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Asian Journal of Finance & Accounting  
ISSN 1946-052X 

2015, Vol. 7, No. 1 

www.macrothink.org/ajfa 76

Determinants of Value Creation of GCC Firms - An 
Application of PLS SEM Model 

 

Rajesh Kumar B 

Associate Professor, Finance 

Institute of Management Technology, Dubai Academic City 

Dubai, UAE 

E-mail: rajesh@imtdubai.ac.ae 

 

Received: Oct. 17, 2014    Accepted: Dec 30, 2014     Published:June 1, 2015 

doi:10.5296/ajfa.v7i1.6464   URL: http://dx.doi.org/10.5296/ajfa.v7i1.6464 

 

 

Abstract  

Value creation for a firm is a function of identifying and managing value drivers. This study 
aims to understand the drivers of value creation for GCC listed firms. The paper proposes a 
model for value creation through the application of Partial Least Square Structural Equation 
Modeling (PLS-SEM).The model proposes value creation of a firm as a function of critical 
drivers like size of firm, dividend policy, investment policy, capital structure and risk 
characteristics. Higher the leverage for the firm, lesser will be the value creation for the firm. 
Investors are skeptical about whether firms with high leverage would create value. Firm size 
is negatively related to value creation.  

Keywords: Value Creation, PLS SEM, Path Diagram, Bootstrapping, Reflective 
Measurement models, Heterotrait-monotrait ratio, Blind Folding. 

 

 

 

 

 

 



Asian Journal of Finance & Accounting  
ISSN 1946-052X 

2015, Vol. 7, No. 1 

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1. Introduction 

The determinants of value creation can be categorized in terms of growth, size, efficiency, 
capital structure and profitability drivers. Value creation for a firm is a function of identifying 
and managing value drivers which have the greatest impact on value creation. A focused 
approach would enable management to transform the goals of value creation into specific 
actions. Value driver analysis is an important tool in strategic planning analysis. 
Organizations which create long term value in terms of shareholder wealth are expected to 
create value for all stakeholders. From the perspective of economist’s value viewpoint, value 
is created when revenues exceed all costs. Value is created when management generates 
revenues over and above the economic costs incurred to generate revenues. The costs come 
from sources like employee wages and benefits, material, supplies, economic depreciation of 
physical assets, taxes and opportunity cost of capital. Shareholders expect management to 
generate value over and above the costs of resources consumed which includes the cost of 
using capital. Shareholders require an adequate level of return for the risk they take in. Stock 
prices reflect investors’ expectations about future cash flows. Wealth for shareholders will be 
created only if firms undertake investment decisions which have a positive net present value. 
Value creation is used in the perspective of value derived from accounting based information. 
Wealth creation is based on stock market information. 

Shareholders’ wealth maximization is theoretically logical and operationally feasible 
normative goal for guiding the financial decision making. From the shareholders’ point of 
view, the wealth created by a company through its actions is reflected in the market value of 
the company’s shares. 

Profitability and growth are basically considered as the major determinants of firm value. 
Corporate strategies can be assessed on the basis of their expected effect on profitability, 
growth and firm value. The value based planning models suggests that management of a firm 
aims to create shareholder wealth by maximizing market value of the equity thereby creating 
excess value over the book value of the firm. A firm’s management must focus on strategies 
that creates excess value attributed to market value (MV) compared to the book value (BV) 
of equity. A firm’s management creates value for shareholders if MV>BV, destroys value if 
MV<BV and maintains value if MV=BV. Many researchers have focused on establishing the 
linkage between the strategic position of a company and its financial performance. 

Identifying and selecting strategies that create value for shareholders is a major challenge 
facing management in the modern era. The identification of financial factors which have the 
highest impact on value creation in a business can facilitate establishment of criteria for 
appropriate strategy selection in that direction. The ability of a firm to create value by 
distributing cash flows to its stakeholders depend on its ability for cash generation from its 
operating activities and access of additional funds through external financing. The two basic 
sources of external financing are debt and equity financing. A company’s ability to borrow 
today is based on projections of its future cash flow generation.  

The shareholder returns basically depends on prices, costs, investments, volume of products 
sold and riskiness of firms in an industry. The variables representing these factors can be 



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considered as determinants of shareholder value. Working capital and fixed capital 
investment are the two components of investment value drivers. Management’s investment 
choices and financial policy are also value drivers in the context of riskiness of cash flows for 
the company. Scale economies for firms in purchasing, manufacturing, distribution and 
research can generate value drivers in operating margin, working capital investment and fixed 
capital investment. The link between value chains and value drivers as reflected by sales 
growth rate, operating profit margin, income tax rate, working capital investment, fixed 
capital investment and cost of capital are basic building blocks of shareholder value creation. 

Total risk is the combination of business risk and financial risk. Business risk is the 
uncertainty inherent in the business operations. Financial risk arises for shareholders on 
account of the increased leverage due to additional debt in the capital structure. The financial 
leverage increases would lead to increased variability of cash flows since fixed interest 
payment is bound to increase. Hence shareholders expect higher returns for highly leveraged 
firms. Strategies which increase business risk can increase systematic risk which is measured 
by betacoefficient. Investors expect higher rate of return as the systematic risk of the firm 
increases. 

Earnings is considered an important variable which affects the market value of equity shares. 
The investment decisions aimed at expansion of scale of operations ultimately is focused on 
earnings generation. Hence earnings enhancement could affect market value of a company. 
Many studies have considered measures of market value of equity in excess of book value 
like Tobin q, market to book value, price to earnings ratio or price to sales ratio as the 
variable representing value created in a firm. Studies have also highlighted the positive 
contribution of research and development (R&D) investments to economic growth, 
productivity and profitability.  

