Financing Rural Futures: Governance and Contextual Challenges of Village Fund Management in Underdeveloped Regions

Abstract

Effective management of village funds is central to financing sustainable and equitable rural futures, particularly in underdeveloped and resource-diverse regions such as Papua, Indonesia. This study explores the governance factors that shape the sustainability of village fund management (VFM) by examining institutional, financial, and socio-cultural dimensions across 212 villages. Primary data from village heads and secondary data on village-owned enterprises (BUMDes) and 2024 village fund allocations were analyzed using exploratory factor analysis (EFA), partial least squares structural equation modeling (PLS-SEM), and multi-group analysis (MGA). Seven key governance constructs emerged, with ethical governance, implementation capacity, mandatory disclosure and reporting, community participation, and financial management capacity demonstrating significant positive effects on sustainable VFM outcomes. In contrast, perceived social and economic impacts were negatively associated with performance, and planning quality exerted an influence only under specific contextual conditions. These relationships proved highly context-dependent, varying by geography, natural resource availability, transport accessibility, and demographic composition. The findings underscore the need for adaptive and context-sensitive governance strategies to strengthen institutional resilience, enhance fiscal equity, and maximize the developmental impact of village funds in underdeveloped rural regions.

1. Introduction

Addressing rural underdevelopment remains a central challenge in the global pursuit of sustainable and inclusive growth. While urban centers continue to benefit from concentrated infrastructure, investment, and services, many rural communities, particularly in developing and underdeveloped regions, continue to face persistent barriers such as limited access to public goods, financial exclusion, and fragile institutional support (Martínez et al., 2020; Roldan et al., 2023). In response, fiscal decentralization has been promoted as a way to transfer resources and authority closer to local communities, enabling more adaptive and locally grounded development strategies (Siburian, 2022; Sohail et al., 2022).

Indonesia offers a distinctive case for examining this approach through its Village Fund (Dana Desa) program. Launched in 2015, the program channels substantial fiscal transfers to more than 74,000 rural villages with the objectives of reducing poverty, improving infrastructure, and fostering entrepreneurship through Village-Owned Enterprises (Badan Usaha Milik Desa or BUMDes) (Arifin et al., 2020; Ginting et al., 2024). The initiative is closely aligned with the Sustainable Development Goals (SDGs), particularly those on poverty reduction (SDG 1), decent work and economic growth (SDG 8), and reduced inequalities (SDG 10) (Hartojo et al., 2022; Permatasari et al., 2021).

Despite its scale and ambition, the outcomes of the village fund have been uneven. In underdeveloped provinces such as Papua, large fiscal transfers have often failed to translate into meaningful development outcomes. Many villages remain affected by high poverty rates, limited infrastructure, weak administrative capacity, and fragmented oversight (Ernawati et al., 2021; Fauzi et al., 2019; Manurung et al., 2022). Geographic isolation, low levels of human capital, and complex demographic structures further complicate the management of funds and the planning of development initiatives (Badrudin et al., 2021; Simangunsong & Wicaksono, 2017). Beyond these socioeconomic constraints, Papua represents a unique governance landscape shaped by post-decentralization administrative fragmentation, diverse customary governance systems, and challenges of intergovernmental coordination (Manurung et al., 2022; Oktarini & Kawano, 2019). These conditions make Papua an ideal case for studying how governance diversity and post-decentralization fragmentation influence the performance of fiscal transfers in underdeveloped rural regions.

Previous studies have examined factors such as leadership quality, community participation, and internal control mechanisms (Phoek et al., 2024; Wahyudi et al., 2022). Other works have highlighted the roles of administrative capacity, transparency, and risk management in ensuring effective fund utilization (Ginting et al., 2024; Kewo & Kewo, 2024). At a broader level, several studies have analyzed the village fund’s contribution to national development, demonstrating its positive links with employment creation, economic growth, and poverty reduction (Arifin et al., 2020; Hartojo et al., 2022; Smas et al., 2025). However, most of these studies rely on predefined theoretical variables and primarily focus on well-developed regions such as Java and Sumatra, providing limited insight into how governance mechanisms operate in underdeveloped or frontier contexts. They also offer little explanation of why performance outcomes vary across regions when contextual factors such as geography, institutional capacity, and demographic diversity are considered.

This study addresses that gap by exploring the governance factors that shape village fund management (VFM) in Papua. Using a quantitative approach, we employ Exploratory Factor Analysis (EFA) to extract underlying governance constructs from primary data collected from 212 village heads. These constructs are then tested through Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess their influence on VFM outcomes, measured through Village Fund Allocation and BUMDes’ Contribution. To capture local variations, Multi-Group Analysis (MGA) is applied across subgroups defined by regional topography, infrastructure accessibility, natural resource availability, and demographic composition. Accordingly, the main objective of this study is to identify and test the key governance dimensions influencing the effectiveness of VFM in Papua and to examine how contextual factors moderate these relationships.

This study contributes to the rural development literature by examining how governance pathways interact with contextual challenges to influence fiscal performance in underdeveloped regions. The analysis offers evidence-based insights for designing more equitable and sustainable village fund strategies, with practical implications for policy reforms in Papua and other disadvantaged rural settings. From a theoretical perspective, the research advances understanding of how decentralization interacts with local governance capacities to produce sustainable fiscal outcomes. By identifying latent governance dimensions through rigorous empirical modeling, this study provides a nuanced framework for financing rural futures that emphasizes context-sensitive strategies for equitable and impactful rural development.

This paper is structured as follows: Section 2 reviews the relevant literature on rural development, fiscal decentralization, and village governance. Section 3 outlines the research methods and analytical procedures. Section 4 presents the empirical results. Section 5 discusses the key findings in light of existing research. Finally, Section 6 concludes the study, presents policy implications, acknowledges limitations, and suggests avenues for future research.

2. Literature Review

2.1. Sustainable Rural Development and the Role of Decentralization

Efforts to strengthen rural development have gained renewed global attention through the Sustainable Development Goals (SDGs), which emphasize poverty reduction, social inclusion, and equitable access to infrastructure and governance. Achieving sustainability in rural areas requires not only economic progress but also effective institutions and inclusive governance structures (Castro-Arce & Vanclay, 2020; Roldan et al., 2023).

