M. Ichsan Rouyas Sitorus
| Satia Negara Lubis* | Meilita Tryana Sembiring | Sinar Indra Kesuma | Arga Abdi Rafiud Darajat Lubis
© 2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
This study develops a Bottom-Up Inclusive Growth Model to examine the structural pathways linking participatory planning, inclusive economic growth, and regional development in peripheral coastal cities of post-decentralized Indonesia. Using the case of Tanjung Balai, the research investigates whether citizen participation in planning processes significantly contributes to equitable economic and spatial outcomes in marginalized urban areas. Employing Partial Least Squares-Structural Equation Modeling (PLS-SEM), the study analyzes data from stakeholder surveys and secondary development indicators. Results show that participatory planning has a direct and significant effect on inclusive growth (β=0.614) and regional development (β=0.390), with inclusive growth also mediating the relationship (β=0.422). The model exhibits robust explanatory power (R²=0.378-0.532) and predictive relevance (Q²=0.235-0.357). The findings offer a replicable framework for enhancing planning effectiveness in coastal regions, providing empirical support for integrating participatory governance into inclusive development agendas. The model is applicable for policy alignment with SDGs 1, 10, and 11, particularly in resource-constrained and spatially disadvantaged settings.
participatory planning, inclusive growth, regional development, PLS-SEM, decentralization, coastal cities, Sustainable Development Goals (SDGs)
Inclusive growth has become a central theme in development discourse, recognizing that economic expansion alone cannot resolve structural inequalities [1-4]. In decentralized contexts like Indonesia, the need for locally adapted development strategies is especially urgent—particularly in peripheral coastal cities that face institutional neglect and infrastructure gaps [5-7]. These areas often endure persistent social exclusion, which undermines long-term development resilience. Tanjung Balai exemplifies such challenges and is used here as a strategic case to explore the role of participatory planning in promoting inclusive growth.
True inclusive growth requires more than redistributive economic policies; it calls for rethinking governance structures to allow marginalized communities genuine influence in shaping development priorities [8-10]. When participatory planning is institutionalized within formal governance systems, it helps align policy actions with local socio-economic realities [11, 12]. However, in Indonesia, implementation of such frameworks often falls short due to administrative inertia, elite capture, and uneven institutional capacities across regions [13].
While the literature on inclusive growth and spatial development is extensive, these areas are often studied in isolation. There is a lack of integrated empirical work linking them through the lens of participatory planning especially in the Southeast Asian context, where most research has focused on large metropolitan areas [14-16]. Moreover, existing studies that examine participatory processes are often qualitative, leaving a gap in model-based, quantitative validation of their development impacts.
An emerging body of research supports the transformative role of bottom-up governance, particularly in improving resource targeting, mobilizing social capital, and strengthening legitimacy in long-term development [17]. Case studies from Latin America and the EU suggest that participatory regional planning frameworks are more effective in achieving social equity goals [18, 19]. However, there remains a lack of robust empirical models that quantify how participatory planning influences inclusive growth and regional development—especially in post-decentralized settings.
In Indonesia, the Musrenbang system was designed to institutionalize multi-stakeholder planning. Yet, in practice, its influence on fiscal and spatial outcomes is often limited [20, 21]. Coastal cities like Tanjung Balai face compounded disadvantages: geographic peripherality and weak leverage within national development strategies [22]. This highlights the need to empirically examine how participatory mechanisms affect development outcomes beyond their procedural function.
To address this gap, the present study develops a structural model in which participatory planning acts as a key driver of inclusive growth and regional development. The model is tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) [23]. It involves three constructs: participatory planning (exogenous), inclusive economic growth (mediator), and regional development (endogenous). Using data from Tanjung Balai, the study explores both direct and mediated effects of bottom-up planning on development trajectories.
As a coastal border city, Tanjung Balai is characterized by low investment levels, a high informal sector presence, and strategic yet underutilized geographic advantages [24, 25]. These characteristics make it a compelling site to assess whether bottom-up planning models can address persistent exclusion. The research also aligns with Sustainable Development Goals—particularly SDG 1 (No Poverty), SDG 10 (Reduced Inequality), and SDG 11 (Sustainable Cities and Communities)—and aims to offer insights applicable to other coastal cities in decentralized nations [26, 27].
