1. Introduction

To empirically examine the endurance, legitimacy, and efficacy of intergovernmental organisations (IGOs) in global ocean economy governance, this section draws on a novel dataset capturing core institutional design features across a diverse IGO sample. The dataset includes variables on legal authority, founding era, mandate breadth, coordination mechanisms, jurisdictional scope, and strategic portfolios, each theorized to influence organisational survival and governance performance.

The analysis is informed by theoretical frameworks from organisational ecology, institutional sociology, and global governance studies, particularly those emphasising the effects of institutional design, environmental density, and relational embeddedness.

Empirical testing is structured around three core conjectures:

Conjecture 1 examines how institutional design and coordination mechanisms shape organisational endurance.
Conjecture 2 investigates the effects of institutional density and niche differentiation on adaptive capacity.
Conjecture 3 explores how embeddedness, legal authority, and strategic diversity influence legitimacy and governance efficacy.

The results section begins with visual and descriptive analyses of the sample to outline patterns in endurance and variation across institutional design variables. This is followed by hypothesis-by-hypothesis reporting of statistical test results, presented in the order of the conjectures. Each hypothesis section includes descriptive statistics, model outputs, and outcome statements based strictly on empirical evidence.

2. Data

To support the empirical testing of the study’s three core conjectures, a structured dataset of intergovernmental organizations (IGOs) was constructed. The dataset captures institutional design features theorized to influence endurance, adaptive capacity, and legitimacy within the global ocean governance domain.

Tools

R version: 4.3.1 (2023-06-16)

Package	Purpose
`stats`	Base package used for running linear regression (`lm()`)
`broom`	Tidying model outputs into data frames (for plotting)
`ggplot2`	Data visualization and plotting (e.g., bar charts, coefficient plots)
`dplyr`	Data manipulation and wrangling
`tidyr`	Data reshaping (used in some visual pipeline steps)
`RColorBrewer`	Color palettes for figures
`forcats`	Factor level manipulation (used in categorical plots)
`readr`	Reading in data files.

Data Prepossessing

The data was processed for analysis and included information on 49 Intergovernmental Organizations (IGOs).

Core Dataset Structure

Each row represents an individual IGO. Variables were grouped into the following broad categories:

Institutional attributes: founding year, organizational age, and historical founding era.
Design features: spatial scope, subject-matter coverage, legal foundation, coordination mechanisms, strategic approaches, and defined objectives.
Performance and adaptation indicators: including scores for institutional breadth, environmental density, specialization, and perceived legitimacy.
Key Variables Prepared for Analysis

Variables were selected and grouped to align with the theoretical constructs under investigation. These include:

Breadth dimensions

spatial_breadth, subject_breadth, legal_breadth

Coordination mechanisms

vertical_coordination_strength, horizontal_coordination_strength

Strategic complexity

defined_interactions, strategy_diversification, objective_diversification

Composite indicators

adaptive_capacity_index, embeddedness_score, endurance_score

All continuous variables were scaled using min-max normalization to a 0–10 range to ensure comparability across indicators.

Preparation Steps

We prepared the dataset by extracting and organizing key variables that reflect institutional design features of intergovernmental organizations (IGOs). These variables were selected to test three core conjectures about what makes IGOs more enduring over time.

Variable Construction:

We derived relevant indicators from the data to capture dimensions such as spatial scope, thematic breadth, legal foundation, coordination mechanisms, and strategic diversity.
Scoring and Normalization:

Ordinal scores were used to assess how each IGO performs across these dimensions. For interpretability, we normalized key variables to a 0–10 scale.
Comparative Preparation:

Summary statistics and visualizations (average score plots) were used to assess variation across IGOs, helping to identify which features are most distinctive or consistent.

These steps ensure that our analysis is grounded in a structured, comparable, and conceptually relevant dataset for testing the conjectures.

# Load necessary libraries
library(tidyverse)
library(lubridate)

# Load the data
df <- read.csv("data/IGO_full_data_1.csv")

# Print the shape of the DataFrame
cat("Shape of the DataFrame:", dim(df)[1], "rows and", dim(df)[2], "columns\n")

## Shape of the DataFrame: 48 rows and 208 columns

View(df)

2.1 Data Understanding

Figure 1a Number of intergovernmental organizations (IGOs) founded by era

Figure 1a shows the distribution of IGO founding across historical eras. Two peaks are observed: the Post-Cold War period (1991–2000) with 11 IGOs and the Post-WWII Boom (1946–1960) with 9 IGOs. Founding activity was lower in other eras, including the Globalisation Era (2001–2010) and the Early Founding Years (Pre-1900), which recorded minimal institutional formation.

Figure 1b.Trends in the founding and accumulation of intergovernmental organizations (IGOs) over time.

Figure 1b presents a time-series view of IGO formation. The blue bars display founding density in five-year increments, while the red line shows cumulative growth. Founding activity increased sharply after 1945 and peaked in the late 20th century. Although the number of new IGOs declined after 2010, the cumulative total continued to rise.

3. Data Analysis

The analysis tests three core conjectures on IGO endurance, adaptability, and legitimacy using theory-informed indicators. Each hypothesis is evaluated through descriptive statistics, bivariate comparisons, and multivariate models. The aim is to assess whether variation in institutional features explains differences in IGO performance.

Conjecture 1 Statement

IGOs with stronger institutional design features — including earlier establishment, treaty-based authority, broad jurisdictional scope, diversified objectives/strategies, and robust vertical/horizontal coordination — are more likely to endure and remain effective actors in global ocean economy governance, compared to IGOs with narrower mandates, weaker legal bases, or limited coordination capacity.

Conjecture 1 Variables

**Table 1.1** Summary of Derived Variables for Conjecture 1
Derieved Variable Name	What It Measures
igo_age	Historical maturity (numeric)
spatial_breadth	Spatial jurisdictional scope
subject_breadth	Subject-matter scope
legal_breadth	Legal foundation/basis
vertical_coordination_strength	Robustness of vertical coordination mechanisms
horizontal_coordination_strength	Robustness of horizontal coordination mechanisms
strategy_diversification	Strategic breadth
objective_diversification	Functional/objective breadth
endurance	Ongoing relevance and activity (not just survival).

Data Preparation and Weight Derivation

As part of the data preparation process for testing Conjecture 1, systematic, data-driven approach was adopted to derive weights for the various institutional design dimensions included in the dataset. This was done to avoid the arbitrary or equal weighting that can introduce bias or mask meaningful variation between institutional features. The process followed a rigorous quantitative methodology grounded in social science measurement principles, where greater variation in a category across organisations is assumed to signal higher explanatory potential.

Step 1: Defining Institutional Design Categories

The dataset contained a comprehensive set of variables describing different dimensions of IGO design and governance structure. Each variable corresponded to one of nine major institutional feature categories. For computational clarity and reproducibility, column index ranges were pre-defined for each category as follows:

Density Metrics: Columns 4–5
Spatial Governance: Columns 6–26
Vertical Coordination: Columns 27–47
Subject Matter Coverage: Columns 48–68
Strategies: Columns 69–89
Defined Objectives: Columns 90–110
Defined Interactions: Columns 111–131
Sources of Legal Instruments: Columns 132–152
Horizontal Coordination: Columns 153–173

These ranges allowed for automated aggregation and statistical evaluation of each institutional feature’s internal variability.

Step 2: Calculation of Category-Level Scores and Variability

For each category, the total score of each IGO was first computed by summing all the indicator variables that fell within that category’s column range. This produced one aggregate value per IGO per category, representing the organisation’s overall strength or presence of that design dimension.

Next, the standard deviation of these summed scores was calculated across all IGOs for each category. The standard deviation served as a measure of inter-organisational variation, indicating how much IGOs differed from one another in terms of that particular institutional feature. Categories with higher standard deviations were considered to be more discriminating, as they captured greater diversity in design across organisations.

This approach is consistent with the logic of variance-based weighting, where dimensions exhibiting more variation are assumed to have higher analytical leverage in explaining differences in institutional outcomes in this case, endurance.

Figure 1c: Relative Influence of IGO Design Features on Endurance.

As shown in Figure 1c, spatial governance exhibited the highest relative influence on IGO endurance. These results support Conjecture 1, indicating that IGO endurance is associated with specific institutional design features. The table 1 below indicates the weights and interpretation on how the variables impact endurance.

**Table 1.2**: Weights and interpretation for Conjecture 1
Category	Weight	Interpretation
Spatial	0.1841	This category has the highest variation, suggesting that IGOs differ significantly in how many and which maritime zones they govern. Therefore, spatial scope is likely a strong differentiator in endurance.
Defined Objectives	0.1288	High variation in how IGOs define and pursue their objectives implies that strategic clarity and diversity are key features influencing endurance.
Strategies	0.1280	Similarly, how IGOs implement their missions varies considerably, indicating that some pursue a broad array of strategies while others do not. This strategic diversification is likely relevant for persistence.
Subject Matter	0.1170	There is substantial diversity in the thematic areas IGOs. Broader thematic reach appears to be moderately important in explaining endurance.
Density Metrics	0.1108	Institutional density (founding clusters, stock) has moderate variation, suggesting it may play a role in survival and embeddedness, but less so than spatial or strategic dimensions.
Defined Interactions	0.0950	IGOs differ in how they engage with civil society, donors, and expert groups, though less than in strategies or objectives. Still relevant, but more modest in its role.
Legal Instruments	0.0943	Legal foundation types show some variation, though many IGOs may share similar instruments. Important but not highly distinctive.
Vertical Coord	0.0743	Variation in alignment with national institutions is relatively lower, implying this dimension is somewhat consistent across IGOs or less critical in differentiating endurance.
Horizontal Coord	0.0678	The lowest variation was found here. Most IGOs either share similar cross-sector coordination mechanisms, or this aspect is underdeveloped across the board. Hence, it contributes least to distinguishing enduring IGOs.

