Introduction

This study explores how newly public firms perform in the critical years following their initial public offering (IPO), with a particular focus on the role of corporate governance, executive leadership, and sustainability orientation.

While the IPO marks a pivotal shift—granting access to capital and market visibility—it also introduces increased accountability and strategic complexity. Drawing on upper echelons theory, we examine how characteristics of the top management team and board—such as board size, independence, skill composition, meeting frequency, and attendance—shape post-IPO outcomes.

Furthermore, we incorporate insights from agency theory, emphasizing the mechanisms firms use to align managerial actions with long-term value creation. This includes linking executive compensation to performance metrics like total shareholder return (TSR) and sustainability goals.

By integrating ESG scores, compensation structure, and governance practices, this study asks:

How do leadership composition, governance design, and sustainability commitment influence firm performance in the years following an IPO?

Motivation

Recent evidence suggests that many firms experience a sharp decline in performance in the years following their IPO, a phenomenon observed consistently across sectors and market cycles. In our own analysis of Tobin’s Q—an established proxy for firm value—this pattern is striking: for a substantial number of firms, the ratio between market value and assets drops significantly from the first to the fifth year post-IPO. This widespread depreciation raises a critical question: what internal organizational factors account for firms’ ability (or inability) to sustain value after going public?

While investors and policymakers often focus on market conditions and industry dynamics to explain post-IPO volatility, much less attention has been paid to the role of internal decision-making structures—specifically governance, leadership incentives, and sustainability orientation. This is a significant omission, especially in an era where firms are expected not only to grow financially but also to demonstrate legitimacy, transparency, and environmental and social responsibility.

This study is motivated by the need to unpack this underexplored terrain. It seeks to understand why some firms are better equipped to navigate the post-IPO transition, while others struggle despite similar market opportunities. By focusing on variables such as board configuration, executive pay design, and ESG performance, the research aims to identify internal levers that may help preserve or even enhance firm value after the IPO event.

TobinsQ visualization

The calculation to create this graph is the difference of Tobin’s Q in the first year minus the last year, if the value is negative it means that the value out of the calculation Tobin’s Q = Market Value / Total Asset (when total asset is not zero) is smaller. For example, a company with Tobin’s Q = 10 in the first that got a Tobin’s Q = 5 in the last year would be shown as -5 in this graph.

knitr::include_graphics("C:/workspace/IPO/Fig/TobinsQ.png")

Load Data

# Load your merged IPO dataset
ipo_data <- read.csv("C:/workspace/IPO/data/merged_IPO_2014.csv")

# Ensure year and firm ID are treated as factors
#ipo_data$Year <- as.factor(ipo_data$Year)
ipo_data$ID <- as.factor(ipo_data$ID)

Descriptive Statisitcs

# Define the variables used in your models
vars <- c(
  "log_Net_Income", "Independent_Board", "log_Board_Size", "log_Debt_Total",
  "log_Revenue", "log_Assets_Total", "Comp_LT_Objectives", "CEO_Comp_Link_TSR",
  "log_Executives_Compensation", "Leverage", "Board_skills_percent", "log_Capex_Total",
  "log_ESG_Score", "Board_Committee", "Audit_Expertise", "log_Market_Cap",
  "log_Governance_Score", "log_Workforce_Score", "log_TobinsQ", "W_TobinsQ",
  "W_Leverage", "log_Total_Liabilities", "log_Net_Cash_Flow_Opera", "ROA_Actual",
  "CEO_Chairman_Duality", "CEO_Board_Member", "Founder_is_CEO", "CEO_Change_after_IPO",
  "ESG_Score", "Governance_Score", "Comp_Controversies_Score", "W_ESG_Score",
  "Num_Board_Meetings"
)

# Filter to available columns
vars <- intersect(vars, names(ipo_data))

# Select data and drop rows with missing values
desc_data <- ipo_data %>%
  select(all_of(vars)) %>%
  na.omit()

# Compute descriptive stats and round to 2 decimals
desc_stats <- desc_data %>%
  summarise(across(everything(), list(
    Mean = ~round(mean(.), 2),
    SD   = ~round(sd(.), 2),
    Min  = ~round(min(.), 2),
    Max  = ~round(max(.), 2)
  ), .names = "{.col}_{.fn}")) %>%
  t() %>%
  as.data.frame()

# Add readable variable names as a column
colnames(desc_stats) <- "Value"
desc_stats$Variable <- rownames(desc_stats)
rownames(desc_stats) <- NULL

