2. Data

The raw data set contained 173 columns capturing institutional identifiers, historical context, spatial and subject-matter jurisdictions, vertical and horizontal coordination mechanisms, strategic orientations, objectives, and sources of legal authority.

To align with the theoretical framework, we prepared three tailored data sets:

Conjecture 1 data set (conj1_df): Focuses on institutional design coherence, combining measures of spatial and subject-matter breadth, vertical coordination, strategies, defined objectives, and sources of jurisdiction.
Conjecture 2 data set (conj2_df): Focuses on ecological pressures, retaining measures of density and cumulative stock alongside subject–spatial niches and adaptive strategies/objectives.
Conjecture 3 data set (conj3_df): Focuses on legitimacy and embeddedness, emphasizing inter-institutional relationships, compliance mechanisms, strategies linked to inclusion and accountability, objectives tied to governance and partnerships, and sources of authority.

Each data set was created using systematic column selection from the master file. The R code ensured reproducibility by grouping columns into families (e.g., spatial, subject-matter, strategies) and extracting them consistently across “within-IGO” and “across-IGO” dimensions. This produced three clean analytical subsets, each suited to test a distinct conjecture.

# Load datasets
df_year <- read_csv("Data/year_data.csv")
df_spatial <- read_csv("Data/spatial_jurisdiction_data.csv")
df_vertical <- read_csv("Data/vertical_coordinations_data.csv")
df_subject <- read_csv("Data/subject_matter_jurisdiction_data.csv")
df_strategies <- read_csv("Data/strategies_data.csv")
df_objectives <- read_csv("Data/defined_objectives_data.csv")
df_relationships <- read_csv("Data/defined_inter_institutional_relationships_data.csv")
df_sources <- read_csv("Data/sources_of_jurisdiction_data.csv")
df_horizontal <- read_csv("Data/horizontal_coordination.csv")

# Put all dataframes in a list
all_dfs <- list(
  df_year, df_spatial, df_vertical, df_subject, 
  df_strategies, df_objectives, df_relationships, 
  df_sources, df_horizontal
)

# Standardize column names: make sure all have "Institution" as key
all_dfs <- lapply(all_dfs, function(df) {
  colnames(df) <- gsub("^[Ii]nstitution$", "Institution", colnames(df))
  df
})

# Merge using full_join on "Institution"
merged_df <- reduce(all_dfs, function(x, y) full_join(x, y, by = "Institution"))

Data Preparation

library(stringr)

clean_to_snake_case <- function(names_vector) {
  names_vector %>%
    # Convert to lowercase
    str_to_lower() %>%
    # Replace spaces, slashes, question marks, commas, ampersands, parentheses, and other special chars with underscore
    str_replace_all("[ /?&(),.-]+", "_") %>%
    # Replace multiple underscores with a single underscore
    str_replace_all("_+", "_") %>%
    # Remove trailing or leading underscores
    str_replace_all("^_|_$", "")
}

# Suppose your dataframe is merged_df
colnames(merged_df) <- clean_to_snake_case(colnames(merged_df))

full <- merged_df
nms <- names(full)

# helper: build family columns (within/across variants)
fam_cols <- function(terms, nms) {
  pat <- paste0("^(", paste(terms, collapse="|"), ")_(withinigo|acrossigo)$")
  out <- grep(pat, nms, value = TRUE)
  return(out)
}

