Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11, 674-687 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 4 September 2025; Revised: 14 October 2025; Accepted: 17 October 2025; Published: 11 November 2025 * Correspondence: bachtq@vinhuni.edu.vn Impacts of linkage risks on the sustainable development of agricultural tourism value chains: Evidence from the North Central region of Vietnam Thi Bich Thuy Nguyen1, Quang Bach Tran2*, Thi Thu Cuc Nguyen3, Thi Dieu Anh Ho4, Thi Quynh Liên Duong5, Thi Viet Hoang6 1,2,4,5,6College of Economics, Vinh University, Nghe an Province, Vietnam; bichthuytcnhdhv@gmail.co (T.B.T.N.). bachtq@vinhuni.edu.vn (Q.B.T.) anhhtd@vinhuni.edu.vn (T.D.A.H.) quynhliendhv@gmail.com (T.Q.L.D.) hoangviet.kkt@gmail.com (T.V.H.). 3Vinh University, Nghe an Province, Vietnam; cucntt@vinhuni.edu.vn (T.T.C.N.). Abstract: In the current context, the development of agricultural tourism with its diverse activities has gradually become a global trend. This study aims to examine the relationships between linkage risks and the sustainable development of agricultural tourism value chains in the North Central region of Vietnam. A quantitative research method was employed through structural equation modeling (SEM). Research data were collected from a survey of 471 participants involved in the agricultural tourism value chain, including tourism companies and agricultural organizations. The findings reveal that among the identified risk factors, only supply-side risks and customer-related risks exert direct and negative impacts on the sustainable development of agricultural tourism value chains. Meanwhile, linkage integration plays a mediating role in the relationships between information risks, environmental risks, and sustainable development of agricultural tourism value chains. These results highlight the valuable contributions of the study and provide a foundation for future research. Based on the findings, several recommendations are proposed to foster the sustainable development of agricultural tourism value chains in the North Central region of Vietnam. Keywords: Customer-related risks, Environmental risks, Information risks, Linkage integration, Linkage risks in value chains, Supply-side risks, Sustainable development of agricultural tourism value chains. 1. Introduction The agricultural sector contributes to economic growth through its linkages with other sectors [1]. Increasing competition, globalization, and an increasingly volatile environment make it difficult for firms to achieve sustainable competitive advantages over time [2]. Today, companies are striving to balance economic objectives with the creation of social welfare while addressing the environmental needs of stakeholders. In this context, organizations must be capable of improving economic performance while simultaneously accounting for the negative impacts arising from their activities and enhancing the livelihoods of the societies in which they operate [3]. In the current context, the development of agricultural tourism with its diverse activities has gradually become a global trend. A value chain encompasses all activities undertaken by firms and their partners to create products, from planning to final use, including value-creating activities in agricultural areas, particularly where development strategies require the promotion of trade in goods and services [4]. The sustainable development of agricultural tourism value chains is one of the approaches to ensuring sustainable income generation in rural areas, especially for farmers [5]. Agricultural tourism value chains establish networks linking local resources with target markets, thereby generating added value for stakeholders, particularly farmers, while creating distinctive tourism products that foster the development of multifunctional agriculture [6]. https://orcid.org/0000-0002-5405-2725 https://orcid.org/0000-0001-5473-4657 https://orcid.org/0009-0000-6377-8408 675 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate Many studies have shown that the sectors linked to agriculture are predominantly informal, and while these sectors generate less added value, they create more employment than formal sectors. Downstream agricultural linkages have high potential for both job creation and value addition, and therefore should be supported by development policies [1]. The alignment between information technology and business strategy has long been a challenge for corporate executives. Although previous studies have confirmed the value of such alignment, questions remain regarding how alignment creates value and the extent of that value [7]. In addition, the study by Adiyia and Vanneste [8] revealed inconsistencies in the supply of local products, which undermine the sustainability of supply chain linkages with local farmers, while favoring business linkages with local intermediary suppliers, ultimately shaping the regional development potential of supply chain linkages. The management of agricultural value chains also faces challenges arising from multiple factors, such as the post-COVID-19 economy and climate change [9]. Agricultural tourism is increasingly being developed and invested in by countries worldwide as a means to transform rural economies. In Vietnam, in recent years, the agricultural tourism model has been adopted by many localities and has initially been evaluated as appropriate, contributing to poverty reduction and promoting socio-economic development in an ecological and sustainable direction. Tourism associated with the reconstruction of new rural areas has become an inevitable trend, in which agriculture remains a key economic sector, accounting for 72.84% of the national economic structure [10]. The North Central region of Vietnam, with a natural area of approximately 5.15 million hectares, holds a particularly important strategic role and position. In this region, tourism is not only oriented toward becoming a leading economic sector but also serves as a bridge for cultural exchange and inter- provincial connectivity, creating distinctive highlights that link national tourism with neighboring countries in the region. However, the development of agricultural tourism value chains in this