# Reorder columns
desc_stats <- desc_stats %>%
  tidyr::separate(Variable, into = c("Variable", "Statistic"), sep = "_(?=[^_]+$)") %>%
  tidyr::pivot_wider(names_from = Statistic, values_from = Value)



#kable(desc_stats, digits = 2, caption = "Descriptive Statistics of Model Variables")

desc_stats %>%
  kable(digits = 2, caption = "Descriptive Statistics of Model Variables") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  scroll_box(height = "400px")

Descriptive Statistics of Model Variables
Variable	Mean	SD	Min	Max
log_Net_Income	11.81	9.16	0.00	22.30
Independent_Board	76.35	13.79	33.33	93.33
log_Board_Size	2.22	0.23	1.61	2.71
log_Debt_Total	16.48	8.10	0.00	25.03
log_Revenue	19.75	4.16	0.00	25.60
log_Assets_Total	21.41	1.71	18.01	25.92
Comp_LT_Objectives	0.19	0.40	0.00	1.00
CEO_Comp_Link_TSR	0.20	0.40	0.00	1.00
log_Executives_Compensation	16.18	0.90	11.63	17.98
Leverage	0.28	0.25	0.00	1.04
Board_skills_percent	61.55	21.27	16.67	100.00
log_Capex_Total	17.17	2.01	11.16	22.27
log_ESG_Score	3.31	0.39	2.26	4.28
Board_Committee	0.97	0.16	0.00	1.00
Audit_Expertise	0.97	0.16	0.00	1.00
log_Market_Cap	21.52	1.27	18.16	25.22
log_Governance_Score	3.26	0.74	0.76	4.55
log_Workforce_Score	3.48	0.74	0.76	4.48
log_TobinsQ	0.97	0.73	0.00	3.37
W_TobinsQ	2.32	2.57	0.00	9.47
W_Leverage	0.28	0.25	0.00	1.04
log_Total_Liabilities	20.88	1.97	16.51	25.84
log_Net_Cash_Flow_Opera	14.56	8.35	0.00	23.65
ROA_Actual	-0.02	0.23	-1.60	0.35
CEO_Chairman_Duality	0.54	0.50	0.00	1.00
CEO_Board_Member	0.98	0.14	0.00	1.00
Founder_is_CEO	0.28	0.45	0.00	1.00
CEO_Change_after_IPO	0.28	0.52	0.00	2.00
ESG_Score	28.58	12.30	8.57	71.23
Governance_Score	31.76	20.50	1.14	93.52
Comp_Controversies_Score	51.62	0.21	51.45	52.70
W_ESG_Score	28.58	12.30	8.57	71.23
Num_Board_Meetings	7.39	3.35	3.00	23.00

Correlation

selected_data <- ipo_data[, c('W_Leverage',
                  'log_Total_Liabilities',
                  'log_Net_Cash_Flow_Opera',
                  'log_Revenue',
                  'ROA_Actual',
                  'CEO_Comp_Link_TSR',
                  'CEO_Chairman_Duality',
                  'Independent_Board',
                  'CEO_Board_Member',
                  'Num_Board_Meetings',
                  'Founder_is_CEO',
                  'CEO_Change_after_IPO',
                  'Comp_LT_Objectives',
                  'ESG_Score',
                  'Governance_Score',
                  'Comp_Controversies_Score',
                  'Audit_Expertise')]
# Check if all columns are numeric

# Convert all columns to numeric, handling conversion issues and NAN
selected_data[] <- lapply(selected_data, function(x) {
  x <- as.numeric(as.character(x))
  x[is.infinite(x)] <- NA
  x
})

# Recompute the correlation matrix handling NA values
correlation_matrix <- cor(selected_data, use = "pairwise.complete.obs")

if (all(is.finite(correlation_matrix))) {
  corrplot(correlation_matrix, method = "number", type = "upper", order = "hclust",
           tl.cex = 0.8, tl.col = "black", tl.srt = 45,
           number.cex = 0.7, addCoef.col = "black", cl.cex = 0.8)
}

TobinsQ as DV

Hypotheses Development

Going public represents a pivotal moment in a firm’s lifecycle, marking a transition from private governance to public scrutiny. While an IPO provides firms with access to capital markets, it also introduces new accountability structures, managerial pressures, and strategic complexities. Understanding the mechanisms that influence firm valuation in the post-IPO period is therefore critical.

Drawing on agency theory, upper echelons theory, and legitimacy theory, this study explores how governance characteristics, executive incentives, and sustainability commitments shape post-IPO market performance, as captured by Tobin’s Q—a forward-looking measure of firm valuation.

