A Structural Equation Model of the Factors Affecting the Belt and Road Initiative (BRI) on the Perceived Benefits and Intention of Thai People to Use the China-Laos High-Speed Rail Service

A Structural Equation Model of the Factors Affecting the Belt and Road Initiative (BRI) on the Perceived Benefits and Intention of Thai People to Use the China-Laos High-Speed Rail Service | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Structural Equation Model of the Factors Affecting the Belt and Road Initiative (BRI) on the Perceived Benefits and Intention of Thai People to Use the China-Laos High-Speed Rail Service Kestsirin Theerathitichaipa, Panuwat Wisutwattanasak, Chamroeun Se, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4740250/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Belt and Road Initiative (BRI) policy, with the China-Laos high-speed railway being a part of the BRI project. As it is well known, Thailand shares a border with Laos and has significant cross-border trade with China. In terms of tourism, Thailand is a globally popular destination, with Chinese tourists being the largest group of visitors. This study takes place in Thailand, a developing country, to examine the opportunities that may arise if Thailand prepares to handle the China-Laos high-speed railway. The objective of this research is to study the relationship between factors affecting BRI and Thai people's perceived benefits and their intention to use the China-Laos high-speed railway. Data was collected through a survey conducted in key trade gateway areas along the Thailand-Laos border, using Stratified Random Sampling of 1,540 Thai residents living in border provinces along the BRI route. The research findings indicate that Perceived Emotional is the most important factor in explaining Thai people's perception. Additionally, foreign direct investment, tourism, employment, living standards and society, economy, and logistics transportation positively influence the perceived benefits of BRI. Furthermore, the perceived benefits of BRI have a positive relationship with the attitudes of Thai people. The results also reveal that perceived benefits, attitudes, and subjective norms positively correlate with the intention to use the China-Laos high-speed railway. These findings can be utilized to provide in-depth insights to relevant agencies and assist in formulating policies and measures that align with the BRI policy. This alignment will enhance development to sustainable changes. Policy Belt and Road Initiative (BRI) High-speed railway The Perception of Thai people Theory of Planned Behavior (TPB) Structural Equation Modeling (SEM) Figures Figure 1 Figure 2 Figure 3 1. Introduction The Belt and Road Initiative (BRI), also known as One Belt One Road (OBOR), is a global cooperation policy initiated by China. It focuses on economic significance, promoting trade, and fostering cultural and technological exchanges (Ohashi, 2018 ).Consequently, the Chinese government is actively involved in developing infrastructure such as transportation routes and constructing high-speed railways across Asia and Europe to boost trade and improve logistics networks among participating countries (Prachachat, 2023 ). The China-Laos high-speed railway is a key connectivity project under the BRI plan, aiming to link over 70 countries across Asia, Africa, and Europe through the construction of roads, railways, ports, and airports. This aligns with Laos' strategy to transform itself from a landlocked country into a land-linked hub (Kapook Travel, 2021 ). The railway connects Vientiane, the capital of Laos, with Kunming, the capital city of Yunnan province in China, covering a total distance of 1,035 kilometers, of which 414 kilometers lie within Laos. The railway commenced service on December 3, 2021, offering passenger trains that can reach speeds of up to 160 kilometers per hour (Thai Public Broadcasting Service, 2022 ). This route has since become popular among tourists worldwide. Currently, countries around the world place significant importance on the BRI route. For example, the Republic of Korea (ROK) has shown interest in exploring its stance and policies towards China's Belt and Road Initiative (BRI), focusing on the economic benefits that can be gained through participation in the BRI and linking it with South Korea's regional policies (Pugacheva & Piatachkova, 2021 ). Additionally, under the ASEAN Connectivity concept, member countries aim to enhance connections between China and ASEAN, believing that this will benefit their nations and people across various dimensions, including the economy, tourism, society, and culture. This is especially relevant today as China has become the world's largest market, second only to the US consumer goods market (Schulhof, Van Vuuren, & Kirchherr, 2022 ). Thailand plays a significant role in driving various ASEAN cooperation frameworks (ASEAN Community, 2022 ). Currently, Thailand is classified as a developing country due to issues such as unequal access to transportation systems (Theerathitichaipa, Wisutwattanasak, Se, et al., 2024 ). Therefore, advancing logistics system development to ensure regional and international connectivity has become a crucial issue for Thailand and is set to become a key policy objective for the future. Thailand aims to position itself as a central transportation hub connecting ASEAN regions with the rapidly growing China in all dimensions (Thai Civil Rights and Investigative Journalism, 2024 ). Thailand shares a border with Laos that stretches 1,810 kilometers, comprising 12 provinces: Chiang Rai, Phayao, Nan, Uttaradit, Phitsanulok, Loei, Nong Khai, Bueng Kan, Nakhon Phanom, Amnat Charoen, Mukdahan, and Ubon Ratchathani. Thailand has the most border checkpoints with Laos, totaling 49 points, which include 20 permanent checkpoints and 29 temporary trade checkpoints. The Nong Khai checkpoint holds the highest trade value, accounting for 34.93% of total border trade. According to the 2020 statistics from the Department of Foreign Trade, Ministry of Commerce of Thailand, the border trade between Thailand and Laos represents 99.2% of the total border trade value when compared to other countries bordering Thailand. Furthermore, Thailand has the highest cross-border trade with China, with a value of 341,180.69 million baht (Ministry of Commerce, 2020 ). Thailand's tourism industry significantly impacts the national economy. According to the UNWTO Tourism Highlights report by the World Tourism Organization in 2019, Thailand ranked 7th globally as a tourist destination, attracting 40 million international visitors. Notable tourist attractions include Bangkok and Phuket (The nation, 2022 ). Thailand was also ranked 4th in terms of tourism revenue, generating a total of 61 billion USD with a growth rate of 3%. The Ministry of Tourism and Sports of Thailand's 2019 statistics indicate that China is the leading country of origin for tourists visiting Thailand, accounting for 28% of total international arrivals (Tourism Council of Thailand, 2023 ). In recent years, numerous studies have sought to understand the public's perception of the benefits of the BRI across various aspects, given its potential to bring significant economic and social changes to participating countries. For instance, in the USA, a study (O’Trakoun, 2018 ) on China's BRI and regional perceptions used survey data to analyze these views. The findings indicated that an increase in Chinese Foreign Direct Investment (FDI) in a country improved respondents’ perception of China’s influence on their nation. It also showed that these perceptions correlated with future business confidence in the Asia-Pacific region. In India, a study (Sachdeva, 2018 ) explored the country's awareness of China's BRI. The research gathered broad perceptions from the developing nation, highlighting that as the BRI progresses, India focuses more on domestic connectivity plans. This study also pointed out that the BRI is increasingly analyzed through the lens of the political economy of participating countries, considering challenges such as debt traps, corruption, political disputes, environmental impacts, and the overall sustainability of the project. A joint study in China and Pakistan examined the social impacts, (Mahmood, Ali, Menhas, & Sabir, 2022 )infrastructure development, and tourism related to the China-Pakistan Economic Corridor (CPEC). This research collected data from respondents living along the CPEC route through face-to-face interviews and used structural equation modeling techniques to analyze the results. The study found that CPEC plays a significant role in the socio-economic and rural development of Pakistan. The expectations from the BRI could lead to positive changes in infrastructure, energy sectors, and social development projects in Pakistan. It also indicated that CPEC would connect rural areas to urban centers, offering development opportunities to achieve sustainable development. In Laos, a study (Khamphengvong, Zhang, Wu, & Thavisay, 2022 ) investigated the economic and social impacts on Laotian attitudes towards the benefits received from the BRI. Using structural equation modeling and multigroup analysis, the study assessed the research model. The findings showed that economic and social determinants positively influence perceived benefits of the BRI, with education, tourism, and foreign direct investment (FDI) being the primary drivers of economic and social benefits. Previous studies in Thailand have examined perceptions of the BRI, but they mainly focused on the strategic perspective of Thailand towards China's BRI expansion. These studies reflected only the views of academics, government officials, and politicians in Thailand (Punyaratabandhu & Swaspitchayaskun, 2021 ). However, there is a lack of comprehensive research on the perceptions of the general Thai population, including public and private sectors and residents in border provinces adjacent to Laos along the BRI route. Punyaratabandhu and Swaspitchayaskun ( 2021 ) also revealed that projects and collaborations in Thailand under the BRI have not progressed significantly. To fill this gap, this study was conducted within the context of a developing country like Thailand. It encompasses all relevant factors influencing public perception and the impact on the BRI, including foreign direct investment, tourism, employment, education, living standards, social conditions, international relations, economy, and logistics and transportation. If Thailand adequately prepares for the China-Laos high-speed rail, it could lead to significant opportunities for the country (Punyaratabandhu & Swaspitchayaskun, 2018 ). For instance, in the trade sector, Thailand could increase its exports to Laos and China, given that Thai products are known for their quality, and China has enormous purchasing power. Additionally, high-speed rail transportation can reduce both the time and cost of shipping goods (Vickerman, 2018 ). In terms of services and tourism, the high-speed rail could make it more convenient for Chinese and Laotian tourists to visit Thailand, providing an opportunity for Thailand to attract more tourists. Finally, in the realm of foreign investment, Chinese investors have shown growing interest in Thailand. Currently, China is increasingly relocating its production bases to Thailand. If this trend continues, it could result in more job creation and help reduce income inequality through the decentralized distribution of investment across various regions (Bank of Thailand, 2021 ). Based on the importance stated above, this research is considered novel due to the aforementioned importance. Its objective is to investigate indicators of benefit perception factors. It includes studying the factors influencing the BRI and analyzing how these factors relate to the intention of Thai people to use the China-Laos high-speed rail service. The study will apply the Theory of Planned Behavior (TPB) to appropriately adapt to and manage the changes that Thai people will face with the implementation of the BRI policy and accompanying technological advancements affecting both the economic and social realms. This aims to enhance developmental opportunities across various dimensions and assist in formulating effective policies and measures to support these changes for relevant agencies in Thailand. 2. Literature Review In this section, a literature review is presented that comprehensively examines factors influencing Thai people's intention to use the China-Laos high-speed railway. The review focuses on three main issues: TPB, Perception, and factors impacting BRI. These factors have been thoroughly investigated to provide in-depth insights into Thai perceptions of the benefits associated with the BRI policy. 2.1 Theory of Planned Behavior (TPB) The Theory of Planned Behavior (TPB) explains human behavior based on three core beliefs that influence intention to use a service (Conner & Armitage, 1998 ). These beliefs are: 1) Attitudes: This refers to personal beliefs about the outcomes of a behavior. If individuals believe that performing a particular behavior will result in positive outcomes, they tend to develop a favorable attitude towards that behavior. Conversely, if they believe it will result in negative outcomes, they develop an unfavorable attitude. Positive attitudes lead to the intention to engage in the behavior. 2) Subjective Norms: These are perceptions of social pressures or norms related to the behavior. It involves the individual's perception of whether significant others (such as family and peers) perform the behavior and whether they approve of or expect the individual to perform it. If individuals perceive that important others engage in or support the behavior, they are more likely to conform to those expectations and intend to perform the behavior. and 3) Perceived Behavioral Control: This refers to the individual's perception of the ease or difficulty of performing the behavior. If individuals believe they have the capability to perform the behavior under the given circumstances and can control the outcomes as intended, they are more likely to have the intention to perform the behavior. In summary, TPB posits that these beliefs (attitudes, subjective norms, and perceived behavioral control) collectively influence human intentions regarding behaviors, including the intention to use services. This theoretical framework helps explain how attitudes, social norms, and perceived control interact to shape human behavior intentions (Ajzen, 2002 ). Currently, this concept is widely used to study the intention to use railway transportation services (Borhan, Ibrahim, & Miskeen, 2019 ; Brohi, Kalwar, Memon, & Ghaffar, 2021 ; Hou, Liang, Meng, & Choi, 2021 ) 2.2 Perception The concept of perception is an explanation of how humans understand and perceive their surroundings, encompassing thoughts, feelings, and decision-making influenced by various sensory perceptions (Efron, 1969 ). Currently, this concept is widely used to study people's perceptions of the BRI policy (Khamphengvong et al., 2022 ; Kuek, 2020 ; Lubis, Rini, & Sembiring, 2021 ). Key aspects of perception include: 1) Perceived benefits Product: This refers to how humans perceive or understand products and express feelings towards specific products. Perception is influenced by various dimensions such as product characteristics and quality. 2) Perceived Service: This concept is crucial in marketing and service management contexts. It involves the intangible aspects of service perception that are palpable and affect human perception significantly. 3) Perceived Cultural: This type of perception relates to cultural norms, artistic culture, and cultural values. Understanding and adapting to different cultures can influence decision-making in service selection. and 4) Perceived Emotional: This involves emotional responses or reactions towards products, services, or overall consumer experiences. It often relates to feelings or understanding of services. Overall, these factors collectively influence human decision-making in choosing services (Merleau-Ponty, 2004 ). 2.3 BRI Impact Factors Based on a review of previous literature, this paper has collected and analyzed data on 15 research studies across 9 countries that attempt to examine factors influencing the Belt and Road Initiative (BRI) policies, as presented in Table 1 . Therefore, to fill the research gap in the context of developing countries, the researchers have gathered various factors including foreign direct investment, tourism, employment, education, living standards and social aspects, international relations, economics, and logistics and transportation. This comprehensive approach aims to scrutinize and evaluate the impacts on people, providing a genuine understanding of Thai perspectives. Table 1 Summary of previous studies on factors affecting the BRI. Author Country Factor impact of BRI Method FDI Tourism Employment Education Standard of living and social International relations Economic Logistics and transportation (Menhas, Mahmood, Tanchangya, Safdar, & Hussain, 2019 ) Pakistan ✓ ✓ SPSS and binary logistic regression (Prates & Lages, 2019 ) Brazil ✓ Qualitative Data Analysis (Masabo, 2019 ) China ✓ ✓ ✓ Qualitative data analysis (Choi, Xia, & Lee, 2020 ) Korea ✓ SEM (Daye, Charman, Wang, & Suzhikova, 2020 ) Kazakhstan ✓ Social Exchange Theory (Daye et al., 2020 ) China ✓ Pearson Chi-square test (Khan, Chenggang, Bano, & Hussain, 2020 ) China ✓ ✓ PCA, PVAR-GMM and Driscoll and Kraay regression (Yue, Gong, & Ma, 2021 ) China ✓ Sequential explanatory mixed method (An, Razzaq, Nawaz, Noman, & Khan, 2021 ) China ✓ ✓ ✓ ✓ FGLS and Sys-GMM (Abdulsalam, Xu, Ameer, Abdo, & Xia, 2021 ) China ✓ ✓ Johansen Fisher Panel Cointegration,.PDOLS and the Toda and Yamamoto technique (Carlucci, Corcione, Mazzocchi, & Trincone, 2021 ) Italy ✓ Theoretical model and two partial non-parametric techniques (Liu & Suk, 2021 ) Azerbaijan ✓ SWOT and AHP (Khamphengvong et al., 2022 ) Laos ✓ ✓ ✓ ✓ ✓ SEM and multi-group analysis (Ashraf, Luo, & Anser, 2022 ) China ✓ ARDL (Jinrui, 2023 ) Malaysia ✓ Multiple regression analysis Present study Thailand ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ EFA, CFA and SEM Note : SPSS = Statistical Package for Social Sciences; PCA = Principal component analysis; PVAR-GMM = Panel vector autoregressive model based on a generalized method of moment approach; FGLS = Feasible generalized least squares; Sys-GMM = System generalized method of moments; PDOLS = Panel Dynamic Ordinary Least Squares; SWOT = Strengths, Weaknesses, Opportunities, and Threats; AHP = Analytic Hierarchy Process; ARDL = Autoregressive distribution lag; SEM = Structural equation modeling; EFA = Exploratory factor Analysis; CFA = Confirmatory factor analysis. 3. Research methodology The study process began with a literature review focusing on factors influencing perceptions of benefits from the China-Laos high-speed railway project, considering economic, tourism, and social impacts. Identifying research gaps, the researchers examined statistical methods applicable to the study, developed a questionnaire grounded in relevant principles and theories, and designed a conceptual framework. Data was collected through face-to-face interviews with 1,540 respondents from Thai provinces bordering Laos along the BRI route. Analysis utilized structural equation modeling (SEM), incorporating exploratory factor analysis (EFA) on a 30% sample (462 respondents) to group economic, tourism, and social impact variables, and confirmatory factor analysis (CFA) on the remaining 70% (1,078 respondents) to validate measures and study variable relationships. Statistical results and policy recommendations for Thailand's railway system development were subsequently presented based on this comprehensive research process, as outlined in Fig. 1 . 3.1 Research Conceptual Framework For the research framework in Fig. 2 , it illustrates the format to be used for presentation, aiming to examine the relationships between factors impacting the BRI on Thai perceptions of benefits and intention to use the China-Laos high-speed railway service. The framework includes factors related to the Theory of Planned Behavior (TPB), which serves as a central mediator. The use of indicators in measuring the perception of benefits from the BRI policy is based on the framework of Perception (Sánchez-Fernández & Iniesta-Bonillo, 2007 ), which assesses various dimensions such as Perceived Product Benefits, Perceived Service Benefits, Perceived Cultural Benefits, and Perceived Emotional Benefits. These factors can significantly predict perceptions of benefits regarding the BRI policy. Therefore, these indicators can be utilized as components in analyzing perceived benefits. In the context of this study, to measure the impact dimensions of the BRI, all relevant factors have been compiled including foreign direct investment, tourism, employment, education, standards of living and social aspects, international relations, economics, and logistics and transportation. These factors are utilized to assess people's perceptions of the perceived benefits of the BRI. The underlying hypothesis of the model can be defined as follows. Hypothesis 1 (H1) : Foreign direct investment will positively influence perceived benefits. Hypothesis 2 (H2) : Tourism will positively influence perceived benefits. Hypothesis 3 (H3) : Employment will positively influence perceived benefits. Hypothesis 4 (H4) : Education will positively influence perceived benefits. Hypothesis 5 (H5) : Standards of living and social aspects will positively influence perceived benefits. Hypothesis 6 (H6) : International relations will positively influence perceived benefits. Hypothesis 7 (H7) : Economics will positively influence perceived benefits. Hypothesis 8 (H8) : Logistics and transportation will positively influence perceived benefits. In addition, correlations were also examined between the perceived benefits of the BRI policy and the intention to use the China-Laos high-speed railway service. Based on these findings, hypotheses regarding the intention of Thai people to use the high-speed railway service were formulated as follows: Hypothesis 9 (H9) : Perceived benefits have a positive influence on attitudes. Hypothesis 10 (H10) : Perceived benefits have a positive influence on intention. Hypothesis 11 (H11) : Perceived behavior control has a positive influence on intention. Hypothesis 12 (H12) : Attitude has a positive influence on intention. Hypothesis 13 (H13) : Subjective norm has a positive influence on intention. 