# Families (prefixes as in your dataset)
spatial_terms <- c(
  "archipelago","coastal_zone","contiguous_zone_cz","enclosed_or_semi_enclosed_sea",
  "exclusive_economic_zone_eez","extended_continental_shelf_cs","high_seas",
  "internal_waters","territorial_sea_ts","the_area"
)

subject_terms <- c(
  "biodiversity_ecosystem_conservation","cultural_heritage_traditional_knowledge_data_governance",
  "disaster_risk_reduction_resilience","environmental_protection_climate_change",
  "human_rights_social_justice_advocacy","international_cooperation_governance",
  "research_science_innovation","security_safety",
  "sustainable_development_capacity_building","trade_investment_economic_cooperation"
)

vertical_terms <- c(
  "data_integration_systems","global_regional_national_coordination",
  "intergovernmental_to_national_institutions","multi_level_planning_structures",
  "policy_alignment_with_national_plans","regional_implementing_partners",
  "reporting_compliance_mechanisms","sectoral_to_national_coordination",
  "technical_assistance_to_states","un_to_member_states"
)

horizontal_terms <- c(
  "advocacy_and_communication","cross_border_initiatives",
  "cross_sectoral_collaboration","inter_agency_technical_cooperation",
  "joint_research_and_projects","multi_stakeholder_platforms",
  "peer_to_peer_learning_mechanisms","regional_economic_community_coordination",
  "shared_monitoring_frameworks","thematic_working_groups" 
)

strategy_terms <- c(
  "capacity_development_operational_delivery","collaboration_partnerships_networks",
  "environmental_climate_biodiversity_action","financial_budgetary_management",
  "inclusion_rights_social_justice","innovation_technology_development",
  "knowledge_research_data_systems","monitoring_evaluation_accountability",
  "policy_regulation_legal_frameworks","strategic_institutional_planning"
)

objective_terms <- c(
  "environmental_action","financial_stewardship","governance_planning","inclusion_rights",
  "innovation_technology","knowledge_data","monitoring_accountability","operational_delivery",
  "partnerships_networks","policy_regulation"
)

inter_terms <- c(
  "civil_society_engagement","donor_partnerships","intergovernmental_consultations",
  "ngo_engagement","private_sector_partnerships","regional_body_coordination",
  "scientific_community_linkages","technical_or_expert_groups",
  "treaty_body_coordination","un_system_collaboration"
)

source_terms <- c(
  "bilateral_multilateral_arrangements","binding_secondary_law","compliance_oversight",
  "customary_soft_law","delegated_or_derived_powers","foundational_treaties_charters",
  "non_binding_secondary_law","other_governance_instruments",
  "strategic_frameworks","technical_norms_standards"
)

# --- collect actual column names from families -------------------------------
spatial_cols   <- fam_cols(spatial_terms, nms)
subject_cols   <- fam_cols(subject_terms, nms)
vertical_cols  <- fam_cols(vertical_terms, nms)
strategy_cols  <- fam_cols(strategy_terms, nms)
objective_cols <- fam_cols(objective_terms, nms)
inter_cols     <- fam_cols(inter_terms, nms)
source_cols    <- fam_cols(source_terms, nms)
horizontal_cols <- fam_cols(horizontal_terms, nms)

# Scores and identifiers
score_cols <- intersect(c(
  "ordinal_score_spatial","ordinal_score_vertical_coordination",
  "ordinal_score_subject_matter","ordinal_score_strategies",
  "ordinal_score_defined_objectives","ordinal_score_defined_inter",
  "ordinal_score_sources", "ordinal_score_horizontal"
), nms)

id_cols <- intersect(c("institution","year_cleaned","founding_era_category"), nms)
density_cols <- intersect(c("foundingdensity_5yr","cumulativestock"), nms)

# --- Conjecture 3: embeddedness & legitimacy --------------------------------
strategy_legit <- grep("^(inclusion_rights_social_justice|financial_budgetary_management|monitoring_evaluation_accountability|policy_regulation_legal_frameworks|strategic_institutional_planning)_(withinigo|acrossigo)$", nms, value = TRUE)
objective_legit <- grep("^(inclusion_rights|financial_stewardship|monitoring_accountability|governance_planning|policy_regulation|partnerships_networks)_(withinigo|acrossigo)$", nms, value = TRUE)
vertical_compliance <- grep("^reporting_compliance_mechanisms_(withinigo|acrossigo)$", nms, value = TRUE)

c3_cols <- unique(c(
  id_cols,
  intersect(c("ordinal_score_defined_inter","ordinal_score_sources",
              "ordinal_score_defined_objectives","ordinal_score_strategies"), score_cols),
  inter_cols, strategy_legit, objective_legit, vertical_compliance, source_cols
))
conj3_df <- dplyr::select(full, all_of(c3_cols))