To guide the inquiry, the study addresses the following questions:
1. To what extent does participatory bottom-up planning influence inclusive economic growth in peripheral coastal cities?
2. What is the role of inclusive growth in mediating the relationship between participatory planning and regional development outcomes?
3. How can a structural model of inclusive growth inform more effective spatial development strategies in post-decentralized contexts?
2.1 Research design
This study adopts a quantitative, explanatory research design to explore the causal relationships among participatory planning, inclusive economic growth, and regional development in peripheral coastal cities. Tanjung Balai, located in North Sumatra, Indonesia, was selected as the case study due to its representative characteristics of coastal marginality and post-decentralization challenges.
As a maritime border city near the Malacca Strait, it faces limited domestic investment, a high proportion of informal economic activity, and weak institutional leverage—challenges also prevalent in other second-tier coastal municipalities such as Sibolga (North Sumatra), Lhokseumawe (Aceh), and Bau-Bau (Southeast Sulawesi). These cities are typically marked by lower Human Development Index (HDI) scores, limited intergovernmental fiscal support, and fragmented spatial planning. Tanjung Balai’s active participation in Indonesia’s Musrenbang (community planning) framework also makes it an appropriate site to evaluate the functional role of participatory mechanisms in shaping inclusive and regional development outcomes.
The analytical approach is grounded in Structural Equation Modeling (SEM), specifically using Partial Least Squares-SEM (PLS-SEM), which is appropriate for models with complex latent constructs, small to medium sample sizes, and data that do not meet normality assumptions [28]. The research integrates primary survey responses with secondary socio-economic indicators to empirically validate the proposed conceptual model and test the hypothesized relationships.
2.2 Data sources and collection
Data was collected through a combination of primary and secondary sources between July and September 2024. Primary data involved a structured questionnaire distributed to stakeholders in Tanjung Balai, including government officials, community leaders, local entrepreneurs, and planning officers. A proportionate stratified sampling technique was employed to ensure representation across key stakeholder groups. Secondary data were sourced from the Central Bureau of Statistics (BPS), local development planning documents (RPJMD), and relevant planning agency archives. The triangulation of these data sources ensures reliability and contextual depth in model estimation (see Table 1 for details on data sources and instruments).
Table 1. Data sources and instruments
|
No. |
Source Type |
Description |
Method |
Respondent/Unit |
|
1 |
Primary |
Perceptions on participatory planning, inclusivity, and development outcomes |
Structured questionnaire |
110 stakeholders (multi-sector) |
|
2 |
Secondary |
Socio-economic indicators, spatial development metrics |
Document review |
BPS, RPJMD, Local Planning Office |
|
3 |
Expert Input |
Validation of indicator weights & relevance |
Expert judgment panel |
5 regional development experts |
2.3 Variables and operational definitions
To strengthen construct validity, each indicator used to operationalize the latent variables was selected based on both theoretical grounding and empirical precedent.
Participatory Planning (X) is measured through:
·Frequency of community consultation: Captures how often stakeholders are engaged in formal planning processes such as Musrenbang forums. This reflects the degree of institutional openness and responsiveness, as emphasized in participatory governance literature [8, 9].
·Musrenbang quality: A composite indicator comprising (a) diversity of stakeholder participation, (b) proportion of community proposals integrated into final plans, and (c) transparency of documentation and outcomes.
·Access to decision-making: Measured by the perceived influence of community members on final planning outcomes, based on their representation in formal committees or advisory roles.
Inclusive Growth (Y) includes:
·Poverty rate and unemployment rate: Standard macroeconomic indicators reflecting basic access to income and livelihoods [1, 4].
·SME participation: Measured as the number of active micro, small, and medium enterprises (MSMEs) per 1,000 residents. It captures inclusive economic engagement, especially for low-income populations [2, 14].
·Gini coefficient: A conventional measure of income inequality, used to assess the distributional fairness of economic growth [3, 12].