In many developing countries, rural populations continue to face significant disadvantages stemming from inadequate infrastructure, under-resourced institutions, and limited economic opportunities (Kiryluk-Dryjska & Beba, 2018; Martinez-Vazquez et al., 2017). Indonesia presents a vivid example of this challenge. While national poverty levels have declined in recent years, rural poverty, especially in eastern regions like Papua, remains persistently high (Smas et al., 2025). This rural–urban development gap has prompted a shift toward more localized, decentralized governance models.

Fiscal decentralization is viewed as a key mechanism for improving local responsiveness and ensuring that public spending reflects community priorities (Alfada, 2019; Pal & Wahhaj, 2017). Yet, its effectiveness depends not only on the size of transfers but on local institutional capacity. Regions with fragile administrative structures often struggle to translate financial resources into development outcomes (Maier et al., 2022). In Papua, governance fragmentation, limited human capital, and logistical barriers constrain the expected benefits of decentralization (Indrawan et al., 2019; Simangunsong & Wicaksono, 2017). Although decentralization aligns well with SDGs related to poverty eradication (SDG 1), reducing inequality (SDG 10), and institutional strengthening (SDG 16), its impact can vary widely depending on local conditions.

As such, the literature increasingly advocates for integrated governance approaches that combine fiscal decentralization with local capacity-building, participatory planning, and robust accountability frameworks (Dushkova & Ivlieva, 2024; Yu et al., 2024). Within this broader context, the performance of village fund management becomes a critical indicator of how rural development strategies unfold at the village level.

2.2. The Village Fund Policy in Indonesia

Indonesia’s village fund, introduced in 2015 through Law No. 6/2014, is one of the largest fiscal decentralization initiatives globally. It aims to stimulate rural development through direct fiscal transfers to over 74,000 villages, prioritizing poverty reduction, infrastructure development, and community-based economic initiatives through village-owned enterprises (BUMDes) (Arifin et al., 2020; Permatasari et al., 2021). Between 2015 and 2022, allocations exceeded IDR 400 trillion, illustrating the program’s scale (Sidik & Habibi, 2024).

The establishment of BUMDes embodies a bottom-up, entrepreneurial approach to rural growth by promoting innovation and employment generation (Putra et al., 2025; Sidik & Habibi, 2024). Meanwhile, substantial resources are also directed toward roads, sanitation, and clean water projects (Hilmawan et al., 2023; Kania et al., 2021). Despite this, implementation gaps persist, particularly in remote and underdeveloped regions such as Papua, where administrative inefficiency and limited internal control hinder performance (Fauzi et al., 2019; Manurung et al., 2022).

According to Ernawati et al. (2021), logistical isolation, weak planning systems, and limited digital infrastructure reduce the capacity of village governments to absorb and manage funds effectively. Previous evaluations have primarily focused on Java and Sumatra, with limited attention to Papua. Studies by Diansari et al. (2023) and Wahyudi et al. (2022) highlight leadership and financial systems, while Phoek et al. (2024) emphasize community participation and transparency. However, the main research gap remains the limited use of exploratory models that integrate diverse governance dimensions and objective financial data from frontier regions, where governance conditions are far more diverse and constrained.

2.3. Key Governance Concepts Relevant to Village Fund Management

Village fund management (VFM) in Indonesia refers to the processes through which village governments receive, allocate, and utilize public funds to support rural development in line with national policy goals. While fiscal transfers create financial capacity, governance quality determines whether those resources translate into effective results (Akita et al., 2021; Antlöv et al., 2016).

At its core, VFM reflects the principle of local autonomy, where accountability, transparency, and community involvement are central. Studies consistently link good governance to successful fiscal implementation (Handayani et al., 2023; van Doeveren, 2011). Commonly recognized governance dimensions include transparency, accountability, participation, and institutional capacity, yet their relative importance and interactions remain underexplored, especially in frontier regions like Papua, where formal and customary institutions coexist (Ananta et al., 2016; Badrudin et al., 2021). This underscores the need for an exploratory-explanatory approach that can capture the complexity of local governance ecosystems.

Community participation, encouraged through musyawarah desa (village deliberations), is essential but often constrained by low civic literacy and elite dominance, especially in isolated communities (Badrudin et al., 2021; Phoek et al., 2024). Similarly, institutional capacity, encompassing budgeting skills and internal control systems, underpins successful implementation (Diansari et al., 2023; Wahyudi et al., 2022). Yet in Papua, persistent deficits in human resources and governance continuity weaken these mechanisms (Siburian, 2022).

Several studies have investigated governance factors influencing village fund outcomes. Diansari et al. (2023), Ginting et al. (2024), and Phoek et al. (2024) found that planning quality, internal control, and participation mechanisms enhance fund utilization. These studies highlight the importance of administrative capacity and citizen engagement but rely mainly on confirmatory approaches and focus on well-developed regions such as Java and Sumatra. Consequently, empirical evidence remains limited for frontier areas like Papua, where geographical isolation, weak infrastructure, and customary governance systems shape different governance dynamics (Ananta et al., 2016; Simangunsong & Wicaksono, 2017).

To fill this gap, this study applies Exploratory Factor Analysis (EFA) to uncover latent governance dimensions emerging from local contexts rather than predetermined models. It further employs Multi-Group Analysis (MGA) to explore variations in governance–performance relationships across diverse geographic and socio-demographic settings. Papua’s unique mix of mountainous and coastal regions, indigenous communities, and resource heterogeneity provides a robust setting to understand how governance pathways interact with contextual realities in decentralized rural development (Fauzi et al., 2019; Indrawan et al., 2019; Pal & Wahhaj, 2017).

2.4. Theoretical Perspectives, Research Propositions, and Research Questions

This study is guided by four interrelated theoretical perspectives: agency theory, institutional theory, contingency theory, and the good governance framework. Together, these perspectives offer a robust conceptual foundation to explicitly link governance mechanisms to measurable performance outcomes.

Agency theory provides a foundational lens for understanding the relationship between the village government (as the agent) and the rural community (as the principal) in managing public funds (Kewo & Kewo, 2024). Delegation of authority inherently involves risks of moral hazard and information asymmetry, particularly in remote areas where monitoring systems are weak or underdeveloped (Jensen & Meckling, 1976; Jubery et al., 2017). This perspective justifies the inclusion of accountability and oversight mechanisms, such as external auditing and community review, as crucial institutional tools to align the interests of agents with those of the principals (Indraningsih et al., 2021; Suffah et al., 2020).