# --- sanity checks ----------------------------------------------------------
cat("Conj3 cols:", length(c3_cols), "\n")

## Conj3 cols: 71

Analysis

# Columns for Legal Authority
lai_cols <- c(
  "binding_secondary_law_withinigo", "binding_secondary_law_acrossigo",
  "compliance_oversight_withinigo", "compliance_oversight_acrossigo",
  "delegated_or_derived_powers_withinigo", "delegated_or_derived_powers_acrossigo",
  "foundational_treaties_charters_withinigo", "foundational_treaties_charters_acrossigo",
  "non_binding_secondary_law_withinigo", "non_binding_secondary_law_acrossigo",
  "strategic_frameworks_withinigo", "strategic_frameworks_acrossigo",
  "technical_norms_standards_withinigo", "technical_norms_standards_acrossigo",
  "bilateral_multilateral_arrangements_withinigo", "bilateral_multilateral_arrangements_acrossigo",
  "customary_soft_law_withinigo", "customary_soft_law_acrossigo",
  "other_governance_instruments_withinigo", "other_governance_instruments_acrossigo"
)

# Compute LAI as row mean (index)
conj3_df <- conj3_df %>%
  mutate(LAI = rowMeans(across(all_of(lai_cols)), na.rm = TRUE))

# Columns that contribute to legitimacy participation
lpi_cols <- c(
  "civil_society_engagement_withinigo", "civil_society_engagement_acrossigo",
  "ngo_engagement_withinigo", "ngo_engagement_acrossigo",
  "private_sector_partnerships_withinigo", "private_sector_partnerships_acrossigo",
  "scientific_community_linkages_withinigo", "scientific_community_linkages_acrossigo",
  "treaty_body_coordination_withinigo", "treaty_body_coordination_acrossigo",
  "un_system_collaboration_withinigo", "un_system_collaboration_acrossigo",
  "reporting_compliance_mechanisms_withinigo", "reporting_compliance_mechanisms_acrossigo"
)

# Construct Legitimacy Participation Index (LPI)
conj3_df <- conj3_df %>%
  mutate(LPI = rowMeans(across(all_of(lpi_cols)), na.rm = TRUE))

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.

# Model: Legal Authority → Legitimacy
m_h31 <- lm(LPI ~ LAI, data = conj3_df)
summary(m_h31)

## 
## Call:
## lm(formula = LPI ~ LAI, data = conj3_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7391 -0.6141 -0.0427  0.3997  2.4008 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   1.7760     0.5264   3.374  0.00151 **
## LAI           0.4093     0.2297   1.781  0.08146 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9224 on 46 degrees of freedom
## Multiple R-squared:  0.06453,    Adjusted R-squared:  0.04419 
## F-statistic: 3.173 on 1 and 46 DF,  p-value: 0.08146

The regression estimates the effect of Legal Authority (LAI) on Legitimacy (LPI). The coefficient on LAI is positive (0.41), suggesting that IGOs with stronger legal authority tend to score higher on legitimacy. The relationship is marginally significant (p ≈ 0.08), meaning it is just outside the conventional 5% threshold but within the 10% range. The model explains about 6.5% of the variance in legitimacy, indicating a modest relationship.
This result aligns with the hypothesis: legal authority contributes to legitimacy, but the effect is not overwhelmingly strong on its own. In practice, IGOs that rely on treaties, binding legal frameworks, and compliance mechanisms do appear to enjoy somewhat greater legitimacy, as measured by stakeholder engagement and participation. However, the borderline significance suggests that legal authority may not be the sole or dominant driver of legitimacy. Instead, it likely interacts with other factors, such as embeddedness (network ties), strategy breadth, or inclusiveness, which we explore in later hypotheses.

