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Capital Structure and Firm Performance:
Moderating Role of Business Strategy and
Competitive Intensity
By
Sheikh Naveed Ahmed
CIIT/FA10-PMS-006/LHR
PhD Thesis
In
Management Sciences
COMSATS University Islamabad
Lahore Campus - Pakistan
Spring, 2017
ii
COMSATS University Islamabad, Lahore Campus
Capital Structure and Firm Performance:
Moderating Role of Business Strategy and
Competitive Intensity
A Thesis Presented to
COMSATS University Islamabad
In partial fulfillment
of the requirement for the degree of
PhD (Management Sciences)
By
Sheikh Naveed Ahmed
CIIT/FA10-PMS-006/LHR
Spring, 2017
iii
Capital Structure and Firm Performance: Moderating
Role of Business Strategy and Competitive Intensity
A Post Graduate Thesis submitted to the Department of Management Sciences as
partial fulfillment of the requirement for the award of Degree of PhD in
Management Sciences.
Name Registration Number
Sheikh Naveed Ahmed CIIT/FA10-PMS-006/LHR
Supervisor
Prof. Dr. Talat Afza
Vice Chancellor
The Government Sadiq College Women University
Bahawalpur, Pakistan
Co-Supervisor
Prof. Dr. Mahmood Ahmad Bodla
Advisor, Virtual University of Pakistan
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DEDICATION
This thesis work is dedicated to my beloved family, who always provided unconditional love, endless support and
encouragements during all the phases of my life.
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ACKNOWLEDGEMENTS In the name of Allah, the Most Gracious and the Most Merciful
First and above every one, all praises to Almighty ALLAH, Who has strengthen
me and provided me the capability to successfully accomplish this task. Next to all,
ALLAH’s Messenger Hazrat Muhammad (peace be upon him), Who is an eternal
torch of guidance and knowledge for whole mankind. This dissertation reached in current
shape with the help of productive guidance, fruitful contribution and step by step support
of a few individuals and I would like to offer my heartfelt gratitude to all of them.
I am extremely grateful to my PhD research supervisor Prof. Dr. Talat Afza
(Vice Chancellor, The Government Sadiq College Women University Bahawalpur)
whose continues support, thoughtful guidance, constructive feedback and warm
encouragements were truly exceptional. I have learnt an enormous amount of knowledge
from her regarding all the stages of conducting research. It would not be an exaggeration
to say that if she had not been there, I may not have reached the finishing line.
I am also deeply thankful to my incredible and kind Parents who provided me
endless and unconditional support, guidance and encouragements in all phases of life. In
addition, I deeply appreciate the efforts of my beloved Sister whose abundant skills have
contributed a lot in the completion of this research work. I would also like to express my
appreciation to my Wife for her continuous moral support and cooperation during the
whole time period of the study.
I am also particularly thankful to Dr. Sajid Nazir (Assistant Professor,
COMSATS University) for his valuable comments to the improvement of my thesis. In
addition, I am also warmly grateful to Mr. Umar Farooq (Lecturer, FAST-NU) for
helping me in the collection of financial data.
Sheikh Naveed Ahmed (CIIT/FA-10-PMS-06/LHR)
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ABSTRACT
Capital Structure and Firm Performance: Moderating Role of Business Strategy and Competitive Intensity
The optimal blend of debt and equity financing plays a vital role not only in reducing the overall cost of capital but also helps in enhancing the overall performance of the firms. The purpose of the present research was three folds. Firstly, this study investigated the relationship between capital structure and performance of non-financial firms of Pakistan. Secondly, the novel contribution of the current study was to examine the moderating role of business strategy between the relationship of capital structure and firm’s performance. Thirdly, the present study also contributed in the existing literature by exploring the extent to which firm’s competitive intensity moderated the leverage-performance relationship. The data of 333 listed non-financial firms of Pakistan over the period of eight years (2006-2013) was selected for the final analysis. Both book and market based measures were utilized to compute the performance of the selected firms whereas capital structure of the firms was measured through three different proxies. Business strategy was divided into four strategic categories and Herfindahl-Hirschman index was selected to compute the competitive intensity of the firms. The results of the study depicted that capital structure negatively and significantly influence the accounting measures of performance whereas the relationship between capital structure and market performance (Q ratio) was significantly positive. In addition, the results showed that 31% of the selected sample firms were inclined towards the cost leadership strategy to accomplish their business objectives. The results of moderating analysis showed that cost leadership strategy positively moderate the relationship between capital structure and firm performance. It implies that debt financing is financially viable for the cost leadership firms. In addition, the results specified that when the firms try to maintain high debt ratio while pursuing a product differentiation or hybrid strategy, incur a significant performance penalty. Moreover, the results showed that debt financing is also harmful for the performance of “stuck in the middle” firms but the results were statistically insignificant in most cases. Furthermore, the results also revealed that product market competition can be used as a substitute for debt to limit the discretionary resources of the managers. Consequently, debt financing cannot create real financial benefits in the presence of high product market competition. Finally, based on the findings of the research, the present study also suggested some policy implications for the regulators, policy makers and firm’s managers.
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TABLE OF CONTENTS
1. Introduction 1
1.1 Capital Structure………………………………………………….. 2
1.2 Capital Structure and Firm Performance ………………………… 3
1.3 Business Strategy and High Debt ratio…………………………… 5
1.4 Competitive Intensity and High Debt ratio...……………………... 12
1.5 Research Objectives…………………………………..….………... 13
1.6 Research Questions…………………………………..…………… 13
1.7 Significance of Study…………………………….…………..…… 13
2. Literature Review…………………………………………………….. 15
2.1 Theoretical Review of Capital Structure and Firm performance..... 16
2.1.1 Net Income and Net Operating Income Approaches….………... 16
2.1.2. Traditional Approach of Capital Structure…………… 17
2.1.3. Irrelevance Theory of Capital Structure……………… 17
2.1.4. Trade-off Theory……………………………………... 18
2.1.5 Pecking Order Theory………………………………… 19
2.1.6 Agency Theory of Capital Structure………………….. 20
2.1.7 Signaling Theory of Capital Structure………………... 21
2.1.8 Market Timing Theory………………………………... 22
2.2 Empirical Review of Capital Structure and Firm Performance….... 23
2.3 Capital Structure, Business Strategy and Firm Performance…… 39
2.4 Capital Structure, Competitive Intensity and Firm Performance.. 43
2.5 Empirical Literature of Pakistan………………………………. 44
2.6 Research Gap…………………………………………………… 49
3. Research Design………………………………………………………. 50
3.1 Sample Description…………………………………………….. 51
3.2 Empirical Models………………………………………………. 54
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3.3 Variables of the Study…………………………………………... 62
3.4 Estimation Techniques…………………..……………………… 72
4. Results and Discussion……………………………………………….. 74
4.1 Descriptive Statistics……………………………………………. 75
4.2 Correlation Analysis……………………………………………. 80
4.3 Regression Analysis…………………………………………….. 83
4.3.1 Capital Structure and Firm Performance…………….. 83
4.3.2 Capital Structure, Business Strategy and Firm Performance………………………………………….
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4.3.3 Capital Structure, Competitive Intensity and Firm Performance………………………………………….
132
5. Conclusion and Policy Recommendations…………….……………. 142
6. References……………………………………………………………..
Appendices……………………………………………………………..
147
175
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LIST OF FIGURES
Figure 3.1 Theoretical Framework……….……………………………………… 71
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LIST OF TABLES
Table 3.1 Sample of the Study………………………………………………… 53
Table 4.1 Descriptive Statistics………………………………………………… 77
Table 4.2 Correlation Analysis…………………………………………………… 82
Table 4.3 Capital Structure and Firm Performance (ROA)…………………………. 87
Table 4.4 Capital Structure and Firm Performance (ROE)………………………….. 90
Table 4.5 Capital Structure and Firm Performance (Q Ratio)………………………. 93
Table 4.6 Capital Structure, Cost Leadership Strategy and Firm Performance (ROA)……………………………………………………………………..
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Table 4.7 Capital Structure, Cost Leadership Strategy and Firm Performance (ROE)……………………………………………………………………...
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Table 4.8 Capital Structure, Cost Leadership Strategy and Firm Performance (Q Ratio)…………………………………………………………………..
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Table 4.9 Capital Structure, Product Differentiation Strategy and Firm Performance (ROA)……………………………………………………...
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Table 4.10 Capital Structure, Product Differentiation Strategy and Firm Performance (ROE)………………………………………………………
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Table 4.11 Capital Structure, Product Differentiation Strategy and Firm Performance (Q Ratio)…………………………………………………..
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Table 4.12 Capital Structure, Hybrid Strategy and Firm Performance (ROA)……… 116
Table 4.13 Capital Structure, Hybrid Strategy and Firm Performance (ROE)……… 118
Table 4.14 Capital Structure, Hybrid Strategy and Firm Performance (Q Ratio)…… 120
Table 4.15 Capital Structure, Unclear Strategy and Firm Performance (ROA)…….. 126
Table 4.16 Capital Structure, Unclear Strategy and Firm Performance (ROE)……... 128
Table 4.17 Capital Structure, Unclear Strategy and Firm Performance (Q Ratio)….. 130
Table 4.18 Capital Structure, Competitive Intensity and Firm Performance (ROA)... 135
Table 4.19 Capital Structure, Competitive Intensity and Firm Performance (ROE)... 137
Table 4.20 Capital Structure, Competitive Intensity and Firm Performance (Q Ratio)……………………………………….
139
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_____________________________________________________________LIST OF ABBREVIATIONS _____________________________________________________________
AG Age of the Firms
AUE Asset utilization Efficiency
CL Cost Leadership
EPS Earnings per Share
EBIT Earnings before Interest and Tax
HB Hybrid Strategy
GT Growth of the Firms
GPM Gross Profit Margin
HHI Herfindahl-Hirschman Index
INT Competitive Intensity
LDR Long Term Debt Ratio
LQ Liquidity of the Firms
NPM Net profit Margin
PD Product Differentiation
Per Performance
PPC Premium Price Capability
ROA Return on Assets
RK Risk of the Firms
ROE Return on Equity
ROIC Return on Invested capital
SBP State Bank of Pakistan
SDR Short Term Debt Ratio
SM Stuck in the Middle
STRA Business Strategy
STRA_CL Cost Leadership Strategy
STRA_HB Combine Strategy/Hybrid Strategy
STRA_PD Product Differentiation Strategy
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STRA_UC Stuck in Middle/Unclear Strategy
SZ Size of the Firms
TDR Total Debt Ratio
VIF Variance Inflation Factor
1
Chapter 1
Introduction
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The current chapter illustrates the significance of capital structure along with the
moderating role of competitive intensity and business strategy. The first and second
sections describe the implication and formation of capital structure respectively. The third
section exhibits the theories of capital structure. Section four and five exhibits the
significance of business strategy and competitive intensity along with their moderating
roles. The objectives and the significance of the current research are presented in the
sixth and seven sections respectively.
1.1 Capital Structure
All the financial and non-financial decisions made by firm managers are in the
endeavor of enhancing its performance or wealth of the shareholders. Among the
pecuniary decisions, capital structure decision or the choice of debt/equity financing is
not only linked with determining the suitable type of asset financing but is also related to
determining an appropriate blend of the financing options for successfully running the
firm’s operations. Consequently, the firm’s management devises its debt ratio in an
appropriate manner which enhances the firm performance. More precisely, capital
structure specifies the approach wherein the firms finance their assets through issuing
debt capital, equity capital and hybrid securities.
Debt financing certainly categorized into long term debt (debentures, bonds, notes
payables) and short term debt (commercial papers, short term bank credit, trade payable)
while equity instigates from issuing stocks and retained earnings. In addition, firms also
issue hybrid securities (contain features of both debt and equity) for financing their assets
such as preference shares and convertible bonds. Therefore, capital structure is the
optimal blend of equity capital and debt capital and it is essential for effectively
achieving the goals of the firm.
In Pakistan, firms also utilize various sources of debt capital to finance their assets
or meet their both long term and short term business requirements. The foremost source
of debt financing of the non-financial firms of Pakistan is Bank debts (State Bank of
Pakistan) whereas bonds, commercial papers and term finance certificates are also
utilized by the firms as a source of capital but at slightly small-scale level. Non banking
financial companies also operate in Pakistan to provide debt capital to the firms. In
addition, few venture capital funds are also working in Pakistan but usually provide funds
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to the small, infant or rapidly growing small companies which are normally not eligible
to obtain bank loans. The venture capital companies also provide technical or managerial
expertise to venture projects.
Corporate finance literature illustrated that few academicians describe capital
structure or leverage in a narrow sense, as they incorporated only long term debt in its
composition (Graham, 1996; Devic & Krstic, 2001). However, short term debt should
also be incorporated along with the long-term equity and debt capital in the formation of
structure of capital because of many reasons. Firstly, Lindsay and Sametz (1963)
indicated that the exclusion of short term debt seems erroneous while describing the
capital structure because firms prefer short term debt when they face uncertainty in tax
status due to high tax rates. In this case, short term debt becomes least costly and the
firms easily adjust their debt levels.
Harwood and Manzon (2000) also specified that rate of taxes are directly
associated to debt maturity as long term debt creates higher current interest payments.
Secondly, growing firms with the potential funding need to issue short term and less
secured debt caused by the high risk related with asymmetric information and inefficient
marketplace (Demirguc-Kunt & Maksimovic, 1998). Thirdly, during the period of
unfavorable interest rates, a firm may keep short-term debt i.e. bank credit and
marketable securities etc. as a temporary source of financing. In addition, monitoring role
of firm managers through creditors becomes more effective due to short term debt
financing (Delcoure, 2007; Fung & Goodwin, 2013). Consequently, it is better to contain
long term and short term securities in the formation of capital structure of a firm (Omet &
Mashharawe, 2003; Nikolaos et al., 2007; Joeveer, 2006; Gaud et al., 2005;).
1.2 Capital Structure and Firm Performance
The relationship between performance and capital structure/debt ratio for value
creation is a topic of continuing interest among the academic researchers. The literature
stated that an optimal combination of equity and debt is essential while formulating the
capital structure of the business ventures as it is considered as a significant antecedent for
enhancing the firm’s performance. If the firm raises funds through the issuance of large
portion of equity, then it enhances the firm’s performance due to numerous reasons.
Firstly, it has no fixed maturity period and fixed charges. Secondly, equity financing
4
improves the credit worthiness due to reduction in leverage or bankruptcy risk. Thirdly,
such firms do not face any kind of restrictive covenants while issuing the equity.
Furthermore, asset financing through equity lessens the dissimilarities of interests
between creditors and stockholders and the managers of business ventures can invest in
risky but more profitable projects. In contrast, when firms make an announcement
regarding the issuance of new equity then they provide a negative signal to the
shareholders and as an outcome, lessens the market value of the shares. In addition, firms
with poor financial positions finance their assets through equity only to share losses with
the investors.
Additionally, equity financing also raises agency cost due to the interests’
differences between the managers and stockholders of the firm. Moreover, equity capital
is also considered as an expensive source of financing. Therefore, equity financing should
be considered as a last option for raising funds (Myers & Majluf, 1984; Kim et al., 2006).
In this scenario, finance managers may choose the right kind of financing option which
increases the firm’s value through minimization of cost. One possible solution is to utilize
fixed cost of financing i.e. debt to finance the firm’s assets which not only declines the
overall cost of capital but also raises the value of the firms. Therefore, debt serves as a
valuable instrument for the operational growth of the firms and it also alerts the firms’
managers to generate adequate cash flows that enable them to pay principal amount along
with interest.
Furthermore, when firms shift their capital structure towards more debt; the issue
of free cash flow reduces and the mangers become further disciplined (investments and
operational activities of the business) and refrain from meeting their personal objectives.
Debt financing also provides tax shelter as the extra amount paid on the principal amount
of loan (interest) is tax free. Moreover, retirement of the loan obligation makes firms’
manager more efficient to generate sufficient cash flows through investing in positive
NPV projects. The issuance of debt also positively signals the market that a firm has
positive cash flows to pay the debt obligations. Modigliani and Miller (1963) also
proposed that market value of the firm can be enhanced through large portion of debt
financing in its capital structure.
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However, a large portion of debt financing also possesses different types of costs
i.e. bankruptcy cost and agency cost of debt. The later cost emerges from the conflicts of
interests between the debt holders and stockholders. These conflicts arise when firms’
management engages in investments or projects which benefits shareholders more than
debt holders (Myers, 1977). Alternatively, the bankruptcy cost exits when firm is unable
to pay interest liability on high debt financing. These circumstances may also compel the
firms to sell or liquidate their assets for less than their market value. Moreover, suppliers
may decline to extend loans, debt holders may require high interest rates and
staff/employees of the firms may discontinue their jobs due to the bankruptcy threat. In
addition, debt holders are more risk averse (Smith & Warner, 1979) and they force firm
managers to restrain from risky projects and cut back on R&D expenditures under
restrictive covenants (Baysinger & Hoskisson, 1989).
As a result, leverage inversely affects the value of the firm. Moreover, “Pecking
Order” hypothesis also described that debt capital in the presence of appropriate retained
earnings may harmful for firms value. Many studies (e.g. Abor, 2005; Baum, Schafer &
Talavera, 2006; Dwilaksono, 2010; Gill, et al., 2011; Shubita & Alsawalhah, 2012) have
also reported an inverse association between high debt ratio and value of the firm. To
apprehend this problem, a firm should have optimal capital structure or appropriate blend
of equity and debt through keeping a balance between the benefits and cost of debt and
equity financing (Myers & Majluf, 1984). Optimal capital structure maximizes the
shareholders’ wealth through reduction in cost of financing choices.
However, optimal capital structure not only varies across industries but also
differs among firms in the same industry due to dissimilar attributes. Consequently, it is
difficult to employ a specific target capital structure among all dissimilar nature of
businesses to maximize their value. Therefore, various financial approaches or theories of
“capital structure” may be helpful for achieving the desired debt ratio. In addition, capital
structure theories have also provided the guidelines regarding suitable financing options.
1.3 Business Strategy and High Debt Ratio
Business strategy is considered an effective mechanism of every business and is a
combination of a diverse set of activities for the creation of valuable and unique position
in the marketplace. It addresses the competitive aspect whom the business should serve,
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what needs should be satisfied, how core competencies can be developed and how the
business can be positioned. In other words, business strategy emphasizes on enhancing
the competitive position of a business unit or a firm within the particular business
industry (Misangyi et al., 2006).
One way of addressing the formulation of business strategy is to determine
whether the business seeks new opportunities, focuses on exploiting the current
opportunities, or attempts to balance the two. Exploitation creates only short term returns
while exploration can generate long term sustainable competitive advantage for the
business. So, the business strategy developed for the specific business organization
attempts to resolve this challenge (He & Wong, 2004).
Each firm within the same industry has its unique mission or business objectives.
The target of its strategic managers is to formulate competitive strategies for the firm to
accomplish the desired objectives and to maintain a competitive advantage in the
marketplace (Cockburn et. al., 2000). Strategic planning or the formulation of strategy is
a key concern of the practitioners and business managers as it may lead to considerable
long term competitive strength and produce higher rate of return than its rivals.
Therefore, effective business strategy is a significant factor which can alter the firm
performance.
Porter (1980) made a valuable addition in the literature by establishing a generic
business strategy structure that specifies how firms select a business strategy for
outperforming and effectively competing other ventures or business units in a specific
industry. This strategic framework is named generic because it can be followed by any
size or type of firm operating either for profit or non-profit purposes. According to the
Porter’s topology, strategic managers must decide whether a business ventures will
compete in the marketplace through minimizing their costs relative to their competitors
(i.e. cost leadership strategy) or will compete by offering the distinctive, high quality
feature products (product differentiation strategy) in the market.
Although several kinds of business strategies have been developed with the
passage of time (e.g. Chrisman et al.; 1988 Miles & Snow, 1978), however competitive
business strategies of the Porter’s remain the most generally accepted and supported in
the literature (Miller, 1998; Miller & Dess, 1993; Kim & Lim, 1988; Thompson &
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Stickland, 1998; David, 2000; Jermias, 2008). Consequently, Porter proposed two broad
kinds of “generic” business strategies i.e. Cost Leadership Strategy and Product
Differentiation strategy. He argued that firm must select and pursue any single strategy
for the accomplishment of its goals, otherwise, a firm will “stuck in the middle” in the
competitive marketplace and will face poor financial conditions.
Porter (1980) suggested that the business companies may become “Stuck in the
middle” for one of the two rationales. First, if the firms try to adopt both strategies
simultaneously or second, if the firms fail to develop and implement any single strategy.
However, numerous researchers (see for example, Hill, 1988; Kim & Lim, 1988;
Chakraborty & Philip, 1996; Dess et.al., 1999; Parnell, 2000; Barney, 2002; Barney &
Hesterley, 2006; Minarik, 2007) latterly argued that hybrid strategy is quite effective in
terms of generating high margins or profits due to premium prices and low product costs.
Consequently, the current study classifies the firms under “hybrid strategy” group which
are putting emphasis on product differentiation strategy and cost Leadership strategy
simultaneously whereas firms with the poor strategic condition or “Stuck in the Middle”
firms are those business ventures which have failed to pursue any single strategy (product
differentiation strategy or cost Leadership strategy).
Porter (1985) contended that Cost Leadership is a lower cost competitive strategy
that entails the aggressive establishment of efficient scale amenities, tight control of
overhead expenditures, attain cost efficient techniques through past experiences,
prevention of marginal consumer accounts and lessen the cost in other different areas like
sales force, services, R&D, advertisement etc. Hence, cost leadership strategy is the
capability of the business unit or a firm to design, produce and sale the similar products
and services more efficiently than its counterparts or competitors. Although the cost
leader charges low prices than its competitors but still yields a suitable profit to sustain in
the market.
There are many benefits attached with the cost leaders. First, lower costs permit to
continue earning profit during the periods of heavy competition. Second, low price
position of business units provide a protection against their rivals. Third, low prices
enhance the market share of the business (Caves & Ghemawat, 1992) and as a result, the
firm gains the immense bargaining power relative to their suppliers. Forth, lower cost
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firms are also considered as barriers for new entrants and earn above average return on
investments because only a few new entrants are able to compete with the advantages of
cost leaders. Fifth, low cost producers have access to scarce raw materials, easily achieve
high degree of capitalization and maintain a large market share (Parnell, 1997).
Therefore, the firms which pursue the cost leadership strategy not only attain the better
profit margins but also gain more market share than their counterparts.
The firms which follow the cost leadership strategy can enhance both accounting
and market performance through debt financing. As discussed, the cost leadership firms
need to be more efficient, require tight control of overhead expenditures and desire to
minimize their R&D expenditures etc, so debt financing is the most suitable instrument to
accomplish these objectives as it enhances the efficiency of firm’s managers due to the
monitoring role of lenders (Jordan et al., 1998; Jermias, 2008). This role of debt providers
put pressure on managers to avoid unnecessary overhead expenditures and provides a
path to produce cost effective products.
In addition, debt financing also limits the opportunistic behaviors of managers and
refrains them from the investments in the risky projects. As a result, cost leadership firms
reduce their undesirable expenditures and produce desirable monetary benefits than their
rivals. Moreover, asset financing through debt also aligns the interest of shareholders and
managers which is suitable for the cost leadership firms. Therefore, firms should finance
their assets through debt if they wish to be efficient and are interested to pursue the cost
leadership strategy. The present study also anticipates that the moderating variable (the
interaction term between capital structure and cost leadership strategy) will be positive in
all the estimated models.
On the other hand, Product Differentiation Strategy of the firm comprises the
creation of the products and services with unique characteristics i.e. technology, quality,
design, features and brand image etc. and charge premium prices than its counterparts
(Porter, 1980, 1987, 1996; Cross, 1999; Hyatt, 2001; Hlavacka et al., 2001; Bauer and
Colgan, 2001). The duration in the product development cycles, speed in the product
delivery, quality of work and marketing approach can also be the key differentiators. This
strategy normally attempts to establish new products with distinguished physical
characteristics and market opportunities through employing the latest scientific
9
technology. As an outcome, the customers are usually willing to pay above an average
price due to the perceived significant differences in the features of the products. When
the consumers are somewhat price insensitive, a firm can choose a differentiation strategy
and can put emphasis on quality. In addition, the core objective in developing a product
differentiators’ is to establish what makes a firm unique from its competitors (Rajecki,
2002; Berthoff, 2002; McCracken, 2002).
This strategy is feasible in earning high profits in a specific business unit or a firm
because brand loyalty decreases the customers’ sensitivity to price. The brand loyalty of
the customers is also considered as an entry barrier because it is very tough for the new
business units to establish distinctive products with the intention to effectively compete in
the product marketplace. Caves and Ghemawat (1992) also claimed that the product
differentiation strategy of the firms is more likely to produce higher return on
investments or profits than the cost leader firms as a product differentiation creates better
entry barrier.
The firms which pursue product differentiation strategy may not attain suitable
monetary benefits of debt financing. As product differentiation firms spend heavily in
research and development activities with the aim to boost their innovative capabilities; so
more debt financing along with its covenants is probable to hinder the innovative
qualities and creativity of the firm’s managers which are required for achieving the
competitive advantage. Moreover, product differentiation firms usually face high
uncertainty due to the involvement in high risk but profitable investment activities. So,
restrictive covenants may not allow to invest in these kinds of risky projects to earn high
margins (Baginski & Wahlen, 2003; O’Brien, 2003). Furthermore, asset financing
through debt is also considered as an obstruction for the product differentiators because it
creates mismatching of interests among the creditors and firm’s stockholders. Therefore,
the present study anticipates that the interaction term between high debt ratio and product
differentiation strategy will be negative in all the estimated models.
Strategic management literature stated that many firms also adopt both strategies
simultaneously (hybrid strategy) to accomplish their business goals. Porter (1985)
contended that the firms turn into “stuck in the middle” if they follow hybrid strategies in
chorus as effectively adopting the “Single” strategy requires enormous resources and
10
total commitment. He further argued that extra efforts are required to implement both
business strategies simultaneously. Irrespective of this fact, many firms strive to pursue
both strategies simultaneously (hybrid strategy) and gained more financial benefits along
with the market share (Barney, 2002; Minarik, 2007). For instance, Hill (1988), Parnell
(2000) and Minarik (2007) stated that these two approaches i.e. product differentiation
strategy and cost leadership strategy are not necessarily unanimously exclusive and many
business ventures start with the differentiation strategy and integrate low costs as they
grow and develop economies of scale along with the competitive advantage in the
product market. In addition, hybrid strategy improves flexibility and make it easier for
the organizations to adopt the modifications e.g. industry changes and advancement in
technology (Parker & Helms, 1992).
Furthermore, any inaptness between product differentiation strategy and cost
leadership strategy may have apprehended true in 1980s when business environment was
relatively stable. However, mass customization, development of network organizations
and rapid transformation in the business competitive environment demands more
flexibility for the selection of more than one strategy simultaneously (Goldman et al,
1995; Kim et al., 2004).
Evans and Wurster (1999) also argued that accessibility of internet disbands the
conventional value chains, establishes new competitive environment, diminishes
information asymmetries and transactions costs (Afuha & Tucci, 2001) and creates
opportunities to adopt more than one strategies for successfully competing in product
marketplace. Moreover, combining business strategies may create synergy that
overcomes any trade-off which arises due to the combination. Certainly, hybrid strategy
is feasible to attain and can be quite effective in terms of generating high margins or
profits due to premium prices and low product costs.
When the firms try to pursue both strategies simultaneously (hybrid strategy),
debt financing is not considered as a useful financing option for the financial health of the
firms. As the firms try to pursue mixed strategies in chorus, so at one end, debt financing
reduces the opportunistic behavior of firms’ managers due to restrictive covenants
imposed by lenders. This situation creates tight control on the overhead expenditures,
11
generates efficiency in production process, refrain managers from the high risk business
investments and diminishes the unnecessary sales and advertisement expenditures.
On the other hand, restrictive covenants attached with the debt also impede
managers from R&D activities, intensive advertisement expenditures and from indulging
in high risk business investments etc. As a result, debt financing generates obstacles for
the firms to become product differentiators in the marketplace and refrains them to
charge premium prices from the customers. Therefore, high debt financing is not
considered a useful tool for the firms who are interested in pursuing both strategies
simultaneously and it may prove destructive for the accomplishment of the business
objectives of the firms.
As stated, choosing the generic strategy means successfully putting serious
attention to all the components of competitive plan. However, many firms are unable to
make necessary plan due to vague corporate culture and such firms are considered “Stuck
in the Middle” (Cronshaw et al., 1994). These firms are laid down under the poor
strategic conditions and fail to become either product differentiators or cost leaders. As a
result, these firms face poor financial performance and suffer when competition
intensifies. “Stuck in the Middle” firms are usually small, deal with the strategy on
informal basis and cannot maintain any competitive advantage in the marketplace. In
addition, these firms normally do not offer unique featured products as per the demand
but charge high prices from their customers and duck out from the product market
competition.
Several studies (see for example, Dess & Davis, 1984;; Farrell et al., 1992; Miller,
1986; Kim et al., 2004; Powers & Hahn, 2004; Torgovicky et al., 2005; Parnell, 2010;
Nandakumar et al., 2011) also illustrated that “Stuck in the Middle” firms negatively
influence the financial performance.
It is affirmed that high debt financing cannot enhance the financial health of the
firms with unclear strategic situation. Although the lenders are reluctant to provide funds
to “stuck in the middle” firms, however, if they do so then these firms are unable to
utilize these funds in an appropriate manner due to the inappropriate or uncertain
investment decisions. As a result, the firms may not generate enough funds to pay back
the loan payments and they ultimately face financial distress. Therefore, the present study
12
also anticipates that the moderating term between high debt ratio and unclear strategy
will be negative in all the estimated models.