region still faces significant limitations. Sectoral linkages among tourism, agriculture, and rural areas to form comprehensive product value chains remain ineffective. Agricultural tourism in the North Central region is still seasonal, with fragmented destination images. There is a lack of connection among stakeholders engaged in core and supporting activities to holistically exploit agricultural tourism values based on local characteristics. Furthermore, systematic linkages from local tourism governance to community-level participation remain weak. In addition, agricultural tourism models are not yet well integrated into agricultural development planning in a scientific manner, which hinders the creation of competitive, distinctive tourism products that capitalize on local strengths. This study aims to examine the relationships between linkage risks and the sustainable development of agricultural tourism value chains in the North Central region of Vietnam. A quantitative approach was employed, using structural equation modeling (SEM) for data analysis. The findings indicate that among the identified risk factors, only supply-side risks and customer-related risks exert direct and negative impacts on the sustainable development of agricultural tourism value chains. Meanwhile, linkage integration plays a mediating role in the relationships between information risks, environmental risks, and the sustainable development of agricultural tourism value chains. These findings highlight the valuable contributions of the study. Based on the results, the paper proposes several recommendations to promote the sustainable development of agricultural tourism value chains in the North Central region of Vietnam. 2. Literature Review and Hypotheses 2.1. Literature Review 2.1.1. Risks in Value Chain Linkages Risk is defined in various ways depending on the field of study [11]. In the context of value chains, risk refers to fluctuations or disruptions that, when they occur, affect the flow of information, raw materials, and finished products from the initial supplier to the final customer, thereby breaking the supply chain and reducing the revenue of enterprises. According to Jüttner et al. [12], risks in the value chain are those related to information, the flow of raw materials and products from suppliers to end 676 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate consumers, and the potential impact of mismatches between market supply and demand. Risks stem from various causes, among which uncertainty and the ability to predict future events [13, 14] may influence business decisions and organizational performance. According to Kaplinsky and Morris [15], a value chain refers to a series of activities required to transform a product (or a service) from the initial idea, through various stages of production, to distribution to the final consumer and disposal after use. According to Chopra and Meindl [16], a value chain encompasses all stages directly or indirectly involved in fulfilling a customer’s request. Spekman and Davis [17] classified risks in value chain linkages into six groups, including: risks occurring in the physical flow (goods) along the supply chain; information flow; financial flow; the organization’s internal information systems; relationships; and the social responsibility of supply chain members. Cavinato [18] divided value chain linkage risks into five sources: risks in the physical flow; financial flow; information; collaborative relationships; and innovational opportunities for supply chain members. Jüttner et al. [12] grouped value chain linkage risks into three categories: environmental risks stemming from business uncertainty, such as disasters or crises; organizational risks caused by members within the chain, such as failures in production and distribution systems; and risks related to labor strikes or weaknesses in the structure and characteristics of the supply chain, such as lack of cooperation, integration, and information sharing among its members. Among the different approaches to risk classification, the framework proposed by Jüttner et al. [12] and Punniyamoorthy et al. [19] is widely adopted in studies on value chain linkages. This framework identifies four major categories of risk: supply risk, market risk, information risk, and environmental risk. Supply-side risks may arise from various issues such as new product development, disruptions in distribution activities or member relationships, product quality problems, input price fluctuations, inability to meet customer demand, outdated technology, scarcity of resources, shortages or price pressures, and finally, the geographical distance between buyers and suppliers [20]. Environmental risks are associated with external factors beyond the value chain, including the political environment, macroeconomic conditions, legal and regulatory frameworks, government policies, social factors, labor sources, and natural conditions [19]. Risks may also stem from information instability, such as unavailable data, delayed information provision, damaged information infrastructure causing interruptions, or a lack of information security [19]. 2.1.2. Integration in Linkages According to Lee et al. [21], supply chain integration should be considered from three perspectives: (1) integration with customers; (2) integration with suppliers; and (3) internal integration. Coordination among members of the chain is necessary to ensure that the flow of information, raw materials, and products is accurate and timely [22]. The study by Lee and Lee [23] also emphasized that integration in supply chains should be viewed from these three dimensions. Ensuring close linkages among chain members facilitates the smooth transfer of information, materials, and products [22]. Particularly, integration within agricultural value chains and sustainability aspects, with a focus on cooperation among stakeholders in the chain, are critical factors for achieving sustainable development [24]. Integration in value chain linkages has been defined in different ways, generally falling into two conceptual groups: process-oriented and relationship-oriented. On the one hand, integration is considered a business process in which