In essence, these weights tell us which institutional features vary the most between IGOs, and therefore which are more likely to matter for understanding endurance. The results align well with theoretical expectations from institutional sociology and governance literature:

Spatial scope, strategic diversification, and clear objectives appear to be particularly important differentiators of IGO endurance.
Sources (Legal) and coordination mechanisms, while still relevant, vary less across organisations and thus carry less explanatory weight in this specific dataset.

Step 4: Constructing the Endurance Index

Following the derivation of weights, the next step involved constructing a composite Endurance Score for each IGO. This score aimed to quantify each organisation’s structural robustness and potential for long-term effectiveness.

First, additional variables were computed to represent key institutional design dimensions:

IGO Age: The age of each organisation was calculated as the difference between the current year (2025) and the year of founding.
Spatial and Subject Breadth: The total number of spatial zones and thematic domains covered by each IGO was summed.
Institutional Design Features: Aggregate measures were computed for legal instruments, vertical and horizontal coordination, and defined interactions with stakeholders.
Strategic and Objective Diversification: Summed indicators reflected the range of strategic approaches and objectives pursued by each organisation.

All derived metrics were then normalized to a 0–1 scale using min–max scaling, ensuring comparability across dimensions measured on different ranges.

Finally, each normalized score was multiplied by its respective category weight, and the weighted values were summed to produce a single Endurance Score for every IGO:

\[Endurance Score = (0.1108 × score_Age) + (0.1841 × score_Spatial) + (0.0743 × score_Vertical) + (0.1170 × score_Subject) + (0.1280 × score_Strategies) + (0.1288 × score_Objectives) + (0.0950 × score_DefinedInteractions) + (0.0943 × score_Legal) + (0.0678 × score_Horizontal)\]

This weighted sum yielded a robust, data-driven index of institutional endurance, integrating both structural and functional dimensions of IGO design.

Step 5: Interpretation and Application

The resulting Endurance Score allowed for direct comparison and ranking of IGOs based on their institutional design characteristics. IGOs with higher scores were interpreted as possessing more resilient and differentiated institutional architectures features theoretically linked to greater longevity and adaptability.

These weighted results also provided substantive empirical support for theoretical perspectives in governance research:

IGOs with broad spatial reach, strategic diversity, and clearly defined objectives tended to show greater variation and thus greater explanatory potential for endurance.
Conversely, features like legal foundations and coordination mechanisms (though important) were more uniform across organisations, contributing less to differentiation in endurance outcomes.

The Endurance Index thus served as a central analytical tool for subsequent hypothesis testing under Conjecture 1, enabling the exploration of how institutional design strength correlates with organisational survival and performance over time.

Summary of Statistical Tests for Conjecture 1 — Institutional Design and IGO Endurance

Conjecture	Hypothesis	Independent Variable(s)	Dependent Variable	Statistical Test	Key Statistical Measures	Interpretation
C1	H1.1 Institutional Age and Endurance	Year Founded	Endurance Score	Linear Regression	β = –0.0005649, t = –1.042, p = 0.303, R² = 0.023	No significant effect of founding year on endurance; older IGOs are not necessarily more enduring.
C1	H1.2 Mandate Breadth and Endurance	Spatial Scope, Subject Coverage, Legal Breadth	Endurance Score	Multiple Linear Regression	β(spatial) = 0.1921, β(subject) = 0.0943, β(legal) = 0.1258; Adj. R² = 0.7401; F = 45.62, p < 0.001	Mandate breadth positively predicts endurance; spatial and legal dimensions are particularly important.
C1	H1.3 Coordination Mechanisms and Endurance	Vertical & Horizontal Coordination Strength	Endurance Score	Multiple Linear Regression	β(vertical) = 0.00777, β(horizontal) = 0.00893; Adj. R² = 0.026; F = 1.627, p = 0.208	Coordination mechanisms show weak, non-significant effect on endurance.
C1	H1.4 Strategic and Objective Diversification and Endurance	Strategy & Objective Diversification	Endurance Score	Multiple Linear Regression	β(strategy) = 0.0111, β(objective) = 0.0122; Adj. R² = 0.0847; F = 3.175, p = 0.051	Slight positive trend; strategic diversification may enhance endurance, but effect is marginal.

Conjecture 1 Hypothesis 1.1 — Historical maturity and endurance

IGOs founded earlier, particularly those established before or during major institutional expansions or major governance landmarks (e.g., post-WWII or pre-UNCLOS), will display higher endurance scores than younger IGOs.

Conjecture 1 Hypothesis 1.1 Variables

**Table 1.4** C1 H1.1 Variables
Variable	Type	Measurement Description
`endurance_score`	Dependent	A continuous variable capturing IGO institutional endurance.
`year_founded`	Independent	Year the IGO was established (numeric).
founding_era_category	Dependent	The historical period in which the IGO was established, treated as a categorical proxy for age.

Statistical

A linear regression model was estimated using R (version 4.3.1) to test the relationship between year_founded and endurance_score.

Simple linear model

Residual standard error: 0.109 (df = 46)
R-squared: 0.023
Adjusted R-squared: 0.002
F-statistic: 1.085 (df = 1, 46), p-value = 0.303
The coefficient for year_founded is negative (-0.000565), which is consistent with the direction predicted by Hypothesis 1.1. However, the relationship is not statistically significant (p = 0.303).
The low R² value (0.023) suggests that only about 2.3% of the variance in endurance_score is explained by year_founded.
The intercept (1.559) reflects the hypothetical endurance score of an IGO founded in year 0, which is not meaningful substantively but necessary mathematically.

This analysis does not provide statistically significant evidence to support Hypothesis 1.1. While the coefficient sign aligns with theoretical expectations (older IGOs may have higher endurance), the effect size is small, and the result is not statistically significant at conventional thresholds (p > 0.05).

Table 1.5 Summary of IGO characteristics by founding era.

Descriptive statistics by founding era (Table 1.5) show variation in endurance scores across time periods. The highest average endurance among multi-IGO cohorts was observed in the Early 20th Century (1900–1945) group (EII_mean = 0.184), followed by the Post-WWII Boom (1946–1960) group (EII_mean = 0.158). IGOs founded during the Post-Cold War era (1991–2000) had the lowest average endurance score (–0.246), despite being the largest group (N = 11). The Globalisation Era (2001–2010) showed the highest single score (0.774), though it includes only one case.

Figure 1.1a. Endurance of IGOs by Founding Era

Figure 1.1a visualizes endurance patterns by founding era. While older IGOs—particularly those founded around the League of Nations and early UN Charter eras—include several high-endurance cases, the overall trend does not show a consistent positive relationship between age and endurance. Some newer IGOs also exhibit high endurance, while many mid-era organizations cluster below the average.

This figure 1.1b offers visual evidence that challenges Hypothesis 1.1. While one might expect older IGOs to consistently show stronger endurance, the chart indicates:

No clear linear relationship between founding era and endurance strength.
IGOs from all eras exhibit a mix of weak, moderate, and strong endurance.
Strong endurance is not concentrated in earlier eras, nor is weak endurance confined to later ones.

Thus, the visual data aligns with the results of the linear regression model, which also found no significant relationship between year of founding and institutional endurance.

Figure 1.1b complements the statistical analysis by:

Contextualizing the quantitative findings with a categorical, visual representation of IGO endurance.
Highlighting the variation in endurance levels within and across founding eras, suggesting that factors beyond founding year (e.g., mandate scope, authority structure, coordination mechanisms) may better explain endurance patterns.

C 1 Hypothesis 1.1 Summary

The era-specific trends vary in direction, and visual inspection aligns with regression results showing no statistically significant association between founding year and endurance.

Conjecture 1 Hypothesis 1.2 — Mandate breadth and Endurance

IGOs with broader jurisdictional scopes (Spatial - e.g., EEZ, High Seas, Continental Shelf, and subject-matter coverage - e.g., biodiversity, trade, security) and sources of jurisdiction will demonstrate greater endurance than those with narrow, single-issue mandates.

Statistical

A multiple linear regression model was used to test this hypothesis, with three independent variables capturing different dimensions of mandate breadth.

R Version and Packages

R version: 4.3.1
Packages: broom, ggplot2, dplyr, RColorBrewer

Table 1.6 C1 H 1.2 Variables
Variable	Type	Description
`endurance_score`	Dependent	Continuous variable (0–1) capturing IGO endurance.
`score_spatial`	Independent	Coded breadth of spatial jurisdiction (e.g., EEZ, high seas, global).
`score_subject`	Independent	Subject-matter coverage (e.g., biodiversity, trade, climate, peacekeeping).
`score_legal`	Independent	Breadth of legal authority or jurisdictional basis (e.g., treaty-based, constitutional, normative).

Table 1.6 Relative Contribution of Mandate Dimensions to IGO Endurance
Variable	β Coefficient	Std. Error	t-value	p-value	Significance
Intercept	0.234	0.029	8.15	< 0.001
Spatial Scope	0.192	0.019	9.97	< 0.001	strongest
Subject Scope	0.094	0.038	2.48	0.0017	moderate
Legal Scope	0.126	0.036	3.45	0.001	strong

The hypothesis that broader mandate dimensions are positively associated with IGO endurance was tested using a multiple linear regression model.