1.4 Competitive Intensity and High Debt Ratio
The competitive intensity among the rivals refers to the probability to which
business units or firms put pressure in terms of limiting other’s profits or market share
within the same industry. In other words, product market competition is a constructive
battle among the firms to steal other firms’ profit and to get more market share in the
industry. Therefore, competitive intensity not only enhances the overall performance of
the firm (Baggs & Bettignies 2007), but also permits the firms to judge the efficiency of
the managers compared with their rivals (Meyer & Vickers, 1997).
Chhaochharia et al. (2016) also stated that the business ventures working in the
high competitive sectors are more efficient and face fewer financial frauds than less
competitive rivals. This suggested that product market competition defends shareholders
and other investors against expropriation by corporate insiders. In addition, intense
product market competition enhances the firm’s performance because it enforces a
discipline and serves a significant influential tool in reducing the agency problem
between managers and owners (Allen & Gale, 2000; Baggs & Bettignies, 2007).
Moreover, firm managers are more inclined towards making a value maximizing
decisions in the more competitive business environment in order to save their financial
benefits and jobs as intense competition creates performance and productivity
comparison with their rivals (Meyer & Vickers, 1997). Maksimovic (1988), Brander and
Lewis (1986), and Phillips (1993) argued that selection of the appropriate financing
option also depends upon the competitive intensity faced by the firms. Jensen (1986)
suggested that protective covenants of debt financing compel managers to be efficient in
generating principal and interest payments and reduced the discretionary earnings
accessible to managers for meeting their personal goals. In the same manner, vigorous
competition averts managers from the misuse of funds for their personal goals. While
stumpy competition produces deviation between the interests of stockholders and the
managers of the firms which leads to the accomplishment of the personal goals of the
managers. If managers waste the corporate resources under high competitive
environment, the firms become incapable of competing in the market and may exit from
13
the competition. Baggs and Bettignies (2007) indicated that competitive intensity reduces
the firm’s cost, improves the product quality and makes employees more efficient,
consistent with the alignment of interest’s argument.
Faure-Grimaud (2000) also stated that high debt ratio persuade the companies to
compete less aggressively in the product market because the firms with high debt ratio
desire to lessen the probability of default and enhance the prospect of superior credit
record. Therefore, vigorous competition may perform as a substitute of debt financing in
reducing the agency problem. In this scenario, debt financing becomes more expensive
due to high risk and return (Botosan & Plumlee, 2005). Consequently, the current study
anticipates that competitive intensity will inversely moderate the relationship between
capital structure and performance of the firm.
1.5 Research Objectives
The current study attempts to meet the following core objectives:
To examine the impact of capital structure on performance of the non-financial
firms of Pakistan.
To explore the moderating role of business strategy between the relationship of
capital structure and firm performance.
To investigate the moderating role of competitive intensity between the
relationship of capital structure and firm performance.
1.6 Research Questions
Does debt ratio affect the performance of non-financial firms of Pakistan?
Does business strategy of the sample firms moderate the relationship between
capital structure and firm performance?
Does competitive intensity of the sample firms moderate the relationship between
capital structure and firm performance?
1.7 Significance of Study
During the last five decades, academicians, policy makers, regulators and
investors have given a lot of weightage to the financing options and their appropriate
combination to enhance the firm performance. Most of the studies have focused on
exploring the direct impact of capital structure on firm’s performance by considering
14
limited measures. However, the present study investigates the leverage-performance
relationship by incorporating more comprehensive measures of capital structure and
alternative performance measures.
Moreover, the present study also significantly contributes to the existing literature
in two more ways. First, to the best of author’s knowledge, this is the first study in
Pakistan to explore the moderating role of business strategy between the relationship of
capital structure and firm’s performance. Second, this study examines the extent to which
the relationship between capital structure and firm performance depends upon the
competitive intensity faced by the firms for the first time in Pakistan.
This research has significance for the firms’ mangers, policy makers, regulators,
investors and academicians. Business strategy emphasizes on enhancing the competitive
position of a firm within the specific industry. Therefore, empirical findings of the
current study provide guidelines to the firms’ managers and policy makers that the
business strategy of the firms must be carefully formulated before the selection of
financing option(s). In addition, this study will also be helpful in suggesting a better or
appropriate type of business strategy that should be adopted to maximize both accounting
and market measures of performance of the firms. Moreover, competitive intensity refers
to the probability to which firms put pressure in terms of limiting other’s profits or
market share within the same industry. Therefore, the findings of the present research
also provide guidelines to the policy makers and firm managers that product market
competition should also be analyzed before the selection of debt or equity financing.
The remainder of the thesis is arranged as follows: Chapter two presents the
relevant review of literature whereas Chapter three demonstrates the research design
which comprises of research models, sample selection and measurements of variables.
Chapter four illustrates the empirical findings and discussions while last chapter of the
thesis exhibits the conclusion of the research and policy recommendations.
15
Chapter 2
Literature Review
16
Over the past six decades, academicians have showed enormous attempts in
exploring the appropriate mixture of debt and equity and its relationship with firm’s
performance, however so far no consensus has been developed on the estimated results.
The current chapter demonstrates the holistic view of existing theoretical and empirical
studies and explains the research gaps on the basis of past literature. This chapter is
distributed into six sections. First section describes the theoretical review whereas
empirical literature on capital structure and firm performance is exhibited in the second
section. Third section contains the empirical studies on moderating role of business
strategy between the relationship of capital structure and firm performance. Forth section
covers the review of existing studies on the moderating role of competitive intensity
between the relationship of capital structure and firm performance. The relevant studies
conducted in Pakistan are presented in the section five while the last section explains the
research gaps on the basis of existing literature.
2.1 Theoretical Review of Capital Structure and Firm Performance:
2.1.1 Net Operating Income and Net Income Approaches of Capital Structure
Durand (1952) proposed “Net Income Approach and Net Operating Income
Approach of Capital structure”. He is considered as one of the pioneers who examined
capital structure theoretically. Under the Net Operating Income Approach, he proposed
that in the perfect capital market, weighted average cost of capital and performance of the
firm remains constant even if the debt ratio is changed. In other words, the market
performance of the company is irrelevant with the equity or debt financing. Therefore,
capital structure becomes irrelevant to the investors while making investment decisions.
Conversely, Net Income Approach stated that a business unit or a company can
enhance its value and reduce its cost of capital by changing the financing options in the
debt ratio. If the firm prefers to finance its assets through debt, then a weighted average
cost of capital declines and firm’s performance increases. Conversely, decrease in the
debt ratio enhances the overall cost of financing which also leads to decline in the
stockholders’ wealth. The implication of using net income approach is that the business
units can continuously lessen their cost of capital by enhancing the debt financing.
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2.1.2 Traditional Approach of Capital Structure
Traditional approach of capital structure was proposed by Solomon (1963) and it
is also treated as an intermediate approach as it lies between the net income approach and
net operating income approach. The current theory describes that both cost of capital and
firms’ value are dependent upon the choice of financing options. The firm can achieve
optimal capital structure by employing judicious portion of debt capital as it diminishes
the overall cost of capital and enhances the performance of the firm.
The rationale is that the debt financing is relatively an inexpensive source of
funding than equity, hence higher leverage declines the firm’s overall cost of capital.
However, financing through debt enhances the value of the firm and reduces the cost of
capital up to the certain optimal level and when firms utilize debt capital beyond that
level, overall cost of capital becomes high and resultantly, market performance of the
firm declines. Therefore, firms should mix the debt and equity capital in a manner which
maximizes its wealth.
This approach can be divided in three phases. In the first stage, the market value
of the companies’ overall cost of capital decreases as the debt ratio increases under the
certain assumptions (a) the cost of equity capital also remains constant but may rise with
more debt financing and (b) the cost of debt financing remains constant. In the second
stage, when firm reaches the certain level of debt ratio (optimal level), any addition of
debt financing will insignificantly affect the companies’ value. Resultantly, value of the
firm and cost of capital remain relatively constant with in specific range. More precisely,
there is a range of debt ratio in which firm’s value stands at its highest level with the
minimal cost of capital. At the next level, when firms cross this critical point of capital
structure due to increase in debt financing, both cost of equity and debt rises and value of
the firm declines due to high degree of bankruptcy risk.
2.1.3 Irrelevance Theory of Capital Structure
Modern concept of capital structure started with the landmark “Irrelevance
Theory of Capital Structure” presented by the Modigliani and Miller (1958) which also
validates the Net Operating Income Approach. They examined the impact of capital
structure on firm’s performance under the assumption of perfect capital markets i.e., there
is no transaction cost, there are no corporate taxes, investors having homogeneous
18
expectations of future earnings, all earnings are distributed to stockholders etc. and
proposed that firm’s value is irrelevant with the debt or equity financing or the entire cost
of capital is not influenced by the debt ratio of the firm.
They argued that although the debt financing is considered a cheaper source of
financing than other financing options, but the inclusion of a large portion of debt
financing in the formation capital structure will not enhance the firms’ value as it also
enhances the firm’s cost of equity. The enhancement of this equity cost exactly pay offs
the financial rewards of low cost of debt and as a result, the firms performance and the
entire cost of capital remains constant.
2.1.4 Trade-Off Theory
The “Trade-off Theory” was derived from the dialogue over the Irrelevance
theory of Modigliani and Miller when tax shield benefit was observed due to debt
financing (Modigliani & Miller, 1963). This hypothesis comprises of Static Trade-off
Theory and Dynamic Trade-off Theory. Kraus and Litzenberger (1973) made a valuable
addition in the capital structure literature by proposing the “Static Trade-off Theory”.
This hypothesis stated that the firm formulates the target debt financing/ratio as per the
business requirements and then steadily progress to attain it. He also proposed that
although debt capital is more beneficial than equity capital as interest paid on debt is tax
deductible and agency benefits (whereas dividend payments cannot avail the tax
benefits), but a large portion of debt financing also raises the bankruptcy or financial risk
which ultimately declines the firm’s value. Consequently, the firm should have optimal
blend of equity and debt capital depending upon the benefits and cost of debt. This theory
also predicts that the uncertain business units and the firms with elevated growth
prospects prefer to finance their assets through equity while high lucrative firms and the
business units with more fixed assets choose debt capital for enhancing their value and
shareholders’ wealth.
“Dynamic Trade-off Theory” of capital structure is based on the assumptions of
static trade-off theory. This model describes that instead of placing a fix/constant optimal
leverage, an optimal range of debt ratio is determined and this range depends upon the
financing margins as per company forecasts for the upcoming time phase. In addition,
there are also some facts such as changes in the stock price, market timing, financial
19
deficits and other relevant variables which cause the firm to move away from their target
capital structure. So, due to these deviations, the firm will make a trade-off between the
marginal costs and marginal benefits for maximize the shareholders’ wealth.
2.1.5 Pecking Order Theory
As opposed to the “Static Trade-off Theory”, pecking order hypothesis of capital
structure claimed that there is no specific target debt ratio for maximizing the company’s
value. This hypothesis was presented by Myers and Majluf (1984) and is dependent on
the asymmetric information and signaling problems. This theory stated that when firm’s
managers possess more information regarding the operational and investment activities of
the firm than the shareholder; then investors prefer to purchase stocks only at discount.
Consequently, information asymmetries subsist between the shareholders and
firm managers and it also negatively influences the firm’s value. To apprehend this
problem, firms should follow the particular hierarchy to finance their assets, explicitly, a
preference for internal financing (retained earnings) over external financing (debt and
equity) and for debt financing over equity capital. If the firm requires funds for running
its business activities then initially, firm should utilize retained earnings (internally
generated funds). If retained earnings become inadequate for running the operations of
the business than the firms should raise additional capital through debt instruments and as
a last resort, firms should rely on equity financing for raising the additional funds. There
are two main rationales behind the preference of pattern of financing (a) External
financing transaction cost (b) Asymmetric information.
According to the first rational, transaction cost allied with the financing options
play an important role while formation of capital structure. There is no transaction cost
associated with the utilization of internally generated funds (retained earnings) while the
firms bear transaction cost when their assets are financed through external sources (debt
or equity). However, the transaction cost of debt financing is lower than the equity
financing.
In relation to the notion of asymmetric information, asset financing with the
retained earnings avoid scrutiny from any external supplier of capital while issuance of
the debt instrument show a positive indication to the investors that the business unit has
adequate cash flows in the future due to the attachment of restrictive covenants.
20
Therefore, the firm should opt internal financing than external financing and debt
financing over the equity financing.
However, Chen (2004) challenged this order of financing and proposed “New
Pecking Order Theory” in the Chinese prospects. He stated that Chinese firms pursue
new pecking order i.e. retained earnings, equity and long term debt. It means a preference
for equity financing over debt capital due to financial constraints and institutional
differences in the Chinese banking system. If the firms require funds then initially, firms
utilize internally generated funds i.e. retained earnings. If retained earnings become
inadequate for running the operations of the business then the firms raise additional
capital through issuing equity and as a last resort, firms depend on long term debt
financing.
In China, most of the corporations are controlled and owned by the state and
financial sector is also under the strong influence of the Chinese government. This
monopoly obstructs the development and efficiency of financial markets especially a
bond market and as a result, asset financing through debt is complicated. In addition,
institutional and legal framework in China is immature, rights of lenders are vague,
stockholders have more power in the liquidation or bankruptcy process of the firms than
the debt holders; therefore, Chinese firms prefer to finance their assets through equity
than debt.
2.1.6 Agency Theory of Capital Structure
The association between leverage and the value of firm is also elaborated by
Jensen and Meckling (1976) in their renowned theory named “Agency Theory of Capital
Structure”. This theory deals with the agency’s problem that takes place because of
conflict of interests either between the stockholders and managers or between the debt
holders and stockholders. He proposed that “agency problem” arises between the
managers and the shareholders of the firm when managers indulge in moral hazard
problems and are interested in increasing their own wealth than shareholders.
As a result, the shareholders face high monitoring and incentive costs i.e. agency
cost for the reduction of moral hazard problems. However, higher debt financing not only
reduces the business conflicts between the managers and stockholders but also enhances
the performance. This is because debt acts as a disciplinary instument to decrease the
21
cash flow wastage of the managers through the threat of bankruptcy, pre-commitment of
interest payments, attachment of protective covenants and informational content of debt.
Therefore, debt financing may be utilized as a reliable instrument to lessen the agency
cost of the firm.
Jensen and Meckling (1976) indicated another kind of agency issue which
upraises from the conflict of interests between the stockholders and debt providers due to
default risk as the shareholders may be engage in risky projects for getting higher
financial benefits because payment obligations remain unchanged and the excess cash
inflows enhances the shareholders wealth. In this scenario, performance of the firms will
be negatively influenced by debt financing. However, if the risky investments do not pay
off, debt holders also share in the losses. To evade this situation, debt holders impose
certain protective covenants or monitoring tools on debtors to protect themselves which
as a result incurs agency costs.
Theses protective covenants include the loan agreement provisions like fixation of
dividend payout ratio, maintenance of minimum level of liquidity, amount for spending
acquisition of assets, standardization of executive salaries etc. Therefore, containing the
suitable provisions in the loan agreement, creditors protect themselves from the adverse
consequences of agency problems and on the other hand, managers become bound to
utilize the free cash flow in the best interest of shareholders.
2.1.7 Signaling Theory
The theoretical framework developed by Ross (1977) explicates that asset
financing through debt capital is valuable for the firm because raising funds through debt
instrument gives a positive indication to the investors that the business unit can generates
adequate pecuniary resources in the future or in a stable financial position to accomplish
outstanding debt liabilities. Additionally, utilization of debt capital is also a reliable sign
to the stockholders that mangers of the firm consider that the stock is undervalued.
However, firms with poor prospects could be reluctant to issue debt due to the
incapability of loan repayment along with the threat of bankruptcy. In other words, debt
financing is a reliable tool to increase the future value of the firm and also build the
investors’ trust on the business venture.
22
On the contrary, when firms raise their funds through the issuance of shares,
investors and stakeholders perceive it as a bad news or negative signal because it
specifies that firms’ management is certain that the value of the stocks are currently
overvalued and future profitability of the firms may also decline. Hence, firms’ share
price rises when it generates funds through issuing debt and falls when it issues equity,
indicating a positive association between capital structure and firm value.
2.1.8 Market Timing Theory
Another significant theoretical hypothesis of capital structure is the “Market
Timing Theory”, established by Baker and Wurgler (2002). This hypothesis suggested
that the current formulation of equity and debt capital of the firms is an outcome of the
past equity market timings. Market timings entail that the firm issues new stocks when
prices of the shares supposed to be overvalued and repurchase their own stocks when the
shares are deemed to be undervalued. Subsequently, current structure of the company’s
capital is strongly rerated with the past market values of the stocks or the fluctuation in
the market value of the stocks affects the debt ratio of the firm.
The literature stated two forms of equity market timings. Under the assumption
i.e. economic agents are rational, the first version elaborated that firms are supposed to
issue stocks after the releases of positive information which also lessens the asymmetric
issues between the shareholders and managers of the firms. This reduction in the
asymmetric information enhances the stock prices and as a result, firms generate their
own timing opportunities. Whereas the later version of the theory presumes that the
economic agents are irrational and there is a time-varying mispricing of the firm’s share.
The firm’s managers issue equity when they are certain that its cost is irrationally low
while firms management buys back its own shares when they consider its cost is
irrationally high and this notion builds the trust in the managers that they can time the
market and enhance the firm’s value.
All the above theories provide guidelines about how firms can enhance their value
through suitable blend of equity and debt in their capital structure. However, these
cannot be generalized for every kind of firm or industry due to the dissimilar attributes of
the business and economic environment. Therefore, many attributes like the nature of the
firm, objectives and funds availability, regulations of the financial markets and
23
accessibility of the funds from these financial markets etc. specify which kind of
theoretical approach plays an important role in order to maximize firm’s value. For
instance, Shyam-Sunder and Myers (1999) stated that USA business companies pursue
the pecking order hypothesis to finance their assets while Dang et al., (2012) illustrated
that UK firms support the financing patterns of trade-off theory of capital structure.
2.2 Empirical Review of Capital Structure and Firm Performance:
The current section exhibits the comprehensive view of existing empirical
researches conducted in both underdeveloped and develop countries regarding capital
structure and performance of the firms. The current section further divides into four sub
sections based on the relationship directions between the explanatory and dependent
variables and consequently, the present research will not only be able to identify the
research gap but also it will be able to anticipate the direction of relationship for the
current study. The first sub-section exhibits the studies which demonstrated the positive
association between capital structure and firm value followed by the researches which
illustrated the inverse association in the next section. In addition, the studies which
exhibited the mixed or insignificant association between debt ratio and performance of
the firms are presented in the last two sections.
The debate on the capital structure started with the pioneer work of Modigliani
and Miller (1958). They argued that firm’s performance is irrelevant to debt ratio under
the perfect capital market i.e. no corporate tax, no transaction cost, investors having
homogeneous expectations of future earnings, all earnings are distributed among stock
holders etc. However, they altered their claim and explored that the firm’s value varies
with the changes in the debt ratio. They further stated that the value of the firm can be
maximized when assets are financed through debt (Modigliani & Miller, 1963).
Later on, Jensen and Meckling (1976) also proposed that debt ratio affects the
value of the comapny in their renowned agency hypothesis of capital structure. He stated
that agency cost rises due to conflict of interests between stockholders and managers.
This conflict arises when the managers start working for the accomplishment of their
personal objectives and as a result, the shareholders face high monitoring and incentive
costs To apprehend this problem, Jensen and Meckling (1976) suggested that high debt
ratio lessens the agency cost of the firms because of the liquidation threat and risk of
24
reduction in perquisites of the firm managers. In addition, monitoring role of debt put
pressure on the managers to produce positive cash flow to pay interest along with the
principal payments of debt. Heinkel (1982) also pointed out that high debt ratio enhances
the firm’s performance caused by the disciplinary role of debt. Moreover, Harris and
Raviv (1991) also explored that high debt ratio aligns the interests of managers and
shareholders’ for ehancing the shareholders wealth.
Late on, various authors also demonstrated that leverage enhance the firm
performance. For instance, Ross (1977) extended the analysis and proposed that high debt
ratio of the firm gives a positive sign to the market that the business unit has positive cash
flow for meeting the prospect operational and financing requirements of the firm. It also
signals that substandard business firms may not hold large portion of debt due to high
risk of bankruptcy (Barclay et al., 1995). Masulis (1983) also reported that when firms
finance their assets through debts, they enhance both accounting and market based
performance.
Myers and Majluf (1984) stated that equity financing is an costly source of
financing than debt. Asset financing through debt is considered more financially viable
for the firms than equity capital as interest paid on debt capital is tax deductible. In
another development, Lubatkin and Chatterjee (1994) argued that debt can enhance the
firm’s performance as a portion of debt’s cost (interest) is tax deductible. Wruck (1990)
pointed out that the performance of the firm positively and significantly related to debt
ratio because it plays a disciplinary role in the business activities of the companies;
consequently, avoiding the financial distress.
Safieddine and Titman (1999) examined the relationship between leverage and
firms’ value by taking the sample of 573 US unsuccessful takeovers attemps from the
period of 1982 to 1991. The study found that high debt ratio declines the probability of
the firm being taken over. The failed targets enhance their debt financing which mostly
reduce employment, asset sales, share price, capital expenditues and insignificant
changes in the operating cash flow. In addition, high debt ratio improve targets in the
favour of sharholders at the time of termination of takeover offers. Moreover, Champion
(1999) argued that debt financing is considered one of the significant devices for
enhancing the firm’s performance.
25
Whiting and Gilkison (2000) also showed positive association between debt ratio
and firm’s performance. They selected forty five listed firms of New Zealand covering
the period of 10 years from 1985 to 1994. The results specified that the stock returns
positively relate with the long term debt and the firm’s probability of dividend
distributions decreases when the firms utilize higher level of both short term and total
debts in their capital structure. In addition, the firms with the poor performance cut their
asset levels while utilizing higher debt ratio.
By covering the sample of Brazilian companies over the time period of 7 years
from 1995 to 2001, Mesquita and Lara (2003) investigated the impact of short, long and
total debt ratio on the firm’s profitability. The results of the study indicated a positive
relationship between short term debt financing and corporate performance while inverse
association was found between long term debt ratio and return on assets. Wet & Hall
(2004) explored the impact of operating and financial leverage on firm’s sales, economic
value added (EVA) and market value added (MVA). The results of spreadsheet models
indicated that fixed cost capital enhances the profitability of the firms. They also found
that organization’s sensitivity to changes in sales volume is established by the degree of
operating leverage (DOL) and total cost of capital (COC)
Ahn et. al. (2006) used the sample of 8674 US diversified firm-year observations
for exploring the impact of debt ratio on investment decisions and the firm’s value.
Investment of the firms increases with the increase in the leverage for high q firms and
for non-core segments of diversified firms. In addition, focused diversified firms depend
on high debt for creating their value than low-growth firms. Moreover, the disciplinary
role of debt can be partially offset by the business segments used by the firms. Hadlock
and James (2002) also identified that the firms utilize more debt for getting higher rate of
return. Many researches (e.g. Baker, 1973; Taub, 1975; Roden & Lewellen, 1995; Ghosh
et al., 2000) also exhibited that high debt ratio directly related with the performance of
the sample firms.
Berger and Patti (2006) stated that debt financing lessens agency conflicts of
outside equity and therefore persuade the managers to perform for maximizing the
stockholder’s wealth. They selected the data of 695 USA banks for the time period of
1990 to 1995 and showed that profitability was directly related to leverage. Kim et al.,
26
(2006) selected 16,276 firm year observations of chaebol and non-chaebol Korean firms
for the period of eight years between 1985-2002 to investigate the relationship between
financing choices and firm’s performance. The results of the research revealed that
financing while non- chaebol firms showed better performance with low debt ratio.
By covering the sample of publically held firms of USA over the period of 1970
to 2005, Kale et al. (2007) studied the disciplinary role of debt financing by examining
the relationship between leverage and firm performance. The result of the study found a
positive concave relationship between high debt ratio and firm performance. In addition,
when opportunities of outside employment increases then this positive relationship
became weaker and vice versa. These findings proposed that the firms’ employees
realistically trade-off the personal cost of financial distress and leaving the employment.
By covering the ten years panel data of 52 Ghanaian microfinance intuitions,
Kyereboah-Coleman (2007) explored the leverage-performance relationship. He
described that microfinance banks normally finance their assets with long term debts. In
addition, microfinance institutions with high debt ratio may better deal with moral
hazards, adverse selection, achieve economies of scale and are easily reach out for more
clients. Moreover, the results of regression analysis indicated that leverage positively
affect the overall performance of the microfinance institutions.
King and Santor (2008) examined the relationship between capital structure,
family ownership and value of the firm by selecting the sample of 613 Canadian
companies over the period of 1998-2005. The research found that single share-class
family owned firms normally finance their assets through debt and their both market and
accounting measures of performance are also superior than the other firms. Liew (2010)
examined the relationship between leverage and firm’s performance of 336 successful
international listed real estate firms from a time period of 2000 to 2006. Jensen’s alpha
and Sharpe ratio were used to measure the firm’s value while debt ratio was a proxy of
leverage. The study found that successful real estate firms had larger size and their
market value was greater than the book value. In addition, profitable and high growth
firms generally used high debt ratio, indicated positive relationship between debt ratio
and profitability.
27
By utilizing the sample of French high and low growth manufacturing industries,
Margaritis and Psillaki (2010) examined the relationship among debt ratio, ownership
structure and firms’ efficency. Efficiency was measured through non-parametric data
envelopment analysis and the results of quantile regressions consistent with the agency
hypothesis of capital structure. Results specified that a sefficiency of the firm enhances
with the high debt financing. Moreover, the outcomes also showed that firm’s efficiency
and more concentrated ownership possitively affects the capital structure of the firms.
Bei and Wijewardanab (2012) investigated the relationship between financial
leverage and the financial health of the Srilanka’s firms by taking the sample of 10 years
from 2000 to 2009. The outcomes of the analysis indicated that the financial health of the
firms was positively related to debt ratio. By selecting the financial panel data of top 400
Malaysian business comapnies for the period of 2000 to 2009, Matemilola et al. (2012)
explored the impact of capital structure, managerial skills on the shareholders return. The
results of fixed effect regression model revealed that both total debt and long term debt
ratio positively affect the shareholders returns.
Gonzalez (2013) explored the impact of debt ratio on the operating performance
of the 10375 companies of 39 countries. The study selected the panel data from 1995 to
2004 and the results of Generalized Method of Moments (GMM) analysis illustrated that
the firms with the high debt ratio significantly reduce the operating performance.
However, this relationship varies with the financial development, legal origin and
financial structure of the countries. The followers of French civil laws depicted a direct
relationship between debt capital and firm performance while development of the
banking system than the stock markets increased the disciplinary role of debt. In addition,
debt ratio was positively related to performance in those countries which protect their
shareholder’s rights and have strong legal enforcement system.
Park and Jang (2013) jointly analyzed leverage, divercification, free cash flows
and firm’s performance. They collected panel data of 308 resturants including 77
diversified resturants from COMPUSTAT for the period of 1995 to 2008. Debt ratio was
measured through the ratio of total debt to total assets while Tobin’s Q was utilized for
measuring the firm’s performance. The results of the study showed that free cash flow
was improved in both related and unrelated diversification business companies. The study
28
also indicated that debt financing not only reduced the free cash flows but also enhanced
the firm’s performance, particularly lessoned the harmful effets of unrelated
diversification on the value of the firms.
O’Brien et al., (2014) explored the association between leverage, R&D expenses
and firm returns by collecting the sample of 1986 Japanese comapnies for a period of
1991-2001 and found significant positive association between debt ratio and firm returns.
The outcomes also indicated that high debt ratio facilitated the firms to easily enter into
new markets due to controlling structure of debt financing. Moreover, the harmfull
effects of debt financing may become worsen for R&D intensive firms than the firms
who manageda established portfolio in the market.
In the recent years, private equity Leverage Buyouts (LBOs) are considered one
of the significant tool for corporate control. Therefore, Cohn et al., (2014) selected the
data of 317 U.S LBOs which passed between 1995 to 2007 to explore the relationship
between financing choice and operating perofrmnace. They found that debt financing was
enhanced after LBOs which not only lowered the corporate tax payments but also
improved the opertaing performance.
By selecting the sample of 41 listed firms of plantation sector of Malaysia for the
period of 5 years from 2007 to 2011, Tan and Hamid (2016) explored the relationship
between the debt ratio and the performance of firms. The research utilized long term debt
ratio, short term debt ratio, total debt ratio and debt to equity ratio to measure the capital
structure whereas performance of the Malaysian firms was computed through ROA,
GMS, EPS, PE ratios. The outcomes of the research depicted a positive and significant
relationship between high debt financing and the performance of selected Malaysian
companies.
In contrary, several researches have also described a significantly inverse
relationship between firms’ performance and capital structure. For instance, Myers
(1977) explored that high debt ratio leads to underinvestment. As part of the investment’s
benefits are transferred to the debt holders, lucrative business prospects may be
abandoned by the high levered firms, consequently declines in the market value of the
firm. Huberman (1984) examined the impact of external financing on the firm’s
performance and exhibited that the market performance of the firms negatively influences
29
the debt financing and suggested that the firms should expect decrease in the earnings
when favors external financing.
Titman and Wessels (1988) also proposed that highly profitable business
companies have lesser level of debt financing than fewer lucrative companies as
profitable firms prefer to utilize their retained earnings than external financing.
Moreover, stock prices reflect the level of firm’s performance so companies are more
inclined to utilize equity capital than debt capital.
Jensen (1989) illustrated that less-leveraged firms respond slower to decline in
firm’s value or have less frequent financial distress than highly leveraged firms because
the firms with the high debt ratio are more likely to pay its financial claims quickly and
restructure its operations for maintaining their firm’s value. Later on, Jensen et al., (1992)
also constructed simultaneous equation model and cross-sectional analysis validate that
debt ratio negatively affects the firm’s future cash flows.