two or more partners in the chain work together toward shared objectives [25-28]. On the other hand, it is also seen as the establishment of close and long-term partnerships where chain members collaborate and share information, resources, and risks to achieve common goals [29, 30]. Over the past decade, companies have increasingly sought opportunities outside their own organizations to collaborate with partners in value chain linkages, in order to ensure efficiency and responsiveness, as well as to leverage the resources and knowledge of their suppliers and customers [31]. Such collaboration leads to faster product development, reduced development costs, technological improvements, and enhanced product quality [32]. Partners in the value chain face growing demands to 677 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate be more dynamic and responsive in order to create additional value for customers. Business objectives that may seem difficult for individual organizations to achieve can often be attained more easily through collaborative value chain relationships. Consequently, collaborative behaviors and practices in supply chain management have gained significant importance [33]. 2.1.3. Sustainable Development of Agricultural Value Chains According to Hugos [34], value chain management involves the coordination of production, inventory, location, and transportation processes among participants in a value chain in order to achieve optimal responsiveness and efficiency for the target market. Pretty et al. [35] proposed strategies to enhance production without causing environmental harm while reducing dependence on non-renewable resources, thereby strengthening sustainability in agricultural systems. Wheeler and Von Braun [36] analyzed the impact of climate change on food security and highlighted the necessity of sustainable agricultural development in the global context, demonstrating that integration in resource management and utilization is a crucial factor influencing sustainable agricultural development. Bryan et al. [37] further emphasized the need for supportive policies to enhance the adaptability of agriculture under changing conditions. 2.2. Research Hypothesis 2.2.1. Risks in Linkages and the Sustainable Development of Agricultural Tourism Value Chains For value chains, risks refer to disruptions or variations that, when they occur, affect the flow of information, raw materials, and finished products from initial suppliers to final customers, thereby breaking supply chains and reducing firms’ revenues. According to Jüttner et al. [12], risks in value chains are associated with information flows, the transportation of raw materials and products from suppliers to end consumers, and the potential to create mismatches between market supply and demand. Risks arise from various causes, among which uncertainty and the inability to predict future events [13, 14] can significantly affect business decisions and organizational performance. High levels of market risk compel producers to frequently adjust products, output, and orders [38]. Market demand volatility and fluctuations make it more difficult for producers to determine market needs and respond to customers, thereby complicating customer linkages [39]. Agricultural information systems have been established to connect all stakeholders in agriculture, enabling the creation, collection, sharing, and use of agricultural data [40]. Therefore, to clarify the relationships between risk factors in linkages and the sustainable development of agricultural tourism value chains, this study proposes the following hypotheses: H1: Supply-side risks negatively affect the sustainable development of agricultural tourism value chains in the North Central region of Vietnam. H2: Customer-related risks negatively affect the sustainable development of agricultural tourism value chains in the North Central region of Vietnam. H3: Information risks negatively affect the sustainable development of agricultural tourism value chains in the North Central region of Vietnam. H4: Environmental risks negatively affect the sustainable development of agricultural tourism value chains in the North Central region of Vietnam. 2.2.2. Risks and Integration in Linkages Fawcett et al. [41] argue that linkage in value chains refers to the ability to work across organizations to build and manage value-adding processes that better meet customer needs. Such linkages result in faster product development processes, reduced development costs, improved technologies, and enhanced product quality, adaptability, diversity, and flexibility [32, 42]. Linkage within value chains also leads to performance improvements [43]. In demand chain management, when the value chain structure aligns with customer needs, it results in better performance. Therefore, close integration between suppliers and customers is necessary to create a 678 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate successful value chain [43]. Companies are increasingly building relationships with their value chain partners to achieve efficiency, flexibility, and sustainable competitive advantage [44]. However, increased risks can weaken the linkages within value chains. Accordingly, this study proposes the following hypotheses: H5: Supply-side risks negatively affect integration in the agricultural tourism value chain linkages in the North Central region of Vietnam. H6: Customer-related risks negatively affect integration in the agricultural tourism value chain linkages in the North Central region of Vietnam. H7: Information risks negatively affect integration in the agricultural tourism value chain linkages in the North Central region of Vietnam. H8: Environmental risks negatively affect integration in the agricultural tourism value chain linkages in the North Central region of Vietnam. 