Fishermen Capacity Model After Sedimentation in Kendari Bay

Fishermen Capacity Model After Sedimentation in Kendari Bay | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fishermen Capacity Model After Sedimentation in Kendari Bay Muhammad Aswar Limi, R. Marsuki Iswandi, Weka Widayati, Yani Taufik, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8342660/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Small-scale fisheries in coastal areas are increasingly facing chronic environmental pressures that have the potential to limit the adaptive capacity of fishermen. One of the increasingly dominant pressures is sedimentation, but the mechanism of its influence on the capacity of fishermen is often assumed to work through the performance of fishing businesses without adequate empirical testing. This study aims to analyze the impact of sedimentation and business diversification on fishermen's capacity, both directly and indirectly through fishing business performance, in the context of the Kendari Bay coast, Southeast Sulawesi. This study challenges the dominant assumption that fishermen's capacity is primarily shaped through economic performance by showing that under chronic environmental pressures, capacity is shaped through direct adaptive pathways rather than through mediated economic mechanisms. This study uses an explanatory cross-sectional design with a Structural Equation Modeling–Partial Least Squares (SEM-PLS) approach. Data were collected through structured interviews with 200 small-scale fishing households in 2025. Four reflective latent constructs were analyzed, namely the impact of sedimentation, business diversification, fishery business performance, and fisher capacity. Model evaluation included convergent validity, internal reliability, discriminant validity (Fornell–Larcker and HTMT) tests, as well as structural model evaluation through path coefficients, R², f², Q², SRMR, and bootstrapping-based mediation testing. The results show that the impact of sedimentation has a positive and significant effect on fisher capacity, but does not significantly affect fishery business performance. Business diversification has a positive and significant effect on fishery business performance, but does not directly affect fisher capacity. Furthermore, fishery business performance does not mediate the effect of sedimentation or business diversification on fisher capacity. These findings confirm that fisher capacity is a socio-ecological outcome that is not entirely shaped through economic channels, particularly in the context of chronic environmental pressures. Practically, the results indicate that strategies to strengthen fisher capacity in areas affected by sedimentation need to go beyond a business performance-based approach, emphasizing the strengthening of adaptive capacities and institutional networks. sedimentation business diversification fisheries business performance fisher capacity SEM-PLS Kendari Bay Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Small-scale fisheries in coastal areas are increasingly under double pressure due to environmental degradation and economic uncertainty, which disrupt business stability, household welfare, and the adaptive capacity of fishermen. One of the increasingly dominant environmental pressures that is still limited in quantitative modeling in socio-economic fisheries studies is sedimentation. Sedimentation triggered by land use practices and runoff into coastal areas has been shown to degrade aquatic ecosystems, reduce fish stocks and biodiversity, and impact fishermen's productivity and income [ 1 ] [ 2 ]. Empirical evidence from Poyang Lake shows that increased phosphorus loads from land-based activities have a direct impact on local water and fishery conditions [ 2 ]. Most of the literature positions sedimentation as an external environmental pressure whose impact on fishermen is assumed to work through changes in catch and business performance. Physically, sedimentation is explained through the budget–capacity framework, which describes sediment fluxes and long-term system equilibrium [ 3 ], as well as the concept of sediment transport capacity, which determines erosion, deposition, and changes in aquatic habitats [ 4 ]. Modeling studies show that sediment accumulation alters fish habitats and reduces catch potential [ 5 ]. However, this approach tends to treat sedimentation as an ecological shock or disturbance, whereas in many coastal systems, sedimentation operates as a chronic environmental stress that can structurally limit fishermen's adaptive capacity, even when business performance is relatively stable. In response to these pressures, fishermen generally respond through business diversification as an adaptation strategy to reduce risk and income volatility. A number of studies show that diversification, whether through a variety of fishing or non-fishing activities, contributes to income stability and risk management [ 6 ] [ 7 ] [ 8 ] [ 9 ]. However, the literature also reveals a conceptual tension: specialized fishermen often earn higher profits, while diversification functions primarily as a risk mitigation mechanism [ 7 ]. The effectiveness of diversification is greatly influenced by fisheries governance [ 8 ] and economic conditions that can either strengthen or limit business performance [ 10 ] [ 11 ]. This shows that diversification does not always lead to an increase in fishermen's capacity, especially under persistent ecological pressures. Kendari Bay, Southeast Sulawesi, represents a relevant coastal system for testing these dynamics. Water quality pressures are reflected in the distribution of total suspended solids (TSS) and water productivity parameters [ 12 ]. Changes in water quality after sedimentation have proven to require technical adaptations in milkfish farming, which have impacted the economic performance of businesses [ 13 ]. For crab fishing households, livelihood diversification has become an important strategy in maintaining livelihood stability after the revitalization of Kendari Bay [ 14 ]. These ecological pressures are exacerbated by heavy metal contamination [ 15 ] [ 16 ] and mangrove degradation that reduces coastal habitat functions [ 17 ]. Thus, Kendari Bay serves as a socio-ecological laboratory for testing how chronic environmental pressures shape fishermen's economic responses and capacities. Although sedimentation and diversification have been extensively studied, there are still conceptual gaps in understanding the mechanisms of fisher capacity formation. From an adaptive capacity perspective, fisher capacity should not be reduced to economic outcomes alone. Rather, it reflects the ability to manage resources, reorganize livelihoods, and maintain control under persistent ecological constraints. Within this framework, capacity can emerge through direct adaptive responses to environmental pressures, rather than being mediated by short-term economic performance. This study explicitly tests whether the capacity of fishermen in sedimentation-affected coastal systems is formed through economic mediation or direct adaptive pathways under chronic sedimentation pressures. Most existing studies implicitly assume that sedimentation limits fisher capacity through reduced catch yields and decreased economic performance. However, this assumption remains under-explored empirically in degraded coastal systems characterized by persistent ecological pressures. Although sedimentation is generally expected to constrain fisher capacity, adaptive responses under chronic environmental pressures may also trigger compensatory capacity-building mechanisms at the household and institutional levels. From the perspective of capability and adaptive capacity, fisher capacity is not synonymous with business performance or welfare, but rather reflects the ability to manage resources, make economic choices, and build social-institutional networks in the context of ecological constraints. Based on this gap, this study develops and tests the Post-Sedimentation Fishermen Capacity Model in Kendari Bay by placing the impact of sedimentation (X1) and business diversification (X2) as the main determinants, fisheries business performance (Z1) as an explicitly tested mediating variable, and fishermen capacity (Y1) as a socio-ecological outcome. Fishermen's capacity is defined as the ability to manage household and business finances and build institutional and economic networks, while fisheries business performance reflects income stability and the sustainability of fishing activities. Direct and indirect relationships between constructs are analyzed using SEM-PLS to capture the complexity of socio-ecological interactions in coastal fisheries systems. Theoretically, this study contributes by testing and challenging the assumption that the formation of fisher capacity is always mediated by fishery business performance. Findings regarding the presence or absence of mediation are positioned as theoretical insights into how chronic environmental pressures and adaptation strategies shape fisher capacity directly and indirectly. Practically, the research results provide an empirical basis for formulating management policies for Kendari Bay and strategies for increasing fisher capacity that do not solely focus on improving short-term business performance, but also on strengthening adaptive capacity in the face of ongoing sedimentation pressures. Method Research design and data collection This study uses a cross-sectional explanatory study design to analyze the causal relationship between environmental pressures, adaptation strategies, business performance, and fisher capacity under conditions of chronic sedimentation pressure in Kendari Bay. Data collection was conducted in 2025 through direct structured interviews with 200 small-scale fisher households residing in the coastal area of Kendari Bay, Kendari City. The term post-sedimentation in this study refers to the condition of fishermen after experiencing prolonged sediment accumulation and deterioration of water quality, as reported in various previous studies in Kendari Bay [ 12 ] [ 15 ] [ 16 ]. A cross-sectional approach was used to capture variations in fishermen's responses to structural and recurring environmental pressures. Research instruments were developed to measure four reflective latent constructs, namely sedimentation impact (X1), business diversification (X2), fisheries business performance (Z1), and fisher capacity (Y1), which were tested using the SEM-PLS approach [ 18 ]. Research location and rationale The research was conducted in the coastal area of Kendari Bay, Southeast Sulawesi (Fig. 1 ), which is a center of small-scale fishing activities as well as an area with high sedimentation pressure and water quality degradation. These conditions have a direct impact on access to fishing areas, the efficiency of fishing operations, and the sustainability of fishing businesses. In addition, Kendari Bay has strong links between fishermen and marketing systems, fisheries infrastructure, and local institutions (fishermen's groups, cooperatives, and financial institutions), making it a relevant context for examining the formation of fishermen's capacity in coastal socio-ecological