2. Objective of the Study  

The basic objective of the paper is to examine the determinants of value creation in terms of 
drivers.In other words study aims to understand the drivers of value creation for GCC listed 
firms. 

3. Review of Literature  

The study by Sam Ben et al (2002) uses random probit model estimation procedure to 
estimate the determinants of value creation among companies listed in Tunisia stock 
exchange. The study finds that probability of creating future value is significantly correlated 
with profitability. The study also finds that value creation is affected by industry patterns, 
size and nature of property. The linkage between strategic position of a company and its 
financial performance have been advocated by studies of De Bodinat (1978), Pene (1983), 
Degos et al (1988) etc. The study by Rappaport (1987) suggests the determinants of value 
creation as growth rate, operating profit margin, income tax rate, working capital investment, 
fixed capital investment, cost of capital and value growth duration. Caby et al (1996) based 
on a sample of French companies find that the determinants of value creation are variables 
based on profitability, activity, financial policy, investment policy and dividend policy. The 



Asian Journal of Finance & Accounting  
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study by Varaiya (1987) highlights the significance of Return on Equity (ROE) as a signal of 
profitable investment. The results of this study indicate that profitability and growth do 
influence shareholder value and the market to book value of equity ratio, Tobin‘s q ratio are 
theoretically and empirically equivalent measures of value creation. The studies by Ross 
(1977); Bhattacharya (1979), Hakansson (1982), Miller et al (1985) suggests that dividend 
payment signals the market about the higher cash flow generation potential of firms. The 
choice of debt level is a signal of firm quality (Leland 1977, Ross 1977, Myers 1977). 
Rappaport (1986) suggests that profitability is an important determinant of value creation. 
Profitability improvement can result from economies of scale, cost reducing linkages with 
suppliers and channels. 

Banz (1981) advocates size effects (measured by market capitalization) as a significant 
determinant of average returns provided by market beta. This study finds that average returns 
on small size ( low market capitalization) stocks are too high given their beta estimates and 
average returns on large size (high market capitalization) stocks are low. Bhandari (1988) 
documents positive relationship between leverage and average returns. Studies by Stattman 
(1980) and Rosenberg et al (1985) finds that average returns on stocks are positively related 
to the ratio of firm’s book value of equity to market value of equity. The study by Chan et al 
(1991) finds that the ratio of book value to market value of equity is a significant determinant 
in explaining the cross section of average returns on Japanese stocks. Chen et al (1991) 
postulate that the earning prospects of firms are associated with a risk factor in returns. Firms 
with low stock prices and high ratio of book to market equity which are characterized having 
poor prospects by market are considered risky and have higher expected stock returns than 
firms with strong prospects.  

Basu (1983) suggests that the earning-price ratios (E/P) is a variable that explain the cross 
section of average returns on US stocks which includes size and market beta variables. The 
studies by Black et al (1972) and Fama et al (1976) find positive relation between average 
stock returns and beta. The study by Fama and French (1992) suggests that size (measured by 
market value of equity) and book to market equity are important determinants which reflect 
powerful characterization of the cross section of average stock returns during the period 
1963-1990. The main results of the Fama and French (1992) study indicates that for the 
1963-1990 period, size and book to market equity capture the cross sectional variation in 
average stock returns associated with size, E/P, book to market equity and leverage. It can be 
stated that if the stocks are priced rationally, systematic differences in average returns can be 
attributed to differences in risk. In the perspective of rational pricing, the variables size 
measured by the total market capitalization (price multiplied by number of shares) and 
BE/ME can be considered as proxy variables to sensitivity to common risk factors in returns.  

Fama and French (1995) study the behavior of stock prices in relation to size and book to 
market equity (BE/ME), which reflects the behavior of earnings. Specifically the study 
explores whether the behavior of stock prices in relation to size and book to market equity is 
consistent with the behavior of earnings. In the context of rational pricing, the study indicates 
that high BE/ME signals persistent poor earnings and low BE/ME signals strong earnings. A 
low stock price relative to book value (high BE/ME) signals sustained lower earnings on 



Asian Journal of Finance & Accounting  
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book equity. In summary low BE/ME (high stock price relative to book value) is typical of 
firms with high average returns on capital (growth stocks), whereas high BE/ME is typical of 
firms that are relatively distressed. Fama and French (1995) also suggest that size is related to 
profitability. Controlling for BE/ME, small stocks tend to have lower earnings on book equity 
than do big stocks. Penmann (1991) suggests that low book to market equity firms remain 
more profitable than high BE/ME firms. 

Firms with higher required equity returns will have higher book to market ratios. This 
prediction is consistent with the positive relation between average stock return and BE/ME 
observed by Fama and French (1992, 1995). Fama and French (1995) predicts that high 
BE/ME should be associated with a persistently low ratio of earnings to book equity, while 
low BE/ME should be persistently associated with strong earnings to book value of equity. In 
other words low BE/ME stocks are on average more profitable than high BE/ME stocks.  

Debt equity ratio (DER) is used as a variable to explain the expected common stock returns. 
An increase in debt equity ratio of a firm increases the risk of its common equity. Cross 
sectionally the common equity of a firm with higher debt equity ratio always have higher risk 
since the firm level risk may vary, DER is expected to be positively correlated to the risks of 
common equity across firms (Bhandari 1988).Beta is based on a market proxy and calculated 
for a period. 

