Willingness to participate (WP) in community-based management depends on various factors such as a sense of community, perceptions, beliefs, and demographic characteristics. This study first evaluates the WP of locals (n = 100) in water management in the Ba River Basin – Highlands of Vietnam, where ethnic minority groups are living and working in the agricultural sector. The multivariable linear regression model is applied to explain WP through the factors including the sense of community (SC), dependance on river water (DW), perceptions of current water resource management (PW), level of income (IN), gender (GE), education levels (ED), and place of residence (PR). The results showed that the final regression model explains 67.9% of the variation in WP levels (R2 = 0.679, p < 0.001). The variables SC and PW had a significant level of p < 0.001, while the figures for DW and IN were 0.018 and 0.012 (p < 0.05), respectively, and the p-value of PR was 0.008 (p < 0.01). However, gender and education levels were not statistically significant (p > 0.05) in this model. Sense of community and perceptions greatly influence community participation. To boost willingness, periodic training and campaigns are needed. Additionally, improving legal frameworks can encourage greater community involvement in water management programs.

  • Community participation is a key component of water management projects.

  • The legal framework should focus on improving the willingness to participate.

  • The final model explains 67.9% of the variation in willingness to participate levels.

  • Sense of community is the most important factor in the model of Ba River Basin.

The concept of integrated water resources management has been mentioned and discussed in recent years (Butterworth et al. 2010; Gallego-Ayala 2013). The first coordinated approach to integrated water resources management was introduced at the United Nations Conference on Water held in Mar del Plata, Argentina, in 1977. The main goal of implementing integrated water resources management was to use water efficiently and ensure equitable and sustainable access (Butterworth et al. 2010). In 2012, the Dublin Conference was held to introduce sustainable water policies and action programs to be considered by the Rio United Nations Conference on Environment and Development. Integrated water resources management is a process that promotes the coordination of water and land resource management in order to equitably maximize economic and social well-being in the present and near future and the sustainability of ecosystems (Agarwal et al. 2000). The key aim of integrated water resources management is to promote coordination and integration as a means to achieve holistic water management and improve the sustainability of water resources (Jønch-Clausen & Fugl 2001). The concept of sustainability is not a final state to be achieved but a continuous process to create a closer relationship and a better understanding of human and natural needs, as well as their interactions (Wallace et al. 2003). Integrated river basin management is a subset of integrated water resources management, and its application is facing many difficulties in practice. First, multi-stakeholder collaboration and public-private partnerships for the implementation of comprehensive governance are needed. However, the participation and cooperation of many parties are a difficult task in the different institutional contexts of many regions (Calizaya et al. 2010). Second, the management of water resources across borders, between countries and regions within a country will face difficulties due to different mechanisms in water resources management, leading to conflicts (UNDP 2006). Therefore, collaboration has been recognized as a key driver for the achievement of integrated river basin management outcomes, which aim at safeguarding economic, social, and environmental interests within a basin (Galvez & Rojas 2019). The successful implementation of integrated river basin management principles depends on a participative approach that includes local stakeholders in a managerial capacity. In particular, community-based management is one strategy that can be used as part of an integrated management approach. In community-based management, local groups are in charge of managing resources, which are then tied to larger management goals at basin or national levels. Therefore, community participation plays a key role in the successful implementation of integrated river basin management as well as water governance. Willingness to participate in community-based management depends on various factors such as dependance on natural resources, sense of community, and demographic characteristics (income, gender, and education). Some studies revealed that the greater the dependance on a particular resource, the more likely individuals are to participate in management activities designed to protect those same resources (Zanetell & Knuth 2004; Coulibaly-Lingani et al. 2011; Jennewein & Jones 2016; Mussehl et al. 2023). The level of knowledge or perceptions on environmental and water issues was considered for investigating the willingness to participate (Jingling et al. 2010; Hua & Marsuki 2014; Stoutenborough & Vedlitz 2014). In addition, a sense of community (SC) is the inherent level of connection, trust, belongingness, and cohesiveness established through living and working in shared physical spaces. This definition includes both personal connections and social characteristics of communities (Jennewein & Jones 2016). Another case study found that social division, social disincentive, inequity, and lack of social trust and capacities were barriers to community participation in water management programs (Shunglu et al. 2022). The importance of socio-economic-demographic variables in participatory management has been emphasized in the previous findings (Lise 2000; Coulibaly-Lingani et al. 2011; Ahmed et al. 2020). Table 1 shows some previous studies related to public participation in water management.