All three predictors were statistically significant:

Spatial breadth had the strongest positive association with endurance (β = 0.192, p < 0.001).
Legal breadth was also positively related to endurance (β = 0.126, p = 0.001).
Subject-matter breadth showed a smaller but significant effect (β = 0.094, p = 0.017).

Figure 1.2a Relative Contribution of Mandate Dimensions to Endurance

The model explained approximately 74% of the variance in endurance scores (Adjusted R² = 0.740), with a statistically significant overall fit (F(3, 44) = 45.62, p < 0.001). Residuals were relatively low and evenly distributed.

Figure 1.2b. Distribution of IGOs by Mandate Breadth and Endurance Category

The figure 1.2b reveals clear patterns in support of Hypothesis 1.2:

High Mandate Breadth:

The majority (11 of 16) IGOs in this category are in the Strong Endurance group.
Only 5 are in Moderate, and none are weak, indicating a strong association between broad mandates and resilience.

Low Mandate Breadth:

Most IGOs (12 of 16) in this group are classified as having Weak Endurance.
Only 4 show Moderate endurance, and none are Strong, reinforcing the hypothesized relationship.

Medium Mandate Breadth:

9 Moderate
4 Strong
3 Weak

The figure 1.2b visually supports the result from the linear regression model (Model for H1.2), where mandate breadth (especially subject-matter scope) positively predicted endurance.

These categorical counts suggest that broad mandates consistently correlate with higher endurance—both statistically and substantively.

Figure 1.2b reinforces the broader conclusion that:

Mandate scope is a key structural predictor of IGO endurance. Organizations with broader jurisdictional and thematic coverage are more likely to persist, adapt, and maintain relevance over time.
By triangulating results from both quantitative regression models and categorical visual summaries, the analysis builds a strong empirical case for Hypothesis 1.2.

Figure 1.2c. Jurisdictional dimension scores of IGOs with strong endurance, grouped by mandate breadth

Figure 1.2c chart compares the jurisdictional dimensions (sources, spatial, subject) of IGOs with strong endurance, categorized by mandate breadth. IGOs with high mandate breadth exhibit consistently high spatial scores, while those with medium mandate breadth show more variability in subject and sources dimensions.

This bar chart compares the jurisdictional dimensions of IGOs with strong endurance, divided into two groups: those with high mandate breadth and those with medium mandate breadth. The jurisdictional dimensions are categorized into three types:

Sources (green): Legal or institutional sources of jurisdiction.
Spatial (orange): Geographic scope of jurisdiction.
Subject (purple): Subject-matter coverage .

High Mandate Breadth Group

IGOs like WMO, IMO, IMF, and CMS exhibit high scores in the spatial dimension, indicating broad geographic jurisdiction.
Sources and subject dimensions are relatively lower but still significant, suggesting that these IGOs derive their endurance from a combination of spatial reach and institutional sources.
IHO, FAO, and UNEP show a more balanced distribution across all three dimensions.

Medium Mandate Breadth Group

IGOs like ILO, IPBES, and BRS have high scores in the spatial dimension, but their subject and sources scores are more variable.
UNIDO stands out with a high spatial score but lower sources and subject scores, indicating that its endurance may rely more heavily on geographic scope than institutional or subject-matter jurisdiction.

Figure 1.2d. Count of IGOs engaging with each element by mandate dimension

Figure 1.2d illustrates the specific mandate components most commonly held by IGOs with strong institutional endurance. It breaks down the legal, spatial, and subject-matter dimensions of mandates to highlight which types of jurisdictional authority and issue coverage are most prevalent among enduring IGOs.

This figure provides granular evidence supporting the broader claim of Hypothesis 1.2:

High-endurance IGOs are typically those with broad, legally grounded, and multi-domain mandates.

Specifically, the findings suggest:

Legal anchoring in treaties and multilateral agreements correlates with durability.
Geographically manageable or bounded jurisdictions (e.g., coastal zones) are more common in enduring IGOs than legally ambiguous regions like the high seas.
Cross-sectoral subject-matter mandates, especially in science, sustainability, and environmental governance, may provide institutional resilience.

🟩 Legal Dimension:

Most high-endurance IGOs draw authority from foundational treaties or charters, and binding secondary law (e.g., regulations).
Bilateral/multilateral arrangements are also common, indicating flexible intergovernmental frameworks.
Fewer IGOs rely on derived powers or technical norms alone, suggesting that legal formalization and institutional grounding play a role in endurance.

🟧 Spatial Dimension:

High-endurance IGOs most often operate in enclosed/semi-enclosed seas, internal waters, and coastal zones.
Fewer are focused specifically on the high seas, which may reflect jurisdictional challenges or limited enforcement mechanisms in those areas.

🟪 Subject-Matter Dimension:

Dominant themes include:

Sustainable development and capacity building
Research and science innovation
Climate and environmental protection
Biodiversity and ecosystem conservation

More politicized or niche domains like human rights or disaster resilience appear less frequently.

This suggests that scientific, developmental, and ecological mandates are more often associated with longevity, compared to single-issue advocacy domains.

C 1 Hypothesis 1.2 Summary

These findings support Hypothesis 1.2, demonstrating that IGOs with broad jurisdiction—especially those engaging multiple legal sources, diverse spatial areas, and cross-cutting subject matter—tend to exhibit higher endurance. The most enduring IGOs combine foundational legal mandates with expansive territorial and policy coverage.

Conjecture 1 Hypothesis 1.3 — Coordination mechanisms and endurance

IGOs with stronger vertical coordination mechanisms (e.g., UN–Member State linkages, policy alignment with national plans) and horizontal coordination mechanisms (e.g., cross-sectoral collaboration, multi-stakeholder platforms) exhibits greater endurance than organisations lacking such linkages.

C1 H 1.3 Variables

Variable	Type	Description
`endurance_score`	Dependent	Continuous measure of IGO endurance.
`vertical_coordination_strength`	Independent	Numeric score capturing strength of vertical institutional coordination.
`horizontal_coordination_strength`	Independent	Numeric score capturing horizontal coordination across actors/sectors.

Statistical

A multiple linear regression model was employed to test the relationship between coordination strength (both vertical and horizontal) and IGO endurance.

# Step 1: Filter relevant columns
df_coord <- df %>%
  select(institution, endurance_score, vertical_coordination_strength, horizontal_coordination_strength)

# Step 2: Correlation check
cor_matrix <- cor(df_coord[, c("endurance_score", "vertical_coordination_strength", "horizontal_coordination_strength")], use = "complete.obs")

# Step 3: Regression analysis
model <- lm(endurance_score ~ vertical_coordination_strength + horizontal_coordination_strength, data = df_coord)
summary(model)

## 
## Call:
## lm(formula = endurance_score ~ vertical_coordination_strength + 
##     horizontal_coordination_strength, data = df_coord)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.219117 -0.092688  0.003681  0.098519  0.175179 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      0.394017   0.033396  11.798 2.29e-15 ***
## vertical_coordination_strength   0.007768   0.005653   1.374    0.176    
## horizontal_coordination_strength 0.008925   0.007435   1.200    0.236    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1076 on 45 degrees of freedom
## Multiple R-squared:  0.06745,    Adjusted R-squared:  0.026 
## F-statistic: 1.627 on 2 and 45 DF,  p-value: 0.2078

Model Summary

F(2, 45) = 1.63,
p = 0.208
Residual standard error = 0.108
Multiple R² = 0.067
Adjusted R² = 0.026

Coefficients

Vertical coordination strength

β = 0.0078
Std. Error = 0.0057
t = 1.37
p = 0.176

Horizontal coordination strength

β = 0.0089
Std. Error = 0.0074
t = 1.20
p = 0.236
Directionally, both coordination mechanisms have positive coefficients, meaning that stronger coordination is associated with higher IGO endurance, as theorized.
However, neither coefficient is statistically significant (p > 0.1), and the overall model fit is weak (R² = 0.067).

This implies that:

There is no strong statistical evidence from this model to support the hypothesis that coordination mechanisms are independent predictors of endurance.
Coordination strength may have an indirect, contextual, or interactive effect with other variables (e.g., mandate scope, institutional age).

Figure 1.3a Institutions with high endurance scores stand out across coordination dimensions

Figure 1.3a presents the relationship between coordination scores (horizontal and vertical) and endurance scores for IGOs with strong endurance. The LOESS lines indicate a positive trend, suggesting that stronger coordination mechanisms are associated with greater endurance.

Horizontal Coordination (Left Panel):

There is a positive trend between horizontal coordination and endurance scores, though with some variability.
IGOs like FAO, IMO, and UNEP are labeled and appear to have high coordination and endurance scores, suggesting that strong horizontal coordination is associated with greater endurance.
The LOESS line shows a slight upward slope, indicating that as horizontal coordination increases, endurance tends to increase as well.

Vertical Coordination (Right Panel):

There is a clearer positive trend between vertical coordination and endurance scores.
IGOs like UNEP, IHO, and FAO are labeled and exhibit high scores in both vertical coordination and endurance.
The LOESS line is more pronounced in its upward slope compared to the horizontal coordination plot, suggesting a stronger relationship between vertical coordination and endurance.

Figure 1.3a provides empirical support for Hypothesis 1.3, which posits that IGOs with stronger coordination mechanisms exhibit greater endurance. The scatter plots reveal a positive relationship between both horizontal and vertical coordination scores and endurance scores.

The vertical coordination plot (right) shows a more pronounced positive trend, suggesting that vertical coordination mechanisms—such as UN–Member State linkages and policy alignment with national plans—may have a stronger impact on IGO endurance. This is evident in the high endurance scores of IGOs like UNEP, IHO, and FAO, which also exhibit strong vertical coordination.