Using the sample of 358 U.S listed firms, Ofek (1993) exhibited the association
between debt ratio and the firm’s financial and operational short term responses to poor
performance. The study covered the data of 358 US firms from 1983 to1987 having a
year of poor performance followed by a year of average performance. The study found an
inverse association between external financing options and performance of the firms. The
outcomes also indicated that the higher pre distress-debt ratio increased the employee
layoffs, asset restructuring, debt restructuring and dividend cuts of the firms.
Gu (1993) examined the association between the usage of debt financing and the
performance of the listed restaurant firms of Taiwan. The outcomes of the study indicated
that dining restaurants employed low level of debts while food restaurants utilized
moderate and excessive debt financing respectively. In addition, low debt financing
produced an optimal level of capital structure and generated high profitability while
moderate usage of debt showed higher firms’ performance but also created risk for the
investors. Moreover, high debt financing was reported harmful for the profitability of the
restaurants.
Using the financial data of listed Indian firms, Balakrishnan and Fox (1993)
claimed that leverage enhances the risk aversions of the managers and lessens their
inclination to invest in uncertain but lucrative business which ultimately reduced the
30
firm’s value. Opler and Titman (1994) also showed that higher debt ratio negatively
influenced the sales growth of the firms especially in distressed industries which also
inversely affected the profitability. Pushner (1995) explored the impact of debt ratio on
the performance of firms by taking 1247 Japanese firms for the span of 1976 to 1989.
The results revealed that the productivity of Japanese firms was reduced by increasing
debt financing.
Fama and French (1998) examined the role of tax benefits on the value of firm
through debt financing. They determined that leverage does not create tax benefits as
high debt ratio produces agency problems among the creditors and shareholders of the
firm. As a result, asymmetric information cancels the benefits of the debt financing.
Consistent with the findings, Wald (1999), Friend and Lang (1988) and Rajan and
Zingales (1995) also showed a negative relationship between the debt ratio and the firm’s
value.
By selecting the sample of 37 US restaurants for the period of 1992 to 1996, Sheel
and Wattanasuttiwong (1998) studied the impact of capital structure on risk/size adjusted
equity returns. The findings of the generalized least square regression (GLM) revealed
that equity returns was significantly and negatively associated with the debt equity ratio.
This negative relationship holds regardless of use of real/nominal returns and January
effect. Majumdar and Chhibber (1999) also explored that leverage negatively affects the
financial performance. In addition, industrial grouping, excise duty and age were also
inversely related to the corporate performance of Indian firms. Majumdar and Chhibber
(1999) also found negative influence of capital structure on the return on net worth of
Indian listed firms. Shyam-Sunder and Myers (1999) also concluded that the most
profitable firms across many industries often have the lowest leverage.
Gleason et al. (2000) explored the impact of capital structure on the corporate
performance of retailers of 14 European countries. They grouped the sample into four
clusters based on the culture and revealed that corporate performance was inversely
influenced by the high debt ratio. This negative relationship existed due to the agency
issues which lead to utilize higher than the optimal level of debt financing. Chen and Tsai
(2000) applied two-stage least square method and reported a negative relationship
between capital structure and the performance of life insurance firms of USA.
31
Dewenter and Malatesta (2001) carried out a longitudinal and cross-sectional
analysis for determining the leverage-corporate performance relationship of private and
State Owned Enterprises (SOEs). An inverse relationship between capital structure and
firm’s performance was reported as Private firms were more profitable and less leverage
while State Owned Enterprises (SOEs) were less profitable due to high debt financing.
Chiang et al. (2002) also selected the construction and property sectors of the Hong Kong
to investigate the leverage-performance relationship and concluded that capital structure
was inversely related to profit margins.
There are few researches which observed leverage-performance association in
property and construction sectors. Hung et al., (2002) examined the impact of capital
structure and cost of capital on the profitability of 35 labor intensive constructors and
capital intensive property developers of Hong Kong for a period of 1993 to 2000. The
debt ratio of developers seemed lower than the contractors. This lower level of debt can
be related to their being bigger (Ooi, 1999). Furthermore, they argued that firm’s
profitability is inversely related to leverage, consistent with the Pecking Order hypothesis
of capital structure.
By selecting the panel-sample of listed Hungarian and Polish firms, Hammes
(2003) studied the leverage-performance relationship. He employed different sources of
debts i.e. bank debts and trade credits to measure the leverage of the firms. Results
revealed that regardless of the type of debt, firm’s profitability was negatively and
significantly associated with the capital structure of the firms. Mesquita and Lara (2003)
also found that firm’s rate of return was inversely related to long term debt financing.
Chen, Chen and Lin (2004) also utilized Structural Equation Modeling (SEM) and found
negative influence of debt ratio on the performance of property-liability insurance sector
of Taiwan.
Yoon and Jang (2005) empirically examined the impact of financial leverage on
return on equity and the firm’s operational risk of 62 US restaurants over the period of six
years from 1998 to 2003. Leverage was measured through long term and total debt ratio
while return on equity was used to measure firm’s performance. The findings indicated
that the restaurants with high debt ratio performed poorly in terms of both accounting and
32
market measures. Moreover, highly leveraged restaurants were less risky than equity
based restaurants.
Hossain et al. (2005) analyzed the impact of financial structure on the
productivity growth and profitability of the manufacturing food industry of USA. The
food industry of USA attained a 0.9% average productivity growth due to capital
adjustments and technological advancement. The findings of the study revealed that the
rapid use of debt financing was responsible for reducing the pace of productivity growth
as the associated agency cost of debt inversely affected the firm’s profitability, input
demand, output growth and overall productivity growth. Therefore, equity financing was
the only source which could improve the firm’s profitability and overall productivity.
The relationship between capital structure, R&D expenditures and the firm’s
future growth of large US manufacturing firms was analyzed by Singh and Faircloth
(2005). They collected panel data from COMPUSTAT for 392 US firms for a period of
1996 to 1999. Results reported a negative relationship between the degree of R&D
expenses and leverage. Moreover, high debt financing harmfully influenced the future
investments in R&D which ultimately deteriorated the future growth and long term
financial performance of the firms.
By selecting the weekly data of more than 700 UK firms covering the period of
twenty six years from 1976 to 2001, Chelley-Steeley and Steeley (2005) analyzed the
impact of capital strucrue on the firm’s stock returns. The research found that small firms
had more tendency of higher debt financing than the large firms as systematic volatility
asymmetries were found in the UK firms. In addition, the outcomes of the GARCH
analysis elaborated an inverse association between leverage and stock returns.
Aivazian et al., (2005) collected panel data of 863 publically traded Canadian
firms over the period of 1982 to 1999 to examine the association between financial
leverage and investment decisions. The results of fixed effects and random effects
multiple regression models indicated that leverage negatively influenced the investment
decisions of the firms and this inverse relationship became more stronger for the
companies with low growth. Moreover, the results of the robustness models supported the
agency theory of capital structure i.e. debt financing has a disciplinary role for the firms
with low growth oppertunities.
33
Ghosh (2007) employed cross sectional data of 543 companies for the year of
2000 and the data of 576 firms for the year of 2005 to explore the relationship between
debt ratio and value of the firm. Capital structure was measured through debt ratio while
ROA, EPS and Tobin’s Q were selected for measuring the firm’s value. The outcomes of
OLS regression analysis explored that the leverage negatively influenced the firm’s
value. Moreover, internal monitoring through managers can substitute debt financing.
Aggarwal and Zhao (2007) controlled the industry effects of leverage in
investagating the leverage-value relationship. They selected 81,711 firm-year
observations of US firms from the “Research Annual Industrial Tapes” and
COMPUSTAT covering the period from 1980 to 2003 for conducting the analysis.
Tobin’s Q ratio was opted for measuring the firm’s value while capital structure was
assessed through total market leverage and total book leverage. The results of regression
analysis designated that high debt financing negatively impacted the value of both low
and high growth US firms. This study controlled the industry leverage effects in
estimation the leverage–performance relationship and showed that debt ratio had a
negative relationship with performance for both low and high growth business ventures.
Abor (2007) employed the panel data of 160 and 100 medium and small-sized
South African and Ghanaian firms respectively from 1998 to 2003 to explore the impact
of debt ratio on the performance of the firms. Long, short and total debt ratios were used
as explanatory variables while performance was measured by gross profit margins,
Return on Assets and Q ratio. The outcomes of generalized least squares regression
analysis revealed that due to agency issues, performance of medium and small-sized
enterprises was harmfully affected by the leverage.
Zeitun and Tian (2007) explored the association between the performance and the
capital structure of the Jordan listed companies and showed that both market and
accounting performances were inversely affected by leverage. Arcas and Bachiller (2008)
explored the impact of debt financing on the performance of recently privatized firms and
private firms of European Union from 1999 to 2002. The results depicted that recently
privatized firms were having dissimilar debt ratios, profitability and labor intensity than
private firms. In the Scandinavian and French zones, private firms were less profitable,
highly leveraged and high labor intensive than privatized firms. In the British zone,
34
private firms were more profitable and less leveraged than the privatized firms.
Therefore, an inverse relationship existed between capital structure and firms’ value.
However, the results were quite different in Eastern zone where privatized firms had
more leverage and generated more earnings.
Nieh et al. (2008) also utilized non-linear framework to examine the optimal level
of capital structure of listed electronic firms of Taiwan and indicated that the firm’s
value drops when debt ratio exceeds 51.57 percent. Firth et. al. (2008) examined the
leverage-investment relationship of 1203 Chinese listed firms over the period of fourteen
years (1991-2004). Investment of the Chinese firms was inversely related to leverage.
This relationship became stronger for the firms having high operating performance,
enormous growth opportunities and lower level of state shareholdings. Ghosh (2008)
examined the association between corporate profitability and capital structure of 1390
Indian manufacturing firms for the span of 1995-2004. The results of the research
depicted that the firm’s cash flows and profitability deteriorate as debt level rises. In
addition, this negative impact was more significant for the corporations who participated
in global debt markets than other firms.
The overall performance of insurance companies was closely related to
compensation of policy holders and this performance was majorly depended upon the
formation of capital structure (Chen et al., 2009). Therefore, they investigated the
relationship between capital structure and the firm’s profitability by selecting the panel
data of 13 firms from life insurance sector of Taiwan covering the time period of 1993 to
2003. The ratio of total debt to total assets was used to compute the capital structure
while firm’s performance was measured through net profit margin and return on assets.
The results of the Structural Equation Modeling (Path and Factor analysis) directed that
capital structure was inversely associated with the capital structure of the life insurance
industry of Taiwan.
By selecting the data of listed firms of Egyptian stock exchange from 1997 to
2005, Ebid (2009) empirically inspected the relationship between the capital structure and
firm’s performance. The performance of the firm was computed through Return of Assets
(ROA), Return on Equity (ROE) and Gross Profit Margin (GPM) while short, long and
total debt ratio were utilized for measuring capital structure of the firms. The outcomes of
35
the multiple regression analysis revealed that leverage negatively influenced the
performance of Egyptian firms in case of ROA while no statistically significant results
were reported in case of ROE and GPM.
Based on the monthly data of listed non-financial U.S companies covering the the
sample of twenty eight years from 1975 to 2002, Cai & Zhang (2011) analyzed the
relationship between debt ratio and firms’ stock prices. The results of the study
designated that stock prices of the sample firms were inversly related to leverage. This
negative relationship was more stronger for the firms who faced intense financial
constraints and higher probability of defaults. Theses results were also consistent with the
hypothesis of Myers' (1977), which stated that high debt financing directs future
underinvetment, hence dropping a firm's value.
Francis et al. (2011) explored the relationship between leverage and the firms’
growth of 680 U.S. listed manufacturing firms fromg 1993 to 2005. The results exhibited
that high debt ratio was inverely associated to the firm’s growth. However, this negative
relationship was significantly reduced during the sample period and disappeared when
stock prices were at higher level. It predicted that stock based reward schemes were
helpful to reduce the agency issues between stockholders and managers and consequently
mitigated the disciplinary role of debt. Furthermore, the negative leverage-growth
relationship turned into more significant in case of focused segment than the diversified
firms. Moreover, the results of the study also showed that high debt financing was
harmful for the the poorly governed firms in contrast to well-governed firms.
Coricelli et al. (2012) explored the impact of leverage on the firm’s performance
by taking sample of 16 Eastern and Central Europe countries from the period of 10 years
from 1999 to 2008. The results of a threshold regression analysis exhibited that the firm’s
performance was negatively associated with capital structure. Salim and Yadav (2012)
examined the impact of capital structure on the firms’ performance based on the 237
Malaysian firms from 1995 to 2011. The study employed return on asset, return on
equity, earning per share and Tobin’s Q to measure the firm’s performance while total,
long term and short term debt ratio were utilized to quantify the capital structure. The
outcomes indicated a inverse relationship between three performance measures (earning
per share, return on equity and return on asset) and capital strucrure of the firms while
36
Tobin’s Q positively and significantly impacted the long term debt and short tem debt
ratios.
Ahmad and Abdullah (2013) not only determined the optimum level of capital
structure of the listed Malaysian firms but also explored the impact of this optimum level
of capital structure on the firm’s value. They collected panel data for 467 Malaysian
firms from 2005 to 2009 and employed the advanced panel threshold regression
estimation. The results exhibited that the firm’s value is at maximum level when the debt
ratio is 64.33 percent and the further debt can be harmful for the business operations of
the selected Malaysian firms. By selecting the panel data of 218 oil exploration firms for
the period of 1970 to 2007, Chung et al., (2013) investigated the impact of capital
structure on the firm’s survival. The results support the pecking order hypothesis and
illustarted that debt financing declines the financial performance of the firms in the
presence of retained earnings.
Yazdanfar and Ohman (2015) selected the cross sectional sample of 15,897
Swedish small and medium enterprises (SMEs) during the span of 2009 to 2012 to
investigate the leverage-performance relationship. The outcomes of the research depicted
that both short term and long term debt ratios negatively influence the firm performance
of selected SMEs. In addition, the owners of SMEs preferred equity financing due to
agency cost and threat of losing control on firms associated with the debt capital.
By choosing the 136 listed industrial firms of Turkey over the period of
2005-2012, Nassar (2016) examined the relationship between debt ratio and firm’s
performance. Earnings per Share (EPS), Return on Equity (ROE) and Return on Asset
(ROA) were utilized to compute the performance while capital structure of the selected
firms was computed through debt ratio. The outcomes of the multivariate regression
analysis indicated the leverage was harmful for the performance of Turkish companies as
all the measures of performance were inversely related to debt ratio.
Abata and Migiro (2016) empirically examined the impact of capital structure on
the performance of listed firms of Nigeria. A sample of 30 companies was selected over
the period of 2005 to 2014. Results of multivariate regression analysis indicated that both
proxies of the performance i.e. ROA and ROE decline with the increase of debt
financing. Mauwa et al., (2016) used both primary and secondary data of listed firms in
37
Rwanda Stock Exchange (RSE) Nigeria to investigate the leverage-performance
relationship. The results of the regression analysis depicted that all measures of firm
performance i.e. ROE and ROA were inversely and significantly related with the firm’s
performance.
Vithessonthia and Tonguraib (2016) explored the impact of capital structure and
the firm’s performance by collecting the financial data of 159,375 non-financial firms of
Thailand during the phase of financial crises (2007-2009). The outcomes of the study
revealed that high debt financing was harmful for the financial performance of the whole
selected sample. The explanatory power of the leverage-performance relationship was
higher in the case of domestically oriented firms.
In addition to the inverse leverage-performance relationship, few studies have
presented the mixed relationships. For instance, Shenoy and Koch (1996) developed a
simultaneous equation model to investigate the relationship between leverage and firm’s
profitability. They selected 162 US listed firms of six industries from 1979 to 1989 for
conducting the analysis. The empirical results indicated a negative cross-sectional
relationship between capital structure and firm’s future value. However, positive
relationship was found between debt ratio and firm’s performance across the time
periods.
Abor (2005) exhibited the impact of leverage on the profitability of 22 listed firms
of Ghana Stock Exchange over the period of five years (1998 to 2002). The results of
regression analysis specified that Ghanaian firms more relied on debt as the short term
and total debt ratio were positively related to profitability of the firms. However, this
study found inverse and significant association between the long term debt ratio and
firm’s profitability.
Cheng et al. (2010) selected the advanced panel threshold regression model to
explore the optimal level of capital structure in Chinese listed firms. The firm’s value was
measured through Return on Equity (ROE) while debt to total assets was used as a proxy
to measure capital structure. The results indicated an inverted U shaped relationship
between leverage and firm’s value, showing that the firm’s value deteriorates after the
threshold level of debt ratio i.e. 70.48 percent level.
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The optimal capital structure is a trade-off between the cost and benefits of the
debt. Consequently, Yang et al., (2010) not only investigated the leverage-performance
relationship but also identified the optimal capital structure under quadratic framework
by using the data of listed non-financial firms of Taiwan 50 and Mid-Cap 100. The study
found a significant curvilinear relationship between the debt ratio and the firm’s
performance. In addition, larger firms had more ability to sustain higher performance
than Mid-Cap 100 corporations under the same capital structure. Moreover, the financing
behavior of Taiwan mid-cap 100 and Taiwan 50 firms followed the static trade-off theory
of capital structure.
By taking sample of 196 listed Taiwanese firms from 1993 to 2005, Lin and
Chang (2011) employed an advanced panel threshold regression model to test whether
asymmetrical relationship existed between capital structure and firms performance. The
study found two threshold levels of capital structure i.e. 9.86 percent and 33.33 percent
respectively. The firm’s value was harmfully affected when debt ratio exceeded 33.33
percent or decreased from 9.86 percent. These results were also consistent with the static
trade off theory of capital structure.
Bistrova et al. (2011) considered the sample size of 36 Baltic “blue chip” firms
covering the period of 4 years (2007 to 2010) to investigate the impact of leverage on the
firm’s profitability and equity performance. The results were consistent with the pecking
order hypothesis as the inverse relationship was found between capital structure and
firm’s profitability. However, the relationship between equity capital and stock
performance was found positive.
Capital structure literature also specifies few studies which showed insignificant
relationship between capital structure and firm’s value. For example, Krishnan and
Moyer (1997) collected panel data from “Disclosure and Worldscope” databases for a
total of 81 firms to empirically explore the leverage-corporate performance
relationship of large enterprises of four emerging economies of Asia i.e. Korea, Malaysia,
Singapore and Hong Kong. The study measured capital structure through total and long
term debt ratio while performance was determined by Return On Equity (ROE), Return
On Capital Employed (ROIC), share return and operating profit margin. The outcomes of
the study indicated that Korean firms employed more debt for financing their assets than
39
other countries. In addition, capital structure was not influencing the firm’s value as the
results were statistically insignificant. Phillips and Sipahioglu (2004) also tested the
irrelevance theory of capital structure of Modgliani and Miller (1958) and found that
debt-equity ratio does not significantly influence the firm’s value of listed organizations
of UK.
As stated, various studies have shown a positive relationship between debt
ratio/capital structure and firm performance; however a large number of studies have also
exhibited that high debt ratio is harmful for the financial health of the firms. On the basis
of the literature, the present study also anticipates an inverse relationship between capital
structure and the performance of non-financial firms of Pakistan.
2.3 Capital Structure, Business Strategy and Firm Performance
Business strategy is considered one of the imperative and essential elements of
every effective business and the firm can enhance its value and compete in the
marketplace by pursuing the suitable competitive business strategy. Porter (1980) made a
valuable addition in the strategic management literature by proposing the generic
business strategies. According to his topology, a firm can attain better performance by
either pursuing the cost leadership strategy or product differentiation strategy. In
addition, he also proposed that a firm becomes “stuck in the middle” and face poor
financial performance if it tries to adopt both strategies simultaneously or fails to adopt
and implement any single strategy.
Later on, numerous researchers have investigated the strategy-performance
relationship by selecting the Porter generic business strategies. For instance, Hambrick
(1983) selected the data of industrial product manufacturers and the producers of capital
goods to explore the strategy-performance relationship and the outcome supported the
porter’s topology and found that “stuck in the middle” firms showed poor performance
than the cost leadership firms or product differentiators.
Dess and Davis (1984) investigated the business strategy–performance
relationship by using the sample of industrial products and argued that higher
performance may be attained either through pursuing cost leadership strategy and product
differentiation strategy. Akan et al., (2006) explored the validity of widely accepted
porter’s topology by using the data of 226 adult employees of different
40
organizations. The results of regression analysis indicated that both cost leadership and
product differentiation strategy significantly and positively influence the firm’s
performance.
Chathoth and Olsen (2007) considered the data of 48 US restaurants for the period
of six years from 1995 to 2000 to analyze the impact of capital structure, corporate
strategy and environmental risk on the financial performance. They computed leverage
through the ratio of total debt to total assets and ROE was selected to compute the firm’s
performance. The results showed that leverage, strategy and business risk significantly
affects the corporate performance. Nandakumar et al. (2011) also investigated the impact
of business strategy on the organizational performance to assess the applicability of
porter’s generic strategies. The data was collected from the CEOs of 124 mechanical and
electrical engineering firms from UK through postal survey. The firm’s performance was
computed through both objective and subjective measures. The outcomes of the study
revealed that the firms pursuing either product differentiation strategy or cost leadership
strategy performs better than stuck in the middle firms.
By using the sample of 101 listed firms of Thailand, Teeratansirikool et al. (2013)
illustrated that differentiation strategy of the firms significantly enhances their financial
performance than the cost leadership firms due to the high prices and featured products.
Pulaj et al. (2015) also explored the relationship between Porter’s generic business
strategies and the organizational performance and the outcome depicted that both both
cost leaders and product differentiators positively and significantly influenced the firm’s
performance.
In addition to the above studies, numerous researchers have also argued that
hybrid or integrated strategy was associated with the superior performance. Parnell
(2010) explored the association between Porter’s generic business strategy and firm’s
performance by selecting 277 retail business firms of USA and the results revealed that
the firms pursuing single strategy and hybrid strategy performed better than the firms
without strategic clarity or “stuck in the middle”.
White (1986) explored that 19 of the 69 firms from the selected sample were
pursuing hybrid strategy and showed the highest performance (ROI) than the firms which
adopted either cost leadership firms or product differentiation strategy. Wright and
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Parsinia (1988) selected the sample of diversified industries including creative business
firms, distributing and retail firms and banking industry and found that successful firms
were following hybrid strategy.
Hill (1988) argued that porter’s both generic business strategies were not
incompatible and may be integrated in same business organizations to attain the
competitive advantage in the marketplace. Parker and Helms (1992) explored the impact
of porter’s business strategies on the firm’s performance and illustrated that higher
performance was associated with the firms who either adopted single strategy or pursued
both strategies simultaneously (cost leadership strategy and product differentiation
strategy).
Slocum et al. (1994) argued that business organizations can gain maximum
financial benefits if they adopt and implement both porters’ generic strategies (cost
leadership firms or product differentiation strategy) simultaneously. In an endeavor to
explore whether differentiation and low cost strategy are mutually exclusive or can be
implemented simultaneously, Helms et al. (1997) discovered that firms with integrated
strategy attained higher Return On Investments (ROI) than other firms. Many studies
(e.g. Spanos et al., 2004; Lubatkin et al., 2006; Uotila et al., 2009) have also reported
positive relationship between the hybrid business strategy and firms performance.
Kim et al. (2004) selected the sample of 75 e-businesses to explore the
validity of the Porter’s business strategy and the outcomes of the research illustrated that
Porter’s generic strategies were also valid to e-business firms. Business units pursuing the
integrated (Hybrid) strategy exhibited highest performance than the firms which adopted
single strategy. These results also proposed that product differentiation strategy and cost
leadership strategy can be integrated at the similar span to accomplish the firm’s
objectives in a better way.
As stated, strategic management literature showed that the firms either choose
single strategy (cost leadership strategy or product differentiation strategy) or pursue
integrated strategy to enhance performance. Consequently, the current study also
anticipates that “stuck in the middle” firms will perform poorly while the firm’s pursuing
either single or hybrid business strategy will show better performance.
42
Most of the prior researches have only investigated the direct relationship
between leverage and performance of the firm. However, O’Brien (2003) and Jermias
(2008) argued that impact of capital structure on the firm’s performance may be
dependent upon the business strategy followed by the business units/firms. For Instance,
O’Brien (2003) not only explored the relationship between capital structure and firm’s
performance but also investigated the moderating role of product differentiation strategy
on the leverage-value relationship. The data was collected from 16,358 firms listed on
Compustat for the period of 1980 to 1999. The results illustrated that the capital structure
of the firms had a negative relationship with firm’s performance. In addition, the
outcomes also designated that the coefficient of moderating variable (interaction term
between product differentiation and leverage) was significantly negative. It implied that
product differentiation strategy negatively moderates the relationship between capital
structure and firm’s performance.
Using the financial data of 176 U.S manufacturing listed firms, Jermias (2008)
empirically examined the moderating role of business strategy on the relationship
between capital structure and firm performance. He divided the business strategy into
cost leadership and product differentiation strategy and the outcomes of the study
indicated that leverage negatively affects the performance of the firms. In addition, the
outcomes also confirmed the moderating role of business strategy on the leverage-
performance relationship as the association between capital structure and the firm’s
performance is more negative for the firms which are pursuing product differentiation
strategy than cost leadership strategy. He further stated that debt financing was not
suitable for the product differentiators because these firms spend more in R&D activities
and indulges in risky investment projects; so, restrictive covenants attached with the debt
financing refrain them from featuring the products. The results also suggested that debt
financing is beneficial for cost leadership firms because of the monitoring role of lenders,
which match the interests of stockholders and firm managers.
To the best of author’s knowledge, existing literature reported only two studies
which employed business strategy as a moderating variable between capital structure and
performance of the firms. Out of these two studies, O’Brien (2003) considered only one
type of Porter’s business strategy while Jermias (2008) opted two categories of business
43
strategy as a moderating variable and conducted the research in USA. Consequently, the
current research contributes in the existing literature by choosing all the categories of
business strategy i.e. cost leadership strategy, product differentiation strategy, hybrid
strategy and unclear strategy (stuck in the middle) of the firms as a moderating variable
between the leverage-performance relationship.
2.4 Capital Structure, Competitive Intensity and Firm Performance
The “Competitive Intensity” among the rivals refers to the probability to which
business units or firms put pressure in terms of limiting other’s profits or market shares
within the same industry. It not only improves the productivity but also enhances the
overall value of the firms. Few studies have examined the extent to which the
leverage-performance relationship is contingent upon the product market competition.
For instance, Jermias (2008) investigated the moderating role of competitive
intensity between capital structure and firm’s performance. The study selected 176 U.S
manufacturing firms and Herfindahl index (HHI) was utilized to compute the product
market competition of the companies. The results of regression analysis illustrated that
high debt ratio was harmful for the financial performance of the US firms. In addition,
the outcome also showed that the interaction term between the product market
competition and leverage was negative and significant, indicating that competition
negatively moderates the relationship between capital structure and firm’s performance.
If the firms maintain high debt ratio during high product market competition then they
incur a significant performance penalty. This also suggested that competitive intensity
lessens the benefits of debt and competitive intensity can be used as a substitute of debt
financing because it also limits the opportunistic behavior of the managers.
Using the date of 257 South African listed firms, Fosu (2013) explored the
moderating impact of product market competition between debt ratio and firm’s
performance. The outcomes depicted that high debt financing had a positive and
significant relationship with the performance of the firms. Furthermore, debt financing
declines the firm’s value during the high product market competition.
As stated, a small number of empirical studies investigated the moderating impact
of competitive intensity between capital structure and firm’s performance in the
international literature. In addition, to the best of author’s knowledge, no single study
44
selected the sample of Pakistani firms to examine the moderating role of competitive
intensity between the relationship of capital structure and firm performance. Therefore,
the current study fulfills this research gap in the existing literature by investigating the
moderating role of product market competition between the leverage-performance
relationship.
2.5 Empirical Literature of Pakistan
Most of the empirical studies of Pakistan have only investigated the direct impact
of capital structure on the firm’s performance by utilizing the sample of various
industries of Pakistan. For instance, by selecting 20 listed firms of energy and fuel sector
from 2000 to 2005, Akhtar et al. (2012) explored the relationship between financial
leverage and firm’s performance. Gearing ratio and debt to equity ratio were selected to
compute the capital structure of the firms whereas firm’s performance was measured
through return on equity, return on assets, dividend ratio to equity, earnings per share,
and net profit margin. The outcomes of the research showed a positive relationship
between debt ratio and firm’s performance. Khan (2012) selected 36 listed engineering
firms of Pakistan covering the span of 2003 to 2009 to explore the impact of capital
structure on the firm’s performance and the results of the study stated a inverse
relationship between financial leverage and firm’s performance.
Using the sample of top 100 listed firms of Karachi Stock Exchange for the period
of four years (2006 to 2009), Umar et al. (2012) explored the relationship between debt
ratio and firms financial performance. Six measures i.e. ROA, ROE, EPS, EBIT, P/E
ratio and NPM were utilized to measure the company’s performance whereas capital
structure of the firms was measured through short, long and total debt ratios. The
empirical results of generalized least square regression analysis stated that all proxies of
capital structure were inversely associated to NPM, EPS, ROA and EBIT. Conversely,
P/E ratio exhibited a positive relationship with short term debt ratio.
Sheikh and Wang (2013) explored the impact of debt financing on the corporate
performance by selecting the 240 listed non-financial firms of Pakistan from 2004 to
2009. Return on Assets (ROA) and Market to Book ratio (M/B) were employed to
measure performance while three proxies i.e. short term, long term and total debt ratio
were used to measure the capital structure of the firms. The results indicated that all three
45
proxies of capital structure were inversely predicting the accounting and market
performance of Pakistani firms.
By incorporating the panel data of 482 listed non-financial firms of Pakistan over
the period of six years (2004 to 2009), Raza (2013) attempted to explore the impact of
capital structure on the performance of the selected sample firms. Debt to equity ratio
was selected to compute the leverage while financial performance was determined
through return on total assets and return on equity. The key finding of the study pointed
out a inverse relationship between performance and debt ratio of the selected firms. In
addition, highest debt ratio was found in the textile sector with the lowest level of
profitability. Moreover, it was reported that long term debt financing aggressively
deteriorates the profitability of the firms.