3.2 Development of the Questionnaire In order to design and establish guidelines for study, from the research framework to the creation of tools used for questionnaire analysis, it is divided into three parts: (1) General information and travel behavior of respondents, such as gender, age, marital status, residential area, education level, occupation, income, and preferred travel modes. (2) Behaviors influencing Thai people's perceptions of the benefits of the Belt and Road Initiative (BRI), consisting of 37 questions based on the Theory of Planned Behavior (TPB) principles like Intention to Use, Attitudes, Subjective Norm, and Perceived Behavioral Control, as well as Perception principles such as Perceived Benefits, Product Perception, Service Perception, Cultural Perception, and Emotional Perception. (3) Impact factors of BRI, including 40 questions across 8 factors: foreign direct investment, tourism, employment, education, living standards and society, international relations, economy, and logistics and transportation. The questionnaire responses are based on a 7-point Likert scale (Harpe, 2015 ), where 1 indicates strongly disagree and 7 indicates strongly agree. Furthermore, the content accuracy of the questionnaire has been validated using the Index of Content Validity (IOC), assessed by 3 experts. This study identified questions with IOC values exceeding 0.50 (Fouzul Kareema & Bt Zubairi, 2021 ), and found that our questionnaire includes questions with IOC values ranging from 0.67 to 1.00. 3.3 Participants and Data Collection In this study, conducted research with a sample group consisting of Thai citizens residing in the border provinces of Thailand adjacent to the Lao PDR along the BRI route. The sample was selected using Stratified Random Sampling method across key border trade checkpoints: Chiang Saen checkpoint, Chiang Khong checkpoint, Huai Kon checkpoint, Phu Doo checkpoint, Ban Nakraseng checkpoint, Chiang Khan checkpoint, and Nong Khai checkpoint. This method ensured that the sample represented a diverse and quality representation of the population. This study has obtained useful responses from 1,540 respondents, as presented in Table 2 . This sample size is deemed sufficient for analysis, considering that previous literature recommends a minimum sample size for Structural Equation Modeling (SEM) analysis to be at least 15 times the number of observed variables (Cangur & Ercan, 2015 ; Hair, 2009 ). With a total of 77 variables in this study, the minimum sample size required is 1,155 sets. The sample selection process for this study adhered rigorously to these guidelines to ensure that the sample size was strategically aligned with the initial statistical requirements for analysis. Furthermore, the interview process was conducted only with individuals who willingly agreed to participate in the questionnaire. Additionally, prior to the interview, respondents were asked for their consent. Interviews were conducted face-to-face with respondents aged 18 and above, and each respondent spent no more than 15 minutes answering the questionnaire. Moreover, our data collection process was approved by the Ethics Committee of Suranaree University of Technology (30 January 2024; COE No. 7/2567). Table 2 Characteristics of respondents. Characteristics Category 1540 Respondents Frequency Percentage Gender Male 738 47.9 Female 802 52.1 Age 18–25 years old (Gen Alpha) 174 11.3 26–43 years old (Gen Y) 874 56.8 44–58 years old (Gen X) 380 24.7 59–77 years old (Baby Boomer) 112 7.3 Status Single 616 40.0 Married 822 53.4 Widowed/divorced/separated 102 6.6 Residential area Live in the city 571 37.1 Live outside the city 867 56.3 Live in the suburbs 102 6.6 Education Primary education 126 8.2 High school education 321 20.8 Vocational education 238 15.5 Associate Degree 277 18.0 Bachelor's Degree 503 32.7 Master's Degree 65 4.2 Doctoral Degree 10 0.6 Occupation Agriculturist/Agricultural Organization 269 17.5 Entrepreneur 377 24.5 Private Employee 445 28.9 Government Employee 199 12.9 Student 108 7.0 Others 142 9.2 Income Less than or equal to 10,000 Baht 490 31.8 10,001–15,000 Baht 178 11.6 15,001–20,000 Baht 575 37.3 More than 20,000 Baht 297 19.3 Modes of travel used Private vehicle (Car/Motorbike) 1,288 83.6 Bus 221 14.4 Railway 16 1.0 Other (Airplane and Boat) 15 1.0 3.4 Statistical Method 3.4.1 Exploratory factor analysis (EFA) To establish a latent variable model emphasizing various factors based on a literature review, typically when a clear theoretical framework isn't available for measuring relationships, Exploratory Factor Analysis (EFA) is employed. EFA is an analytical approach used to explore and identify factors (Stevens, 2012 ) that explain relationships among observed variables. Furthermore, EFA results can reduce observed variables by creating new variables in the form of composite factors (Fabrigar & Wegener, 2011 ; Williams, Onsman, & Brown, 2010 ). 3.4.2 Confirmatory factor analysis (CFA) To confirm the relationships between components of variables, Confirmatory Factor Analysis (CFA) is used. This method is employed when researchers know that indicators are components based on theory or a literature review, and it verifies the consistency of measures with an academic understanding of relevant factors. The primary objective of CFA is to assess whether the collected data aligns with the research assumptions (Kline, 2023 ; Theerathitichaipa, Wisutwattanasak, Banyong, et al., 2024 ; Wisutwattanasak et al., 2023 ). 3.4.3 Modeling Structural Equations Structural Equation Modeling (SEM) is utilized because it is grounded in theory that emphasizes relationships between observable and latent variables. SEM encompasses both measurement models and structural models to establish causal links between variables (Byrne, 2013 ; Iamtrakul, Chayphong, & Yoshitsugu, 2024 ; Kaiser, Samuel, & Burger, 2024 ; Raykov & Marcoulides, 2012 ). To assess data appropriateness for SEM guidelines, various statistical techniques are employed, including factor analysis, path analysis, and regression modeling. These methods are typically analyzed using software such as Mplus 7 (Muthén & Muthén, 2017 ). These techniques collectively enable a comprehensive evaluation of data within the SEM framework. We use criteria for assessing the adequacy of component data proposed by Fornell and Larcker ( 1981 ) and Hair ( 2009 ) to statistically test the results of each indicator model. This involves evaluating Composite Reliability (CR) values and Average Variance Extracted (AVE). CR values and AVE should ideally exceed 0.7 and 0.5, respectively. These statistical values can be computed using Equations ( 1 ) and ( 2 ) as follows: $$\:\text{AVE=}\:\frac{\sum\:_{\text{i=1}}^{\text{n}}{\text{λ}}_{\text{i}}^{\text{2}}}{\text{n}}$$ 1 , $$\:\text{CR}\text{=}\:\frac{{\text{(}\sum\:_{\text{i=1}}^{\text{n}}{\text{λ}}_{\text{i}}\text{)}}^{\text{2}}}{{\text{(}\sum\:_{\text{i=1}}^{\text{n}}{\text{λ}}_{\text{i}}\text{)}}^{\text{2}}\text{+}\text{(}\sum\:_{\text{c=1}}^{\text{n}}{\text{δ}}_{\text{i}}\text{)}}$$ 2 where i denotes the component loading of each indicator, and i represents the error terms. 4. Results 4.1 Descriptive statistics The preliminary statistical analysis included measures of mean, standard deviation, skewness, and kurtosis. Before conducting Confirmatory Factor Analysis (CFA), we examined these descriptive statistics to confirm the suitability of the data for analysis. Table 3 in the Appendix. It shows that across all 77 items, skewness ranges from − 0.639 to 0.196, and kurtosis ranges from − 0.767 to 0.938, respectively. These values align with the established criteria where skewness is ideally between − 2 and 2, and kurtosis between − 7 and 7 (Kline, 2023 ). Therefore, it can be concluded that our sample statistics indicate a normally distributed data set and are acceptable for analysis. 4.2 The Exploratory Factor Analysis We used EFA to establish observable indicators representing components of each latent factor and calculate main factors. In Table 4 , we present factor analysis results categorized into 3 groups. Group 1: The results of EFA for TPB factors indicate high reliability and acceptability, with a Kaiser-Meyer-Olkin (KMO) measure of 0.936, indicating excellent sampling adequacy. The model explains 79.597% of the variance, and the EFA yields 19 items grouped into 4 factors. The combined factors include Intention, Attitude, Subjective Norm, and Perceived Behavioral Control. Group 2: The results of EFA for Perception factors indicate high reliability and acceptability, with a Kaiser-Meyer-Olkin (KMO) measure of 0.959, indicating excellent sampling adequacy. The model explains 78.870% of the variance, and the EFA yields 18 items grouped into 4 factors. The combined factors include Perceived Benefits, Product Perception, Perceived Service, Perceived Cultural, and Perceived Emotional. Group 3: The results of EFA for BRI impact factors indicate high reliability and acceptability, with a Kaiser-Meyer-Olkin (KMO) measure of 0.961, indicating excellent sampling adequacy. The model explains 75.877% of the variance, and the EFA yields 40 items grouped into 8 factors. The combined factors include Foreign Direct Investment, Tourism, Employment, Education, Standard of Living and Society, International Relations, Economy, and Logistics and Transportation. When examining the accuracy and reliability in terms of Cronbach's alpha for all variables, it was found that values ranged from 0.800 to 0.923. These values exceed the minimum standard recommended in previous research, which is 0.70 (Bujang, Omar, & Baharum, 2018 ; Tavakol & Dennick, 2011 ) 4.3 Confirmatory Factor Analysis Results Considering the results from the EFA, we proceeded to evaluate and elucidate the importance of each item. The CFA results were analyzed using Mplus 7 software to confirm that the indicators could indeed be components of each factor. In Table 4 , the results of the CFA are presented. It was found that all indicators were significant at the 0.01 level as components of each factor. The Critical Ratio (CR) should ideally not be less than 0.7, and the Average Variance Extracted (AVE) should be at least 0.5 (Ab Hamid, Sami, & Sidek, 2017 ; Alarcón, Sánchez, & De Olavide, 2015 ) According to the results, the TPB factor exhibited component loadings ranging from 0.680 to 0.882, the Perception factor showed component loadings ranging from 0.684 to 0.867, and the BRI Impact factor had component loadings ranging from 0.585 to 0.863. All factors demonstrated construct reliability (CR) and average variance (AVE) values exceeding 0.7 and 0.5, respectively. Therefore, these findings confirm that all factors were suitable for CFA analysis. Table 4 Factor analysis results. Variable EFA CFA Loading Item Cronbach's Alpha Loading S.E. t-Stat CR AVE TPB Intention 4 0.911 0.868 0.622 IN1 0.824 0.751 0.016 47.357 IN2 0.838 0.737 0.017 44.165 IN3 0.769 0.783 0.015 53.472 IN4 0.725 0.876 0.011 78.728 Attitude 6 0.911 0.896 0.590 A1 0.602 0.757 0.015 52.110 A2 0.770 0.758 0.014 52.394 A3 0.793 0.800 0.013 63.801 A4 0.819 0.736 0.015 47.771 A5 0.780 0.774 0.014 56.502 A6 0.705 0.782 0.013 58.851 Subjective Norm 4 0.891 0.877 0.643 SN1 0.702 0.825 0.012 67.078 SN2 0.793 0.882 0.010 86.562 SN3 0.864 0.714 0.017 41.347 SN4 0.853 0.777 0.015 53.433 Perceived Behavioral Control 5 0.918 0.897 0.636 BC1 0.830 0.680 0.018 38.242 BC2 0.840 0.774 0.014 55.900 BC3 0.801 0.831 0.011 74.617 BC4 0.847 0.834 0.011 75.754 BC5 0.786 0.857 0.010 86.189 Goodness of fit : Kaiser-Meyer-Olkin (KMO) = 0.936, Bartlett’s test approx. \(\:{\chi\:}^{2}\) = 8273.228, Degrees of freedom (df) = 171, p < 0.001 Perception Perceived Benefits Product 4 0.897 0.898 0.689 PP1 0.686 0.786 0.012 65.616 PP2 0.792 0.867 0.009 92.836 PP3 0.744 0.817 0.012 69.379 PP4 0.756 0.847 0.011 80.514 Perceived Service 3 0.902 0.891 0.731 PS1 0.573 0.843 0.011 78.701 PS2 0.617 0.866 0.010 90.326 PS3 0.622 0.856 0.010 85.885 Perceived Cultural 5 0.898 0.883 0.602 PC1 0.755 0.727 0.016 44.481 PC2 0.834 0.774 0.014 54.051 PC3 0.826 0.757 0.015 50.181 PC4 0.807 0.828 0.012 69.294 PC5 0.701 0.789 0.014 58.144 Perceived Emotional 6 0.919 0.914 0.640 PE1 0.759 0.796 0.012 64.965 PE2 0.749 0.861 0.010 90.313 PE3 0.775 0.831 0.011 77.946 PE4 0.753 0.832 0.011 78.493 PE5 0.665 0.684 0.017 39.941 PE6 0.529 0.784 0.013 58.949 Goodness of fit : Kaiser-Meyer-Olkin (KMO) = 0.959, Bartlett’s test approx. \(\:{\chi\:}^{2}\) = 7859.706, Degrees of freedom (df) = 153, p < 0.001 Factor impact of BRI Factor 1: Foreign Direct Investment (FDI) 3 0.800 0.772 0.531 B1 0.787 0.693 0.019 35.731 B2 0.683 0.734 0.018 41.877 B3 0.498 0.758 0.016 46.623 Factor 2: Tourism 6 0.923 0.908 0.622 C1 0.760 0.727 0.016 46.228 C2 0.765 0.781 0.013 59.252 C3 0.825 0.792 0.013 62.009 C4 0.821 0.835 0.011 77.550 C5 0.793 0.826 0.011 73.952 C6 0.721 0.765 0.014 54.798 Factor 3: Employment 5 0.899 0.894 0.630 D1 0.771 0.680 0.019 36.611 D2 0.842 0.796 0.015 53.775 D3 0.854 0.863 0.012 70.347 D4 0.860 0.762 0.014 52.920 D5 0.724 0.855 0.015 57.796 Factor 4: Education 5 0.870 0.858 0.549 F1 0.519 0.585 0.022 26.973 F2 0.600 0.758 0.015 50.501 F3 0.679 0.798 0.013 59.512 F4 0.773 0.772 0.015 52.203 F5 0.774 0.773 0.015 52.756 Factor 5: Standard of living and social 7 0.900 0.882 0.517 E1 0.443 0.708 0.017 42.276 E2 0.513 0.718 0.016 44.532 E3 0.525 0.760 0.014 52.751 E4 0.748 0.664 0.019 35.543 E5 0.819 0.645 0.019 33.211 E6 0.666 0.706 0.017 41.992 E7 0.455 0.820 0.012 70.453 Factor 6: International relations 5 0.902 0.899 0.640 R1 0.663 0.799 0.012 64.333 R2 0.726 0.811 0.012 67.848 R3 0.728 0.765 0.014 54.450 R4 0.610 0.833 0.011 76.945 R5 0.595 0.790 0.013 61.060 Factor 7: Economic 4 0.886 0.868 0.622 M1 0.522 0.808 0.012 66.345 M2 0.459 0.732 0.016 46.815 M3 0.525 0.789 0.013 59.822 M4 0.588 0.823 0.012 70.969 Factor 8: Logistics and transportation 5 0.906 0.895 0.631 L1 0.769 0.725 0.016 45.307 L2 0.711 0.799 0.013 62.694 L3 0.727 0.807 0.012 65.455 L4 0.687 0.798 0.013 62.660 L5 0.663 0.839 0.011 78.031 Goodness of fit : Kaiser-Meyer-Olkin (KMO) = 0.961, Bartlett’s test approx. \(\:{\chi\:}^{2}\) = 16485.083, Degrees of freedom (df) = 780, p < 0.001 Note: EFA = exploratory factor analysis, CFA = confirmatory factor analysis, CR = composite reliability, AVE = average variance extracted. For the TPB factors, the results indicate that the indicator representing the intention to use the China-Laos high-speed train service among Thai people is highest for In4, " I plan to use the China-Laos high-speed rail service if I have the opportunity to access it" (γ = 0.876, t = 78.728). Following this, the indicator explaining attitude factors most is A3, " I feel that the China-Laos high-speed rail will help promote tourism in Thailand" (γ = 0.800, t = 63.801). The indicator explaining subjective norms the most is SN2, " If a friend recommends that I try using the China-Laos high-speed rail service, I think this recommendation would have a greater impact on my decision to use it" (γ = 0.882, t = 86.562). Finally, the indicator explaining Perceived Behavioral Control the most is BC5, " I think that even though I have never used the China-Laos high-speed rail before, it is easy to use and everyone can do it" (γ = 0.857, t = 86.189). In addition, for the Perception factors, the results show that the indicator representing Perceived Benefits Product factors the most is PP2, "I think that the China-Laos high-speed rail makes travel safer" (γ = 0.867, t = 92.836). Following this, the indicator explaining Perceived Service factors the most is PS2, "I think that the China-Laos high-speed rail service is impressive" (γ = 0.866, t = 90.326). The indicator explaining Perceived Cultural factors the most is PC4, "I think that the China-Laos high-speed rail has a positive influence on cultural exchange between countries" (γ = 0.828, t = 69.294). Lastly, the indicator explaining Perceived Emotional factors the most is PE2, "I think that the China-Laos high-speed rail is interesting" (γ = 0.861, t = 90.313). Furthermore, for the BRI impact factors, the results indicate that the indicator representing Direct Foreign Investment factors the most is B3, " The China-Laos high-speed rail helps increase trade and investment between Thailand and foreign countries, such as Laos and China" (γ = 0.758, t = 46.623). Following this, the indicator explaining Tourism factors the most is C4, " The China-Laos high-speed rail can encourage more people to choose train travel" (γ = 0.835, t = 77.550). The indicator explaining Employment factors the most is D3, " The China-Laos high-speed rail has helped me earn more compensation from work" (γ = 0.863, t = 70.347). The indicator explaining Education factors the most is F3, "The China-Laos high-speed rail contributes to the development of educational personnel in Thailand" (γ = 0.798, t = 59.512). The indicator explaining Standard of Living and Society factors the most is E7, " The potential of the surrounding Thai border communities along the China-Laos high-speed rail route has developed increased" (γ = 0.820, t = 70.453). The indicator explaining International Relations factors the most is R4, " The China-Laos high-speed rail helps enhances good relations between Thailand and foreign countries" (γ = 0.833, t = 76.945). The indicator explaining Economic factors the most is M4, " The China-Laos high-speed rail contributes to the growth of Thailand's tourism industry, such as hotels, restaurants, and service businesses" (γ = 0.823, t = 70.969). Lastly, the indicator explaining Logistics and Transport factors the most is L5, " The China-Laos high-speed rail contributes to the growth of Thailand's logistics industry, including e-commerce and transportation businesses" (γ = 0.839, t = 78.031). 4.4 Structural Equation Modeling Results The analysis of hypotheses in Table 5 and Fig. 3 illustrates the influence of BRI factors on perceptions of benefits and intentions to use the China-Laos high-speed railway service. These factors were initially hypothesized in the conceptual model, which incorporated indicators derived from CFA for testing the proposed hypotheses. SEM was utilized to explain behavioral intentions, and detailed findings will be presented in the following section. For the overall goodness-of-fit assessment of the model, this study used absolute and incremental fit indices (D Hooper, Coughlan, & Mullen, 2008 ) The results of the CFA estimation are as follows: (1) Chi-square test of model fit \(\:{\chi\:}^{2}\) = 7623.019, df = 2752, \(\:{\chi\:}^{2}\) /df = 2.770, which aligns with the research by Bagozzi and Yi ( 1988 ) and Deb and Ahmed ( 2018 ) recommending that the Chi-square value or the ratio between the chi-square and the number of degrees of freedom ( \(\:{\chi\:}^{2}\) /df) should be less than 3; (2) Comparative fit index (CFI) = 0.929, which aligns with the research by Hu and Bentler ( 1999 ) and (Cangur & Ercan, 2015 ) recommending that CFI should be greater than 0.9; (3) Tucker–Lewis index (TLI) = 0.924, which aligns with the research by Daire Hooper and Coughlan (2008) recommending that TLI should be greater than 0.8; (4) Root mean square error of approximation (RMSEA) = 0.041, which aligns with the research by Deb and Ahmed ( 2018 ) and Xia and Yang ( 2019 ) recommending that RMSEA should be less than 0.06 and (5) Standardized root mean square Residual (SRMR) = 0.039, p < 0.001, which aligns with the research by Schreiber, Nora, Stage, Barlow, and King ( 2006 ) recommending that SRMR should be less than 0.08. Therefore, based on the examination of all indices meeting their respective criteria, it can be concluded that this model fits the empirical data well. In Table 5 , it is evident that Perceived Benefits Product, Perceived Service, Perceived Cultural, and Perceived Emotional are statistically significant indicators of the perceived benefits of the BRI policy. Additionally, the results show that foreign direct investment, tourism, employment, education, living standards and social economy, and logistics and transportation significantly influence the perceived benefits of the BRI policy in a statistically significant manner. Therefore, hypotheses H1–H5 and H7–H8 are supported. Conversely, H6 is not supported due to the lack of statistically significant international relationships. Furthermore, the perceived benefits of the BRI policy significantly influence attitudes, supported by hypothesis H9. Additionally, perceived benefits, attitudes, and conformity to reference groups significantly influence the intention to use the China-Laos high-speed railway service, supporting hypotheses H10 and H12–H13. On the other hand, H11 is not supported as behavioral control does not have statistically significant influence on the intention to use the China-Laos high-speed railway service. Table 5 Hypothesis results (SEM). Hypothesis Variable Standardized Coefficient S.E. p-Value Results Measurement model: Perception Measurement by; Perceived Benefits Product 0.875 0.009 0.000 Supported Perceived Service 0.882 0.010 0.000 Supported Perceived Cultural 0.884 0.010 0.000 Supported Perceived Emotional 0.911 0.007 0.000 Supported Structural model: Perception Affected on; H1 Foreign Direct Investment 0.314 0.014 0.000 Supported H2 Tourism 0.290 0.011 0.000 Supported H3 Employment 0.282 0.015 0.000 Supported H4 Education –0.279 0.042 0.000 Supported H5 Standard of living and social 0.279 0.012 0.000 Supported H6 International relations –0.053 0.042 0.205 Not supported H7 Economic 0.117 0.004 0.000 Supported H8 Logistics and transportation 0.199 0.008 0.000 Supported Attitude Affected on; H9 Perception 0.935 0.007 0.000 Supported Intention Affected on; H10 Perception 0.177 0.008 0.000 Supported H11 Perceived Behavioral Control –0.016 0.048 0.740 Not supported H12 Attitude 0.622 0.050 0.000 Supported H13 Subjective Norm 0.146 0.036 0.000 Supported 5. Discussion From our research findings, this study has revealed several significant discoveries and can provide further in-depth explanations. Details are outlined below. 