systems [ 19 ]. Respondents and sampling techniques Respondents were selected using a spatially stratified sampling approach to ensure representation of fishermen along the Kendari Bay coast. Spatial units were defined at the coastal village level, which included the main fishing settlements. In each village, active fishermen were identified with the help of fishermen's groups and community leaders. The criteria for respondent inclusion were: (1) actively fishing in Kendari Bay in the last 12 months, (2) residing in the coastal area of Kendari Bay, and (3) willing to participate in interviews. A sample size of 200 respondents was considered adequate for reflective SEM-PLS, as it exceeded the general rule of thumb (e.g., 10-times rule) and was sufficient to detect moderate effects in a model with multiple structural paths. Conceptual model and hypothesis testing This study developed the Fishermen Capacity Model Under Sedimentation Pressure, which was translated into a SEM-PLS structural model (Fig. 2 ). The impact of sedimentation (X1) and business diversification (X2) were positioned as exogenous constructs, fishing business performance (Z1) as a mediator construct, and fishermen capacity (Y1) as an endogenous construct. The hypotheses tested are: H1 The impact of sedimentation (X1) has a negative effect on fishery business performance (Z1). H2 Business diversification (X2) has a positive effect on fishery business performance (Z1). H3 The impact of sedimentation (X1) has a negative effect on fisher capacity (Y1). H4 Business diversification (X2) has a positive effect on fisher capacity (Y1). H5 Fisheries business performance (Z1) has a positive effect on fishing capacity (Y1). H6 Fisheries business performance (Z1) mediates the effect of sedimentation (X1) on fisher capacity (Y1). H7: Fishing business performance (Z1) mediates the effect of business diversification (X2) on fisher capacity (Y1). Measurement model (all constructs are reflective) All constructs are modeled reflectively to capture latent adaptive orientation rather than composite activity levels. In this specification, indicators are treated as manifestations of underlying latent capacities and strategies and are expected to vary in response to ongoing environmental pressures. This reflective approach is consistent with the adaptive capacity and capability framework of this study, which conceptualizes fishermen's responses as latent orientations rather than as additional livelihood components. 1. The impact of sedimentation (X1) is measured through fishermen's perceptions of the intensity of sedimentation pressure: X1.1 Decreased water clarity at fishing sites has led to a decline in catch compared to five years ago. X1.2 Reduction in traditional fishing areas due to siltation. X1.3 Increased frequency of fishing gear damage due to sediment. 2. Business diversification (X2) is modeled as a reflective construct, reflecting the level of diversification of fishermen's livelihoods as an adaptation strategy. The indicators used are: X2.1 Fishermen have more than one source of livelihood besides fishing. X2.2 Non-fishing businesses contribute significantly to household income. X2.3 Fishermen are actively involved in alternative economic activities (aquaculture, livestock farming, trade, and the like). These indicators are treated as reflections of the same level of business diversification and are measured using a 1–5 Likert scale. Theoretical justification: Diversification is positioned as a latent adaptive orientation, where variation in economic activities is an expression of fishermen's adaptive tendencies, not merely a sum of activities (in line with the capability and adaptive capacity approaches) [ 20 ] [ 21 ]. 3. Fisheries business performance (Z1) reflects the stability and sustainability of fishing activities, measured by: Z1.1 Stability of income from fisheries. Z1.2 Perception of the sustainability of the fishing industry and consistency in fishing intensity (frequency of going to sea). 4. Fishermen's capacity (Y1) is defined as socio-ecological capability, and is measured through: Y1.1 Ability to manage household and business finances. Y1.2 The ability to build and utilize institutional/economic networks. Y1.3 The ability to make adaptive economic decisions in response to environmental changes. All reflective indicators are measured using a 1–5 Likert scale (1 = strongly disagree; 5 = strongly agree). Data analysis using SEM-PLS (SmartPLS 4) Data analysis in this study was conducted using the Structural Equation Modeling–Partial Least Squares (SEM-PLS) approach with the assistance of SmartPLS version 4 software. The SEM-PLS approach was chosen because it is suitable for analyzing causal models involving latent constructs, does not require strict multivariate normality assumptions, and is capable of handling the complexity of direct and indirect relationships between constructs in the context of small-scale fisheries socio-ecological systems. All constructs in this model are modeled as reflective constructs, so that indicators are treated as manifestations of the underlying latent variables. The SEM-PLS evaluation was conducted in two main stages, namely the measurement model evaluation and the structural model evaluation. In the measurement model evaluation stage, the quality of reflective constructs was assessed through convergent validity, internal reliability, and discriminant validity tests. Convergent validity is evaluated based on indicator outer loadings (≥ 0.70), Composite Reliability (≥ 0.70), and Average Variance Extracted (AVE ≥ 0.50). The internal reliability of the construct is examined using Cronbach's alpha and rho_A values to ensure the internal consistency of the indicators in representing the latent construct. Discriminant validity was evaluated using two complementary approaches. First, the Fornell–Larcker criteria, where the square root of AVE (√AVE) of each construct must be greater than the correlation of that construct with other constructs. The √AVE value was placed on the diagonal of the Fornell–Larcker matrix as the main indicator of discriminant validity. Second, discriminant validity is also reinforced using the Heterotrait–Monotrait Ratio (HTMT), with a threshold of < 0.85 (strict) or < 0.90 (more lenient), to ensure that each construct is empirically distinct from other constructs. After the measurement model met the quality criteria, the evaluation continued with the structural model. The significance of the causal relationships between constructs was tested using a bootstrapping procedure with 5,000 resamples. The explanatory power of the model was assessed through the coefficient of determination (R²) on the endogenous constructs, while the relative contribution of each causal path was evaluated using effect sizes (f²). The predictive ability of the model is examined through the Q² value (predictive relevance), with a Q² value > 0 indicating that the model has adequate predictive power. The overall model fit is also evaluated using the Standardized Root Mean Square Residual (SRMR) as an indicator of global model fit in PLS-SEM. Mediation test Mediation testing in SEM-PLS was performed using bootstrapping on indirect effects and direct effects according to the procedure recommended by Hair[ 22 ]. In this study, p1 represents the coefficient of the X → Z path, p2 represents the coefficient of the Z → Y path, and p3 represents the coefficient of the X → Y path. First, the significance of the indirect effect (p1 × p2) was tested through bootstrapping (5,000 resamples) with a p-value criterion of < 0.05 (or a bootstrap confidence interval that did not cross zero). Second, the significance of the direct effect (p3) was also tested using the same bootstrapping procedure. Third, if both the indirect and direct effects are significant, the type of mediation is determined based on the direction/sign of the coefficients (whether p1 × p2 and p3 are in the same direction or opposite), so that it can be classified into: complementary mediation (complementary partial mediation; p1×p2 significant, p3 significant, and the effect sign is in the same direction/product p1·p2·p3 positive) or competitive mediation (competitive partial mediation; p1×p2 significant, p3 significant, but the effect sign is opposite/product p1·p2·p3 negative). If the indirect effect is significant but the direct effect is not significant, it is categorized as indirect-only mediation (full mediation). Conversely, if the indirect effect is not significant but the direct effect is significant, it is categorized as direct-only (no mediation). If both the indirect and direct effects are insignificant, the result is classified as no effect (no mediation). This procedure is applied to the two mediation paths tested in the study, namely X1 → Z1 → Y1 and X2 → Z1 → Y1, so that the interpretation of mediation is based on the significance of the indirect effect, the direct effect, and the consistency of the direction of the effect according to Hair's framework [ 22 ] . Ethical considerations Before the interview, respondents were given an explanation of the research objectives, data confidentiality, and the right to withdraw from participation at any time. Verbal consent for participation was obtained. The research protocol was confirmed by the Research Ethics Committee of the Department of Agribusiness, Halu Oleo University, as a non-invasive study that did not require formal ethical approval. Results 1) Evaluation of the Measurement Model (Outer Model) The measurement model evaluation was conducted to ensure that the reflective latent constructs met convergent validity, internal reliability, and discriminant validity before structural model testing was conducted in SEM-PLS [ 22 ] [ 23 ] [ 24 ]. In this study, all constructs Impact of Sedimentation (X1), Business Diversification (X2), Fisheries Business Performance (Z1), and Fisherman Capacity (Y1) were modeled as reflective constructs (Fig. 3 ). 1.1 Convergent Validity and Internal Reliability Convergent validity and internal reliability were evaluated using outer loading, Cronbach's alpha, composite reliability (ρa and ρc), and average variance extracted (AVE). The commonly used criteria are outer loading ≥ 0.70, composite reliability ≥ 0.70, and AVE ≥ 0.50 [ 22 ] [ 25 ]. Table 1 Outer Loading, Reliability, AVE, and AVE Root (√AVE) Indicators Outer loading Cronbach's alpha ρa ρc AVE √AVE X1.1 ← Impact of Sedimentation (X1) 0.769 0.740 0.745 0.852 0.657 0.811 X1.2 ← Impact of Sedimentation (X1) 0.845 X1.3 ← Impact of Sedimentation (X1) 0.817 X2.1 ← Business Diversification (X2) 0.720 0.743 0.830 0.846 0.648 0.805 X2.2 ← Business Diversification (X2) 0.814 X2.3 ← Business Diversification (X2) 0.874 Y1.1 ← Fisherman Capacity (Y1) 0.872 0.651 0.654 0.851 0.741 0.861 Y1.2 ← Fisherman Capacity (Y1) 0.850 Z1.1 ← Fisheries Business Performance (Z1) 0.925 0.669 0.770 0.852 0.743 0.862 Z1.2 ← Fisheries Business Performance (Z1) 0.794 In Table 1 and Fig. 3 , all indicators have outer loadings ≥ 0.70, and the composite reliability (ρc) values of all constructs are ≥ 0.70 and AVE ≥ 0.50, so that the criteria for convergent validity and internal reliability in reflective constructs are considered to be met [ 22 ] [ 25 ] [ 26 ]. 