Table 1

Literature review on public participation in water management

AuthorYearCountryObjectivesVariables/ issuesMethodsResults
Jingling et al. (2010)  2010 China To clarify people's awareness and participation in water resource management of the river basin Environmental knowledge and information, water satisfaction, WP Questionnaire survey, interview
Descriptive Statistical analysis 
The actual participation rate is lower than the percentage of the willingness to participate 
Stoutenborough & Vedlitz (2014)  2013 The USA To determine people's willingness to conserve water Public perceptions of water availability and willingness to conserve water Online surveys
Descriptive Statistical analysis 
The public is willing to support government efforts to manage water 
Hua & Marsuki (2014)  2014 Malaysia To find out people's awareness about water pollution and river ecology Perceptions on river ecology, perceptions of water pollution Descriptive Statistical analysis Moral and ethical values could have a significant impact on caring for the environment 
Jennewein & Jones (2016)  2016 El Salvador, Guatemala, and Honduras To identify what factors motivate people's WP in water management via community-based management Sense of community, DW resources, level of concern for water resources, and socio-economic characteristics (age, sex, education level, and income) Multivariate regression analysis Levels of people's willingness to participate, sense of community, wealth, and perceptions of watershed management are also statistically significant 
Ahmed et al. (2020)  2020 Malaysia To identify factors affecting people'willing to participate in sustainable water resources management Participation in water resource management programs, Perception and attitude, awareness of the river and drinking water, perceived health risk, trust in institutes and policymakers, people's perceived water quality, residence places, the value of the river and drinking water Partial Least Squares Structural Equation Modeling (PLS–SEM) The public's perceptions and attitudes regarding rivers and drinking water, as well as their perceptions of water quality, were found to be the most influential factors in their participation in water management platforms 
Shunglu et al. (2022)  2022 Turkey, India, and Sri Lanka To analyze barriers to Participative Water Governance The use of participatory approaches
To what degree are power relationships 
Qualitative methods (field studies, interviews, literature review on laws, regulations, and policies) Lack of social trust, elite capture of participatory processes, power heterogeneity and imbalances at the micro-level, and a lack of inclusive participation in decision-making 
AuthorYearCountryObjectivesVariables/ issuesMethodsResults
Jingling et al. (2010)  2010 China To clarify people's awareness and participation in water resource management of the river basin Environmental knowledge and information, water satisfaction, WP Questionnaire survey, interview
Descriptive Statistical analysis 
The actual participation rate is lower than the percentage of the willingness to participate 
Stoutenborough & Vedlitz (2014)  2013 The USA To determine people's willingness to conserve water Public perceptions of water availability and willingness to conserve water Online surveys
Descriptive Statistical analysis 
The public is willing to support government efforts to manage water 
Hua & Marsuki (2014)  2014 Malaysia To find out people's awareness about water pollution and river ecology Perceptions on river ecology, perceptions of water pollution Descriptive Statistical analysis Moral and ethical values could have a significant impact on caring for the environment 
Jennewein & Jones (2016)  2016 El Salvador, Guatemala, and Honduras To identify what factors motivate people's WP in water management via community-based management Sense of community, DW resources, level of concern for water resources, and socio-economic characteristics (age, sex, education level, and income) Multivariate regression analysis Levels of people's willingness to participate, sense of community, wealth, and perceptions of watershed management are also statistically significant 
Ahmed et al. (2020)  2020 Malaysia To identify factors affecting people'willing to participate in sustainable water resources management Participation in water resource management programs, Perception and attitude, awareness of the river and drinking water, perceived health risk, trust in institutes and policymakers, people's perceived water quality, residence places, the value of the river and drinking water Partial Least Squares Structural Equation Modeling (PLS–SEM) The public's perceptions and attitudes regarding rivers and drinking water, as well as their perceptions of water quality, were found to be the most influential factors in their participation in water management platforms 
Shunglu et al. (2022)  2022 Turkey, India, and Sri Lanka To analyze barriers to Participative Water Governance The use of participatory approaches
To what degree are power relationships 
Qualitative methods (field studies, interviews, literature review on laws, regulations, and policies) Lack of social trust, elite capture of participatory processes, power heterogeneity and imbalances at the micro-level, and a lack of inclusive participation in decision-making 

Therefore, to demonstrate how key factors affect willingness to participate (WP) in water management programs, the critical factors determined in previous studies, including a sense of community, dependance on river water (DW), perceptions of current water management, and socio-economic information (the level of income, gender, and education level), were analyzed in the model of this study. Second, this study was conducted in the context of the highlands of Vietnam, where ethnic minority groups live along the river, and agricultural activities are dominant in the study area. Third, river basin governance in Vietnam was analyzed to clarify the roles and responsibilities of stakeholders and community participation. The multivariable regression method will be applied to establish the model, which supports the prediction of WP levels in river water management in the Ba River Basin. Moreover, it is the first study to illustrate and analyze the holistic system from the legal framework to participation willingness in river water management in Vietnam.