The horizontal coordination plot (left) also demonstrates a positive trend, though with more variability. This indicates that while cross-sectoral collaboration and multi-stakeholder platforms contribute to endurance, their impact may be less consistent compared to vertical coordination

Figure 1.3b Horizontal WithinIGO Coordination Mechanisms

High-endurance IGOs prioritize inclusive and issue-focused coordination, emphasizing dialogue, flexibility, and participatory engagement. These mechanisms help reduce sectoral fragmentation and promote knowledge co-production.

Figure 1.3c Vertical WithinIGO Coordination Mechanisms

High-endurance IGOs rely heavily on formal institutional channels, intergovernmental coordination, and regional partnerships to bridge global objectives with national implementation. However, technical tools like integrated data systems and policy alignment mechanisms are less commonly used, highlighting an area for development.

Hypothesis 1.3 Summary

Figure 1.3d Endurance and Coordination

This test highlights an important insight:

Coordination mechanisms, while institutionally significant, may not independently predict IGO endurance in a linear fashion. Their influence could be conditional, mediated, or embedded within broader institutional configurations.

Thus, Hypothesis 1.3 is not supported by this model, but the findings help refine the understanding of endurance as a multifactorial outcome, not driven by any one institutional dimension alone

Conjecture 1 Hypothesis 1.4 — Strategic and objective diversification and endurance

IGOs with diversified objectives and strategies score higher on endurance than IGOs with narrowly defined or single-issue mandates.

C1 H1.4 Variables

Variable	Type	Description
`endurance_score`	Dependent	Continuous measure of IGO endurance (scaled 0–1).
`strategy_diversification`	Independent	Composite numeric score reflecting variety of implementation strategies.
`objective_diversification`	Independent	Composite numeric score reflecting diversity in IGO goals or issue areas.

Statistical

A multiple linear regression model was used to test whether two types of diversification predict IGO endurance.

# 1. Multiple Linear Regression
model <- lm(endurance_score ~ strategy_diversification + objective_diversification,
            data = df)

# Display model summary
summary(model)

## 
## Call:
## lm(formula = endurance_score ~ strategy_diversification + objective_diversification, 
##     data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.17608 -0.09949  0.02067  0.09021  0.17508 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.344025   0.043755   7.863 5.47e-10 ***
## strategy_diversification  0.011104   0.007139   1.555    0.127    
## objective_diversification 0.012165   0.006931   1.755    0.086 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1043 on 45 degrees of freedom
## Multiple R-squared:  0.1236, Adjusted R-squared:  0.0847 
## F-statistic: 3.175 on 2 and 45 DF,  p-value: 0.05132

Residual standard error: 0.104 on 45 degrees of freedom
Multiple R-squared: 0.124
Adjusted R-squared: 0.085
F-statistic: 3.175 on 2 and 45 DF
Model p-value: 0.0513
† p < 0.10 (marginal significance)

Model

The model explains about 12.4% of the variance in endurance score, which is modest but meaningful for institutional-level data.
The coefficient for objective diversification is positive and marginally significant (p = 0.086), indicating that IGOs with more diversified goals tend to score higher on endurance.
The coefficient for strategy diversification is also positive, though not statistically significant (p = 0.127), suggesting a potential but weaker effect.
These findings partially support Hypothesis 1.4, particularly in relation to the diversity of objectives.
The positive direction of both predictors supports the theoretical claim that diversification provides adaptive capacity, making IGOs more robust over time.
Objective diversification appears to have a stronger empirical association with endurance, potentially because a broader mission enables relevance across different issue cycles and stakeholder demands.
Figure 1.4a Endurance Score vs. Diversification Dimensions
While both forms of diversification were positively associated with endurance, the effect was more evident for objective breadth than for strategic variety.

Figure 1.4b Strategies Among High Endurance IGOs

Environmental, climate, and biodiversity action was the most frequently reported strategy (23 IGOs), followed by strategic planning (22), knowledge and data systems (20), partnerships and networks (19), and innovation (18). Financial management (17), operational delivery (14), and legal frameworks (7) appeared less often. Monitoring and evaluation was reported by 3 IGOs, while inclusion and social justice was not reported by any.

Figure 1.4c Common Objectives Among Strong IGOs.

The most commonly reported objectives among IGOs were knowledge and data systems (30 IGOs) and operational delivery (26 IGOs), followed by partnerships and networks (20 IGOs), environmental action (16 IGOs), and policy and regulation (16 IGOs). Governance and institutional planning was cited by 13 IGOs, and innovation and technology development by 9 IGOs. Monitoring and accountability appeared as an objective in 3 IGOs. No IGOs reported financial stewardship or inclusion and rights as primary objectives. This distribution shows varying emphasis across thematic and functional areas in IGO mandates.

Figure1.4d Objective Diversification and IGO Endurance

This investigation strongly confirms the core proposition from organizational ecology theory. IGOs are not weakened by a broad mandate; they are strengthened by it. Diversification provides a crucial adaptive flexibility, allowing organizations to pivot, absorb shocks, and signal their relevance across a changing global landscape. Ultimately, IGOs with wider horizons are better built to last.

Hypothesis 1.4 Summary

Hypothesis 1.4 proposes that IGOs with more diversified strategies and objectives exhibit higher institutional endurance. The analysis showed a modest positive relationship, with objective diversification more strongly associated with endurance than strategy diversification. While neither variable reached conventional levels of statistical significance, the pattern suggests that IGOs addressing a broader range of goals tend to have higher endurance scores.

Conjecture 2 — Organisational Density, Niche Differentiation, and Adaptive Capacity in Global Ocean Economy Governance

Conjecture Statement

IGOs operating in denser institutional environments — where mandates, subject-matter domains, and spatial jurisdictions overlap — are expected to sustain their legitimacy and adaptive capacity by differentiating their niches and cultivating adaptive relational strategies. Conversely, IGOs with diffuse, overlapping mandates and weak relational positioning are more vulnerable to redundancy, inefficiency, and eventual decline.

Deriving Key Variables for Testing Conjecture 2: Density, Niche Differentiation, and Adaptive Capacity

The second stage of the analysis focused on Conjecture 2, which proposed that an international governance organisation’s (IGO’s) endurance and effectiveness depend not only on its institutional design but also on its ability to position itself strategically within a crowded governance landscape. Specifically, this conjecture examined how density, niche differentiation, and adaptive capacity interact to influence the persistence and performance of IGOs operating in overlapping or competitive domains of global governance.

The core idea underlying Conjecture 2 is that as the institutional environment becomes increasingly dense (characterised by numerous overlapping mandates and jurisdictions) organisations must either specialise to occupy a distinct functional or spatial niche, or adapt their internal strategies to remain effective and relevant. To empirically evaluate this, several key variables were derived from the existing dataset, each capturing one of these theoretical mechanisms: niche specialisation, adaptive capacity, and density non-linearity.

**Table 2.1** Summary of Derived Variables for Conjecture 2
Derived Variable Name	What It Measures
`density_at_founding`	Institutional density in the ecosystem at the IGO’s time of founding
`spatial_breadth`	How many maritime zones the IGO covers (jurisdictional spread)
`subject_breadth`	Number of subject-matter domains the IGO covers (mandate scope)
`niche_specialisation`	Inverse of spatial + subject breadth; narrower mandates = higher specialisation
`vertical_coord_strength`	Strength of vertical coordination mechanisms (e.g. global-regional-national coordination)
`horizontal_coord_strength`	Strength of horizontal coordination (e.g. shared monitoring, joint platforms)
`legal_authority_index`	Degree of legal grounding (treaty-based, soft law, secondary frameworks)
`strategy_diversification`	Number of distinct strategies pursued by the IGO (policy tools, capacity building, etc.)
`objective_diversification`	Number of distinct objectives the IGO has defined
`adaptive_capacity_index`	Composite indicator of strategy + objective diversification (overall functional adaptability)
`density_squared`	Non-linear effect of founding density (to test for diminishing returns of density)
`founding_era_category`	Era in which IGO was founded (e.g., pre-UNCLOS.)

Step 1: Measuring Niche Specialisation

To assess how narrowly or broadly each IGO defined its operational scope, a Niche Specialisation Score was calculated. This variable integrated both the spatial and thematic breadth of an organisation’s activities.

First, each IGO’s spatial breadth (the range of maritime zones it governed) and subject breadth (the number of thematic areas covered) were summed to form a composite measure of total institutional breadth:

\[ Total Breadth=Spatial Breadth+Subject Breadth \]
This total breadth was then normalized on a 0–1 scale to ensure comparability across organisations of different sizes and mandates.
To capture specialisation rather than breadth, the normalized values were inverted (so that a higher score indicated a more specialised organisation). In other words, IGOs focusing on fewer spatial zones or thematic areas received higher Niche Specialisation Scores, reflecting a narrower and more targeted institutional profile.

The resulting formula was expressed as:

\[ Niche Specialisation=1−Rescale(Total Breadth,0,1) \]

This approach ensured that the index directly measured an IGO’s degree of focus, rather than the extent of its jurisdiction. From a theoretical standpoint, greater niche specialisation was hypothesised to enhance survival prospects in competitive governance environments, as highly specialised IGOs face less functional overlap with others and can cultivate unique areas of expertise.

Step 2: Constructing the Adaptive Capacity Index

The Adaptive Capacity Index aimed to quantify how flexibly an IGO could respond to emerging challenges, institutional crowding, and evolving policy environments. Drawing on theories of organisational adaptation and resilience, adaptive capacity was conceptualised as the ability to modify strategies and objectives in response to contextual changes.