Mumtaz et al., (2013) selected 83 listed firms from KSE 100 index of Pakistan to
examine the association between capital structure and firm’s financial value. Leverage
was measured through total debt ratio while earnings per share, return on equity, return
on assets and net profit margin were selected to compute the performance of the firms.
The outcomes of the study reported that firm’s accounting performance was
significantly and negatively affected by the formation of debt and equity financing.
Moreover, market performance and level of risk was inversely related to debt financing.
Khan (2012) investigated that stock returns of the 189 listed textile firms of
Pakistan are sensitive to change in the debt equity choice. The financial data was
collected over the period of 2003 to 2009. Findings of the ordinary least square
regression analysis suggested that variations in the stock returns were not significantly
affected by the financing choices of the firms
Badar and Saeed (2013) selected the financial data of 10 listed food firms of
Pakistan during the period of 2007 to 2011 to empirically examine the impact of capital
structure on firm’s performance. The study utilized return on assets and assets turnover
ratio as response variables to compute the firm’s value. On the other hand, capital
structure of the firm was computed through long term debt ratio, short term debt ratio and
debt to equity ratio. The results of the multiple regression models showed significant and
inverse relationship of debt to equity ratio and short term debt ratio with firm’s
46
performance while long term debt financing significantly enhances the company’s
performance.
Rehman (2013) conducted a research to examine the leverage-performance
relationship of 35 listed firms from sugar industry of Pakistan. The study selected debt
ratio to estimate the capital structure whereas return on total assets, earnings per share,
net profit margin, return on total equity, and sales growth were chosen to measure firms
performance. The findings of the research demonstrated mixed results as capital structure
positively was related to sales growth and return on total assets while debt ratio was
negatively related to ROE, NPM and EPS.
Using the sample of 25 listed cement firms of Pakistan over the period of 2009-
2013, Muhammad et al. (2014) examined the association between debt ratio and firm’s
performance. They selected debt to assets ratio and debt to equity ratio to measure the
capital structure of the selected firms while firm’s performance was measured through
return on total assets, return on equity, gross profit margin and net profit margin. The
outcome of the research revealed a significant and negative relationship between leverage
and firm performance.
Mujahid and Akhtar (2014) considered the 155 listed textile firms of Pakistan
covering the apan of six years (2006 to 2011) to explore the impact of capital structure on
the firm’s financial performance. The research used EPS, ROE and ROA to compute the
firm’s performance and shareholders wealth and the outcomes of the research showed
that leverage was significantly and positively associted to shareholders wealth and firm’s
performance.
Inam and Mir (2014) investigated the impact of debt ratio on the firm’s
performance by selecting the sample of all listed energy and fuel sectors of Pakistan.
Financial performance of the firms was computed through earnings per share, return on
capital employed, return on equity, return on total assets and net profit margin and while
capital structure was measured through gearing ratio and debt to equity ratio. The
outcomes of the study showed that leverage had a positive relationship with financial
performance of the firms. They also proposed that Pakistan’s energy and fuel sectors can
uplift their future growth by incorporating the appropriate capital structure or more debt
financing.
47
Kausar et al. (2014) explored the association between firm performance and
capital structure of 197 listed firms of Pakistan during the period of 2004 to 2011. Capital
structure was determined through long term, short term and total debt ratio while price
earnings ratio and Q ratio were selected to determine the performance of the selected
firms. The outcomes of the Ordinary Least Square and panel regression analysis indicated
that all proxies of the capital structure were inversely and significantly related to P/E ratio
and Tobin’s Q. Moreover, the study also showed that the listed firms of Pakistan were
largely dependent upon the short term debt financing.
Javed et al. (2014) empirically examined the leverage-performance relationship
by selecting 63 listed firms of Karachi Stock Exchange over the period of five years from
2007 to 2011. Performance of the selected sample was calculated through return on sales,
return on assets and return on equity while long term debt ratio, total debt ratio and equity
to assets ratio were selected to compute the capital structure of the firms. The results of
the study revealed mixed results as leverage positively predated ROA and ROE when
measured through equity to assets ratio. Conversely, LDR and TDR significantly and
negatively predicted ROS.
Tauseef et al. (2015) explored the leverage-performance relationship by selecting
the sample of 95 listed textile firms of Pakistan from 2002 to 2008. Return on equity was
selected to measure the financial performance while capital structure computed through
debt to assets ratio. Results showed a non-liner relationship between debt to asset ratio
and return on equity. It implied that as the leverage increases, performance of the firms
enhances till optimal level of capital structure and then starts declining. The study also
designated that optimal level of debt ratio is around 56 percent in textile sector of
Pakistani. Moreover, firm’s sales growth was positively predicting return on equity while
size was not predicting performance of the firms.
Shahzad et al. (2015) explored the relationship between capital structure and
financial performance of panel data of 112 listed textile firms of Pakistan over the period
of fourteen years i.e. 1999 to 2008. The study utilized both accounting i.e. return on total
assets and market measures i.e. Q ratio to compute the financial performance while total
debt ratio, long term debt ratio, short term debt ratio and debt to equity ratio were
selected to measure the capital structure. The findings of the research illustrated mixed
48
results as capital structure of the selected sample showed negative impact on accounting
performance and positive impact onmarket performance of the firms.
Ahmad and Mohsin (2016) studied the impact of financial leverage on the
performance of the cement sector of Pakistan. They selected 18 listed firms of cement
sector over the period of six years from 2009 to 2015. The perfroamnce was measured
through return on total assets while debt ratio was utilized to compute the financial
leverage. The outcomes illustrated a statistically significant negative relationship between
debt ratio and profitability.
The appropriate mixture of debt and equity financing is considered one of the
fundamental and critical decisions faced by the comapny’s top management to enhance
the shareholders wealth or overall performance of the organization. In this context, Awais
et al. (2016) explored the impact of capital structure on the firm’s financial performance.
The financial data of 100 listed non-financial firms of Pakistan was selected over the
period of 2004 to 2012. Study showed that total debt ratio enhanced all indicators of the
firm’s performance i.e. ROA, ROE, Q ratio and EPS while long term and short term debt
ratios negatively influenced the firm’s corporate performance.
Habib et al. (2016) expanded the existing literature by empirically examining the
leverage-performance relationship by selecting the financial data of 340 listed firms of
Pakistan over the period of ten years (2003 to 2012). The study used SDR, LDR and TDR
as explanatory variables while firm’s performance was measured through return on
assets. The outcomesof the panel regression analysis indicated that the relationship
between all proxies of capital structure and firm performance were significantly negative.
. As stated, most of the empirical studies of Pakistan have only examined the
relationship between capital structure and firm performance by utilizing the financial data
of Pakistani firms. To the best of the author’s knowledge, no single study was found in
the literature which explored the moderating role of business strategy between the
leverage-performance relationship especially in case of Pakistan. Moreover, corporate
finance literature of Pakistan is also silent regarding the investigation of moderating role
of competitive intensity between the relationship of leverage and firm’s performance.
Therefore, the current study fulfills this research gap and examines the extent to which
49
competitive intensity and business strategy moderates the leverage-performance
relationship.
2.6 Research Gap
The present study significantly contributes to the existing literature in the
following ways:
1. Academic researchers have mostly focused to investigating the impact of capital structure
on the firm’s value by considering limited measures of both leverage and firm’s
performance; however, the present study employs more comprehensive measures of both
capital structure and firm performance to draw the inferences.
2. The existing literature selected limited sample or specific industries to conduct the
research while the current study selected the large sample size of non-financial firms of
Pakistan.
3. This study examines the moderating role of firm’s business strategy on the relationship
between capital structure and firms performance. To the best of author’s knowledge, no
single study undertaken in Pakistan has selected business strategy as moderating variable
by incorporating all of its categories.
4. Moreover, the current study also significantly contributes in the current literature by
investigating the moderating role of competitive intensity on the relationship between
capital structure and firms performance. Very few studies have used competitive intensity
as a moderator, however, no study (to the best of author’s knowledge) was found in
Pakistan which investigated this moderating relationship.
50
Chapter 3
Research Design
51
The current chapter exhibits the research methodology used to attain the
objectives of the current research. This chapter is comprised of 4 sub-sections: first
section describes the sample and its selection procedure while the second section
exhibited the estimated empirical models. The third section describes the selected
variables of the study whereas estimation techniques used in the present current study are
illustrated in the last section of the chapter.
3.1 Sample Description
In order to establish the relationship between capital structure and the
performance of the firms and to determine the moderating role of business strategy and
competitive intensity between this established relationship; the current study initially
selected all 396 non-financial firms listed at Pakistan Stock Exchange (Formerly Karachi
Stock Exchange) for the span of eight years (2006-2013). According to the Pakistan
Stock Exchange (PSE), listed non-financial firms of Pakistan have been classified into 28
sectors; however, as per the criteria of State Bank of Pakistan, the present study merged
many small sectors of the similar businesses and finally 14 sectors emerged.
Non-financial firms are enormously different from the financial firms in terms of
their business activities. The primary business activity of the non-financial firms is the
production of market goods and non-financial services and is considered a significant,
sound and stable segment of any economy. On the other hand, financial firms are
primarily deals with the financial products, e.g. banks, insurance companies mutual funds
etc.
These selected sectors of non-financial firms include textile sector, food sector,
chemical products and pharmaceuticals sector, manufacturing sector, mineral products,
cement sector, motor vehicle sector, auto parts trailers sector, fuel and energy sector,
information sector, transport services & communication sector, refined petroleum
products sector, paper sector, products and paperboard sector, apparatus and electrical
machinery sector and other services sector.
The objectives of the current research require to utilize balanced panel data
because it allows an observation of the same unit (firm) in every time period (year),
which reduces the noise produced by unit heterogeneity (Hsiao, 2007). In addition,
selection of balanced panel data also reduces the self selection and attrition bias (Baltagi
52
and Song, 2006). Furthermore, as stated, the current study divided the selected sample
firms further into four different strategic categories on the basis of the company’s
business strategy and these sub-samples of the research require complete observations of
the entire sample period to draw the accurate inferences. Thus, another significant
rationale to consider the balanced panel data is to fulfill the requirements of moderating
variables i.e., business strategy. The same rationale is also applicable on the second
moderating variable of the study i.e., competitive intensity. Hence, out of 396 listed non-
financial firms of Pakistan, 63 firms are not selected in the final sample due to
unavailability of the financial data of the firms.
Although, few firms from the unselected sample become defaulted as reported by
State Bank of Pakistan but the financial data of these firms was also not available (as
mentioned). Therefore, the selected sample does not create the survivorship bias, which
only occurs when the selection of the sample is based on the ground to produce favorable
results. Here, unselected sample firms also do not create survivorship bias because of
non-existence of self selection and attrition bias and the firms are also not ignored
intentionally.
Moreover, as per the guidelines of State Bank of Pakistan, default does not only
mean that the firm is not capable to provide its debt obligations; however the firm is
considered to be defaulter due to some other conditions. The firm is considered to be
defaulted if it fulfills one of the following conditions: (1) if the market performance of
the share is quoted less than 50% of the face value for continuous period of three years,
(2) if the firm failed to declare dividends for five years from the date of last declaration,
(3) if the firm failed to hold Annual General Meeting for the consecutive period of three
years, (4) if the firm is under liquidation and (5) if the firm failed to pay annual listing fee
for continues period of two years
Out of these specified unselected firms, 40 firms were excluded from textile
sector, 4 from food products sector, 1 from sugar sector, 7 from manufacturing sector, 3
from mineral products sector, 1 from fuel and energy, 3 firms from paper, paperboard and
products sector, 2 from electrical machinery and apparatus sector and from other services
sector. These firms were excluded on the basis of the above mentioned criteria (State
Bank of Pakistan, 2013). Thus, final sample of the research includes of 2664 firm year
53
observations of 333 listed non-financial firms (14 sectors) of Pakistan for the period of
eight years i.e., 2006 to 2013 to accomplish the objectives of the study. The details
regarding the number of firms initially selected, unselected firms due to non-availability
of data along with their sector name are exhibited in table 3.1.
Table 3.1: Sample of the Study
Type of Sector
Initial Sample
(No. of Firm)
Unselected Firms
(No. of Firm)
Final Sample
(No. of Firm)
“Textile 153 40 113
Sugar 31 1 30
Food Products 17 4 13
Chemicals, chemical products
and Pharmaceuticals
44 - 44
Manufacturing” 32 7 25
Mineral products 8 3 5
Cement 20 - 20
Motor vehicle, trailers and
auto parts
22 - 21
Fuel and energy 19 1 18
Information, communication
and transport Services
13 - 13
Refined petroleum products 9 - 9
Paper, paperboard and
products
9 3 6
Electrical machinery and
apparatus
8 2 6
Other services sector” 11 2 9
Total firms 396 63 333
54
Various sources have been used for the purpose of collection of the financial data.
Both the market and book value based financial data of listed non-financial firms of
Pakistan has been collected. Book value based yearly financial data is collected from the
annual reports of the sample companies and from the financial reports entitled “Financial
Statement Analysis of Non-financial Companies” published by the “State Bank of
Pakistan”. The publication of State Bank of Pakistan presents reliable financial
information of non-financial firms of Pakistan listed at Pakistan Stock Exchange
(Formerly Karachi Stock Exchange). Moreover, market prices of the selected sample
firms are obtained from the website of KSE as it provides the reliable and authentic
information regarding the firm’s market activities.
3.2 Empirical Models
As stated, the purpose of the research is three folds. Firstly, the present research
explores the impact of capital structure on the performance of the listed non-financial
firms of Pakistan. Secondly, the current study examines the moderating role of business
strategy between capital structure and firms performance. Lastly, the moderating role of
competitive intensity between the relationship of capital structure and firms performance
is also examined. Consequently, regression models are designed on the basis of the
study’s objectives. As the present study uses balanced panel data, therefore fixed and
random effects estimation techniques are used to capture the findings. These models are
selected as they are considered more suitable for the panel data in order to establish the
relationship among the selected variables (Greenne, 2002; Chen, 2004; Salawu, 2007).
Many studies (e.g. Arellano & Bond, 1991; Antoniou et al., 2007; Abor, 2007;
Gropp & Heider, 2010) have also applied panel regression estimation techniques to
capture the findings. Moreover, Ordinary Least Square (OLS) estimation technique is
also applied for affirmation of the relationships. Numerous researchers also utilized OLS
estimation technique for the panel data set (Barclay & Smith, 1995; Demirguc-Kent &
Maksimovic, 1998; Scherr & Hulburt; 2001; Cai et al., 2008). The estimated models
along with the relationship justifications are presented below:
55
Regression Model to Measure the Relationship between Capital Structure and firm
Performance:
Capital structure is considered one of the significant antecedents in determining
the firm’s performance (e.g. Majumdar & Chhibber, 1999; Simerly & Li, 2000; Chathoth
& Olsen, 2007; Phillips & Sipahioglu, 2004; Singh & Faircloth, 2005; Ebid, 2009;
Margaritis & Psillaki, 2010; Lin & Chang, 2011; Sheikh & Wang, 2013). Numerous
researchers (Yoon & Jang, 2005; Abor, 2007; Chen et al., 2009; Francis et al., 2011; Nieh
et al., 2008; Nassar, 2016) stated that capital structure is harmful for the financial health
of the firms. Muradoglu (2012) also argued that debt holders are more risk averse and
force firm mangers to refrain from risky but profitable projects. In addition, lenders also
cut back on most of the firms R&D expenditures under restrictive covenants which also
inversely affects the firm’s value. Furthermore, the firm’s financial risk is also enhanced
due to greater inclusion of fixed cost financing i.e. debt in the formation of capital
structure. It suggests a negative relationship between capital structure and firm’s
performance. On the basis of these arguments, the present study also hypothesized a
inverse relationship between capital structure and firm performance. The hypothesis is as
follows:
H1: There is a negative relationship between capital structure and firm performance
In order to study this relationship, the following estimation model is designed in
the light of the studies conducted by Whiting and Gilkison (2000); Abor (2005); Abor
(2007); Kyereboah-Coleman (2007); Sheikh and Wang (2013). All these studies used
three proxies to measure the capital structure i.e. total debt ratio, long term debt ratio and
short term debt ratio. In addition, numerous studies have also used control variables along
with the capital structure in order to conduct pertinent analysis between capital structure
and firm performance. In the same manner, the current study also utilizes some
significant firm level control variables in the light of studies conducted by Singh and
Faircloth (2005); Margaritis and Psillaki (2010); Chathoth and Olsen (2007); Loderer et
al., (2011). Thus, the relationship between capital structure and firm performance is
estimated utilizing the following model:
56
������ = � + ��(��)�� + �′���� + ��� … … … … … … … … . (�. �)
Where,
FPER = Firm Performance
CS = Capital Structure
C' = Vector of Control variables – [Size, Growth, Liquidity, Age, Risk]
As the diverse proxies have been utilized to compute both the explanatory and
dependent variables, so leverage-performance relationship is also estimated by
incorporating all the proxies in different models.
Regression Models to Determine the Moderating Role of Business Strategy between
the Relationship of Capital Structure and firm Performance:
The second model is used to estimate the moderating role of business strategy
between the relationship of debt ratio and firm performance. Porter (1985) proposed two
broad categories of business strategy i.e. “Cost Leadership Strategy” and “Product
Differentiation Strategy”. The firms either choose a single strategy or hybrid strategy for
successfully running the business operations; however, many firms fail to implement any
single strategy and get stuck in the middle (unclear strategic situation). Consistent with
the literature, present study also splits the strategic choices of the firms into cost
leadership strategy, product differentiation strategy, hybrid strategy and the unclear
strategy (fails to adopt any single strategy).
Porter (1985) reported that cost leadership is a lower cost competitive strategy
that entails the aggressive establishment of efficient scale amenities, tight control of
overhead expenditures etc. Hence, cost leadership strategy is the capability of the
business unit or a firm to design, produce and sale the similar products and services more
efficiently than its counterparts or competitors.
Jermias (2008) argued that the firms which follow the cost leadership strategy
can enhance their performance through debt financing as monitoring role of debt puts
pressure on managers to avoid unnecessary overhead expenditures to produce cost
effective products. In addition, debt financing also reduces the free cash flow which
limits the opportunistic behaviors of managers and refrains them from the investments in
57
the risky projects. This argument suggests that the relationship between capital structure
and the firm’s performance is positively moderated by the cost leadership strategy. The
hypothesis is as follows:
H2a: Cost leadership strategy positively moderates the relationship between high debt
ratio and firm performance.
In order to explore the moderating affect of cost leadership strategy between the
established relationship of capital structure and the performance of the firms, the current
study designs the model in the light of the study conducted by Jermias (2008). The model
is presented below:
������ = � + ��(��)�� + ��(������)�� + ���� ∗ �������� + �′����
+ ��� … … … … … … … … . (�. �)
Where,
FPER = Firm Performance
CS = Capital Structure
CLSTRA = Cost Leadership Strategy
CLSTRA = 1 (for cost leaders), otherwise= 0
C' = Vector of Control variables – [Size, Growth, Liquidity, Age, Risk]
The second category of business strategy that the firms can adopt to enhance their
performance is product differentiation strategy. This strategy normally attempts to
establish products with unique features by employing the latest scientific technology. As
an outcome; the consumers are usually agree to pay premium prices for the featured
products. It is argued that product differentiation strategy is feasible to enhance
performance of firm due to the brand loyalty of the customers and more inclination
towards insensitivity of product prices.
O’Brien (2003) emphasized that the firms which pursue the product
differentiation strategy may not attain suitable monetary benefits of debt financing. He
58
further stated that product differentiation firms heavily spend on R&D activities and also
indulge in risky projects, however restrictive covenants attached with the debt financing
are probable to impede innovation qualities and creativity of the firm’s managers. As a
result, firms may be unable to achieve competitive advantage as product differentiators. It
suggests that the relationship between capital structure and the performance of the firms
is negatively moderated by the product differentiation strategy. These results are also
consistent with O’Brien (2003) and Jermias (2008). The hypothesis is as follows:
H2b: Product differentiation strategy negatively moderates the relationship between high
debt ratio and firm performance.
Following model is used to estimate the moderating role of product differentiation
strategy between leverage- performance relationship.
������ = � + ��(��)�� + ��(������)�� + ���� ∗ ������ �� + �′����
+ ��� … … … … … … … … . (�. �)
Where,
FPER = Firm Performance
CS = Capital Structure
PDSTRA = Product Differentiation Strategy
PDSTRA = 1 (for product differentiators), otherwise 0
C' = Vector of Control variables – [Size, Growth, Liquidity, Age, Risk]
Strategic management literature showed that many firms pursue both cost
leadership strategy and product differentiation strategy (hybrid) simultaneously to
achieve their business objectives. Parnell (2000) stated that many business ventures start
with the differentiation strategy and develop economies of scale as they grow. Minarik
(2007) also argued that the firms adopt hybrid strategy to gain more financial benefits
along with the market share.
59
However, it is argued that high debt financing is not suitable for the financial
performance of the firms while pursuing both strategies simultaneously. Although debt
financing reduces the opportunistic behavior of the firm managers but high debt ratio also
generates obstacles for the firms to become product differentiators due to restrictive
covenants associated with the debt. Therefore, debt financing cannot create financial
benefits for the firms if they pursue both product differentiation strategy and cost
leadership strategy simultaneously.
The present study also postulates that the relationship between capital structure
and firm performance is negatively moderated by hybrid strategy of the firms. The
hypothesis is as follows:
H2c: Hybrid strategy negatively moderates the relationship between high debt ratio and
firm performance.
The following model is designed in the light of the studies conducted by Kim et
al. (2004) and Baroto et al. (2012). The model is presented below:
������ = � + ��(��)�� + ��(������)�� + ���� ∗ �������� + �′����
+ ��� … … … … … … … … . (�. �)
Where,
FPER = Firm Performance
CS = Capital Structure
HBSTRA = Hybrid Strategy
HBSTRA = 1 (for hybrid strategy), otherwise 0
C' = Vector of Control variables – [Size, Growth, Liquidity, Age, Risk]
Kim et al. (2004) specified that firms either choose a single generic strategy or
pursue hybrid strategy to to gain competitive advantage in the market place. However,
White (1986) and Dostaler and Flouris (2006) stated that many firms are unable to choose
any single strategy due to their vague corporate culture and poor decision making and get
60
“stuck in the middle”. As a result, these firms perform poorly and face competitive
disadvantage. The firms with unclear strategy cannot attain real financial benefits from
the debt financing. Normally, lenders are reluctant to provide funds to the firms with
unclear strategy. If they do so then these firms are unable to utilize the funds properly due
to inappropriate investment decisions. As a result, firms may also fail to return the debt
capital to the lenders and face poor financial conditions. This argument suggests that the
relationship between capital structure and the firm’s performance is negatively moderated
by the unclear strategic situations of the firms. The hypothesis is as follows:
H2d: Unclear strategy negatively moderates the relationship between high debt ratio and
firm performance.
Following model is used to estimate the moderating role of unclear strategy
between the relationship of capital structure and firm performance:
������ = � + ��(��)�� + ��(������)�� + ���� ∗ �������� + �′����
+ ��� … … … … … … … … . (�. �)
Where,
FPER = Firm Performance
CS = Capital Structure
UCSTRA = Unclear Strategy
UCSTRA = 1 (for unclear strategy), otherwise 0
C' = Vector of Control variables – [Size, Growth, Liquidity, Age, Risk]
The current study will regress the above four estimated moderated regression
models by employing both continuous and dummy variables at the same model. Type of
business strategy (moderating variable) is measured through dummy variable while rest
of the variables are continues in nature. As the moderating variable affect the strength of
the existing causal relationship between dependent and independent variable, therefore
the slope is also shifted/change by using the moderating variable in the regression model.
61
Numerous studies (See for example, Zeitun & Tian, 2007; Jensen, 2008; Obrien, 2013;
Trinh & Phuong, 2016) also utilized both continuous and dummy variables in same
multiple regression model to draw the inferences.
Regression Model to Determine the Moderating Role of Competitive Intensity
between the relationship of Capital Structure and firm Performance:
Product market competition is also considered one of the significant determinants
that can enormously influence a firm’s performance (Baggs & Bettignies, 2007). Jensen
(1986) stated that restrictive covenants associated with debt financing not only reduce the
discretionary earnings accessible to managers but also compels them to be efficient in
order to generate funds for repayment of debt obligations. Similarly, intense product
market competition serves as a significant tool to reduce the agency problem between
managers and owners as competitive intensity also enforces a discipline and averts
managers from the misuse of the funds for their personal goals (Allen & Gale, 2000).
Chhaochharia et al. (2016) also argued that the firms operating in high competitive
industries are more efficient. He proposed that intense product market competition
enhances the firm’s performance. Therefore, competition acts as a substitute of debt
financing to produce the monitory and disciplinary role. In this scenario, high debt capital
becomes more expensive for the firms and may harm their financial health. This
argument suggests that the relationship between capital structure and the firm’s
performance is negatively moderated by the competitive intensity of the firms.
Jermias (2008) also indicated that the relationship between capital
structure and firm performance becomes more negative with the increasing level of
product market competition. In order to evaluate the moderating role of competitive
intensity between capital structure and firm performance, the current study designs the
following model in line with Jermias (2008); Guney et al. (2011), Fosu (2013). The
hypothesis is as follows:
H3: Intense product market competition negatively moderates the relationship between
high debt ratio and firm performance.
62
The model is presented below:
������ = � + ��(��)�� + ��(���)�� + ���� ∗ ����� + �′����
+ ��� … … … … … … … … . (�. �)
Where,
FPER = Firm Performance
CS = Capital Structure
INT = Competitive intensity of the firm and is measured through Herfindahl–Hirschman
Index (HHI)
C' = Vector of Control variables – [Size, Growth, Liquidity, Age, Risk]
The moderating role of business strategy and competitive intensity between the
leverage-performance relationship is also estimated by incorporating all the proxies of the
performance and capital structure in different models.
3.3 Variables of the Study
In the light of literature, the current study selects three proxies i.e. “Total Debt
Ratio” (TDR), “Long Term Debt Ratio” (LDR) and “Short Term Debt Ratio” (SDR) to
measure the capital structure. The performance of Pakistan’s non-financial firms is
computed through Return on Total Assets (ROA), Return on Equity (ROE) and Q Ratio.
In addition, the current study selects business strategy and competitive intensity as
moderating variables between capital structure and firm performance. Moreover, five
control variables i.e. liquidity, growth, size, risk and age are selected to capture the
pertinent analysis.
Firm Performance: To measure the performance of listed non-financial firms of
Pakistan, the present research divides the firm’s performance into two general categories
i.e. accounting based performance measures and market based performance measure. The
accounting based measures include Return on Equity (ROE) and Return on Assets
(ROA). ROA is considered one of the suitable accounting measures to compute firm
performance (Gentry and Shen, 2010) as it explicitly considers the assets utilized to
support all the business activities of the firms. ROA determines whether the firm
63
produces sufficient returns by efficiently utilizing the assets or just wasting the
investments. The higher the ratio, the firm is more efficient to produces the earnings.
As a key performance indicator, firm’s ROA is also helpful for investors,
management and analysts to evaluate the firm’s financial strength and to take future
decisions accordingly. Various studies regarding the leverage-performance relationship
(see for example, Abor, 2005; Berger & Bonaccorsi di Patti, 2006; Arcas & Bachiller,
2008; Ebid, 2009; Chen et.al., 2009; Gentry and Shen, 2010; Majumdar & Chhibber,
1999; Yang et. al., 2010; Bistrova, Lace & Peleckiene, 2011; Sheikh & Wang, 2012;
Chadha and Sharma, 2016; Vy and Nguyet, 2017) have also utilized ROA to compute the
performance of the firms.
The present research also selects Return on Equity (ROE) to measures the firm’s
performance which is measured through net income divided by equity. ROE evaluates
how well a firm utilizes shareholders investments to generate net income and it is also
considered one of the key performance indicators of the firms (Abor, 2005). The
investors are always interested to know that how their investments accelerate against their
rivals and therefore, ROE allows them to benchmark and devise business decisions
accordingly. In addition, when investors invest in the firm’s stock then they basically put
their money to the discretion of the managers, then how the inventors know that they
(managers) are competent enough to utilize their investments in the best manner? Here,
ROE is one of the significant indicators to determine the performance of the firm’s
managers.
A higher the ROE demonstrates that the managers efficiently utilize the
shareholder investments to generate earnings. Numerous studies (Abor, 2005;
Kyereboah-Coleman, 2007; Arcas and Bachiller, 2008; Nieh et al., 2008; Ebaid, 2009;
Yang et al., 2010; Cheng et al., 2010; Foong and Idris, 2012; Chadha and Sharma, 2016;
Vy and Nguyet, 2017) have also opted ROE to measure the firm’s performance. Many
studies have also utilized these accounting measures to compute the firm performance
(Majumdar & Chhibber, 1999; Abor, 2005; Berger & Bonaccorsi di Patti, 2006; Arcas &
Bachiller, 2008; Chen et.al., 2009; Sheikh & Wang, 2012; Yang et. al., 2010; Bistrova,
Lace & Peleckiene, 2011). Consistent with the literature, the proxies are presented below:
64
Return on Total Assets (ROA) = Net Profit after Tax
Total Assets
Return on Total Equity (ROE) = Net Profit after Tax
Total Equity
Capital structure literature also indicated that along with the accounting measures
of firm performance. Many studies also utilized market measure (Q ratio) to compute the
firm market value. For instance, Aggarwal & Zhao, 2007; Abor, 2007; King & Santor,
2008; Ghosh, 2007; Salim & Yadav, 2012; Park & Jang, 2013) used Q ratio to measure
the market performance of the firms. Q ratio is computed through:
Tobin’s Q (Q Ratio) = “Market value of the firm”
“Book value of asset”
Where,
Market value of the firm = Book value of debt + market value of equity.