Board Independence and Market Valuation

Agency theory suggests that independent boards improve oversight and reduce agency costs by aligning managerial actions with shareholder interests (Fama & Jensen, 1998). Post-IPO, when ownership becomes more dispersed, the presence of independent directors may enhance market confidence and firm valuation.

Hypothesis 1 (H1):
Board independence is positively associated with Tobin’s Q in the post-IPO period.

# Fixed effects model
model_h1 <- plm(W_TobinsQ ~ Independent_Board + Leverage+ log_Board_Size +
                  log_Debt_Total + log_Revenue + log_Assets_Total + factor(Year),
                data = ipo_data, index = c("ID", "Year"), effect = "individual", model = "within")

# Robust SEs clustered at firm level
coeftest(model_h1, vcov = vcovHC(model_h1, type = "HC1", cluster = "group"))

## 
## t test of coefficients:
## 
##                     Estimate Std. Error t value  Pr(>|t|)    
## Independent_Board -0.0013378  0.0090777 -0.1474   0.88299    
## Leverage           1.4902166  1.2562664  1.1862   0.23698    
## log_Board_Size    -0.7345796  0.6204840 -1.1839   0.23790    
## log_Debt_Total    -0.0406256  0.0265354 -1.5310   0.12739    
## log_Revenue        0.0285921  0.0748111  0.3822   0.70274    
## log_Assets_Total  -1.6108363  0.3793151 -4.2467 3.356e-05 ***
## factor(Year)2015   0.0024719  0.3241539  0.0076   0.99392    
## factor(Year)2016  -0.5715180  0.2340653 -2.4417   0.01551 *  
## factor(Year)2017  -0.1714678  0.2226472 -0.7701   0.44215    
## factor(Year)2018  -0.3875821  0.2304865 -1.6816   0.09425 .  
## factor(Year)2019  -0.1844452  0.2783775 -0.6626   0.50839    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Long-Term Incentives and Market Confidence

Executive compensation structured around long-term goals reduces short-termism and aligns managers’ incentives with sustained value creation (Brauer, 2013). Investors may reward firms that implement such mechanisms, interpreting them as signals of long-term strategic focus.

Hypothesis 2 (H2):
The use of long-term performance-based executive compensation is positively associated with Tobin’s Q after the IPO.

#log_Assets_Total   when included makes  Comp_LT_Objectives insignificant
model_h2 <- plm(W_TobinsQ ~ Comp_LT_Objectives + CEO_Comp_Link_TSR + Leverage +
                  log_Executives_Compensation  + log_Revenue +
                   factor(Year),
                data = ipo_data, index = c("ID", "Year"), model = "within", effect= "individual")

coeftest(model_h2, vcov = vcovHC(model_h2, type = "HC1", cluster = "group"))

## 
## t test of coefficients:
## 
##                              Estimate Std. Error t value Pr(>|t|)   
## Comp_LT_Objectives          -0.602500   0.257636 -2.3386 0.020408 * 
## CEO_Comp_Link_TSR           -0.275867   0.210804 -1.3086 0.192253   
## Leverage                     0.264936   1.822542  0.1454 0.884577   
## log_Executives_Compensation -0.077478   0.161933 -0.4785 0.632879   
## log_Revenue                 -0.039047   0.076625 -0.5096 0.610937   
## factor(Year)2015            -0.288006   0.485334 -0.5934 0.553614   
## factor(Year)2016            -0.769588   0.298784 -2.5757 0.010771 * 
## factor(Year)2017            -0.475469   0.350757 -1.3555 0.176869   
## factor(Year)2018            -0.902514   0.358422 -2.5180 0.012636 * 
## factor(Year)2019            -0.897563   0.332134 -2.7024 0.007514 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Board Expertise and Strategic Decision Quality

According to upper echelons theory (Hambrick & Mason, 1984), the background and competencies of board members shape strategic outcomes. Boards with more domain-specific expertise can enhance firm decision-making and adaptability, thereby improving market valuation.

Hypothesis 3 (H3):
A higher proportion of directors with industry or financial expertise is positively associated with Tobin’s Q post-IPO.