Institutional theory complements this perspective by emphasizing how both for-mal institutions (laws, regulations) and informal norms (customary or social practices) shape governance behavior (DiMaggio & Powell, 1983). In Papua, village administrations must operate within overlapping state and customary systems, where local legitimacy often depends on alignment with indigenous governance traditions (Permatasari et al., 2021; Putra et al., 2025). This makes ethical governance and participatory decision-making essential for maintaining trust and ensuring institutional coherence.

Contingency theory adds an environmental dimension, suggesting that no single governance model universally guarantees success. Instead, effectiveness depends on how internal management practices fit with contextual conditions such as geography, accessibility, and resource endowment (Donaldson, 2001; Lawrence & Lorsch, 1967). This is particularly relevant in Papua, where mountainous terrain, diverse ethnic compositions, and uneven infrastructure access can significantly influence how governance mechanisms operate (Indrawan et al., 2019; Phoek et al., 2024). The theory therefore supports this study’s use of multi-group analysis (MGA) to assess how governance–performance relationships vary across different local environments.

Finally, the good governance framework provides a normative foundation that connects these theoretical strands to measurable governance constructs. It posits that effective governance requires accountability, transparency, participation, responsiveness, and ethical conduct (Beshi & Kaur, 2020; van Doeveren, 2011). While these principles are interrelated, they are conceptually distinct: transparency reflects openness and accessibility of information (e.g., publicizing reports); accountability refers to answerability and enforcement mechanisms (e.g., internal and external audit); and ethical governance highlights integrity, fairness, and the prevention of misuse by village leaders. In the village fund context, these principles are vital in fostering public trust, encouraging citizen involvement, and reducing inefficiency and corruption (Antlöv et al., 2016; Handayani et al., 2023).

Integrating these theories provides a cohesive explanation of how governance operates under decentralization: agency theory explains incentive alignment, institutional theory captures normative and cultural influences, contingency theory accounts for environmental fit, and good governance theory anchors the discussion in normative principles. Together, they link conceptual reasoning with the empirical constructs examined in this study. The conceptual framework guiding this research is presented in Figure 1.

This study adopts an exploratory–explanatory strategy. The EFA phase is employed to identify the actual latent governance constructs from local data (RQ1). These empirically derived factors are then used in the PLS-SEM phase to test theory-driven directional expectations about how governance quality enhances fiscal outcomes (RQ2) and how contextual diversity influences these relationships (RQ3).

Based on these theoretical perspectives, the study proposes the following research propositions:

Proposition 1.

Governance quality, encompassing the empirically derived latent factors (e.g., Institutional Capacity, Accountability, Transparency, and Integrity), is positively associated with objective Village Fund Management Outcomes (Village Fund Allocation and BUMDes’ Contribution to Local Revenue).

Proposition 2.

The structural relationship between latent governance factors and objective VFM Out-comes is significantly moderated by regional and ecological Contextual Factors (as tested via MGA), supporting the Contingency Theory perspective.

This study is guided by the following research questions:

RQ1. 

What latent governance-related factors influence village fund management in Papua, as revealed through exploratory factor analysis?

RQ2. 

To what extent do these factors affect village fund management outcomes, specifically in terms of village fund allocation and BUMDes’ contribution, as estimated using PLS-SEM?

RQ3. 

Do the structural relationships between governance factors and village fund management outcomes differ across various regional and ecological contexts (e.g., topography, transportation access, natural resources, and population makeup) as shown through multi-group analysis?

3. Materials and Methods

3.1. Materials

This study investigates the governance-related factors influencing sustainable and equitable village fund management (VFM) in Papua, Indonesia. The initial stage involved collecting secondary data on village-owned enterprises (Badan Usaha Milik Desa or BUMDes) from the Ministry of Villages’ official database (KEMENDESA, 2025) and complementary administrative records obtained through institutional collaboration with BKKBN Papua. This led to the use of a purposive sampling approach, where the sampling frame was defined by 212 villages across Papua that possessed complete and reliable financial/administrative records necessary for the objective VFM Outcomes metrics. We justify the sample’s representativeness by ensuring the final sample reflects proportional distribution across the key geographic and administrative regions of Papua, thus capturing the required regional and contextual diversity for the Multi-Group Analysis (MGA).

To obtain primary data, structured questionnaires were administered to the heads of the 212 selected villages. Village heads were deliberately chosen as respondents due to their official role in overseeing village finances and supervising BUMDes operations, ensuring they possess the contextual and institutional knowledge critical for governance assessment (Ginting et al., 2024; Wahyudi et al., 2022). To reduce self-reporting and social desirability bias, the survey included internal consistency checks and the study design employs objective financial metrics as a vital triangulation mechanism against perceptual data.

The questionnaire instrument was adapted from validated scales used in prior research on public financial management and governance. Key references include Diansari et al. (2023), Ginting et al. (2024), Wahyudi et al. (2022), Udjianto et al. (2021), Sisoumang et al. (2011), Sukosol et al. (2020), Boufounou et al. (2024), Hossain et al. (2024), Li et al. (2024), and Ye et al. (2022). The instrument underwent expert review, back-translation, and a pilot test with 30 village officials to confirm clarity and contextual fit. Reliability and validity were established through Cronbach’s alpha (>0.70) and average variance extracted (AVE > 0.50) on the final measurement model. A total of 32 indicators, rated on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree), were used to capture the perceptions of village leaders regarding local governance processes.

VFM was measured using two objective indicators, both reflecting 2024 financial outcomes at the village level: village fund allocation (Minister of Finance Decree No. 352/2024) (DJPK KEMENKEU, 2024) and BUMDes’ contribution to local revenue. The allocation reflects fiscal input capacity, while the BUMDes’ contribution captures output performance in generating sustainable local revenue.

3.2. Methods

This research employed a two-stage exploratory–explanatory quantitative approach. In the first stage, Exploratory Factor Analysis (EFA) was conducted using SPSS 25 with Principal Component Analysis extraction and Varimax rotation to identify latent governance constructs. In the second stage, Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4 examined relationships between governance factors and VFM. Governance constructs were modeled as reflective, while VFM was modeled as formative since its indicators are conceptually distinct.

To assess contextual heterogeneity, Multi-Group Analysis (MGA) was performed using four binary classifications reflecting Papua’s socio-geographic diversity:

• Regional Topography: Mountainous (villages in Papua Pegunungan Province and other highland regencies) vs. coastal (villages with direct sea access).
• Natural Resource Endowment: Classified using a resource endowment classification score constructed from four binary indicators that reflect the presence of major commercial activities (mining, plantation, forestry, and fishing). Thresholds: Villages with a score of ≥1 (indicating at least one major commercial activity) were classified as resource-rich; villages with a score of =0 (indicating reliance primarily on subsistence activities) were classified as resource-poor.
• Transport Accessibility: Easy access (year-round four-wheel road connectivity) vs. difficult access (dependent on river, sea, or air transport).
• Population Structure: Indigenous Majority (≥50% Original Papuan People) vs. Migrant Majority (<50%).