Capital Structure: Capital structure of the firm is the combination of debt and equity or a
proportion of assets which are financed by debt capital. In the previous literature, both
market and book value based financial data have been used to measure the capital
structure. However, the current study uses only the book based values to measure capital
structure because of two major reasons. First, cash benefits provided by debt financing in
terms of shield cannot be availed on the market value of debt once it has been provided to
the creditors. In this scenario, market value of debt is unrelated for the computation of tax
calculations. Second, when the company enters into bankruptcy then only book value of
debt is considered as a suitable one for pertinent computations. Hence, the present study
utilizes only the book based values of debt and equity to compute the capital structure of
the firms.
The literature of capital structure also revealed that usually Total Debt Ratio
(TDR) and Long Term Debt Ratio (LTR) were selected to calculate the capital structure
65
of the firms. However, the present study also chooses Short Term Debt Ratio (STR)
along with the Total and Long Term Debt Ratios (LTR) because of following reasons.
First, firms prefer short term debt when they face uncertainty in tax status due to high tax
rates (Scholes & Wolfson, 1990). In this case, short term debt becomes least costly and
the firms can easily adjust their debt levels. Second, short term financing alleviate the
problem of underinvestment in growing firms (Titman & Wessels, 1988).Third, growing
firms with the potential financing prefers to issue short term less secured debt due to the
higher likelihood connected with asymmetric information (pecking order).
In addition, under the inefficient financial system, firms prefer to finance their
assets through short term debt financing (Demirgüç-Kunt & Maksimovic, 1998).
Furthermore, firms in Pakistan also prefer to finance their assets through short term debt
over long term debt financing (Booth et. al.2001) because the average size of the firms is
small. Moreover, capital market of Pakistan is still underdeveloped and it is also difficult
to access these markets in term of technical formalities and high cost (Shah & Hijazi,
2004). Consequently, the current study utilizes total, long term and short term debt ratios
to compute the capital structure of the firms:
Total Debt Ratio (TDR) = Total Debt
Total Assets
Long Term Debt Ratio (LDR) = Long Term Debt
Total Assets
Short Term Debt Ratio (SDR) = Short Term Debt
Total Assets
Five control variables are also explored and selected as standard determinants of
performance. These variables comprise of firms size, liquidity, growth, risk and age of
the firms and have been extensively employed by various researchers. The relationship
justification along with the measurements of the selected variables is as follows:
Size of the Firm: Size of the firm is one of the key factors in formulating the firm’s value
(Surajit & Saxena, 2009). The firms with larger size normally attain economies of scale
66
along with expertise in terms of human capital and operating business activities that may
lead to better performance. In addition, large size firms are normally more diverse, well
controlled and have better risk tolerance (Berger & Patti, 2006). Moreover, larger firms
have more organizational resources (Capon et al., 1990), superior technology and hold
more bargaining power over suppliers (Onder, 2003). Conversely, small firms find it hard
to lessen the asymmetric issue and as a result they may face poor financial performance
Previous researches (e.g. Frank & Goyal, 2003; Majumdar & Chhibber, 1999;
Ramaswamy, 2001) have reported a positive association between firm performance and
the size of the firm. Therefore, the present research also anticipates the positive
relationship between size and firms performance and it is measured through:
Size (SZ) = Log of Total Assets
Liquidity: Another important determinant of the performance is liquidity position of the
firms. It is utilized to evaluate the capability of the firms to meet its short term
obligations. A firm holding high proportion of its current assets with respect to current
liabilities is less volatile to the unexpected changes in its balance sheet and therefore,
high liquidity lessens the risk of short-term monetary obligations and increases the firm’s
profitability (Nickell & Nicolitsas, 1999).
Moreover, firms with liquidity have appropriate capacity to adopt frequent
changes in the business structure or uncertain circumstances. This adaptation positively
affects firm’s performance (Goddard et al., 2005). Deloof (2003) also stated that effective
liquidity management is an imperative driver of financial performance. Consequently, the
present study also anticipates a positive relationship between liquidity and performance
of the firms and is measured through:
Liquidity (LQ) = Current Assets
Current Liabilities
Growth: Attaining firm’s sales growth is usually a key objective of each business unit or
a firm in the business and it is essential for the success of any business (McGrath et al.,
2001). High sales growth of a firm not only boosts the overall revenue but also generates
67
the economies of the scale and as a result enhances both accounting and market
performance of the firms.
In addition, a firm that accurately manages and uplifts the sales output not only
enhances its current financial performance but also creates ample funds for the future
business expansion (Asimakopoulos et al., 2009). High sales growth may also increase
the firm’s profitability when investment in extra capability does not hinder demands
(Scherer and Ross, 1990). Consequently, the present study also anticipates a positive
relationship between liquidity and firm’s performance and it is measured through:
Growth (GT) = Percentage change in the net Sales
Age of the Firm: Corporate finance literature illustrated that the age of the firm is also a
key antecedents of the firm’s performance. Experienced firms positively predict the
performance because as the firm grows older, investors’ uncertainty lessens (Pastor &
Veronesi, 2003). They become more specialized and find ways to coordinate, regulate
and accelerate their manufacturing procedures as well as improve quality, reduce costs
and enhance profitability (Jovanovic, 1982; Loderer et al., 2011). In addition, older firms
also have updated knowledge, skills and abilities to induce organization more profitable
(Agarwal & Gort, 2002). Consistent with the literature, the present study also proposes a
positive relationship between age of the firms and performance and the following proxy
is utilized to measure the age:
Age (AG) = “Difference between Observation Year and Year of Establishment”
Risk: Another significant determinant of the firm performance is firm’s risk which is
measured through Altman Z score. It is the yield of credit strength which measures the
probability of the firm’s bankruptcy. The parameters of Altman Z-score are different for
non-financial firms as compare to financial firms. The current study utilizes the sample of
only non-financial firms of Pakistan (312 manufacturing and 21 non-financial
services/non-manufacturing firms) to draw the inferences and the parameters of Z-score
are not considered dissimilar among the firms working under the non-financial sectors.
68
If the value of Z-score is high (especially equal or greater than 2.9) then the firms are
most likely to be safe from the probable insolvency or bankruptcy. In addition, firms are
considered in gray zone if the Altman Z score falls between 1.81 to 2.99. The descriptive
statistics of the current study shows that on average, non-financial firms of Pakistan are
probably secure from bankruptcy but exist in the gray area and they should be cautious.
The risk of the firms is calculated through:
Risk (RK) = “1.2 A + 1.4 B + 3.3 C + 0.6 D + 1.0 E”
Where:
A = “Working Capital/Total Assets”
B = “Retained Earnings/Total Assets”
C = “Earnings before Interest & Tax/Total Assets”
D = “Market Value of Equity/Total Liabilities”
E = “Sales/Total Assets”
Business Strategy: As stated, there are two broad kinds of business strategy i.e., cost
leadership strategy and product differentiation strategy. According to Porter (1985), the
firm must choose any single strategy for successful attainment of its business objectives.
He further stated that if the firm either fails to implement any single strategy or tries to
adopt both strategies simultaneously, the firms become stuck in the middle and face poor
financial conditions. However, numerous researchers (Chakraborty & Philip, 1996; Dess
et. al., 1999; Parnell, 2000; Barney, 2002; Barney & Hesterley, 2006; Minarik, 2007)
latterly argued that hybrid strategy was quite effective in terms of generating high
margins or profits due to premium prices and low product costs. Consistent with the
literature, the current study describes “Stuck in the Middle” firms as those which fail to
pursue any single strategy. In addition, present study classifies the firms under “hybrid
strategy” group which were using cost Leadership strategy and product differentiation
strategy simultaneously. Consistent with the findings of Singh and Agarwal (2002) and
Jermias (2008), cost leadership strategy is measured through:
69
Asset Utilization Efficiency (AUE) = Total Sales
Total Assets
While product differentiation strategy is computed through:
Premium Price Capability (PPC) = Selling and Administrative Expenses
Total sales
After applying the above measurements, the following methodology (suggested
by Nandakumar et al., 2011) was adopted to examine (1) number of firms using cost
leadership strategy, (2) number of firms following product differentiation strategy, (3)
number of firms pursuing both strategies simultaneously (hybrid strategy) and (4) number
of firms falling under the poor strategic situation or were “stuck in the middle”.
Cost Leadership Strategy: The firm is considered to follow cost leadership strategy if it
fulfills the following two conditions:
a) If the firm’s value of Asset Utilization Efficiency is greater than its median value
and
b) If the firm’s value of Premium Price Capability is lesser than its median value
Product Differentiation strategy: The firm is considered to pursue product differentiation
strategy if it fulfills the following two conditions:
a) If the firm’s value of Asset Utilization Efficiency is lesser than its median value
and
b) If the firm’s value of Premium Price Capability is greater than its median value
Hybrid Strategy: The firm is considered to follow hybrid strategy if it fulfills the
following two conditions:
a) If the firm’s value of Asset Utilization Efficiency is greater than its median value
And suppose
b) If the firm’s value of Premium Price Capability is greater than its median value
70
Stuck in the Middle: The firm is considered “stuck in the middle” if it fulfills the
following two conditions:
a) If the firm’s value of Asset Utilization Efficiency is lesser than its median value
and
b) If the firm’s value of Premium Price Capability is lesser than its median value
Competitive Intensity: According to Jaworski & Kohli (1993) “Competitive intensity
refers to the degree of competition a firm faces in a particular market”. Ramaswamy
(2001) indicated that intense competitive environment depends on the number of firms
that exists in that particular market. Numerous academicians (e.g. Nauenberget al., 1997;
Jermias, 2008; Laksmana and Yang, 2015; Mnasri and Ellouze, 2015; Tessema, 2016)
measured the competitive intensity through Herfindahl–Hirschman Index (HHI) which is
the measure of industry concentration. This index is the reverse measure of competitive
intensity; so higher industrial concentration is negatively related to product market
competition and vice versa. Consistent with the literature, present study also computes
the competitive intensity through Herfindahl–Hirschman Index. HHI is calculated
through a sum of squares of the market shares held by all the firms in the industry.
����������– ��������� ����� = ��� = � ���
�
���
71
Figure: 3.1Theoretical Framework
Capital
Structure
Firm
Performance
Cost Leadership Strategy
ROA
ROE
Q Ratio
Business
Strategy
Product Differentiation Strategy
Hybrid
Strategy
Unclear Strategy
TDR
LDR
SDR
Competitive
Intensity
72
3.4 Estimation Techniques:
Among the widely used estimation technique of analyzing the dependence of
firm’s performance on capital structure is the Ordinary Least Square (OLS) Regression.
Various studies (e.g. Hung et al., 2002; Abor, 2005; Yoon and Jang, 2005; Muradoglu
and Sivaprasad, 2012) have also utilized OLS as a primary estimation technique to
investigate the leverage-performance relationship. Consistent with the literature, the
current study also employs OLS estimation technique to explore the impact of capital
structure on firm’s performance. Although OLS regression is extensively used in the
existing literature, however; this technique ignores the panel nature of the data set by not
considering the time and firm-specific cross-sectional/industry effects (Johnston &
DiNardo, 1997). In this regard, Kyereboah-Coleman (2007) stated that fixed effect and
random effect regressions (panel regression) are considered most appropriate estimation
techniques for panel data based studies. In addition, Hausman (1978) specification test is
also applied to formulate an appropriate choice between fixed effect and random effect
technique.
Consistent with the studies (see for example, Pesaran et al., 2000; Greenne, 2002;
Chen, 2004; Kim et al., 2006; Salawu, 2007; Delcoure, 2007; Zeitun & Tian, 2007;
Muradoglu & Sivaprasad, 2012; Chung et al., 2013; Cohn et al., 2014), the current
research also selected the fixed effect and random effect estimation techniques to
accomplish the objectives of the research while OLS regression is utilized for the
rationale of robustness analysis. In order to ensure the suitability of panel data analysis
and to control the sector specific unobserved heterogeneity, the present research follow
the diagnostics suggested by Oscar (2007) and hence, the current study has controlled
both the sector and time specific dummies while conducting the analysis.
As stated, for the panel nature of data, both OLS and fixed/random effect
estimation techniques have been selected to predict the relationship between response and
explanatory variables (Park, 2009).These regression equations are usually estimated
either through linear or non-linear form. The choice of nonlinear or linear regression
depends on linearity of the residuals of the regression equations and various other
assumptions must be fulfilled to apply the estimation techniques. For instance, linear
regression analysis can only be performed when explanatory variables are linearly
73
independent with each other and the selected sample of the study should be true
representative of the population. In addition, explanatory variables should be determined
with “zero error”. Moreover, errors should not be associated / correlated with selected
dependent variable and regression error should be random with the mean value of zero.
Various diagnostic tests are also performed to validate the appropriateness of the
estimation techniques. For instance, 1-sample KS Kolmogorov-Smirnov (KS) test is
applied to ensure that the study variables are normally distributed whereas Modified
Wald test is also applied to check the heteroscedasticity problem. The current study has
also applied Levin-Lin-Chu test (Panel unit root test) to check the stationarity of the panel
data and the outcome of the p-value exhibits the stationarity of the data.
The present study also performed “Variance Inflation Factor” (VIF) and “Durbin–
Watson” (DW) tests to test the multicollinearity and autocorrelation (serial correlation),
respectively. The estimated results and the discussion in the light of existing literature are
presented in next chapter.
74
Chapter 4
Results and Discussion
75
The current chapter describes the empirical results along with the discussion in
the light of the literature. This chapter starts with the descriptive statistics of the selected
variables of the study followed by the correlation analysis. Finally, detailed empirical
results based on fixed effect, random effect and OLS estimation techniques are presented
in the last section of the chapter.
4.1 Descriptive Statistics
Overall descriptive statistics of firm specific response, explanatory, control and
moderating variables of the study are illustrated in the table 4.1. The current study
employs the financial data of 333 listed non-financial firms of Pakistan for the span of
eight years from 2006-2013 for conducting the whole analysis. The first two variables
named ROA and ROE of column one are the accounting measures of performance while
marketing performance of the firms is quantified through Tobin’s Q.
The selected sample of non-financial firms of Pakistan is reasonably profitable in
terms of all four measures of performance as the mean value of ROA for the whole
sample is 3.50% with the standard deviation of 8.59%. The minimum value of
performance in terms of ROA is minus 23% while one of the selected firms generates
maximum 29% profit out of their total allocation of resources. On average, the sample
firms generate 7.83% profit with the funds provided by the shareholders in terms of ROE.
The minimum value of firm performance in terms of ROE is minus 45%, shows one of
the sample firm produces loss by utilizing the funds of shareholders whereas maximum
value of ROE exhibits that one of the selected sample non-financial firms of Pakistan
generate 61% profit out of equity funds. However, the value of standard deviation is quite
high i.e. 18% as compare to ROA due to the wide range that exists between the minimum
and maximum values of ROE.
As stated, this study measures the market performance of the firms through
Tobin’s Q ratio which specifies the fair market value of the firm’s shares along with the
book value of the debt to the replacement cost of the assets. The outcomes of table 4.1
indicates that the average value of Tobin’s Q is greater than 1, which showed that
investors place the higher market value of assets than the recorded value. This higher
value also suggests that sample firms should avail more investment opportunities as they
are worth more than the price they paid for them. The minimum value (0.21) of Q ratio
76
indicates that investors of the one of the sample firms consider that the firm market value
in lower than its book value. However, the value of standard deviation is quite high i.e.
around 24% as compare to the rest of the performance measures because the market price
of selected Pakistani firms are quite volatile within the selected period of the study. The
wide range that exists between the maximum and minimum values also point out the
volatile position of the Tobin’s Q values.
Table 4.1 also indicates the descriptive statistics of three independent variables
namely total debt ratio (TDR), long term debt ratio (LDR) and short term debt ratio
(SDR). The TDR of the selected sample firms indicates that the selected Pakistani non-
financial firms are relatively highly leveraged as the mean value of the TDR is around
60%. Total debt ratio ranges from 1% to 140% with the standard deviation of around
20%. The maximum value of total debt ratio (more than 1) is indicating that one of the
sample firms has a negative equity during any year within the whole sample period. This
negative equity shows that the firms incur losses which entirely offset any funds provided
by the investors to the firms for their stocks along with any retained earnings from the
previous periods. Negative equity is also a strong indicator of imminent bankruptcy of the
firms. The minimum value of TDR shows that only 1% debt capital is utilized by one of
the sample firms to finance its assets.
The present study also measures the capital structure through long term debt ratio
(LDR).On average, 18% of the Pakistani non-financial firm’s assets are financed through
long term debts and this ratio deviates by around 15% from its mean value. The minimum
value of LDR depicts that the sample also includes the firm which does not utilize long
term debt or entirely depend upon the short term debt financing or equity financing while
several firms heavily employ long term debts for running their operations as the
maximum value of LDR is around 80%.
77
Table 4.1: Descriptive Statistics
Variable Minimum Maximum Mean Std. Deviation
ROA (%) -.23 .29 .0350 .08591
ROE(%) -.45 .61 .0783 .18002
Tobin’s Q (%) .21 1.63 1.08 .24144
TDR(%) .01 1.41 .5940 .19990
LDR(%) .00 .79 .1800 .15028
SDR(%) .00 .97 .4140 .18646
Size(Billions-PAK) 0.01 414.02 10.76 29.9865
Liquidity (%) .08 15.36 1.3898 1.36306
Growth(%) -1.46 9.55 .2268 .57344
Age(Years) 2 153 33.01 16.901
Risk(Z-Scores) .00 9.58 1.9328 .76660
SRTA_CL(Dummy) 0 1 .31 .465
STRA_PD(Dummy) 0 1 .28 .443
STRA_HB(Dummy) 0 1 .19 .385
STRA_UC(Dummy) 0 1 .22 .428
INT(Herfindahl
Index)
207.52 2572.80 761.7262 613.80886
Firm-Year Observations = 2664
78
Table 4.1 also depicts that short term debt ratio (SDR) varies from 0% to 97%
with the mean value and standard deviation of around 41% and 19% respectively. The
maximum value of SDR represents the significant dependence of Pakistani non-financial
firms on the short term debt financing in the formation of capital structure. The rationale
behind this approach may be that the Pakistani fund providers or lenders are reluctant to
provide long term funds due to high business uncertainty, high credit and interest risk rate
that prevails in the economy. In addition, business firms in Pakistan generally encourage
short term debt financing due to their on average small infrastructure or size and it is also
complicated to access capital market due to technical hitches and high transactions cost
(Shah & Hijazi, 2004). Booth et al. (1999) also stated that underdeveloped and
developing economies (including Pakistan) discourage long term debt capital and prefer
to utilize short term debt financing.
Table 4.1 also depicts the descriptive statistics of selected five control variables of
the study i.e. size, liquidity (CR), growth, age and risk. Average size of sample firms is
around Rs. 11 Billion with the standard deviation around 29.986 which is higher than the
mean value. The largest firm holds the total assets of around Rs. 414 billion while assets
of Rs. 0.01 billion are contained by the smallest firm in the entire sample. The mean
value of current ratio shows that on average, sample non-financial firms of Pakistan
maintains more current assets than short term liabilities and are smoothly able to pay their
short term obligations as depicted in the mean value of around 1.38.
The value of standard deviation is quite higher i.e. around 1.363 as it is closer to
the mean value of current ratio. In addition, a wide range exists between the minimum
and maximum values of liquidity, demonstrating that one of the sample firms has
extremely low capacity to pay their short term obligations while another extreme points
out that one firm has around 15 times capacity to pay its short term obligations. On
average, the selected sample firms illustrate moderate sales growth of 22.68% during the
entire sample period; however, the deviation from the mean value is quite higher. One of
the selected firms show a negative growth in its sales as per the minimum value of -1.46
while another extreme show 9.55 time growth by one of the sample firms of Pakistan.
Another control variable of the study is the age of the firms measured through the
difference between the establishment year and the observation year of the firms. The
79
average age of the sample non-financial firms of Pakistan is 33 years with a quite small
variation of around 17% ranges from 2 years to 153 years. Table 4.1 also indicates that
average value of firm’s risk is 1.93 which is calculated by Altman Z score. This implies
that on average, selected non-financial firms of Pakistan are probably secure from
bankruptcy but exist in the gray area and caution should be taken. This value of Z score
might subsist due to the existence of high debt ratio of the Pakistani firms.
The second objective of the current research is to examine the moderating role of
business strategy between the relationship of capital structure and the performance of the
firms. As discussed, there are two broad categories of business strategy titled cost
leadership strategy and product differentiation strategy. The firms either follow single
strategy or pursue both strategies simultaneously to accomplish their business objectives.
However, many firms are unable to select or pursue any single strategy due to their vague
corporate culture and get stuck in the middle (unclear strategic situation). Thus, the firm
falls one of the following strategies (1) cost leadership strategy, (2) product
differentiation strategy, (3) Implement both strategies simultaneously, or (4) fails to adopt
any single strategy for running its business operations.
The current study considers all these four strategic choices of the firms as
moderating variables in different models and are measured through dummy coding; code
1 is assigned to the firms if they follow the cost leadership strategy otherwise it is 0.
Similarly, the firms which follow the product differentiation strategy, the study allocates
code 1 otherwise 0. In addition, code 1 is also allocated to the firms which pursue both
strategies simultaneously otherwise 0. The same coding is followed if the firm does not
follow any single strategy for the accomplishment of its goals i.e. code 1 for “stuck in the
middle” firms, otherwise 0.
The descriptive statistics show that the non-financial firms of Pakistan mostly
follow the cost leadership strategy for the accomplishment of their business goals. The
mean value of the cost leadership strategy (STRA_CL) is 0.31, which indicates that
around 31% non-financial firms of Pakistan follow cost leadership strategy among the
whole selected sample firms with the 0.465 standard deviation.
Moreover, product differentiation strategy (STRA_PD) is considered the second
preferred strategy of non-financial firms of Pakistan. On average, 28% firms choose this
80
strategy for effectively running their operations with relatively low standard deviation as
compare to the cost leadership strategy. The statistics also show that the average value of
hybrid strategy (STRA_HB) is around 0.19; showing that 19% firms have adopted both
strategies simultaneously (cost leadership strategy and product differentiation strategy)
during the sample period with the deviation of around 38.5% from its mean value.
Moreover, descriptive statistics indicate that around 22% non-financial Pakistani firms
(STRA_UC) are unable to become either cost leaders or grow as product differentiators
(stuck in the middle). In other words, these firms failed to follow cost leadership strategy
or product differentiation strategy. However, table 4.1 shows that the chances of
deviation from this strategic situation are around 42.8%, which is quite high.
The third key objective of the present research is to examine the moderating role
of competitive intensity between the relationship of capital structure and firm
performance. Competitive intensity (INT) refers to the “Degree of competition a firm
faces in a particular market”. INT is measured through Herfindahl–Hirschman Index
(HHI) which describes the level of industry concentration. HHI is calculated through a
sum of squares of the market shares held by all the firms in the industry and it is
considered an inverse measure of product market competition.
Table 4.1 indicates that minimum and maximum values of Herfindahl–Hirschman
Index are 207.52 and 2572.80 respectively. Higher value of HHI means that the industry
is more concentrated while low value shows high degree of competition a firm faces in
the particular market or within the specific industry. However, the value of standard
deviation of HHI is quite high i.e. around 613.81 as compare to the rest of variables due
to the wide range exists between the minimum and maximum values.
4.2 Correlation Analysis
In order to examine the stability of the regression models, the present study
applies both traditional and modern techniques for the collinearity diagnostics. The
traditional measurement of multicollinearity is Pearson Product Moment correlation
analysis while variance inflation factor (VIF) analysis is performed to check
multicollinearity issues under the modern notion. Many studies of corporate finance
literature also utilized VIF to identify the collinearity issue (Belsley et al., 1980;Jermias,
2008; Garson, 2012).
81
The correlation analysis depicts the association between the variables of the
research and it is helpful to determine multicollinearity between the explanatory variables
of the research. Table 4.2 portrays the findings of correlation analysis of the selected
explanatory and control variables of the study. The results signifies the absence of
multicollinearity among the variables as the reported correlation coefficients lies under
the threshold level i.e. 0.60 except the correlation between the Total Debt Ratio (TDR)
and Short Term Debt Ratio (SDR) which is around 0.69. However, it does not create
multicollinearity as both independent variables TDR and SDR will regress separately in
different regression models and will not be considered in the a single regression model.
Two moderating variables i.e. business strategy and competitive intensity of the
firms are also regressed as an independent variables in the current study, so
multicollinearity of these variables is also checked along with other independent
variables. As per the results, it is evident that multicollinearity issue does not exist along
with other variables.
Alternatively, numerous studies argued that Variance Inflation Factor (VIF) is
also an accurate and reliable technique to find the multicollinearity between the
independent variables. It has been reported that if the value of VIF is closer to 1 then it
indicates the absence of multicollinearity while strong multicollinearity exists when value
of VIF is equal to or greater than 10 (Hartmann& Moers, 1999; Pallant, 2005). Therefore,
VIF is also computed for each independent and control variables of the current research
in order to ensure the absence of multicollinearity. The outcome shows that all the scores
of VIF are under 3, which confirms the absence of multicollinearity (relevant information
is shown at regression models). Therefore, it can be concluded that there is no evidence
of multicollinearity issue among the selected variables of the study.
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Table 4.2: Correlation Analysis
TDR LDR SDR Size Liquidity Growth Age Risk STRA_CL STRA_PD STRA_HB STRA_UC INT
TDR 1
LDR .462** 1
SDR .699** -.310** 1
Size .003 .067** -.051* 1
Liquidly -.584** -.147** -.506** -.033 1
Growth .021 .107** -.064** .018 -.020 1
Age -.080** -.090** -.013 .079** .067** -.065** 1
Risk -.080** -.266** .129** -.046* .095** .015 .076** 1
STRA_CL .092** -.010 .106** -.159** -.039 -.027 -.143** .283** 1
STRA_PD -.025 .107** -.114** .145** .006 .029 .133** -.347** -.378** 1
STRA_HB -.071** -.192** .079** -.073** .034 -.023 .081** .241** -.301** -.277** 1
STRA_UC -.014 .066** -.068** .109** .006 .032 -.077** -.160** -.358** -.288** -.236** 1
INT -.207** -.155** -.097** .146** .195** -.023 .060** .135** -.215** .058** .259** -.066** 1
***, **, and * designate the level of significance at 1%, 5% and 10%
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4.3 Regression Analysis
The current study proposes three models based on firm specific dependent,
independent and moderating variables. These models are estimated using ordinary Least
Square (OLS), fixed and random effect estimation techniques. The present study also
performed Variance Inflation Factor (VIF) and Durbin–Watson (DW) tests to check the
multicollinearity and autocorrelation (serial correlation) respectively. The detailed
analysis and estimated results are presented below.
4.3.1 Capital Structure and Firm Performance
Various researchers (see for example, Huberman, 1984; Opler& Titman, 1994;
Whiting & Gilkison, 2000; Abor, 2005; Yang et al., 2010; Park & Jang, 2013; Gonzalez,
2013; Cohn et al., 2014) emphasized the significance of firm’s capital structure in
determining the performance of the companies. The results of fixed effect, random effect
and pooled OLS estimation techniques are displayed in table 4.3 to table 4.5. Table 4.3
exhibits the estimated outcomes of regression analysis using total debt ratio (TDR), long
term debt ratio (LDR) and short term debt ratio (SDR) as an independent variables and
return on assets (ROA) as a dependent variable. Moreover, each model is estimated with
control variables i.e. size, growth, liquidity, age and risk. Model 1 indicates the impact of
total debt ratio (TDR) on the performance of the firms while model 2 and model 3 reports
the results when capital structure is computed through long term debt ratio (LDR) and
short term debt ratio (SDR) respectively.
Under fixed effect multiple regression model, the results of model 1 report that
the relationship between Total Debt Ratio (TDR) and ROA is significantly negative
(β= -0.20; p-value < 1%). This estimated relationship indicates that a unit increase in the
capital structure (TDR) significantly decrease the 0.20 units in Return on Assets (ROA)
of listed non-financial firms of Pakistan. The explanatory power of the model is around
32% and significant F value exhibits the fitness of the model. Model 2 reports the results
when Long Term Debt (LDR) ratio is selected as an explanatory variable. Many studies
have also utilized long term debt ratio to measure the capital structure of the firms (Chen,
2004; Dwilaksono, 2010; Oyesola, 2007; Serrasqueiro & Nunes, 2008; Teker et al.,
2009). The relationship between LDR and ROA is also negative and significant at 1%
level with the slightly lower beta value (β= -0.06).
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The results show that one unit enhances in long term debt ratio, declines 0.06
units in return on assets. The explanatory power of the model is around 23% whereas
significant F value ensures the overall fitness of the model. The results of Model 3 also
show almost consistent results with the model 1 and model 2 as the coefficient of SDR is
statistically negative at the level of 1% with the negative beta value (β= -0.11), indicating
that short term debt ratio also inversely effects ROA of the sample firms. Many studies
(e.g. Abor, 2005; Baum, Schafer and Talavera, 2006; Dwilaksono, 2010; Shubita and
Alsawalhah,, 2012; Gill, et al., 2011) also reported the inverse relationship between short
term debt ratio and firm performance.
Consequently, the reported outcomes show that high debt ratio is harmful for the
accounting measures of performance (ROA) of the selected Pakistan’s firms. Variance
Inflation Factor (VIF) is calculated for each variable of all the models and the values
ensure the absence of multicollinearity (VIF < 5) whereas the value of DW test also lies
within acceptable range. The results of random effect models also illustrate almost
consistent results with the fixed effect model, however; statistically significant value of
Hausman test ensures that fixed effect model is more appropriate to draw the inferences.
Though, there is a mechanical relationship exists between debt financing and the
firm’s performance (ROE and ROA) i.e. interest increases with the increase in the debt
capital which eventually declines the accounting measures of performance. However, it is
not necessary that interest expense leads to the significant change (decline) in the
accounting measures of firm performance. There are many other rationales/reasons which
cause the negative relationship between capital structure and firm performance. For
instance, negative relationship between capital structure and firm performance (ROA)
also supports the pecking order hypothesis which describes that debt financing is more
expensive and has more information asymmetry than the internal resources. So, the firm
value declines when the firm finances its assets through debt in the appropriate
availability of retained earnings. Therefore, a preference should be given to internal
financing over external financing (Myers, 1984).