5.1 Factors perceptions of the benefits of BRI When evaluating overall perceptions of the benefits of the Belt and Road Initiative (BRI), Thai people generally have a high perception of its benefits. The component with the highest weight influencing perceptions of BRI benefits is "Perceived Emotional," with a value of 0.911. This indicates that emotional perception has the strongest impact on Thai perceptions of the China-Laos high-speed rail project. This finding is consistent with studies suggesting that emotional perception plays a significant role in BRI projects, highlighting that emotional perspectives form the basis of how BRI benefits are perceived (Mostafanezhad, Farnan, & Loong, 2023 ) Following this are "Perceived Cultural," "Perceived Service," and "Perceived Benefits Product," respectively. 5.2 The Relationship Between the Impacts of BRI and Perception of Benefits Based on the findings of this study, factors such as foreign direct investment (FDI), tourism, employment, education, living standards and social conditions, economics, and logistics and transportation have significant correlations with the perception of the benefits of BRI. Among these, foreign direct investment (FDI) emerged as the most influential factor affecting Thai people’s perceptions of BRI benefits. This indicates that FDI positively influences the perception of BRI benefits, as it is seen to bring increased investment opportunities to Thailand. Additionally, it facilitates enhanced trade and investment between Thailand and other countries. These findings align with studies that reveal the BRI projects are beneficial in creating opportunities for foreign direct investment, which positively impacts various economic sectors within the country (Khamphengvong et al., 2022 ; O’Trakoun, 2018 ; Yu, Qian, & Liu, 2019 ). In tourism, this factor influences perceived benefits second to FDI. This indicates that tourism factors have a positive influence on perceived BRI benefits, which are seen as beneficial in promoting tourism to Thailand. It can also attract more tourists to Thailand and helps promote the growth of tourism businesses such as hotels, restaurants, and tourist attractions. This finding is consistent with studies revealing that BRI projects have a significant positive relationship between tourism motivation and satisfaction, as well as between tourism motivation and tourism satisfaction (Yingzhi, Ningning, Huimin, & Jiaying, 2020 ). Regarding employment, this indicates that employment factors have a positive influence on the perception of BRI benefits. It is viewed as beneficial as it can promote more employment opportunities for people in Thailand, such as opportunities to find a variety of jobs, receive potentially higher compensation, and work with more foreign people. This finding is consistent with studies revealing that BRI projects create new business opportunities and jobs, and have a positive impact on policy-making. This creates opportunities for both local and Chinese workers to coexist positively (Fienena, Sorn, Ge, & Wang, 2023 ). In terms of living standards and society, this indicates that living standards and social factors have a positive influence on the perception of BRI benefits. These are seen as beneficial to living standards and Thai society by fostering increased development. It affects the development of the potential of communities around the Thai border along the BRI railway line, helping further develop Thailand's infrastructure and facilities, such as road networks. This finding is consistent with studies revealing that BRI projects bring positive changes in infrastructure and social development projects (Mahmood et al., 2022 ). In the field of logistics and transportation, this indicates that logistics and transportation factors have a positive influence on the perception of BRI benefits, which are seen as beneficial to Thailand's logistics and transportation system. It can promote the growth of Thailand's logistics industry, such as e-commerce and transportation businesses. It also helps improve and develop travel routes connecting to Thailand. This finding is consistent with studies revealing that BRI projects affect the growth of logistics and transportation systems (Chhetri, Nkhoma, Peszynski, Chhetri, & Lee, 2018 ) Finally, regarding the economy, this indicates that economic factors have a positive influence on the perception of BRI benefits, which are seen as beneficial to economic development in Thailand. It helps stimulate Thailand's tourism industry, create opportunities for Thai people to establish businesses or become entrepreneurs, and boost Thailand's economy. It also contributes to the development of Thailand's cross-border trade economy. This finding is consistent with studies revealing that BRI projects play an important role in socio-economic development and influence the economic and social success of participating countries (Mahmood et al., 2022 ; Ur Rehman, Ahmed, Ali, Khattak, & Sameer, 2020 ) On the other hand, this result also reveals that in the field of education, it is of statistical significance. However, it shows a negative relationship with the perception of BRI benefits. This means that the educational factor has a negative influence on the perception of BRI benefits, suggesting that education may not directly benefit Thailand. This finding is consistent with studies that revealed that in the perception aspect of education, it may be influenced by cross-cultural contexts. This factor may require understanding the learning perceptions of international students, as attitudes are extremely complex (Li, 2021 ). 5.3 The Relationship Between Perception of Benefits and Attitudes According to the study's findings, perceived benefits of the BRI are positively related to Thai people's attitudes. This indicates that when Thai people are aware of the benefits of the BRI, it promotes even more positive attitudes towards the China-Laos high-speed rail. This positive attitude helps them realize the importance of the BRI railway line in developing Thailand's economy. Additionally, they have a favorable perspective on the awareness of Thailand's railway development plan, which will connect with the China-Laos high-speed rail line, benefiting Thai society. This finding is consistent with studies indicating that perceived direct benefits have a positive and significant effect on attitudes (Chanthanasinh, Khamphengvong, Soukavong, Vongsavanthong, & Laungphonexay, 2022 ; Rosyidin, Sugandhini, & Harjanti, 2023 ). 5.4 The Relationship Between Perception of Benefits and Intention to Use Services Using the Theory of Planned Behavior (TPB) In the context of factors influencing intentions to use the China-Laos high-speed rail service, as clearly evidenced by the study findings, the results unequivocally demonstrate that perceived benefits, attitudes, and subjective norms are positively related to the intention to use the China-Laos high-speed rail service. Indicators of attitudes have the most pronounced impact on the intention to use the China-Laos high-speed rail service. This finding aligns with studies indicating that positive attitudes influence intentions to use rail transport (Kwan, Sutan, & Hashim, 2020 ), while perceived benefits show a positive relationship with intentions to use the China-Laos high-speed rail service. This finding is consistent with studies indicating that perceived benefits positively influence service use intentions (Choi et al., 2020 ), and subjective norms show a positive relationship with intentions to use the China-Laos high-speed rail service. This finding is consistent with studies indicating that conformity to reference groups directly influences intentions to use the service positively (Borhan et al., 2019 ). 6. Conclusions and Implementation The Belt and Road Initiative (BRI) is recognized globally as a highly impactful trade route capable of bringing about significant changes in the economic and social systems of all participant countries. This initiative is expected to substantially reduce transportation time and trade costs (De Soyres, Mulabdic, Murray, Rocha, & Ruta, 2019 ). Thailand, classified as a developing country, benefits significantly due to its proximity to the high-speed railway line connecting China and Laos. This geographical advantage directly impacts Thailand and affects a majority of Thai people, who may experience both positive and negative effects from the changes occurring as a result. Therefore, this study aims to investigate indicators of benefit perception factors, including analyzing the factors influencing the BRI and examining the relationship between these factors and the intention of Thai people to use the China-Laos high-speed rail service using the Theory of Planned Behavior. (TPB). We have presented findings from a combination of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM) by surveying 1,540 Thai individuals in border provinces adjacent to Laos along the BRI route. This study reveals several significant findings that can inform the development of policies tailored to Thailand's context and aligned with global changes arising from the BRI policy. The findings of this study revealed that Perceived Emotion was identified as the indicator exerting the greatest influence on perceived benefits of the BRI, followed by Perceived Culture, Perceived Service, and Perceived Product Benefits, respectively. These results showed relatively high component weights, indicating high reliability within the domain. Additionally, regarding the perception benefits of BRI, the results also indicate a positive correlation with the attitudes of Thai individuals. This means that when Thai people perceive the benefits of the BRI, it enhances their positive attitude towards the China-Laos high-speed rail. This positive attitude helps them recognize the significance of the BRI railway. We can further explore the significant role of BRI policies, as our results indicate. Direct foreign investment, tourism, employment, living standards, economic impact, and logistics and transportation are perceived positively in terms of their contribution to the benefits of the BRI among Thai people. Conversely, education shows a negative relationship with perceived benefits, suggesting that education may not be seen as directly benefiting Thailand. Additionally, the study provides insights into Thai people's intentions to use the China-Laos high-speed rail service, revealing that perceived benefits, attitudes, and subjective norms influence this intention. Thus, the structural equation model used in this study effectively examines the relationships between these factors and accurately reflects Thai people's perspectives on BRI policies. 6.1 Recommendation for implementation Based on the statistical data analysis and the key findings mentioned earlier, we propose policy recommendations to develop Thailand, preparing it to adapt and manage the forthcoming changes associated with the Belt and Road Initiative (BRI). Our recommendations stem from significant variables identified within the model and are crafted to align Thailand's development with BRI policies. The detailed recommendations include: Policy on direct foreign investment development This particular finding, this study propose guidelines to establish a policy promoting foreign trade and investment aimed at attracting investors and entrepreneurs to invest in Thailand. This initiative serves as an additional avenue for generating income for Thailand, fostering expanded trade cooperation with China and other promising countries. These recommendations align with studies advocating for policies that enhance opportunities for foreign direct investment (FDI) across sectors such as infrastructure, energy, and technology to bolster the domestic economy (Abdulsalam et al., 2021 ). Policy on tourism development This particular finding, this study propose guidelines to establish a policy promoting tourism activities aimed at enhancing the quality and diversity of tourist destinations in Thailand. This initiative aims to create distinctive identities recognized globally based on Thai culture. Additionally, it focuses on improving the quality and standards of tourism-related businesses such as spas, hotels, and restaurants. This strategy aims to attract tourists traveling on the China-Laos high-speed railway. It aligns with studies suggesting cooperative tourism development strategies to formulate sustainable tourism development strategies between countries (Liu & Suk, 2021 ) Policy on employment development This particular finding, this study propose guidelines for stimulating employment according to the needs of the new economic system, both domestically and internationally. This includes developing workforce skills to enhance the quality of labor, preparing for employment opportunities from the BRI route. This aligns with Thailand's Ministry of Labour policy aimed at driving labor-related initiatives to foster stable economic growth (Ministry of Labour, 2022). Policy on standard of living and social development This particular finding, this study propose guidelines to establish a policy for developing societal standards to reduce inequality and promote equitable prosperity distribution regionally. This would lead to the development of infrastructure and amenities within Thailand, such as improving road networks connecting the Thai-Lao border areas. This aligns with studies suggesting policies that consider societal well-being and address environmental degradation in economic activities, aiming for sustainable development goals (Khan et al., 2020 ). Policy on logistics and transportation development This particular finding, this study propose guidelines to establish a policy for developing transportation systems with integrated multimodal transport networks linking the high-speed railway between China and Laos with Thailand. This initiative aims to seamlessly connect Thailand's transportation networks, enhancing logistics infrastructure to facilitate efficient and rapid movement of goods. Moreover, it aims to efficiently link Thai goods transportation with other countries. This aligns with national strategies for developing China's transportation connectivity, included in the 14th National Economic and Social Development Plan (2021–2025) (Department of International Trade Promotion, 2024 ) Policy on economic development This particular finding, this study propose guidelines to establish a policy aimed at driving economic growth and enhancing competitiveness by creating opportunities for business development. This includes elevating the agricultural sector towards high-value agricultural and agro-processing industries. This initiative aims to propel Thailand's industrial sector to greater heights, enabling efficient exportation of various goods and services to global markets. This aligns with the National Economic and Social Development Plan (2023–2027), targeting the accelerated advancement of Thailand towards an advanced society with a sustainable economy (Office of the National Economic and Social Development Council, 2022 ) All of the policies mentioned have been directly informed by current public sentiment in Thailand. These policies will involve relevant stakeholders in shaping national-level policies. For instance, the Ministry of Foreign Affairs can apply policies for direct foreign investment development. The Ministry of Tourism can implement policies for tourism development. Additionally, the Ministry of Labour can apply policies for employment development. Furthermore, the Ministry of Transport (including the Department of Highways, Department of Rail Transport, and State Railway of Thailand) can apply policies for standard of living and societal development, as well as logistics and transportation development. The Office of the National Economic and Social Development Council can apply policies for economic development, aiming to align Thailand's national development plans with global cooperation policies such as the BRI, efficiently and effectively benefiting Thai citizens in all aspects going forward. 6.2 Limitation of the study However, amidst the limitations of the research, this study focuses solely on examining the perception of benefits related to the BRI from the perspective of Thai residents living in border provinces along the BRI routes. Therefore, future studies should expand to cover other regions across all areas of Thailand. This may help reveal different perspectives that arise based on varying local contexts within Thailand. Moreover, this study could be further developed and conducted in numerous countries along the BRI routes to gather new insights and findings from various aspects for future researchers. Declarations Author Contribution Author Contributions: Conceptualization, K.T. and R.K.; methodology, K.T. and P.W.; software, K.T.; validation, T.C., P.W., and C.S.; formal analysis, K.T. and R.K.; investigation, K.T.; resources, K.T. and M.S.; data curation, K.T. and M.S.; writing—original draft preparation, K.T. and R.K.; writing—review and editing, K.T., R.K., P.W., C.S., S.J., V.R., and T.C.; visualization, K.T. and R.K.; supervision, V.R., R.K. and S.J.; project administration, V.R., R.K. and S.J.; funding acquisition, V.R., R.K., and S.J. All authors have read and agreed to the published version of the manuscript. References Ab Hamid, M. R., Sami, W., & Sidek, M. M. (2017). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. Paper presented at the Journal of physics: Conference series. Abdulsalam, A., Xu, H., Ameer, W., Abdo, A.-B., & Xia, J. (2021). Exploration of the impact of China’s outward foreign direct investment (FDI) on economic growth in Asia and North Africa along the Belt and Road (B&R) Initiative. Sustainability, 13(4), 1623. Ajzen, I. (2002). Constructing a TPB questionnaire: Conceptual and methodological considerations. Alarcón, D., Sánchez, J. A., & De Olavide, U. (2015). Assessing convergent and discriminant validity in the ADHD-R IV rating scale: User-written commands for Average Variance Extracted (AVE), Composite Reliability (CR), and Heterotrait-Monotrait ratio of correlations (HTMT). Paper presented at the Spanish STATA meeting. An, H., Razzaq, A., Nawaz, A., Noman, S. M., & Khan, S. A. R. (2021). Nexus between green logistic operations and triple bottom line: evidence from infrastructure-led Chinese outward foreign direct investment in Belt and Road host countries. Environmental Science and Pollution Research, 28(37), 51022-51045. ASEAN Community. (2022). Thailand's Role in ASEAN. Retrieved from https://image.mfa.go.th/mfa/0/OcXc7u4THG/infographic/%E0%B8%9A%E0%B8%97%E0% B8%9A%E0%B8%B2%E0%B8%97%E0%B9%84%E0%B8%97%E0%B8%A2%E0%B9%83%E0%B8%99%E0%B8%AD %E0%B8%B2%E0%B9%80%E0%B8%8B%E0%B8%B5%E0%B8%A2%E0%B8%99/%E0%B8%9A%E0%B8%97%E0%B8%9A%E0%B8%B2%E0%B8%97%E 0%B8%82%E0%B8%AD%E0%B8%87%E0%B9%84%E0%B8%97%E0%B8%A2%E0%B9%83%E0%B8%99%E0%B8%AD%E0%B8%B2%E0%B9%80%E 0%B8%8B%E0%B8%B5%E0%B8%A2%E0%B8%99.pdf Ashraf, J., Luo, L., & Anser, M. K. (2022). Do BRI policy and institutional quality influence economic growth and environmental quality? An empirical analysis from South Asian countries affiliated with the Belt and Road Initiative. Environmental Science and Pollution Research, 29(6), 8438-8451. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16, 74-94. Bank of Thailand. (2021). Opening the China-Laos train whistle: implications for the Thai economy. Retrieved from https://www.bot.or.th/content/dam/bot/documents/th/research-and-publications/article/regional/2021/2564_RI_05_chinalaosrailway.pdf Borhan, M. N., Ibrahim, A. N. H., & Miskeen, M. A. A. (2019). Extending the theory of planned behaviour to predict the intention to take the new high-speed rail for intercity travel in Libya: Assessment of the influence of novelty seeking, trust and external influence. Transportation Research Part A: Policy and Practice, 130, 373-384. Brohi, S., Kalwar, S., Memon, I. A., & Ghaffar, A. (2021). Using the theory of planned behavior to identify the behavioral intention to use public transportation service: the case study of Karachi circular railway. Int. J. Emerg. Technol, 12(6), 317-322. Bujang, M. A., Omar, E. D., & Baharum, N. A. (2018). A review on sample size determination for Cronbach’s alpha test: a simple guide for researchers. The Malaysian journal of medical sciences: MJMS, 25(6), 85. Byrne, B. M. (2013). Structural equation modeling with Mplus: Basic concepts, applications, and programming : routledge. Cangur, S., & Ercan, I. (2015). Comparison of model fit indices used in structural equation modeling under multivariate normality. Journal of Modern Applied Statistical Methods, 14(1), 14. Carlucci, F., Corcione, C., Mazzocchi, P., & Trincone, B. (2021). The role of logistics in promoting Italian agribusiness: The Belt and Road Initiative case study. Land Use Policy, 108, 105560. Chanthanasinh, V., Khamphengvong, V., Soukavong, B., Vongsavanthong, V., & Laungphonexay, K. (2022). The Perception and Attitude of Government Official on the Bilateral between Lao-China Initiatives under the Belt and Road Initiative (BRI) Case Study: Vientiane Capital. sujournal, 8(2), 342-352. Chhetri, P., Nkhoma, M., Peszynski, K., Chhetri, A., & Lee, P. T.-W. (2018). Global logistics city concept: a cluster-led strategy under the belt and road initiative. Maritime Policy & Management, 45(3), 319-335. Choi, K.-S., Xia, T.-S., & Lee, P. T.-W. (2020). Structural relationships among shipper’s perception, value, and choice intention of Korea–China train ferry service. Maritime Policy & Management, 47(6), 703-717. Conner, M., & Armitage, C. J. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of applied social psychology, 28(15), 1429-1464. Daye, M., Charman, K., Wang, Y., & Suzhikova, B. (2020). Exploring local stakeholders’ views on the prospects of China’s Belt & Road Initiative on tourism development in Kazakhstan. Current Issues in Tourism, 23(15), 1948-1962. De Soyres, F., Mulabdic, A., Murray, S., Rocha, N., & Ruta, M. (2019). How much will the Belt and Road Initiative reduce trade costs? International Economics, 159, 151-164. Deb, S., & Ahmed, M. A. (2018). Determining the service quality of the city bus service based on users’ perceptions and expectations. Travel Behaviour and Society, 12, 1-10. Department of International Trade Promotion. (2024). One Belt One Route (BRI) with the transportation of goods from Thailand to China. Retrieved from https://www.ditp.go.th/post/173487 Efron, R. (1969). What is perception? Paper presented at the Proceedings of the Boston Colloquium for the Philosophy of Science 1966/1968. Fabrigar, L. R., & Wegener, D. T. (2011). Exploratory factor analysis : Oxford University Press. Fienena, R. L., Sorn, M. K., Ge, W., & Wang, E. (2023). Exploring the Perceived Prospects of the Belt & Road Initiative on Policy Impacts for Madagascar. Journal of Business and Management Studies, 5(2), 83-100. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. Fouzul Kareema, M. I., & Bt Zubairi, A. M. (2021). Item objective congruence analysis for multidimensional items content validation of a reading test in Sri Lankan university. Hair, J. F. (2009). Multivariate data analysis. Harpe, S. E. (2015). How to analyze Likert and other rating scale data. Currents in pharmacy teaching and learning, 7(6), 836-850. Hooper, D., & Coughlan, J. (2008). i Mullen, MR (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60. Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electron J Bus Res Methods. 2008; 6 (1): 53–60. In. Hou, Z., Liang, L. J., Meng, B., & Choi, H. C. (2021). The role of perceived quality on high-speed railway tourists’ behavioral intention: An application of the extended theory of planned behavior. Sustainability, 13(22), 12386. Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55. Iamtrakul, P., Chayphong, S., & Yoshitsugu, H. (2024). Exploring the perception of quality of life in urban daily commuting for sustainable urban transport in Bangkok, Thailand. Transportation , 1-27. Jinrui, X. (2023). Popular Perceptions of China's Influence in Malaysia since the BRI: An Economic Perspective. China: An International Journal, 21(1), 135-159. Kaiser, A., Samuel, R., & Burger, P. (2024). Toward a low-pesticide agriculture: bridging practice theory and social-psychological concepts to analyze farmers’ routines. Sustainability: Science, Practice and Policy, 20(1), 2306731. Kapook Travel. (2021). Laos-China Railway Tourist routes along the BRI high-speed rail line. Retrieved from https://travel.kapook.com/view250243.html Khamphengvong, V., Zhang, H., Wu, Q., & Thavisay, T. (2022). Examine the economic and social effects on Lao people’s perceived benefit attitudes towards BRI. Sustainability, 14(9), 5088. Khan, A., Chenggang, Y., Bano, S., & Hussain, J. (2020). The empirical relationship between environmental degradation, economic growth, and social well-being in Belt and Road Initiative countries. Environmental Science and Pollution Research, 27, 30800-30814. Kline, R. B. (2023). Principles and practice of structural equation modeling : Guilford publications. Kuek, Y. K. B. K. (2020). Malaysians’ Perceptions and Socio-Cultural Behaviors to the Belt and Road Initiative–a Perspective From the People-to-People Bond. Ritsumeikan Asia Pacific University, Kwan, S. C., Sutan, R., & Hashim, J. H. (2020). Behavioral Framework of Attitude, Service Quality, Environmental Health Concerns and Intention to Use Rail Transport among Private Motor Vehicle Users. Malaysian Journal of Medicine & Health Sciences, 16(4). Li, J. (2021). Shaping “The Belt and Road Initiative” international higher education: A qualitative study of international students from South-Asian countries in Chinese universities. Beijing International Review of Education, 3(1), 22-37. Liu, Y., & Suk, S. (2021). Influencing factors of Azerbaijan and China’s sustainable tourism development strategy under the one belt one road initiative. Sustainability, 14(1), 187. Lubis, K., Rini, E., & Sembiring, B. (2021). The influence of E-service quality and perceived value on the positive E-word of mouth through satisfaction of customers users of internet banking BRI in the city of medan. International Journal of Research and Review, 8(1), 2454-2237. Mahmood, S., Ali, G., Menhas, R., & Sabir, M. (2022). Belt and road initiative as a catalyst of infrastructure development: Assessment of resident’s perception and attitude towards China-Pakistan Economic Corridor. PloS one, 17(7), e0271243. Masabo, C. J. (2019). Implications of Chinese diplomacy and foreign policy on foreign students’ perception of Belt and Road Initiative. Cappadocia Journal of Area Studies, 1(1), 25-47. Menhas, R., Mahmood, S., Tanchangya, P., Safdar, M. N., & Hussain, S. (2019). Sustainable development under belt and road initiative: A case study of China-Pakistan economic corridor’s socio-economic impact on Pakistan. Sustainability, 11(21), 6143. Merleau-Ponty, M. (2004). The world of perception : Routledge. Ministry of Commerce. (2020). Current Border Trade Situation between Thailand and Lao PDR. Retrieved from https://www.itd.or.th/wp-content/uploads/2020/10/itdevent30102020-02.pdf Ministry of Labour. (2022). Ministry of Labour Policy Fiscal Year 2022. Retrieved from https://www.mol.go.th/wp- content/uploads/sites/2/2022/03/%E0%B9%81%E0%B8%9C% E0%B9%88%E0%B8%99%E0%B8%9E%E0%B8%B1%E0%B8%9A%E0%B8%99%E0% B9%82%E0%B8%A2%E0%B8%9A%E0%B8%B2%E0%B8%A2%E0%B8%81%E0%B8% A3%E0%B8%B0%E0%B8%97%E0%B8%A3%E0%B8%A7%E0%B8%87-%E0%B8%9B%E0%B8%B5-2565-%E0%B9%80%E0%B8%9C%E0%B8%A2%E0%B9%81%E0%B8%9E%E0%B8%A3 %E0%B9%88%E0%B8%9A%E0%B8%99%E0%B9%80%E0%B8%A7%E0%B9%87%E0%B8%9A%E0%B9%84%E0%B8%8B%E0%B8%95%E0%B9%8C.pdf Mostafanezhad, M., Farnan, R. A., & Loong, S. (2023). Sovereign anxiety in Myanmar: An emotional geopolitics of China's Belt and Road Initiative. Transactions of the Institute of British Geographers, 48(1), 132-148. Muthén, B., & Muthén, L. (2017). Mplus. In Handbook of item response theory (pp. 507-518): Chapman and Hall/CRC. O’Trakoun, J. (2018). China’s belt and road initiative and regional perceptions of China. Business Economics, 53(1), 17-24. Office of the National Economic and Social Development Council. (2022). National Economic and Social Development The Thirteenth Plan (2023-2027). Retrieved from http://nscr.nesdc.go.th/wp-content/uploads/2022/11/plan13-rkt-011165.pdf Ohashi, H. (2018). The Belt and Road Initiative (BRI) in the context of China’s opening-up policy. Journal of Contemporary East Asia Studies, 7(2), 85-103. Prachachat. (2023). Belt and Road (BRI), a strategic project used by China to expand its influence on the world stage. Retrieved from https://www.prachachat.net/world-news/news-1416799 Prates, I., & Lages, C. (2019). The Brazilian Perception of China’s Belt and Road Initiative. Available at SSRN 3681467. Pugacheva, O., & Piatachkova, A. (2021). South Korea’s Policy Toward Belt and Road Initiative. Mirovaia ekonomika i mezhdunarodnye otnosheniia, 65(10), 81-90. Punyaratabandhu, P., & Swaspitchayaskun, J. (2018). The political economy of China–Thailand development under the one belt one road initiative: Challenges and opportunities. The Chinese Economy, 51(4), 333-341. Punyaratabandhu, P., & Swaspitchayaskun, J. (2021). Thailand’s perception and strategy toward China’s BRI expansion: hedging with cooperating. The Chinese Economy, 54(1), 69-77. Raykov, T., & Marcoulides, G. A. (2012). A first course in structural equation modeling : routledge. Rosyidin, M. A. F., Sugandhini, D., & Harjanti, S. (2023). THE EFFECT OF PERCEPTIONS OF EASE OF USE AND PERCEPTIONS OF USEFULNESS ON INTEREST IN MEDIATION BY ATTITUDES OF USERS OF BRI INTERNET BANKING SERVICES IN THE SPECIAL AREA OF YOGYAKARTA. International Journal of Economics, Business and Accounting Research (IJEBAR), 7(2), 674-688. Sachdeva, G. (2018). Indian perceptions of the Chinese Belt and Road initiative. International Studies, 55(4), 285-296. Sánchez-Fernández, R., & Iniesta-Bonillo, M. Á. (2007). The concept of perceived value: a systematic review of the research. Marketing theory, 7(4), 427-451. Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of educational research, 99(6), 323-338. Schulhof, V., Van Vuuren, D., & Kirchherr, J. (2022). The Belt and Road Initiative (BRI): What will it look like in the future? Technological Forecasting and Social Change, 175, 121306. Stevens, J. P. (2012). Applied multivariate statistics for the social sciences : Routledge. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International journal of medical education, 2, 53. Thai Civil Rights and Investigative Journalism. (2024). Thailand moves forward with transportation-logistics route to become ASEAN hub. Retrieved from https://www.tcijthai.com/news/2013/17/scoop/3247 Thai Public Broadcasting Service. (2022). China-Laos Railway connects economic opportunities. Retrieved from https://www.thaipbs.or.th/news/content/319522 The nation. (2022). Bangkok and Thailand ranked among top 10 destinations by travel magazine readers. Retrieved from https://www.nationthailand.com/thailand/tourism/40020794 Theerathitichaipa, K., Wisutwattanasak, P., Banyong, C., Seefong, M., Jomnonkwao, S., Champahom, T., . . . Kasemsri, R. (2024). Measurement Model for Determining the Disparity Factors of Intercity Railway Transportation. Civil Engineering Journal, 10(3), 668-688. Theerathitichaipa, K., Wisutwattanasak, P., Se, C., Seefong, M., Jomnonkwao, S., Champahom, T., . . . Kasemsri, R. (2024). Assessment of Disparity in Accessing Railway Stations in Thailand: an Application Geographic Information System Network Analysis. Journal of Geovisualization and Spatial Analysis, 8(1), 6. Tourism Council of Thailand. (2023). National Tourism Policy (2023 - 2027). Retrieved from https://secretary.mots.go.th/ewtadmin/ewt/secretary/download/article/article_20230327145011.pdf Ur Rehman, Z., Ahmed, D. M., Ali, D. S. A., Khattak, A., & Sameer, I. (2020). Entrepreneurial factors affecting perceived socioeconomic success in one belt one road initiative–a study of China-Pakistan economic corridor. International Journal of Management, 11(9). Vickerman, R. (2018). Can high-speed rail have a transformative effect on the economy? Transport policy, 62, 31-37. Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian journal of paramedicine, 8, 1-13. Wisutwattanasak, P., Champahom, T., Jomnonkwao, S., Seefong, M., Theerathitichaipa, K., Kasemsri, R., & Ratanavaraha, V. (2023). Modeling Extended Service Quality for Public Transportation in the Post-Pandemic Period: Differentiating between Urban and Rural Areas: A Case Study of Intercity Railway, Thailand. Logistics, 7(4), 93. Xia, Y., & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior research methods, 51, 409-428. Yingzhi, G., Ningning, X., Huimin, T., & Jiaying, X. (2020). An empirical study on the impacts of tourist risk perception on tourist motivation and satisfaction in Xinjiang of China under the background of the Belt and Road Initiative. Journal of Tourism and Hospitality Management, 8(1), 14-34. Yu, S., Qian, X., & Liu, T. (2019). Belt and road initiative and Chinese firms' outward foreign direct investment. Emerging Markets Review, 41, 100629. Yue, Y., Gong, L., & Ma, Y. (2021). Factors influencing international student inward mobility in China: A comparison between students from BRI and non-BRI countries. Educational Studies , 1-19. Table 3 Table 3 is not available with this version Additional Declarations No competing interests reported. Supplementary Files 2AppendixPaperBRI.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4740250","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335335504,"identity":"aa27d23d-0c6c-4ada-a410-06b0da07ba95","order_by":0,"name":"Kestsirin Theerathitichaipa","email":"","orcid":"","institution":"Suranaree University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kestsirin","middleName":"","lastName":"Theerathitichaipa","suffix":""},{"id":335335505,"identity":"07239a41-6cbd-4f68-8855-d9b64793d8f5","order_by":1,"name":"Panuwat Wisutwattanasak","email":"","orcid":"","institution":"Suranaree 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19:59:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102356,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Process.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4740250/v1/dd6bee3a9601f86b0bef3fe2.png"},{"id":62056610,"identity":"67ca0206-c5a2-4594-b7e7-d4e519501749","added_by":"auto","created_at":"2024-08-08 19:59:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":216228,"visible":true,"origin":"","legend":"\u003cp\u003eConceptualization of the proposed model.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4740250/v1/ebc40f6ff4070eaae475cd3b.png"},{"id":62056611,"identity":"bf3fa233-899d-4aec-a8ec-1c118a495591","added_by":"auto","created_at":"2024-08-08 19:59:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":216333,"visible":true,"origin":"","legend":"\u003cp\u003eSEM Model Results.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4740250/v1/7d1731d2097bbe2b5fd085a8.png"},{"id":70494911,"identity":"62815d19-f8bf-44e3-bf2e-c3c50c5ca28c","added_by":"auto","created_at":"2024-12-03 18:23:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2072236,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4740250/v1/36768141-112d-4dfa-b40b-965131d44d1e.pdf"},{"id":62056612,"identity":"8fc949fc-2f9b-48e7-8c3b-0fc084f624ec","added_by":"auto","created_at":"2024-08-08 19:59:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22764,"visible":true,"origin":"","legend":"","description":"","filename":"2AppendixPaperBRI.docx","url":"https://assets-eu.researchsquare.com/files/rs-4740250/v1/815d20fc55e69dbe83d71eeb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Structural Equation Model of the Factors Affecting the Belt and Road Initiative (BRI) on the Perceived Benefits and Intention of Thai People to Use the China-Laos High-Speed Rail Service","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Belt and Road Initiative (BRI), also known as One Belt One Road (OBOR), is a global cooperation policy initiated by China. It focuses on economic significance, promoting trade, and fostering cultural and technological exchanges (Ohashi, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).Consequently, the Chinese government is actively involved in developing infrastructure such as transportation routes and constructing high-speed railways across Asia and Europe to boost trade and improve logistics networks among participating countries (Prachachat, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The China-Laos high-speed railway is a key connectivity project under the BRI plan, aiming to link over 70 countries across Asia, Africa, and Europe through the construction of roads, railways, ports, and airports. This aligns with Laos' strategy to transform itself from a landlocked country into a land-linked hub (Kapook Travel, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The railway connects Vientiane, the capital of Laos, with Kunming, the capital city of Yunnan province in China, covering a total distance of 1,035 kilometers, of which 414 kilometers lie within Laos. The railway commenced service on December 3, 2021, offering passenger trains that can reach speeds of up to 160 kilometers per hour (Thai Public Broadcasting Service, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This route has since become popular among tourists worldwide.\u003c/p\u003e \u003cp\u003eCurrently, countries around the world place significant importance on the BRI route. For example, the Republic of Korea (ROK) has shown interest in exploring its stance and policies towards China's Belt and Road Initiative (BRI), focusing on the economic benefits that can be gained through participation in the BRI and linking it with South Korea's regional policies (Pugacheva \u0026amp; Piatachkova, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, under the ASEAN Connectivity concept, member countries aim to enhance connections between China and ASEAN, believing that this will benefit their nations and people across various dimensions, including the economy, tourism, society, and culture. This is especially relevant today as China has become the world's largest market, second only to the US consumer goods market (Schulhof, Van Vuuren, \u0026amp; Kirchherr, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThailand plays a significant role in driving various ASEAN cooperation frameworks (ASEAN Community, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Currently, Thailand is classified as a developing country due to issues such as unequal access to transportation systems (Theerathitichaipa, Wisutwattanasak, Se, et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, advancing logistics system development to ensure regional and international connectivity has become a crucial issue for Thailand and is set to become a key policy objective for the future. Thailand aims to position itself as a central transportation hub connecting ASEAN regions with the rapidly growing China in all dimensions (Thai Civil Rights and Investigative Journalism, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thailand shares a border with Laos that stretches 1,810 kilometers, comprising 12 provinces: Chiang Rai, Phayao, Nan, Uttaradit, Phitsanulok, Loei, Nong Khai, Bueng Kan, Nakhon Phanom, Amnat Charoen, Mukdahan, and Ubon Ratchathani. Thailand has the most border checkpoints with Laos, totaling 49 points, which include 20 permanent checkpoints and 29 temporary trade checkpoints. The Nong Khai checkpoint holds the highest trade value, accounting for 34.93% of total border trade. According to the 2020 statistics from the Department of Foreign Trade, Ministry of Commerce of Thailand, the border trade between Thailand and Laos represents 99.2% of the total border trade value when compared to other countries bordering Thailand. Furthermore, Thailand has the highest cross-border trade with China, with a value of 341,180.69\u0026nbsp;million baht (Ministry of Commerce, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thailand's tourism industry significantly impacts the national economy. According to the UNWTO Tourism Highlights report by the World Tourism Organization in 2019, Thailand ranked 7th globally as a tourist destination, attracting 40\u0026nbsp;million international visitors. Notable tourist attractions include Bangkok and Phuket (The nation, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thailand was also ranked 4th in terms of tourism revenue, generating a total of 61\u0026nbsp;billion USD with a growth rate of 3%. The Ministry of Tourism and Sports of Thailand's 2019 statistics indicate that China is the leading country of origin for tourists visiting Thailand, accounting for 28% of total international arrivals (Tourism Council of Thailand, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, numerous studies have sought to understand the public's perception of the benefits of the BRI across various aspects, given its potential to bring significant economic and social changes to participating countries. For instance, in the USA, a study (O\u0026rsquo;Trakoun, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) on China's BRI and regional perceptions used survey data to analyze these views. The findings indicated that an increase in Chinese Foreign Direct Investment (FDI) in a country improved respondents\u0026rsquo; perception of China\u0026rsquo;s influence on their nation. It also showed that these perceptions correlated with future business confidence in the Asia-Pacific region. In India, a study (Sachdeva, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) explored the country's awareness of China's BRI. The research gathered broad perceptions from the developing nation, highlighting that as the BRI progresses, India focuses more on domestic connectivity plans. This study also pointed out that the BRI is increasingly analyzed through the lens of the political economy of participating countries, considering challenges such as debt traps, corruption, political disputes, environmental impacts, and the overall sustainability of the project. A joint study in China and Pakistan examined the social impacts, (Mahmood, Ali, Menhas, \u0026amp; Sabir, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)infrastructure development, and tourism related to the China-Pakistan Economic Corridor (CPEC). This research collected data from respondents living along the CPEC route through face-to-face interviews and used structural equation modeling techniques to analyze the results. The study found that CPEC plays a significant role in the socio-economic and rural development of Pakistan. The expectations from the BRI could lead to positive changes in infrastructure, energy sectors, and social development projects in Pakistan. It also indicated that CPEC would connect rural areas to urban centers, offering development opportunities to achieve sustainable development. In Laos, a study (Khamphengvong, Zhang, Wu, \u0026amp; Thavisay, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated the economic and social impacts on Laotian attitudes towards the benefits received from the BRI. Using structural equation modeling and multigroup analysis, the study assessed the research model. The findings showed that economic and social determinants positively influence perceived benefits of the BRI, with education, tourism, and foreign direct investment (FDI) being the primary drivers of economic and social benefits. Previous studies in Thailand have examined perceptions of the BRI, but they mainly focused on the strategic perspective of Thailand towards China's BRI expansion. These studies reflected only the views of academics, government officials, and politicians in Thailand (Punyaratabandhu \u0026amp; Swaspitchayaskun, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, there is a lack of comprehensive research on the perceptions of the general Thai population, including public and private sectors and residents in border provinces adjacent to Laos along the BRI route. Punyaratabandhu and Swaspitchayaskun (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also revealed that projects and collaborations in Thailand under the BRI have not progressed significantly. To fill this gap, this study was conducted within the context of a developing country like Thailand. It encompasses all relevant factors influencing public perception and the impact on the BRI, including foreign direct investment, tourism, employment, education, living standards, social conditions, international relations, economy, and logistics and transportation.