library(ggeffects)
library(ggrepel)

pred_h31 <- ggpredict(m_h31, terms = "LAI [all]")

p_h31 <- ggplot(pred_h31, aes(x = x, y = predicted)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, fill = "skyblue") +
  geom_line(size = 1.2, color = "blue") +
  # add actual data points with IGOs
  geom_point(data = conj3_df, aes(x = LAI, y = LPI, color = founding_era_category), size = 3, alpha = 0.7) +
  geom_text_repel(data = conj3_df, aes(x = LAI, y = LPI, label = institution, color = founding_era_category),
                  size = 3, max.overlaps = 12) +
  scale_color_brewer(palette = "Dark2") +
  theme_minimal(base_size = 13) +
  labs(
    title = "Figure 3.1 Legal Authority and Legitimacy",
    x = "Legal Authority Index (LAI)",
    y = "Legitimacy Participation Index (LPI)",
    color = "Founding Era",
    caption = "IGOs with stronger legal authority."
  )

print(p_h31)

The trend line confirms that IGOs with stronger legal authority are more likely to exhibit higher legitimacy scores, supporting Hypothesis 3.1. However, the dispersion of points indicates variation: some IGOs with strong legal authority score relatively low on legitimacy (suggesting authority alone is insufficient), while certain IGOs with moderate legal authority (like CITES) achieve exceptionally high legitimacy. Founding era also matters — early and mid-20th century IGOs (e.g., ILO, FAO, WHO) cluster around the middle but remain durable, while more recent IGOs show greater spread.
This suggests that legal authority contributes to legitimacy, but its effect depends on context and complementary factors such as embeddedness, inclusiveness, and strategic breadth. For global ocean economy governance, the implication is that formal treaties and binding legal frameworks (UNCLOS-based bodies, regional fisheries treaties) are advantageous, but legitimacy cannot be fully secured through law alone. IGOs must build participatory practices and stakeholder trust alongside legal mandates to sustain legitimacy in increasingly competitive governance landscapes.

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.

# Construct Mandate Breadth Score
conj3_df$Mandate_Breadth <- rowMeans(conj3_df[, c(
  "ordinal_score_defined_objectives",
  "ordinal_score_strategies"
)], na.rm = TRUE)

# Standardize for comparability
conj3_df$Mandate_Breadth_z <- scale(conj3_df$Mandate_Breadth)

# Regression: Mandate breadth → Legitimacy
model_h3_2 <- lm(LPI ~ Mandate_Breadth_z, data = conj3_df)
summary(model_h3_2)

## 
## Call:
## lm(formula = LPI ~ Mandate_Breadth_z, data = conj3_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9630 -0.4342 -0.1213  0.5517  2.5370 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         2.6833     0.1349  19.887   <2e-16 ***
## Mandate_Breadth_z   0.1870     0.1364   1.371    0.177    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9348 on 46 degrees of freedom
## Multiple R-squared:  0.03927,    Adjusted R-squared:  0.01839 
## F-statistic:  1.88 on 1 and 46 DF,  p-value: 0.1769

The coefficient for Mandate Breadth (standardized) is positive (0.187), suggesting that IGOs with broader mandates tend to have slightly higher legitimacy scores. However, the effect is not statistically significant (p = 0.177). The R² is very low (0.039), meaning mandate breadth alone explains little variation in legitimacy.
While the direction of the relationship supports the hypothesis (broader mandates → higher legitimacy), the evidence is weak. This implies that mandate scope, in isolation, does not strongly predict legitimacy. IGOs with wide-ranging objectives and strategies may project inclusiveness and systemic relevance, but this breadth does not automatically translate into stakeholder recognition or legitimacy gains. In practice, legitimacy may depend on how effectively IGOs operationalize their broad mandates—mere scope without delivery may dilute credibility.