These thresholds were based on Official Government Statistics (e.g., Central Bureau of Statistics—BPS and PODES), complemented by local government transport and demographic records from BKKBN, ensuring replicability.

Potential endogeneity concerns were mitigated by employing PLS-SEM, which emphasizes variance explanation rather than strict causal inference. The use of EFA-derived latent constructs also reduced risks of multicollinearity and omitted variable bias.

All procedures followed standard ethical and empirical protocols. Participation was voluntary with informed consent obtained from all respondents. Only official government data sources were used, and survey data are available upon reasonable request. No generative AI tools were used in the design, analysis, or interpretation process.

4. Results

4.1. Respondent Profile

Table 1. Demographic Characteristics of Village Head Respondents.

Characteristics	Category	Frequency	Percentage
Province	Papua	100	47.17%
	South Papua	70	33.02%
	Central Papua	24	11.32%
	Mountain Papua	18	8.49%
Gender	Male	200	94.34%
	Female	12	5.66%
Age	30–39	66	31.13%
	40–49	49	23.11%
	50–59	64	30.19%
	60–64	33	15.57%
Education	Primary/Middle School	23	10.85%
	High School	77	36.32%
	Bachelor’s	76	35.85%
	Diploma	36	16.98%
Tenure	1–5 years	86	40.57%
	6–10 years	68	32.08%
	>10 years	58	27.36%

presents the demographic characteristics of the 212 village heads who participated in this study, each serving as the primary authority responsible for village governance and the management of village-owned enterprises (BUMDes). The sample covers four provinces in Papua, with the largest representation from Papua Province (47.17%), followed by South Papua (33.02%), Central Papua (11.32%), and Mountain Papua (8.49%). The overwhelming majority of village heads were male (94.34%), with only 5.66% being female.

In terms of age, the distribution shows substantial representation of leaders in both the 30–39 years (31.13%) and 50–59 years (30.19%) categories, followed by those aged 40–49 years (23.11%) and 60–64 years (15.57%). Educational attainment varied, with 36.32% having completed high school, 35.85% holding a bachelor’s degree, 16.98% a diploma, and 10.85% only primary or middle school education. Leadership tenure also showed diversity: 40.57% had served between 1–5 years, 32.08% for 6–10 years, and 27.36% for more than 10 years.

These characteristics suggest that the village heads in Papua bring a mix of educational backgrounds, leadership experiences, and age-related perspectives, all of which are likely to influence how governance practices are implemented and how effectively village funds are managed across different local contexts.

4.2. Exploratory Factor Analysis

Exploratory Factor Analysis (EFA) was conducted to uncover the underlying governance-related dimensions influencing village fund management (VFM) in Papua. The analysis began with an assessment of data suitability, yielding a Kaiser–Meyer–Olkin (KMO) measure of 0.935, indicating meritorious sampling adequacy, and a statistically significant Bartlett’s Test of Sphericity (χ²(561) = 4830.256, p < 0.001), confirming that the correlation matrix was appropriate for factor analysis.

Prior to analysis, the dataset was confirmed to have no missing values, and outliers were inspected via standardized z-scores and the Mahalanobis distance, with none exceeding the recommended thresholds.

Using principal component analysis as the extraction method and Varimax rotation to achieve orthogonal solutions, the EFA identified seven distinct factors with eigen values greater than 1, jointly explaining 68.99% of the total variance. Factor retention followed the Kaiser criterion (eigenvalues > 1) and was further supported by the scree plot and cumulative variance exceeding 60%. A total of 34 indicators met the loading threshold of 0.4, with most exceeding 0.6, demonstrating strong associations with their respective factors. Indicators with loadings below 0.50 were generally excluded; however, one indicator (X10) was retained. This retention is justified due to its critical theoretical relevance to the local governance context of Papua, where sufficient staffing (X10) is a well-documented bottleneck for effective community-level engagement. A summary of these results, including measures of sampling adequacy, variance explained, and extraction methods, is provided in

Table 2. Summary of Exploratory Factor Analysis (EFA).

Test	Result
KMO Measure	0.935
Bartlett’s Test of Sphericity	χ2(561) = 4830.256, p < 0.001
Total Variance Explained	68.988% (7 factors)
Eigenvalues > 1	7
Extraction Method	Principal Component Analysis
Rotation Method	Varimax

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The seven factors were then conceptually named by the authors based on the thematic similarity of the indicators grouped under each factor. These factors are: (1) village fund planning quality (8 indicators), reflecting the alignment, clarity, and capacity involved in formulating fund allocation plans; (2) fund implementation and utilization (6 indicators), emphasizing efficiency, timeliness, and responsiveness to community needs; (3) financial management capacity (5 indicators), capturing managerial skills, technological use, and financial recording practices; (4) social and economic impact (4 indicators), indicating the tangible development outcomes generated by fund utilization; (5) ethical governance and oversight (4 indicators), highlighting integrity, transparency, and both internal and external monitoring; (6) community participation in planning (4 indicators), focusing on inclusivity and consensus in decision-making; and (7) mandatory fund disclosure and reporting (3 indicators), representing the timely dissemination and accessibility of fund realization information. Overall, these seven governance-related factors provide a multidimensional representation of institutional and procedural quality in village-level fund management, serving as the independent variables for subsequent structural modeling. The results of the factor rotation, including loadings, eigenvalues, and variance explained for all retained indicators, are presented in

Table 3. Rotated Component Matrix of Governance Factors.