The financial leverage hypothesis suggests that increase in debt is a signal to the market that 
the firm’s prospects have improved. The dividend payout hypothesis suggests that value 
creation is a function of the dividend payout of companies. Higher the dividend payout more 
is the value creation for the company. Ross (1977) suggests that companies that increase 
dividend payout signal to the market that it has the potential to generate future cash flows to 
meet future dividends. The value of a company is expected to increase on account of dividend 
payment as it signals to the market that the firm is expected to have higher cash flows. The 
profitability hypothesis suggests that higher the profits generated by firms, greater would be 
the value creation.  

The study by Gamba and Triantis (2008) develop a model that endogenizes dynamic 
financing, investment and cash retention/payout policies in order to analyze the financial 
flexibility on firm value. The study demonstrate that value of financing flexibility depends on 
the costs of external financing, the level of corporate and personal tax, the firm’s growth 
potential.  

Michael et al (2001) suggests integration of entrepreneurial and strategic thinking for value 
creation in entrepreneurial firms. The paper by Amhud (2002) shows that over time, expected 
market illiquidity positively affects ex ante stock excess return, suggesting that expected 
stock excess return partly represents an illiquidity premium. Severine et al (2004) examines 
the determinants of stock returns in a small open economy using an APT framework and 
finds that statistical factors yield a better representation of the determinants of stock returns 
than macroeconomic variables. Boyer et al (2007) find that the return of Canadian energy 
stock is positively associated with the Canadian stock market return, with appreciations of 
crude oil and natural gas prices, with growth in internal cash flows and proven reserves, and 



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negatively with interest rates. The study by Fang L et al (2009) find that stocks with no media 
coverage earn higher returns than stocks with high media coverage even after controlling for 
well-known risk factors.  

4. Methodology 

4.1 Model Discussion  

The proposed model for value creation is framed with respect to latent constructs as given in 
the diagrammatic design below. It is assumed that value creation is a function of critical 
variables of size of the firm, dividend policy of the firm, investment decisions, capital 
structure and risk characteristics of the firm. It is hypothesized that investment decisions 
would lead to growth which in turn would lead to profitability and results in value creation.  

 

Figure 1. Initial Path Diagram for Value Creation 

The relationship between the constructs is based on the direction of arrows. The latent 
constructs of Dividend Policy (DIVPO), Firm Size, Growth, Investment Decision 
(INVESTDECI), Risk, Capital Structure are designed with reflective indicators. The final 
latent construct of value creation is designed with formative indicators of average stock 
returns, earnings to book value of equity and ratio of market value to book value of the firm. 
The description of the reflective and formative indicators is provided in the table below. 



Asian Journal of Finance & Accounting  
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Table 1. Reflective and Formative indicator definitions 

Variables Definitions 
MVt / BVt Market Value of Firm / Book Value of Firm in year t 
LNSA Natural Log of Average Revenue in t-1 to t-3 
Dividend Payout (DPO) Total Dividends / Total Earnings in t-1 to t-3 
LNTA Natural log of Total Assets in t-1 to t-3 
Lev(Leverage) Total debt / Total Equity in t-1 to t-3 
LNME Natural log of Market Equity in t-1 
Return on Equity (ROE) Net Income / Total Equity in t-1 to t-3 
Return on Assets (ROA) Net Income / Total Assets in t-1 to t-3 
CAPEX / TA Capital expenditure / Total Assets in t-1 to t-3 
WC / TA Working capital / Total Assets in t-1 to t-3 
SG Sales Growth rate in t-1 to t-3 
EG Earnings Growth rate in t-1 to t-3 
Earnings / Price (E/P) Total Earnings / Market Capitalization in t-1 to t-3. Market 

Capitalization is price multiplied by number of shares. 
Return Average monthly returns in July of year t to June of year t+1. 
Beta Measures the systematic risk of the stock. Beta is found out by 

regressing stock returns for a stock on respective market indices 
returns based on one year of data. Beta is calculated for one 
year period t-1. 

Div Yield Dividend per Share / Market Price per Share in t-1 to t-3. 
Et / BEt-1 Total Earnings in t / Book value of Equity in t-1 

 

The formative indicator variables of market value of firm to book value of firm, average 
stock returns and earnings were based on the year 2012. One of the formative indicator for 
final latent construct of value creation was Et / BEt-1 where Et was in the year 2012 and. 
BEt-1 was based in the previous year, The average accounting data for the year’s t-1 to t-3 is 
matched with the average monthly returns for July of year t to June of year t+1. The values 
for variables of size, dividend payout, profitability are average values for period t-1 to t-3 
(2009-2011). The financial data was collected from the balance sheets of the firms. The stock 
market data was collected from the seven stock exchange websites.  

4.2 Sample Selection 

For the empirical modelling part, we selected eight companies with highest average market 
capitalization from Saudi Tadawul, and seven companies each from other six stock markets 
-Dubai Financial Markets, Abu Dhabi Securities Exchange, Muscat Securities Market, Qatar 
Exchange and Kuwait Stock Exchange The average market capitalization was based on five 
years during the period 2009-2013. The value of average market capitalization was calculated 



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in US dollars. All financial variables in terms of value were converted to US dollars based on 
the exchange rate of US dollar vis a vis GCC currencies1. 