library(ggplot2)
library(ggrepel)
library(dplyr)

# --- data ---
conj3_df <- conj3_df %>%
  mutate(
    Mandate_Breadth = rowSums(select(., 
                                     financial_budgetary_management_withinigo,
                                     inclusion_rights_social_justice_withinigo,
                                     monitoring_evaluation_accountability_withinigo,
                                     policy_regulation_legal_frameworks_withinigo,
                                     strategic_institutional_planning_withinigo,
                                     financial_budgetary_management_acrossigo,
                                     inclusion_rights_social_justice_acrossigo,
                                     monitoring_evaluation_accountability_acrossigo,
                                     policy_regulation_legal_frameworks_acrossigo,
                                     strategic_institutional_planning_acrossigo
    ), na.rm = TRUE),
    Mandate_Breadth_z = scale(Mandate_Breadth)[,1]
  )

# --- Identify IGOs with both high mandate breadth & high legitimacy ---
threshold_mb <- quantile(conj3_df$Mandate_Breadth, 0.75, na.rm = TRUE) # top 25%
threshold_lpi <- quantile(conj3_df$LPI, 0.75, na.rm = TRUE)

high_igos <- conj3_df %>%
  filter(Mandate_Breadth >= threshold_mb & LPI >= threshold_lpi)

# --- Plot ---
p_h32 <- ggplot(conj3_df, aes(x = Mandate_Breadth, y = LPI, 
                              color = founding_era_category)) +
  geom_point(alpha = 0.6, size = 3) +
  geom_smooth(method = "lm", se = TRUE, color = "black", linetype = "dashed") +
  geom_point(data = high_igos, aes(x = Mandate_Breadth, y = LPI), 
             size = 4, color = "red", shape = 17) +  # highlight in red triangles
  geom_text_repel(data = high_igos, aes(label = institution),
                  size = 3.5, color = "red", fontface = "bold") +
  scale_color_brewer(palette = "Dark2") +
  labs(
    title = "Figure 3.2 Hypothesis 3.2 — Mandate Breadth and Legitimacy",
    subtitle = "Highlighted: IGOs with broad mandates AND high legitimacy",
    x = "Mandate Breadth (count of domains)",
    y = "Legitimacy Index (LPI)",
    caption = "Red triangles = IGOs in the top quartile for both mandate breadth and legitimacy.\nDashed line = overall linear trend."
  ) +
  theme_minimal(base_size = 8) +
  theme(
    legend.position = "bottom",
    plot.caption = element_text(hjust = 0, face = "italic", size = 10)
  )

print(p_h32)

The regression output shows a positive but statistically insignificant relationship between mandate breadth and legitimacy. While the estimated coefficient suggests that IGOs with broader mandates tend to score higher on legitimacy, the effect size is modest and does not reach conventional significance thresholds. In the visualization, most IGOs cluster in the middle range, but a few outliers — such as UN-Habitat and UNRISD — demonstrate both high mandate breadth and high legitimacy, illustrating the logic of the hypothesis.
The results suggest that mandate breadth alone is not a universal driver of legitimacy across IGOs. Instead, it may function as an enabling condition, amplifying legitimacy when combined with other institutional strengths (e.g., legal authority, coordination, or operational delivery). This has important implications for IGO design: expanding mandates without parallel strengthening of capacity and governance structures may lead to dilution rather than consolidation of legitimacy

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.

library(dplyr)

# --- Derive IEI ---
conj3_df <- conj3_df %>%
  mutate(
    IEI_raw = rowSums(select(., 
      civil_society_engagement_withinigo, donor_partnerships_withinigo,
      intergovernmental_consultations_withinigo, ngo_engagement_withinigo,
      private_sector_partnerships_withinigo, regional_body_coordination_withinigo,
      scientific_community_linkages_withinigo, technical_or_expert_groups_withinigo,
      treaty_body_coordination_withinigo, un_system_collaboration_withinigo,
      civil_society_engagement_acrossigo, donor_partnerships_acrossigo,
      intergovernmental_consultations_acrossigo, ngo_engagement_acrossigo,
      private_sector_partnerships_acrossigo, regional_body_coordination_acrossigo,
      scientific_community_linkages_acrossigo, technical_or_expert_groups_acrossigo,
      treaty_body_coordination_acrossigo, un_system_collaboration_acrossigo
    ), na.rm = TRUE),
    