\u003c/p\u003e \u003cp\u003eIf Thailand adequately prepares for the China-Laos high-speed rail, it could lead to significant opportunities for the country (Punyaratabandhu \u0026amp; Swaspitchayaskun, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, in the trade sector, Thailand could increase its exports to Laos and China, given that Thai products are known for their quality, and China has enormous purchasing power. Additionally, high-speed rail transportation can reduce both the time and cost of shipping goods (Vickerman, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In terms of services and tourism, the high-speed rail could make it more convenient for Chinese and Laotian tourists to visit Thailand, providing an opportunity for Thailand to attract more tourists. Finally, in the realm of foreign investment, Chinese investors have shown growing interest in Thailand. Currently, China is increasingly relocating its production bases to Thailand. If this trend continues, it could result in more job creation and help reduce income inequality through the decentralized distribution of investment across various regions (Bank of Thailand, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the importance stated above, this research is considered novel due to the aforementioned importance. Its objective is to investigate indicators of benefit perception factors. It includes studying the factors influencing the BRI and analyzing how these factors relate to the intention of Thai people to use the China-Laos high-speed rail service. The study will apply the Theory of Planned Behavior (TPB) to appropriately adapt to and manage the changes that Thai people will face with the implementation of the BRI policy and accompanying technological advancements affecting both the economic and social realms. This aims to enhance developmental opportunities across various dimensions and assist in formulating effective policies and measures to support these changes for relevant agencies in Thailand.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eIn this section, a literature review is presented that comprehensively examines factors influencing Thai people's intention to use the China-Laos high-speed railway. The review focuses on three main issues: TPB, Perception, and factors impacting BRI. These factors have been thoroughly investigated to provide in-depth insights into Thai perceptions of the benefits associated with the BRI policy.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theory of Planned Behavior (TPB)\u003c/h2\u003e \u003cp\u003eThe Theory of Planned Behavior (TPB) explains human behavior based on three core beliefs that influence intention to use a service (Conner \u0026amp; Armitage, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). These beliefs are: 1) Attitudes: This refers to personal beliefs about the outcomes of a behavior. If individuals believe that performing a particular behavior will result in positive outcomes, they tend to develop a favorable attitude towards that behavior. Conversely, if they believe it will result in negative outcomes, they develop an unfavorable attitude. Positive attitudes lead to the intention to engage in the behavior. 2) Subjective Norms: These are perceptions of social pressures or norms related to the behavior. It involves the individual's perception of whether significant others (such as family and peers) perform the behavior and whether they approve of or expect the individual to perform it. If individuals perceive that important others engage in or support the behavior, they are more likely to conform to those expectations and intend to perform the behavior. and 3) Perceived Behavioral Control: This refers to the individual's perception of the ease or difficulty of performing the behavior. If individuals believe they have the capability to perform the behavior under the given circumstances and can control the outcomes as intended, they are more likely to have the intention to perform the behavior. In summary, TPB posits that these beliefs (attitudes, subjective norms, and perceived behavioral control) collectively influence human intentions regarding behaviors, including the intention to use services. This theoretical framework helps explain how attitudes, social norms, and perceived control interact to shape human behavior intentions (Ajzen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Currently, this concept is widely used to study the intention to use railway transportation services (Borhan, Ibrahim, \u0026amp; Miskeen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Brohi, Kalwar, Memon, \u0026amp; Ghaffar, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hou, Liang, Meng, \u0026amp; Choi, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Perception\u003c/h2\u003e \u003cp\u003eThe concept of perception is an explanation of how humans understand and perceive their surroundings, encompassing thoughts, feelings, and decision-making influenced by various sensory perceptions (Efron, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). Currently, this concept is widely used to study people's perceptions of the BRI policy (Khamphengvong et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kuek, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lubis, Rini, \u0026amp; Sembiring, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Key aspects of perception include: 1) Perceived benefits Product: This refers to how humans perceive or understand products and express feelings towards specific products. Perception is influenced by various dimensions such as product characteristics and quality. 2) Perceived Service: This concept is crucial in marketing and service management contexts. It involves the intangible aspects of service perception that are palpable and affect human perception significantly. 3) Perceived Cultural: This type of perception relates to cultural norms, artistic culture, and cultural values. Understanding and adapting to different cultures can influence decision-making in service selection. and 4) Perceived Emotional: This involves emotional responses or reactions towards products, services, or overall consumer experiences. It often relates to feelings or understanding of services. Overall, these factors collectively influence human decision-making in choosing services (Merleau-Ponty, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 BRI Impact Factors\u003c/h2\u003e \u003cp\u003eBased on a review of previous literature, this paper has collected and analyzed data on 15 research studies across 9 countries that attempt to examine factors influencing the Belt and Road Initiative (BRI) policies, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Therefore, to fill the research gap in the context of developing countries, the researchers have gathered various factors including foreign direct investment, tourism, employment, education, living standards and social aspects, international relations, economics, and logistics and transportation. This comprehensive approach aims to scrutinize and evaluate the impacts on people, providing a genuine understanding of Thai perspectives.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of previous studies on factors affecting the BRI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e \u003cp\u003eFactor impact of BRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTourism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard of living and social\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInternational relations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEconomic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLogistics and transportation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Menhas, Mahmood, Tanchangya, Safdar, \u0026amp; Hussain, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSPSS and binary logistic regression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Prates \u0026amp; Lages, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eQualitative Data Analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Masabo, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eQualitative data analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Choi, Xia, \u0026amp; Lee, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Daye, Charman, Wang, \u0026amp; Suzhikova, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKazakhstan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSocial Exchange Theory\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Daye et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePearson Chi-square test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Khan, Chenggang, Bano, \u0026amp; Hussain, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePCA, PVAR-GMM and Driscoll and Kraay regression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Yue, Gong, \u0026amp; Ma, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSequential explanatory mixed method\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(An, Razzaq, Nawaz, Noman, \u0026amp; Khan, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFGLS and Sys-GMM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Abdulsalam, Xu, Ameer, Abdo, \u0026amp; Xia, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eJohansen Fisher Panel Cointegration,.PDOLS and the Toda and Yamamoto technique\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Carlucci, Corcione, Mazzocchi, \u0026amp; Trincone, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTheoretical model and two partial non-parametric techniques\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Liu \u0026amp; Suk, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAzerbaijan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSWOT and AHP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Khamphengvong et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSEM and multi-group analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Ashraf, Luo, \u0026amp; Anser, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eARDL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Jinrui, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMultiple regression analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eEFA, CFA and SEM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eNote\u003c/b\u003e: SPSS\u0026thinsp;=\u0026thinsp;Statistical Package for Social Sciences; PCA\u0026thinsp;=\u0026thinsp;Principal component analysis; PVAR-GMM\u0026thinsp;=\u0026thinsp;Panel vector autoregressive model based on a generalized method of moment approach; FGLS\u0026thinsp;=\u0026thinsp;Feasible generalized least squares; Sys-GMM\u0026thinsp;=\u0026thinsp;System generalized method of moments; PDOLS\u0026thinsp;=\u0026thinsp;Panel Dynamic Ordinary Least Squares; SWOT\u0026thinsp;=\u0026thinsp;Strengths, Weaknesses, Opportunities, and Threats; AHP\u0026thinsp;=\u0026thinsp;Analytic Hierarchy Process; ARDL\u0026thinsp;=\u0026thinsp;Autoregressive distribution lag; SEM\u0026thinsp;=\u0026thinsp;Structural equation modeling; EFA\u0026thinsp;=\u0026thinsp;Exploratory factor Analysis; CFA\u0026thinsp;=\u0026thinsp;Confirmatory factor analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research methodology","content":"\u003cp\u003eThe study process began with a literature review focusing on factors influencing perceptions of benefits from the China-Laos high-speed railway project, considering economic, tourism, and social impacts. Identifying research gaps, the researchers examined statistical methods applicable to the study, developed a questionnaire grounded in relevant principles and theories, and designed a conceptual framework. Data was collected through face-to-face interviews with 1,540 respondents from Thai provinces bordering Laos along the BRI route. Analysis utilized structural equation modeling (SEM), incorporating exploratory factor analysis (EFA) on a 30% sample (462 respondents) to group economic, tourism, and social impact variables, and confirmatory factor analysis (CFA) on the remaining 70% (1,078 respondents) to validate measures and study variable relationships. Statistical results and policy recommendations for Thailand's railway system development were subsequently presented based on this comprehensive research process, as outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Conceptual Framework\u003c/h2\u003e \u003cp\u003eFor the research framework in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it illustrates the format to be used for presentation, aiming to examine the relationships between factors impacting the BRI on Thai perceptions of benefits and intention to use the China-Laos high-speed railway service. The framework includes factors related to the Theory of Planned Behavior (TPB), which serves as a central mediator.\u003c/p\u003e \u003cp\u003eThe use of indicators in measuring the perception of benefits from the BRI policy is based on the framework of Perception (S\u0026aacute;nchez-Fern\u0026aacute;ndez \u0026amp; Iniesta-Bonillo, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which assesses various dimensions such as Perceived Product Benefits, Perceived Service Benefits, Perceived Cultural Benefits, and Perceived Emotional Benefits. These factors can significantly predict perceptions of benefits regarding the BRI policy. Therefore, these indicators can be utilized as components in analyzing perceived benefits.\u003c/p\u003e \u003cp\u003eIn the context of this study, to measure the impact dimensions of the BRI, all relevant factors have been compiled including foreign direct investment, tourism, employment, education, standards of living and social aspects, international relations, economics, and logistics and transportation. These factors are utilized to assess people's perceptions of the perceived benefits of the BRI. The underlying hypothesis of the model can be defined as follows.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H1)\u003c/b\u003e: Foreign direct investment will positively influence perceived benefits.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H2)\u003c/b\u003e: Tourism will positively influence perceived benefits.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H3)\u003c/b\u003e: Employment will positively influence perceived benefits.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H4)\u003c/b\u003e: Education will positively influence perceived benefits.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 5\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H5)\u003c/b\u003e: Standards of living and social aspects will positively influence perceived benefits.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 6\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H6)\u003c/b\u003e: International relations will positively influence perceived benefits.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 7\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H7)\u003c/b\u003e: Economics will positively influence perceived benefits.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 8\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H8)\u003c/b\u003e: Logistics and transportation will positively influence perceived benefits.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn addition, correlations were also examined between the perceived benefits of the BRI policy and the intention to use the China-Laos high-speed railway service. Based on these findings, hypotheses regarding the intention of Thai people to use the high-speed railway service were formulated as follows:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 9\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H9)\u003c/b\u003e: Perceived benefits have a positive influence on attitudes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 10\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H10)\u003c/b\u003e: Perceived benefits have a positive influence on intention.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 11\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H11)\u003c/b\u003e: Perceived behavior control has a positive influence on intention.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 12\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H12)\u003c/b\u003e: Attitude has a positive influence on intention.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 13\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H13)\u003c/b\u003e: Subjective norm has a positive influence on intention.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Development of the Questionnaire\u003c/h2\u003e \u003cp\u003e In order to design and establish guidelines for study, from the research framework to the creation of tools used for questionnaire analysis, it is divided into three parts: (1) General information and travel behavior of respondents, such as gender, age, marital status, residential area, education level, occupation, income, and preferred travel modes. (2) Behaviors influencing Thai people's perceptions of the benefits of the Belt and Road Initiative (BRI), consisting of 37 questions based on the Theory of Planned Behavior (TPB) principles like Intention to Use, Attitudes, Subjective Norm, and Perceived Behavioral Control, as well as Perception principles such as Perceived Benefits, Product Perception, Service Perception, Cultural Perception, and Emotional Perception. (3) Impact factors of BRI, including 40 questions across 8 factors: foreign direct investment, tourism, employment, education, living standards and society, international relations, economy, and logistics and transportation. The questionnaire responses are based on a 7-point Likert scale (Harpe, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), where 1 indicates strongly disagree and 7 indicates strongly agree.\u003c/p\u003e \u003cp\u003eFurthermore, the content accuracy of the questionnaire has been validated using the Index of Content Validity (IOC), assessed by 3 experts. This study identified questions with IOC values exceeding 0.50 (Fouzul Kareema \u0026amp; Bt Zubairi, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and found that our questionnaire includes questions with IOC values ranging from 0.67 to 1.00.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Participants and Data Collection\u003c/h2\u003e \u003cp\u003eIn this study, conducted research with a sample group consisting of Thai citizens residing in the border provinces of Thailand adjacent to the Lao PDR along the BRI route. The sample was selected using Stratified Random Sampling method across key border trade checkpoints: Chiang Saen checkpoint, Chiang Khong checkpoint, Huai Kon checkpoint, Phu Doo checkpoint, Ban Nakraseng checkpoint, Chiang Khan checkpoint, and Nong Khai checkpoint. This method ensured that the sample represented a diverse and quality representation of the population. This study has obtained useful responses from 1,540 respondents, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This sample size is deemed sufficient for analysis, considering that previous literature recommends a minimum sample size for Structural Equation Modeling (SEM) analysis to be at least 15 times the number of observed variables (Cangur \u0026amp; Ercan, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hair, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). With a total of 77 variables in this study, the minimum sample size required is 1,155 sets. The sample selection process for this study adhered rigorously to these guidelines to ensure that the sample size was strategically aligned with the initial statistical requirements for analysis.\u003c/p\u003e \u003cp\u003eFurthermore, the interview process was conducted only with individuals who willingly agreed to participate in the questionnaire. Additionally, prior to the interview, respondents were asked for their consent. Interviews were conducted face-to-face with respondents aged 18 and above, and each respondent spent no more than 15 minutes answering the questionnaire. Moreover, our data collection process was approved by the Ethics Committee of Suranaree University of Technology (30 January 2024; COE No. 7/2567).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of respondents.