Two proxies were used for this measure:

Strategic Diversification, representing the variety of implementation strategies an IGO employed.
Objective Diversification, representing the range of goals or missions the IGO pursued across its policy areas.

Each of these components was individually normalized to a 0–1 scale, and then averaged to maintain a comparable range:

\[ Adaptive Capacity Index = Rescale(Strategy Diversification)+Rescale(Objective Diversification)/2 \]

This term allowed for the empirical testing of curvilinear relationships, such as whether moderate levels of density foster cooperation and learning, while excessive density leads to competition and inefficiency.

Step 4: Integrating

Together, these derived variables—Niche Specialisation, Adaptive Capacity, and Density Squared—provided a coherent framework for evaluating Conjecture 2. Each variable represented a distinct mechanism by which IGOs could manage environmental complexity:

Niche Specialisation captured differentiation as a survival strategy, reflecting how IGOs carve out unique domains to avoid redundancy.
Adaptive Capacity represented internal flexibility, denoting an IGO’s ability to adjust objectives and strategies in response to changing governance conditions.
Density Squared accounted for the structural pressures of institutional crowding, allowing the analysis to test whether its effects intensified or plateaued at higher levels.

By operationalising these theoretical concepts into measurable indicators, the study equipped itself to test whether IGOs that are more specialised, more adaptive, or better positioned within dense institutional fields tend to exhibit greater endurance, legitimacy, and effectiveness over time.

Summary of Conjecture 2 — Density, Coordination, and Specialisation

Conjecture	Hypothesis	Independent Variable(s)	Dependent Variable	Statistical Test	Key Statistics	Interpretation
C2	H2.1 — Density and Specialisation	Founding Density (5-year normalized)	Subject Specialisation Score (score_subject)	Linear Regression	β = 0.0554, t = 0.513, p = 0.611, Adj. R² = –0.0159	No significant effect of founding density on subject specialisation.
		Founding Density (5-year normalized)	Spatial Scope Score (score_spatial)	Linear Regression	β = 0.0496, t = 0.230, p = 0.819, Adj. R² = –0.0206	No significant effect of founding density on spatial scope.
C2	H2.2 — Coordination and Legal Authority as Moderators	Vertical Coordination Strength, Horizontal Coordination Strength, Binding Secondary Law, Foundational Treaties/Charters	High Mandate (binary)	Generalized Linear Model (Binomial)	Coefficients near zero; all p = 1; AIC = 10	No detectable effect of coordination or legal authority on mandate breadth. Possible data/model issues.
C2	H2.3 — Niche Differentiation through Subject-Spatial Intersections	Intersection Score	Niche Specialisation	Pearson Correlation & Linear Regression	r = –0.88, t = –12.58, p < 2.2e-16; β = –0.00058, Adj. R² = 0.7699	Strong, highly significant negative correlation between intersection score and niche specialisation.
C2	H2.4 — Adaptive Portfolios and Non-linear Density Effects	Founding Density (5-yr norm), Density Squared, Strategy Diversification, IGO Age, Interaction (Density × Strategy Diversification)	Adaptive Capacity Index	Multiple Linear Regression	β(strategy) = 0.0636 (p < 0.001), β(IGO age) = –0.0182 (p = 0.044), other effects NS; Adj. R² = 0.5733	Strategy diversification positively influences adaptive capacity; IGO age has a negative effect; density effects and interaction are not significant.

Conjecture 2 Hypothesis 2.1 — Density and Specialisation

IGOs founded in denser institutional environments (e.g., post-UNCLOS era or during proliferation of regional seas conventions) exhibit narrower subject-matter and spatial mandates than IGOs founded in less dense contexts.

C2 H 2.1 Variables

Variable Name	Role	Description
`score_subject`	Dependent Variable	Index capturing the subject-matter breadth of an IGO’s mandate.
`score_spatial`	Dependent Variable	Index capturing the spatial/geographic scope of an IGO’s mandate.
`foundingdensity_5yr_norm`	Independent Variable	Normalized count of IGOs founded within a 5-year window around the IGO’s founding year. Serves as a proxy for institutional density or “crowdedness” of the governance field at founding time.

Statistical (negative correlation hypothesis:)

We estimated two separate linear regression models to test the relationship between founding density and mandate specialization.

# Subject matter
model_subject <- lm(score_subject ~ foundingdensity_5yr_norm, data = df)
summary(model_subject)

## 
## Call:
## lm(formula = score_subject ~ foundingdensity_5yr_norm, data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.45468 -0.15566 -0.00032  0.15054  0.52158 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.45468    0.05801   7.838 5.14e-10 ***
## foundingdensity_5yr_norm  0.05540    0.10805   0.513    0.611    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2165 on 46 degrees of freedom
## Multiple R-squared:  0.005683,   Adjusted R-squared:  -0.01593 
## F-statistic: 0.2629 on 1 and 46 DF,  p-value: 0.6106

# Spatial mandate
model_spatial <- lm(score_spatial ~ foundingdensity_5yr_norm, data = df)
summary(model_spatial)

## 
## Call:
## lm(formula = score_spatial ~ foundingdensity_5yr_norm, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5229 -0.4874  0.2259  0.3631  0.5055 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                0.4733     0.1157   4.092 0.000171 ***
## foundingdensity_5yr_norm   0.0496     0.2154   0.230 0.818938    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4316 on 46 degrees of freedom
## Multiple R-squared:  0.001151,   Adjusted R-squared:  -0.02056 
## F-statistic: 0.053 on 1 and 46 DF,  p-value: 0.8189

**Table 1.3** Model 1 : Subject Breadth
Term	Estimate	Std. Error	t value	p-value
Intercept	0.455	0.058	7.838	<0.001
Institutional Density	0.055	0.108	0.513	0.611

Residual standard error = 0.22
R² = 0.006
F(1, 46) = 0.263, p = 0.611
The regression revealed no statistically significant relationship between institutional density and subject breadth. The hypothesis was not supported.

Table 1.4 Model 2: Spatial Breadth
Term	Estimate	Std.Error	t value	p-value
Intercept	0.473	0.116	4.092	<0.001
Institutional Density	0.050	0.215	0.230	0.819

Residual standard error = 0.43
R² = 0.001
F(1, 46) = 0.053, p = 0.819
Similarly, institutional density did not significantly predict spatial breadth. This result also does not support the hypothesis.

Both models yielded non-significant results, with minimal variance explained. The findings do not support the hypothesis that higher institutional density is associated with narrower subject or spatial mandates.

Figure 2.1a: Foundational Density and Niche Specialization

Figure 2.1a visualizes the relationship between founding institutional density and mandate breadth. The plot shows no clear trend or directional association, consistent with the regression and t-test results reported above.

Figure 2.1b: Number of narrow-mandate IGOs engaged in subject-matter coordination mechanisms, disaggregated by Within-IGO and Across-IGO types. Research and Sustainable Development domains show the highest level of activity across both coordination types.

Conjecture 2 Hypothesis 2.2 — Coordination and Legal Authority as Moderators

IGOs with stronger coordination mechanisms (vertical or horizontal) and legally binding sources of jurisdiction (treaty-based mandates) will sustain broader mandates even in dense institutional fields.

C2 H2.2 Variables

Variable Name	Role	Description
Mandate_Category	Dependent Variable	Classification of Igo based on mandate score
vertical_coordination_strength	Independent Variable	Strength of vertical coordination mechanisms
horizontal_coordination_strength	Independent Variable	Strength of horizontal coordination mechanisms
binding_secondary_law_withinigo	Independent Variable	Presence of legally binding secondary laws within the IGO.
foundational_treaties_charters_withinigo	Independent Variable	Presence of foundational treaties or charters within the IGO

The logistic regression model did not detect any meaningful association between the selected predictors (coordination mechanisms and legal authority indicators) and the likelihood of having a high mandate breadth.
All predictors had effect estimates near zero, extremely large standard errors, and non-significant p-values (all = 1.000).
The model’s deviance is near-zero, which suggests complete or quasi-complete separation — likely due to:
Low sample size (N = 29).
Little variance in the outcome or predictors.
Perfect prediction or lack of cases in one of the outcome categories (e.g., almost all IGOs might have high mandate breadth in this filtered dataset).
No statistical support was found for the hypothesis in this test.

Figure 2.2a: Coordination Strength by Mandate Breadth

This figure displays boxplots comparing vertical and horizontal coordination strength across mandate breadth categories.

The boxplots illustrate that vertical coordination tends to be somewhat higher among high mandate IGOs, though variance is substantial.
Horizontal coordination is more evenly distributed, with no clear visual trend across categories.

Summary of Results for Hypothesis 2.2

Hypothesis 2.2 was not supported by the data. No significant association was found between coordination strength or binding legal authority and the breadth of IGOs’ mandates within the tested sample.

H2.3 — Niche Differentiation through Subject–Spatial Intersections

IGOs adapt by differentiating across specific subject–spatial intersections (e.g., biodiversity in enclosed seas; trade in EEZs).

C2 H2.3 Variables

Variable Name	Role	Description
`niche_specialisation`	Dependent variable	Index capturing how narrowly defined and specialized an IGO’s mandate is
`intersection_score`	Independent variable	Composite score of the IGO’s breadth across subject-spatial intersections

Construction of Composite Scores

Subject-Matter and Spatial Domain Coding

To assess the scope and specialization of intergovernmental organizations (IGOs), binary coding was applied to each IGO across two domains:

Subject-matter domains: 10 thematic areas (e.g., biodiversity, trade, security).
Spatial domains: 10 geographic areas (e.g., high seas, internal waters, EEZs).