    IEI = scale(IEI_raw)[,1]
  )

library(dplyr)
library(ggplot2)

# --- Model: Embeddedness and Legitimacy ---
m_h33 <- lm(LPI ~ IEI, data = conj3_df)
summary(m_h33)

## 
## Call:
## lm(formula = LPI ~ IEI, data = conj3_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.81228 -0.36003 -0.05038  0.31298  2.03231 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2.6833     0.1054   25.46  < 2e-16 ***
## IEI           0.6071     0.1065    5.70 8.13e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7301 on 46 degrees of freedom
## Multiple R-squared:  0.414,  Adjusted R-squared:  0.4012 
## F-statistic: 32.49 on 1 and 46 DF,  p-value: 8.131e-07

# --- Visualization ---
p_h33 <- ggplot(conj3_df, aes(x = IEI, y = LPI, color = founding_era_category, label = institution)) +
  geom_point(size = 3, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "darkblue") +
  ggrepel::geom_text_repel(
    data = conj3_df %>% filter(LPI == max(LPI) | LPI == min(LPI) | IEI == max(IEI) | IEI == min(IEI)),
    aes(label = institution),
    size = 3.5,
    box.padding = 0.3,
    point.padding = 0.3,
    max.overlaps = 10,
    color = "black"
  ) +
  labs(
    title = "Figure 3.3 Hypothesis 3.3 — Embeddedness and Legitimacy",
    x = "Inter-Institutional Embeddedness Index (IEI)",
    y = "Legitimacy Participation Index (LPI)",
    caption = "IGOs embedded in broader governance networks (coordination, partnerships, UN ties) are hypothesized to have higher legitimacy. 
The fitted line shows predicted legitimacy with 95% CI. IGOs with extreme values of embeddedness or legitimacy are labeled for interpretation."
  ) +
  scale_color_brewer(palette = "Set2", name = "Founding Era") +
  theme_minimal(base_size = 13) +
  theme(
    plot.caption = element_text(hjust = 0, face = "italic", size = 10, color = "gray40"),
    plot.title = element_text(size = 14, face = "bold")
  )
print(p_h33)

The graph empirically supports Hypothesis 3.3: Embedded IGOs tend to be more legitimate.
The time of founding adds context but is not the primary determinant of legitimacy—embeddedness is.
Outliers (e.g., IFAD, UNCCD) show that some IGOs are less legitimate or embedded despite their age or mandate, which could warrant further study

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.

# Define strategy-related columns
strategy_cols <- c(
  "financial_budgetary_management_withinigo",
  "policy_regulation_legal_frameworks_withinigo",
  "strategic_institutional_planning_withinigo",
  "monitoring_evaluation_accountability_withinigo",
  "inclusion_rights_social_justice_withinigo",
  "technical_or_expert_groups_withinigo",
  "civil_society_engagement_withinigo",
  "private_sector_partnerships_withinigo",
  "scientific_community_linkages_withinigo",
  "un_system_collaboration_withinigo",
  "ngo_engagement_withinigo"
)

# Create Strategy Breadth Index (SBI)
conj3_df$SBI <- rowSums(conj3_df[, strategy_cols], na.rm = TRUE)

# Basic model
model <- lm(LPI ~ SBI, data = conj3_df)
summary(model)

## 
## Call:
## lm(formula = LPI ~ SBI, data = conj3_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.78364 -0.33894 -0.08566  0.35422  2.15162 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20503    0.46272   0.443     0.66    
## SBI          0.06115    0.01111   5.505 1.59e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7405 on 46 degrees of freedom
## Multiple R-squared:  0.3971, Adjusted R-squared:  0.384 
## F-statistic:  30.3 on 1 and 46 DF,  p-value: 1.588e-06

