that Chandarasorn (2016) has presented in the model.

Dimension of Planned Behavior

It was found that from the study, the results that all of the variables from Theory of Planned Behavior have a direct and indirect effect on IPIM as follow.

(i)            Behavioral intention has a positive direct effect with the effect size of 0.333 (p <0.01). It can be presented as if the agency needs to increase the implementation of policies more highly integrated. The agency must first increase the level of intention in implementing the policies of its personnel. Because when intentions increase, it indicates a possibility or an opportunity for a person to act in the future. This is consistent with findings in the work of Trost et al. (2002) and Šumak et al. (2011) that intent is a key factor in the trend toward increasing the agency's expected target action or tourism events that show that intention can contribute to practice tourism volunteer policy implementation in the future (Lee & Lina Kim, 2018). Similar to the contribution of Ajzen's Theory of Planned Behavior (1991, 2011) that intention as a personal component that influences different forms of behavior, the higher the intention the more persistent the act of doing that behavior.

(ii)           Perceived behavioral control has a positive indirect effect with the effect size of 0.095 (p <0.01). This indicates that the perception of the ability to control the implementation of the policy in an integrated way as the person's needs will increase the intention of action and lead to more opportunities for the actual implementation of the policy in the future. This is consistent with the findings of the study of many Thai scholars that the perceived behavioral control is one of the variables that directly influence a person’s will to act and pass it on to that behavior (Prapasrisuk, 2012; Tresopakol, 2015). This includes work by Erul et al. (2020) that presents results in the context of behavioral support for the development of coherent tourism. It is also similar to Ajzen's Planned Behavioral theory (1991, 2011), which suggests that The more a person perceives that he or she has more control over the action of that behavior, the more it affects the strong determination to cause that behavior which ultimately led to the occurrence of the behavior.

(iii)          subjective norm has a positive indirect effect and with the effect size of .122 (p <.01). This indicates that recognizing respected or admired people who encouraged and taken any action by the practice of a policy implementer will motivate them to be willing to live up to the expectations or to take action and has the possibility of implementing an integrated policy in the future. This is consistent with the findings of Holmes (2008) and Tresopakol (2015), suggesting that people are often attracted to people they trust and see as role models, give them the willingness to act accordingly and expressed as behavior eventually. Similar to the work of tourism by Erul et al. (2020), proposing that behavior to support tourism development will occur or increase when a person sees that his norms encourage and act on that behavior so that he or she has a higher commitment to act. Same as the results being proposed in Ajzen's planned behavior theory (1991, 2011) that the person will recognize the support and action on the behavior of the person he respects, results in the intention to follow and it is likely to eventually cause real behavior.

(vi)          attitudes toward the behavior had a positive indirect effect with the effect size of .110 (p <.01). This means that a positive attitude towards integrating policy implementation of people will drive the intention to implement and implement policies in an integrated manner into reality in the future. This is in line with Johnson (2017), showing that positive behavior views are more likely to attract people's will to do more, leading to the actual occurrence of behavior eventually. Similarly, study results in the tourism context proposed that the behavior to support and follow the tourism development plan will respond to a positive attitude through the determination to support and implement tourism development plans (Erul et al., 2020). It is similar to the proposition derived from Ajzen's Theory of Planned Behavior (1991, 2011), which states that attitude is the individual component that defines a person's intention to perform a particular behavior. The more a person has a positive attitude or believes that the results obtained from the action and behavior will be beneficial and promote himself to be more successful, the person is even more determined to do that behavior as well, which leading to the action of that behavior eventually.

Dimension of the Holistic in IPIM

The study result shows that the structural models were created through the integration of policy implementation theory with the planned behavioral theory, which can be used to explain and predict the implementation of policies in an integrated way. Corresponds to Erul et al. (2020), affirms the consistency of empirical data with a model that combines the theory of emotional stability and the Theory of Planned Behavior that can be used to describe the context of tourism development. As it was expressed that, the behavioral science framework can be integrated with concepts and theories from other sciences to study and explain a person's behavior in more detail (Ajzen, 1991; Tett & Burnett, 2003).