Table 2. Sample segregation  

Sector  Number of Companies  
Banking  22 
Industrial  6 
Telecommunication & IT 5 
Investment and Financials 5 
Petrochemical and energy 
utilities  

3 

Real Estate and 
Construction 

3 

Consumer services  3 
Insurance  2 
Transport  1 
Total  50  

 

The following table gives the descriptive statistics for 50 sample companies based on five 
yearly average values.The table below e gives the descriptive statistics of the sample firms 
involved in the empirical study. The descriptive statistics of variables like total assets, 
revenues, cash flows and net profit are given in the table. The average, mean, standard 
deviation, the maximum value and minimum value of the sample statistics are given. The 
descriptive statistics are for 50 sample companies based on five yearly average values.  

The average total assets and revenues of the sample firms amounted to $23.65 billion and 
$3.2 billion respectively. The standard deviations for the total assets and revenues were 28.59 
and 6.47 respectively. Hence the variability in asset size and revenues were higher in the 
sample firms. The analysis for the maximum and minimum value for assets and revenues 
suggests huge difference in values. The sample firms had an average cash flow and net profit 
of $8.7 billion and $0.416 billion respectively. The standard deviation for cash flow and net 
profit were $46.82 billion and $0.521 billion respectively. 

Table 3. Descriptive Statistics (Values in millions of dollars)  

Variables  Average  Standard deviation Maximum Minimum  
Total Assets  23656.39 28599.43 140216.56 463.45 
Revenues  3200.91 6472.93 42266.60 31.68 
Cash Flow  8703.81 46824.72 330799.50 -68.47 
Net Profit  416.06 521.15 2111.88 -413.85 
 
 

                                                        
11USD=3.67AED; 3.75 Saudi Riyal; 3.64 Qatari riyal; 0.28 Kuwaiti Dinar; 0.38 Bahrani dinar ; 0.38 Omani rial. 



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The following table provides the cross sectional statistics of the reflective and formative 
indicators. Thecross-sectional statistics are for averages, standard deviation, maximum and 
minimum values for the sample firms. The variables indicate growth, earnings, leverage, 
profitability and risk measures. Size is measured by book value of total assets and market 
value of equity. Risk is measured by beta.  

Table 4. Cross sectional variable statistics  

Variables  Average 
Standard 
Deviation Maximum Minimum 

MV / BV 1.05 0.50 2.36 0.15 
Log (Sales) 6.90 1.74 10.58 0.0000 

Earnings 
Growth 1.80 9.53 66.70 -4.6800 

Log (Total 
Assets) 9.09 1.91 12.02 0.0000 

Dividend 
Payout 0.38 0.35 1.70 0.0000 

Leverage 3.61 3.12 12.91 0.0000 
ROE 1.60 8.6 46.8 -8.3 
ROA -0.27 2.14 0.17 -15.16 

CAPEX/TA 0.0040 0.06 0.26 0.0000 
WC/TA -0.28 0.68 1.06 -4.3 

Sales 
Growth 0.12 0.28 1.23 -0.23 

E/P 0.98 1.67 6.1200 -1.32 
Return -0.0009 0.0322 0.12 -0.11 
Beta 0.94 0.61 2.64 0.0000 

ln (ME) 9.3 1.5705 11.74 4.3 
ln BV/MV 0.08 0.56 1.86 -0.85 
Dividend 

Yield 0.09 0.40 2.75 0.0000 

 

The partial least squares structural equations modeling (PLS-SEM) was used to test the 
determinants of value creation in the GCC firms. The Structural Equation Modelling 
procedure can be done in two ways, namely covariance based structural equation modeling 
(CB-SEM) and partial least squares modeling (PLS-SEM). Recently, there is an increased use 
of PLS-SEM 

rather than CB-SEM due to both theoretical and methodological reasons (Hair et al., 
2012).Partial least square modeling is used to tackle many multi equation econometric 
models. Since PLS also reflects the sum of the diagonal in the covariance matrix, it is also 
suited for prediction. Basically a path model is a diagram that connects variables/constructs 
based on theory and logic to visually display the hypothesis that will be tested. Structural 



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equation models are based on an analysis of correlation or covariance. PLS-SEM is more 
preferable than CB-SEM in case of small samples and when the focus is on prediction and 
theory development. With respect to methodological reasons,CB-SEM models require larger 
sample size and normal data for better goodness of fit indices in comparison with PLS-SEM.  

5. Results and Discussion  

In PLS SEM, the significance of factor loadings are assessed by the bootstrapping procedure 
with minimum samples of 5000 and the number of cases equivalent to sample size (n=50). 
Bootstrapping is a non-parametric procedure which can be applied to test whether 
coefficients such as outer weights, outer loadings and path coefficients are significant by 
estimating standard errors for the estimates. In bootstrapping, the subsamples are created with 
observations randomly drawn from the original data set (with replacement). 

Through the process of bootstrapping, reflective and formative indicators which were not 
significantly loading with their respective loadings or not meeting the criteria were removed 
from the model. For the reflective indicators, outer loading relevance testing is carried out 
based on the following criteria. If the outer loading is less than 0.40, the reflective indicator is 
deleted. If the outer loading is less than 0.40 but greater than 0.40, then the impact of 
indicator deletion on AVE and composite reliability is analyzed. If the deletion increases the 
measures above the threshold, then the reflective indicator is deleted. If the measures already 
meets the threshold, then the reflective indicator is retained. If the outer loading is greater 
than 0.70, then the reflective indicator is retained. The reflective indicators of dividend yield 
(DIVYIELD), LNTA, LNSA and EG were removed from their respective constructs.  