1.2 Discriminant Validity Based on Fornell–Larcker Discriminant validity is evaluated using the Fornell–Larcker criteria by comparing the √AVE of each construct with the inter-construct correlations. Discriminant validity is met if √AVE is greater than the correlation of that construct with other constructs [ 25 ] [ 22 ]. The √AVE values for each construct are presented in Table 1 . 1.3 Discriminant Validity Based on HTMT Discriminant validity was also tested using HTMT. An HTMT value < 0.85 (or < 0.90) indicates that discriminant validity is met [ 23 ] [ 22 ]. Table 2 Heterotrait–Monotrait Ratio (HTMT) Construct Pair HTMT Fisheries Business Performance (Z1) ↔ Business Diversification (X2) 0.647 Fisherman Capacity (Y1) ↔ Business Diversification (X2) 0.165 Fisherman Capacity (Y1) ↔ Fisheries Business Performance (Z1) 0.149 Impact of Sedimentation (X1) ↔ Business Diversification (X2) 0.098 Impact of Sedimentation (X1) ↔ Fisheries Business Performance (Z1) 0.166 Impact of Sedimentation (X1) ↔ Fisherman Capacity (Y1) 0.372 In Table 2 , all HTMT values are below the recommended threshold, so the discriminant validity is declared to be fulfilled [ 23 ] [ 27 ] [ 26 ]. 2) Structural Model Evaluation (Inner Model) The significance of the path coefficients was tested using the bootstrapping procedure (Fig. 4 ), with a significance criterion at a 95% confidence level (t > 1.96; p < 0.05) [ 22 ] [ 28 ]. Table 4 Direct Effects Path Original sample (O) Sample mean (M) STDEV t-statistics p-values Description X2 → Z1 0.510 0.513 0.041 12.430 0.000 Significant X2 → Y1 0.074 0.073 0.086 0.870 0.384 Not significant Z1 → Y1 0.101 0.105 0.080 1.260 0.208 Not significant X1 → Z1 −0.079 −0.082 0.062 1.280 0.200 Not significant X1 → Y1 0.276 0.284 0.071 3.860 0.000 Significant 3) Mediation Test Mediation testing was conducted by evaluating the significance of indirect effects using bootstrapping (Fig. 4 ). This procedure assesses the significance of indirect effects and direct effects to determine the mediation pattern [ 22 ] [ 29 ]. Table 5 Indirect Effects Indirect Paths Original sample (O) Sample mean (M) STDEV t-statistics p-values Description X2 → Z1 → Y1 0.052 0.054 0.042 1.240 0.215 Not significant X1 → Z1 → Y1 −0.008 −0.008 0.010 0.779 0.436 Not significant 4) Structural Model Quality The evaluation of structural model quality was conducted by assessing the coefficient of determination (R² and adjusted R²), effect size (f²), predictive relevance (Q²predict), and model fit as recommended in SEM-PLS [ 22 ] [ 30 ]. 4.1 Coefficient of Determination (R² and Adjusted R²) The R² value is used to indicate the proportion of variance in the endogenous construct that can be explained by the exogenous construct in the model. An R² value of 0.25 is categorized as weak–moderate, 0.50 as moderate, and 0.75 as strong [ 22 ]. Table 6 Coefficient of Determination Endogenous Construct Adjusted R² Fisheries Business Performance (Z1) 0.264 Fisherman Capacity (Y1) 0.078 In Table 6 , the adjusted R² value shows that the exogenous construct explains 26.4% of the variance in fisheries business performance (Z1) and 7.8% of the variance in fisherman capacity (Y1), in accordance with the R² interpretation criteria in SEM-PLS [ 22 ]. 4.2 Effect Size (f²) Effect size (f²) is used to assess the relative contribution of each exogenous construct to the endogenous construct. An f² value of 0.02 indicates a small effect, 0.15 a moderate effect, and 0.35 a large effect [ 31 ] [ 32 ] [ 22 ]. Table 7 Effect Size (f²) Path f² Business Diversification (X2) → Fisheries Business Performance (Z1) 0.357 Business Diversification (X2) → Fisherman Capacity (Y1) 0.004 Fisheries Business Performance (Z1) → Fisherman Capacity (Y1) 0.008 Impact of Sedimentation (X1) → Fisheries Business Performance (Z1) 0.009 Impact of Sedimentation (X1) → Fisherman Capacity (Y1) 0.083 In Table 7 , the f² values in the table show the variation in the contribution of effects between paths according to the guidelines for interpreting effect sizes in SEM-PLS [ 31 ] [ 32 ] [ 22 ]. 4.3 Predictive Relevance (Q²predict) Predictive relevance is evaluated using the PLS-predict procedure. A Q²predict value > 0 indicates that the model has predictive power for endogenous constructs [ 27 ]. Table 8 Q²predict Endogenous Construct Q²predict Fisherman Capacity (Y1) 0.064 Fisheries Business Performance (Z1) 0.208 In Table 8 , the positive Q²predict value indicates the predictive relevance of the model to the endogenous construct of the [ 27 ] 4.4 Model Fit Model fit was evaluated using several approximate model fit indicators recommended in SEM-PLS, specifically SRMR, d_ULS, d_G, and NFI [ 23 ] [ 22 ]. Table 9 Model Fit Indicators Indicator Value SRMR 0.088 d_ULS 0.423 d_G 0.201 Chi-square (df10 = 18.307) 261.577 NFI 0.497 In Table 9 , SRMR values < 0.10 indicate an acceptable level of model fit in SEM-PLS, while other fit indices are presented as supporting information in accordance with variance-based model reporting practices [ 30 ] [ 22 ]. Discussion Measurement Model Analysis The results of the SEM-PLS analysis show that the reflective measurement model meets all recommended quality criteria. All indicators have outer loadings > 0.70, Composite Reliability values > 0.70, and AVE > 0.50, thus fulfilling convergent validity and internal reliability[ 22 ] Discriminant validity was also confirmed through the Fornell–Larcker criteria (√AVE greater than the correlation between constructs) and HTMT < 0.85 for all construct pairs [ 23 ]. In the structural model, business diversification (X2) had a positive and significant effect on fishery business performance (Z1) (β = 0.510; p < 0.001) with a large effect size (f² = 0.357). Conversely, business diversification does not have a direct effect on fisher capacity (Y1), and fishery business performance also does not have a significant effect on fisher capacity. The impact of sedimentation (X1) does not significantly affect fishery business performance, but shows a direct positive and significant effect on fisher capacity (β = 0.276; p < 0.001) with a small to medium effect size (f² = 0.083). The adjusted R² value shows that the model explains 26.4% of the variance in fishery business performance and 7.8% of the variance in fisher capacity, while the positive Q²predict value indicates the predictive relevance of the model. The SRMR of 0.088 indicates an acceptable level of model fit in the context of variance-based SEM-PLS [ 30 ] [ 27 ]. Mediation testing confirmed that fishing business performance did not mediate the effect of business diversification or the impact of sedimentation on fishermen's capacity, so all indirect paths were declared insignificant. The Impact of Sedimentation and the Formation of Fishermen's Capacity The results show that the impact of sedimentation has a positive and significant effect on fisher capacity, while its effect on fishery business performance is not significant. This finding indicates that environmental pressures due to sedimentation are not always directly reflected in short-term fishery business performance, but rather trigger stronger adaptive responses at the household and institutional levels of fishermen. Thus, sedimentation operates as a chronic environmental stress that shapes fishermen's capacity directly, rather than through conventional economic channels. Conceptually, these results are consistent with the model-based estimation approach, which emphasizes that socio-economic responses to ecological pressures are often non-linear and not always reflected in business output indicators [ 18 ] [ 21 ] [ 20 ]. Fishermen's capacity, as reflected in their financial management skills, adaptive decision-making, and institutional networks, is a more sensitive construct in capturing the impact of ecological pressures than fisheries business performance. Business Diversification, Business Performance, and Fishermen Capacity This study found that business diversification has a positive and significant effect on fishery business performance, but does not directly affect fisher capacity. These findings indicate that diversification functions primarily as an operational economic strategy that increases income stability and perceptions of business sustainability, but does not automatically strengthen fisher capacity in the social and institutional dimensions. These results are in line with the literature emphasizing that diversification improves economic performance through risk mitigation, but capacity building requires more complex social processes, including learning, access, and network quality [ 33 ] [ 34 ]. Thus, variation in economic activities alone is insufficient to build adaptive capacity among fishermen, especially in the context of structural ecological pressures. Failure of Mediation of Fishing Business Performance Testing for indirect effects shows that fishery business performance does not mediate the relationship between the impact of sedimentation and business diversification on fishermen's capacity. This finding is a major theoretical contribution of the study, as it empirically challenges the dominant assumption that fishermen's capacity is always formed through improved economic performance first. The absence of a significant indirect effect indicates that fishery business performance does not mediate the relationship between environmental pressures and fisher capacity. This finding provides empirical evidence that under chronic environmental pressures, economic performance fails to function as an effective transmission mechanism for adaptive capacity, thus challenging the linear livelihood model commonly assumed in small-scale fishery research. Instead, adaptive capacity appears to be shaped through direct behavioral and social responses to persistent ecological constraints, rather than through short-term economic outcomes. These results support the view that when pressures stem from structural factors such as environmental degradation, economic mediation pathways become weak or even irrelevant [ 35 ]. In the context of Kendari Bay, fishers appear to build capacity through direct adaptation of household strategies, social relations, and resource management without necessarily being preceded by improved fisheries business performance. Theoretical and Practical Implications Overall, the findings of this study indicate that fisher capacity is a socio-ecological outcome that is not entirely economy-driven. Business diversification remains important in maintaining economic performance, but fisher adaptive capacity is shaped primarily by direct responses to environmental pressures. This model expands the coastal livelihoods literature by showing that capacity formation pathways are non-linear and contextual, particularly in coastal systems experiencing ongoing environmental degradation. The practical implication is that fisheries development policies in areas affected by sedimentation should not only focus on increasing production or economic diversification, but should also be directed at strengthening household management capacity, access to institutional networks, and fishermen's ability to adapt to long-term ecological pressures. Conclusion This study develops and