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1540 Respondents\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;25 years old (Gen Alpha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026ndash;43 years old (Gen Y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u0026ndash;58 years old (Gen X)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u0026ndash;77 years old (Baby Boomer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed/divorced/separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive in the city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive outside the city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive in the suburbs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVocational education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociate Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor's Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster's Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctoral Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgriculturist/Agricultural Organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntrepreneur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate Employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment Employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than or equal to 10,000 Baht\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,001\u0026ndash;15,000 Baht\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,001\u0026ndash;20,000 Baht\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 20,000 Baht\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModes of travel used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate vehicle (Car/Motorbike)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRailway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther (Airplane and Boat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Statistical Method\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Exploratory factor analysis (EFA)\u003c/h2\u003e \u003cp\u003eTo establish a latent variable model emphasizing various factors based on a literature review, typically when a clear theoretical framework isn't available for measuring relationships, Exploratory Factor Analysis (EFA) is employed. EFA is an analytical approach used to explore and identify factors (Stevens, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) that explain relationships among observed variables. Furthermore, EFA results can reduce observed variables by creating new variables in the form of composite factors (Fabrigar \u0026amp; Wegener, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Williams, Onsman, \u0026amp; Brown, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Confirmatory factor analysis (CFA)\u003c/h2\u003e \u003cp\u003eTo confirm the relationships between components of variables, Confirmatory Factor Analysis (CFA) is used. This method is employed when researchers know that indicators are components based on theory or a literature review, and it verifies the consistency of measures with an academic understanding of relevant factors. The primary objective of CFA is to assess whether the collected data aligns with the research assumptions (Kline, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Theerathitichaipa, Wisutwattanasak, Banyong, et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wisutwattanasak et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 Modeling Structural Equations\u003c/h2\u003e \u003cp\u003eStructural Equation Modeling (SEM) is utilized because it is grounded in theory that emphasizes relationships between observable and latent variables. SEM encompasses both measurement models and structural models to establish causal links between variables (Byrne, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Iamtrakul, Chayphong, \u0026amp; Yoshitsugu, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kaiser, Samuel, \u0026amp; Burger, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Raykov \u0026amp; Marcoulides, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). To assess data appropriateness for SEM guidelines, various statistical techniques are employed, including factor analysis, path analysis, and regression modeling. These methods are typically analyzed using software such as Mplus 7 (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These techniques collectively enable a comprehensive evaluation of data within the SEM framework.\u003c/p\u003e \u003cp\u003eWe use criteria for assessing the adequacy of component data proposed by Fornell and Larcker (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) and Hair (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) to statistically test the results of each indicator model. This involves evaluating Composite Reliability (CR) values and Average Variance Extracted (AVE). CR values and AVE should ideally exceed 0.7 and 0.5, respectively. These statistical values can be computed using Equations (\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{AVE=}\\:\\frac{\\sum\\:_{\\text{i=1}}^{\\text{n}}{\\text{\u0026lambda;}}_{\\text{i}}^{\\text{2}}}{\\text{n}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e,\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{CR}\\text{=}\\:\\frac{{\\text{(}\\sum\\:_{\\text{i=1}}^{\\text{n}}{\\text{\u0026lambda;}}_{\\text{i}}\\text{)}}^{\\text{2}}}{{\\text{(}\\sum\\:_{\\text{i=1}}^{\\text{n}}{\\text{\u0026lambda;}}_{\\text{i}}\\text{)}}^{\\text{2}}\\text{+}\\text{(}\\sum\\:_{\\text{c=1}}^{\\text{n}}{\\text{\u0026delta;}}_{\\text{i}}\\text{)}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere i denotes the component loading of each indicator, and i represents the error terms.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eThe preliminary statistical analysis included measures of mean, standard deviation, skewness, and kurtosis. Before conducting Confirmatory Factor Analysis (CFA), we examined these descriptive statistics to confirm the suitability of the data for analysis. Table\u0026nbsp;3 in the Appendix. It shows that across all 77 items, skewness ranges from \u0026minus;\u0026thinsp;0.639 to 0.196, and kurtosis ranges from \u0026minus;\u0026thinsp;0.767 to 0.938, respectively. These values align with the established criteria where skewness is ideally between \u0026minus;\u0026thinsp;2 and 2, and kurtosis between \u0026minus;\u0026thinsp;7 and 7 (Kline, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, it can be concluded that our sample statistics indicate a normally distributed data set and are acceptable for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 The Exploratory Factor Analysis\u003c/h2\u003e \u003cp\u003eWe used EFA to establish observable indicators representing components of each latent factor and calculate main factors. In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, we present factor analysis results categorized into 3 groups.\u003c/p\u003e \u003cp\u003eGroup 1: The results of EFA for TPB factors indicate high reliability and acceptability, with a Kaiser-Meyer-Olkin (KMO) measure of 0.936, indicating excellent sampling adequacy. The model explains 79.597% of the variance, and the EFA yields 19 items grouped into 4 factors. The combined factors include Intention, Attitude, Subjective Norm, and Perceived Behavioral Control.\u003c/p\u003e \u003cp\u003eGroup 2: The results of EFA for Perception factors indicate high reliability and acceptability, with a Kaiser-Meyer-Olkin (KMO) measure of 0.959, indicating excellent sampling adequacy. The model explains 78.870% of the variance, and the EFA yields 18 items grouped into 4 factors. The combined factors include Perceived Benefits, Product Perception, Perceived Service, Perceived Cultural, and Perceived Emotional.\u003c/p\u003e \u003cp\u003eGroup 3: The results of EFA for BRI impact factors indicate high reliability and acceptability, with a Kaiser-Meyer-Olkin (KMO) measure of 0.961, indicating excellent sampling adequacy. The model explains 75.877% of the variance, and the EFA yields 40 items grouped into 8 factors. The combined factors include Foreign Direct Investment, Tourism, Employment, Education, Standard of Living and Society, International Relations, Economy, and Logistics and Transportation.\u003c/p\u003e \u003cp\u003eWhen examining the accuracy and reliability in terms of Cronbach's alpha for all variables, it was found that values ranged from 0.800 to 0.923. These values exceed the minimum standard recommended in previous research, which is 0.70 (Bujang, Omar, \u0026amp; Baharum, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tavakol \u0026amp; Dennick, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Confirmatory Factor Analysis Results\u003c/h2\u003e \u003cp\u003eConsidering the results from the EFA, we proceeded to evaluate and elucidate the importance of each item. The CFA results were analyzed using Mplus 7 software to confirm that the indicators could indeed be components of each factor.\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the results of the CFA are presented. It was found that all indicators were significant at the 0.01 level as components of each factor. The Critical Ratio (CR) should ideally not be less than 0.7, and the Average Variance Extracted (AVE) should be at least 0.5 (Ab Hamid, Sami, \u0026amp; Sidek, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Alarc\u0026oacute;n, S\u0026aacute;nchez, \u0026amp; De Olavide, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) According to the results, the TPB factor exhibited component loadings ranging from 0.680 to 0.882, the Perception factor showed component loadings ranging from 0.684 to 0.867, and the BRI Impact factor had component loadings ranging from 0.585 to 0.863. All factors demonstrated construct reliability (CR) and average variance (AVE) values exceeding 0.7 and 0.5, respectively. Therefore, these findings confirm that all factors were suitable for CFA analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor analysis results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eEFA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003eCFA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach's Alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLoading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et-Stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eTPB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c4\" namest=\"c3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c9\" namest=\"c8\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAttitude\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"5\" nameend=\"c4\" namest=\"c3\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"5\" nameend=\"c9\" namest=\"c8\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubjective Norm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c4\" namest=\"c3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c9\" namest=\"c8\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Behavioral Control\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c4\" namest=\"c3\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c9\" namest=\"c8\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGoodness of fit\u003c/b\u003e: \u003cem\u003eKaiser-Meyer-Olkin (KMO)\u0026thinsp;=\u0026thinsp;0.936, Bartlett\u0026rsquo;s test approx.\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e= 8273.228, Degrees of freedom (df) = 171, p \u0026lt; 0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerception\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Benefits Product\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c4\" namest=\"c3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c9\" namest=\"c8\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Service\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c4\" namest=\"c3\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c9\" namest=\"c8\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Cultural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c4\" namest=\"c3\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c9\" namest=\"c8\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Emotional\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"5\" nameend=\"c4\" namest=\"c3\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"5\" nameend=\"c9\" namest=\"c8\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGoodness of fit\u003c/b\u003e: \u003cem\u003eKaiser-Meyer-Olkin (KMO)\u0026thinsp;=\u0026thinsp;0.959, Bartlett\u0026rsquo;s test approx.\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e= 7859.706, Degrees of freedom (df) = 153, p \u0026lt; 0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor impact of BRI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 1: Foreign Direct Investment (FDI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c4\" namest=\"c3\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c9\" namest=\"c8\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 2: Tourism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"5\" nameend=\"c4\" namest=\"c3\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"5\" nameend=\"c9\" namest=\"c8\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 3: Employment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c4\" namest=\"c3\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c9\" namest=\"c8\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 4: Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c4\" namest=\"c3\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c9\" namest=\"c8\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 5: Standard of living and social\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"6\" nameend=\"c4\" namest=\"c3\" rowspan=\"7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"6\" nameend=\"c9\" namest=\"c8\" rowspan=\"7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 6: International relations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c4\" namest=\"c3\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c9\" namest=\"c8\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 7: Economic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c4\" namest=\"c3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c9\" namest=\"c8\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 8: Logistics and transportation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c4\" namest=\"c3\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c9\" namest=\"c8\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGoodness of fit\u003c/b\u003e: \u003cem\u003eKaiser-Meyer-Olkin (KMO)\u0026thinsp;=\u0026thinsp;0.961, Bartlett\u0026rsquo;s test approx.\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e= 16485.083, Degrees of freedom (df) = 780, p \u0026lt; 0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: EFA\u0026thinsp;=\u0026thinsp;exploratory factor analysis, CFA\u0026thinsp;=\u0026thinsp;confirmatory factor analysis, CR\u0026thinsp;=\u0026thinsp;composite reliability, AVE\u0026thinsp;=\u0026thinsp;average variance extracted.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the TPB factors, the results indicate that the indicator representing the intention to use the China-Laos high-speed train service among Thai people is highest for In4, \" I plan to use the China-Laos high-speed rail service if I have the opportunity to access it\" (γ\u0026thinsp;=\u0026thinsp;0.876, t\u0026thinsp;=\u0026thinsp;78.728). Following this, the indicator explaining attitude factors most is A3, \" I feel that the China-Laos high-speed rail will help promote tourism in Thailand\" (γ\u0026thinsp;=\u0026thinsp;0.800, t\u0026thinsp;=\u0026thinsp;63.801). The indicator explaining subjective norms the most is SN2, \" If a friend recommends that I try using the China-Laos high-speed rail service, I think this recommendation would have a greater impact on my decision to use it\" (γ\u0026thinsp;=\u0026thinsp;0.882, t\u0026thinsp;=\u0026thinsp;86.562). Finally, the indicator explaining Perceived Behavioral Control the most is BC5, \" I think that even though I have never used the China-Laos high-speed rail before, it is easy to use and everyone can do it\" (γ\u0026thinsp;=\u0026thinsp;0.857, t\u0026thinsp;=\u0026thinsp;86.189).\u003c/p\u003e \u003cp\u003eIn addition, for the Perception factors, the results show that the indicator representing Perceived Benefits Product factors the most is PP2, \"I think that the China-Laos high-speed rail makes travel safer\" (γ\u0026thinsp;=\u0026thinsp;0.867, t\u0026thinsp;=\u0026thinsp;92.836). Following this, the indicator explaining Perceived Service factors the most is PS2, \"I think that the China-Laos high-speed rail service is impressive\" (γ\u0026thinsp;=\u0026thinsp;0.866, t\u0026thinsp;=\u0026thinsp;90.326). The indicator explaining Perceived Cultural factors the most is PC4, \"I think that the China-Laos high-speed rail has a positive influence on cultural exchange between countries\" (γ\u0026thinsp;=\u0026thinsp;0.828, t\u0026thinsp;=\u0026thinsp;69.294). Lastly, the indicator explaining Perceived Emotional factors the most is PE2, \"I think that the China-Laos high-speed rail is interesting\" (γ\u0026thinsp;=\u0026thinsp;0.861, t\u0026thinsp;=\u0026thinsp;90.313).\u003c/p\u003e \u003cp\u003eFurthermore, for the BRI impact factors, the results indicate that the indicator representing Direct Foreign Investment factors the most is B3, \" The China-Laos high-speed rail helps increase trade and investment between Thailand and foreign countries, such as Laos and China\" (γ\u0026thinsp;=\u0026thinsp;0.758, t\u0026thinsp;=\u0026thinsp;46.623). Following this, the indicator explaining Tourism factors the most is C4, \" The China-Laos high-speed rail can encourage more people to choose train travel\" (γ\u0026thinsp;=\u0026thinsp;0.835, t\u0026thinsp;=\u0026thinsp;77.550). The indicator explaining Employment factors the most is D3, \" The China-Laos high-speed rail has helped me earn more compensation from work\" (γ\u0026thinsp;=\u0026thinsp;0.863, t\u0026thinsp;=\u0026thinsp;70.347). The indicator explaining Education factors the most is F3, \"The China-Laos high-speed rail contributes to the development of educational personnel in Thailand\" (γ\u0026thinsp;=\u0026thinsp;0.798, t\u0026thinsp;=\u0026thinsp;59.512). The indicator explaining Standard of Living and Society factors the most is E7, \" The potential of the surrounding Thai border communities along the China-Laos high-speed rail route has developed increased\" (γ\u0026thinsp;=\u0026thinsp;0.820, t\u0026thinsp;=\u0026thinsp;70.453). The indicator explaining International Relations factors the most is R4, \" The China-Laos high-speed rail helps enhances good relations between Thailand and foreign countries\" (γ\u0026thinsp;=\u0026thinsp;0.833, t\u0026thinsp;=\u0026thinsp;76.945). The indicator explaining Economic factors the most is M4, \" The China-Laos high-speed rail contributes to the growth of Thailand's tourism industry, such as hotels, restaurants, and service businesses\" (γ\u0026thinsp;=\u0026thinsp;0.823, t\u0026thinsp;=\u0026thinsp;70.969). Lastly, the indicator explaining Logistics and Transport factors the most is L5, \" The China-Laos high-speed rail contributes to the growth of Thailand's logistics industry, including e-commerce and transportation businesses\" (γ\u0026thinsp;=\u0026thinsp;0.839, t\u0026thinsp;=\u0026thinsp;78.031).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Structural Equation Modeling Results\u003c/h2\u003e \u003cp\u003eThe analysis of hypotheses in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the influence of BRI factors on perceptions of benefits and intentions to use the China-Laos high-speed railway service. These factors were initially hypothesized in the conceptual model, which incorporated indicators derived from CFA for testing the proposed hypotheses. SEM was utilized to explain behavioral intentions, and detailed findings will be presented in the following section.\u003c/p\u003e \u003cp\u003eFor the overall goodness-of-fit assessment of the model, this study used absolute and incremental fit indices (D Hooper, Coughlan, \u0026amp; Mullen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) The results of the CFA estimation are as follows: (1) Chi-square test of model fit \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 7623.019, df\u0026thinsp;=\u0026thinsp;2752, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e/df\u0026thinsp;=\u0026thinsp;2.770, which aligns with the research by Bagozzi and Yi (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) and Deb and Ahmed (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) recommending that the Chi-square value or the ratio between the chi-square and the number of degrees of freedom (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e/df) should be less than 3; (2) Comparative fit index (CFI)\u0026thinsp;=\u0026thinsp;0.929, which aligns with the research by Hu and Bentler (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and (Cangur \u0026amp; Ercan, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) recommending that CFI should be greater than 0.9; (3) Tucker\u0026ndash;Lewis index (TLI)\u0026thinsp;=\u0026thinsp;0.924, which aligns with the research by Daire Hooper and Coughlan (2008) recommending that TLI should be greater than 0.8; (4) Root mean square error of approximation (RMSEA)\u0026thinsp;=\u0026thinsp;0.041, which aligns with the research by Deb and Ahmed (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Xia and Yang (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) recommending that RMSEA should be less than 0.06 and (5) Standardized root mean square Residual (SRMR)\u0026thinsp;=\u0026thinsp;0.039, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, which aligns with the research by Schreiber, Nora, Stage, Barlow, and King (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) recommending that SRMR should be less than 0.08. Therefore, based on the examination of all indices meeting their respective criteria, it can be concluded that this model fits the empirical data well.\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, it is evident that Perceived Benefits Product, Perceived Service, Perceived Cultural, and Perceived Emotional are statistically significant indicators of the perceived benefits of the BRI policy. Additionally, the results show that foreign direct investment, tourism, employment, education, living standards and social economy, and logistics and transportation significantly influence the perceived benefits of the BRI policy in a statistically significant manner. Therefore, hypotheses H1\u0026ndash;H5 and H7\u0026ndash;H8 are supported. Conversely, H6 is not supported due to the lack of statistically significant international relationships. Furthermore, the perceived benefits of the BRI policy significantly influence attitudes, supported by hypothesis H9. Additionally, perceived benefits, attitudes, and conformity to reference groups significantly influence the intention to use the China-Laos high-speed railway service, supporting hypotheses H10 and H12\u0026ndash;H13. On the other hand, H11 is not supported as behavioral control does not have statistically significant influence on the intention to use the China-Laos high-speed railway service.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHypothesis results (SEM).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMeasurement model:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerception Measurement by;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Benefits Product\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Cultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Emotional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eStructural model:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerception Affected on;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForeign Direct Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard of living and social\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternational relations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogistics and transportation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude Affected on;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention Affected on;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Behavioral Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubjective Norm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eFrom our research findings, this study has revealed several significant discoveries and can provide further in-depth explanations. Details are outlined below.