Study area

Ba River Basin has an area of 13,900 km2, located in the Central Highlands of Vietnam. The Ba River supplies water for domestic water treatment plants such as Ayun Pa, Phu Thien – Iapa, and Saigon – An Khe Water Plants. In addition, the water source is used for agricultural irrigation purposes, as agricultural activities are common in the Ba River Basin. The main crops are mainly coffee, rubber, pepper, and some vegetables that are a livelihood source for people living in the Ba River Basin (DONRE 2021). Figure 1 illustrates the study area of Ba River Basin in Gia Lai Province.
Figure 1

Study area of the Ba River Basin.

Figure 1

Study area of the Ba River Basin.

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Data collection and analysis methods

A household survey (n = 100) was conducted to collect some information on demographics, WP, SC, DW, perceptions of current water management, and land possession. With a population of around 151,818 households and a 10% margin of error (Yamane 1967), the study sampled around 100 households randomly based on the calculation from the following equation:
(1)
whereas N = 151,818 and e = 0.1 (10%)

The five-point Likert scale was used to evaluate the degree from strongly disagree to strongly agree, and then to apply quantitative analysis with a dependent variable (WP) and independent variables, including the sense of community, DW, perceptions of current water management, and socio-economic information (the level of income, gender, and education level). To analyze the effect of each independent variable on willingness to participate, the linear multivariate cross-sectional regression model was applied. Table S1 shows regression variables and measurements in the survey.

WP is a key component of community-based management that is a holistic approach to sustainable water resource management (Zanetell & Knuth 2004). Individual perceptions, attitudes, beliefs, and demographic characteristics are underlying factors affecting WP in water management (Ahmed et al. 2020). In addition, personal participation, the change of practises and willingness to work in groups or with other villagers have been used for the investigation of WP (Zanetell & Knuth 2004). The SC was defined to comprise both interpersonal relationships and communal social norms. The degree of connection, trust, belongingness, and cohesion created by residing in and working in communal places (Jennewein & Jones 2016). The five-point Likert scale and 18 questions of Buckner (1988) were applied to investigate a SC (Buckner 1988). DW can affect the WP because it brings direct value or benefits to users. By using a Likert scale (five points), an evaluation can be carried out using questions about the role of water in daily activities and its economic benefits. Perceptions of the current water resource management of respondents can be investigated through responses to what factors or pollution sources affect these sources of water quality and quantity and the necessary level to implement solutions to protect water resources (Jennewein & Jones 2016). In addition, socio-economic-demographic variables have been used for evaluating the WP in recent studies (Lise 2000; Coulibaly-Lingani et al. 2011; Ahmed et al. 2020). In this study, these variables, including a sense of community, DW, perceptions of current water resource management, level of income, gender, education levels, and place of residence, are investigated for the influence of the levels of WP.

Data analysis

Quantitative analyses were conducted using R Language version 4.2.2. A linear multivariate cross-sectional regression model was applied with the variables including WP, SC, DW, perceptions of current water resource management (PW), level of income (IN), gender (GE), education levels (ED), and place of residence (PR). In addition, Spearman's rank correlation was employed to test multicollinearity among independent variables. The linear multivariate cross-sectional regression formula is as follows:
(2)
whereas is the dependent variable WP, β is representative of independent variables including SC, DW, PW, IN, GE, ED, and PR; α is the constant regression coefficient for the intercept.

Vietnam River Basin management and governance

Vietnam's laws on water resources, environmental protection, and irrigation regulate aspects related to river basin management. The law on water resources provides for the management, protection, exploitation, and use of water resources. The law on environmental protection stipulates water environmental protection, control, and treatment of water pollution in river basins. The law on irrigation is related to the management and exploitation of irrigation works and the operation of hydroelectric reservoirs for irrigation (MONRE 2019). However, there are still some shortcomings in the legal documents on water environmental protection that have not yet been adjusted and developed accordingly (Diep et al. 2007). The practical applications still encountered difficulties due to a lack of resources such as human and financial, especially in rural or small-sized urban areas in Vietnam. The issue of water environmental protection has not been focused on in relevant specialized plans such as urban planning (MONRE 2019).