IGOs with broader strategic portfolios tend to have significantly higher legitimacy. This supports Hypothesis 3.4.

model_control <- lm(LPI ~ SBI + IEI + founding_era_category, data = conj3_df)
summary(model_control)

## 
## Call:
## lm(formula = LPI ~ SBI + IEI + founding_era_category, data = conj3_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.53384 -0.35557  0.01635  0.32084  1.73232 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                                0.74343    0.58935
## SBI                                                        0.03751    0.01338
## IEI                                                        0.36700    0.12716
## founding_era_categoryCold War Era II (1971-1980)           0.50593    0.33884
## founding_era_categoryEarly 20th Century (1900-1945)        0.12589    0.34021
## founding_era_categoryEarly Founding Years (Pre-1900)       0.13827    0.67791
## founding_era_categoryGlobalisation Era (2001-2010)         0.58063    0.69122
## founding_era_categoryLate Cold War (1981-1990)             1.42257    0.50354
## founding_era_categoryPost-Cold War (1991-2000)             0.40520    0.29011
## founding_era_categoryPost-WWII Boom (1946-1960)            0.53643    0.31209
## founding_era_categorySDG & Climate Action Era (2011-2020)  1.17307    0.42863
##                                                           t value Pr(>|t|)   
## (Intercept)                                                 1.261  0.21504   
## SBI                                                         2.803  0.00801 **
## IEI                                                         2.886  0.00647 **
## founding_era_categoryCold War Era II (1971-1980)            1.493  0.14388   
## founding_era_categoryEarly 20th Century (1900-1945)         0.370  0.71346   
## founding_era_categoryEarly Founding Years (Pre-1900)        0.204  0.83950   
## founding_era_categoryGlobalisation Era (2001-2010)          0.840  0.40630   
## founding_era_categoryLate Cold War (1981-1990)              2.825  0.00757 **
## founding_era_categoryPost-Cold War (1991-2000)              1.397  0.17082   
## founding_era_categoryPost-WWII Boom (1946-1960)             1.719  0.09400 . 
## founding_era_categorySDG & Climate Action Era (2011-2020)   2.737  0.00948 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6425 on 37 degrees of freedom
## Multiple R-squared:  0.635,  Adjusted R-squared:  0.5364 
## F-statistic: 6.437 on 10 and 37 DF,  p-value: 1.18e-05

Even after controlling for embeddedness and founding era, strategy breadth remains a strong, independent predictor of legitimacy.
Each additional strategy domain adds about 0.038 points to LPI, all else equal.
IEI also plays a strong and complementary role.
Some eras matter more, possibly due to alignment with emerging norms (e.g., SDG, climate governance).

library(ggrepel)

high_LPI <- 4   
high_SBI <- 55  

# Filter for high-scoring IGOs
high_IGOs <- conj3_df[conj3_df$LPI >= high_LPI | conj3_df$SBI >= high_SBI, ]

# Plot
ggplot(conj3_df, aes(x = SBI, y = LPI)) +
  geom_point(aes(color = founding_era_category), alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "blue") +
  geom_text_repel(
    data = high_IGOs,
    aes(label = institution),
    size = 3.5,
    max.overlaps = 15
  ) +
  labs(
    title = "H3.4 — Strategy Breadth and Legitimacy",
    x = "Strategy Breadth Index (SBI)",
    y = "Legitimacy Participation Index (LPI)"
  ) +
  theme_minimal()

IGOs with more diversified strategy portfolios show significantly higher legitimacy, both in isolation and when controlling for other key factors.

This aligns with the theory that diversity enhances adaptability, responsiveness, and stakeholder alignment, boosting perceived legitimacy.

Conjecture 3 IGO’s Analysis Report

Introduction