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Factors perceptions of the benefits of BRI\u003c/h2\u003e \u003cp\u003eWhen evaluating overall perceptions of the benefits of the Belt and Road Initiative (BRI), Thai people generally have a high perception of its benefits. The component with the highest weight influencing perceptions of BRI benefits is \"Perceived Emotional,\" with a value of 0.911. This indicates that emotional perception has the strongest impact on Thai perceptions of the China-Laos high-speed rail project. This finding is consistent with studies suggesting that emotional perception plays a significant role in BRI projects, highlighting that emotional perspectives form the basis of how BRI benefits are perceived (Mostafanezhad, Farnan, \u0026amp; Loong, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) Following this are \"Perceived Cultural,\" \"Perceived Service,\" and \"Perceived Benefits Product,\" respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.2 The Relationship Between the Impacts of BRI and Perception of Benefits\u003c/h2\u003e \u003cp\u003eBased on the findings of this study, factors such as foreign direct investment (FDI), tourism, employment, education, living standards and social conditions, economics, and logistics and transportation have significant correlations with the perception of the benefits of BRI. Among these, foreign direct investment (FDI) emerged as the most influential factor affecting Thai people\u0026rsquo;s perceptions of BRI benefits. This indicates that FDI positively influences the perception of BRI benefits, as it is seen to bring increased investment opportunities to Thailand. Additionally, it facilitates enhanced trade and investment between Thailand and other countries. These findings align with studies that reveal the BRI projects are beneficial in creating opportunities for foreign direct investment, which positively impacts various economic sectors within the country (Khamphengvong et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; O\u0026rsquo;Trakoun, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yu, Qian, \u0026amp; Liu, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn tourism, this factor influences perceived benefits second to FDI. This indicates that tourism factors have a positive influence on perceived BRI benefits, which are seen as beneficial in promoting tourism to Thailand. It can also attract more tourists to Thailand and helps promote the growth of tourism businesses such as hotels, restaurants, and tourist attractions. This finding is consistent with studies revealing that BRI projects have a significant positive relationship between tourism motivation and satisfaction, as well as between tourism motivation and tourism satisfaction (Yingzhi, Ningning, Huimin, \u0026amp; Jiaying, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Regarding employment, this indicates that employment factors have a positive influence on the perception of BRI benefits. It is viewed as beneficial as it can promote more employment opportunities for people in Thailand, such as opportunities to find a variety of jobs, receive potentially higher compensation, and work with more foreign people. This finding is consistent with studies revealing that BRI projects create new business opportunities and jobs, and have a positive impact on policy-making. This creates opportunities for both local and Chinese workers to coexist positively (Fienena, Sorn, Ge, \u0026amp; Wang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In terms of living standards and society, this indicates that living standards and social factors have a positive influence on the perception of BRI benefits. These are seen as beneficial to living standards and Thai society by fostering increased development. It affects the development of the potential of communities around the Thai border along the BRI railway line, helping further develop Thailand's infrastructure and facilities, such as road networks. This finding is consistent with studies revealing that BRI projects bring positive changes in infrastructure and social development projects (Mahmood et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the field of logistics and transportation, this indicates that logistics and transportation factors have a positive influence on the perception of BRI benefits, which are seen as beneficial to Thailand's logistics and transportation system. It can promote the growth of Thailand's logistics industry, such as e-commerce and transportation businesses. It also helps improve and develop travel routes connecting to Thailand. This finding is consistent with studies revealing that BRI projects affect the growth of logistics and transportation systems (Chhetri, Nkhoma, Peszynski, Chhetri, \u0026amp; Lee, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) Finally, regarding the economy, this indicates that economic factors have a positive influence on the perception of BRI benefits, which are seen as beneficial to economic development in Thailand. It helps stimulate Thailand's tourism industry, create opportunities for Thai people to establish businesses or become entrepreneurs, and boost Thailand's economy. It also contributes to the development of Thailand's cross-border trade economy. This finding is consistent with studies revealing that BRI projects play an important role in socio-economic development and influence the economic and social success of participating countries (Mahmood et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ur Rehman, Ahmed, Ali, Khattak, \u0026amp; Sameer, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eOn the other hand, this result also reveals that in the field of education, it is of statistical significance. However, it shows a negative relationship with the perception of BRI benefits. This means that the educational factor has a negative influence on the perception of BRI benefits, suggesting that education may not directly benefit Thailand. This finding is consistent with studies that revealed that in the perception aspect of education, it may be influenced by cross-cultural contexts. This factor may require understanding the learning perceptions of international students, as attitudes are extremely complex (Li, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.3 The Relationship Between Perception of Benefits and Attitudes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAccording to the study's findings, perceived benefits of the BRI are positively related to Thai people's attitudes. This indicates that when Thai people are aware of the benefits of the BRI, it promotes even more positive attitudes towards the China-Laos high-speed rail. This positive attitude helps them realize the importance of the BRI railway line in developing Thailand's economy. Additionally, they have a favorable perspective on the awareness of Thailand's railway development plan, which will connect with the China-Laos high-speed rail line, benefiting Thai society. This finding is consistent with studies indicating that perceived direct benefits have a positive and significant effect on attitudes (Chanthanasinh, Khamphengvong, Soukavong, Vongsavanthong, \u0026amp; Laungphonexay, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rosyidin, Sugandhini, \u0026amp; Harjanti, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e5.4 The Relationship Between Perception of Benefits and Intention to Use Services Using the Theory of Planned Behavior (TPB)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the context of factors influencing intentions to use the China-Laos high-speed rail service, as clearly evidenced by the study findings, the results unequivocally demonstrate that perceived benefits, attitudes, and subjective norms are positively related to the intention to use the China-Laos high-speed rail service. Indicators of attitudes have the most pronounced impact on the intention to use the China-Laos high-speed rail service. This finding aligns with studies indicating that positive attitudes influence intentions to use rail transport (Kwan, Sutan, \u0026amp; Hashim, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while perceived benefits show a positive relationship with intentions to use the China-Laos high-speed rail service. This finding is consistent with studies indicating that perceived benefits positively influence service use intentions (Choi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and subjective norms show a positive relationship with intentions to use the China-Laos high-speed rail service. This finding is consistent with studies indicating that conformity to reference groups directly influences intentions to use the service positively (Borhan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions and Implementation","content":"\u003cp\u003eThe Belt and Road Initiative (BRI) is recognized globally as a highly impactful trade route capable of bringing about significant changes in the economic and social systems of all participant countries. This initiative is expected to substantially reduce transportation time and trade costs (De Soyres, Mulabdic, Murray, Rocha, \u0026amp; Ruta, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thailand, classified as a developing country, benefits significantly due to its proximity to the high-speed railway line connecting China and Laos. This geographical advantage directly impacts Thailand and affects a majority of Thai people, who may experience both positive and negative effects from the changes occurring as a result. Therefore, this study aims to investigate indicators of benefit perception factors, including analyzing the factors influencing the BRI and examining the relationship between these factors and the intention of Thai people to use the China-Laos high-speed rail service using the Theory of Planned Behavior. (TPB). We have presented findings from a combination of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM) by surveying 1,540 Thai individuals in border provinces adjacent to Laos along the BRI route. This study reveals several significant findings that can inform the development of policies tailored to Thailand's context and aligned with global changes arising from the BRI policy.\u003c/p\u003e \u003cp\u003e The findings of this study revealed that Perceived Emotion was identified as the indicator exerting the greatest influence on perceived benefits of the BRI, followed by Perceived Culture, Perceived Service, and Perceived Product Benefits, respectively. These results showed relatively high component weights, indicating high reliability within the domain. Additionally, regarding the perception benefits of BRI, the results also indicate a positive correlation with the attitudes of Thai individuals. This means that when Thai people perceive the benefits of the BRI, it enhances their positive attitude towards the China-Laos high-speed rail. This positive attitude helps them recognize the significance of the BRI railway.\u003c/p\u003e \u003cp\u003eWe can further explore the significant role of BRI policies, as our results indicate. Direct foreign investment, tourism, employment, living standards, economic impact, and logistics and transportation are perceived positively in terms of their contribution to the benefits of the BRI among Thai people. Conversely, education shows a negative relationship with perceived benefits, suggesting that education may not be seen as directly benefiting Thailand. Additionally, the study provides insights into Thai people's intentions to use the China-Laos high-speed rail service, revealing that perceived benefits, attitudes, and subjective norms influence this intention. Thus, the structural equation model used in this study effectively examines the relationships between these factors and accurately reflects Thai people's perspectives on BRI policies.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Recommendation for implementation\u003c/h2\u003e \u003cp\u003eBased on the statistical data analysis and the key findings mentioned earlier, we propose policy recommendations to develop Thailand, preparing it to adapt and manage the forthcoming changes associated with the Belt and Road Initiative (BRI). Our recommendations stem from significant variables identified within the model and are crafted to align Thailand's development with BRI policies. The detailed recommendations include:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolicy on direct foreign investment development\u003c/strong\u003e \u003cp\u003e This particular finding, this study propose guidelines to establish a policy promoting foreign trade and investment aimed at attracting investors and entrepreneurs to invest in Thailand. This initiative serves as an additional avenue for generating income for Thailand, fostering expanded trade cooperation with China and other promising countries. These recommendations align with studies advocating for policies that enhance opportunities for foreign direct investment (FDI) across sectors such as infrastructure, energy, and technology to bolster the domestic economy (Abdulsalam et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolicy on tourism development\u003c/strong\u003e \u003cp\u003e This particular finding, this study propose guidelines to establish a policy promoting tourism activities aimed at enhancing the quality and diversity of tourist destinations in Thailand. This initiative aims to create distinctive identities recognized globally based on Thai culture. Additionally, it focuses on improving the quality and standards of tourism-related businesses such as spas, hotels, and restaurants. This strategy aims to attract tourists traveling on the China-Laos high-speed railway. It aligns with studies suggesting cooperative tourism development strategies to formulate sustainable tourism development strategies between countries (Liu \u0026amp; Suk, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolicy on employment development\u003c/strong\u003e \u003cp\u003e This particular finding, this study propose guidelines for stimulating employment according to the needs of the new economic system, both domestically and internationally. This includes developing workforce skills to enhance the quality of labor, preparing for employment opportunities from the BRI route. This aligns with Thailand's Ministry of Labour policy aimed at driving labor-related initiatives to foster stable economic growth (Ministry of Labour, 2022).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolicy on standard of living and social development\u003c/strong\u003e \u003cp\u003e This particular finding, this study propose guidelines to establish a policy for developing societal standards to reduce inequality and promote equitable prosperity distribution regionally. This would lead to the development of infrastructure and amenities within Thailand, such as improving road networks connecting the Thai-Lao border areas. This aligns with studies suggesting policies that consider societal well-being and address environmental degradation in economic activities, aiming for sustainable development goals (Khan et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolicy on logistics and transportation development\u003c/strong\u003e \u003cp\u003e This particular finding, this study propose guidelines to establish a policy for developing transportation systems with integrated multimodal transport networks linking the high-speed railway between China and Laos with Thailand. This initiative aims to seamlessly connect Thailand's transportation networks, enhancing logistics infrastructure to facilitate efficient and rapid movement of goods. Moreover, it aims to efficiently link Thai goods transportation with other countries. This aligns with national strategies for developing China's transportation connectivity, included in the 14th National Economic and Social Development Plan (2021\u0026ndash;2025) (Department of International Trade Promotion, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolicy on economic development\u003c/strong\u003e \u003cp\u003e This particular finding, this study propose guidelines to establish a policy aimed at driving economic growth and enhancing competitiveness by creating opportunities for business development. This includes elevating the agricultural sector towards high-value agricultural and agro-processing industries. This initiative aims to propel Thailand's industrial sector to greater heights, enabling efficient exportation of various goods and services to global markets. This aligns with the National Economic and Social Development Plan (2023\u0026ndash;2027), targeting the accelerated advancement of Thailand towards an advanced society with a sustainable economy (Office of the National Economic and Social Development Council, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAll of the policies mentioned have been directly informed by current public sentiment in Thailand. These policies will involve relevant stakeholders in shaping national-level policies. For instance, the Ministry of Foreign Affairs can apply policies for direct foreign investment development. The Ministry of Tourism can implement policies for tourism development. Additionally, the Ministry of Labour can apply policies for employment development. Furthermore, the Ministry of Transport (including the Department of Highways, Department of Rail Transport, and State Railway of Thailand) can apply policies for standard of living and societal development, as well as logistics and transportation development. The Office of the National Economic and Social Development Council can apply policies for economic development, aiming to align Thailand's national development plans with global cooperation policies such as the BRI, efficiently and effectively benefiting Thai citizens in all aspects going forward.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Limitation of the study\u003c/h2\u003e \u003cp\u003eHowever, amidst the limitations of the research, this study focuses solely on examining the perception of benefits related to the BRI from the perspective of Thai residents living in border provinces along the BRI routes. Therefore, future studies should expand to cover other regions across all areas of Thailand. This may help reveal different perspectives that arise based on varying local contexts within Thailand. Moreover, this study could be further developed and conducted in numerous countries along the BRI routes to gather new insights and findings from various aspects for future researchers.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: Conceptualization, K.T. and R.K.; methodology, K.T. and P.W.; software, K.T.; validation, T.C., P.W., and C.S.; formal analysis, K.T. and R.K.; investigation, K.T.; resources, K.T. and M.S.; data curation, K.T. and M.S.; writing\u0026mdash;original draft preparation, K.T. and R.K.; writing\u0026mdash;review and editing, K.T., R.K., P.W., C.S., S.J., V.R., and T.C.; visualization, K.T. and R.K.; supervision, V.R., R.K. and S.J.; project administration, V.R., R.K. and S.J.; funding acquisition, V.R., R.K., and S.J. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAb Hamid, M. R., Sami, W., \u0026amp; Sidek, M. M. (2017). \u003cem\u003eDiscriminant validity assessment: Use of Fornell \u0026amp; Larcker criterion versus HTMT criterion.\u003c/em\u003e Paper presented at the Journal of physics: Conference series.\u003c/li\u003e\n\u003cli\u003eAbdulsalam, A., Xu, H., Ameer, W., Abdo, A.-B., \u0026amp; Xia, J. (2021). Exploration of the impact of China\u0026rsquo;s outward foreign direct investment (FDI) on economic growth in Asia and North Africa along the Belt and Road (B\u0026amp;R) Initiative. \u003cem\u003eSustainability, \u003c/em\u003e13(4), 1623.\u003c/li\u003e\n\u003cli\u003eAjzen, I. (2002). Constructing a TPB questionnaire: Conceptual and methodological considerations.\u003c/li\u003e\n\u003cli\u003eAlarc\u0026oacute;n, D., S\u0026aacute;nchez, J. A., \u0026amp; De Olavide, U. (2015). \u003cem\u003eAssessing convergent and discriminant validity in the ADHD-R IV rating scale: User-written commands for Average Variance Extracted (AVE), Composite Reliability (CR), and Heterotrait-Monotrait ratio of correlations (HTMT).\u003c/em\u003e Paper presented at the Spanish STATA meeting.\u003c/li\u003e\n\u003cli\u003eAn, H., Razzaq, A., Nawaz, A., Noman, S. M., \u0026amp; Khan, S. A. R. (2021). Nexus between green logistic operations and triple bottom line: evidence from infrastructure-led Chinese outward foreign direct investment in Belt and Road host countries. \u003cem\u003eEnvironmental Science and Pollution Research, \u003c/em\u003e28(37), 51022-51045.\u003c/li\u003e\n\u003cli\u003eASEAN Community. (2022). Thailand\u0026apos;s Role in ASEAN. Retrieved from\u003cbr\u003ehttps://image.mfa.go.th/mfa/0/OcXc7u4THG/infographic/%E0%B8%9A%E0%B8%97%E0%\u003cbr\u003eB8%9A%E0%B8%B2%E0%B8%97%E0%B9%84%E0%B8%97%E0%B8%A2%E0%B9%83%E0%B8%99%E0%B8%AD\u003cbr\u003e%E0%B8%B2%E0%B9%80%E0%B8%8B%E0%B8%B5%E0%B8%A2%E0%B8%99/%E0%B8%9A%E0%B8%97%E0%B8%9A%E0%B8%B2%E0%B8%97%E\u003cbr\u003e0%B8%82%E0%B8%AD%E0%B8%87%E0%B9%84%E0%B8%97%E0%B8%A2%E0%B9%83%E0%B8%99%E0%B8%AD%E0%B8%B2%E0%B9%80%E\u003cbr\u003e0%B8%8B%E0%B8%B5%E0%B8%A2%E0%B8%99.pdf\u003c/li\u003e\n\u003cli\u003eAshraf, J., Luo, L., \u0026amp; Anser, M. K. (2022). Do BRI policy and institutional quality influence economic growth and environmental quality? An empirical analysis from South Asian countries affiliated with the Belt and Road Initiative. \u003cem\u003eEnvironmental Science and Pollution Research, \u003c/em\u003e29(6), 8438-8451.\u003c/li\u003e\n\u003cli\u003eBagozzi, R. P., \u0026amp; Yi, Y. (1988). On the evaluation of structural equation models. \u003cem\u003eJournal of the academy of marketing science, \u003c/em\u003e16, 74-94.