tests a model of fisher capacity under chronic sedimentation pressure in Kendari Bay by integrating the impacts of sedimentation, business diversification, and fisheries business performance using a full reflective SEM-PLS approach. The results show that the impact of sedimentation is a significant direct determinant of fisher capacity, while its effect on fisheries business performance is not significant. These findings confirm that structural environmental pressures work directly on the formation of fisher capacity, without having to be mediated by short-term economic performance. Business diversification has been shown to play an important role in improving fishery business performance, but it does not directly affect fisher capacity. In addition, fishery business performance does not mediate the influence of sedimentation impacts or business diversification on fisher capacity. The failure of this mediation pathway is a key finding that challenges the dominant assumption that improvements in fisher capacity are always shaped through improvements in fishery business performance. By empirically demonstrating the failure of the economic mediation pathway, this study reframes fisher capacity as a direct adaptive response to chronic environmental pressures, rather than as a derivative of economic performance. Theoretically, this research contributes by showing that fisher capacity is a socio-ecological outcome that is not entirely economy-driven, especially in the context of chronic environmental pressures such as sedimentation. The resulting model expands the coastal livelihoods literature by asserting that capacity building is direct, non-linear, and contextual. Practically, these findings imply that policies to strengthen the capacity of fishers in areas affected by sedimentation need to go beyond approaches based on business performance and economic diversification, with an emphasis on strengthening adaptive capacity, household management, and fishers' institutional networks. Research Limitations and Future Research Agenda This study has several limitations that need to be considered when interpreting the findings. First, the study design used a cross-sectional approach, so that the causal relationships identified reflect structural associations at a single point in time and do not capture the dynamics of fishermen's adaptation in the long term. Sedimentation pressure as a chronic stressor likely shapes fishermen's capacity through a gradual process that is not fully observable in a one-time survey design. Second, the measurement of sedimentation impacts and fisher capacity was primarily based on household perception indicators. Although this approach is relevant for capturing the socio-ecological responses of fishers, this limitation opens up opportunities for the integration of biophysical data (e.g., TSS, sedimentation rate) and objective economic indicators in future research to enrich the external validity of the findings. Third, the research model focused on a single coastal context, namely Kendari Bay, which has specific ecological and institutional characteristics. Therefore, generalizing the results to other coastal systems needs to be done with caution, especially in areas with different levels of environmental degradation, market structures, and fisheries governance. Given these limitations, future research is advised to adopt a longitudinal or panel design to trace changes in fisher capacity over time under sustained environmental pressure. In addition, developing models that incorporate institutional variables, access to information, and social capital as mediators or moderators has the potential to provide a more comprehensive understanding of the pathways to fisher capacity formation. Cross-site comparisons are also needed to assess the consistency of these findings across different coastal socio-ecological contexts. Declarations Clinical trial number not applicable Ethics approval This study employed an observational research design involving fishermen as primary respondents. Ethical approval was deemed exempt by the Research Ethics Committee of the Department of Agribusiness, Halu Oleo University, because the study relied solely on voluntary survey responses and did not collect sensitive personal data. All procedures were conducted in accordance with established research ethics principles, including informed consent, confidentiality, and anonymity, and complied with relevant national and institutional research guidelines and regulations. Consent to participate Informed consent was obtained from all participants included in the study and adhered to the Declaration of Helsinki Consent to publish Not applicable Competing interests The authors declare no financial or personal conflict of interest. Funding This research did not receive any specific grant from any source. Author Contribution Muhammad Aswar Limi, R. Marsuki Iswandi, Weka Widayati, Hartina Batoa, and Iskandar Zainuddin Rela have collected data. Yani Taufik screened samples and prepared the first draft of the manuscript. Muhammad Aswar Limi reviewed and edited the final version of the manuscript. All the authors reviewed and approved the final draft of the manuscript Acknowledgement The author would like to express his gratitude to Halu Oleo University for its institutional support through the Halu Oleo University Internal Basic Research programme, which enabled this research to be carried out and completed successfully. Special thanks go to Armid Rahimahullah, who provided support to all professors conducting research. Data Availability The author confirms that all data generated or analyzed during this study are included in this published article. Furthermore, data supporting the findings of this study is available from the corresponding author upon reasonable request. 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PLoS One. 2015;10(6):e0131765. https://dx.plos.org/10.1371/journal.pone.0131765 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Jan, 2026 Reviews received at journal 28 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 11 Jan, 2026 Reviews received at journal 09 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers invited by journal 09 Jan, 2026 Editor assigned by journal 21 Dec, 2025 Submission checks completed at journal 21 Dec, 2025 First submitted to journal 21 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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increasingly under double pressure due to environmental degradation and economic uncertainty, which disrupt business stability, household welfare, and the adaptive capacity of fishermen. One of the increasingly dominant environmental pressures that is still limited in quantitative modeling in socio-economic fisheries studies is sedimentation. Sedimentation triggered by land use practices and runoff into coastal areas has been shown to degrade aquatic ecosystems, reduce fish stocks and biodiversity, and impact fishermen's productivity and income [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Empirical evidence from Poyang Lake shows that increased phosphorus loads from land-based activities have a direct impact on local water and fishery conditions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost of the literature positions sedimentation as an external environmental pressure whose impact on fishermen is assumed to work through changes in catch and business performance. Physically, sedimentation is explained through the budget\u0026ndash;capacity framework, which describes sediment fluxes and long-term system equilibrium [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], as well as the concept of sediment transport capacity, which determines erosion, deposition, and changes in aquatic habitats [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Modeling studies show that sediment accumulation alters fish habitats and reduces catch potential [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, this approach tends to treat sedimentation as an ecological shock or disturbance, whereas in many coastal systems, sedimentation operates as a chronic environmental stress that can structurally limit fishermen's adaptive capacity, even when business performance is relatively stable.\u003c/p\u003e \u003cp\u003eIn response to these pressures, fishermen generally respond through business diversification as an adaptation strategy to reduce risk and income volatility. A number of studies show that diversification, whether through a variety of fishing or non-fishing activities, contributes to income stability and risk management [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the literature also reveals a conceptual tension: specialized fishermen often earn higher profits, while diversification functions primarily as a risk mitigation mechanism [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The effectiveness of diversification is greatly influenced by fisheries governance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and economic conditions that can either strengthen or limit business performance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This shows that diversification does not always lead to an increase in fishermen's capacity, especially under persistent ecological pressures.\u003c/p\u003e \u003cp\u003eKendari Bay, Southeast Sulawesi, represents a relevant coastal system for testing these dynamics. Water quality pressures are reflected in the distribution of total suspended solids (TSS) and water productivity parameters [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Changes in water quality after sedimentation have proven to require technical adaptations in milkfish farming, which have impacted the economic performance of businesses [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For crab fishing households, livelihood diversification has become an important strategy in maintaining livelihood stability after the revitalization of Kendari Bay [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These ecological pressures are exacerbated by heavy metal contamination [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and mangrove degradation that reduces coastal habitat functions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Thus, Kendari Bay serves as a socio-ecological laboratory for testing how chronic environmental pressures shape fishermen's economic responses and capacities.\u003c/p\u003e \u003cp\u003eAlthough sedimentation and diversification have been extensively studied, there are still conceptual gaps in understanding the mechanisms of fisher capacity formation. From an adaptive capacity perspective, fisher capacity should not be reduced to economic outcomes alone. Rather, it reflects the ability to manage resources, reorganize livelihoods, and maintain control under persistent ecological constraints. Within this framework, capacity can emerge through direct adaptive responses to environmental pressures, rather than being mediated by short-term economic performance. This study explicitly tests whether the capacity of fishermen in sedimentation-affected coastal systems is formed through economic mediation or direct adaptive pathways under chronic sedimentation pressures.