2.2.3. Integration in Linkages and the Sustainable Development of Agricultural Tourism Value Chains Organizations increasingly seek closer collaboration in order to manage supply sources and distribution channels more effectively, thereby optimizing costs while enhancing customer satisfaction, improving competitiveness, and increasing profitability for participants [45]. Collaborative relationships among organizations within value chains have recently attracted significant attention [46]. Agricultural value chain linkages enable fragmented actors to connect, thereby increasing value, enhancing the competitiveness of outputs, and creating a new, dynamic, and efficient production ecosystem [47]. Collaboration in value chains also leads to performance improvements [43]. In demand chain management, when the value chain structure is aligned with customer needs, performance is enhanced. Therefore, close cooperation between suppliers and customers is essential to make the value chain successful [43]. Companies are building cooperative relationships with their value chain partners to achieve efficiency, flexibility, and sustainable competitive advantages [44]. Integration in value chain linkages creates sustainable value and positively influences the benefits of each member, thereby laying a solid foundation for the sustainable development of agricultural tourism value chains. Accordingly, the study proposes the following hypothesis: H9: Integration in linkages positively affects the sustainable development of agricultural tourism value chains in the North Central region of Vietnam. Figure 1. Proposed research model. 679 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate 3. Research Methodology 3.1. Study Scale Based on the theoretical review and related studies, the research proposes a model with six constructs. Among them, the independent variables belong to the group of risks in the agricultural tourism value chain linkages, including: (1) Supply-side risks (RIS); (2) Customer-related risks (RIC); (3) Information risks (RII); and (4) Environmental risks (RIE). The mediating variable is integration in linkages (ISC), while the dependent variable is the sustainable development of agricultural tourism value chains (SDA). The study employs a five-point Likert scale with levels corresponding to scores from 1 to 5 (1: Strongly disagree; 2: Disagree; 3: Neutral; 4: Agree; 5: Strongly agree). The indicators used in this research were adapted from previous studies and adjusted to fit the context of the research subjects, which consist of tourism enterprises and agricultural organizations participating in agricultural tourism value chains in the North Central region of Vietnam. Table 1. Origin of the scale of variables. No. Variable Code Number of observations Origin of scale 1 Supply-side risks RIS 4 Wagner and Bode [11] and Zhao et al. [48] 2 Customer-related risks RIC 4 Wagner and Bode [11] and Zhao et al. [48] 3 Information risks RII 6 Punniyamoorthy et al. [19] 4 Environmental risks RIE 6 Wagner and Bode [11] and Zhao et al. [48] 5 Integration in linkages ISC 6 Ipek [49] 6 Sustainable development of agricultural tourism value chains SDA 6 Bryan et al. [37] 3.2. Research Samples This study employs a non-probability sampling method, specifically convenience sampling. To collect data, the researchers designed and distributed questionnaires to respondents who are stakeholders participating in agricultural tourism value chains, including tourism enterprises and agricultural organizations in the North Central region of Vietnam. The total sample size used in the study is 471, obtained through both on-site and online surveys. For the on-site survey, 400 questionnaires were distributed, 258 were returned, and 217 were valid. For the online survey, data were collected via Google Forms, with 350 questionnaires sent out, 269 returned, and 254 deemed valid. Thus, the total number of valid questionnaires used for analysis was 471. According to Hair et al. [50], the minimum sample size should be at least five times the total number of observed variables. With 32 observed variables in this study, the final sample of 471 fully meets the analytical requirements. Data collection was conducted from January 2025 to June 2025. 3.3. Data Processing The collected data were processed using SPSS and AMOS 22.0 software. First, the reliability of the measurement scales was assessed, requiring Cronbach’s Alpha values greater than 0.7 and corrected item-total correlations greater than 0.3. In addition, if the Cronbach’s Alpha if Item Deleted value was higher than the overall Cronbach’s Alpha, the corresponding indicator was considered for removal. Next, exploratory factor analysis (EFA) was conducted to determine the convergent validity and discriminant validity of the scales, with the following requirements: factor loadings greater than 0.5; KMO coefficient between 0.5 and 1; significance value (Sig.) less than 0.05; and extracted variance exceeding 50%. Subsequently, confirmatory factor analysis (CFA) was performed using AMOS to evaluate the model fit. Finally, structural equation modeling (SEM) was applied to test the research 680 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate hypotheses, with the following requirements: chi-square/df < 3 [50]; P < 0.05; GFI, TLI, and CFI > 0.8 [51]; and RMSEA < 0.05 [52]. 4. Research Results and Discussion 4.1. Testing the Reliability of the Scale To assess the reliability of the measurement scales, Cronbach’s Alpha was calculated for each group of variables. Cronbach’s Alpha is a statistical test of the internal consistency or explanatory power of a set of observed variables measuring a construct. This method is used to eliminate unsuitable indicators and reduce measurement errors in the research model. The results show that the scales are reliable, as Cronbach’s Alpha coefficients for all variables are greater than 0.7, and corrected item-total correlations are greater than 0.3. Moreover, the Cronbach’s Alpha if Item Deleted values were all smaller than the overall Cronbach’s Alpha, indicating that no indicators needed to be removed. Table 2. Assess the reliability of the scale. No. Variable Code Cronbach's Alpha 1 Supply-side risks RIS 0.767 2 Customer-related risks RIC 0.728 3 Information risks RII 0.832 4 Environmental risks RIE 0.837 5 Integration in linkages ISC 0.840 6 Sustainable development of agricultural tourism value chains SDA 0.871 4.2. Exploratory Factor Analysis (EFA) After testing the reliability, exploratory factor analysis (EFA) was conducted for the independent, mediating, and dependent