Factor	Indicator	Loading	Eigenvalue	% of Variance
Factor 1: Village Fund Planning Quality	X25. Government agencies provide coaching and mentoring in planning the village fund.	0.808	5.665	16.663
	X4. The plan is based on the village’s priority needs.	0.780
	X2. The plan aligns with village needs, priorities, and potentials.	0.762
	X5. The plan considers local village resources and potentials.	0.760
	X1. The village fund spending plan is developed effectively.	0.724
	X3. The plan aims to foster development of the local economic area.	0.715
	X14. The village government possesses the capability to formulate the fund plan.	0.702
	X15. The village fund budget is formulated clearly and purposefully.	0.574
Factor 2: Fund Implementation and Utilization	X18. It is used for empowering villagers.	0.787	4.022	11.830%
	X33. The fund is disbursed efficiently and in a timely manner.	0.732
	X17. The fund employs local labor.	0.729
	X16. The fund is used to utilize local natural resources.	0.644
	X20. Fund usage follows principles of accountability and transparency.	0.616
	X19. It is utilized according to the villagers’ expectations.	0.534
Factor 3: Financial Management Capacity	X9. Officials managing the fund have competence in finance and accounting.	0.756	3.460	10.176%
	X26. Regular training is provided to fund managers on financial management.	0.744
	X27. Fund transactions are recorded and reported digitally.	0.680
	X11. A technology-based financial management system (e.g., SISKEUDES) is used.	0.633
	X34. Village officials are skilled in using IT for managing funds.	0.620
Factor 4: Social and Economic Impact	X21. The fund increases agricultural production and income.	0.844	2.951	8.679%
	X24. It improves the village’s infrastructure quality.	0.811
	X22. It enhances villagers’ welfare.	0.802
	X23. It contributes to more equitable income distribution among villagers.	0.744
Factor 5: Ethical Governance and Oversight	X29. External or independent oversight monitors fund usage.	0.710	2.758	8.111%
	X13. Village leaders exhibit high integrity in public fund management.	0.676
	X28. Financial decisions are made transparently and accountably.	0.646
	X12. There is an internal audit mechanism verifying fund use.	0.639
Factor 6: Community Participation in Planning	X8. Community proposals and aspirations are used as input in planning.	0.827	2.463	7.245%
	X7. Villagers are actively involved in fund planning.	0.758
	X6. The fund plan is formulated through consensus with villagers.	0.717
	X10. The number of staff handling village funds is sufficient.	0.459
Factor 7: Mandatory Fund Disclosure and Reporting	X31. Fund realization is communicated to villagers through village deliberations.	0.702	2.137	6.284%
	X32. The realization is publicized via local information media (e.g., village noticeboard).	0.701
	X30. Fund realization is reported in a timely manner.	0.698

Note: Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Loadings less than 0.4 are suppressed for clarity. One indicator, X10, loads below the recommended threshold of 0.5, but is retained for exploratory purposes.

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4.3. Measurement Model Analysis

The measurement model was assessed separately for the reflective governance constructs and the formative VFM construct. For the reflective constructs, convergent validity was examined using outer loadings, Composite Reliability (CR), Cronbach’s alpha (CA), and Average Variance Extracted (AVE), as shown in

Table 4. Reflective Measurement Model Assessment.

Construct	Item	Outer Loadings	CA	CR	AVE
Factor 1: Village Fund Planning Quality	X1	0.861	0.936	0.947	0.692
	X2	0.874
	X3	0.748
	X4	0.871
	X5	0.868
	X14	0.797
	X15	0.742
	X25	0.877
Factor 2: Fund Implementation and Utilization	X16	0.794	0.875	0.906	0.619
	X17	0.739
	X18	0.838
	X19	0.660
	X20	0.819
	X33	0.853
Factor 3: Financial Management Capacity	X9	0.880	0.855	0.897	0.638
	X11	0.819
	X26	0.883
	X27	0.675
	X34	0.714
Factor 4: Social and Economic Impact	X21	0.881	0.817	0.891	0.732
	X22	Not valid
	X23	0.844
	X24	0.840
Factor 5: Ethical Governance and Oversight	X12	0.770	0.783	0.860	0.607
	X13	0.757
	X28	0.745
	X29	0.840
Factor 6: Community Participation in Planning	X6	0.722	0.765	0.849	0.586
	X7	0.746
	X8	0.838
	X10	0.750
Factor 7: Mandatory Fund Disclosure and Reporting	X30	0.959	0.959	0.973	0.924
	X31	0.964
	X32	0.960

. Most indicators recorded loadings above the recommended 0.70 threshold, except for one item, X22 (It enhances villagers’ welfare), from Factor 4 (Social and Economic Impact), which had an outer loading below 0.60. In accordance with the guidelines of Hair et al. (2019), for exploratory research in the social sciences, a 0.60 cut-off was applied, with lower loadings only retained if they carry a strong theoretical justification. Since X22 did not meet this criterion, it was removed from the model to enhance construct reliability.

After removing X22 and re-estimating the model, all remaining indicators displayed satisfactory outer loadings, with CA values ranging from 0.765 to 0.959, CR values from 0.817 to 0.973, and AVE values from 0.586 to 0.924, well above the minimum recommended thresholds of CA > 0.70, CR > 0.70, and AVE > 0.50 (Hair et al., 2019). Discriminant validity, evaluated through the Heterotrait–Monotrait (HTMT) ratio in

Table 5. Discriminant Validity (HTMT) and Collinearity Statistics (Inner Model VIF).

Construct	1	2	3	4	5	6	VIF
1. Village Fund Planning Quality							2.497
2. Fund Implementation and Utilization	0.741						2.367
3. Financial Management Capacity	0.648	0.615					2.086
4. Social and Economic Impact	0.387	0.316	0.441				1.203
5. Ethical Governance and Oversight	0.662	0.715	0.672	0.405			1.883
6. Community Participation in Planning	0.503	0.503	0.667	0.304	0.561		1.527
7. Mandatory Fund Disclosure and Reporting	0.733	0.744	0.622	0.305	0.644	0.459	2.437

, confirmed that all governance constructs were empirically distinct, with HTMT values below 0.85 (Hair et al., 2019). Furthermore, the Collinearity Statistics (Inner Model VIF) are all significantly below the 3.3 threshold, indicating that Common Method Bias is not a concern.

To verify robustness, an alternative specification of the measurement model was tested by sequentially removing low-loading indicators and re-estimating the constructs. The direction and significance of the factor loadings remained consistent, indicating model stability.

For the formative construct of VFM, two objective financial indicators were employed: village fund allocation (sourced from the 2024 Minister of Finance Decree No. 352) and BUMDes’ contribution to local revenue (sourced from the 2024 Ministry of Villages dataset), both expressed in Indonesian Rupiah. As presented in

Table 6. Formative Measurement Model Assessment for Village Fund Management.

Construct	Indicator	Outer Weight	VIF
Village Fund Management	Contribution	0.859	1.203
	Allocation	0.269	1.203

, outer weight analysis indicated that BUMDes’ contribution (0.859) made a stronger relative contribution than fund allocation (0.269). Both indicators were statistically significant and exhibited no multicollinearity concerns (VIF = 1.203 < 5). The formative specification is theoretically appropriate, as these indicators are non-interchangeable and reflect distinct aspects of VFM performance.