Each domain variable was coded as:

1 if the IGO’s mandate explicitly includes that area, and
0 otherwise.

This resulted in two sets of binary variables per IGO:

Subject vector: subject_cols (10 variables)
Spatial vector: spatial_cols (6 variables)

Intersection Score: Subject–Spatial Differentiation

To measure how precisely targeted an IGO is across specific subject–spatial intersections, we developed a composite score termed the intersection score.

For each IGO:

Extracted its subject vector and spatial vector (binary).
Calculated the outer product of the two vectors (a 6×10 matrix).

This matrix captures all possible subject–spatial combinations targeted by the IGO.

Each element is 1 if both the corresponding subject and spatial domain are present; 0 otherwise.
Sum all elements in the matrix to get the total number of unique subject–spatial intersections.

This sum reflects the intersectional breadth of the IGO’s mandate.

IGOs with broad mandates across many areas score higher; those with narrow or focused mandates score lower.

Statistical

A Pearson correlation test was run between:

intersection_score: a composite metric capturing how specific an IGO’s mandate is at the intersection of subject and spatial domains
niche_specialisation: an index capturing the degree of specialization in institutional design
Test if intersection_score is correlated with niche_specialisation

Pearson Correlation

r = -0.88: This is a strong negative correlation between intersection_score and niche_specialisation.
p < 2.2e-16: This is highly statistically significant (well below 0.05).
95% CI: The correlation is confidently between -0.93 and -0.79.

The more broadly an IGO intersects across multiple spatial and subject domains, the less niche-specialised it tends to be. IGOs with highly focused mandates (high niche scores) typically operate within fewer or more specific spatial-subject combinations.

Figure 2.3a — Subject–Spatial Intersections

Figure 2.3a Subject–Spatial Intersections in International Governance

This Figure 2.3a illustrates the intersections between spatial domains (e.g., the area, internal waters, high seas) and subject areas (e.g., biodiversity, climate change, sustainable development) in international governance. The color intensity represents the total score of institutional focus or activities at each intersection, with darker shades indicating higher scores. Notable intersections include strong institutional focus on biodiversity and conservation in enclosed or semi-enclosed seas, security and sustainable development in internal waters, and traditional knowledge and cultural heritage in archipelagic regions. This visualization highlights the prioritization of governance efforts across different spatial and subject areas.

Hotspots: “The Area” and “Internal Waters” in Biodiversity, Sustainable Development, Trade, and Science have the highest cumulative scores — reflecting IGOs with broad mandates.
Coldspots: Domains like “Archipelago” or “Coastal Zone” have zero or low interaction with fields like International Cooperation, Cultural Heritage, or Security — likely covered by more niche-focused IGOs.

Summary Hypothesis

This supports your statistical findings — spatial-subject concentration is inversely related to niche specialisation.

H2.4 — Adaptive Portfolios and Non-linear Density Effects

IGOs with diversified strategies (e.g., Innovation, Climate, Inclusion, Trade) adapt better in dense environments than IGOs with narrow portfolios. The effect of density is non-linear: moderate density fosters adaptive value, while extreme density yields diminishing returns unless mitigated by strategic breadth.

C2 H 2.4 Variables

Variable	Type	Description
`adaptive_capacity_index`	Dependent	Composite index of innovation, responsiveness, and institutional agility
`foundingdensity_5yr_norm`	Independent	Z-scored IGO density within ±5 years of founding
`density_squared`	Independent	Quadratic term to test non-linear (curvilinear) effects of density.
`strategy_diversification`	Independent	Composite score of strategic breadth
`foundingdensity_5yr_norm:strategy_diversification`	Interaction	Tests whether strategy diversification moderates the effect of density.
`igo_age`	Control	Controls for maturity or institutional path dependency

Strategic Diversification is Key: The highly significant, positive coefficient for strategy_diversification (p < 0.001) is the standout result. It clearly shows that IGOs with a wider toolkit of strategies have a significantly higher adaptive capacity. This is the single most important predictor in the model.
Density’s Effect is Complex: The non-linear terms for density were not significant, suggesting the relationship might be more complex than a simple curve. However, the significant effect of igo_age (p < 0.05) indicates that older IGOs have a slightly lower adaptive capacity, perhaps because they are less agile or face more institutional inertia.

Conjecture 3 — Embeddedness, Legitimacy, and Efficacy in Global Ocean Economy Governance

Conjecture Statement

IGOs with stronger legal authority, broader mandates, deeper relational embeddedness, and more diversified strategies are more likely to enjoy higher legitimacy and demonstrate greater efficacy in global ocean economy governance. Conversely, IGOs with weaker legal bases, narrow mandates, limited relational integration, or restricted strategic portfolios are more vulnerable to diminished legitimacy and governance effectiveness.

Data Preparation

The third stage of the analysis focused on Conjecture 3, which examined the determinants of legitimacy and efficacy among international governance organisations (IGOs) operating within the global ocean governance regime. The core hypothesis underlying this conjecture posited that organisations more deeply embedded within governance networks (legally, strategically, and institutionally) are likely to be more legitimate and effective.

To empirically test this, a set of key indicators was derived to quantify four principal dimensions: legal authority, mandate breadth, relational embeddedness, and strategic diversification.

Conjecture 3 Variables

Variable	What It Measures (Conceptual Dimension)
`ordinal_score_sources`	Legal Authority Strength — Measures the extent of formal legal basis (e.g., treaties, binding law)
`subject_breadth`	Subject-Matter Mandate Breadth — Number of distinct issue areas (e.g., biodiversity, trade)
`spatial_breadth`	Jurisdictional Breadth — Number of maritime zones under IGO’s governance
`vertical_coordination_strength`	Relational Embeddedness (Vertical) — Strength of intergovernmental coordination mechanisms
`horizontal_coordination_strength`	Relational Embeddedness (Horizontal) — Involvement in partnerships, platforms, and cross-sector ties
`strategy_diversification`	Strategic Breadth — Range of strategies employed (e.g., climate action, finance, innovation, inclusion)
`objective_diversification`	Operational Breadth — Breadth of functional goals and planning objectives

Deriving Variables for Conjecture 3: Embeddedness, Legitimacy, and Efficacy

To enhance the precision of the legal authority dimension under Conjecture 3, the analysis was refined to distinguish between soft law and strong law foundations. This refinement acknowledges that not all legal instruments confer the same degree of legitimacy or enforceability upon an international governance organisation (IGO). While some organisations are embedded within binding treaties or charters (strong law), others rely primarily on voluntary frameworks, strategic plans, or technical standards (soft law). Recognising this distinction is crucial, as the nature of an IGO’s legal foundation directly affects both its formal authority and perceived legitimacy within the broader governance system.

Conceptual Rationale

In the context of global ocean governance, soft law refers to non-binding or partially binding instruments (such as strategic frameworks, guidelines, resolutions, or technical standards) that provide normative direction without formal legal enforceability. These instruments facilitate cooperation, policy diffusion, and innovation but depend largely on voluntary compliance.

In contrast, strong law encompasses legally binding agreements and mechanisms with clear obligations and compliance oversight. This category includes foundational treaties, charters, secondary legislation, and delegated powers, which institutionalise authority and accountability. IGOs grounded in strong law are typically perceived as having greater legitimacy, authority, and capacity to enforce norms, though sometimes at the cost of flexibility.

Step 1: Categorising Soft and Strong Law Instruments

The dataset contained a variety of variables describing the legal bases of each IGO. These were systematically grouped into two categories—soft law and strong law—to reflect their respective normative force:

Soft Law Instruments:

customary_soft_law
non_binding_secondary_law
other_governance_instruments
strategic_frameworks
technical_norms_standards
bilateral_multilateral_arrangements

Strong Law Instruments:

binding_secondary_law
compliance_oversight
delegated_or_derived_powers
foundational_treaties_charters

Quantifying Legal Strength

To operationalise these categories, we computed a count-based measure that captured how many types of legal instruments each IGO possessed in each category. For every organisation, the number of active (non-zero) entries was summed across the relevant columns. This produced two numerical indicators:

Soft Law Count – representing the number of non-binding or voluntary frameworks associated with the organisation.
Strong Law Count – representing the number of binding legal instruments and enforcement mechanisms underpinning its operations.

A higher value on this measure indicated that an IGO was grounded in more binding or formalised legal arrangements, conferring greater legitimacy in the eyes of member states and other international actors. This indicator therefore provided a direct quantitative measure of the legal embeddedness and credibility of each IGO’s institutional foundation.

\[ Legal Authority=ordinal_score_sources \]

This dimension was crucial because legal grounding not only legitimises organisational mandates but also constrains arbitrariness and strengthens compliance mechanisms, contributing to perceived fairness and procedural legitimacy in the governance process.

\[ Legal Strength Index=Rescale(Soft Law Count)+Rescale(Strong Law Count) \]

This composite index complemented the original ordinal_score_sources variable, offering a more empirically grounded representation of each organisation’s legal foundation

Step 2: Constructing Mandate Breadth

Next, the analysis sought to measure how comprehensive or narrow an organisation’s mandate was. Mandate breadth reflects an IGO’s scope of authority, both spatially (across maritime zones) and thematically (across issue areas). It thus captures the organisation’s representational reach and ability to engage with diverse governance challenges.

To derive this variable, two previously computed measures were combined:

Spatial Breadth – representing the number of maritime zones in which the organisation operates.
Subject Breadth – representing the number of thematic policy domains covered.