#log_Assets_Total   when included makes  Comp_LT_Objectives insignificant
model_H3 <- plm(W_TobinsQ ~ Board_skills_percent + log_Board_Size  + log_Revenue +log_Capex_Total + Leverage,
                data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_H3, vcov = vcovHC(model_H3, type = "HC1"))

## 
## t test of coefficients:
## 
##                        Estimate Std. Error t value Pr(>|t|)  
## Board_skills_percent -0.0103418  0.0045798 -2.2581  0.02507 *
## log_Board_Size       -0.5959233  0.8479911 -0.7027  0.48307  
## log_Revenue          -0.0588356  0.0804434 -0.7314  0.46544  
## log_Capex_Total       0.0690236  0.0551695  1.2511  0.21242  
## Leverage             -0.1892797  1.9441155 -0.0974  0.92254  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# If we include Year we got Capex significant, but we lose Board Skills
# model_H3 <- plm(W_TobinsQ ~ Board_skills_percent + log_Board_Size  + log_Revenue +log_Capex_Total + Leverage + Year,
#                 data = ipo_data, index = c("ID", "Year"), model = "within")
# coeftest(model_H3, vcov = vcovHC(model_H3, type = "HC1", cluster = "group"))

CSR Governance and Market Perception

Legitimacy theory (Suchman, 1995) and signaling theory (Connelly et al., 2011) suggest that formal CSR structures signal commitment to broader stakeholder goals, environmental responsibility, and social ethics. These structures may be viewed favorably by investors seeking sustainable, forward-thinking firms.

Hypothesis 4 (H4):
Firms with formal CSR governance structures exhibit higher Tobin’s Q in the post-IPO period.

model_h4 <- plm(W_TobinsQ ~ log_ESG_Score + Board_Committee + Audit_Expertise +
                  Comp_LT_Objectives + CEO_Comp_Link_TSR + log_Revenue+ Leverage+
                  log_Market_Cap + log_Governance_Score + log_Workforce_Score,
                data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_h4, vcov = vcovHC(model_h4, type = "HC1", cluster = "group"))

## 
## t test of coefficients:
## 
##                       Estimate Std. Error t value  Pr(>|t|)    
## log_ESG_Score        -0.372125   0.357964 -1.0396   0.29986    
## Board_Committee       0.637127   0.538914  1.1822   0.23858    
## Audit_Expertise      -0.156231   0.352025 -0.4438   0.65769    
## Comp_LT_Objectives   -0.476167   0.186847 -2.5484   0.01161 *  
## CEO_Comp_Link_TSR    -0.349125   0.207291 -1.6842   0.09377 .  
## log_Revenue          -0.117180   0.059131 -1.9817   0.04895 *  
## Leverage             -0.416639   1.576244 -0.2643   0.79182    
## log_Market_Cap        1.261915   0.259624  4.8605 2.436e-06 ***
## log_Governance_Score -0.135487   0.272207 -0.4977   0.61924    
## log_Workforce_Score   0.160173   0.148339  1.0798   0.28160    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#Year diminish the significance 
#However it shows that ESG matters in some years more than others, trends are changing over time


# model_h4 <- plm(W_TobinsQ ~ log_ESG_Score + Board_Committee + Audit_Expertise +
#                   Comp_LT_Objectives + CEO_Comp_Link_TSR + log_Revenue+ Leverage+
#                   log_Market_Cap + log_Governance_Score + log_Workforce_Score +
#                   factor(Year)+log_ESG_Score * factor(Year),
#                 data = ipo_data, index = c("ID", "Year"), model = "within")
# coeftest(model_h4, vcov = vcovHC(model_h4, type = "HC1", cluster = "group"))
# 
# esg_effects <- c(2014, 2015, 2016, 2017, 2018)
# estimates <- c(0.288, 0.288 - 2.086, 0.288 + 0.326, 0.288 - 0.044, 0.288 + 0.116)
# plot(esg_effects, estimates, type = "b", ylab = "Marginal Effect of log_ESG_Score on Tobin's Q", xlab = "Year")
#abline(h = 0, lty = 2)

ESG Performance and Firm Valuation

Strong ESG performance can serve as a strategic asset—enhancing reputation, reducing regulatory risk, and attracting capital. From a strategic management perspective, these factors contribute to competitive advantage and superior market valuation (Eccles et al., 2014).

Hypothesis 5 (H5):
Higher ESG scores are positively associated with Tobin’s Q in the post-IPO period.