Figure 2 illustrates the final validated measurement model after the iterative refinement process, showing X22 removed from Factor 4. The resulting model meets all established reliability and validity standards, providing a robust measurement framework for the subsequent structural analysis.

In sum, the refined measurement model demonstrates strong psychometric quality, with the reflective constructs reliably capturing governance dimensions and the formative VFM construct accurately representing its multidimensional financial outcomes. These results establish a solid foundation for further structural evaluation.

4.4. Structural Model Analysis

The structural model was evaluated to examine the hypothesized relationships between the seven governance-related constructs and VFM. A bootstrapping procedure (5000 subsamples) was applied to confirm the significance and reliability of the outer weights, consistent with the consistent-PLS algorithm, providing further evidence of the model’s robustness. Model quality was first assessed using the coefficient of determination (R²), effect size (f²), and predictive relevance (Q²). As shown in

Table 7. Structural Model Fit and Predictive Metrics.

Metric	Construct	Value	Description
Coefficient of Determination	Village Fund Management	R2 = 0.499 R2 Adjusted = 0.482	This value indicates that the seven factors collectively explain 49.9% of the variance in village fund management, which is considered a moderate predictive power.
	Community Participation in Planning	f2 = 0.027	Small effect size
	Ethical Governance and Oversight	f2 = 0.082	Small to medium effect size
	Financial Management Capacity	f2 = 0.018	Small effect size
Effect Size	Fund Implementation and Utilization	f2 = 0.030	Small effect size
	Social and Economic Impact	f2 = 0.028	Small effect size
	Mandatory Fund Disclosure and Reporting	f2 = 0.021	Small effect size
	Village Fund Planning Quality	f2 = 0.001	No effect
Predictive Relevance	Village Fund Management	Q2 = 0.303	The value of Q2 > 0 indicates that the model has predictive relevance.

, the governance constructs collectively explain 49.9% of the variance in VFM (R² = 0.499; R² adjusted = 0.482), which represents moderate predictive power according to established guidelines (Hair et al., 2019). The Stone–Geisser Q² value of 0.303 is greater than zero, confirming the model’s predictive relevance for VFM outcomes.

Effect size analysis (Table 7) indicates that ethical governance and oversight (f² = 0.082) exerts the strongest relative influence on VFM (classified as a small to medium effect). The remaining factors show small but meaningful effect sizes (f² ranging from 0.018 to 0.030), except for village fund planning quality (f² = 0.001), which has a negligible effect.

Path coefficient estimates, presented in

Table 8. Path Coefficient Estimates.

Relationship	Path Coefficient	t-Value	p-Value	Result
Community Participation in Planning → Village Fund Management	0.144	2.461	0.014 **	Supported
Ethical Governance and Oversight → Village Fund Management	0.278	3.898	0.000 ***	Supported
Financial Management Capacity → Village Fund Management	0.138	1.968	0.049 **	Supported
Fund Implementation and Utilization → Village Fund Management	0.190	3.576	0.000 ***	Supported
Social and Economic Impact → Village Fund Management	−0.130	2.558	0.011 **	Supported
Mandatory Fund Disclosure and Reporting → Village Fund Management	0.159	2.967	0.003 ***	Supported
Village Fund Planning Quality → Village Fund Management	0.028	393	0.695	Not Supported

Note: *** p < 0.01; ** p < 0.05.

, reveal that six governance factors significantly influence VFM. Ethical governance and oversight (β = 0.278, p < 0.01) emerges as the most substantial positive driver, highlighting the importance of integrity, transparency, and independent oversight in ensuring effective fund utilization. fund implementation and utilization (β = 0.190, p < 0.01), mandatory fund disclosure and reporting (β = 0.159, p < 0.01), community participation in planning (β = 0.144, p < 0.05), and financial management capacity (β = 0.138, p < 0.05) also exert significant positive effects. Interestingly, social and economic impact displays a statistically significant but negative association (β = −0.130, p < 0.05). This finding implies that perceived socio-economic benefits alone may not directly translate into better fund management, possibly reflecting a lag between infrastructure or welfare improvements and governance capacity enhancement.

In contrast, village fund planning quality (β = 0.028, p = 0.695) does not significantly affect VFM. This finding suggests that while planning quality is essential for setting priorities, it may not directly determine financial performance unless supported by effective implementation, oversight, and stakeholder engagement.

Overall, the structural model results indicate that VFM in Papua is driven less by planning documents and more by active governance practices, financial capacity, and transparent execution. This finding underscores the need for capacity-building initiatives and governance reforms emphasizing accountability, stakeholder participation, and implementation quality over procedural compliance alone.

4.5. Multi-Group Analysis (MGA)

The multi-group analysis examined whether the structural relationships between governance factors and VFM varied significantly across different contextual categories, thereby testing Proposition 2 (contingency theory). For the regional comparison between coastal and mountainous areas (

Table 9. Multi-Group Analysis by Region and Natural Resources.

Relationship	Region			Natural Resources
	Coastal	Mountainous	Diff.	Rich	Minimal	Diff.
CPP → VFM	0.145 **	−0.049	0.194 *	0.155 **	0.175	−0.020
EGO → VFM	0.185 **	0.060	0.125	0.256 **	0.405 **	−0.150
FMC → VFM	0.191 **	0.224	−0.033	0.186 ***	0.419	0.605 **
FIU → VFM	0.257 ***	0.051	0.206	0.250 ***	0.013	0.263 *
SEI → VFM	−0.151 **	−0.140	−0.011	−0.104 *	0.259	0.155
MFD → VFM	0.094	−0.105	0.199 *	0.105	0.338 *	−0.233
FPQ → VFM	0.846 ***	0.189 ***	0.657 ***	−0.001	0.383	−0.384 *

Note: CPP = Community Participation in Planning; EGO = Ethical Governance and Oversight; FMC = Financial Management Capacity; FIU = Fund Implementation and Utilization; SEI = Social and Economic Impact; MFD = Mandatory Fund Disclosure and Reporting; FPQ = Village Fund Planning Quality; VFM = Village Fund Management. * p < 0.10; ** p < 0.05; *** p < 0.01.