The Vietnamese government has tasked the Ministry of Natural Resources and Environment with managing river basins and water resources. The Ministry of Natural Resources and Environment is mainly responsible for river basins' environmental protection, such as guiding, inspecting, and organising the implementation of policies, laws, strategies, master plans, and programs related to water environmental protection; exploitation and use of water resources; preventing depletion of water resources; and restoration of water quality and quantity in river basins. The Ministry of Agriculture and Development manages issues related to irrigation, flood and storm control, rural domestic water supply, irrigation works, dikes, and the exploitation and development of aquatic products. The Ministry of Construction manages urban water supply and drainage works, while the Ministry of Health monitors the quality of drinking water. The Ministry of Transport is responsible for the management and development of waterway transport, and the Ministry of Industry and Trade manages the hydroelectric dams. The environmental public security department has the power to supervise, investigate, and punish individuals or organisations that can be harmful to the water environment. The management of water resources in the river basins involves many agencies, organisations, and stakeholders, so it is difficult to implement integrated water resource management. For instance, water sources in river basins are used for multi-purpose, such as drinking water, irrigation, and hydroelectric dams, and companies, the Ministry of Natural Resources and Environment, the Ministry of Agriculture and Development, and the Ministry of Industry and Trade are the main stakeholders in this case. As a result, managing water sources is sometimes not consistent and encounters conflicts in some practical cases. In 2020, community participation was first regulated in the scope and subjects of application of the Law on Environmental Protection. For instance, community participation was prescribed in collecting and providing information on environmental protection and evaluating the environmental protection results of enterprises in their living areas. However, there are various factors, including internal and external ones, that hinder community participation in practice. WP is considered one of the key components of water action programs. Figure 2 shows the structure of river basin governance in Vietnam.
Figure 2

Vietnam river water governance and organizational structure. MPS: Ministry of Public Security; MOC: Ministry of Construction; MT: Ministry of Transport; MARD: Ministry of Agriculture and Development; MOIT: Ministry of Industry and Trade; MOH: Ministry of Health; MONRE: Ministry of Natural Resource and Environment. (D: Department – Corresponding to Ministerial level). Managing Supervising River basin Management Committee River Basin Planning Management Committee River basin Management Board.

Figure 2

Vietnam river water governance and organizational structure. MPS: Ministry of Public Security; MOC: Ministry of Construction; MT: Ministry of Transport; MARD: Ministry of Agriculture and Development; MOIT: Ministry of Industry and Trade; MOH: Ministry of Health; MONRE: Ministry of Natural Resource and Environment. (D: Department – Corresponding to Ministerial level). Managing Supervising River basin Management Committee River Basin Planning Management Committee River basin Management Board.

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The Ba River Basin is a medium-sized river basin that has not yet experienced significant impacts from big cities. Small or medium-sized towns and agricultural activities are predominant in the Ba River Basin. Waste or wastewater from domestic and agricultural activities can directly discharge into rivers, which can significantly affect the variations in river water quality (Brontowiyono et al. 2022; Anh et al. 2023). Human activities in buffer zones could have significant effects on river water quality (Chimwanza et al. 2006; Ou et al. 2016; Estrada-Rivera et al. 2022). Therefore, the willingness of the community to participate will help to protect the quality of river water through sustainable land use, keeping the coverage rate, and protecting the riverbank buffer zone.

Descriptive analysis

Table S2 illustrates that the percentages of males and females in the survey are 46 and 54%, respectively. In terms of education levels, 55% (n = 55) of respondents are below the completion of secondary school, while the figures for high school level and higher education are 45% (n = 45). More than a third of younger adults aged 18–30 (34%) participated in the survey campaigns, while approximately two-thirds (64%) agreed to answer the questionnaire in this study. Agriculture is the most popular occupation in the Ba River Basin, with 58% (n = 58) of the total respondents. Business, freelance, officer, and worker occupy 42% (n = 42) of the total locals surveyed.