Regional Development (Z) is evaluated using:
·Infrastructure access: The percentage of households with access to key infrastructure—roads, electricity, and clean water—which are fundamental to spatial equity [7].
·Human Development Index (HDI): A composite metric combining health, education, and income dimensions; widely used to benchmark regional progress [25].
·Investment flow: Refers to domestic investment (RDI) per capita, indicating economic vitality and attractiveness.
·Spatial equity: Assessed through distribution maps of services and access indices across sub-districts, following UN-Habitat’s spatial inclusion framework [17].
All indicators were reviewed and validated by a panel of five regional development experts. Their relevance was further confirmed by high factor loadings in the measurement model. All indicators were validated by a panel of experts to ensure construct validity and reliability (see Table 2 for a summary of the variables and indicators used).
Table 2. Variables and operational
|
Variable |
Code |
Definition |
Indicators |
|
Participatory Planning |
X |
Stakeholder engagement in planning, budgeting, and monitoring |
Frequency of consultation, Musrenbang quality, access to decision-making |
|
Inclusive Growth |
Y |
Economic growth accompanied by equity and opportunity |
Poverty rate, unemployment, SME participation, Gini ratio |
|
Regional Development |
Z |
Spatial and socio-economic enhancement of the city |
Infrastructure access, HDI, investment flow, spatial equity |
2.4 Hypothesis formulation
The research model proposes direct and mediated relationships among the three latent variables. Hypotheses were formulated based on theoretical and empirical findings in participatory planning, inclusive development, and regionalism literature. The three hypotheses derived from the conceptual model are summarized in Table 3.
Table 3. Hypothesis
|
Hypo. |
Statement |
|
H1 |
Participatory planning positively influences inclusive growth. |
|
H2 |
Participatory planning positively influences regional development. |
|
H3 |
Inclusive growth positively influences regional development. |
2.5 Analytical tools, model estimation, and validation
This study employs Partial Least Squares-Structural Equation Modeling (PLS-SEM) using SmartPLS 4.0. PLS-SEM is well-suited for prediction-oriented models with complex constructs and smaller sample sizes, particularly when data do not meet normality assumptions.
2.5.1 Model estimation
Estimation used bootstrapping with 5,000 resamples to assess the significance of path coefficients. The structural model includes the following latent variables:
- Participatory Planning (X)
- Inclusive Growth (Y)
- Regional Development (Z)
The structural equations are:
η₁ = γ₁ξ + ζ₁
η₂ = γ₂ξ + β₂₁η₁ + ζ₂
Figure 1 presents the conceptual framework linking participatory planning (X), inclusive growth (Y), and regional development (Z). The arrows indicate the hypothesized paths: X is expected to influence both Y and Z directly, while Y is also posited to mediate the effect of X on Z. This structure reflects the theoretical assumption that participatory governance affects development not only through economic inclusivity but also by shaping spatial and infrastructural outcomes directly.
Figure 1. Methodological framework
2.5.2 Model validation
PLS-SEM was chosen over covariance-based SEM (CB-SEM) due to its advantages in handling small sample sizes, non-normal data, and predictive modeling contexts. Given the study’s sample size (n = 110) and its aim to explore and validate a novel structural framework, PLS-SEM provides greater flexibility and statistical robustness. Unlike CB-SEM, which requires larger samples and strict distributional assumptions, PLS-SEM allows for the estimation of complex models with both reflective and formative indicators. These features make it the most suitable analytical approach for examining causal relationships in this research. Accordingly, two levels of validation were conducted:
a. Measurement Model (Outer Model):
1. Convergent Validity: Outer loadings > 0.70, AVE > 0.50
2. Discriminant Validity: Fornell-Larcker Criterion and HTMT
3. Reliability: Cronbach’s alpha and Composite Reliability > 0.70
b. Structural Model (Inner Model):
1. Path Coefficient Significance: Based on t-values and p-values from bootstrapping
2. R² (Coefficient of Determination): Indicates explanatory power
3. Q² (Predictive Relevance): Obtained via blindfolding
4. Effect Size (f²) and Goodness-of-Fit (GoF): Evaluate individual path contribution and overall model fit
3.1 Measurement model evaluation (outer model)
The measurement model was assessed to evaluate the reliability and validity of the indicators used to reflect each latent variable: Participatory Planning (X), Inclusive Growth (Y), and Regional Development (Z). The outer loadings of all observed indicators exceeded 0.70 (Table: Outer Loadings), meeting the standard for convergent validity [22]. Each construct’s Average Variance Extracted (AVE) also surpassed the 0.50 threshold (see Table 4).