# If we include + log_Assets_Total we got no significance
# log_Governance_Score largely diminish the significance of W_ESG_Score
#+ factor(Year) Makes the results insignificant

model_H5 <- plm(W_TobinsQ ~ W_ESG_Score  + log_Revenue + Leverage + log_Workforce_Score +log_Capex_Total,
                data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_H5, vcov = vcovHC(model_H5, type = "HC1"))

## 
## t test of coefficients:
## 
##                       Estimate Std. Error t value Pr(>|t|)   
## W_ESG_Score         -0.0278063  0.0087265 -3.1864 0.001683 **
## log_Revenue         -0.0615562  0.0785512 -0.7836 0.434220   
## Leverage            -0.1308543  1.8670913 -0.0701 0.944200   
## log_Workforce_Score  0.1975716  0.1857475  1.0637 0.288827   
## log_Capex_Total      0.0615151  0.0511171  1.2034 0.230305   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

coeftest(model_H5, vcov = vcovHC(model_H5, type = "HC1", cluster = "group"))

## 
## t test of coefficients:
## 
##                       Estimate Std. Error t value Pr(>|t|)   
## W_ESG_Score         -0.0278063  0.0087265 -3.1864 0.001683 **
## log_Revenue         -0.0615562  0.0785512 -0.7836 0.434220   
## Leverage            -0.1308543  1.8670913 -0.0701 0.944200   
## log_Workforce_Score  0.1975716  0.1857475  1.0637 0.288827   
## log_Capex_Total      0.0615151  0.0511171  1.2034 0.230305   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Using LASSO to select the best model

I believe that our main analysis should focus on the variables that were selected using Lasso this would give us a good justification of what are the main influences for a Tobin’s Q in a firm in the long run.

# Load required libraries


# STEP 1: Select relevant variables and remove missing values
lasso_data <- ipo_data %>%
  select(W_TobinsQ, W_Leverage, log_Total_Liabilities, log_Net_Cash_Flow_Opera,
         log_Revenue, ROA_Actual, CEO_Comp_Link_TSR, CEO_Chairman_Duality,
         CEO_Board_Member, Founder_is_CEO, CEO_Change_after_IPO, Comp_LT_Objectives,
         Board_Size, Board_skills_percent, Independent_Board, Num_Board_Meetings,
         ESG_Score, Governance_Score, Comp_Controversies_Score, Audit_Expertise) %>%
  na.omit()

# STEP 2: Prepare predictor matrix (X) and response vector (y)
x <- model.matrix(W_TobinsQ ~ ., data = lasso_data)[, -1]  # remove intercept column
y <- lasso_data$W_TobinsQ

# STEP 3: Run cross-validated LASSO
set.seed(123)
cv_lasso <- cv.glmnet(x, y, alpha = 1, nfolds = 10)  # alpha = 1 for LASSO

# Plot CV errors and selected lambda
plot(cv_lasso)
abline(v = log(cv_lasso$lambda.min), col = "red", lty = 2)
title("LASSO Cross-Validation", line = 2.5)

# STEP 4: Fit LASSO with best lambda
best_lambda <- cv_lasso$lambda.min
lasso_model <- glmnet(x, y, alpha = 1, lambda = best_lambda)

# STEP 5: Show coefficients retained by LASSO
selected_coefs <- coef(lasso_model)
nonzero_coefs <- selected_coefs[which(selected_coefs != 0), , drop = FALSE]
print(nonzero_coefs)

## 10 x 1 sparse Matrix of class "dgCMatrix"
##                                   s0
## (Intercept)             12.384581215
## W_Leverage              -1.082310588
## log_Total_Liabilities   -0.603190165
## log_Net_Cash_Flow_Opera  0.014566154
## CEO_Comp_Link_TSR       -0.567857062
## Comp_LT_Objectives       0.608577311
## Board_skills_percent     0.010718057
## Independent_Board        0.012848014
## Num_Board_Meetings      -0.005566579
## Audit_Expertise          1.097483594

# STEP 6: Refit a standard linear model using selected variables
# Get variable names of selected predictors (excluding intercept)
selected_vars <- rownames(nonzero_coefs)[-1]

# Build formula dynamically
lasso_formula <- as.formula(paste("W_TobinsQ ~", paste(selected_vars, collapse = " + ")))

# Refit model on original data (you can optionally standardize predictors here)
final_model <- lm(lasso_formula, data = lasso_data)