5.1 Validity Assessment of Reflective Measurement Models 

The PLS SEM algorithm converged. The assessment of reflective measurement model is 
done through indicator reliability, internal consistency reliability, convergent liability and 
discriminant validity. 

It is required that at least 50 per cent of each indicator’s variance must be accounted for by 
the underlying construct.The indicator reliabilities for reflective measures are analyzed by 
examining the outer loadings. Indicator reliability is also known as indicator communality. 
Reflective indicators of E/P for latent construct profitability, ROE for profitability and 
WC/TA are found to be statistically significant. Loadings of value one indicate that the latent 
construct had only one reflective indicator. For example the only reflective indicator for the 
latent construct RISK is beta. The indicator reliability loading of E/P and WC/TA were above 
0.70. For the latent construct profitability, E/P indicator has a higher loading than ROE.  

 



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Table 5. Outer loadings of indicator reliabilities 

  
Loadings for 
Originals  Sample Mean  P Values 

BETA <- RISK 1 1   
CAPEX/TA <- INVESTDECI 0.488 0.465 0.28 
DPO <- DIVPO 1 1   
E/P <- PROFITABILITY 0.809 0.658 0.002 
LEV <- CAPITAL 
STRUCTURE 1 1   
LNME <- FIRMSIZE 1 1   
ROE <- PROFITABILITY 0.602 0.707 0.002 
SG <- GROWTH 1 1   
WC/TA <- INVESTDECI 0.889 0.728 0.017 

The internal consistency reliability of reflective measures is analyzed through composite 
reliability and Cronbach’s alpha.  

Table 6. Composite Reliability  

  Values  
P 
Values 

CAPITAL 
STRUCTURE 1   
DIVPO 1   
FIRMSIZE 1   
GROWTH 1   
INVESTDECI 0.661 0.002
PROFITABILITY 0.67 0
RISK 1   

Composite reliability values of greater than 0.6 are generally acceptable. The p values are 
statistically significant for the latent construct of INVESTDECI and PROFITABILITY. The 
reflective indicators of INVESTDECI (Investment Decisions) are CAPEX/TA (ratio of 
capital expenditure to total assets) and WC/TA (ratio of working capital to total assets). The 
reflective measures of PROFITABILITY are the ratio of earnings to price and return on 
equity. All the reflective measures have composite reliability. It has to be noted that other 
than the above discussed constructs, all the rest have value one since they are represented by 
only one reflective measure. 

The Cronbach’s alpha measure for the majority of the construct was above 0.7. The 
Cronbach’s Alpha can be considered as the lower bound and the composite reliability as the 
upper bound of the true internal consistency reliability.  

 



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Table 7. Average Variance Extracted (AVE) 

  
Original 
Sample  

Sample 
Mean  

P 
Values 

CAPITAL 
STRUCTURE 1 1   
DIVPO 1 1   
FIRMSIZE 1 1   
GROWTH 1 1   
INVESTDECI 0.514 0.543 0 
PROFITABILITY 0.509 0.519 0 
RISK 1 1   

 

The average variance extracted (AVE) is the measure of convergent validity. AVE is the 
grand mean value of the squared loadings of all indicators associated with the construct. Each 
construct should account for atleast 50 per cent of the assigned indicators’ variance. It is also 
referred to as construct communality. All the construct satisfy the convergent validity 
criterion. 

Discriminant Validity  

Discriminant validity is tested by means of assessment like Fornell Larcker, Cross loadings 
and the Heterotrait -monotrait ratio (HTMT)  

Table 8. Fornell Larcker Measure  

  
CAPITAL 
STRUCTURE DIVPO FIRMSIZE GROWTH INVESTDECI PROFITABILITY

CAPITAL 
STRUCTURE 1           
DIVPO -0.198 1         
FIRMSIZE 0.053 0.081 1       
GROWTH 0.041 -0.313 -0.131 1     
INVESTDECI -0.462 -0.128 0.063 0.187 0.717   
PROFITABILITY -0.068 0.148 0.017 -0.53 -0.164 0.713
RISK -0.251 0.325 0.319 -0.111 0.033 -0.019
VALUE CREATION -0.404 0.159 -0.259 -0.096 0.146 0.107

The Fornell Larcker criterion suggests that the square root of AVE must be greater than the 
correlation of the construct with all other constructs including the formative measures in the 
structural model. This criterion is satisfied in this case. 

 



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Table 9. Cross loadings  

  CAPITAL STRUCTURE DIVPO FIRMSIZE GROWTH INVESTDECI PROFITABILITY RISK VALUE CREATION 

BETA -0.251 0.325 0.319 -0.111 0.033 -0.019 1 0.145 

CAPEX/TA -0.438 0.145 0.107 0.088 0.488 0.003 0.308 0.023 

DPO -0.198 1 0.081 -0.313 -0.128 0.148 0.325 0.159 

E t / BE (t-1) -0.187 0.045 -0.297 -0.023 0.162 0.137 -0.013 0.649 

E/P 0.054 0.158 -0.039 -0.428 -0.2 0.809 0.001 0.099 

LEV 1 -0.198 0.053 0.041 -0.462 -0.068 -0.251 -0.404 

LNME 0.053 0.081 1 -0.131 0.063 0.017 0.319 -0.259 

ROE -0.19 0.037 0.083 -0.32 -0.008 0.602 -0.034 0.049 

Return -0.384 0.174 -0.107 -0.108 0.065 0.034 0.2 0.805 

SG 0.041 -0.313 -0.131 1 0.187 -0.53 -0.111 -0.096 

WC/TA -0.298 -0.223 0.016 0.167 0.889 -0.19 -0.124 0.155 

 

According to cross loading criteria, each indicator must load highest on the construct on 
which it is the indicator measure both in terms of reflective and formative measures. 