\u003c/p\u003e \u003cp\u003eMost existing studies implicitly assume that sedimentation limits fisher capacity through reduced catch yields and decreased economic performance. However, this assumption remains under-explored empirically in degraded coastal systems characterized by persistent ecological pressures. Although sedimentation is generally expected to constrain fisher capacity, adaptive responses under chronic environmental pressures may also trigger compensatory capacity-building mechanisms at the household and institutional levels. From the perspective of capability and adaptive capacity, fisher capacity is not synonymous with business performance or welfare, but rather reflects the ability to manage resources, make economic choices, and build social-institutional networks in the context of ecological constraints.\u003c/p\u003e \u003cp\u003eBased on this gap, this study develops and tests the Post-Sedimentation Fishermen Capacity Model in Kendari Bay by placing the impact of sedimentation (X1) and business diversification (X2) as the main determinants, fisheries business performance (Z1) as an explicitly tested mediating variable, and fishermen capacity (Y1) as a socio-ecological outcome. Fishermen's capacity is defined as the ability to manage household and business finances and build institutional and economic networks, while fisheries business performance reflects income stability and the sustainability of fishing activities. Direct and indirect relationships between constructs are analyzed using SEM-PLS to capture the complexity of socio-ecological interactions in coastal fisheries systems.\u003c/p\u003e \u003cp\u003eTheoretically, this study contributes by testing and challenging the assumption that the formation of fisher capacity is always mediated by fishery business performance. Findings regarding the presence or absence of mediation are positioned as theoretical insights into how chronic environmental pressures and adaptation strategies shape fisher capacity directly and indirectly. Practically, the research results provide an empirical basis for formulating management policies for Kendari Bay and strategies for increasing fisher capacity that do not solely focus on improving short-term business performance, but also on strengthening adaptive capacity in the face of ongoing sedimentation pressures.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eResearch design and data collection\u003c/h2\u003e\n \u003cp\u003eThis study uses a cross-sectional explanatory study design to analyze the causal relationship between environmental pressures, adaptation strategies, business performance, and fisher capacity under conditions of chronic sedimentation pressure in Kendari Bay. Data collection was conducted in 2025 through direct structured interviews with 200 small-scale fisher households residing in the coastal area of Kendari Bay, Kendari City.\u003c/p\u003e\n \u003cp\u003eThe term post-sedimentation in this study refers to the condition of fishermen after experiencing prolonged sediment accumulation and deterioration of water quality, as reported in various previous studies in Kendari Bay [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. A cross-sectional approach was used to capture variations in fishermen\u0026apos;s responses to structural and recurring environmental pressures.\u003c/p\u003e\n \u003cp\u003eResearch instruments were developed to measure four reflective latent constructs, namely sedimentation impact (X1), business diversification (X2), fisheries business performance (Z1), and fisher capacity (Y1), which were tested using the SEM-PLS approach [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eResearch location and rationale\u003c/h3\u003e\n\u003cp\u003eThe research was conducted in the coastal area of Kendari Bay, Southeast Sulawesi (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), which is a center of small-scale fishing activities as well as an area with high sedimentation pressure and water quality degradation. These conditions have a direct impact on access to fishing areas, the efficiency of fishing operations, and the sustainability of fishing businesses.\u003c/p\u003e\n\u003cp\u003eIn addition, Kendari Bay has strong links between fishermen and marketing systems, fisheries infrastructure, and local institutions (fishermen\u0026apos;s groups, cooperatives, and financial institutions), making it a relevant context for examining the formation of fishermen\u0026apos;s capacity in coastal socio-ecological systems [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eRespondents and sampling techniques\u003c/h3\u003e\n\u003cp\u003eRespondents were selected using a spatially stratified sampling approach to ensure representation of fishermen along the Kendari Bay coast. Spatial units were defined at the coastal village level, which included the main fishing settlements. In each village, active fishermen were identified with the help of fishermen\u0026apos;s groups and community leaders. The criteria for respondent inclusion were: (1) actively fishing in Kendari Bay in the last 12 months, (2) residing in the coastal area of Kendari Bay, and (3) willing to participate in interviews.\u003c/p\u003e\n\u003cp\u003eA sample size of 200 respondents was considered adequate for reflective SEM-PLS, as it exceeded the general rule of thumb (e.g., 10-times rule) and was sufficient to detect moderate effects in a model with multiple structural paths.\u003c/p\u003e\n\u003ch3\u003eConceptual model and hypothesis testing\u003c/h3\u003e\n\u003cp\u003eThis study developed the Fishermen Capacity Model Under Sedimentation Pressure, which was translated into a SEM-PLS structural model (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The impact of sedimentation (X1) and business diversification (X2) were positioned as exogenous constructs, fishing business performance (Z1) as a mediator construct, and fishermen capacity (Y1) as an endogenous construct.\u003c/p\u003e\n\u003cp\u003eThe hypotheses tested are:\u003c/p\u003e\n\u003cp\u003eH1 The impact of sedimentation (X1) has a negative effect on fishery business performance (Z1).\u003c/p\u003e\n\u003cp\u003eH2 Business diversification (X2) has a positive effect on fishery business performance (Z1).\u003c/p\u003e\n\u003cp\u003eH3 The impact of sedimentation (X1) has a negative effect on fisher capacity (Y1).\u003c/p\u003e\n\u003cp\u003eH4 Business diversification (X2) has a positive effect on fisher capacity (Y1).\u003c/p\u003e\n\u003cp\u003eH5 Fisheries business performance (Z1) has a positive effect on fishing capacity (Y1).\u003c/p\u003e\n\u003cp\u003eH6 Fisheries business performance (Z1) mediates the effect of sedimentation (X1) on fisher capacity (Y1).\u003c/p\u003e\n\u003cp\u003eH7: Fishing business performance (Z1) mediates the effect of business diversification (X2) on fisher capacity (Y1).\u003c/p\u003e\n\u003ch3\u003eMeasurement model (all constructs are reflective)\u003c/h3\u003e\n\u003cp\u003eAll constructs are modeled reflectively to capture latent adaptive orientation rather than composite activity levels. In this specification, indicators are treated as manifestations of underlying latent capacities and strategies and are expected to vary in response to ongoing environmental pressures. This reflective approach is consistent with the adaptive capacity and capability framework of this study, which conceptualizes fishermen\u0026apos;s responses as latent orientations rather than as additional livelihood components.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. The impact of sedimentation (X1) is measured through fishermen\u0026apos;s perceptions of the intensity of sedimentation pressure:\u003c/p\u003eX1.1 Decreased water clarity at fishing sites has led to a decline in catch compared to five years ago.\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eX1.2 Reduction in traditional fishing areas due to siltation.\u003c/p\u003e\n\u003cp\u003eX1.3 Increased frequency of fishing gear damage due to sediment.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e2. Business diversification (X2) is modeled as a reflective construct, reflecting the level of diversification of fishermen\u0026apos;s livelihoods as an adaptation strategy. The indicators used are:\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eX2.1 Fishermen have more than one source of livelihood besides fishing.\u003c/p\u003e\n\u003cp\u003eX2.2 Non-fishing businesses contribute significantly to household income.\u003c/p\u003e\n\u003cp\u003eX2.3 Fishermen are actively involved in alternative economic activities (aquaculture, livestock farming, trade, and the like).\u003c/p\u003e\n\u003cp\u003eThese indicators are treated as reflections of the same level of business diversification and are measured using a 1\u0026ndash;5 Likert scale. Theoretical justification: Diversification is positioned as a latent adaptive orientation, where variation in economic activities is an expression of fishermen\u0026apos;s adaptive tendencies, not merely a sum of activities (in line with the capability and adaptive capacity approaches) [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e3. Fisheries business performance (Z1) reflects the stability and sustainability of fishing activities, measured by:\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eZ1.1 Stability of income from fisheries.\u003c/p\u003e\n\u003cp\u003eZ1.2 Perception of the sustainability of the fishing industry and consistency in fishing intensity (frequency of going to sea).\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e4. Fishermen\u0026apos;s capacity (Y1) is defined as socio-ecological capability, and is measured through:\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eY1.1 Ability to manage household and business finances.\u003c/p\u003e\n\u003cp\u003eY1.2 The ability to build and utilize institutional/economic networks.\u003c/p\u003e\n\u003cp\u003eY1.3 The ability to make adaptive economic decisions in response to environmental changes.\u003c/p\u003e\n\u003cp\u003eAll reflective indicators are measured using a 1\u0026ndash;5 Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree; 5\u0026thinsp;=\u0026thinsp;strongly agree).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis using SEM-PLS (SmartPLS 4)\u003c/h2\u003e\n \u003cp\u003eData analysis in this study was conducted using the Structural Equation Modeling\u0026ndash;Partial Least Squares (SEM-PLS) approach with the assistance of SmartPLS version 4 software. The SEM-PLS approach was chosen because it is suitable for analyzing causal models involving latent constructs, does not require strict multivariate normality assumptions, and is capable of handling the complexity of direct and indirect relationships between constructs in the context of small-scale fisheries socio-ecological systems. All constructs in this model are modeled as reflective constructs, so that indicators are treated as manifestations of the underlying latent variables.