Table 4. Average variance extracted (AVE)
|
Construct |
AVE |
Threshold |
Status |
|
Participatory Planning (X) |
0.694 |
> 0.50 |
Valid |
|
Inclusive Economic Growth (Y) |
0.651 |
> 0.50 |
Valid |
|
Regional Development (Z) |
0.692 |
> 0.50 |
Valid |
Additionally, composite reliability and Cronbach’s alpha values were all above the 0.70 criterion, confirming strong internal consistency across all constructs (Table: Composite Reliability and Cronbach’s Alpha).
The discriminant validity was verified using Fornell-Larcker Criterion and cross-loading analysis. Each construct’s AVE square root was greater than its correlation with other constructs, and indicator cross-loadings were highest with their own construct. Thus, both methods confirmed satisfactory discriminant validity.
3.2 Structural model results (inner model)
The inner (structural) model was then tested using bootstrapping with 5,000 resamples to determine the significance of the hypothesized relationships between latent variables. The results, presented in Table 5, show that all path coefficients are statistically significant. Specifically, Participatory Planning (X) strongly influences Inclusive Economic Growth (Y), with a path coefficient of β = 0.614, t = 8.307, and p < 0.001 (confirming H1). Additionally, Participatory Planning directly affects Regional Development (Z) (H2) with β = 0.390, t = 2.737, and p = 0.006. The effect of Inclusive Growth on Regional Development (H3) is also significant (β = 0.422, t = 3.024, p = 0.003).
These findings demonstrate that all three hypothesized direct relationships (H1-H3) are statistically supported at the 0.05 level, confirming the critical role of participatory planning mechanisms in shaping inclusive and regional development outcomes.
To evaluate the model’s predictive power and explanatory strength, the R² and Q² values for the two endogenous constructs were examined. As shown in Table 6, the R² value for Inclusive Growth is 0.378, indicating a moderate level of explanatory power. Meanwhile, Regional Development’s R² value is 0.532, demonstrating a moderate to substantial proportion of explained variance. Predictive relevance, as measured by Q² values, is also acceptable—0.235 for Inclusive Growth and 0.357 for Regional Development—both exceeding the minimum threshold of 0.
The Q² values, which reflect predictive relevance, were also acceptable. The Q² for Inclusive Growth was 0.235, while Regional Development scored 0.357, both exceeding the threshold of 0, which confirms that the model has good predictive capability. These results are summarized in Table 6.
These findings confirm that the model is statistically robust, well-fitted, and has both explanatory strength and predictive power.
Figure 2 visually displays the bootstrapped path coefficients from the PLS-SEM estimation. The model confirms all hypothesized relationships: Participatory Planning strongly affects Inclusive Growth (β = 0.614), which in turn significantly influences Regional Development (β = 0.422). The direct path from Participatory Planning to Regional Development (β = 0.390) is also statistically significant. These path coefficients, along with their associated t-values and p-values, validate the structural assumptions embedded in the conceptual model.