), community participation in planning had a significant positive effect on VFM in coastal areas (β = 0.145, p < 0.05) but was not significant in mountainous areas (β = −0.049). The difference of 0.194 was statistically significant (p < 0.10), suggesting that participatory planning processes are more effective in coastal settings. Village fund planning quality exhibited a very strong positive effect in coastal areas (β = 0.846, p < 0.01) compared to a moderate positive effect in mountainous areas (β = 0.189, p < 0.01), with the difference of 0.657 being highly significant (p < 0.01). Mandatory fund disclosure and reporting was positive in coastal areas (β = 0.094) but negative in mountainous areas (β = −0.105), with a difference of 0.199 (p < 0.10). Other governance factors did not show significant regional differences.

In the natural resource endowment comparison, fund implementation and utilization (FIU) was significantly stronger in resource-rich areas (β = 0.250, p < 0.01) compared to resource-minimal areas (β = 0.013), with a significant difference of 0.263 (p < 0.10). Conversely, financial management capacity (FMC) displayed a stronger influence in resource-minimal areas (β = 0.419) than in resource-rich areas (β = 0.186, p < 0.01), yielding a significant difference of 0.605 (p < 0.05) between the two groups. Village fund planning quality exhibited a significant difference of −0.384 (p < 0.10), being negligible in resource-rich areas (β = −0.001) but showing a positive trend in resource-minimal areas (β = 0.383). Other factors did not display significant differences between the two resource categories.

When comparing transport access (

Table 10. Multi-Group Analysis by Transport Access and Population Makeup.

Relationship	Transport Access			Population Makeup
	Easy	Difficult	Diff.	Many Migrants	The Majority of Papuan People	Diff.
CPP → VFM	0.190 **	0.085	0.105	0.170 ***	−0.045	0.214 *
EGO → VFM	0.206 **	−0.034	0.241	0.240 ***	0.054	0.186
FMC → VFM	0.183 **	0.200	−0.017	0.142 *	0.245 **	−0.103
FIU → VFM	0.249 ***	0.166 *	0.083	0.231 ***	0.221 **	0.010
SEI → VFM	−0.130 **	−0.024	−0.106	−0.135	−0.017	−0.119
MFD → VFM	0.045	−0.173 *	0.219 **	0.061	−0.149	0.210
FPQ → VFM	0.831 ***	0.218 ***	0.613 ***	0.197 ***	0.721 **	−0.524 **

Note: CPP = Community Participation in Planning; EGO = Ethical Governance and Oversight; FMC = Financial Management Capacity; FIU = Fund Implementation and Utilization; SEI = Social and Economic Impact; MFD = Mandatory Fund Disclosure and Reporting; FPQ = Village Fund Planning Quality; VFM = Village Fund Management. * p < 0.10; ** p < 0.05; *** p < 0.01.

), mandatory fund disclosure and reporting were positive in areas with easy access (β = 0.045) but negative in those with difficult access (β = −0.173, p < 0.10), resulting in a significant difference of 0.219 (p < 0.05). Village fund planning quality was substantially stronger in areas with easy access (β = 0.831, p < 0.01) than in those with difficult access (β = 0.218, p < 0.01), with a difference of 0.613 that was highly significant (p < 0.01). No other factors showed significant differences based on transport accessibility.

For population makeup, community participation in planning was significantly positive in areas with a high migrant presence (β = 0.170, p < 0.01) but negative in areas dominated by indigenous Papuan populations (β = −0.045). The difference of 0.214 was significant (p < 0.10). Village fund planning quality was stronger in indigenous Papuan majority areas (β = 0.721, p < 0.05) than in migrant-majority areas (β = 0.197, p < 0.01), with a significant difference of −0.524 (p < 0.05). Other governance factors did not present significant differences across the two population structures.

Overall, these results strongly support Proposition 2, indicating that the influence of specific governance dimensions on VFM is highly context-dependent, with village fund planning quality and community participation emerging as the most sensitive to geographical, economic, and demographic variations.

5. Discussion

The analysis of governance-related determinants of VFM in Papua provides a layered understanding of how institutional quality, community engagement, and local contextual factors collectively influence the sustainability, equity, and impact of rural fund utilization. Addressing the first research question (RQ1) and Proposition 1, the exploratory factor analysis distilled a broad range of governance indicators into seven latent constructs, confirming the necessity of a nuanced, multidimensional approach to governance in complex settings like Papua. This configuration reflects statistical robustness and theoretical coherence, aligning with governance frameworks linking institutional integrity, participatory decision-making, and managerial capacity to effective public resource allocation (Castro-Arce & Vanclay, 2020; Hao et al., 2022; Smas et al., 2025).

The structural model results, addressing the second research question (RQ2), reveal that governance factors differ substantially in their influence on VFM outcomes. Ethical governance and oversight emerged as the strongest positive driver, reaffirming agency theory by demonstrating that robust accountability structures and integrity norms are the primary strategic levers for delivering VFM outcomes (Brinkerhoff & Brinkerhoff, 2015; Phoek et al., 2024; Wahyudi et al., 2022). This finding is particularly crucial in Papua’s dispersed governance environment, where embedding independent oversight within community structures is essential to strengthen compliance and reduce opportunities for elite capture. Fund implementation and utilization and mandatory fund disclosure and reporting also demonstrated substantial positive effects. This underscores that de facto execution capability and transparent execution are more decisive than mere financial capacity or procedural compliance alone (Smoke, 2015). Transparency and accountability likewise showed a significant positive relationship with VFM, reinforcing the participatory governance proposition that open access to budget and implementation information empowers local actors to demand timely corrections when deviations occur (Castro-Arce & Vanclay, 2020; Ginting et al., 2024; Rodríguez-Navas et al., 2021).

Community participation in planning also contributed positively, indicating that when communities are actively involved in identifying priorities and designing interventions, resource allocation tends to be more relevant and legitimate (Akbar et al., 2020; Ledezma, 2023; Sabet & Khaksar, 2024). In Papua, however, the quality of participation varies, as shown in the multi-group analysis, where geography and demographics strongly influence its impact. Financial management capacity similarly had a significant positive effect, highlighting the importance of skilled personnel, sound accounting systems, and the ability to manage intergovernmental transfers effectively (Ginting et al., 2024; Lin & Peng, 2025). This capacity can determine whether development budgets are fully absorbed or left idle in resource-constrained or remote settings.