These two elements were summed to create a composite Mandate Breadth Score, reflecting the overall jurisdictional and thematic range of each IGO:

\[ Mandate Breadth=Spatial Breadth+Subject Breadth \]

IGOs with broad mandates were expected to have greater capacity to coordinate across multiple issue areas and constituencies, thereby enhancing both their efficacy and their perceived legitimacy. However, excessively broad mandates may also dilute focus, a nuance that would be explored in subsequent model testing.

Step 3: Quantifying Relational Embeddedness

The third dimension captured the degree to which an organisation was embedded within broader institutional networks, reflecting both vertical and horizontal coordination mechanisms. Relational embeddedness was operationalised as the sum of coordination mechanisms across two categories:

Vertical Coordination (Columns 27–47) – partnerships and formal relationships between the IGO and national or subnational governments.
Horizontal Coordination (Columns 153–173) – collaboration with other IGOs, UN agencies, and transnational networks.

By summing these two dimensions, we produced an Embeddedness Score, which quantified how well-connected each IGO was within the larger institutional system:

\[ Embeddedness Score=∑(Vertical Coordination)+∑(Horizontal Coordination) \]

Organisations with higher embeddedness scores were interpreted as being more institutionally networked, capable of mobilising resources and legitimacy through cooperative relationships.

Step 4: Incorporating Strategic Diversification

Finally, strategic diversification (previously derived in earlier conjectures) was included as a key indicator of organisational adaptability and operational legitimacy. This variable measured the range of strategic tools and approaches employed by an IGO, such as capacity-building, policy coordination, data sharing, or innovation programs.

IGOs that pursue a diverse portfolio of strategies are generally more resilient and better equipped to address multifaceted global challenges. They are also more likely to be perceived as competent and legitimate actors capable of bridging interests across stakeholder groups. Thus, strategic diversification served as a dynamic complement to the more structural indicators of embeddedness and authority.

\[ Strategic Diversification=Strategy Diversification Score \]

Step 5: Constructing the Composite Legitimacy & Efficacy Score

To synthesise these four theoretical dimensions into a single comparative measure, a Composite Legitimacy & Efficacy Score was developed. Each component variable (legal authority, mandate breadth, relational embeddedness, and strategic diversification) was standardised to a 0–1 range using min–max normalization.

The standardized components were then summed to yield a single composite score:

\[ Legitimacy & Efficacy Score=Rescale(Legal Authority)+Rescale(Mandate Breadth)+Rescale(Embeddedness Score)+Rescale(Strategic Diversification) \]

This formula ensured that each dimension contributed equally to the composite measure while allowing for direct cross-organisational comparison.

Summary of ConjTable X. Summary of Conjecture 2 — Density, Coordination, and Specialisationecture 3 — Institutional Design and Legitimacy

Conjecture	Hypothesis	Independent Variable(s)	Dependent Variable	Statistical Test	Key Statistics	Interpretation
C3	H3.1 — Legal Authority and Legitimacy	Legal Authority Type	Legitimacy–Efficacy Score	One-Way ANOVA	F(2,45) = 0.663, p = 0.52	No significant differences in legitimacy across legal authority types.
C3	H3.2 — Mandate Breadth and Legitimacy	Mandate Breadth (Narrow / Medium / Broad)	Legitimacy–Efficacy Score	One-Way ANOVA	p = 0.622 (n.s.)	No significant effect of mandate breadth on legitimacy–efficacy.
C3	H3.3 — Relational Embeddedness and Legitimacy	Embeddedness Score	Legitimacy–Efficacy Score	Correlation / Descriptive	Embeddedness: M = 16.83, Legitimacy: M = 2.05	Descriptive evidence suggests higher embeddedness may relate to greater legitimacy, but no formal test reported.
C3	H3.4 — Strategy Breadth and Legitimacy	Strategy Breadth	Legitimacy–Efficacy Score	One-Way ANOVA	F(1,46) = 14.13, p = 0.00048 **	Strong positive and significant effect — broader strategy breadth is associated with higher legitimacy–efficacy.

H3.1 — Legal authority and legitimacy

IGOs endowed with stronger legal authority (e.g., treaty-based jurisdiction, binding secondary law) will display higher legitimacy and efficacy compared to IGOs whose authority rests primarily on soft law or technical standards. Binding legal mandates stabilize authority and enhance perceived legitimacy (Abbott et al., 2016).

C3 H 3.1 Variables

Variable Name	Type	Role	Description
`legal_authority_type`	Categorical (3 levels: soft, strong, balanced)	Independent variable	Classifies legal authority based on the presence of soft vs. hard legal instruments
`legitimacy_efficacy_score`	Continuous	Dependent variable	Composite score capturing perceptions of IGO legitimacy and institutional effectiveness

Operationalization of Legal Authority

To classify the legal authority type of IGOs, we constructed two additive indices:

Soft Law Indicators (soft_count)

Summing the presence (1) of the following soft legal instruments:

Customary or non-binding law
Technical norms and standards
Strategic frameworks
Bilateral/multilateral arrangements

Strong Legal Indicators (strong_count)

Summing the presence (1) of stronger legal instruments:

Foundational treaties or charters
Binding secondary law
Compliance or oversight mechanisms
Delegated or derived powers

Each IGO was assigned a legal authority type:

“strong” if strong law count > soft law count
“soft” if soft > strong
“balanced” if equal

Statistical

A one-way ANOVA was conducted to examine whether the type of legal authority (strong, soft, balanced) is associated with differences in legitimacy and efficacy scores.

p = 0.52: The difference in mean legitimacy/efficacy scores across the three legal authority types is not statistically significant.
The effect size is also small (F = 0.66), suggesting that legal authority type alone does not meaningfully predict perceived legitimacy in this sample.

Figure 3.1a Legitimacy & Efficacy by Legal Authority Type

Figure 3.1a This boxplot compared legitimacy and efficacy scores of IGOs categorized by legal authority type: balanced, soft, and strong. The median scores are similar across all categories, with balanced and strong legal authority types showing slightly higher medians (~2.25) compared to soft (~2.0). The interquartile ranges and presence of outliers are also depicted, indicating no significant differences in perceived legitimacy/efficacy based on legal authority type.

Figure 3.1b IGO Legitimacy & Efficacy by Legal Authority Type

Figure 3.1b, the dot plot displays the legitimacy and efficacy scores of individual IGOs categorized by their legal authority type: balanced (green), soft (yellow), and strong (blue). IGOs with balanced and strong legal authority types generally exhibit higher scores, while those with soft legal authority tend to have lower scores. This visualization highlights the variation in perceived legitimacy and efficacy across different types of legal authority.

Balanced vs. Soft:

IGOs with balanced legal authority tend to have higher legitimacy/efficacy scores compared to those with soft legal authority. This suggests that a mix of legal instruments may contribute to higher perceived legitimacy and efficacy.

Balanced vs. Strong:

IGOs with balanced and strong legal authority have similar ranges of legitimacy/efficacy scores, with both categories showing high scores. This indicates that both balanced and strong legal frameworks can support high legitimacy and efficacy.

Soft vs. Strong:

IGOs with strong legal authority generally have higher legitimacy/efficacy scores compared to those with soft legal authority. This supports the idea that stronger legal frameworks may enhance perceived legitimacy and efficacy.

H3.2 — Mandate breadth and legitimacy

IGOs with broader subject-matter mandates and spatial jurisdictional coverage will demonstrate higher legitimacy and efficacy than narrowly focused IGOs. Broad mandates engage multiple constituencies, signalling representatives and systemic relevance (Tallberg et al., 2013).

C3 H3.2 Variables

Variable Name	Type	Role	Description
`mandate_category`	Categorical (narrow, medium, broad)	Independent variable	Coded category of each IGO’s mandate breadth, based on subject-matter and spatial jurisdiction.
`legitimacy_efficacy_score`	Continuous	Dependent variable	Composite index measuring perceptions of IGO legitimacy and effectiveness.

Statistical

The model tests whether differences in IGO mandate breadth, classified as narrow, medium, or broad significantly affect their legitimacy and efficacy scores, using a one-way ANOVA

The analysis tested whether legitimacy and efficacy scores differed significantly across IGOs grouped by mandate category — combinations of spatial and subject-matter breadth (e.g., High–High, Medium–Low).
The F-statistic was 5.673, indicating a strong signal that at least one group mean differs from the others.
The p-value was 0.000231, which is highly significant (p < 0.001). This means the differences in legitimacy scores across mandate categories were statistically significant and unlikely to be due to chance.
The Sum of Squares for mandate category (5.804) compared to the residuals (6.991) shows that a substantial portion of the variance in legitimacy scores was explained by mandate category.

Figure 3.2a Legitimacy & Efficacy by Mandate Category

Figure 3.2a compares the legitimacy and efficacy scores of IGOs across different mandate breadth categories. The x-axis represents the mandate breadth categories, while the y-axis represents the legitimacy and efficacy scores. Each boxplot shows the distribution of scores within each category, including the median, interquartile range (IQR), and potential outliers.

High-Low

Median Score: Approximately 2.0 IQR: Ranges from about 1.75 to 2.5 Outliers: None visible
Interpretation: IGOs in this category have moderate legitimacy and efficacy scores with a relatively narrow range.

High-Medium

Median Score: Approximately 2.5 IQR: Ranges from about 2.0 to 2.75 Outliers: None visible
Interpretation: IGOs in this category have higher legitimacy and efficacy scores, indicating that a high-medium mandate breadth is associated with better performance.

Low-High

Median Score: Approximately 2.0 IQR: Ranges from about 1.75 to 2.25 Outliers: Present below 1.5
Interpretation: IGOs in this category have moderate legitimacy and efficacy scores, but with some lower outliers.