Table 5. Structural model results (Hypothesis testing)
|
Hypothesis |
Path |
Standardized Coefficient (β) |
T-Value |
P-Value |
Significance |
|
H1 |
Participatory Planning → Inclusive Growth |
0.614 |
8.307 |
0.000 |
Significant |
|
H2 |
Participatory Planning → Regional Development |
0.390 |
2.737 |
0.006 |
Significant |
|
H3 |
Inclusive Growth → Regional Development |
0.422 |
3.024 |
0.003 |
Significant |
Figure 2. Bootstrapping model testing result
Table 6. Model fit summary - R² and Q²
|
Endogenous Variable |
R² |
R² Interpretation |
Q² |
Q² Interpretation |
|
Inclusive Growth |
0.378 |
Moderate |
0.235 |
Predictive relevance |
|
Regional Development |
0.532 |
Moderate to Substantial |
0.357 |
Predictive relevance |
3.3 Discussion of hypotheses
The results of the structural model offer significant insights into the causal pathways linking participatory planning, inclusive growth, and regional development, as hypothesized in the conceptual framework. The statistical support for all three hypotheses (H1-H3) affirms the theoretical position that bottom-up governance mechanisms can substantially enhance both economic equity and territorial development, particularly in peripheral or coastal regions such as Tanjung Balai.
3.3.1 Participatory planning → Inclusive growth
H1, which posited that Participatory Planning positively influences Inclusive Growth, is strongly supported (β = 0.614, t = 8.307, p < 0.001). This finding confirms earlier empirical and conceptual works [11] that underscore the role of inclusive governance in expanding economic opportunity. In the context of Indonesia, where planning decentralization mechanisms like Musrenbang exist, this result emphasizes that citizen engagement in planning processes is not only a democratic exercise but also a driver of tangible economic outcomes, such as reductions in unemployment, poverty, and income inequality.
3.3.2 Participatory planning → Regional development
The result for H2 also showed statistical significance (β = 0.390, t = 2.737, p = 0.006), affirming that Participatory Planning directly influences Regional Development. This supports the growing body of evidence [6] suggesting that participatory mechanisms help local governments allocate resources more efficiently and design infrastructure or investment strategies that reflect the actual needs of the community. The findings imply that participatory governance is particularly crucial in coastal or marginal areas, where traditional top-down development frameworks have historically failed to achieve balanced growth.
3.3.3 Inclusive growth → Regional development
H3, which tested the path from Inclusive Growth to Regional Development, was also validated (β = 0.422, t = 3.024, p = 0.003). This indicates that regions with higher levels of economic inclusiveness—measured through indicators like SME participation, Gini ratios, and employment access—are more likely to achieve comprehensive spatial and socio-economic development. This aligns with Berraies et al. [29] who argue that economic growth without inclusiveness tends to reinforce spatial disparities, especially in geographically disadvantaged areas.
The empirical findings also reveal an indirect pathway: Participatory Planning contributes to Regional Development both directly and indirectly through Inclusive Growth.
3.4 Theoretical and practical implications
The empirical findings of this study contribute to both theoretical advancement and policy practice, particularly in the domains of inclusive development, regional planning, and participatory governance.
From a theoretical standpoint, the study extends existing literature on inclusive growth by positioning participatory planning as a structural enabler within development processes. While earlier works [30] have discussed the benefits of inclusive growth in poverty reduction and inequality mitigation, this research demonstrates that such growth can be predicted and influenced through bottom-up planning models. This supports the argument that governance processes, particularly citizen engagement and planning institutionalization—should be considered core variables in inclusive growth models, not peripheral factors [31].
Furthermore, the study validates the idea that inclusive growth is not merely a by-product of regional development, but a causal mechanism shaping it. This finding reinforces the conceptual shift from linear growth theories toward integrated governance-development frameworks. The model's dual-path structure—where participatory planning affects regional development both directly and via inclusivity—demonstrates a more nuanced understanding of development causality in decentralized and spatially diverse contexts.
From a practical or policy perspective, the findings provide compelling justification for institutional investment in participatory mechanisms, particularly in peripheral and coastal cities like Tanjung Balai. While participatory planning frameworks such as Musrenbang are widely implemented in Indonesia, their impact is often diluted by procedural tokenism, elite capture, or capacity gaps. This research shows that when genuinely implemented, participatory planning can influence inclusive economic outcomes and improve regional infrastructure, spatial equity, and investment attractiveness [32-34].