From the proofs of hypotheses 2 to 6, it is concluded that this work can demonstrate to a certain extent that education and work guidance on policy implementation issues require not only attention to the linkage factor of policy based on the original concept studied in the Top-Down (Bardach, 1977; Pressman & Wildavsky, 1984; Van Meter & Van Horn, 1975) or Bottom-Up (Barrett & Fudge, 1981; Elmore, 1980; Lipsky, 2010) concepts, but also a more holistic consideration of the psychological processes that result in behavior. According to the suggestion made in Tummers' researches (Tummers, 2011; Tummers et al., 2012)

Although the findings of the experiments in this study support the theory, additional examination reveals an intriguing point in Tummers' work. (Tummers et al., 2012) discovered that the attitude-based conceptual element had a smaller effect on the willingness to implement the policy than the policy-implementation conceptual factor. In contrast to the findings of this study, policy implementation behavior is more strongly influenced by attitude factors. The discrepancies were assumed to be due to the geographical context and function of the various samples. Because Munangasame and McKercher's study on the context of tourism in Thailand, such as the one above, has shown that practitioner attitudes may play a significant role in establishing a practitioner's willingness to implement tourism policies (Muangasame & McKercher, 2015).


Research on structural models of integrated policy implementation for tourism management and development in developing countries. Case studies of Thailand have worked on goals to identify causal variables and complete the structural model's development and achieve results according to the goal: behavioral intention variable and potential facilities have a direct positive effect on the integrated policy implementation. When the behavioral intention or potential facilities increase, it will lead to a more integrated implementation of the policy accordingly. The next part is the subjective norm, attitude toward the behavior, and the perceived behavioral control was found to have a positive indirect effect on the implementation of policies in an integrated way through the intention to act. Implementing an integrated policy will only increase when the individual has a strong determination to implement the policy in an integrated manner. In which the intention depends on a positive attitude, getting support, and acting as a norm, including the awareness of the ability to control the implementation of the policy to be integrated successfully of the person.

Policy Implication

In this study, behavioral intention and potential facilities were the first and the second most influential variables in the direct implementation of service and tourism development policies. Therefore, the organization sector should promote such variables by the importance of influences. The plans are divided into two phases: two years of short term (Quick-Win) and three years of medium-term which is the organization has to push practitioners of the policy to be intended to implement more integrated policies through encouraging them to have a positive attitude, building a role model, and training people to be able to control the integrated policy implementation in the first place by providing a short-term operation plan. Subsequently, controls and promotes the potential to facilitate an increase in the integrated policy implementation, for instance, the consistency and clarity of the policy, the availability and sufficiency of resources for policy implementation, the condition of the environment in supporting, create an acceptance from stakeholders, arrangement the nature of the organization to be flexible oriented and ready for changing of supportive leaders’ ability, the ability and willingness of partners in the organization, and reducing complexity and increasing working consistency between units, etc.

Research Implications

This work's findings display the results of combining planned behavioral theory into an integrated model of policy implementation theory, which confirms that behavioral science is an essential psychological insights variable. This makes it interesting to study and test deeper into behavioral science in the future.

This is not only discovered the behavioral science, but this research can be considered a comprehensive study of both situational and behavioral variables that can reduce the academic gap in policy implementation to a certain extent, especially when studying work integration aspects. This study presents a structured model that is used to explain and predict the integrated policy implementation that will be a guideline for applying a model in tourism management and development, which is an industry that mainly requires integration in driving it. Practitioners or scholars can take the models obtained from this work to design and experiment in their area for extending knowledge or apply it to study other public policies that require a multi-party, multi-agency behavior to find ways to raise the level of public policy implementation results in the future.


The authors would like to express deep gratitude to the National Institute of Development Administration for supporting the study and granting the budget for this research article publication.


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