Low-Low

Median Score: Approximately 1.5 IQR: Ranges from about 1.25 to 1.75 Outliers: Present below 1.0
Interpretation: IGOs in this category have the lowest legitimacy and efficacy scores, indicating that narrow mandates may limit perceived legitimacy and efficacy.

Low-Medium

Median Score: Approximately 2.0 IQR: Ranges from about 1.5 to 2.5 Outliers: Present below 1.0
Interpretation: IGOs in this category have moderate legitimacy and efficacy scores, with some variability and lower outliers.

Medium-Low

Median Score: Approximately 2.0 IQR: Ranges from about 1.5 to 2.5 Outliers: Present below 1.0
Interpretation:IGOs in this category have moderate legitimacy and efficacy scores, similar to the Low-Medium category.

Medium-Medium

Median Score: Approximately 2.25 IQR: Ranges from about 2.0 to 2.5 Outliers: None visible
Interpretation: IGOs in this category have relatively high legitimacy and efficacy scores, indicating that a medium breadth in both dimensions is beneficial.

IGOs with different combinations of spatial and subject-matter mandates showed meaningful differences in legitimacy and efficacy. This result supports the hypothesis that broader and more balanced mandates are associated with higher legitimacy, and that mandate composition, not just total breadth, plays a critical role.

Figure 3.2b Legitimacy & Efficacy by Mandate Composition

The analysis of Figure 3.2b revealed that IGOs with broader and more balanced mandates—classified as High–High in both spatial and subject-matter scope—consistently demonstrated higher legitimacy and efficacy scores. Organizations such as UNCCD and UNDP, which operated across diverse domains and regions, appeared in the upper range of the legitimacy scale, supporting the hypothesis that broader mandates enhanced institutional legitimacy. In contrast, IGOs with narrow or uneven mandates, particularly those in the Low–Low and High–Low categories like UNODC, WFP, and IPBES, tended to score lower, suggesting that strength in only one dimension was insufficient to elevate perceived legitimacy. These findings confirmed that mandate composition played a significant role in shaping how effective and representative IGOs were perceived to be.

H3.3 — Relational embeddedness and legitimacy

IGOs with stronger inter-institutional ties and more developed vertical/horizontal coordination mechanisms will achieve higher legitimacy and efficacy than those lacking such linkages. Relational embeddedness reinforces legitimacy by situating IGOs within dense governance networks (Powell et al., 2005; Biermann et al., 2009).

C3 H3.3 Variables

Variable Name	Role	Description
`embeddedness_score`	Independent variable	Composite index.
`legitimacy_efficacy_score`	Dependent variable	Perceived legitimacy and institutional effectiveness.

Statistical

A bivariate correlation analysis was conducted to test whether an IGO’s relational embeddedness score is positively associated with its legitimacy and efficacy score.

Variable	Min	Median	Mean	Max
Embeddedness Score	11.00	17.00	16.83	22.00
Legitimacy Score	0.77	2.08	2.05	3.08

The Pearson correlation of 0.29 suggests a modest positive association between embeddedness and legitimacy.
While not strong, this supports the hypothesis that IGOs more deeply embedded in inter-institutional networks tend to be seen as more legitimate and effective.

Figure 3.3a Heatmap of Relational Embeddedness vs. Legitimacy

Figure 3.3a visualized the relationship between relational embeddedness (measured by inter-institutional ties and vertical coordination) and IGO legitimacy. IGOs with higher embeddedness scores (positioned toward the top right of the heatmap) generally showed darker shading, indicating higher average legitimacy scores. While variation existed across the grid, organizations like ILO, FAO/UN-Habitat, and UNDP/CITES were among those with stronger ties and above-average legitimacy.
Conversely, IGOs with lower embeddedness—such as ISA or Minamata—tended to show lighter tiles, reflecting lower legitimacy. This pattern supported Hypothesis H3.3 in part, reinforcing that dense governance linkages contributed to perceived legitimacy, though not uniformly across all cases.

H3.4 — Strategy breadth and legitimacy

IGOs with more diversified strategy portfolios (spanning multiple domains such as environmental action, trade, technology, and capacity building) will display higher legitimacy and efficacy than IGOs with narrowly focused strategies. Strategy breadth increases adaptability and perceived relevance, thereby sustaining legitimacy in dynamic governance environments (Baum & Singh, 1994; Powell et al., 2005).

C3 H3.4 Variables

Variable Name	Role	Description
`strategy_breadth`	Independent variable	Degree of diversification in the IGO’s strategy portfolio across multiple policy and governance domains.
`legitimacy_efficacy_score`	Dependent variable	Composite index measuring perceived legitimacy and institutional effectiveness.

Statistical

Source	Df	Sum Sq	Mean Sq	F value	Pr(>F)
Strategy Breadth	1	3.007	3.007	14.13	0.00048*
Residuals	46	9.787	0.213

The model finds a highly significant effect of strategy breadth on legitimacy (p < 0.001).
The F-value of 14.13 indicates that variation in strategy breadth explains a substantial portion of variance in legitimacy scores.
This strongly supports the hypothesis that more diversified strategy portfolios enhance IGO legitimacy and efficacy.
The analysis of Hypothesis H3.4 demonstrated a statistically significant relationship between strategy breadth and legitimacy.

Figure 3.4a — Strategy Breadth vs. Legitimacy & Efficacy

Figure 3.4a compares legitimacy and efficacy scores of IGOs categorized by strategy breadth (diverse vs. narrow). IGOs with diverse strategies (green) generally exhibit higher scores, while those with narrow strategies (orange) show greater variability and lower scores.

Diverse Strategies: IGOs with diverse strategies tend to have higher legitimacy and efficacy scores, supporting the hypothesis that strategic diversification enhances perceived legitimacy and efficacy.
Narrow Strategies: IGOs with narrow strategies show more variability in their legitimacy and efficacy scores, suggesting that narrow strategies may not consistently support high legitimacy and efficacy.

Figure 3.4b — Strategy Breadth and Legitimacy of IGOs

Figure 3.4b illustrates the positive relationship between strategy breadth score and legitimacy/efficacy score. IGOs with diverse strategies (green) tend to have higher legitimacy and efficacy scores, supporting the hypothesis that broader strategy portfolios enhance perceived legitimacy and efficacy. The shaded region represents the confidence interval of the trend line.

Positive Correlation: There is a clear positive correlation between strategy breadth and legitimacy/efficacy scores, supporting the hypothesis that broader strategy portfolios enhance perceived legitimacy and efficacy.
Diverse vs. Narrow: IGOs with diverse strategies consistently achieve higher legitimacy and efficacy scores compared to those with narrow strategies.

Both figures 3.4a and 3.4b provide strong visual evidence supporting Hypothesis 3.4, which posits that IGOs with more diversified strategy portfolios achieve higher legitimacy and efficacy.

Figure 3.4a shows that IGOs with diverse strategies tend to have higher legitimacy and efficacy scores compared to those with narrow strategies.
Figure 3.4b further reinforces this by showing a positive correlation between strategy breadth and legitimacy/efficacy scores

The results suggest that strategic diversification across multiple domains enhances an IGO’s legitimacy and efficacy, aligning with theories of organizational adaptability and relevance.

IGO’s Analysis Report

Emmanuel

2025-10-13

1. Introduction

2. Data

Tools

Data Prepossessing

Preparation Steps

2.1 Data Understanding

3. Data Analysis

Conjecture 1 Statement

Data Preparation and Weight Derivation

Step 1: Defining Institutional Design Categories

Step 2: Calculation of Category-Level Scores and Variability

Step 4: Constructing the Endurance Index

Step 5: Interpretation and Application

Summary of Statistical Tests for Conjecture 1 — Institutional Design and IGO Endurance

Conjecture 1 Hypothesis 1.1 — Historical maturity and endurance

Conjecture 1 Hypothesis 1.1 Variables

Conjecture 1 Hypothesis 1.2 — Mandate breadth and Endurance

Conjecture 1 Hypothesis 1.3 — Coordination mechanisms and endurance

Hypothesis 1.3 Summary

Conjecture 1 Hypothesis 1.4 — Strategic and objective diversification and endurance

Hypothesis 1.4 Summary

Conjecture 2 — Organisational Density, Niche Differentiation, and Adaptive Capacity in Global Ocean Economy Governance

Conjecture Statement

Deriving Key Variables for Testing Conjecture 2: Density, Niche Differentiation, and Adaptive Capacity

Step 1: Measuring Niche Specialisation

Step 2: Constructing the Adaptive Capacity Index

Step 4: Integrating

Summary of Conjecture 2 — Density, Coordination, and Specialisation

Conjecture 2 Hypothesis 2.1 — Density and Specialisation

C2 H 2.1 Variables

Conjecture 2 Hypothesis 2.2 — Coordination and Legal Authority as Moderators

C2 H2.2 Variables

H2.3 — Niche Differentiation through Subject–Spatial Intersections

H2.4 — Adaptive Portfolios and Non-linear Density Effects

Conjecture 3 — Embeddedness, Legitimacy, and Efficacy in Global Ocean Economy Governance

Conjecture Statement

Data Preparation

Deriving Variables for Conjecture 3: Embeddedness, Legitimacy, and Efficacy

Summary of ConjTable X. Summary of Conjecture 2 — Density, Coordination, and Specialisationecture 3 — Institutional Design and Legitimacy

H3.1 — Legal authority and legitimacy

H3.2 — Mandate breadth and legitimacy

H3.3 — Relational embeddedness and legitimacy

H3.4 — Strategy breadth and legitimacy