In addition, the research provides an actionable model for development agencies, planners, and local governments to integrate monitoring indicators for inclusive within urban and regional planning documents. Metrics such as SME participation, poverty rates, and unemployment can be linked directly to planning quality indicators, enhancing both accountability and outcome relevance. This integration is crucial for aligning local practices with global development agendas, especially SDG 1 (No Poverty), SDG 10 (Reduced Inequalities), and SDG 11 (Sustainable Cities and Communities).
Ultimately, the model developed in this study has the potential to serve as a diagnostic and planning tool, enabling municipalities to evaluate whether their participatory systems are structurally linked to measurable development results.
In sum, the findings provide a theoretically grounded, empirically tested, and policy-relevant contribution to the study of inclusive regional development—especially in underrepresented geographies such as small, coastal cities in Southeast Asia.
4.1 Conclusion
This study developed and empirically validated a Bottom-Up Inclusive Growth Model tailored for regional development in peripheral coastal cities, with Tanjung Balai serving as the representative case. Using Partial Least Squares-Structural Equation Modeling (PLS-SEM), the research demonstrated that participatory planning exerts both direct and indirect effects on regional development outcomes through the mediating role of inclusive economic growth. Key indicators such as stakeholder engagement, SME participation, and spatial equity were found to be critical in explaining the structural relationships among the studied variables.
Ultimately, the model offers a robust and replicable framework that integrates governance mechanisms with spatial and economic performance metrics. It underscores the necessity of institutionalizing participatory systems not merely for democratic accountability but for their measurable impact on development equity, especially in under-resourced coastal and border cities.
4.2 Policy implications
The findings of this study highlight the need for a phased and data-driven governance approach to stimulate inclusive development in peripheral regions. Effective implementation requires not only stronger participatory platforms but also the integration of inclusivity metrics into planning and budgeting processes. Policymakers are encouraged to adopt a time-horizon strategy to prioritize interventions based on feasibility, urgency, and impact.
Table 7. Strategic policy recommendations (Grouped by time horizon)
|
Time Horizon |
Policy Area |
Recommendation |
|
Short-Term |
Participatory Planning Systems |
Transform participatory platforms like Musrenbang into genuine co-creation processes involving citizens, civil society, and local business stakeholders. |
|
Short-Term |
Institutional Capacity Building |
Enhance local government capacity to manage participatory planning, including facilitation skills, digital tools, and feedback systems. |
|
Medium-Term |
Integration of Inclusive Indicators |
Embed inclusive growth metrics—such as SME participation rates, Gini coefficient (currently 0.41 in Tanjung Balai), and poverty/unemployment data—into regional development plans. |
|
Medium-Term |
Budget Allocation Mechanisms |
Link inclusive development indicators to fiscal allocation criteria, ensuring planning and budgeting reflect equity and impact priorities. |
|
Long-Term |
Alignment with SDGs |
Integrate quantitative SDG targets into local planning: reduce poverty by 50% (SDG 1.2), lower Gini index in line with SDG 10.1, and expand infrastructure access and participatory planning coverage (SDG 11.1 and 11.3). Use local data benchmarks to monitor progress. |
4.3 Limitations and future research
This study has several limitations that should be acknowledged. First, the research is based on a single case study—Tanjung Balai—which may limit the generalizability of findings to other coastal cities with different socio-political contexts. Second, although the sample size (n = 110) was sufficient for PLS-SEM analysis, it still represents a relatively small population, which may constrain the statistical power of broader inference.
Third, the primary data relies on self-reported perceptions from stakeholders, making it susceptible to social desirability bias, especially in politically sensitive topics such as government transparency or planning effectiveness. While stratified sampling and expert validation were applied to reduce this risk, respondent bias remains a potential limitation. Fourth, regional policy heterogeneity across Indonesian provinces means that decentralization is implemented unevenly, which could influence how participatory mechanisms like Musrenbang function in other locales.
Finally, the model captures structural relationships at a single time point and does not account for temporal changes, such as shifts in planning responsiveness or economic cycles. Future studies could benefit from longitudinal designs or comparative analyses across multiple coastal cities to assess temporal dynamics and improve external validity. Integrating qualitative data, such as stakeholder interviews or focus group discussions, could also deepen understanding of the institutional factors influencing participatory planning outcomes.
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