In addition, debt holders are more risk averse than other fund providers (Smith &
Warner, 1979) and they force firm managers to restrain from high profitable risky
projects and cut back on R&D expenditures under restrictive covenants (Baysinger &
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Hoskisson, 1989). When the firm finances its assets largely through debt then the firm
may become dependent upon the lenders regarding the business opportunities and
operational activities of the firm and as a result the firm may confront with the slow
growth and low market share in the industry. One of the major risks of high debt ratio is
the higher % of profit share capitalization which may increase the earnings per share but
on the other hand lowers the value of ordinary shares (Sojeva, 2015).
Furthermore, firm’s financial risk is also increased with more fixed cost financing
i.e. debt in the formation of capital structure and consequently declines the firm’s value.
Moreover, large portion of debt financing also possess the bankruptcy cost and it exits
when firm is unable to pay interest liability on high debt financing. In this situation,
suppliers may refuse to extend or grant credit, lenders may demand high interest rates and
ultimately induce harmful effects on the performance indicators. Additionally, low levels
of leverage limit the agency costs of debt, such as underinvestment (Myers, 1977).
Therefore, debt financing is not only considered beneficial for the shareholders but also
harmful for the long term survival of the firms.
These results can be supported by the previous studies as they also reported a
negative relationship between capital structure and firm value (Majumdar and Chhibber,
1999; Gleason et al., 2000; Chiang et al., 2002; Cassar and Holmes, 2003; Tong & Green,
2005; Singh &Faircloth, 2005; Huang & Song, 2006; Abor, 2007; Sheikh & Wang,
2013).
The current study also selects five significant firms level control variables.
Among these selected control variables, coefficient of variable size is positive and
statistically significant in all three models of table 4.3, indicating a positive relationship
between firm size and the performance (ROA). The firms with larger size normally attain
economies of scale along with the expertise in terms of human capital and operating
business activities which may lead to better performance. This positive relationship is
also consistent with the trade-off hypothesis which argued that large size firms tend to
borrow more due to the capability of risk diversification. Consequently, greater
utilization of debt financing produces tax savings on interest payments which leads to
superior performance (Sheikh & Wang, 2013). This direct relationship between firm
86
performance and the size is consistent with the results of Majumdar and Chhibber (1999),
Ramaswamy (2001) and Frank and Goyal (2003).
The positive and statistically significant coefficient of growth also indicates that
the firms with high sales growth significantly perform better than low growth firms. A
firm that accurately manages and uplifts the sales output not only enhances its current
financial performance but also creates ample funds for further future business expansion
(Asimakopoulos et al., (2009). High sales growth may increase the firm’s profitability
when investment in extra capability has not hindered with demand (Scherer &Ross,
1990).
Furthermore, the outcomes of table 4.3 indicate that liquidity significantly
enhances the firm’s value whereas positive and significant relationship has also been
found between firm’s age and ROA. A firm holding high proportion of its current assets
with respect to current liabilities is less volatile to the unexpected changes in its balance
sheet. Therefore, high liquidity lessens the exposure to the risk of being incapable to meet
short-term monetary obligations and increase the firm’s profitability (Nickell
&Nicolitsas, 1999). Deloof (2003) also stated that efficient liquidity management is an
imperative tool of financial performance.
Moreover, more liquid firms have a larger capacity to adapt to frequent changes in
the business structure or uncertain circumstances and this adaptation positively affect the
firm’s performance (Goddard et al., 2005). Likewise, aged firms positively impact the
performance because as the business grows older, they have learnt how to manage things
in a better way (Jovanovic, 1982), investor uncertainty also lessons (Pastor & Veronesi,
2003) and they become specialized and find ways to coordinate, standardize and
accelerate their production procedures as well as improve quality, reduce costs and
enhance profitability (Loderer et al., 2011).
87
Table 4.3: Capital Structure and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Assets (ROA)
Variables
Fixed Random OLS
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 0.13 (2.14)***
-0.05 (-0.76)**
0.04 (0.62)***
-0.05 (-2.15)***
-019 (-7.60)***
-0.12 (-4.69)***
-0.08 (-5.23)***
-0.20 (-1.15)***
-0.15 (-9.16)***
TDR -0.20 (-14.90)***
-0.18 (-17.33)***
-0.20 (-20.14)***
LDR -0.06 (-4.33)***
-0.06 (-5.47)***
-0.80 (-7.12)***
SDR -0.11 (-9.05)***
-0.11 (-10.76)*
-0.12 (-13.16)***
Size 0.04 0.01 0.03 0.02 0.01 0.01 0.01 0.01 0.04
(0.23)*** (0.03)** (0.04)* (6.14)*** (6.31)** (5.26)*** (12.11)*** (12.11)*** (10.91)***
Growth 0.03 0.02 0.01 0.02 0.02 0.01 0.02 0.02 0.03
(7.14)*** (6.23)*** (6.23)*** (7.31)*** (7.38)*** (6.30)*** (5.83)** (6.11)*** (7.30)*** Liquidity 0.01 0.01 0.01 0.02 0.02 0.01 0.03 0.02 0.01
(3.23)** (8.71)*** (3.64)*** (2.99)** (11.70)*** (5.31)*** (2.69)** (15.36)*** (4.32)*** Risk 0.04 0.05 0.05 0.04 0.04 0.05 0.04 0.04 0.04
(11.40)*** (13.29)*** (14.09)*** (15.99)*** (16.11)*** (18.38)*** (20.81)*** (17.65)*** (22.75)*** Age 0.03 0.01 0.02 0.01 0.01 0.02 0.01 0.01 0.03
(1.16)* (0.54) (1.32) (1.28) (0.45) (0.28) (1.88)** (1.22) (0.15)
F-value 109.86 69.05 81.70 971.86 620.10 734.73 234.69 153.30 181.38 Adjusted R2 0.32 0.23 0.24 0.38 0.30 0.32 0.38 0.28 0.32 Hauman Test 0.0017 0.0219 0.0005 0.0017 0.0219 0.0005 - - -
Note: “t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. Model 1 includes the explanatory variable total debt ratio (TDR) whereas model 2 and model 3 contain the explanatory variables long term debt ratio (LDR) and short term debt ratio (SDR) respectively. The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models”.
88
In addition, old age firms also have updated knowledge, skills and abilities to run
organization in a profitable manner (Agarwal & Gort, 2002). However, age-performance
relationship is statistically insignificant in most of the models. It is not necessary that
with the passage of time the companies can generate ample resources along with the
modern technology to significantly outperform, especially in the case of Pakistan where
organizational rigidities exists in most of the non-financial firms due to the family
ownership. This attitude makes it difficult for the firms to accept and implement changes
in an appropriate manner. It may impair their abilities to considerably establish their
market worth.
Another control variable of the study is firm’s risk which is measured through
Altman Z score. Higher value (equal or greater than 2.9) indicates that the firms are most
likely to be safe from the probable insolvency or bankruptcy. In addition, firms are
considered in gray zone if the Altman Z scores are subsisted between 1.81 to 2.99.The
descriptive statistics of the current study show that on average Pakistan’s non-financial
firms are probably secure from bankruptcy but exist in the gray area and caution should
be taken to protect the firms from bankruptcy. The results of table 4.3 also depict that
proxy of risk positively and significantly influence the firm’s performance. As the risk is
measured through Altman Z scores (an inverse measure of firms risk), so this positive
relationship states that lower bankruptcy risk of firms enhances the performance and vice
versa.
This study also applied OLS regression for the purpose of robustness and the
results of table 4.3 shows almost consistent results with the results of fixed effect model
as the relationship between all proxies of capital structure i.e. TDR, LDR and SDR and
ROA is significantly negative. The explanatory powers of all the OLS models are
relatively higher than the fixed effect models and the significant F-statistics specify the
overall fitness of models.
The present study also selects Return on Equity (ROE) to measures the firm’s
performance (dependent variable) which is computed through net income divided by
equity. ROE evaluates how well a firm utilizes shareholders investments to generate net
income. Various studies (Abor, 2005; Kyereboah-Coleman, 2007; Ebaid, 2009) have also
opted ROE to measure firm’s performance. Table 4.4 describes the estimated results of
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fixed effect, random effect and OLS regression models when return on equity (ROE) is
selected as a dependent variable.
The outcomes illustrate that all measurements of capital structure i.e. total debt
ratio, long term debt ratio and short term debt ratio negatively and significantly influence
the ROE (firm performance) in both fixed and random effect models as all the beta
coefficients are negative and statistically significant, however significant P-value of
Hausman test ensures the suitability of fixed effect model. In addition, the negative beta
values (β= -0.30; β= -0.22; β= -0.25) indicated that a unit increase in total debt ratio
significantly decreases the 0.30 units in firms return on equity. Moreover, 0.22 and 0.25
units decline in LDR and SDR respectively when one unit increases in ROE. This
illustrates that declining intensity of ROE is higher in case of higher total debt ratio of the
firms followed by the short term debt ratio. Consequently, high debt financing or capital
structure is harmful for the return on equity of the selected Pakistani non-financial firms.
The explanatory powers of all three models are 13%, 9% and 11% respectively while
significant F-values ensure fitness of the models.
Among the control variables; sales growth, liquidity and risk positively influence
the performance and all the relationships are statistically significant. Although size and
age of the firm positively influence the firm’s performance but the outcomes are
statistically insignificant. Moreover, significant negative relationship is also reported
between capital structure and firm performance when it is estimated through OLS
regression.
Literature also exhibited that market performance of the firm also depends upon
the variations in the firm’s financing options. Several researchers (e.g. Abor, 2007;
Himmelberg et al., 1999; McConnell & Servaes, 1995; Zhou, 2001; Zeitun& Tian, 2007;
Park& Jang, 2013) have also adopted Q ratio to compute the firm’s market performance.
Consistent with the literature, the present study also selected Q ratio (developed by Nobel
Laureate James Tobin) to compute the market value of the selected firms. Q ratio is
computed as market value of the firms divided by book value of assets. Market value of
the firm is the sum of book value of debt and market value of equity.
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Table 4.4: Capital Structure and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Equity (ROE)
Note: “t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. Model 1 includes the explanatory variable total debt ratio (TDR) whereas model 2 and model 3 contain the explanatory variables long term debt ratio (LDR) and short term debt ratio (SDR) respectively”. The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models.
Variables
Fixed Random OLS Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 0.31 0.11 0.30 -0.28 -0.44 -0.32 -0.36 -0.48 -0.41
(1.90)*** (0.71)* (1.87)** (-4.77)*** (-7.76)*** (-5.37)*** (-8.99)*** (-13.04)*** (-10.69)*** TDR -0.30 -0.21 -0.18
(-7.24)*** (-7.81)*** (-8.08)*** LDR -0.22 -0.03 -0.08
(-0.62)** (-0.99)* (-3.22)*** SDR -0.25 -0.18 -0.13
(-7.36)*** (-6.72)*** (-5.37)***
Size 0.01 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03
(1.10) (1.35) (1.31) (6.52)*** (6.53)*** (5.80)*** (12.15)*** (12.33)*** (11.58)*** Growth 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02
(6.25)*** (5.86)*** (5.69)*** (6.03)*** (5.80)*** (5.41)*** (4.40)*** (4.47)*** (3.73)*** Liquidity 0.01 0.02 0.01 0.01 0.02 0.01 0.01 0.02 0.01
(2.95)*** (5.37)*** (2.07)** (2.38)*** (6.85)*** (2.88)*** (2.31)*** (8.09)*** (4.41)*** Risk 0.08 0.09 0.09 0.09 0.09 0.09 0.09 0.08 0.09
(9.33)*** (10.87)*** (10.73)*** (14.35)*** (14.69)*** (15.75)*** (18.56)*** (17.14)*** (19.31)***
Age 0.01 0.01 0.01 0.02 0.01 0.03 0.01 0.01 0.02
(0.07) (0.97) (0.30) (0.88) (0.40) (0.39) (1.32) (1.07) (0.66)
F value 50.37 40.53 50.70 458.92 387.78 436.58 109.81 98.17 102.08 Adjusted R2 0.13 0.09 0.11 0.23 0.21 0.22 0.23 0.21 0.22 Hauman test 0.0002 0.0002 0.0000 0.0002 0.0002 0.0000 - - -
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A high value of Tobin’s Q (more than one) indicates that value of firm’s equity in
the market is higher than the book value. In these circumstances, financing costs of equity
is cheaper than the debt (Park & Jang, 2013). Table 4.5 presents the estimated outcomes
of regression analysis using Total Debt Ratio (TDR), Long term Debt Ratio (LDR) and
Short term Debt Ratio (SDR) as an independent variables and Q ratio as a dependent
variable. Under fixed effect multiple regression model, the results of model 1 reports that
the relationship between Total Debt Ratio (TDR) and Tobin’s Q (market performance) is
significantly positive (p-value < 1%; β= 0.62).
This estimated relationship indicates that a unit increases in the total debt ratio
(TDR) significantly enhances the 0.62 units in the market performance (Q ratio) of the
listed non-financial firms of Pakistan. This designates that the perception of the investors
about the highly leveraged firms become more constructive and they prefer to invest in
these firms. The explanatory power of the model is around 5% and significant F value
exhibits overall fitness of the model.
In addition, model 2 reports the relationship between LDR and Q ratio is positive
and significant at 1% level. The explanatory power of the model is around 2% whereas
significant F value ensures the overall fitness of the model. Model 3 also shows almost
consist results (p-value < 1%; β= 0.39) with the model 1 and model 2 as short term debt
ratio also (SDR) positively effects the Q ratio of the sample firms. The positive and
significant beta value of SDR shows that one unit change in the short term debt ratio of
non-financial firms of Pakistan significantly enhances the market performance (Q ratio)
of the selected firms
This significant and positive relationship is consistent with the signaling approach
which stated that debt financing can be utilized to signal the information that a firm has
positive cash flows to pay the debt obligations (Ross, 1977). Additionally, utilization of
debt capital is also a reliable indication to the stockholders that executives of the firm
consider the stock as undervalued. As a result, investors prefer to purchase the shares of
the high leveraged firms to safeguard their future earnings (Ross, 1977). Various authors
(see for example, Ravid and Sarig, 1991; Shenoy and Koch, 1995; Baker and Wurgler,
2002; Julan and Yi, 2005; Chou et al., 2011) also supported the signaling approach of
capital structure i.e. investors prefer to invest in the high leveraged firms because high
92
debt ratio exhibits that managers are monitored under the restrictive covenants of debt
capital.
Agency theory also exhibited that higher debt financing not only reduces the
conflict of interests between the shareholders and managers but also enhances the firm’s
market performance. This is because debt acts as a disciplinary device to reduce the
manager’s cash flow wastage through the threat of bankruptcy, pre-commitment of
interest payments, attachment of protective covenants and informational content of debt.
In other words, high debt ratio in the firm makes sure to the investors that managers are
running the business more effectively to manage the firms operations.
Among the control variables, size, liquidity, risk have a positive relationship with
the firm’s market value while growth positively but insignificantly influences the
dependent variable. In addition, the outcomes of OLS estimation technique validate the
results of fixed effect models as market performance of the firms significantly enhances
when assets are financed through debt capital. The explanatory powers of all three OLS
models are relatively high as compare to fixed effect models whereas statistically
significant F-values ensures the fitness of the OLS regression models. 1
Overall, the results of regression analysis indicate that capital structure
significantly declines the accounting measures of performance i.e. ROE and ROA of the
non-financial firms of Pakistan whereas market performance of the sample Pakistan’s
firms significantly enhances when assets are financed through debt capital. Among the
control variables, growth, liquidity, size and risk are the significant determinants of both
accounting and market performance of Pakistan’s firms.
1 The present study lso examined the reverse causality between debt ratio/capital structure and performance
of the firms. Initially, Durbin-Wu-Hausman specification test is performed to find out the potential Endogeneity and the outcomes indicate that there is no issue of endogeneity between the firm performance and capital structure and both the variables are exogenously determined. After that, firm performance is regressed as a antecedent of capital structure and the results showed that firm performance is not a significant factor in determining the capital structure of the firms.
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Table 4.5: Capital Structure and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Tobin’s Q
Variables
Fixed Random OLS Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 2.12 2.76 2.50 0.94 1.39 1.13 -0.00 0.58 0.46 (12.71)*** (15.67)*** (14.54)*** (9.86)*** (14.66)*** (11.65)*** (-2.90)*** (11.00)*** (8.32)***
TDR 0.62 0.61 0.45
(18.42)*** (19.13)*** (15.30) LDR 0.16 0.14 0.21
(4.34)*** (3.98)*** (6.14)*** SDR 0.39 0.42 0.30 (11.96)*** (13.41)*** (9.55)***
Size 0.09 0.09 0.09 0.03 0.03 0.02 0.02 0.02 0.02 (6.96)*** (7.05)*** (7.50)** (4.13)*** (4.23)*** (3.44)*** (5.65)*** (4.77)*** (5.81)***
Growth 0.03 0.01 0.04 0.02 -0.04 0.03 0.01 0.01 0.04 (0.49) (0.76) (0.32) (0.27) (-0.61) (0.53) (0.48) (1.12) (0.46)
Liquidity 0.02 0.01 0.02 0.02 0.02 0.01 0.02 0.01 0.02 (4.00)*** (-2.37)*** (3.47)*** (4.11)*** (3.74)*** (2.92)*** (4.37)*** (4.40)*** (0.59) Risk 0.57 0.03 0.03 0.06 0.04 0.03 0.07 0.08 0.06 (6.20)*** (2.98)*** (2.71)*** (6.70)*** (4.09)*** (3.41)*** (10.28)*** (11.12)*** (7.63)*** Age 0.01 0.01 0.01 0.02 0.01 0.01 0.03 0.01 0.02 (6.95)*** (8.20)*** (5.99)*** (5.01)*** (5.65)*** (5.47)*** (2.90)*** (3.27)*** (4.09)***
F value 118.23 55.67 79.93 471.73 97.35 273.40 65.69 30.75 40.26 Adjusted R2 0.05 0.02 0.04 0.09 0.02 0.04 0.15 0.08 0.07 Hausman test 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 - - - Note: “t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. Model 1 includes the explanatory variable total debt ratio (TDR) whereas model 2 and model 3 contain the explanatory variables long term debt ratio (LDR) and short term debt ratio (SDR) respectively. The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models”.
94
4.3.2 Capital Structure, Business Strategy and Firm Performance:
The existing literature (Kester, 1986; Bhandari, 1988; Wald, 1999; Hodgson &
Stevenson-Clarke, 2000; Hadlock & James; 2002, Abor, 2005; Dimitrov & Jain, 2008;
Arcas and Bachiller, 2008; Cai & Zhang, 2011; Coricelli et al., 2012; Muradoglu &
Sivaprasad, 2012; Yazdanfar and Ohman, 2015; Abata and Migiro, 2016) showed that
most of the researchers have emphasized only on exploring the direct effects of capital
structure on the firm performance. However, O’Brien (2003) and Jermias (2008) argued
that the relationship between capital structure and firm performance is also contingent on
the business strategy pursued by the firms. Consequently, the present study also explores
the moderating role of business strategy between the established relationship of capital
structure and firm performance.
Consistent with the Nandakumar et al., (2011), the current study splits the
strategic choices of the firms into four categories i.e. cost leadership strategy, product
differentiation strategy, hybrid or combine strategy and unclear strategic situation of the
firms (stuck in the middle). The proxy of cost leadership strategy is “asset utilization
efficiency” which is computed through total sales divided by total assets. The “premium
price capability” is utilized to measure the product differentiation strategy which is
computed through selling and administrative expenses divided by total sales.
Subsequently, the current study used the following procedure to identify the form of
business strategy adopted by non-financial firms of Pakistan for running their business
operations:
1. If the value of Cost Leadership > Median and Product Differentiation < Median then the
firm is pursuing the cost leadership strategy.
2. If the value of Cost Leadership < Median and Product Differentiation > Median then the
firm is following the product differentiation strategy.
3. If the value of Cost Leadership > Median and Product Differentiation > Median then the
firm is pursuing the combine strategy.
4. The value of Cost Leadership < Median and Product Differentiation < Median then the
firm fails to adopt any single strategy.
The results of the above analysis reveals that 104 Pakistan’s non-financial firms
from the selected sample follow the cost leadership strategy while 93 firms adopt product
95
differentiation strategy during the sample period to differentiate themselves in terms of
featured products. In addition, statistics show that 73 non-financial firms are stuck in the
middle due to not pursuing any single strategy (poor strategic situation). Finally, out of
333 listed non-financial firms, 63 firms choose both cost leadership strategy and product
differentiation strategy simultaneously for running their business activities. After the
identification of number of firms along with their chosen strategy, present study employs
the regression analysis to explore the extent to which the relationship between capital
structure and firm performance depends upon the nature of business strategy. Consistent
with the stage one, fixed effect, random effect and OLS estimation techniques are applied
to capture the findings. The results are exhibited from table 4.6 to table 4.17.
Capital Structure, Cost Leadership Strategy and Firm Performance:
Table 4.6 presents the estimated results of regression analysis using total debt
ratio (TDR), long term debt ratio (LDR) and short term debt ratio (SDR) as an
independent variables, cost leadership strategy as moderating variable and Return on
Assets (ROA) as a dependent variable. Model 1 reports the results of the moderating role
of cost leadership strategy (STRA_CL) between Total Debt Ratio (TDR) and firm
performance (ROA). In addition, model 2 indicates the extent to which leverage-
performance relationship depends upon the long term debt ratio. Furthermore, model 3
shows the outcomes of moderating effects of cost leadership strategy between short term
debt ratio and firm performance. Furthermore, each model is estimated with control
variables i.e. size, growth, liquidity, age and risk. Table 4.6 illustrates statistically
significant P-value of Hausman test, which confirms the suitability of the fixed effect
models.
The results of model 1, 2 and 3 reports that total, long term and short term debt
ratio have a statistically significant and negative relationship with the accounting measure
of performance i.e. ROA. These results are consistent in terms of direction of relationship
and significance level with the models when regressed without the interaction term. The
positive coefficient of the cost leadership strategy (STRA_CL) in all 3 models specifies
the positive and significant relationship between cost leadership strategy and firm’s
performance.
96
O’Regan and Ghobadian (2005) stated that cost leadership strategy is a lower cost
competitive strategy that entails the aggressive establishment of efficient scale amenities,
tight control of overhead expenditures. In addition, cost leadership strategy is the
capability of a business unit or firm to produce, design and sale similar products and
services more efficiently than its counterparts. As a result, lower costs not only permit to
continue to earn profits but also attain market shares of the business (Caves &
Ghemawat, 1992; Campbell et al., 2002; Balsam et al., 2011). The significance of results
at 5% and 10% levels may be due to the hallmark of cost leadership strategy is a “high-
volume low-margin approach” (Peng et al., 2008).
The results of model 1 of Table 4.6 also illustrates that the interaction term
(moderating variable) between total debt ratio and cost leadership strategy is significantly
positive (β= 0.04) specifying that the estimated relationship between capital structure and
the firm’s performance is positively and significantly moderated by the cost leadership
strategy. This relationship shows that a unit change in the moderating variable
significantly enhances the 0.04 units in return in assets of the firms. Though, the beta
value is slightly low but the relationship is statistically significant. It also implies that
when cost leadership firms try to maintain high debt ratio then their performance (ROA)
significantly enhances.
The explanatory power of the model is around 17% and significant F value
exhibits the fitness of the model. Model 2 and 3 also report that both the interaction terms
i.e. LDR*STRA_CL and SDR*STRA_CL are significantly positive, implies that firms
maintain either long term debt ratio or short term debt ratio while attempting to pursue a
cost leadership strategy, incurs a significant performance (ROA) reward. The values of
adjusted R square of model 2 and model 3 are 7% and 13% respectively whereas F
statistics ensure the fitness of the models. Variance Inflation Factor (VIF) is calculated
for each variable of all the models and the values ensure the absence of multicollinearity
(VIF < 5) whereas the value of DW test also falls within acceptable range. Consequently,
the reported results indicate that high debt financing enhances the performance (ROA) of
the Pakistan’s firms pursuing cost leadership strategy. It is argued that the firm’s financial
structure influences the pricing decisions, production level, and certain strategic output of
the firms. The firm which adopts cost leadership strategy needs to be more cost efficient
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than its counterparts, requires tight control of overhead expenditures, avoids marginal
customer accounts and minimizes cost in different areas like sales force, services, R&D,
advertisement. In this scenario, debt financing is considered a useful tool to accomplish
these objectives of the firm as the firm managers become more efficient due to the
monitoring role of debt providers (Jensen & Meckling 1976; Jordan et al.,1998; Jermias,
2008). In addition, debt financing also puts pressure on them to avoid unnecessary
overhead expenditures and produce cost effective products.
Moreover, restrictive covenants attached with the debt also reduce the free cash
flow available for investments due to the periodic principal and interest payments; hence,
debt financing limits the opportunistic behavior of managers (Jensen, 1986) and refrains
them from the investments in the risky projects. As a conflict of interest between the
shareholders and managers (agency problem) reduces the firm’s efficiency and enhances
the overhead expenditures, so debt financing is also helpful to align the interest of
managers and shareholders. Furthermore, more debt ratio in the firm’s capital structure
also reduces the expense in terms of tax shield benefits. Consequently, cost leaders may
enhance their capability and produce more financial benefits through the utilization of
debt financing, consistent with the results of Jermias (2008).
Among the control variables, size, growth, liquidity and risk positively and
significantly enhance the ROA of selected firms whereas a positive but insignificant
relationship has been found between firm’s age and ROA. Table 4.6 also shows that all
the interaction terms (between capital structure and cost leadership strategy) also have a
significant and positive relationship with ROA in all OLS models, validating the outcome
of fixed effect models. Table 4.7 presents the estimated results of fixed effect, random
effect and OLS regression models when Return on Equity (ROE) is selected as a
dependent variable.
The results show that cost leadership strategy positively influences the ROE in
both fixed and random effect models, however; significant P-value of Hausman test
ensures the suitability of fixed effect model. Consistent with the results of Table 4.6, cost
leadership strategy positively and significantly moderates the relationship between all
proxies of capital structure i.e. total debt ratio, long term debt ratio and short term debt
ratio and ROE. This implies that debt financing also enhances ROE of the firms pursuing
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cost leadership strategy. The explanatory powers of all three models are 9%, 5% and 8%
respectively while significant F-values ensure the fitness of all models.
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Table 4.6: Capital Structure, Cost Leadership Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Assets (ROA) Variables Fixed Random OLS
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Constant 0.21 0.06 0.13 -0.05 -0.17 -0.11 -0.08 -0.19 -0.14 (3.04)*** (0.89)* (1.89)** (-2.10)** (-6.60)*** (-4.07)*** (-4.79)*** (11.90)*** (-8.16)*** TDR -0.15 -0.16 -0.17
(-9.38)*** (-13.05)*** (17.38)*** STRA_CL 0.02 0.01 0.02
(0.39)** (0.43)* (0.56)** TDR*STRA_CL 0.04 0.01 0.03
(1.55)*** (0.54)* (0.70)*** LDR -0.04 -0.05 -0.07
(-2.46)** (-3.60)*** (-5.95)*** STRA_CL 0.06 0.02 0.03
(1.34)* (0.39)* (0.51)* LDR*STRA_CL 0.02 0.02 0.05
(0.93)** (0.77)* (0.01)** SDR -0.09 -0.11 -0.12 (-6.12)*** (-8.43)*** (11.27)*** STRA_CL 0.04 0.03 0.04 (0.85)* (0.40)** (0.88)* SDR*STRA_CL 0.05 0.02 0.05 (0.58)** (0.34)** (0.59)*** Size 0.03 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 (2.04)** (1.86)** (1.81)** (5.77)*** (5.65)*** (4.91)*** (11.58)*** (11.34)*** (10.38)*** Liquidity 0.02 0.01 0.03 0.01 0.02 0.01 0.02 0.02 0.01 (3.56)*** (8.04)*** (3.74)*** (3.19)*** (11.02)*** (5.21)*** (2.97)*** (14.98)*** (7.38)***
Growth 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 (6.50)*** (6.24)*** (5.80)*** (6.24)*** (6.32)*** (5.35)*** (5.02)*** (5.34)*** (3.53)*** Age 0.03 0.01 0.02 0.02 0.01 0.01 0.02 0.01 0.03
(1.48)* (2.76) (1.37) (1.38) (0.76) (0.81) (2.43)** (2.09)** (1.56)
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Risk 0.04 0.04 0.04 0.04 0.04 0.05 0.04 0.04 0.04 (9.77)*** (11.62)*** (12.09)** (14.74)*** (15.08)*** (16.98)*** (19.05)*** (16.51)*** (21.21)*** F value 64.67 44.80 51.28 819.11 543.59 637.24 160.89 108.93 125.98 Adjusted R2 0.17 0.07 0.13 0.36 0.27 0.30 0.36 0.27 0.30 Hausman test 0.0017 0.0021 0.0024 0.0017 0.0021 0.0024 - - - Note: “t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_CL is a cost leadership strategy of the firms and is measured through dummy variable i.e. code 1 is assigned if the firms are pursuing cost leadership strategy otherwise 0. The moderating variable TDR*STRA_CL is the interaction term between total debt ratio and cost leadership strategy while LDR*STRA_CL is the interaction term between long term debt ratio and cost leadership strategy. The moderating variable SDR*STRA_CL is also the interaction term between short term debt ratio and cost leadership strategy of the firms. Model 1 includes total debt ratio (TDR) and cost leadership strategy of the firms (STRA_CL) as an explanatory variables and TDR*STRA_CL is a moderating variable whereas long term debt ratio (LDR), cost leadership strategy (STRA_CL) and the moderating variable LDR* STRA_CL are included in the model 2. Model 3 contains the SDR*STRA_CL as a moderating variable along with the short term debt ratio (SDR) and Cost leadership strategy (STRA_CL). The values of VIF of each variable are less than 4; ensure the absence of Multicollinearity in the regression models”.