# STEP 7: Summary of the final model
summary(final_model)

## 
## Call:
## lm(formula = lasso_formula, data = lasso_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6329 -1.3302 -0.4024  0.8820  6.0812 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             12.569597   1.956000   6.426 1.62e-09 ***
## W_Leverage              -1.113075   0.658181  -1.691  0.09287 .  
## log_Total_Liabilities   -0.745676   0.095625  -7.798 9.55e-13 ***
## log_Net_Cash_Flow_Opera  0.049539   0.022062   2.245  0.02619 *  
## CEO_Comp_Link_TSR       -0.933147   0.403193  -2.314  0.02199 *  
## Comp_LT_Objectives       1.130549   0.418112   2.704  0.00764 ** 
## Board_skills_percent     0.020538   0.007983   2.573  0.01106 *  
## Independent_Board        0.026139   0.011468   2.279  0.02405 *  
## Num_Board_Meetings      -0.004318   0.051452  -0.084  0.93323    
## Audit_Expertise          1.775518   1.012935   1.753  0.08166 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.93 on 151 degrees of freedom
## Multiple R-squared:  0.458,  Adjusted R-squared:  0.4257 
## F-statistic: 14.18 on 9 and 151 DF,  p-value: < 2.2e-16

References

Brauer, M. F. (2013). The effects of short-term and long-term oriented managerial behavior on medium-term financial performance: Longitudinal evidence from Europe. Journal of Business Economics and Management, 14(2), 386–402. https://doi.org/10.3846/16111699.2012.703965
Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39–67. https://doi.org/10.1177/0149206310388419
Eccles, R. G., Ioannou, I., & Serafeim, G. (2014). The impact of corporate sustainability on organizational processes and performance. Management Science, 60(11), 2835–2857. https://doi.org/10.1287/mnsc.2014.1984
Fama, E. F., & Jensen, M. C. (1998). Separation of ownership and control. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.94034
Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. The Academy of Management Review, 9(2), 193–206. https://doi.org/10.2307/258434
Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. The Academy of Management Review, 20(3), 571–610. https://doi.org/10.2307/258788

Extras

# Check for any infinite or NA values in the correlation matrix
if(any(is.infinite(correlation_matrix), na.rm = TRUE) || any(is.na(correlation_matrix), na.rm = TRUE)) {
print("Correlation matrix still contains Inf or NA values")
} else {
  # Perform hierarchical clustering if the correlation matrix is clean
  dist_matrix <- as.dist(1 - correlation_matrix)
  hc <- hclust(dist_matrix, method = "complete")
  plot(hc)  # Plot the dendrogram
}

NOTES

VIF was tested multiple times none of them was above 5

#  management <- plm(W_TobinsQ ~
#                   W_Leverage+
#                   log_Total_Liabilities+
#                   log_Net_Cash_Flow_Opera+ 
#                   log_Revenue+
#                   ROA_Actual+
#                     #log_Capex_Total+
#                   
#                     
#                     #management
#                   CEO_Comp_Link_TSR+
#                     #log_Executives_Compensation+
#                     
#                   CEO_Chairman_Duality+
#                   CEO_Board_Member+
#                   Founder_is_CEO+
#                   CEO_Change_after_IPO+
#                     
#                     Comp_LT_Objectives+
#                     
#                     #governance  
#                     
#                     Board_Size+
#                     Board_skills_percent+
#                     #Board_Committee+
#                     
#                   
#                   Independent_Board+
#                   Num_Board_Meetings+ 
#                     
#                     #ESG Sustainability
#                     #log_Workforce_Score+
#                     
#                   ESG_Score+
#                   Governance_Score+
#                   Comp_Controversies_Score+
#                   Audit_Expertise,
#                   
#                   
#                     
#                  data = ipo_data, effect="twoways", index=c("ID", "Year"),  model="within") 
# summary(management)

                         GVIF Df GVIF^(1/(2*Df))

W_Leverage 1.594694 1 1.262812 log_Total_Liabilities 3.006988 1 1.734067 log_Net_Cash_Flow_Opera 2.153921 1 1.467624 log_Revenue 3.205346 1 1.790348 ROA_Actual 3.056225 1 1.748206 CEO_Comp_Link_TSR 1.320819 1 1.149269 CEO_Chairman_Duality 1.340014 1 1.157590 Independent_Board 1.514847 1 1.230791 CEO_Board_Member 1.292200 1 1.136750 Num_Board_Meetings 1.451529 1 1.204794 Founder_is_CEO 1.562795 1 1.250118 CEO_Change_after_IPO 1.501618 1 1.225405 Comp_LT_Objectives 1.496082 1 1.223144 ESG_Score 2.616260 1 1.617486 Governance_Score 3.516537 1 1.875243 Comp_Controversies_Score 2.282947 1 1.510942 Audit_Expertise 1.148574 1 1.071715 factor(Year) 3.108593 4 1.152313

Post-IPO Performance Analysis: Governance, Compensation, and ESG

Vitor Fernandes M. Ferreira