\u003c/li\u003e\n\u003cli\u003eBank of Thailand. (2021). Opening the China-Laos train whistle: implications for the Thai economy. Retrieved from https://www.bot.or.th/content/dam/bot/documents/th/research-and-publications/article/regional/2021/2564_RI_05_chinalaosrailway.pdf\u003c/li\u003e\n\u003cli\u003eBorhan, M. N., Ibrahim, A. N. H., \u0026amp; Miskeen, M. A. A. (2019). Extending the theory of planned behaviour to predict the intention to take the new high-speed rail for intercity travel in Libya: Assessment of the influence of novelty seeking, trust and external influence. \u003cem\u003eTransportation Research Part A: Policy and Practice, \u003c/em\u003e130, 373-384.\u003c/li\u003e\n\u003cli\u003eBrohi, S., Kalwar, S., Memon, I. A., \u0026amp; Ghaffar, A. (2021). Using the theory of planned behavior to identify the behavioral intention to use public transportation service: the case study of Karachi circular railway. \u003cem\u003eInt. J. Emerg. Technol, \u003c/em\u003e12(6), 317-322.\u003c/li\u003e\n\u003cli\u003eBujang, M. A., Omar, E. D., \u0026amp; Baharum, N. A. (2018). A review on sample size determination for Cronbach\u0026rsquo;s alpha test: a simple guide for researchers. \u003cem\u003eThe Malaysian journal of medical sciences: MJMS, \u003c/em\u003e25(6), 85.\u003c/li\u003e\n\u003cli\u003eByrne, B. M. (2013). \u003cem\u003eStructural equation modeling with Mplus: Basic concepts, applications, and programming\u003c/em\u003e: routledge.\u003c/li\u003e\n\u003cli\u003eCangur, S., \u0026amp; Ercan, I. (2015). Comparison of model fit indices used in structural equation modeling under multivariate normality. \u003cem\u003eJournal of Modern Applied Statistical Methods, \u003c/em\u003e14(1), 14.\u003c/li\u003e\n\u003cli\u003eCarlucci, F., Corcione, C., Mazzocchi, P., \u0026amp; Trincone, B. (2021). The role of logistics in promoting Italian agribusiness: The Belt and Road Initiative case study. \u003cem\u003eLand Use Policy, \u003c/em\u003e108, 105560.\u003c/li\u003e\n\u003cli\u003eChanthanasinh, V., Khamphengvong, V., Soukavong, B., Vongsavanthong, V., \u0026amp; Laungphonexay, K. (2022). The Perception and Attitude of Government Official on the Bilateral between Lao-China Initiatives under the Belt and Road Initiative (BRI) Case Study: Vientiane Capital. \u003cem\u003esujournal, \u003c/em\u003e8(2), 342-352.\u003c/li\u003e\n\u003cli\u003eChhetri, P., Nkhoma, M., Peszynski, K., Chhetri, A., \u0026amp; Lee, P. T.-W. (2018). Global logistics city concept: a cluster-led strategy under the belt and road initiative. \u003cem\u003eMaritime Policy \u0026amp; Management, \u003c/em\u003e45(3), 319-335.\u003c/li\u003e\n\u003cli\u003eChoi, K.-S., Xia, T.-S., \u0026amp; Lee, P. T.-W. (2020). Structural relationships among shipper\u0026rsquo;s perception, value, and choice intention of Korea\u0026ndash;China train ferry service. \u003cem\u003eMaritime Policy \u0026amp; Management, \u003c/em\u003e47(6), 703-717.\u003c/li\u003e\n\u003cli\u003eConner, M., \u0026amp; Armitage, C. J. (1998). Extending the theory of planned behavior: A review and avenues for further research. \u003cem\u003eJournal of applied social psychology, \u003c/em\u003e28(15), 1429-1464.\u003c/li\u003e\n\u003cli\u003eDaye, M., Charman, K., Wang, Y., \u0026amp; Suzhikova, B. (2020). Exploring local stakeholders\u0026rsquo; views on the prospects of China\u0026rsquo;s Belt \u0026amp; Road Initiative on tourism development in Kazakhstan. \u003cem\u003eCurrent Issues in Tourism, \u003c/em\u003e23(15), 1948-1962.\u003c/li\u003e\n\u003cli\u003eDe Soyres, F., Mulabdic, A., Murray, S., Rocha, N., \u0026amp; Ruta, M. (2019). How much will the Belt and Road Initiative reduce trade costs? \u003cem\u003eInternational Economics, \u003c/em\u003e159, 151-164.\u003c/li\u003e\n\u003cli\u003eDeb, S., \u0026amp; Ahmed, M. A. (2018). Determining the service quality of the city bus service based on users\u0026rsquo; perceptions and expectations. \u003cem\u003eTravel Behaviour and Society, \u003c/em\u003e12, 1-10.\u003c/li\u003e\n\u003cli\u003eDepartment of International Trade Promotion. (2024). One Belt One Route (BRI) with the transportation of goods from Thailand to China. Retrieved from https://www.ditp.go.th/post/173487\u003c/li\u003e\n\u003cli\u003eEfron, R. (1969). \u003cem\u003eWhat is perception?\u003c/em\u003e Paper presented at the Proceedings of the Boston Colloquium for the Philosophy of Science 1966/1968.\u003c/li\u003e\n\u003cli\u003eFabrigar, L. R., \u0026amp; Wegener, D. T. (2011). \u003cem\u003eExploratory factor analysis\u003c/em\u003e: Oxford University Press.\u003c/li\u003e\n\u003cli\u003eFienena, R. L., Sorn, M. K., Ge, W., \u0026amp; Wang, E. (2023). Exploring the Perceived Prospects of the Belt \u0026amp; Road Initiative on Policy Impacts for Madagascar. \u003cem\u003eJournal of Business and Management Studies, \u003c/em\u003e5(2), 83-100.\u003c/li\u003e\n\u003cli\u003eFornell, C., \u0026amp; Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. \u003cem\u003eJournal of marketing research, \u003c/em\u003e18(1), 39-50.\u003c/li\u003e\n\u003cli\u003eFouzul Kareema, M. I., \u0026amp; Bt Zubairi, A. M. (2021). Item objective congruence analysis for multidimensional items content validation of a reading test in Sri Lankan university.\u003c/li\u003e\n\u003cli\u003eHair, J. F. (2009). Multivariate data analysis.\u003c/li\u003e\n\u003cli\u003eHarpe, S. E. (2015). How to analyze Likert and other rating scale data. \u003cem\u003eCurrents in pharmacy teaching and learning, \u003c/em\u003e7(6), 836-850.\u003c/li\u003e\n\u003cli\u003eHooper, D., \u0026amp; Coughlan, J. (2008). i Mullen, MR (2008). Structural equation modelling: Guidelines for determining model fit. \u003cem\u003eElectronic Journal of Business Research Methods, \u003c/em\u003e6(1), 53-60.\u003c/li\u003e\n\u003cli\u003eHooper, D., Coughlan, J., \u0026amp; Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electron J Bus Res Methods. 2008; 6 (1): 53\u0026ndash;60. In.\u003c/li\u003e\n\u003cli\u003eHou, Z., Liang, L. J., Meng, B., \u0026amp; Choi, H. C. (2021). The role of perceived quality on high-speed railway tourists\u0026rsquo; behavioral intention: An application of the extended theory of planned behavior. \u003cem\u003eSustainability, \u003c/em\u003e13(22), 12386.\u003c/li\u003e\n\u003cli\u003eHu, L. t., \u0026amp; Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. \u003cem\u003eStructural equation modeling: a multidisciplinary journal, \u003c/em\u003e6(1), 1-55.\u003c/li\u003e\n\u003cli\u003eIamtrakul, P., Chayphong, S., \u0026amp; Yoshitsugu, H. (2024). Exploring the perception of quality of life in urban daily commuting for sustainable urban transport in Bangkok, Thailand. \u003cem\u003eTransportation\u003c/em\u003e, 1-27.\u003c/li\u003e\n\u003cli\u003eJinrui, X. (2023). Popular Perceptions of China\u0026apos;s Influence in Malaysia since the BRI: An Economic Perspective. \u003cem\u003eChina: An International Journal, \u003c/em\u003e21(1), 135-159.\u003c/li\u003e\n\u003cli\u003eKaiser, A., Samuel, R., \u0026amp; Burger, P. (2024). Toward a low-pesticide agriculture: bridging practice theory and social-psychological concepts to analyze farmers\u0026rsquo; routines. \u003cem\u003eSustainability: Science, Practice and Policy, \u003c/em\u003e20(1), 2306731.\u003c/li\u003e\n\u003cli\u003eKapook Travel. (2021). Laos-China Railway Tourist routes along the BRI high-speed rail line. Retrieved from https://travel.kapook.com/view250243.html\u003c/li\u003e\n\u003cli\u003eKhamphengvong, V., Zhang, H., Wu, Q., \u0026amp; Thavisay, T. (2022). Examine the economic and social effects on Lao people\u0026rsquo;s perceived benefit attitudes towards BRI. \u003cem\u003eSustainability, \u003c/em\u003e14(9), 5088.\u003c/li\u003e\n\u003cli\u003eKhan, A., Chenggang, Y., Bano, S., \u0026amp; Hussain, J. (2020). The empirical relationship between environmental degradation, economic growth, and social well-being in Belt and Road Initiative countries. \u003cem\u003eEnvironmental Science and Pollution Research, \u003c/em\u003e27, 30800-30814.\u003c/li\u003e\n\u003cli\u003eKline, R. B. (2023). \u003cem\u003ePrinciples and practice of structural equation modeling\u003c/em\u003e: Guilford publications.\u003c/li\u003e\n\u003cli\u003eKuek, Y. K. B. K. (2020). \u003cem\u003eMalaysians\u0026rsquo; Perceptions and Socio-Cultural Behaviors to the Belt and Road Initiative\u0026ndash;a Perspective From the People-to-People Bond.\u003c/em\u003e Ritsumeikan Asia Pacific University,\u003c/li\u003e\n\u003cli\u003eKwan, S. C., Sutan, R., \u0026amp; Hashim, J. H. (2020). Behavioral Framework of Attitude, Service Quality, Environmental Health Concerns and Intention to Use Rail Transport among Private Motor Vehicle Users. \u003cem\u003eMalaysian Journal of Medicine \u0026amp; Health Sciences, \u003c/em\u003e16(4).\u003c/li\u003e\n\u003cli\u003eLi, J. (2021). Shaping \u0026ldquo;The Belt and Road Initiative\u0026rdquo; international higher education: A qualitative study of international students from South-Asian countries in Chinese universities. \u003cem\u003eBeijing International Review of Education, \u003c/em\u003e3(1), 22-37.\u003c/li\u003e\n\u003cli\u003eLiu, Y., \u0026amp; Suk, S. (2021). Influencing factors of Azerbaijan and China\u0026rsquo;s sustainable tourism development strategy under the one belt one road initiative. \u003cem\u003eSustainability, \u003c/em\u003e14(1), 187.\u003c/li\u003e\n\u003cli\u003eLubis, K., Rini, E., \u0026amp; Sembiring, B. (2021). The influence of E-service quality and perceived value on the positive E-word of mouth through satisfaction of customers users of internet banking BRI in the city of medan. \u003cem\u003eInternational Journal of Research and Review, \u003c/em\u003e8(1), 2454-2237.\u003c/li\u003e\n\u003cli\u003eMahmood, S., Ali, G., Menhas, R., \u0026amp; Sabir, M. (2022). Belt and road initiative as a catalyst of infrastructure development: Assessment of resident\u0026rsquo;s perception and attitude towards China-Pakistan Economic Corridor. \u003cem\u003ePloS one, \u003c/em\u003e17(7), e0271243.\u003c/li\u003e\n\u003cli\u003eMasabo, C. J. (2019). Implications of Chinese diplomacy and foreign policy on foreign students\u0026rsquo; perception of Belt and Road Initiative. \u003cem\u003eCappadocia Journal of Area Studies, \u003c/em\u003e1(1), 25-47.\u003c/li\u003e\n\u003cli\u003eMenhas, R., Mahmood, S., Tanchangya, P., Safdar, M. N., \u0026amp; Hussain, S. (2019). Sustainable development under belt and road initiative: A case study of China-Pakistan economic corridor\u0026rsquo;s socio-economic impact on Pakistan. \u003cem\u003eSustainability, \u003c/em\u003e11(21), 6143.\u003c/li\u003e\n\u003cli\u003eMerleau-Ponty, M. (2004). \u003cem\u003eThe world of perception\u003c/em\u003e: Routledge.\u003c/li\u003e\n\u003cli\u003eMinistry of Commerce. (2020). Current Border Trade Situation between Thailand and Lao PDR. Retrieved from https://www.itd.or.th/wp-content/uploads/2020/10/itdevent30102020-02.pdf\u003c/li\u003e\n\u003cli\u003eMinistry of Labour. (2022). Ministry of Labour Policy Fiscal Year 2022. Retrieved from https://www.mol.go.th/wp-\u003cbr\u003econtent/uploads/sites/2/2022/03/%E0%B9%81%E0%B8%9C%\u003cbr\u003eE0%B9%88%E0%B8%99%E0%B8%9E%E0%B8%B1%E0%B8%9A%E0%B8%99%E0%\u003cbr\u003eB9%82%E0%B8%A2%E0%B8%9A%E0%B8%B2%E0%B8%A2%E0%B8%81%E0%B8%\u003cbr\u003eA3%E0%B8%B0%E0%B8%97%E0%B8%A3%E0%B8%A7%E0%B8%87-%E0%B8%9B%E0%B8%B5-2565-%E0%B9%80%E0%B8%9C%E0%B8%A2%E0%B9%81%E0%B8%9E%E0%B8%A3\u003cbr\u003e%E0%B9%88%E0%B8%9A%E0%B8%99%E0%B9%80%E0%B8%A7%E0%B9%87%E0%B8%9A%E0%B9%84%E0%B8%8B%E0%B8%95%E0%B9%8C.pdf\u003c/li\u003e\n\u003cli\u003eMostafanezhad, M., Farnan, R. A., \u0026amp; Loong, S. (2023). Sovereign anxiety in Myanmar: An emotional geopolitics of China\u0026apos;s Belt and Road Initiative. \u003cem\u003eTransactions of the Institute of British Geographers, \u003c/em\u003e48(1), 132-148.\u003c/li\u003e\n\u003cli\u003eMuth\u0026eacute;n, B., \u0026amp; Muth\u0026eacute;n, L. (2017). Mplus. In \u003cem\u003eHandbook of item response theory\u003c/em\u003e (pp. 507-518): Chapman and Hall/CRC.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Trakoun, J. (2018). China\u0026rsquo;s belt and road initiative and regional perceptions of China. \u003cem\u003eBusiness Economics, \u003c/em\u003e53(1), 17-24.\u003c/li\u003e\n\u003cli\u003eOffice of the National Economic and Social Development Council. (2022). National Economic and Social Development The Thirteenth Plan (2023-2027). Retrieved from http://nscr.nesdc.go.th/wp-content/uploads/2022/11/plan13-rkt-011165.pdf\u003c/li\u003e\n\u003cli\u003eOhashi, H. (2018). The Belt and Road Initiative (BRI) in the context of China\u0026rsquo;s opening-up policy. \u003cem\u003eJournal of Contemporary East Asia Studies, \u003c/em\u003e7(2), 85-103.\u003c/li\u003e\n\u003cli\u003ePrachachat. (2023). Belt and Road (BRI), a strategic project used by China to expand its influence on the world stage. Retrieved from https://www.prachachat.net/world-news/news-1416799\u003c/li\u003e\n\u003cli\u003ePrates, I., \u0026amp; Lages, C. (2019). The Brazilian Perception of China\u0026rsquo;s Belt and Road Initiative. \u003cem\u003eAvailable at SSRN \u003c/em\u003e3681467.\u003c/li\u003e\n\u003cli\u003ePugacheva, O., \u0026amp; Piatachkova, A. (2021). South Korea\u0026rsquo;s Policy Toward Belt and Road Initiative. \u003cem\u003eMirovaia ekonomika i mezhdunarodnye otnosheniia, \u003c/em\u003e65(10), 81-90.\u003c/li\u003e\n\u003cli\u003ePunyaratabandhu, P., \u0026amp; Swaspitchayaskun, J. (2018). The political economy of China\u0026ndash;Thailand development under the one belt one road initiative: Challenges and opportunities. \u003cem\u003eThe Chinese Economy, \u003c/em\u003e51(4), 333-341.\u003c/li\u003e\n\u003cli\u003ePunyaratabandhu, P., \u0026amp; Swaspitchayaskun, J. (2021). Thailand\u0026rsquo;s perception and strategy toward China\u0026rsquo;s BRI expansion: hedging with cooperating. \u003cem\u003eThe Chinese Economy, \u003c/em\u003e54(1), 69-77.\u003c/li\u003e\n\u003cli\u003eRaykov, T., \u0026amp; Marcoulides, G. A. (2012). \u003cem\u003eA first course in structural equation modeling\u003c/em\u003e: routledge.\u003c/li\u003e\n\u003cli\u003eRosyidin, M. A. F., Sugandhini, D., \u0026amp; Harjanti, S. (2023). THE EFFECT OF PERCEPTIONS OF EASE OF USE AND PERCEPTIONS OF USEFULNESS ON INTEREST IN MEDIATION BY ATTITUDES OF USERS OF BRI INTERNET BANKING SERVICES IN THE SPECIAL AREA OF YOGYAKARTA. \u003cem\u003eInternational Journal of Economics, Business and Accounting Research (IJEBAR), \u003c/em\u003e7(2), 674-688.\u003c/li\u003e\n\u003cli\u003eSachdeva, G. (2018). Indian perceptions of the Chinese Belt and Road initiative. \u003cem\u003eInternational Studies, \u003c/em\u003e55(4), 285-296.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Fern\u0026aacute;ndez, R., \u0026amp; Iniesta-Bonillo, M. \u0026Aacute;. (2007). The concept of perceived value: a systematic review of the research. \u003cem\u003eMarketing theory, \u003c/em\u003e7(4), 427-451.\u003c/li\u003e\n\u003cli\u003eSchreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., \u0026amp; King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. \u003cem\u003eThe Journal of educational research, \u003c/em\u003e99(6), 323-338.\u003c/li\u003e\n\u003cli\u003eSchulhof, V., Van Vuuren, D., \u0026amp; Kirchherr, J. (2022). The Belt and Road Initiative (BRI): What will it look like in the future? \u003cem\u003eTechnological Forecasting and Social Change, \u003c/em\u003e175, 121306.\u003c/li\u003e\n\u003cli\u003eStevens, J. P. (2012). \u003cem\u003eApplied multivariate statistics for the social sciences\u003c/em\u003e: Routledge.\u003c/li\u003e\n\u003cli\u003eTavakol, M., \u0026amp; Dennick, R. (2011). Making sense of Cronbach\u0026apos;s alpha. \u003cem\u003eInternational journal of medical education, \u003c/em\u003e2, 53.\u003c/li\u003e\n\u003cli\u003eThai Civil Rights and Investigative Journalism. (2024). Thailand moves forward with transportation-logistics route to become ASEAN hub. Retrieved from https://www.tcijthai.com/news/2013/17/scoop/3247\u003c/li\u003e\n\u003cli\u003eThai Public Broadcasting Service. (2022). China-Laos Railway connects economic opportunities. Retrieved from https://www.thaipbs.or.th/news/content/319522\u003c/li\u003e\n\u003cli\u003eThe nation. (2022). Bangkok and Thailand ranked among top 10 destinations by travel magazine readers. Retrieved from https://www.nationthailand.com/thailand/tourism/40020794\u003c/li\u003e\n\u003cli\u003eTheerathitichaipa, K., Wisutwattanasak, P., Banyong, C., Seefong, M., Jomnonkwao, S., Champahom, T., . . . Kasemsri, R. (2024). Measurement Model for Determining the Disparity Factors of Intercity Railway Transportation. \u003cem\u003eCivil Engineering Journal, \u003c/em\u003e10(3), 668-688.\u003c/li\u003e\n\u003cli\u003eTheerathitichaipa, K., Wisutwattanasak, P., Se, C., Seefong, M., Jomnonkwao, S., Champahom, T., . . . Kasemsri, R. (2024). Assessment of Disparity in Accessing Railway Stations in Thailand: an Application Geographic Information System Network Analysis. \u003cem\u003eJournal of Geovisualization and Spatial Analysis, \u003c/em\u003e8(1), 6.\u003c/li\u003e\n\u003cli\u003eTourism Council of Thailand. (2023). National Tourism Policy (2023 - 2027). Retrieved from https://secretary.mots.go.th/ewtadmin/ewt/secretary/download/article/article_20230327145011.pdf\u003c/li\u003e\n\u003cli\u003eUr Rehman, Z., Ahmed, D. M., Ali, D. S. A., Khattak, A., \u0026amp; Sameer, I. (2020). Entrepreneurial factors affecting perceived socioeconomic success in one belt one road initiative\u0026ndash;a study of China-Pakistan economic corridor. \u003cem\u003eInternational Journal of Management, \u003c/em\u003e11(9).\u003c/li\u003e\n\u003cli\u003eVickerman, R. (2018). Can high-speed rail have a transformative effect on the economy? \u003cem\u003eTransport policy, \u003c/em\u003e62, 31-37.\u003c/li\u003e\n\u003cli\u003eWilliams, B., Onsman, A., \u0026amp; Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. \u003cem\u003eAustralasian journal of paramedicine, \u003c/em\u003e8, 1-13.\u003c/li\u003e\n\u003cli\u003eWisutwattanasak, P., Champahom, T., Jomnonkwao, S., Seefong, M., Theerathitichaipa, K., Kasemsri, R., \u0026amp; Ratanavaraha, V. (2023). Modeling Extended Service Quality for Public Transportation in the Post-Pandemic Period: Differentiating between Urban and Rural Areas: A Case Study of Intercity Railway, Thailand. \u003cem\u003eLogistics, \u003c/em\u003e7(4), 93.\u003c/li\u003e\n\u003cli\u003eXia, Y., \u0026amp; Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. \u003cem\u003eBehavior research methods, \u003c/em\u003e51, 409-428.\u003c/li\u003e\n\u003cli\u003eYingzhi, G., Ningning, X., Huimin, T., \u0026amp; Jiaying, X. (2020). An empirical study on the impacts of tourist risk perception on tourist motivation and satisfaction in Xinjiang of China under the background of the Belt and Road Initiative. \u003cem\u003eJournal of Tourism and Hospitality Management, \u003c/em\u003e8(1), 14-34.\u003c/li\u003e\n\u003cli\u003eYu, S., Qian, X., \u0026amp; Liu, T. (2019). Belt and road initiative and Chinese firms\u0026apos; outward foreign direct investment. \u003cem\u003eEmerging Markets Review,\u003c/em\u003e 41, 100629.\u003c/li\u003e\n\u003cli\u003eYue, Y., Gong, L., \u0026amp; Ma, Y. (2021). Factors influencing international student inward mobility in China: A comparison between students from BRI and non-BRI countries. \u003cem\u003eEducational Studies\u003c/em\u003e, 1-19.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 3","content":"\u003cp\u003eTable 3 is not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Policy Belt and Road Initiative (BRI), High-speed railway, The Perception of Thai people, Theory of Planned Behavior (TPB), Structural Equation Modeling (SEM)","lastPublishedDoi":"10.21203/rs.3.rs-4740250/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4740250/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Belt and Road Initiative (BRI) policy, with the China-Laos high-speed railway being a part of the BRI project. As it is well known, Thailand shares a border with Laos and has significant cross-border trade with China. In terms of tourism, Thailand is a globally popular destination, with Chinese tourists being the largest group of visitors. This study takes place in Thailand, a developing country, to examine the opportunities that may arise if Thailand prepares to handle the China-Laos high-speed railway. The objective of this research is to study the relationship between factors affecting BRI and Thai people's perceived benefits and their intention to use the China-Laos high-speed railway. Data was collected through a survey conducted in key trade gateway areas along the Thailand-Laos border, using Stratified Random Sampling of 1,540 Thai residents living in border provinces along the BRI route. The research findings indicate that Perceived Emotional is the most important factor in explaining Thai people's perception. Additionally, foreign direct investment, tourism, employment, living standards and society, economy, and logistics transportation positively influence the perceived benefits of BRI. Furthermore, the perceived benefits of BRI have a positive relationship with the attitudes of Thai people. The results also reveal that perceived benefits, attitudes, and subjective norms positively correlate with the intention to use the China-Laos high-speed railway. These findings can be utilized to provide in-depth insights to relevant agencies and assist in formulating policies and measures that align with the BRI policy. This alignment will enhance development to sustainable changes.\u003c/p\u003e","manuscriptTitle":"A Structural Equation Model of the Factors Affecting the Belt and Road Initiative (BRI) on the Perceived Benefits and Intention of Thai People to Use the China-Laos High-Speed Rail Service","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-08 19:59:48","doi":"10.21203/rs.3.rs-4740250/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1ab65e0e-d066-4287-9b11-f353723a5558","owner":[],"postedDate":"August 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-03T18:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-08 19:59:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4740250","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4740250","identity":"rs-4740250","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}