Table 10. Discriminant Validity Assessment through Heterotrait -monotrait ratio (HTMT)  

  CAPITAL STRUCTURE DIVPO FIRMSIZE GROWTH INVESTDECI PROFITABILITY RISK 

CAPITAL STRUCTURE               

DIVPO -0.198             

FIRMSIZE 0.053 0.081           

GROWTH 0.041 -0.313 -0.131         

INVESTDECI -2.013 -0.212 0.337 0.698       

PROFITABILITY -0.504 0.722 0.161 -2.778 -2.557     

RISK -0.251 0.325 0.319 -0.111 0.501 -0.122   

HTMT is the average heterotrait -heteromethod correlations relative to the average monotrait 
-heteromethod correlations. Monotrait -heteromethod correlations represent correlations of 
indicators measuring the same construct.Heterotrait -heteromethod correlations are 
correlations of indicators across constructs measuring different phenomena. HTMT values 
close to 1 indicate lack of discriminant validity. The threshold value is considered close to 
0.85. The constructs in the study satisfy the discriminant validity assessment on the basis of 
HTMT. 

5.2 Validity Assessment of Formative Measurement Models 

The validity assessment of formative measurement models consists of assessingthe 
collinearity issues and assessment of significance and relevance of the formative indicators. 
Collinearity issues occurs when two or more indicators of a construct are highly correlated. 
Collinearity assessment is done through the analysis of VIF. If there are no critical levels of 
collinearity ( ie VIF<5), then the analysis enters the next stage. 

 



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Table 11. VIF Values  

  VIF 
BETA 1.000 
CAPEX/TA 1.001 
DPO 1.000 
E t / BE (t-1) 1.005 
E/P 1.000 
LEV 1.000 
LNME 1.000 
ROE 1.000 
Return 1.005 
SG 1.000 
WC/TA 1.001 

The table shows that all VIF values are less than 5. 

The next stage consists of assessing the relevance of the formative indicators in the following 
manner. The outer weight is assessed to be significant or not. If the outer weight is significant, 
then the operation is continued with the interpretation of the outer weight’s absolute and 
relative size. If the outer weight is not significant then the formative indicator’s outer loading 
is analyzed. If the outer loading is less than 0.5, then the significance of the formative 
indicator’s outer loading is tested. If the outer loading is less than 0.5 and not significant, then 
the formative indicator is deleted. If the outer loading is less than 0.5 but is significant, then 
the removal of the indicator may be considered. If the outer loading is greater than 0.5, then 
the indicator is retained even if it is not significant. The processing resulted in the removal of 
the formative indicator variable MV/BV.  

 



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Figure 2. The final path diagram for value creation model 

5.3 Assessment of the Structural Model  

The assessment of the structural model consists of five steps.Firstly the structural model is 
assessed for collinearity issues. The VIF values are found to be less than 5. Hence structurally 
the model has no collinearity issues. The second step consists of assessing the significance 
and relevance of the structural model relationships. This process involves testing the 
significance and relevance of path coefficients. The path coefficients vary between -1 to +1. 
The type of effects is divided into direct effect, indirect effect and total effect. The third step 
involves determination of coefficient of determination R Square. It is a measure of the 
model’s predictive accuracy. The fourth step involves measurement of effect sizes by means 
of F Square. The fifth step involves estimation of q square value to find the predictive 
relevance through Blindfolding. 

 



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Table 12. Path Coefficients  

  Original Sample  Sample Mean T Statistics  P Values 
CAPITAL STRUCTURE -> VALUE 
CREATION -0.337 -0.310 2.195 0.029
DIVPO -> VALUE CREATION 0.060 0.058 0.371 0.711
FIRMSIZE -> VALUE CREATION -0.291 -0.257 1.166 0.244
GROWTH -> PROFITABILITY -0.530 -0.633 3.185 0.002
INVESTDECI -> GROWTH 0.187 0.245 1.194 0.233
PROFITABILITY -> VALUE 
CREATION 0.083 0.066 0.747 0.456
RISK -> VALUE CREATION 0.136 0.136 1.012 0.312

Capital structure variable of leverage is negatively related to value creation. The path 
coefficient is -0.337 for capital structure with statistical significance at 5% level of 
significance. Hence it can be assumed that higher the leverage for the firm, lesser will be the 
value creation for the firm. This result signifies that with respect to stock market valuation, 
investors are skeptical about whether firms with high leverage would create value. Firm size 
is negatively related to value creation. The path coefficient has a value of -0.291 but without 
statistical significance. Growth is negatively related to profitability with statistical 
significance. Investment decisions are positively related to growth. Hence firms which focus 
more on investment decisions like capital expenditures and working capital tend to create 
more value for the firm. Profitable firms tend to create more value for firms. Riskier the firm, 
higher the value creation. The results are not statistically significant.  

Table 13. R Square 

 R Square 
GROWTH 0.035 
PROFITABILITY 0.281 
VALUE CREATION 0.250 

R Square is a measure of the model’s predictive accuracy. It represents the amount of 
variance in the endogenous constructs explained by all of the exogenous constructs linked to 
it. The R Square value for value creation is 0.25. Thus all the exogenous variables accounts 
for 25 per cent of variation in the endogenous construct value creation.  

F Square measures the size effects. It assesses how strongly one exogenous construct 
contributes to explaining a certain endogenous construct in terms of R square. Growth to 
Profitability construct have strong effect and rest of the constructs has weak effects. 