variables. For the independent and mediating variables, the EFA was performed twice. In the first analysis, two indicators (RIE6 and RIC1) were removed due to unsatisfactory convergent validity. In the second analysis, the results met the requirements: KMO = 0.940 (within the range) [0.5, 1]), significance value Sig. = 0.000 (< 0.05), extracted variance = 57.967% (> 50%), and all factor loadings > 0.5. These results confirm both convergent and discriminant validity. For the dependent variable (SDA), only one round of EFA was necessary, showing satisfactory results with KMO = 0.848, Sig. = 0.000 (< 0.05), extracted variance = 60.878% (> 50%), and all factor loadings > 0.5, thereby meeting the requirements for convergent and discriminant validity. Table 3. Exploratory factor analysis (EFA) results. EFA Analysis KMO coefficient P-value Variance extracted (%) Factor loading Conclusion Independent variables and intermediate variables 1st time 0.941 0.000 56.918 All > 0.5 Remove RIE6 and RIC1 indicator 2nd time 0.940 0.000 57.967 All > 0.5 Meet the requirements Dependent variable 1st time 0.878 0.000 60.878 All > 0.5 Meet the requirements Since RIE6 and RIC1 were removed, the reliability of the corresponding constructs was retested. The results confirmed that the Cronbach’s Alpha coefficients for both RIE and RIC remained above 0.7, with corrected item-total correlations > 0.3, and Cronbach’s Alpha if Item Deleted values < overall Cronbach’s Alpha, ensuring reliability. 681 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate 4.3. Confirm Factor Analysis (CFA) Confirmatory factor analysis (CFA) was conducted as the next step after exploratory factor analysis (EFA). The purpose of CFA is to validate the measurement model and assess its goodness-of-fit, providing evidence of convergent and discriminant validity. The CFA results show a good fit of the measurement model: Chi-square = 815.226; df = 390; P = 0.000 (< 0.05); Chi-square/df = 2.090 (< 3); GFI = 0.895 (> 0.8); TLI = 0.923 (> 0.8); CFI = 0.931 (> 0.8); and RMSEA = 0.048 (< 0.05). These indices demonstrate that the measurement model is appropriate and reliable. 4.4. Structural Equation Modeling Analysis (SEM) Structural equation modeling (SEM) was applied to test the research hypotheses. The results indicate that the structural model fits the data well, with indices consistent with accepted thresholds. Specifically, the Chi-square = 815.226; df = 390; P = 0.000 (< 0.05); Chi-square/df = 2.090 (< 3); GFI = 0.895 (> 0.8); TLI = 0.923 (> 0.8); CFI = 0.931 (> 0.8); and RMSEA = 0.048 (< 0.05). 682 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate Figure 2. SEM model analysis. The results of hypothesis testing indicate the appropriateness of the model employed in this study. Among the nine hypotheses incorporated into the model, five were accepted with a significance level above 95% (P < 0.05). Specifically, regarding hypotheses H1 to H4, which examined the impact relationships of risk variables in linkages on the sustainable development of the agricultural tourism value chain, the test results show that two hypotheses, H1 and H2, were accepted, with significance levels of 0.002 and 0.049 (P < 0.05), and regression weights of -0.202 and -0.211 (< 0), respectively. Therefore, it can be concluded that, among risk factors, supply risk and customer risk exert direct and negative effects on the sustainable development of the agricultural tourism value chain in the North Central region of Vietnam. These conclusions are consistent with the findings of Jüttner et al. [12], Chen et al. [13], Beckman et al. [14], and Lilavanichakul [40]. Meanwhile, since the significance levels of hypotheses 683 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate H3 and H4 were greater than 0.05 (0.061 and 0.454), these hypotheses were rejected, indicating that information risk and environmental risk do not affect the sustainable development of the agricultural tourism value chain. For hypotheses H5 to H8, which tested the impact relationships of risk variables on linkage integration, the results reveal that while H5 and H6 were rejected (P > 0.05; 0.121 and 0.078), hypotheses H7 and H8 were accepted, with significance levels below 0.05 and negative regression weights (-0.309 and -0.377). Accordingly, the study demonstrates that, among the four risk variables, only information risk and environmental risk have direct and negative effects on linkage integration. These findings are consistent with the studies of Vereecke and Muylle [43] and Nyaga et al. [44]. In addition, hypothesis H9 was accepted with a significance level below 0.05 and a regression weight of 0.508 (> 0). Therefore, it can be concluded that linkage integration positively affects the sustainable development of the agricultural tourism value chain in the North Central region of Vietnam. This result aligns with the findings of Samaddar and Kadiyala [46] and Tran et al. [47]. Thus, with 5 out of 9 hypotheses being accepted, the study demonstrates valuable contributions in both theoretical and practical aspects. From a scientific perspective, the findings reveal that among the risk factors, supply risk and customer risk exert direct and negative impacts on the sustainable development of the agricultural tourism value chain. Meanwhile, although information risk and environmental risk do not affect the sustainable development of the agricultural tourism value chain, they do exert direct and negative influences on linkage integration. Furthermore, linkage integration, in turn, has a positive effect on the sustainable development of the agricultural tourism value chain. Accordingly, it can also be stated that linkage integration plays a mediating role in the relationships between information risk and environmental risk and the sustainable development of the agricultural tourism value chain. From a practical perspective, the research results serve as useful resources for managers, policymakers, organizations, and enterprises to recognize the specific impacts of risk factors within the value chain linkages. On this basis, they can establish appropriate orientations in decision- making to maximize productivity and efficiency across all business activities. Table 