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Table 4.7: Capital Structure, Cost Leadership Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Equity (ROE)
Variables Fixed Random OLS Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Constant 0.30 0.16 0.30 -0.28 -0.42 -0.32 -0.34 -0.46 -0.39 (1.79)*** (0.94)** (1.78)** (-4.60)*** (-7.28)*** (-5.23)*** (-8.72)*** (12.55)*** (-10.07)*** TDR -0.21 -0.20 -0.19
(-5.01)*** (-6.44)*** (-7.80)*** STRA_CL 0.05 0.05 0.03
(0.45)* (0.32) (1.10)* TDR*STRA_CL 0.11 0.07 0.06
(1.61)** (0.23)* (1.57)*** LDR -0.04 -0.05 -0.10
(-0.06)*** (-1.39)** (-3.38)*** STRA_CL 0.15 0.03 0.03
(1.46) (0.70)* (0.78)* LDR*STRA_CL 0.06 0.05 0.09
(0.02)* (0.73)* (1.58)* SDR -0.19 -0.16 -0.13 (-4.82)*** (-4.98)*** (-4.98)*** STRA_CL 0.08 0.02 0.03 (0.74)** (0.69)* (0.09)**
SDR*STRA_CL 0.07 0.04 0.02 (1.20)** (0.83)** (0.38)*** Size 0.01 0.02 0.02 0.02 0.03 0.02 0.03 0.03 0.03 (0.95) (1.34) (1.24) (6.31)*** (6.34)*** (5.71)*** (11.97)*** (12.01)*** (11.22)*** Liquidity 0.02 0.02 0.01 0.01 0.02 0.01 0.01 0.02 0.01 (2.77)*** (5.09)*** (2.12)** (2.35)** (6.62)*** (2.92)*** (2.50)** (8.17)*** (4.41)*** Growth 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02 (6.05)*** (5.70)*** (5.52)*** (5.82)*** (5.56)*** (5.20)*** (4.25)*** (4.30)*** (3.55)*** Age 0.03 0.01 0.02 0.01 0.01 0.01 0.02 0.01 0.03 (0.06) (1.24) (0.15) (0.87) (0.61) (0.63) (1.17) (1.14) (0.85)
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Risk 0.08 0.10 0.09 0.09 0.09 0.09 0.08 0.08 0.09 (8.81)*** (10.62)*** (10.31)*** (13.62)*** (14.27)*** (15.00)*** (17.51)*** (16.29)*** (18.34)*** F value 36.69 29.47 35.87 445.14 379.22 422.02 81.64 73.19 75.93 Adjusted R2 0.09 0.05 0.08 0.23 0.21 0.22 0.23 0.21 0.22 Hausman test 0.0002 0.0039 0.0002 0.0002 0.0039 0.0002 - - - Note: “t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_CL is a cost leadership strategy of the firms and is measured through dummy variable i.e. code 1 is assigned if the firms are pursuing cost leadership strategy otherwise 0. The moderating variable TDR*STRA_CL is the interaction term between total debt ratio and cost leadership strategy while LDR*STRA_CL is the interaction term between long term debt ratio and cost leadership strategy. The moderating variable SDR*STRA_CL is also the interaction term between short term debt ratio and cost leadership strategy of the firms. Model 1 includes total debt ratio (TDR) and cost leadership strategy of the firms (STRA_CL) as an explanatory variables and TDR*STRA_CL is a moderating variable whereas long term debt ratio (LDR), cost leadership strategy (STRA_CL) and the moderating variable LDR* STRA_CL are included in the model 2. Model 3 contains the SDR*STRA_CL as a moderating variable along with the short term debt ratio (SDR) and Cost leadership strategy (STRA_CL). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models”.
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Table 4.8: Capital Structure, Cost Leadership Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Tobin’s Q
Variables
Fixed Random OLS Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 1.97 2.59 0.40 1.02 1.44 1.19 0.35 0.67 0.57
(11.35)*** (14.08)*** (13.16)*** (10.72)*** (15.30)*** (12.40)*** (6.47)*** (12.57)*** (10.43)*** TDR 0.61 0.58 0.43
(15.44)*** (15.68)*** (13.11)***
STRA_CL 0.04 0.17 0.21
(0.06) (3.85)*** (6.16) TDR*STRA_CL 0.06 0.08 0.18
(0.30) (1.32) (3.44) LDR 0.14 0.14 0.29
(3.13)*** (3.34)*** (7.13)***
STRA_CL 0.05 0.10 0.07
(0.10) (3.72) (3.82) LDR*STRA_CL 0.06 0.07 0.16
(0.87) (0.19) (2.17) SDR 0.40 0.39 0.21
(10.44)*** (10.80)*** (6.13)*** STRA_CL 0.05 0.14 0.25
(0.34) (3.98) (8.41)*** SDR*STRA_CL 0.06 0.07 0.36
(0.92) (1.23) (5.82)
Size 0.06 0.07 0.07 0.03 0.03 0.02 0.02 0.01 0.01
(4.72)*** (4.81)*** (5.27)*** (4.28)*** (4.40)*** (3.61)*** (4.83)*** (3.16)*** (4.49) Liquidity 0.02 -0.01 0.02 0.02 0.02 0.01 0.02 0.02 0.03
(3.91)*** (-2.45)*** (3.39)*** (4.18)*** (3.77)*** (2.87)*** (4.55)*** (4.99)*** (0.27) Growth 0.01 -0.01 0.01 0.02 0.03 0.02 0.01 0.01 0.02
(0.98) (-1.22) (0.16) (0.53) (0.74) (0.33) (0.98) (1.61) (0.02) Age 0.02 0.02 0.02 0.01 0.01 0.02 0.03 0.02 0.03
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(8.78)*** (9.80)*** (7.88)*** (5.86)*** (6.17)*** (5.96)*** (4.20)*** (4.22)*** (4.57)*** Risk 0.06 0.04 0.03 0.07 0.04 0.04 0.09 0.11 0.08
(6.66)*** (3.60)*** (3.24)*** (7.75)*** (4.95)*** (4.43)*** (13.30)*** (13.58)*** (10.60)*** F value 92.61 46.01 63.77 498.46 113.05 290.57 63.43 32.81 42.81 Adjusted R2 0.05 0.03 0.02 0.12 0.02 0.06 0.19 0.11 0.13 Hausman Test 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 - - - Note: “t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_CL is a cost leadership strategy of the firms and is measured through dummy variable i.e. code 1 is assigned if the firms are pursuing cost leadership strategy otherwise 0. The moderating variable TDR*STRA_CL is the interaction term between total debt ratio and cost leadership strategy while LDR*STRA_CL is the interaction term between long term debt ratio and cost leadership strategy. The moderating variable SDR*STRA_CL is also the interaction term between short term debt ratio and cost leadership strategy of the firms. Model 1 includes total debt ratio (TDR) and cost leadership strategy of the firms (STRA_CL) as an explanatory variables and TDR*STRA_CL is a moderating variable whereas long term debt ratio (LDR), cost leadership strategy (STRA_CL) and the moderating variable LDR* STRA_CL are included in the model 2. Model 3 contains the SDR*STRA_CL as a moderating variable along with the short term debt ratio (SDR) and Cost leadership strategy (STRA_CL). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models”.
105
Moreover, the outcomes of OLS models also validate that cost leadership strategy
positively moderates the leverage-performance relationship. Consequently, overall results
entail that debt financing significantly improves the accounting performance of the
Pakistani cost leadership firms.
Consistent with the stage one analysis, the current section also employs Q ratio to
explore the moderating role of cost leadership strategy between capital structure and
marker performance of the firms and the results are presented in Table 4.8. The
statistically significant P-values of Hausman test confirm the validity of fixed effect
models. Table 4.8 shows that market performance of the firms significantly enhances
when assets are financed through debt capital. The results also depict that the cost
leadership strategy has a positive but statistically insignificant relationship with the
market performance of the firms. In addition, the interaction terms between cost
leadership strategy and all measures of capital structure are also positive but statistically
insignificant.
This implies that in the presence of cost leadership strategy, high debt capital does
not significantly enhance the market value of the firms. The explanatory powers of all
three fixed effect models are relatively low as compare to the previous models; however,
the significant F-statistics exhibit the overall fitness of the models. Moreover, the
outcomes of the OLS regression models validate the outcomes of fixed effect models in
terms of relationship directions and significance level.
Capital Structure, Product Differentiation Strategy and Firms’ Performance
The second category which the firms adopt to gain competitive advantage in the
market is a product differentiation strategy. This strategy normally attempts to establish
new products with distinguished physical characteristics and market opportunities
through employing the latest scientific technology. As an outcome, the customers usually
are willing to pay above than average prices due to the perceived significant differences
in the features of the products. In other words, this strategy comprises the creation of the
products and services with the unique characteristics i.e. technology, quality, design,
features and brand image etc. and charge premium prices than its counterparts. O’Brien
(2003) argued that the relationship between capital structure and firm performance also
depends upon the product differentiation strategy pursued by the firms. In the same
106
manner, the present study also examines the moderating role of product differentiation
strategy between capital structure and firm performance. The results of the specified
moderating relationship are presented from table 4.9 to table 4.11.
Table 4.9 presents the estimated outcomes of regression analysis using fixed
effect, random effect and OLS estimation techniques. Total debt ratio (TDR), long term
debt ratio (LDR) and short term debt ratio (SDR) are used as independent variables
whereas product differentiation strategy and Return on Assets (ROA) are selected as
moderating and dependent variables respectively. Table 4.9 illustrates the statistically
significant P-value of Hausman test, which confirms the validity of the fixed effect
models. The values of adjusted R square of fixed effect models are around 20%, 9% and
16% respectively while statistically significant F-values ensure the fitness of the models.
The results specify that all measurements of capital structure i.e. TDR, LDR and SDR
negatively and significantly affect the ROA (firm performance).
107
Table 4.9: Capital Structure, Product Differentiation Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Assets (ROA)
Variables
Fixed Random OLS
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 0.20 0.04 0.11 -0.05 -0.18 -0.11 -0.08 -0.20 -0.14
(2.97)*** (0.60)** (1.72)** (-2.10)** (-6.97)*** (-4.26) (-4.87)*** (12.32)*** (-8.58)*** TDR -0.17 -0.17 -0.16
(-10.84)** (-13.44)*** (-16.41)** STRA_PD 0.05 0.08 0.12
(0.26)** (0.09)* (0.38)** TDR*STRA_PD -0.03 -0.05 -0.06
(-0.44)*** (-0.00)** (-0.62)*** LDR 0.04 -0.05 -0.07
(-2.61)*** (-3.63)*** (-5.39)***
STRA_PD 0.05 0.07 0.09
(0.54)* (0.82) (0.75)* LDR*STRA_ PD -0.02 -0.11 -0.07
(-0.77)** (-0.08) (-0.87)* SDR -0.10 -0.10 -0.12
(-7.16)*** (-8.85)*** (-10.85)** STRA_PD 0.02 0.04 0.09
(0.65)** (0.22)** (0.41)** SDR*STRA_ PD -0.17 -0.08 -0.12
(-0.44)** (-0.14)** (-0.14)*** Size 0.02 0.01 0.01 0.01 0.01 0.01 0.03 0.01 0.01
(1.98)** (1.82)*** (1.76)*** (5.85)*** (5.87)*** (5.00)*** (11.59)*** (11.69)*** (10.63)*** Liquidity 0.01 0.02 0.01 0.02 0.02 0.01 0.01 0.02 0.01
(3.51)*** (8.10)*** (3.72)*** (3.18)*** (11.11)*** (5.22)*** (2.96)*** (15.08)*** (7.41)*** Growth 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
(6.44)*** (6.41)*** (5.78)*** (6.24)*** (6.35)*** (5.39)*** (5.04)*** (5.37)*** (3.61)*** Age 0.02 0.01 0.01 0.01 0.01 0.01 0.03 0.01 0.02
108
(1.50) (2.73)* (1.31) (1.33) (0.71) (0.69) (2.40)* (2.02)** (1.34) Risk 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
(10.07)*** (11.64)*** (12.22)*** (14.70)*** (14.86)*** (16.75)*** (18.58)*** (16.12)*** (20.42)*** F value 73.36 50.81 58.43 818.56 542.67 636.39 160.91 108.96 125.85 Adjusted R2 0.20 0.09 0.16 0.36 0.27 0.30 0.36 0.27 0.30 Hausman test 0.0037 0.0020 0.0027 0.0033 0.0020 0.0008 - - - Note: “t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_PD is a product differentiation strategy of the firms and is measured through dummy variable i.e. code 1 is assigned if the firms are pursuing product differentiation strategy otherwise 0. The moderating variable TDR*STRA_PD is the interaction term between total debt ratio and product differentiation strategy while LDR*STRA_PD is the interaction term between long term debt ratio and product differentiation strategy. The moderating variable SDR*STRA_PD is also the interaction term between short term debt ratio and product differentiation strategy of the firms. Model 1 includes total debt ratio (TDR) and product differentiation strategy of the firms (STRA_PD) as an explanatory variables and TDR*STRA_PD is a moderating variable whereas long term debt ratio (LDR), product differentiation strategy (STRA_PD) and the moderating variable LDR* STRA_PD are included in the model 2. Model 3 contains the SDR*STRA_PD as a moderating variable along with the short term debt ratio (SDR) and Product differentiation strategy (STRA_PD). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models”.
109
Table 4.10: Capital Structure, Product Differentiation Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Equity (ROE)
Variables
Fixed Random OLS Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 0.27 0.09 0.28 -0.28 -0.42 -0.32 -0.35 -0.45 -0.40
(1.69)** (0.61)** (1.71)*** (-4.69)*** (-7.35)** (-5.34)*** (-8.84)*** (12.34)*** (10.39)*** TDR -0.24 -0.19 -0.16
(6.12)*** (-6.40)*** (-6.45)*** STRA_PD 0.04 0.07 0.06
(0.52)** (0.37)* (0.77)**
TDR*STRA_PD -0.02 -0.08 -0.05
(-0.32)** (-0.83)* (-1.36)** LDR -0.08 -0.09 -0.09
(-0.05)** (-1.29)* (-2.88)***
STRA_PD 0.05 0.07 -0.07
(0.23)* (0.64)* (-1.05)** LDR*STRA_ PD -0.03 -0.03 0.04
(-0.33)** (-0.51)* (0.68)* SDR -0.15 -0.15 -0.10
(-5.04)*** (-5.04)*** (-3.93)*** STRA_PD 0.06 0.06 0.05
(1.56)** (0.93)** (1.15)**
SDR*STRA_ PD -0.09 -0.09 -0.09
(-1.35)** (-1.69)** (-1.99)*** Size 0.01 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03
(0.92) (1.27) (1.26) (6.39)*** (6.35) (5.68)*** (11.88)*** (11.88)*** (11.19)***
Liquidity 0.01 0.03 0.01 0.01 0.02 0.01 0.03 0.02 0.01
(2.67)*** (5.09)*** (1.84)*** (2.32)*** (6.62)*** (2.75)*** (2.44)*** (8.06)*** (4.31)***
Growth 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02
(6.02)*** (5.69)*** (5.53)*** (5.87)*** (5.59)*** (5.25)*** (4.30)*** (4.28)*** (3.61)*** Age 0.01 0.01 0.01 0.02 0.01 0.01 0.03 0.02 0.02
110
(0.09) (1.17) (0.16) (0.78) (0.47) (0.44) (1.13) (1.01) (0.69) Risk 0.08 0.09 0.09 0.09 0.09 0.09 0.08 0.08 0.09
(9.12)*** (10.61)*** (10.58)*** (13.50)*** (13.89)*** (14.94)*** (16.94)*** (15.83)*** (17.82)***
F value 41.30 33.37 40.88 446.70 378.96 426.05 81.60 72.91 76.72 Adjusted R2 0.14 0.09 0.12 0.32 0.21 0.22 0.23 0.21 0.22 Hausman test 0.0003 0.0047 0.0002 0.0035 0.0047 0.0002 - - - Note: “t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_PD is a product differentiation strategy of the firms and is measured through dummy variable i.e. code 1 is assigned if the firms are pursuing product differentiation strategy otherwise 0. The moderating variable TDR*STRA_PD is the interaction term between total debt ratio and product differentiation strategy while LDR*STRA_PD is the interaction term between long term debt ratio and product differentiation strategy. The moderating variable SDR*STRA_PD is also the interaction term between short term debt ratio and product differentiation strategy of the firms. Model 1 includes total debt ratio (TDR) and product differentiation strategy of the firms (STRA_PD) as an explanatory variables and TDR*STRA_PD is a moderating variable whereas long term debt ratio (LDR), product differentiation strategy (STRA_PD) and the moderating variable LDR* STRA_PD are included in the model 2. Model 3 contains the SDR*STRA_PD as a moderating variable along with the short term debt ratio (SDR) and Product differentiation strategy (STRA_PD). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models”.
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Table 4.11: Capital Structure, Product Differentiation Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Tobin’s Q
Variables Fixed Random OLS Model 1 Model2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 1.98 2.57 2.35 0.97 1.43 1.16 0.30 0.63 0.46
(11.58)*** (14.60)*** (13.63)*** (10.12)*** (15.18)*** (11.97)*** (5.43)*** (11.79)*** (8.40)***
TDR 0.62 0.62 0.42
(16.58)*** (17.16)*** (12.68)*** STRA_PD 0.03 0.09 0.07
(0.78)** (2.03)* (0.50)** TDR*STRA_PD -0.07 -0.04 -0.11
(-1.04)* (-0.65) (-2.17)** LDR 0.11 0.07 0.11
(2.68)*** (1.60)*** (2.77)** STRA_PD 0.07 0.08 0.02
(1.34)* (0.02)** (1.00)**
LDR*STRA_ PD -0.16 0.26 -0.33
(-2.06)* (3.43)* (-4.67)*** SDR 0.41 0.46 0.33
(11.61)*** (13.39)*** (9.22)*** STRA_PD 0.08 0.14 0.09
(1.43)* (3.86)** (3.43)* SDR*STRA_ PD -0.14 -0.18 -0.09
(-2.12)** (-2.82)* (-1.62)** Size -0.06 -0.07 -0.07 -0.03 -0.03 -0.03 -0.02 -0.01 -0.02
(-4.79)** (-4.70)*** (-5.31)*** (-4.45)* (-4.55) (-3.97) (-5.03)** (-3.87)** (-4.98)***
Liquidity 0.02 0.01 0.03 0.02 0.02 0.01 0.02 0.02 0.00
(3.87)*** (2.57)*** (3.10)*** (4.18)*** (3.79)** (2.75) (4.37) (4.70)*** (0.47)*
Growth 0.01 0.01 0.02 0.01 0.02 0.01 -0.01 0.01 0.02
(0.97)*** (1.23)*** (0.17)** (0.38)*** (0.62)*** (0.46) (-0.68) (1.24)*** (0.33)* Age 0.02 0.02 0.02 0.01 0.02 0.03 0.02 0.01 0.01
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(8.70)* (9.88) (7.83)* (5.65) (5.89) (3.98) (3.25)** (3.35)* (4.03)**
Risk 0.06 0.04 0.03 0.06 0.04 0.03 0.08 0.09 0.07
(6.85)*** (3.58)*** (3.45)*** (7.17)** (4.33)*** (3.98)*** (11.15)*** (11.54)** (8.64)***
F value 106.09 53.20 73.54 480.01 114.84 289.76 52.42 27.85 32.17 Adjusted R2 0.08 0.03 0.05 0.09 0.02 0.04 0.16 0.09 0.10 Hausman test 0.0000 0.0000 0.0306 0.0000 0.0000 0.0306 - - - Note: t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_PD is a product differentiation strategy of the firms and is measured through dummy variable i.e. code 1 is assigned if the firms are pursuing product differentiation strategy otherwise 0. The moderating variable TDR*STRA_PD is the interaction term between total debt ratio and product differentiation strategy while LDR*STRA_PD is the interaction term between long term debt ratio and product differentiation strategy. The moderating variable SDR*STRA_PD is also the interaction term between short term debt ratio and product differentiation strategy of the firms. Model 1 includes total debt ratio (TDR) and product differentiation strategy of the firms (STRA_PD) as an explanatory variables and TDR*STRA_PD is a moderating variable whereas long term debt ratio (LDR), product differentiation strategy (STRA_PD) and the moderating variable LDR* STRA_PD are included in the model 2. Model 3 contains the SDR*STRA_PD as a moderating variable along with the short term debt ratio (SDR) and Product differentiation strategy (STRA_PD). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models
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In addition, the results indicate that the relationship between product
differentiation strategy and ROA is significantly positive in all three models. Many
researchers (see for example, White, 1986; O’Farrell et al., 1992; Cronshaw et al., 1994;
Koo et al., 2004; Kim et al., 2004; Torgovicky et al., 2005; O’Regan and Ghobadian,
2005; Balsam et al., 2011) also showed a positive relationship between product
differentiation strategy and firm performance. Chaganti et al. (1989) argued that the
firm’s differentiation strategy focuses on R&D activities, distinguished physical
characteristics of the products, brand image and advertising intensity. Consequently, they
charge premium prices and earn higher profit margins. Porter (1980) and Caves and
Ghemawat (1992) also claimed that the product differentiation strategy of the firms is
more likely to produce higher return on assets or profits than the cost leader firms as a
product differentiation creates the better entry barrier.
The results of table 4.9 also illustrate that all the coefficients of interaction terms
between product differentiation strategy and capital structure (moderating variable) are
negative and statistically significant (β= -0.03; β= -0.02; β= -0.17). This implies that the
product differentiation strategy negatively and significantly moderates the relationship
between capital structure and firms performance. The return on assets (performance)
significantly declines by 0.03 units with the increase in the one unit of the moderating
variable (TDR*STRA_PD). In addition, 0.02 and 0.17 units decline in ROA when one
unit is enhanced in LDR*STRA_PD and SDR*STRA_PD.
More precisely, when Pakistan’s firms try to maintain high debt ratio while
attempting to pursue a strategy based product differentiation, incur a significant
performance penalty. As product differentiation strategy comprises the creation of the
products and services with unique characteristics (i.e. technology, quality, design,
features and brand image etc.), spend heavily in R&D activities and charge premium
prices than its counterparts (Cross, 1999; Hyatt, 2001). Therefore, more debt financing
along with its covenants is probable to impede innovation and creativity of the firms
which is required to achieve the competitive advantage as product differentiators
(Simerly & Li, 2000). Furthermore, asset financing through debt is also considered as an
obstruction for the product differentiators because it also creates conflict of interests
between the creditors and firm’s managers.
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Moreover, product differentiation firms usually face high uncertainty due to the
involvement in high-risk investment activities. As a result, these firms are unable to
employ high debt capital due to the restrictive covenants (Baginski & Wahlen, 2003).
These results are also consistent with the seminal research work of O’Brien (2003) and
Jermias (2008) which stated that debt financing is harmful for the financial health of the
product differentiators. Among the control variables, size, growth, liquidity and risk have
positive and significant relationship with ROA. Moreover, the results of OLS regression
models also show that the coefficients of all three moderating variables are significantly
negative, validate the results of fixed effect models.
Table 4.10 presents the results using Return on Equity (ROE) as dependent
variable. The results of the table also show that product differentiation strategy negatively
and significantly moderates the relationship between capital structure and firm
performance (ROE). The return on equity (performance) significantly declines by 0.02
units with the increase in the one unit of the moderating variable (TDR*STRA_PD). In
addition, 0.03 and 0.09 units decline in ROE when one unit is enhanced in
LDR*STRA_PD and SDR*STRA_PD respectively. The relationship directions and
significance level of all the rest of results are also approximately consistent with the
outcome of table 4.9. It is argued that product differentiation strategy is also a valuable
strategy to enhance the market performance of the firms. However, when firms try to
maintain high debt capital in the presence of the product differentiation strategy, this
incurs a significant market performance penalty as presented in the results of table 4.11.
Overall the results imply that asset financing through debt significantly declines
both accounting and market measures of performance of the Pakistan’s firms pursuing
product differentiation strategy.
Capital Structure, Hybrid Strategy and Firms Performance
Hill (1988) stated that a large range of business firms normally adopt a single
strategy (Cost Leadership Strategy or Product Differentiation strategy) to successfully run
their business operations. However, numerous firms also pursue both strategies
simultaneously (hybrid strategy) to gain more monetary benefits along with the
competitive advantage in the product market place. Merrilees (2001) argued that the
firms pursuing both the strategies simultaneously exhibit the highest level of financial
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performance. Kim et al., (2004) also stated that any incompatibility between
differentiation strategy and cost leadership strategy may be considered factual in the
nineteen eighties (1980s) when environment of the business were comparatively stable.
However, due to the mass customization, rapidly altering the competitive
environments and speedy growth of network organizations demand the characteristics of
both generic strategies simultaneously for the survival of business (Goldman et al., 1995;
Anderson, 1997). In the light of the literature, the current study not only explores the
impact of hybrid strategy on the performance but also explores the moderating role of
firm’s hybrid strategy between the relationship of capital structure and firm performance.
The present study reveals that 63 non-financial firms of Pakistan have adopted both
strategies i.e. cost leadership strategy and product differentiation strategy simultaneously
for the accomplishment of their business objectives.
Table 4.12 presents the estimated results of multiple regression models using
hybrid strategy as a moderating variable and ROA as a dependent variable. The
significant p-value of Hasuman test shows the validity of fixed effect models. The results
indicate that the impact of TDR, LDR and SDR on ROA is significantly negative. In
addition, positive coefficient of hybrid strategy illustrates that ROA significantly
enhances when the firms adopt both strategies (Differentiation Strategy and Cost
Leadership Strategy) simultaneously. Various authors (Hill, 1988; Parnell, 2000; Kim et
al., 2004; Minarik, 2007) argued that these two approaches are not necessarily mutually
exclusive and many business ventures start with the differentiation strategy and integrate
low costs as they grow and develop economies of scale with the passage of time.
Hybrid strategy is feasible to attain and can be quite effective in terms of
generating high margins or profits due to premium prices and low product costs.
Chakraborty and Philip (1996) also contended that many business firms follow mixed
strategies because of the complexity of devising and implementing an effective long term
approach based solely on one of the two strategies. In addition, hybrid strategy is less
vulnerable to the risks attached with the adaptation of pure “Single” strategy (Miller,
1988; Wright et al, 1991).
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Table 4.12: Capital Structure, Hybrid Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Assets (ROA)
Variables Fixed Random OLS
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 0.19 0.11 0.12 -0.07 -0.19 -0.12 -0.10 -0.20 -0.16
(2.87)*** (0.53)*** (1.71)** (-2.81)*** (-7.29)*** (-4.73)*** (-6.22)*** (-12.84)** (-9.52)*** TDR -0.16 -0.15 -0.15
(-11.27)** (-13.57)** (-15.72)** STRA_HB 0.03 0.06 0.07
(0.40)** (3.48)*** (6.20)*** TDR*STRA_HB -0.05 -0.07 -0.10
(-0.87)** (-2.73)*** ( -5.19)** LDR -0.05 -0.05 -0.06
(-3.12)*** (-3.97)*** (-5.42)*** STRA_HB 0.06 0.05 0.04
(0.18)* (2.48)*** (4.33)*** LDR*STRA_HB -0.08 -0.09 -0.07
(-0.76)* (-1.28) (-2.24)*** SDR -0.10 -0.10 -0.10
(-7.33)*** (-8.87)*** (-10.12)** STRA_HB 0.06 0.04 0.06
(0.08)* (2.68)*** (6.31)*** SDR*STRA_ CS -0.09 -0.03 -0.10
(-0.55)** (-1.19) (-4.50)*** Size 0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
(2.02)*** (1.81)** (1.77)** (6.35)*** (6.21)*** (5.43)*** (12.47)*** (12.19)*** (11.36)***
Liquidity 0.01 0.01 0.01 0.01 0.02 0.01 0.02 0.02 0.01
(3.43)*** (8.07)*** (3.70)*** (3.17)*** (11.15)*** (5.24)*** (3.21)*** (15.13)*** (7.41)*** Growth 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
(6.46)*** (6.42)*** (5.78)*** (6.32)*** (6.43)*** (5.42)*** (5.24)*** (5.47)*** (3.82)*** Age 0.02 0.01 0.01 0.01 0.01 0.03 0.04 -0.01 -0.02
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(1.54) (2.76)* (1.33) (1.43) (0.82) (0.89) (2.40)*** (-2.24)*** (-1.51) Risk 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.04
(10.09)*** (11.62)*** (12.24)*** (14.93)*** (14.84)*** (16.88)*** (18.77)*** (16.01)*** (20.36)*** F value 64.24 44.43 51.12 846.19 552.54 659.92 169.10 112.30 135.05 Adjusted R2 0.20 0.09 0.16 0.37 0.23 0.31 0.37 0.28 0.31 Hausman test 0.0024 0.0011 0.0001 0.0024 0.0011 0.0001 - - Note: t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_HB is a combine strategy of the firms and is measured through dummy variable i.e. code 1 is assigned if the firms are pursuing combine strategy otherwise 0. The moderating variable TDR*STRA_HB is the interaction term between total debt ratio and combine strategy while LDR*STRA_HB is the interaction term between long term debt ratio and combine strategy. The moderating variable SDR*STRA_HB is also the interaction term between short term debt ratio and combine strategy of the firms. Model 1 includes total debt ratio (TDR) and combine strategy of the firms (STRA_HB) as an explanatory variables and TDR*STRA_HB is a moderating variable whereas long term debt ratio (LDR), combine strategy (STRA_HB) and the moderating variable LDR* STRA_HB are included in the model 2. Model 3 contains the SDR*STRA_HB as a moderating variable along with the short term debt ratio (SDR) and Combine strategy (STRA_HB). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models.