The multiple regression model of willingness to participate

The internal consistency and reliability of scaled variables

The variables, including WP (three items), SC (18 items), DW (three items), and PW (three items), were evaluated using a Likert scale. The variables were measured for internal consistency of the scale by Cronbach's alpha (α), and alpha values should be 0.7 or more (Adamson & Prion 2013). Specifically, the alpha values of WP, SC, DW, and PW are 0.77, 0.81, 0.81, and 0.77, respectively, as shown in Table 2. These alpha values are greater than 0.7, which reveals the high internal consistency of scaled data.

Table 2

Descriptive statistics and internal reliability of variables

VariableCodeMean (Std. Dev)Cronbach's (std. )
Willingness to participate WP 4.18 (0.30) 0.77 (0.77) 
Sense of community SC 4.09 (0.42) 0.81 (0.81) 
Dependance on river water DW 3.94 (0.53) 0.81 (0.8) 
Perceptions of current water resource management PW 2.79 (0.49) 0.77 (0.76) 
The level of income IN 3.2 (0.77) – 
Gender GE – – 
Education ED 3.34 (0.81) – 
Place of residence PR – – 
VariableCodeMean (Std. Dev)Cronbach's (std. )
Willingness to participate WP 4.18 (0.30) 0.77 (0.77) 
Sense of community SC 4.09 (0.42) 0.81 (0.81) 
Dependance on river water DW 3.94 (0.53) 0.81 (0.8) 
Perceptions of current water resource management PW 2.79 (0.49) 0.77 (0.76) 
The level of income IN 3.2 (0.77) – 
Gender GE – – 
Education ED 3.34 (0.81) – 
Place of residence PR – – 

The response variable is WP, while the explanatory variables include SC, DW, PW, IN, GE, ED, and PR. The results from the application of multiple linear regression showed that the initial model can explain 59.6% of the variation in the levels of WP (R2 = 0.596, p < 0.001). Figure 3 illustrates that Cook's distances are normally applied to identify outliers, and this method analyzes how much influence a data point has on the regression line. The results showed that seven data points (Cook's distance) including 37 (0.073), 46 (0.055), 65 (0.048), 66 (0.155), 67 (0.05), 68 (0.088), and 88 (0.056), are outliers. These outliers were removed because they could significantly affect the regression model. The coefficient of the final model was improved (R2 = 0.679, p < 0.001).
Figure 3

Cook's distance of residuals.

Figure 3

Cook's distance of residuals.

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The variables SC and PW had a significant level of p < 0.001, while the figures for DW and IN variables were 0.018 and 0.012 (p < 0.05), respectively, and the p-value of PR was 0.008 (p < 0.01). In contrast, gender and education levels were not statistically significant (p > 0.05). Noticeably, SC can make the biggest contribution to the level of WP (0.45), and this means that a 1% increase in SC is associated with a 0.45% increase in the level of willingness to participate. In addition, when the values of DW, PW, and IN increase, there will be an increase in the levels of WP. The details of the regression model are presented in Table 3 and the following equation:
(3)
Table 3

The results of the regression model

EstimateStandard errort-valuep-value
Intercept 0.262 0.357 10.734 0.465 
SC 0.45 0.1 4.484 <0.001*** 
DW 0.139 0.058 2.415 0.018* 
PW 0.292 0.064 4.559 <0.001*** 
IN 0.103 0.04 2.566 0.012* 
GE 0.07 0.05 1.388 0.169 
ED 0.037 0.039 0.952 0.344 
PR 0.138 0.051 2.701 0.008** 
EstimateStandard errort-valuep-value
Intercept 0.262 0.357 10.734 0.465 
SC 0.45 0.1 4.484 <0.001*** 
DW 0.139 0.058 2.415 0.018* 
PW 0.292 0.064 4.559 <0.001*** 
IN 0.103 0.04 2.566 0.012* 
GE 0.07 0.05 1.388 0.169 
ED 0.037 0.039 0.952 0.344 
PR 0.138 0.051 2.701 0.008** 

Note. Significant levels: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05.

Figure 4 shows residuals and fitted values in the regression model. Basically, residual values are above and below the zero value. There should be a linear relationship between the explanatory variables (SC, DW, PW, IN, GE, ED, and PR) and the response variable (WP).
Figure 4

The residual plot of the regression model.

Figure 4

The residual plot of the regression model.

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To check the normality of the model, the histogram and normal QQ plot of residuals were applied to illustrate. Figures 5(a) and 5(b) show that the residual values are normally distributed. In addition, the Shapiro–Wilk test confirms the normality of residuals: W = 0.974, p-value = 0.062 (p > 0.05). Since the p-value is greater than 0.05, the residuals are normally distributed.
Figure 5

(a) Histogram of residuals (b) normal QQ plot.