4. Results of SEM analysis for relationships in the model. Hypothesis Relationship Weight S.E. (Standard Error) C.R. (Criteria Ratio) P Conclusion H1 SDA <-- RIS -0.202 0.065 -3.087 0.002 Accepted H2 SDA <-- RIC -0.211 0.108 -1.964 0.049 Accepted H3 SDA <-- RII -0.169 0.090 -1.872 0.061 Rejected H4 SDA <-- RIE -0.058 0.077 -0.749 0.454 Rejected H5 ISC <-- RIS 0.092 0.059 1.551 0.121 Rejected H6 ISC <-- RIC -0.174 0.098 -1.764 0.078 Rejected H7 ISC <-- RII -0.309 0.083 -3.732 0.000 Accepted H8 ISC <-- RIE -0.377 0.067 -5.600 0.000 Accepted H9 SDA <-- ISC 0.508 0.108 4.701 0.000 Accepted 5. Conclusions and Recommendations 5.1. Conclusions Based on the theoretical review and related studies, this paper proposes a model and tests the impact relationships of risks in linkages on the sustainable development of the agricultural tourism value chain. The research context involves actors participating in the agricultural tourism value chain, including tourism companies and organizations operating in the agricultural sector in the North Central region of Vietnam. The findings indicate that among the risk factors, only supply risk and customer risk have direct and negative effects on the sustainable development of the agricultural tourism value chain. Meanwhile, linkage integration plays a mediating role in the relationships between information risk and environmental risk and the sustainable development of the agricultural tourism value chain. These 684 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate results highlight the novelty and valuable contributions of the study and provide a foundation for future research. 5.2. Recommendations Based on the research findings, this paper proposes several recommendations to promote the sustainable development of the agricultural tourism value chain in the North Central region of Vietnam: First, regarding risk factors in the value chain linkages, in the context of integration and globalization, organizations need to enhance their competitiveness by leveraging their inherent strengths and potential. The increasing expectations of customers and markets are driving the formation of sustainable linkages between organizations operating in the service and agricultural sectors. Accordingly, organizations should pay greater attention to developing smart supply chains and reconfiguring them to ensure higher resilience, transparency, and responsiveness. In addition, to minimize risks, service businesses should prioritize supply chain security, as a single security breach can compromise or disrupt operations. Vulnerabilities in the supply chain can lead to unnecessary costs, inefficient delivery schedules, and the loss of intellectual property rights. Furthermore, the distribution of counterfeit or unauthorized products may harm customers and result in undesirable legal actions. Second, with respect to linkage integration, organizations need to strengthen the integration of service and agricultural value chains. Each organization should capitalize on its strengths in specific areas, foster cooperation, and create collective synergies in order to reduce risks and vulnerabilities in the market. Moreover, to achieve sustainable development of the agricultural tourism value chain, participating organizations should design and implement model frameworks for each stage of the supply chain, allowing tourists to visit and experience these processes. Tour guides should receive professional training to ensure consistency and professionalism. At the same time, there should be collaboration between government support and the mobilization of socialized resources, including sponsorships, contributions from tourism enterprises, professional associations, as well as other organizations and individuals in society. 5.3. Limitations and Future Research In addition to its valuable contributions, this study also acknowledges certain limitations. First, the use of a convenience sampling method, while allowing data collection to be conducted more quickly and easily with lower costs and resource requirements, presents the drawback that the results may not accurately reflect the characteristics of the population. Consequently, this reduces the representativeness of the research sample. Second, the research context is limited to the North Central region of Vietnam. Therefore, the findings open avenues for future research to further examine the impact of relationships of risks in linkages on the sustainable development of the agricultural tourism value chain, incorporating other mediating and moderating variables, as well as extending the research context to other countries in the region and globally. Funding: The present study was conducted with funding support from a ministerial-level research project (Grant Number: B2024-TDV13). Institutional Review Board Statement: The Ethical Committee of the Faculty of Business Administration - School of Economics at Vinh University for this research on September 25, 2025 (Ref. No. 179/XNDD-QTKD-KT-DHV). 685 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] J. Dürr, "The political economy of agriculture for development today: The “small versus large” scale debate revisited," Agricultural Economics, vol. 47, no. 6, pp. 671-681, 2016. https://doi.org/10.1111/agec.12264 [2] U. Khouroh, A. Sudiro, M. Rahayu, and N. K. Indrawati, "The mediating effect of entrepreneurial marketing in the relationship between environmental turbulence and dynamic capability with sustainable competitive advantage: An empirical study in Indonesian MSMEs," Management Science Letters, vol. 10, pp. 709–720, 2020. https://doi.org/10.5267/j.msl.2019.9.007 [3] M. Y. Yusliza, J. Y. Yong, M. I. Tanveer, T. Ramayah, J. N. Faezah, and Z. Muhammad, "A structural model of the impact of green intellectual capital on sustainable performance," Journal of Cleaner Production, vol. 249, p. 119334, 2020. https://doi.org/10.1016/j.jclepro.2019.119334 [4] R. Crescenzi and O. Harman, Harnessing global value chains for regional development, 1st ed. London, U.K: Routledge, 2023. [5] M. Sznajder, L. Prezezborska, and F. Scrimgeour, Agritourism (Electronic version). Wallingford, UK: CABI Publishing, 2009. [6] L. Zheng and H. Liu, "Increased farmer income evidenced