In contrast, social and economic impact negatively correlated with VFM, a counterintuitive yet plausible finding. Visible improvements in infrastructure or welfare may foster a perception of success that reduces pressure for procedural reforms, leading to governance complacency. This finding mirrors critiques that output-focused metrics can mask deficiencies in institutional processes (Bräutigam, 2004; Evans, 2012). While conceptually important, village fund planning quality did not directly affect the pooled model significantly, suggesting that even high-quality plans cannot deliver results without capable execution, rigorous monitoring, and stakeholder engagement (Das & Ngacho, 2017). However, as the multi-group analysis shows, its role becomes more pronounced in specific contexts, indicating that its value is conditional rather than universal.

Addressing the third research question (RQ3) and Proposition 2, the multi-group analysis strongly confirms the contingency theory perspective that governance effectiveness is moderated by local conditions. In coastal versus mountainous regions, community participation in planning had a stronger positive effect in coastal areas, while mandatory fund disclosure was weaker or negative in mountainous areas. These patterns likely stem from differences in transport connectivity and communication networks. Coastal villages have better infrastructure facilitating inclusive meetings and timely information dissemination, whereas isolated mountainous communities rely on traditional governance norms that may not align with standardized transparency models (Bodin & Crona, 2009; Fauzi et al., 2019; Tucker et al., 2021). This finding suggests that basic infrastructure is a prerequisite for effective participatory governance, not merely a development outcome.

Natural resource endowment also shaped governance effectiveness. Financial management capacity and fund implementation were significantly more influential in resource-rich villages, reflecting how resource abundance can expand fiscal space and institutional capability when governance systems are robust (Brunnschweiler & Bulte, 2008; El Anshasy & Katsaiti, 2013; Roldan et al., 2023). Conversely, resource-poor areas face fundamental budget constraints, which limit the operational effectiveness of even well-structured governance systems, contributing to persistent rural inequality (Geiling et al., 2014; Rout et al., 2022).

Transport accessibility emerged as another decisive factor. Villages with easy access benefited more from transparency, accountability, and planning quality, confirming that physical connectivity facilitates project coordination, monitoring, and schedule adherence (Bhattacharyay, 2012; Kaiser & Barstow, 2022). In hard-to-reach areas, governance mechanisms dependent on regular interaction and oversight lose much of their effectiveness, contributing to slower execution and weaker accountability cycles.

Population composition added a socio-cultural layer to governance outcomes. In migrant-majority villages, community participation in planning was more influential, likely due to the social remittances migrants bring, new organizational practices, broader networks, and exposure to participatory norms (Lacroix et al., 2016; Levitt & Lamba-Nieves, 2011). Conversely, in indigenous Papuan-majority communities, planning quality had a greater impact, reflecting the stronger legitimacy of governance processes that align with adat customs and local knowledge systems (Indrawan et al., 2019; Ledezma, 2023; Pal & Wahhaj, 2017). The collectivist ethos described by Rante and Warokka (2013), prioritizing communal obligations over individual gain, further explains why participatory planning resonates more deeply in indigenous contexts, even if it sometimes limits the profitability of village enterprises. This cultural orientation suggests that governance interventions in Papua must balance efficiency goals with respect for social obligations, ensuring reforms are culturally embedded rather than externally imposed.

These findings demonstrate that while core governance dimensions such as ethical oversight, implementation capacity, and financial management are broadly beneficial, their real impact depends heavily on the geographic, economic, infrastructural, and socio-cultural setting in which they are applied. In Papua, designing interventions for Village Fund Management (VFM) therefore requires a context-sensitive approach that adapts governance mechanisms to local realities rather than applying uniform templates. From a sustainability perspective, this resonates with the principles of adaptive governance (Rijke et al., 2012), emphasizing place-based solutions that can build institutional resilience while respecting cultural traditions.

6. Conclusions

This study contributes to the rural development literature by examining how governance pathways interact with contextual challenges to shape fiscal performance in underdeveloped regions. Through exploratory factor analysis, seven latent governance constructs were identified, and their effects on Village Fund Management (VFM) outcomes were assessed using structural modeling. The results demonstrate that ethical oversight, implementation capacity, transparency, community participation, and financial management are central drivers of effective and sustainable fund utilization. These findings confirm that governance quality alone is insufficient without strategies that are sensitive to local realities, underscoring the importance of adaptive and context-specific approaches for financing rural futures.

Theoretically, this study advances our understanding of how decentralization interacts with local governance capacities to generate sustainable fiscal outcomes in fragile and resource-constrained environments. Integrating contextual moderators into the analysis extends existing governance and decentralization frameworks and provides a nuanced lens for evaluating rural fiscal performance. This evidence-based framework highlights that the sustainability of VFM is not only a matter of institutional design but also of how governance systems evolve in response to diverse socio-cultural and ecological settings.

From a practical perspective, the findings offer actionable and context-specific insights for policymakers and development practitioners. The results align with Indonesia’s fiscal decentralization agenda by demonstrating that local governance capacity, not merely fiscal transfers, determines whether decentralization yields sustainable outcomes. The dominance of ethical governance confirms the necessity of strengthening anti-corruption measures to achieve SDG 16 (Peace, Justice, and Strong Institutions). Furthermore, the focus on objective VFM outcomes (BUMDes’ contribution) strengthens both SDG 8 (Decent Work and Economic Growth) and SDG 1 (No Poverty).

These empirical results necessitate differentiated governance strategies based on the following contexts: Strengthening participatory mechanisms and mandatory fund disclosure can be highly effective in coastal regions and in those with easy access due to better communication networks. Conversely, in mountainous/difficult access regions, basic infrastructure investment should be prioritized as a governance prerequisite, and capacity-building should focus on digital reporting tools, rather than costly physical consultation. Furthermore, in indigenous communities, aligning planning processes with adat customs and local norms enhances legitimacy and compliance, ensuring reforms are culturally embedded rather than externally imposed. By highlighting these governance pathways, this study provides a foundation for designing reforms and interventions to deliver equitable and impactful outcomes in Papua and other disadvantaged rural settings.

Despite its contributions, this study is not without limitations. Its cross-sectional design restricts strict causal inference, and although the sample spans many villages in Papua, it does not capture the way local institutions evolve over time. Self-reported governance measures may also carry perceptual bias, especially in politically sensitive settings. Future work should use longitudinal or mixed-method designs to track governance dynamics, triangulate survey evidence with administrative records and ethnographic fieldwork, and test how external shocks or policy reforms alter the governance–VFM link. Researchers could also examine how digital governance tools help overcome remoteness, as well as how traditional institutions interact with modern accountability mechanisms, ensuring that village funds more reliably finance rural futures.