Figure 5

(a) Histogram of residuals (b) normal QQ plot.

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To check multicollinearity, the values of tolerance and variance inflation factor were calculated and examined. Table S3 shows that the tolerance and variance inflation factor (VIF) of variables are more than 0.1 and less than 10, respectively. This means that there is no multicollinearity between variables. In addition, the correlation coefficients of binary variables are weak or medium (Figure 6).
Figure 6

Correlation of variables.

Figure 6

Correlation of variables.

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Discussion on factors affecting WP

SC and PW significantly affect the model of willingness to participate. SC can include personal connections and social characteristics of communities, which can be divided into historical connections, trust, belongingness, and cohesiveness in existing living spaces (Jennewein & Jones 2016). The finding is consistent with some previous studies (Jennewein & Jones 2016; Shunglu et al. 2022). Rural and small-scale urban areas are predominant in the Ba River Basin. More than half of the local people belong to minority ethnic groups, and social bonds play a key role in community participation in all daily activities. Although ED has a minor impact on the model, their low levels of education could lead to a lack of perception of issues related to water management. Therefore, raising public awareness and attitude is important for community participation in water resource management (Ahmed et al. 2020). Obviously, DW can have a direct impact on WP because most people are aware of the importance of water in daily life. Participation as a user was confirmed as an economic actor who holds the right to access waters (Lindsay 2018). In addition, the levels of income in this study were examined based on the average level of monthly income and the area of land owned. Using river water for agriculture could be related to their increased income; it appears to be a significant factor determining their willingness to participate.

PR (urban or rural areas) can have an impact on the cognitive systems of residents who have different perceptions and views on water issues (Rasoolimanesh et al. 2017). Some studies revealed that there was a considerable difference in participation in the support of tourism development between urban and rural residents (Cui & Ryan 2011; Rasoolimanesh et al. 2017). In the Ba River Basin, there are some minority ethnic groups, such as Banah and Jrai, living along the main river, and access to education and information is limited compared to residents in urban areas. In contrast, urban residents have access to formal education and various sources of information, which can improve their perceptions and update their information related to water issues. Although the variables of ED showed no statistical difference in the regression model, the role of formal education in the WP is undeniable (Martínez-Borreguero et al. 2020).

A legal framework has been enacted to facilitate organizations or stakeholders related to water source exploration and protection. However, the implementation of actions or programs has been impractical and not systematic, leading to low performance in the behavior change of locals. Therefore, authorities need to implement various solutions to improve residents' perceptions. Apart from formal education and propaganda campaigns, it is necessary for the community to participate in all project phases, such as idea planning, design, implementation, and evaluation, which provide locals with knowledge and experience to sustain activities or benefits from the project (Ananga et al. 2021).

Notably, the multivariable regression model approach was successfully applied to the Ba River Basin, located in the Highlands of Vietnam, with ethnic minority groups living along the river. The findings provide a good model example for other catchment areas in Vietnam and other nations with similar demographic characteristics. For instance, catchment areas with minorities often have poor economic conditions, a low level of education, and a lack of information. However, the SC should pay more attention to implementing water management programs in these catchment areas.

Community participation is very important for the success of every water management project because this support not only achieves the targets but also sustains the results of the projects toward sustainability. Vietnam's legal framework has regulated and encouraged public participation in activities related to water resource management. However, the state structure of river basin management is linked to many departments or agents, which could result in an overlap of roles and responsibilities. This study revealed that sense of community, DW, perception of water issues, levels of income, and PR are the main factors affecting people's WP in water management programs. This illustrates an overview of factors that should be taken into account before implementing water management programs. Noticeably, an SC is the most important factor in the model, and this could be linked to community characteristics due to over half of the population living in the Ba River Basin in the highlands of Vietnam being ethnic minority people. Furthermore, our results give an indication for managers of other river basins to integrate residents to set a focus on the sense of community.

This research is funded by the Vietnam Ministry of Education and Training (MOET) under grant number B2022-VGU-02.

N.T.A. designed the research, collected and analyzed the data, and wrote the paper; L.D.C. assisted in data collection, and data analysis; B.S. and T.L.L. designed the research, analyzed the data, and reviewed and edited the paper.

This article does not contain any studies with human or animal subjects performed by any of the authors.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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