by a new multifunctional actor network in China," Agronomy for Sustainable Development, vol. 34, no. 2, pp. 515-523, 2014. https://doi.org/10.1007/s13593-013-0169-2 [7] P. P. Tallon, R. V. Ramirez, and J. E. Short, "The information artifact in IT governance: Toward a theory of information governance," Journal of Management Information Systems, vol. 30, no. 3, pp. 141-178, 2013. https://doi.org/10.2753/MIS0742-1222300306 [8] B. Adiyia and D. Vanneste, "Local tourism value chain linkages as pro-poor tools for regional development in western Uganda," Development Southern Africa, vol. 35, no. 2, pp. 210-224, 2018. https://doi.org/10.1080/0376835X.2018.1428529 [9] M. A. Lachaud, B. E. Bravo‐Ureta, and C. E. Ludena, "Economic effects of climate change on agricultural production and productivity in Latin America and the Caribbean (LAC)," Agricultural Economics, vol. 53, no. 2, pp. 321-332, 2022. https://doi.org/10.1111/agec.12682 [10] General Statistics Office of Vietnam, Statistical yearbook. Hanoi, Vietnam: Statistical Publishing House, 2022. [11] S. M. Wagner and C. Bode, "An empirical examination of supply chain performance along several dimensions of risk," Journal of Business Logistics, vol. 29, no. 1, pp. 307-325, 2008. https://doi.org/10.1002/j.2158-1592.2008.tb00081.x [12] U. Jüttner, H. Peck, and M. Christopher, "Supply chain risk management: Outlining an agenda for future research," International Journal of Logistics: Research and Applications, vol. 6, no. 4, pp. 197-210, 2003. https://doi.org/10.1080/13675560310001627016 [13] J. Chen, A. S. Sohal, and D. I. Prajogo, "Supply chain operational risk mitigation: A collaborative approach," International Journal of Production Research, vol. 51, no. 7, pp. 2186-2199, 2013. https://doi.org/10.1080/00207543.2012.727490 [14] C. M. Beckman, C. B. Schoonhoven, R. M. Rottner, and S.-J. Kim, "Relational pluralism in de novo organizations: Boards of directors as bridges or barriers to diverse alliance portfolios?," Academy of Management Journal, vol. 57, no. 2, pp. 460-483, 2014. https://doi.org/10.5465/amj.2011.1068 [15] R. Kaplinsky and M. Morris, Dangling by a thread: How sharp are the Chinese scissors? Cape Town, South Africa: Centre for Social Science Research, University of Cape Town, 2006. [16] S. Chopra and P. Meindl, "Supply chain management: Strategy," Planning and Operation, vol. 15, no. 5, pp. 71-85, 2001. [17] R. E. Spekman and E. W. Davis, "Risky business: Expanding the discussion on risk and the extended enterprise," International Journal of Physical Distribution & Logistics Management, vol. 34, no. 5, pp. 414-433, 2004. https://doi.org/10.1108/09600030410545454 [18] J. L. Cavinato, "Supply chain logistics risks: From the back room to the board room," International Journal of Physical Distribution & Logistics Management, vol. 34, no. 5, pp. 383-387, 2004. https://doi.org/10.1108/09600030410545427 [19] M. Punniyamoorthy, P. Mathiyalagan, and P. Parthiban, "A strategic model using structural equation modeling and fuzzy logic in supplier selection," Expert Systems with Applications, vol. 38, no. 1, pp. 458-474, 2011. https://doi.org/10.1016/j.eswa.2010.06.086 https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1111/agec.12264 https://doi.org/10.5267/j.msl.2019.9.007 https://doi.org/10.1016/j.jclepro.2019.119334 https://doi.org/10.1007/s13593-013-0169-2 https://doi.org/10.2753/MIS0742-1222300306 https://doi.org/10.1080/0376835X.2018.1428529 https://doi.org/10.1111/agec.12682 https://doi.org/10.1002/j.2158-1592.2008.tb00081.x https://doi.org/10.1080/13675560310001627016 https://doi.org/10.1080/00207543.2012.727490 https://doi.org/10.5465/amj.2011.1068 https://doi.org/10.1108/09600030410545454 https://doi.org/10.1108/09600030410545427 https://doi.org/10.1016/j.eswa.2010.06.086 686 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate [20] G. A. Zsidisin, "A grounded definition of supply risk," Journal of Purchasing and Supply Management, vol. 9, no. 5-6, pp. 217-224, 2003. https://doi.org/10.1016/j.pursup.2003.07.002 [21] H. L. Lee, V. Padmanabhan, and S. Whang, "Information distortion in a supply chain: The bullwhip effect," Management Science, vol. 43, no. 4, pp. 546-558, 1997. https://doi.org/10.1287/mnsc.43.4.546 [22] D. M. Lambert and M. C. Cooper, "Issues in supply chain management," Industrial Marketing Management, vol. 29, no. 1, pp. 65-83, 2000. https://doi.org/10.1016/S0019-8501(99)00113-3 [23] K. S. Lee and H. S. Lee, "Factors influencing the adoption behavior of mobile banking: A South Korean perspective," Journal of Internet Banking and Commerce, vol. 12, no. 2, pp. 01-09, 2007. [24] M. J. Maloni and M. E. Brown, "Corporate social responsibility in the supply chain: An application in the food industry," Journal of Business Ethics, vol. 68, no. 1, pp. 35-52, 2006. https://doi.org/10.1007/s10551-006-9038-0 [25] J. T. Mentzer et al., "Defining supply chain management," Journal of Business Logistics, vol. 22, no. 2, pp. 1-25, 2001. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x [26] T. P. Stank, S. B. Keller, and P. J. Daugherty, "Supply chain collaboration and logistical service performance," Journal of Business Logistics, vol. 22, no. 1, pp. 29-48, 2001. https://doi.org/10.1002/j.2158-1592.2001.tb00158.x [27] V. Manthou, M. Vlachopoulou, and D. Folinas, "Virtual e-Chain (VeC) model for supply chain collaboration," International Journal of Production Economics, vol. 87, no. 3, pp. 241-250, 2004. https://doi.org/10.1016/S0925- 5273(03)00218-4 [28] C. Sheu, H. Rebecca Yen, and B. Chae, "Determinants of supplier‐retailer collaboration: Evidence from an international study," International Journal of Operations & Production Management, vol. 26, no. 1, pp. 24-49, 2006. https://doi.org/10.1108/01443570610637003 [29] D. J. Bowersox, D. J. Closs, and T. P. Stank, "How to master cross‐enterprise collaboration," Supply Chain Management Review, vol. 7, no. 4, pp. 18–27, 2003. [30] S. L. Golicic, J. H. Foggin, and J. T. Mentzer, "Relationship magnitude and its role in interorganizational relationship structure," Journal of Business Logistics, vol. 24, no. 1, pp. 57-75, 2003. https://doi.org/10.1002/j.2158- 1592.2003.tb00032.x [31] M. Cao and Q. Zhang, "Supply chain collaboration: Impact on collaborative advantage and firm performance," Journal of Operations Management, vol. 29, no. 3, pp. 163-180, 2011. https://doi.org/10.1016/j.jom.2010.12.008 [32] A. Walter, "Relationship-specific factors influencing supplier involvement in customer new product development," Journal of Business Research, vol. 56, no. 9, pp. 721-733, 2003. https://doi.org/10.1016/S0148-2963(01)00257-0 [33] İ. Koçoğlu, S. Z. İmamoğlu, H. İnce, and H. Keskin, "The effect of supply chain integration on information sharing: Enhancing the supply chain performance," Procedia-Social and Behavioral Sciences, vol. 24, pp. 1630-1649, 2011. https://doi.org/10.1016/j.sbspro.2011.09.016 [34] M. Hugos, Essentials of supply chain management. Hoboken, NJ: John Wiley & Sons, 2003. [35] J. Pretty, C. Toulmin, and S. Williams, "Sustainable intensification in African agriculture," International Journal of Agricultural Sustainability, vol. 9, no. 1, pp. 5-24, 2011. https://doi.org/10.3763/ijas.2010.0583 [36] T. Wheeler and J. Von Braun, "Climate change impacts on global food security," Science, vol. 341, no. 6145, pp. 508- 513, 2013. https://doi.org/10.1126/science.1239402 [37] J. L. Bryan, M. C. Quist, C. M. Young, M.-L. N. Steers, and Q. Lu, "General needs satisfaction as a mediator of the relationship between ambivalence over emotional expression and perceived social support," The Journal of Social Psychology, vol. 156, no. 1, pp. 115-121, 2016. https://doi.org/10.1080/00224545.2015.1041448 [38] P. Trkman and K. McCormack, "Supply chain risk in turbulent environments—A conceptual model for managing supply chain network risk," International Journal of Production Economics, vol. 119, no. 2, pp. 247-258, 2009. https://doi.org/10.1016/j.ijpe.2009.03.002 [39] R. Calantone, R. Garcia, and C. Dröge, "The effects of environmental turbulence on new product development strategy planning," Journal of Product Innovation Management, vol. 20, no. 2, pp. 90-103, 2003. https://doi.org/10.1111/1540-5885.2002003 [40] A. Lilavanichakul, "Development of agricultural e-commerce in Thailand," FFTC Journal of Agricultural Policy, vol. 1, pp. 7-16, 2020. [41] S. E. Fawcett, G. M. Magnan, and M. W. McCarter, "A three‐stage implementation model for supply chain collaboration," Journal of Business Logistics, vol. 29, no. 1, pp. 93-112, 2008. https://doi.org/10.1002/j.2158- 1592.2008.tb00070.x [42] O. Sakka and V. Botta-Genoulaz, "A model of factors influencing the supply chain performance," in 2009 International Conference on Computers & Industrial Engineering (pp. 913-918). IEEE, 2009. [43] A. Vereecke and S. Muylle, "Performance improvement through supply chain collaboration in Europe," International Journal of Operations & Production Management, vol. 26, no. 11, pp. 1176-1198, 2006. https://doi.org/10.1108/01443570610705818 [44] G. N. Nyaga, J. M. Whipple, and D. F. Lynch, "Examining supply chain relationships: Do buyer and supplier perspectives on collaborative relationships differ?," Journal of Operations Management, vol. 28, no. 2, pp. 101-114, 2010. https://doi.org/10.1016/j.jom.2009.07.005 https://doi.org/10.1016/j.pursup.2003.07.002 https://doi.org/10.1287/mnsc.43.4.546 https://doi.org/10.1016/S0019-8501(99)00113-3 https://doi.org/10.1007/s10551-006-9038-0 https://doi.org/10.1002/j.2158-1592.2001.tb00001.x https://doi.org/10.1002/j.2158-1592.2001.tb00158.x https://doi.org/10.1016/S0925-5273(03)00218-4 https://doi.org/10.1016/S0925-5273(03)00218-4 https://doi.org/10.1108/01443570610637003 https://doi.org/10.1002/j.2158-1592.2003.tb00032.x https://doi.org/10.1002/j.2158-1592.2003.tb00032.x https://doi.org/10.1016/j.jom.2010.12.008 https://doi.org/10.1016/S0148-2963(01)00257-0 https://doi.org/10.1016/j.sbspro.2011.09.016 https://doi.org/10.3763/ijas.2010.0583 https://doi.org/10.1126/science.1239402 https://doi.org/10.1080/00224545.2015.1041448 https://doi.org/10.1016/j.ijpe.2009.03.002 https://doi.org/10.1111/1540-5885.2002003 https://doi.org/10.1002/j.2158-1592.2008.tb00070.x https://doi.org/10.1002/j.2158-1592.2008.tb00070.x https://doi.org/10.1108/01443570610705818 https://doi.org/10.1016/j.jom.2009.07.005 687 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 674-687, 2025 DOI: 10.55214/2576-8484.v9i11.10958 © 2025 by the authors; licensee Learning Gate [45] H. L. Lee and S. Whang, "Information sharing in a supply chain," International Journal of Manufacturing Technology and Management, vol. 1, no. 1, pp. 79-93, 2000. https://doi.org/10.1504/IJMTM.2000.001329 [46] S. Samaddar and S. S. Kadiyala, "An analysis of interorganizational resource sharing decisions in collaborative knowledge creation," European Journal of Operational Research, vol. 170, no. 1, pp. 192-210, 2006. https://doi.org/10.1016/j.ejor.2004.06.024 [47] Q. B. Tran, T. T. C. Nguyen, D. A. Ho, and D. A. Duong, "The impact of corporate social responsibility on employee management: A case study in Vietnam," The Journal of Asian Finance, Economics and Business, vol. 8, no. 4, pp. 1033- 1045, 2021. [48] L. Zhao, B. Huo, L. Sun, and X. Zhao, "The impact of supply chain risk on supply chain integration and company performance: A global investigation," Supply Chain Management: An International Journal, vol. 18, no. 2, pp. 115-131, 2013. https://doi.org/10.1108/13598541311318773 [49] I. Ipek, "The effects of text density levels and the cognitive style of field dependence on learning from A CBI tutorial," Turkish Online Journal of Educational Technology, vol. 10, no. 1, pp. 167-182, 2011. [50] F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate data analysis, 7th ed. New York: Pearson, 2010. [51] A. H. Segars and V. Grover, "Re-examining perceived ease of use and usefulness: A confirmatory factor analysis," MIS Quarterly, vol. 17, no. 4, pp. 517-525, 1993. https://doi.org/10.2307/249590 [52] S. A. Taylor, A. Sharland, J. J. Cronin, and W. Bullard, "Recreational service quality in the international setting," International Journal of Service Industry Management, vol. 4, no. 4, pp. 68-86, 1993. https://doi.org/10.1108/09564239310044316 https://doi.org/10.1504/IJMTM.2000.001329 https://doi.org/10.1016/j.ejor.2004.06.024 https://doi.org/10.1108/13598541311318773 https://doi.org/10.2307/249590 https://doi.org/10.1108/09564239310044316