\u003c/p\u003e\n \u003cp\u003eThe SEM-PLS evaluation was conducted in two main stages, namely the measurement model evaluation and the structural model evaluation. In the measurement model evaluation stage, the quality of reflective constructs was assessed through convergent validity, internal reliability, and discriminant validity tests. Convergent validity is evaluated based on indicator outer loadings (\u0026ge;\u0026thinsp;0.70), Composite Reliability (\u0026ge;\u0026thinsp;0.70), and Average Variance Extracted (AVE\u0026thinsp;\u0026ge;\u0026thinsp;0.50). The internal reliability of the construct is examined using Cronbach\u0026apos;s alpha and rho_A values to ensure the internal consistency of the indicators in representing the latent construct.\u003c/p\u003e\n \u003cp\u003eDiscriminant validity was evaluated using two complementary approaches. First, the Fornell\u0026ndash;Larcker criteria, where the square root of AVE (\u0026radic;AVE) of each construct must be greater than the correlation of that construct with other constructs. The \u0026radic;AVE value was placed on the diagonal of the Fornell\u0026ndash;Larcker matrix as the main indicator of discriminant validity. Second, discriminant validity is also reinforced using the Heterotrait\u0026ndash;Monotrait Ratio (HTMT), with a threshold of \u0026lt;\u0026thinsp;0.85 (strict) or \u0026lt;\u0026thinsp;0.90 (more lenient), to ensure that each construct is empirically distinct from other constructs.\u003c/p\u003e\n \u003cp\u003eAfter the measurement model met the quality criteria, the evaluation continued with the structural model. The significance of the causal relationships between constructs was tested using a bootstrapping procedure with 5,000 resamples. The explanatory power of the model was assessed through the coefficient of determination (R\u0026sup2;) on the endogenous constructs, while the relative contribution of each causal path was evaluated using effect sizes (f\u0026sup2;). The predictive ability of the model is examined through the Q\u0026sup2; value (predictive relevance), with a Q\u0026sup2; value\u0026thinsp;\u0026gt;\u0026thinsp;0 indicating that the model has adequate predictive power. The overall model fit is also evaluated using the Standardized Root Mean Square Residual (SRMR) as an indicator of global model fit in PLS-SEM.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMediation test\u003c/h3\u003e\n\u003cp\u003eMediation testing in SEM-PLS was performed using bootstrapping on indirect effects and direct effects according to the procedure recommended by Hair[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study, p1 represents the coefficient of the X \u0026rarr; Z path, p2 represents the coefficient of the Z \u0026rarr; Y path, and p3 represents the coefficient of the X \u0026rarr; Y path. First, the significance of the indirect effect (p1 \u0026times; p2) was tested through bootstrapping (5,000 resamples) with a p-value criterion of \u0026lt;\u0026thinsp;0.05 (or a bootstrap confidence interval that did not cross zero). Second, the significance of the direct effect (p3) was also tested using the same bootstrapping procedure. Third, if both the indirect and direct effects are significant, the type of mediation is determined based on the direction/sign of the coefficients (whether p1 \u0026times; p2 and p3 are in the same direction or opposite), so that it can be classified into: complementary mediation (complementary partial mediation; p1\u0026times;p2 significant, p3 significant, and the effect sign is in the same direction/product p1\u0026middot;p2\u0026middot;p3 positive) or competitive mediation (competitive partial mediation; p1\u0026times;p2 significant, p3 significant, but the effect sign is opposite/product p1\u0026middot;p2\u0026middot;p3 negative). If the indirect effect is significant but the direct effect is not significant, it is categorized as indirect-only mediation (full mediation). Conversely, if the indirect effect is not significant but the direct effect is significant, it is categorized as direct-only (no mediation). If both the indirect and direct effects are insignificant, the result is classified as no effect (no mediation). This procedure is applied to the two mediation paths tested in the study, namely X1 \u0026rarr; Z1 \u0026rarr; Y1 and X2 \u0026rarr; Z1 \u0026rarr; Y1, so that the interpretation of mediation is based on the significance of the indirect effect, the direct effect, and the consistency of the direction of the effect according to Hair\u0026apos;s framework [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e] .\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eBefore the interview, respondents were given an explanation of the research objectives, data confidentiality, and the right to withdraw from participation at any time. Verbal consent for participation was obtained. The research protocol was confirmed by the Research Ethics Committee of the Department of Agribusiness, Halu Oleo University, as a non-invasive study that did not require formal ethical approval.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003e1) Evaluation of the Measurement Model (Outer Model)\u003c/p\u003e \u003cp\u003eThe measurement model evaluation was conducted to ensure that the reflective latent constructs met convergent validity, internal reliability, and discriminant validity before structural model testing was conducted in SEM-PLS [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, all constructs Impact of Sedimentation (X1), Business Diversification (X2), Fisheries Business Performance (Z1), and Fisherman Capacity (Y1) were modeled as reflective constructs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e1.1 Convergent Validity and Internal Reliability\u003c/p\u003e \u003cp\u003eConvergent validity and internal reliability were evaluated using outer loading, Cronbach's alpha, composite reliability (ρa and ρc), and average variance extracted (AVE). The commonly used criteria are outer loading\u0026thinsp;\u0026ge;\u0026thinsp;0.70, composite reliability\u0026thinsp;\u0026ge;\u0026thinsp;0.70, and AVE\u0026thinsp;\u0026ge;\u0026thinsp;0.50 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOuter Loading, Reliability, AVE, and AVE Root (\u0026radic;AVE)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOuter loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCronbach's alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eρc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026radic;AVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX1.1 \u0026larr; Impact of Sedimentation (X1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX1.2 \u0026larr; Impact of Sedimentation (X1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX1.3 \u0026larr; Impact of Sedimentation (X1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX2.1 \u0026larr; Business Diversification (X2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX2.2 \u0026larr; Business Diversification (X2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX2.3 \u0026larr; Business Diversification (X2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY1.1 \u0026larr; Fisherman Capacity (Y1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY1.2 \u0026larr; Fisherman Capacity (Y1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ1.1 \u0026larr; Fisheries Business Performance (Z1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ1.2 \u0026larr; Fisheries Business Performance (Z1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all indicators have outer loadings\u0026thinsp;\u0026ge;\u0026thinsp;0.70, and the composite reliability (ρc) values of all constructs are \u0026ge;\u0026thinsp;0.70 and AVE\u0026thinsp;\u0026ge;\u0026thinsp;0.50, so that the criteria for convergent validity and internal reliability in reflective constructs are considered to be met [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e1.2 Discriminant Validity Based on Fornell\u0026ndash;Larcker\u003c/p\u003e \u003cp\u003eDiscriminant validity is evaluated using the Fornell\u0026ndash;Larcker criteria by comparing the \u0026radic;AVE of each construct with the inter-construct correlations. Discriminant validity is met if \u0026radic;AVE is greater than the correlation of that construct with other constructs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The \u0026radic;AVE values for each construct are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e1.3 Discriminant Validity Based on HTMT\u003c/p\u003e \u003cp\u003eDiscriminant validity was also tested using HTMT. An HTMT value\u0026thinsp;\u0026lt;\u0026thinsp;0.85 (or \u0026lt;\u0026thinsp;0.90) indicates that discriminant validity is met [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterotrait\u0026ndash;Monotrait Ratio (HTMT)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct Pair\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHTMT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisheries Business Performance (Z1) \u0026harr; Business Diversification (X2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisherman Capacity (Y1) \u0026harr; Business Diversification (X2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisherman Capacity (Y1) \u0026harr; Fisheries Business Performance (Z1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpact of Sedimentation (X1) \u0026harr; Business Diversification (X2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpact of Sedimentation (X1) \u0026harr; Fisheries Business Performance (Z1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpact of Sedimentation (X1) \u0026harr; Fisherman Capacity (Y1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all HTMT values are below the recommended threshold, so the discriminant validity is declared to be fulfilled [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e2) Structural Model Evaluation (Inner Model)\u003c/p\u003e \u003cp\u003eThe significance of the path coefficients was tested using the bootstrapping procedure (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with a significance criterion at a 95% confidence level (t\u0026thinsp;\u0026gt;\u0026thinsp;1.96; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect Effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal sample (O)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample mean (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTDEV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX2 \u0026rarr; Z1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX2 \u0026rarr; Y1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ1 \u0026rarr; Y1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX1 \u0026rarr; Z1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX1 \u0026rarr; Y1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3) Mediation Test\u003c/p\u003e \u003cp\u003eMediation testing was conducted by evaluating the significance of indirect effects using bootstrapping (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This procedure assesses the significance of indirect effects and direct effects to determine the mediation pattern [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndirect Effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect Paths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal sample (O)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample mean (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTDEV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX2 \u0026rarr; Z1 \u0026rarr; Y1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX1 \u0026rarr; Z1 \u0026rarr; Y1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e4) Structural Model Quality\u003c/p\u003e \u003cp\u003eThe evaluation of structural model quality was conducted by assessing the coefficient of determination (R\u0026sup2; and adjusted R\u0026sup2;), effect size (f\u0026sup2;), predictive relevance (Q\u0026sup2;predict), and model fit as recommended in SEM-PLS [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e4.1 Coefficient of Determination (R\u0026sup2; and Adjusted R\u0026sup2;)\u003c/p\u003e \u003cp\u003eThe R\u0026sup2; value is used to indicate the proportion of variance in the endogenous construct that can be explained by the exogenous construct in the model. An R\u0026sup2; value of 0.25 is categorized as weak\u0026ndash;moderate, 0.50 as moderate, and 0.75 as strong [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficient of Determination\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndogenous Construct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisheries Business Performance (Z1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisherman Capacity (Y1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the adjusted R\u0026sup2; value shows that the exogenous construct explains 26.4% of the variance in fisheries business performance (Z1) and 7.8% of the variance in fisherman capacity (Y1), in accordance with the R\u0026sup2; interpretation criteria in SEM-PLS [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e4.2 Effect Size (f\u0026sup2;)\u003c/p\u003e \u003cp\u003eEffect size (f\u0026sup2;) is used to assess the relative contribution of each exogenous construct to the endogenous construct. An f\u0026sup2; value of 0.02 indicates a small effect, 0.15 a moderate effect, and 0.35 a large effect [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect Size (f\u0026sup2;)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ef\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness Diversification (X2) \u0026rarr; Fisheries Business Performance (Z1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness Diversification (X2) \u0026rarr; Fisherman Capacity (Y1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisheries Business Performance (Z1) \u0026rarr; Fisherman Capacity (Y1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpact of Sedimentation (X1) \u0026rarr; Fisheries Business Performance (Z1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpact of Sedimentation (X1) \u0026rarr; Fisherman Capacity (Y1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the f\u0026sup2; values in the table show the variation in the contribution of effects between paths according to the guidelines for interpreting effect sizes in SEM-PLS [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e4.3 Predictive Relevance (Q\u0026sup2;predict)\u003c/p\u003e \u003cp\u003ePredictive relevance is evaluated using the PLS-predict procedure. A Q\u0026sup2;predict value\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates that the model has predictive power for endogenous constructs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQ\u0026sup2;predict\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndogenous Construct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ\u0026sup2;predict\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisherman Capacity (Y1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisheries Business Performance (Z1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the positive Q\u0026sup2;predict value indicates the predictive relevance of the model to the endogenous construct of the [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e4.4 Model Fit\u003c/p\u003e \u003cp\u003eModel fit was evaluated using several approximate model fit indicators recommended in SEM-PLS, specifically SRMR, d_ULS, d_G, and NFI [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit Indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed_ULS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed_G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square (df10\u0026thinsp;=\u0026thinsp;18.307)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e261.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e9\u003c/span\u003e, SRMR values\u0026thinsp;\u0026lt;\u0026thinsp;0.10 indicate an acceptable level of model fit in SEM-PLS, while other fit indices are presented as supporting information in accordance with variance-based model reporting practices [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement Model Analysis\u003c/h2\u003e \u003cp\u003eThe results of the SEM-PLS analysis show that the reflective measurement model meets all recommended quality criteria. All indicators have outer loadings\u0026thinsp;\u0026gt;\u0026thinsp;0.70, Composite Reliability values\u0026thinsp;\u0026gt;\u0026thinsp;0.70, and AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.50, thus fulfilling convergent validity and internal reliability[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Discriminant validity was also confirmed through the Fornell\u0026ndash;Larcker criteria (\u0026radic;AVE greater than the correlation between constructs) and HTMT\u0026thinsp;\u0026lt;\u0026thinsp;0.85 for all construct pairs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the structural model, business diversification (X2) had a positive and significant effect on fishery business performance (Z1) (β\u0026thinsp;=\u0026thinsp;0.510; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with a large effect size (f\u0026sup2; = 0.357). Conversely, business diversification does not have a direct effect on fisher capacity (Y1), and fishery business performance also does not have a significant effect on fisher capacity. The impact of sedimentation (X1) does not significantly affect fishery business performance, but shows a direct positive and significant effect on fisher capacity (β\u0026thinsp;=\u0026thinsp;0.276; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with a small to medium effect size (f\u0026sup2; = 0.083).\u003c/p\u003e \u003cp\u003eThe adjusted R\u0026sup2; value shows that the model explains 26.4% of the variance in fishery business performance and 7.8% of the variance in fisher capacity, while the positive Q\u0026sup2;predict value indicates the predictive relevance of the model. The SRMR of 0.088 indicates an acceptable level of model fit in the context of variance-based SEM-PLS [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Mediation testing confirmed that fishing business performance did not mediate the effect of business diversification or the impact of sedimentation on fishermen's capacity, so all indirect paths were declared insignificant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe Impact of Sedimentation and the Formation of Fishermen's Capacity\u003c/h2\u003e \u003cp\u003eThe results show that the impact of sedimentation has a positive and significant effect on fisher capacity, while its effect on fishery business performance is not significant. This finding indicates that environmental pressures due to sedimentation are not always directly reflected in short-term fishery business performance, but rather trigger stronger adaptive responses at the household and institutional levels of fishermen. Thus, sedimentation operates as a chronic environmental stress that shapes fishermen's capacity directly, rather than through conventional economic channels.\u003c/p\u003e \u003cp\u003eConceptually, these results are consistent with the model-based estimation approach, which emphasizes that socio-economic responses to ecological pressures are often non-linear and not always reflected in business output indicators [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Fishermen's capacity, as reflected in their financial management skills, adaptive decision-making, and institutional networks, is a more sensitive construct in capturing the impact of ecological pressures than fisheries business performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBusiness Diversification, Business Performance, and Fishermen Capacity\u003c/h2\u003e \u003cp\u003eThis study found that business diversification has a positive and significant effect on fishery business performance, but does not directly affect fisher capacity. These findings indicate that diversification functions primarily as an operational economic strategy that increases income stability and perceptions of business sustainability, but does not automatically strengthen fisher capacity in the social and institutional dimensions.\u003c/p\u003e \u003cp\u003eThese results are in line with the literature emphasizing that diversification improves economic performance through risk mitigation, but capacity building requires more complex social processes, including learning, access, and network quality [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Thus, variation in economic activities alone is insufficient to build adaptive capacity among fishermen, especially in the context of structural ecological pressures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFailure of Mediation of Fishing Business Performance\u003c/h2\u003e \u003cp\u003eTesting for indirect effects shows that fishery business performance does not mediate the relationship between the impact of sedimentation and business diversification on fishermen's capacity. This finding is a major theoretical contribution of the study, as it empirically challenges the dominant assumption that fishermen's capacity is always formed through improved economic performance first.\u003c/p\u003e \u003cp\u003eThe absence of a significant indirect effect indicates that fishery business performance does not mediate the relationship between environmental pressures and fisher capacity. This finding provides empirical evidence that under chronic environmental pressures, economic performance fails to function as an effective transmission mechanism for adaptive capacity, thus challenging the linear livelihood model commonly assumed in small-scale fishery research. Instead, adaptive capacity appears to be shaped through direct behavioral and social responses to persistent ecological constraints, rather than through short-term economic outcomes.\u003c/p\u003e \u003cp\u003eThese results support the view that when pressures stem from structural factors such as environmental degradation, economic mediation pathways become weak or even irrelevant [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In the context of Kendari Bay, fishers appear to build capacity through direct adaptation of household strategies, social relations, and resource management without necessarily being preceded by improved fisheries business performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical and Practical Implications\u003c/h2\u003e \u003cp\u003eOverall, the findings of this study indicate that fisher capacity is a socio-ecological outcome that is not entirely economy-driven. Business diversification remains important in maintaining economic performance, but fisher adaptive capacity is shaped primarily by direct responses to environmental pressures. This model expands the coastal livelihoods literature by showing that capacity formation pathways are non-linear and contextual, particularly in coastal systems experiencing ongoing environmental degradation.