Blindfolding is an iterative procedure in which different parts of data matrix are omitted. The 
estimates based on the reduced datasets are used to predict the omitted parts. The prediction 
error is used as an indicator of predictive relevance. 

 



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Table 14.  Results of Blind Folding  

  SSO SSE 1-SSE/SSO
CAPITAL 
STRUCTURE 50 50   
DIVPO 50 50   
FIRMSIZE 50 50   
GROWTH 50 50.186 -0.004
INVESTDECI 100 100   
PROFITABILITY 100 91.426 0.086
RISK 50 50   
VALUE 
CREATION 100 97.732 0.023

The q2 square value as given in the last column signify weak effect.  

6. Conclusion  

This paper proposes a theoretical model for value creation. Value creation is analyzed 
through path diagram through PLS SEM algorithm. Value creation for a firm is a function of 
its capital structure, investment decisions, size, growth, profitability and risk measures. The 
study finds that leverage is inversely related to value creation.Investors are skeptical about 
whether firms with high leverage would create value. Firm size is negatively related to value 
creation. 

References  

Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. 
Journal of Financial Markets, 5(1), 31-56. http://dx.doi.org/10.1016/S1386-4181(01)00024-6 

Banz, R.W. (1981). The relationship between return and market value of common stocks. 
Journal of Financial Economics, 9, 3-18. http://dx.doi.org/10.1016/0304-405X(81)90018-0 

Basu, S. (1983). The relationship between earnings yield, market value, and return for NYSE 
common stocks: Further evidence. Journal of Financial Economics, 12, 129-156. 
http://dx.doi.org/10.1016/0304-405X(83)90031-4 

Bhandari, & Laxmi, C. (1988). Debt/Equity ratio and expected common stock returns: 
Empirical evidence. Journal of Finance, 43, 507-528. 
http://dx.doi.org/10.1111/j.1540-6261.1988.tb03952.x 

Bhattacharya, S. (1979). Imperfect information, dividend policy and “the bird in the hand” 
fallacy. Bell Journal of Economics, 10, 259-270. http://dx.doi.org/10.2307/3003330 

Black, F. (1972). Capital market equilibrium with restricted borrowing. Journal of Business, 
45, 444-455. http://dx.doi.org/10.1086/295472 



Asian Journal of Finance & Accounting  
ISSN 1946-052X 

2015, Vol. 7, No. 1 

www.macrothink.org/ajfa 93

Boyer, M., & Didier, F. (2007). Common and fundamental factors in stock returns of 
Canadian oil and gas companies. Energy Economics, 29(3), 428-453. 
http://dx.doi.org/10.1016/j.eneco.2005.12.003 

Caby, J., Clerc, G., & Koch, J. (1996). Strategic et finance: le processus de creation de vaieur. 
Revue Franpalse de Geslion, 108, 49-56. 

Chan, L. K., Yasushi, H., & Josef, L. (1991). Fundamentals and stock returns in Japan. 
Journal of Finance, 46, 1739-1789. http://dx.doi.org/10.1111/j.1540-6261.1991.tb04642.x 

DeBodinat,H.(1978). Strategie et polique financiere. La Revue Banque, 374,750-756. 
Retrieved from http://www.groupeiscae.ma/pdf/Listes des thèses et des mémoires/Les 
publications de l'ISCAE/REVUE GESTION ET SOCIETE/Revue G&S de n°1 au 
n°16/REVUE N°16.1990.pdf 

Degos,J.(1988). Evaluation des socieies el de lews litres. Vuibert. Retrieved from 
http://197.14.51.10:81/.../Petit%20br%C3%A9viaire%20des%20id%C3%A9es%20re%C3%
A7ues%20en% 

Fama, E. F. (1976). Foundations of Finance. New York: Basic Books. 

Fama, E.,& French, K. (1995). Size and Book to Market factors in earnings and returns. The 
Journal of Finance, 50, 131-155. http://dx.doi.org/10.1111/j.1540-6261.1995.tb05169.x 

Fama, E., & French, R. (1992). The cross section of expected stock returns. The Journal of 
Finance, 47(2), 427-465. http://dx.doi.org/10.1111/j.1540-6261.1992.tb04398.x 

Fruhan. (1984). How fast should your company grow? Harvard Business Review, 62, 84-93. 
Retrieved from 
http://web.a.ebscohost.com/ehost/detail/detail?vid=17&sid=eaba2666-a83c-432a-925f-32c39
1b146b7%40sessionmgr4005&hid=4101&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d 

Gamba, A., & Triantis, A. (2008). The Value of financial flexibility. Journal of Finance, 
63(5), 2263-2296. http://dx.doi.org/10.1111/j.1540-6261.2008.01397.x 

Hair J F, C M Ringle, and M Sarstedt (2011). PLS SEM.Indeed a Silver Bullet. Journal of 
Marketing, Theory & Practice, 19(2), 139-151. http://dx.doi.org/10.1016/j.lrp.2012.09.008  

Hair, J F, M Sarstedt, T Pieper, C M Ringle, J A Mena (2012), The Use of Partial Least 
Squares Structural Equation Modelling in Strategic Management Research : A Review of 
Past Practices and Recommendations for Future Applications, Long Range Planning, 
45(5/6),320-340. 

Hair, J F,, Tomas Hult, Christian M Ringle, & Marko Sarstedt. (2014). A Primer on Partial 
Least Squares, Structural Equation Modeling (PLS SEM).Thousand Oaks: Sage. 
http://dx.doi.org/10.1016/j.lrp.2012.09.008  

Hair, J. F. Sarstedt, M. Pieper, T. M. & Ringle, C. M. (2012). The use of partial least squares 
structural equation modeling in strategic management research: A review of past practices 



Asian Journal of Finance & Accounting  
ISSN 1946-052X 

2015, Vol. 7, No. 1 

www.macrothink.org/ajfa 94

and recommendations for future applications. Long Range Planning, 45, Issue 5/6, pp. 
320-340 

Hakansson, N. (1982). To pay or not to pay dividends? Journal of Finance, 37, 415-428. 
http://dx.doi.org/10.2307/2327345 

Jacqueline, L., Garner, N., & Richard, O. (2002).Determinants of corporate growth 
opportunities of emerging firms. Journal of Economics and Business, 54, 73-93. 
http://dx.doi.org/10.1016/S0148-6195(01)00056-X 

James, M. (1973). Risk, return and equilibrium: Empirical tests. Journal of Political 
Economy, 81, 607-636. http://dx.doi.org/10.1086/260061 

Leland, H., & Pyle, D. (1977). Informational asymmetries, financial structure, and financial 
intermediation. Journal of Finance, 32, 371-388. http://dx.doi.org/10.2307/2326770 

Lily, F., &Joel, P. (2009). Media coverage and cross section of stock returns. Journal of 
Finance, 64(5), 2023-2052. http://dx.doi.org/10.1111/j.1540-6261.2009.01493.x 

Michael, H., Duane, I., Michael, C., & Donald, S.(2001). Entrepreneurial strategies for 
wealth creation. Strategic Management Journal, 22, 479-491. 
http://dx.doi.org/10.1002/smj.196 

Miller, M.,& Modigliani, F. (1961).Dividend policy, growth and the valuation of shares. 
Journal of Business, 34, 1031-1051.http://dx.doi.org/10.1086/294442 

Miller, M., & Rock, R. (1985). Dividend policy under asymmetric information. Journal of 
Finance, 40, 1031-1051. http://dx.doi.org/10.1111/j.1540-6261.1985.tb02362.x 

Miller, M.,& Scholes, M. (1978). Dividend and taxes. Journal of Political Economy, 90, 
1118-1141. http://dx.doi.org/10.1086/261114 

Myers, S. C. (1977).Determinants of corporate borrowing. Journal of Financial Economics, 5, 
147-176. http://dx.doi.org/10.1016/0304-405X(77)90015-0 

Pene, D. (1983). Modeles d'evaiuation des entreprises et modeles strategiques. Analyse 
Fmancierc, 54, 55-62. 

Penman, S. H. (1991). An evaluation of accounting rate of return. Journal of Accounting, 
Auditing and Finance, 6, 233-255. Retrieved from 
http://web.a.ebscohost.com/ehost/detail/detail?vid=86&sid=eaba2666-a83c-432a-925f-32c39
1b146b7%40sessionmgr4005&hid=4101&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db
=bth&AN=7256368 

Rappaport, A. (1986). Linking competitive strategy and shareholder value analysis. The 
Journal of Business Strategy, 3, 58-67. Retrieved from 
http://web.a.ebscohost.com/ehost/detail/detail?vid=56&sid=eaba2666-a83c-432a-925f-32c39
1b146b7%40sessionmgr4005&hid=4101&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db
=bth&AN=5691789 



Asian Journal of Finance & Accounting  
ISSN 1946-052X 

2015, Vol. 7, No. 1 

www.macrothink.org/ajfa 95

Rappaport, A. (1987). Corporate performance standards and shareholder value. The Journal 
of Business Strategy, 4, 28-38. Retrieved from 
http://web.a.ebscohost.com/ehost/detail/detail?vid=58&sid=eaba2666-a83c-432a-925f-32c39
1b146b7%40sessionmgr4005&hid=4101&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db
=bth&AN=5689609 

Ringle, C. Wende, S. & Will, A. (2005). SmartPLS, 2.0 (Beta), http://www.smartpls.de, 
Hamburg.  

Rosenberg, B., Kenneth, R.,& Ronald, L. (1985). Persuasive evidence of market inefficiency. 
Journal of Portfolio Management, 11, 9-17.http://dx.doi.org/10.3905/jpm.1985.409007 

Ross, S. (1977). The determination of financial structure: the incentive signaling approach. 
Bell Journal of Economics, 8, 23-40. http://dx.doi.org/10.2307/3003485 

Rozef, F. M. (1982). Growth, beta and agency costs as determinants of dividend payout ratios. 
Journal of Financial Research, 5, 249-259. 
http://dx.doi.org/10.1111/j.1475-6803.1982.tb00299.x 

Samy, N., & Mohamed, G. (2002). The relationship between dividend policy, financial 
structure, profitability and firm value. Applied Financial Economics, 12, 843-849. 
http://dx.doi.org/10.1080/09603100110049457 

Severine, C., Martin, H., & Dusan, I.(2004). The determinants of stock returns in a small 
open economy. International Review of Economics and Finance, 13(2), 167-185. 
http://dx.doi.org/10.1016/j.iref.2003.07.001 

Stattman, D. (1980). Book values and stock returns. The Chicago MBA: A Journal of Selected 
Papers, 4, 25-45. 

Varaiya, N. (1987). The relationship between growth, profitability and firm value. Strategic 
Management Journal, 8, 487-497. http://dx.doi.org/10.1002/smj.4250080507