\u003c/p\u003e \u003cp\u003eThe practical implication is that fisheries development policies in areas affected by sedimentation should not only focus on increasing production or economic diversification, but should also be directed at strengthening household management capacity, access to institutional networks, and fishermen's ability to adapt to long-term ecological pressures.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study develops and tests a model of fisher capacity under chronic sedimentation pressure in Kendari Bay by integrating the impacts of sedimentation, business diversification, and fisheries business performance using a full reflective SEM-PLS approach. The results show that the impact of sedimentation is a significant direct determinant of fisher capacity, while its effect on fisheries business performance is not significant. These findings confirm that structural environmental pressures work directly on the formation of fisher capacity, without having to be mediated by short-term economic performance. Business diversification has been shown to play an important role in improving fishery business performance, but it does not directly affect fisher capacity. In addition, fishery business performance does not mediate the influence of sedimentation impacts or business diversification on fisher capacity. The failure of this mediation pathway is a key finding that challenges the dominant assumption that improvements in fisher capacity are always shaped through improvements in fishery business performance. By empirically demonstrating the failure of the economic mediation pathway, this study reframes fisher capacity as a direct adaptive response to chronic environmental pressures, rather than as a derivative of economic performance. Theoretically, this research contributes by showing that fisher capacity is a socio-ecological outcome that is not entirely economy-driven, especially in the context of chronic environmental pressures such as sedimentation. The resulting model expands the coastal livelihoods literature by asserting that capacity building is direct, non-linear, and contextual. Practically, these findings imply that policies to strengthen the capacity of fishers in areas affected by sedimentation need to go beyond approaches based on business performance and economic diversification, with an emphasis on strengthening adaptive capacity, household management, and fishers' institutional networks.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eResearch Limitations and Future Research Agenda\u003c/h2\u003e \u003cp\u003eThis study has several limitations that need to be considered when interpreting the findings. First, the study design used a cross-sectional approach, so that the causal relationships identified reflect structural associations at a single point in time and do not capture the dynamics of fishermen's adaptation in the long term. Sedimentation pressure as a chronic stressor likely shapes fishermen's capacity through a gradual process that is not fully observable in a one-time survey design.\u003c/p\u003e \u003cp\u003eSecond, the measurement of sedimentation impacts and fisher capacity was primarily based on household perception indicators. Although this approach is relevant for capturing the socio-ecological responses of fishers, this limitation opens up opportunities for the integration of biophysical data (e.g., TSS, sedimentation rate) and objective economic indicators in future research to enrich the external validity of the findings.\u003c/p\u003e \u003cp\u003eThird, the research model focused on a single coastal context, namely Kendari Bay, which has specific ecological and institutional characteristics. Therefore, generalizing the results to other coastal systems needs to be done with caution, especially in areas with different levels of environmental degradation, market structures, and fisheries governance.\u003c/p\u003e \u003cp\u003eGiven these limitations, future research is advised to adopt a longitudinal or panel design to trace changes in fisher capacity over time under sustained environmental pressure. In addition, developing models that incorporate institutional variables, access to information, and social capital as mediators or moderators has the potential to provide a more comprehensive understanding of the pathways to fisher capacity formation. Cross-site comparisons are also needed to assess the consistency of these findings across different coastal socio-ecological contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003e not applicable\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003eThis study employed an observational research design involving fishermen as primary respondents. Ethical approval was deemed exempt by the Research Ethics Committee of the Department of Agribusiness, Halu Oleo University, because the study relied solely on voluntary survey responses and did not collect sensitive personal data. All procedures were conducted in accordance with established research ethics principles, including informed consent, confidentiality, and anonymity, and complied with relevant national and institutional research guidelines and regulations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all participants included in the study and adhered to the Declaration of Helsinki\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no financial or personal conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from any source.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMuhammad Aswar Limi, R. Marsuki Iswandi, Weka Widayati, Hartina Batoa, and Iskandar Zainuddin Rela have collected data. Yani Taufik screened samples and prepared the first draft of the manuscript. Muhammad Aswar Limi reviewed and edited the final version of the manuscript. All the authors reviewed and approved the final draft of the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author would like to express his gratitude to Halu Oleo University for its institutional support through the Halu Oleo University Internal Basic Research programme, which enabled this research to be carried out and completed successfully. Special thanks go to Armid Rahimahullah, who provided support to all professors conducting research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe author confirms that all data generated or analyzed during this study are included in this published article. 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PLoS One. 2015;10(6):e0131765. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dx.plos.org/10.1371/journal.pone.0131765\u003c/span\u003e\u003cspan address=\"https://dx.plos.10.1371/journal.pone.0131765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sedimentation, business diversification, fisheries business performance, fisher capacity, SEM-PLS, Kendari Bay","lastPublishedDoi":"10.21203/rs.3.rs-8342660/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8342660/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmall-scale fisheries in coastal areas are increasingly facing chronic environmental pressures that have the potential to limit the adaptive capacity of fishermen. One of the increasingly dominant pressures is sedimentation, but the mechanism of its influence on the capacity of fishermen is often assumed to work through the performance of fishing businesses without adequate empirical testing. This study aims to analyze the impact of sedimentation and business diversification on fishermen's capacity, both directly and indirectly through fishing business performance, in the context of the Kendari Bay coast, Southeast Sulawesi. This study challenges the dominant assumption that fishermen's capacity is primarily shaped through economic performance by showing that under chronic environmental pressures, capacity is shaped through direct adaptive pathways rather than through mediated economic mechanisms. This study uses an explanatory cross-sectional design with a Structural Equation Modeling\u0026ndash;Partial Least Squares (SEM-PLS) approach. Data were collected through structured interviews with 200 small-scale fishing households in 2025. Four reflective latent constructs were analyzed, namely the impact of sedimentation, business diversification, fishery business performance, and fisher capacity. Model evaluation included convergent validity, internal reliability, discriminant validity (Fornell\u0026ndash;Larcker and HTMT) tests, as well as structural model evaluation through path coefficients, R\u0026sup2;, f\u0026sup2;, Q\u0026sup2;, SRMR, and bootstrapping-based mediation testing. The results show that the impact of sedimentation has a positive and significant effect on fisher capacity, but does not significantly affect fishery business performance. Business diversification has a positive and significant effect on fishery business performance, but does not directly affect fisher capacity. Furthermore, fishery business performance does not mediate the effect of sedimentation or business diversification on fisher capacity. These findings confirm that fisher capacity is a socio-ecological outcome that is not entirely shaped through economic channels, particularly in the context of chronic environmental pressures. Practically, the results indicate that strategies to strengthen fisher capacity in areas affected by sedimentation need to go beyond a business performance-based approach, emphasizing the strengthening of adaptive capacities and institutional networks.\u003c/p\u003e","manuscriptTitle":"Fishermen Capacity Model After Sedimentation in Kendari Bay","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 17:39:19","doi":"10.21203/rs.3.rs-8342660/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-30T06:57:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T05:21:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289704152135359152546533336967977666954","date":"2026-01-27T09:19:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T06:34:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141922440147523082779860900596406381501","date":"2026-01-13T02:33:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136643100770969102561014077145616653707","date":"2026-01-12T02:19:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-09T20:41:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168307481388095621101200110703817569929","date":"2026-01-09T19:37:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-09T16:27:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-22T03:09:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-21T12:24:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-12-21T12:19:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2187204e-668d-42a2-b2a8-7a3b70f7bb98","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T11:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 17:39:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8342660","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8342660","identity":"rs-8342660","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}