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Table 4.13: Capital Structure, Hybrid Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Equity (ROE)
Variables
Fixed Random OLS Model 1 Model2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 0.29 0.09 0.29 -0.28 -0.44 -0.32 -0.36 -0.47 -0.41 (1.74)*** (0.56)** (1.73)** (-4.69)*** (-7.76)*** (-5.37)*** (-9.04)*** (-12.82)** (-10.52)** TDR -0.27 0.21 -0.16
(-7.19)*** (-7.31)*** (-6.68)***
STRA_HB 0.10 0.05 0.07
(0.66)* (0.38)** (2.37)*** TDR*STRA_HB -0.16 -0.09 -0.05
(-1.73)* (-0.72)* (-1.16)** LDR -0.02 -0.02 -0.06
(-0.45)** (-0.69)* (-2.16)*** STRA_HB 0.02 0.05 0.04
(0.16)** (2.56)** (3.06)*** LDR*STRA_ CS -0.12 -0.07 -0.04
(-1.05)* (-0.75)* (-0.50)*
SDR -0.26 -0.19 0.12 (-7.21)*** (-6.54)*** (-4.78)*** STRA_HB 0.12 0.02 0.07 (0.79)* (0.32)** (2.85)**
SDR*STRA_ CS -0.22 -0.09 -0.06 (-2.68)** (-1.44)*** (-1.16)** Size 0.01 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 (0.88) (1.26) (1.18) (6.54)*** (6.66)*** (6.00)*** (12.16)*** (12.20)*** (11.65)***
Liquidity 0.01 0.02 0.01 0.01 0.02 0.02 0.01 0.02 0.01 (2.78)*** (5.07)*** (2.00)*** (2.39)*** (6.66)*** (2.82)*** (2.60)*** (8.16)*** (4.28)***
Growth 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02 (6.01)*** (5.70)*** (5.48)*** (5.86)*** (5.62)*** (5.23)*** (4.35)*** (4.32)*** (3.68)***
119
Age 0.02 0.01 0.01 0.01 0.02 0.01 0.02 0.01 0.03 (0.01) (1.19) (0.04) (1.12) (0.72) (0.88) (1.53) (1.41) (1.18)
Risk 0.08 0.09 0.09 0.08 0.09 0.09 0.08 0.08 0.09 (9.22)*** (10.61)*** (10.57)*** (13.82)*** (14.14)*** (15.17)*** (17.38)*** (16.32)*** (18.14)*** F value 36.54 29.32 36.52 455.77 389.25 437.71 83.72 75.13 79.47 Adjusted R2 0.13 0.09 0.11 0.24 0.22 0.22 0.24 0.22 0.23 Hausman test 0.0008 0.0063 0.0000 0.0008 0.0063 0.0000 - - - Note: t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_HB is a combine strategy of the firms and is measured through dummy variable i.e. code 1 is assigned if the firms are pursuing combine strategy otherwise 0. The moderating variable TDR*STRA_HB is the interaction term between total debt ratio and combine strategy while LDR*STRA_HB is the interaction term between long term debt ratio and combine strategy. The moderating variable SDR*STRA_HB is also the interaction term between short term debt ratio and combine strategy of the firms. Model 1 includes total debt ratio (TDR) and combine strategy of the firms (STRA_HB) as an explanatory variables and TDR*STRA_HB is a moderating variable whereas long term debt ratio (LDR), combine strategy (STRA_HB) and the moderating variable LDR* STRA_HB are included in the model 2. Model 3 contains the SDR*STRA_HB as a moderating variable along with the short term debt ratio (SDR) and Combine strategy (STRA_HB). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models.
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Table 4.14: Capital Structure, Hybrid Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Tobin’s Q
Variables Fixed Random OLS Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 1.98 2.62 2.38 0.88 1.33 1.10 0.19 0.54 0.43
(11.69)*** (14.67)*** (13.55)*** (9.22)*** (14.15)*** (11.45)*** (3.43)*** (10.32)*** (7.78)*** TDR 0.62 0.62 0.52
(17.65)*** (18.80)*** (16.67)*** STRA_HB 0.07 0.22 0.27
(0.45)* (3.89)** (6.78)** TDR*STRA_HB -0.08 -0.17 -0.29
(-0.85)** (-2.04)*** (-4.32)***
LDR 0.19 0.19 0.28
(5.27)*** (5.15)*** (7.65)*** STRA_HB 0.09 0.17 0.13
(0.59)* (5.17) (6.94)** LDR*STRA_ CS -0.40 -0.36 -0.20
(-3.44)* (-3.26)* (-1.84)** SDR 0.36 0.41 0.31
(10.67)*** (12.28)*** (9.33)***
STRA_HB 0.17 0.09 0.16
(1.12)* (2.09)** (4.59)* SDR*STRA_ CS -0.08 -0.04 -0.16
(-1.07)** (-0.09)* (-2.21)*** Size -0.06 -0.07 -0.07 -0.02 -0.02 -0.02 0.02 0.02 0.02
(-4.72)*** (-4.89)*** (-5.30)*** (-3.57)*** (-3.26)*** (-3.16)*** (-6.62)*** (-5.33)*** (-6.24)*** Liquidity 0.02 0.01 0.02 0.02 0.02 0.01 0.02 0.02 0.01
(3.84)*** (2.53)*** (3.34)*** (4.14)*** (3.80)*** (2.92)*** (4.88)*** (4.33)*** (0.42)** Growth 0.02 0.01 0.01 0.01 0.01 0.01 0.02 0.01 0.01
(0.99) (1.13) (0.18) (0.21) (0.45) (0.58) (0.18) (0.98) (0.71)* Age 0.02 0.02 0.02 0.01 0.01 0.02 0.03 0.01 0.02
(8.80)*** (9.72)*** (7.89)*** (5.50)*** (6.00)*** (5.72)*** (2.83)*** (3.36)*** (3.59)***
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Risk 0.06 0.03 0.03 0.05 0.03 0.03 0.06 0.07 0.05
(6.80)*** (3.47)*** (3.35)*** (6.34)*** (3.66)*** (3.09)*** (8.29)*** (9.47)*** (6.15)*** F value 92.85 47.79 63.89 491.09 126.30 281.41 62.19 32.05 36.08 Adjusted R2 0.05 0.03 0.02 0.12 0.03 0.06 0.18 0.10 0.11 Hausman test 0.0000 0.0000 0.0002 0.0000 0.0000 0.0002 - - - Note: t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_HB is a combine strategy of the firms and is measured through dummy variable i.e. code 1 is assigned if the firms are pursuing combine strategy otherwise 0. The moderating variable TDR*STRA_HB is the interaction term between total debt ratio and combine strategy while LDR*STRA_HB is the interaction term between long term debt ratio and combine strategy. The moderating variable SDR*STRA_HB is also the interaction term between short term debt ratio and combine strategy of the firms. Model 1 includes total debt ratio (TDR) and combine strategy of the firms (STRA_HB) as an explanatory variables and TDR*STRA_HB is a moderating variable whereas long term debt ratio (LDR), combine strategy (STRA_HB) and the moderating variable LDR* STRA_HB are included in the model 2. Model 3 contains the SDR*STRA_HB as a moderating variable along with the short term debt ratio (SDR) and Combine strategy (STRA_HB). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models.
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Mixed strategies also improve business flexibility and formulate it easier for the
organizations to adapt to the modifications e.g. industry changes and advancement in
technology (Miller, 1992; Parker & Helms, 1992). Barney (2002) and Barney and
Hesterley (2006) also illustrated that companies which effectively implement both
strategies simultaneously gain continual competitive advantage in the market place. In
addition, combining business strategies may create synergy that overcomes any trade-off
that arises due to the combination. Many researchers (Hall 1980; Phillips et al., 1983;
White 1986; Helms et al., 1997; Hlavacka et al., 2001; Koo et al., 2004; Wright et al.
1991; Kim et al. 2004) also exhibited a positive relationship between hybrid strategy and
firm performance.
Table 4.12 also exhibits that all the interaction terms (moderating variables) are
significantly negative, which implies that hybrid strategy negatively moderates the
relationship between capital structure and firm’s performance (ROA). It denotes that
when firms try to adopt both strategies simultaneously while maintaining the high debt
ratio incurs the significant performance penalty. The negative and significant coefficients
(β= -0.05; β= -0.08; β= -0.09) implies that ROA maximally declines by 0.09 units due to
one unit enhances in the interaction term between short term debt ratio and hybrid
strategy.
When the firms try to pursue the characteristics of both the strategies
simultaneously, restrictive covenants associated with debt financing makes the firm
manager more efficient and reduce their opportunistic behavior. This situation may be
feasible for the firms to become cost leaders. On the other hand, monitoring role of
lenders impede firm managers to incorporate unique characteristics in the products by
imposing constrains in R&D activities, intensive advertisement expenditures and refrain
from high risk business investments activities etc. Therefore, leverage not only creates
hindrances for the accomplishment of the business objectives of hybrid firms but may
also harm their financial performance. The results of OLS regression models also show
that moderating role of hybrid strategy between capital structure and firm performance is
significantly negative.
Table 4.13 presents almost consistent results with respect to the relationship
directions and significance level as all measures of capital structure significantly lessen
123
the ROE of hybrid non-financial firms of Pakistan. In addition, all the negative and
significant beta coefficients (β= -0.16; β= -0.12; β= -0.22) also implies that firm
performance (in terms of ROE) declines by 0.16 units due to one unit increases in the
interaction term between total debt ratio and hybrid strategy. Moreover, 0.12 and 0.22
units decline in ROE when one unit is enhanced in LDR*STRA_HB and
SDR*STRA_HB respectively. These results imply that all kinds of debt financing
negatively and significantly declines the return on equity (performance) while pursuing
the hybrid strategy.
Table 4.14 illustrates the results using firm’s market performance i.e. Q ratio as a
dependent variable. The results indicate that combined business strategy significantly
enhances the market performance of the selected non-financial firms of Pakistan. This
positive relationship states that hybrid strategy not only enhances the firms accounting
performance but also positively contributes towards its market performance. It is argued
that the firms which successfully implement both strategies simultaneously gain continual
competitive advantage in the market place. In addition, mixed strategies also enhance the
flexibility and make it easier for the organizations to adapt to the modifications e.g.
industry changes and advancement in technology which shows a positive signal to the
market. The results also indicate that the relationship between capital structure and firm’s
market performance is negatively moderated by the hybrid strategy as all the beta
coefficients are significantly negative (β= -0.08; β= -0.40; β= -0.08). It implies that
leverage is also significantly harmful for the market performance of the firms which
adopt both the strategies simultaneously.
Capital Structure, Unclear Strategy (Stuck in the Middle) and Firms Performance
The present study reveals that 73 non-financial firms of Pakistan have failed to
implement any single strategy and face poor strategic situation (stuck in the middle)
during the sample period. Porter (1980, 1985) stated that “stuck in the middle” are those
business organizations which fail to successfully adopt and implement any single strategy
(cost leadership strategy or product differentiation strategy) to accomplish their business
objectives. In other words, a business venture that tries to employ one of the generic
business strategies but unsuccessful to accomplish any of them is considered as “Stuck in
the Middle”. Becoming “stuck in the middle” is often a symptom of a company’s
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aversion to devise selection about how to compete in the marketplace. In addition, firms
with the unclear strategy usually face poor strategic situation and competitive
disadvantage in the product market (White, 1986). Pertusa-Ortega et al., (2008) also
stated that unclear strategic situation (stuck in the middle) of the firms leads to lower
level of firm financial performance.
In addition, these firms are unable to offer the featured products which are unique
enough to satisfy the consumers on the stated prices. Powers and Hahn (2004) also
specified that firms with the cost leadership strategy or product differentiation strategy
perform better than “stuck in the middle” firms. It is argued that the relationship between
capital structure and firm performance also depends upon the unclear strategic situation
of the firms. In the same manner, the present research also examines the moderating
affect of unclear strategy between the relationship of capital structure and firm
performance.
The outcomes of the specified moderating relationship are presented in table 4.15
to table 4.17. Table 4.15 exhibits the estimated results of regression analysis using
unclear strategy as a moderating variable and Return on Assets (ROA) as dependent
variable. The statistically significant P-value of Hausman test confirms the validity of the
fixed effect models. The values of adjusted R square of fixed effect models are around
20%, 10% and 16% respectively while statistically significant F-values ensure the fitness
of the models. Table 4.15 specifies that all measures of capital structure (TDR, LDR and
SDR) are negatively and significantly related with the firm’s performance. The estimated
results of all fixed effect models also show that the firms with unclear strategy (stuck in
the middle) face significantly poor performance in terms of ROA.
Though all the beta coefficients (β= -0.05; β= -0.01; β= -0.05) are negative but the
results show that the relationship between ROA and all the moderating variables
(TDR*STRA_UC; LDR*STRA_UC); SDR*STRA_UC) are statistically insignificant.
Numerous researchers (Dess & Davis, 1984; Gibcus & Kemp, 2003; Kim et al., 2004;
Powers & Hahn, 2004; Torgovicky et al., 2005; Nandakumar et al., 2011) also exhibit
that lower financial performance is associated with the “Stuck in the Middle” firms. The
results also illustrate that coefficients of all the interaction terms are negative, implying
that unclear strategic situation of the firms negatively moderates the relationship between
125
capital structure and firms performance. However, the results of this moderated
relationship are statistically insignificant in all three models. The results of OLS
regression models are also consistent with the results of fixed effect model in terms of
significance level and relationship directions. Table 4.16 and 4.17 also illustrates that
both accounting performance (ROE) and market performance (Q ratio) declines with the
increase in the value of moderating variables, but the results are statistically insignificant.
Therefore, stuck in the middle firms cannot generate real benefits of debt financing due to
unclear strategic situation.
The firms usually choose financing option (s) after adopting their business
strategy to align and support the firm’s strategic interests. Cost leadership firms prefer
debt financing whereas product differentiators discourage debt capital to finance their
assets. In the same manner, high debt ratio is also financially harmful for the “stuck in the
middle” firms as it is the strategic situation in which the firms are unable to choose and
pursue any single strategy. These firms are unable to utilize the debt capital in a proper
manner due to vague corporate culture and improper investment decisions. As a result,
firms may fail to return the principal amount of debt along with the interest payments and
face poor financial conditions. Therefore, financing through debt inversely affects the
performance of the “stuck in the middle” firms or the firms with unclear strategic
situation.
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Table 4.15: Capital Structure, Unclear Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Assets (ROA)
Variables Fixed Random OLS Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 0.22 0.04 0.14 -0.04 -0.18 -0.11 -0.07 -0.19 -0.14 (3.34)*** (0.62)** (2.02)*** (-1.64) (-7.01) (-4.08)*** (-4.09)*** (-12.08)*** (-8.51)*** TDR -0.18 -0.18 -0.18
(-11.41)** (-14.70)*** (-18.47)**
STRA_UC -0.08 -0.05 -0.05
(-1.73)*** (-3.57)*** (-5.10)*** TDR*STRA_UC -0.05 0.05 -0.05
-(1.75) (2.27) (-3.08)* LDR -0.05 -0.07 -0.10
(-2.83)*** (-4.93)*** (-8.26)*** STRA_UC -0.02 -0.03 -0.04
(-0.51)* (-3.26)* (-7.12)** LDR*STRA_ SM -0.01 -0.04 -0.10
(-0.32) (-1.61) (-4.39)
SDR -0.12 -0.12 -0.12
(-7.73)*** (-9.30)*** (-10.96)**
STRA_UC -0.06 -0.03 -0.02 (-1.30)* (-3.15)** (-3.06)*** SDR*STRA_ SM -0.05 -0.03 0.02 -(2.15) (-1.38) (0.21) Size 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 (2.06)*** (1.77)** (1.83)** (6.19)*** (6.34)*** (5.38)*** (12.23)** (12.32)*** (11.36)**
Liquidity 0.01 0.01 0.01 0.02 0.02 0.01 0.01 0.02 0.01 (3.41)*** (8.09)*** (3.64)*** (3.13)*** (11.24)** (5.24)*** (2.95)*** (15.22)*** (7.52)***
Growth 0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 (6.49)*** (6.40)*** (5.81)*** (6.37)*** (6.44)*** (5.48)*** (5.32)*** (5.56)*** (3.81)***
Age 0.02 0.01 0.01 0.01 0.01 0.01 0.03 0.01 0.02
127
(1.47) (2.71)* (1.37) (1.68)* (0.94) (0.99) (3.01)* (2.41) (1.94)* Risk 0.06 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.04 (9.86)*** (11.64)** (12.02)*** (14.71)*** (14.95)** (16.86)*** (18.72)** (16.38)*** (20.89)** F value 64.81 44.41 51.86 852.10 563.10 659.24 171.13 118.00 133.17 Adjusted R2 0.20 0.10 0.16 0.37 0.28 0.31 0.37 0.29 0.31 Hausman test 0.0045 0.0000 0.0006 0.0045 0.0000 0.0006 - - - Note: t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_UC is an unclear strategy of the firms (stuck in the middle or failed to choose any single strategy) and is measured through dummy variable i.e. code 1 is assigned if the firms are failed to pursue any single strategy or stuck in the middle otherwise 0. The moderating variable TDR*STRA_UC is the interaction term between total debt ratio and unclear strategy (STRA_UC) while LDR*STRA_UC is the interaction term between long term debt ratio unclear strategy. The moderating variable SDR*STRA_UC is also the interaction term between short term debt ratio and unclear strategy. Model 1 includes total debt ratio (TDR) and unclear strategy of the firms (STRA_UC) as an explanatory variables and TDR*STRA_UC is a moderating variable whereas long term debt ratio (LDR), unclear strategy (STRA_UC) and the moderating variable LDR* STRA_UC are included in the model 2. Model 3 contains the SDR*STRA_UC as a moderating variable along with the short term debt ratio (SDR) and unclear strategy (STRA_UC). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models.
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Table 4.16: Capital Structure, Unclear Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Equity (ROE)
Variables
Fixed Random OLS Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 0.28 0.09 0.28 -0.27 -0.42 -0.31 -0.34 -0.45 -0.39 (1.71)*** (0.54)*** (1.69)*** (-4.49)*** (-7.48)*** (-5.20)*** (-8.51)*** (-12.51)*** (-10.17)*** TDR -0.26 -0.21 -0.18
(-6.25)*** (-6.76)*** (-7.15)***
STRA_UC -0.02 -0.05 -0.05
(-0.15)* (-1.44) (-1.95)**
TDR*STRA_UC -0.03 -0.01 -0.01
(-0.47) (-0.20) (-0.18) LDR 0.01 -0.04 -0.09
(0.22)** (-1.11) (-3.01)*** STRA_UC -0.04 -0.04 -0.05
(-0.40) (-2.34)*** (-4.08)*** LDR*STRA_ SM -0.02 -0.02 -0.06
(-0.21) (-0.24) (-1.10) SDR -0.24 -0.18 -0.12 (-6.11)*** (-5.67)*** (-4.66)*** STRA_UC -0.01 -0.05 -0.04 (-0.13) (-2.02)*** (-2.05)*** SDR*STRA_ SM -0.05 -0.01 -0.02 (-0.72) (-0.28) (-0.40)
0.01 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 (0.92)*** (1.28) (1.24) (6.67)*** (6.70)*** (6.08)*** (12.35)*** (12.43)*** (11.83)***
Liquidity 0.01 0.02 0.01 0.01 0.02 0.01 0.01 0.02 0.01 (2.67)*** (5.10)*** (2.00)*** (2.36)*** (6.71)*** (2.90)*** (2.48)*** (8.21)*** (4.40)*** Growth 0.34 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02 (6.03)*** (5.69)*** (5.51)*** (5.87)*** (5.63)*** (5.24)*** (4.40)*** (4.39)*** (3.68)***
Age 0.02 0.01 0.01 0.01 0.01 0.02 0.03 0.01 0.01
129
(0.08) (1.18) (0.14) (1.17) (0.79) (0.87) (1.86)** (1.60) (1.47) Risk 0.08 0.10 0.09 0.08 0.09 0.09 0.08 0.08 0.09 (9.08)*** (10.60)*** (10.42)*** (13.90)*** (14.33)*** (15.30)*** (17.73)*** (16.61)*** (18.62)*** F value 36.14 29.19 35.56 458.66 390.67 435.82 85.68 77.02 80.56 Adjusted R2 0.14 0.08 0.06 0.24 0.22 0.05 0.24 0.23 0.23 Hausman test 0.0032 0.0051 0.0005 0.0032 0.0051 0.0005 - - - Note: t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_UC is an unclear strategy of the firms (stuck in the middle or failed to choose any single strategy) and is measured through dummy variable i.e. code 1 is assigned if the firms are failed to pursue any single strategy or stuck in the middle otherwise 0. The moderating variable TDR*STRA_UC is the interaction term between total debt ratio and unclear strategy (STRA_UC) while LDR*STRA_UC is the interaction term between long term debt ratio unclear strategy. The moderating variable SDR*STRA_UC is also the interaction term between short term debt ratio and unclear strategy. Model 1 includes total debt ratio (TDR) and unclear strategy of the firms (STRA_UC) as an explanatory variables and TDR*STRA_UC is a moderating variable whereas long term debt ratio (LDR), unclear strategy (STRA_UC) and the moderating variable LDR* STRA_UC are included in the model 2. Model 3 contains the SDR*STRA_UC as a moderating variable along with the short term debt ratio (SDR) and unclear strategy (STRA_UC). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models.
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Table 4.17: Capital Structure, Unclear Strategy and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Tobin’s Q
Variables
Fixed Random OLS Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 1.99 2.59 2.36 0.99 1.41 1.18 0.27 0.59 0.44 (11.70)*** (14.57)*** (13.51)*** (10.17)*** (14.82)*** (12.02)*** (4.75)*** (11.00)*** (7.89)*** TDR 0.58 0.59 0.46
(14.96)*** (16.02)*** (13.81)***
STRA_UC -0.07 -0.05 -0.00
(-0.12) (-1.06) (-0.09) TDR*STRA_UC -0.09 -0.08 -0.03
(-1.35) (-0.80) (-0.47) LDR
0.17
0.17
0.21
(4.08)*** (4.12)*** (5.22)*** STRA_UC -0.03 -0.01 -0.03
(-0.18) (-0.45) (-1.52) LDR*STRA_ SM -0.05 -0.09 -0.07
(-0.72) (-1.29) (-0.45) SDR 0.35 0.39 0.33 (9.51)*** (10.87)*** (8.89)*** STRA_UC -0.03 -0.04 -0.02 (-0.24) (-1.18) (-0.71)
SDR*STRA_ SM -0.08 -0.06 -0.09 (-1.24) (-1.00) (-1.53)
Size -0.06 -0.07 -0.07 -0.03 -0.03 -0.02 0.02 0.02 0.02 (-4.81)*** (-4.77)*** (-5.30)*** (-4.25)*** (-4.25)*** (-3.60)*** (-5.70)*** (-4.78)*** (-5.83)*** Liquidity 0.02 0.01 0.02 0.02 0.01 0.01 0.02 0.02 0.01 (3.84)*** (2.47)*** (3.30)*** (4.15)*** (3.57)*** (2.96)*** (4.51)*** (4.26)*** (0.69) Growth 0.01 0.01 0.02 0.01 0.01 0.01 -0.2 0.01 0.01 (0.96) (1.23) (0.16) (0.25) (0.53) (0.57) (-0.41) (1.03) (0.55)
131
Age 0.02 0.02 0.02 0.02 0.01 0.01 0.03 0.02 0.01 (8.74)*** (9.82)*** (7.83)*** (5.42)*** (5.87)*** (5.59)*** (2.66)*** (2.97)*** (3.31)*** Risk 0.06 0.04 0.03 0.05 0.04 0.03 0.07 0.08 0.06 (6.59)*** (3.62)*** (3.24)*** (6.52)*** (4.01)*** (3.24)*** (9.91)*** (10.79)*** (7.44)*** F value 92.97 45.98 63.84 474.55 103.09 275.25 49.34 23.29 29.94 Adjusted R2 0.08 0.04 0.05 0.09 0.06 0.04 0.15 0.08 0.20 Hausman test 0.0000 0.0000 0.0002 0.0000 0.0000 0.0003 - - - Note: t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. STRA_UC is an unclear strategy of the firms (stuck in the middle or failed to choose any single strategy) and is measured through dummy variable i.e. code 1 is assigned if the firms are failed to pursue any single strategy or stuck in the middle otherwise 0. The moderating variable TDR*STRA_UC is the interaction term between total debt ratio and unclear strategy (STRA_UC) while LDR*STRA_UC is the interaction term between long term debt ratio unclear strategy. The moderating variable SDR*STRA_UC is also the interaction term between short term debt ratio and unclear strategy. Model 1 includes total debt ratio (TDR) and unclear strategy of the firms (STRA_UC) as an explanatory variables and TDR*STRA_UC is a moderating variable whereas long term debt ratio (LDR), unclear strategy (STRA_UC) and the moderating variable LDR* STRA_UC are included in the model 2. Model 3 contains the SDR*STRA_UC as a moderating variable along with the short term debt ratio (SDR) and unclear strategy (STRA_UC). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models.
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The result of tables 4.16 and 4.17 also exhibit almost consistent findings with the
table 4.15, this validates that capital structure is harmful for both accounting and market
performance of the “stuck in the middle”. More precisely, debt financing insignificantly
declines the performance in the presence of unclear strategic situation of the firms.
Overall, the results of regression analysis reveal that capital structure negatively
affects the accounting performance i.e. ROA and ROE of non-financial firms of Pakistan.
Whereas market performance of the sample Pakistani firms enhances when assets are
financed through debt capital. In addition, cost leadership strategy, product differentiation
strategy and hybrid strategy positively impact both the accounting and market
performance of the selected firms. However, Pakistani firms with unclear strategy are
negatively related to the financial performance. Furthermore, the results also state that
debt financing is imperative for both the accounting and market measures of performance
while attempting to pursue the cost leadership strategy. Moreover, debt financing is
harmful for the financial performance of the firms pursuing the product differentiation
strategy, hybrid strategy and unclear strategic situation of the firms.
4.3.3 Capital Structure, Competitive Intensity and Firm Performance:
Competitive intensity or product market competition of the firm is also considered
one of the significant determinants which can enormously alter the firm’s performance
(Ramaswamy & Renforth, 1996; Marciukaityte & Park, 2009; Laksmana & Yang, 2015).
The competitive intensity among the rivals refers to the probability to which business
units or firms put pressure in terms of limiting others’ profits or market shares within the
same industry. More precisely, it signifies the degree of competition company confronta
in a specific product marketplace. The literature shows that most of the researchers
explore the direct impact of capital structure and firm performance; however, Baggs and
Bettignies (2007) and Jermias (2008) argued that relationship between capital structure
and firm performance also depends upon the product market competition faced by the
firms. In the same manner, the present research also explores the moderating role of
competitive intensity between the relationship of capital structure and firm performance.
The results of the moderating relationship are presented in table 4.18 to table 4.20.
Table 4.18 presents the estimated results of regression analysis using fixed
effect, random effect and OLS estimation techniques. Total debt ratio (TDR), long term
133
debt ratio (LDR) and short term debt ratio (SDR) are utilized as independent variables
whereas competitive intensity and Return on Assets (ROA) are selected as a moderating
and dependent variables respectively. Table 4.18 illustrates that the statistically
significant P-value of Hausman test validate the fixed effect models. The values of
adjusted R square of fixed effect models are around 32%, 30% and 33% respectively
while statistically significant F-values ensure the fitness of the models.
Consistent with the previous results, table 4.18 indicates that TDR, LDR and SDR
negatively and significantly influence the ROA (firm’s performance). In addition, the
outcomes of all fixed effect models show that the relationship between competitive
intensity (HHI) and firm performance is significantly negative. As HHI measures the
market concentration, hence higher market concentration is related with the lower
competitive intensity and vice versa. Consequently, inverse relationship indicates that
high product market competition positively and significantly influences the firm’s
performance. In other words, more industry concentration is harmful for the financial
health of the firms.
Intense product market competition enhances the firm’s performance because it
enforces a discipline and serves as a significant instrument to lessen the agency issue
between managers and owners (Holmstrom, 1982; Schmidt, 1997; Baggs & Bettignies,
2007; Mnasriand & Ellouze, 2015). As a result, managers become more inclined towards
maximizing the shareholders wealth (Hermalin, 1992; Nalebuff & Stiglitz, 1983). Allen
and Gale (2000) also stated that competitive intensity also diminishes the managerial
slack. In addition, firms working in high competitive sector are more efficient and are
probable to invest in risky but profitable business projects as competition protects
shareholders against expropriation by corporate insiders (Chhaochharia et al., 2016).
Firm managers are also more inclined towards value maximizing decisions in order to
save their financial benefits and jobs, as intense competition creates a performance
comparison with their rivals (Vickers, 1997; Meyer & Vickers, 1997).
Reenen (2011) also illustrated that competitive intensity boosts the firm performance
through improved management practices. Moreover, in the more competitive
environment, business organizations pay more attention to perform well in order to
ensure their continued existence or survival in the marketplace. Numerous studies have
134
also specified that competitive intensity is a significant tool to improve the firm’s
performance (Nickell, 1996; Allen & Gale, 2000; Januszewski et al., 2002; Grosfeld &
Tressel, 2002; Okada, 2005; Pant & Pattanayak, 2010; Inui et al., 2012; Porras & Mateo,
2011; Ramstetter & Ngoc, 2013). The present study also explores the extent to which the
leverage-performance association is contingent upon the level of competitive intensity of
marketplace. Table 4.18 shows that the coefficients of all moderating variables
(TDR*INT, LDR*INT and SDR*INT) are significantly positive. It implies that product
market competition negatively moderates the relationship between capital structure and
firm performance. All the beta coefficients (β= 0.09; β= 0.11; β= 0.08) are significantly
positive, entails that a unit change in the moderating variable (TDR*INT) significantly
declines the firms ROA i.e. in the presence of high product market competition, high debt
financing may not create real financial benefit to the firms or even a harmful for the
firm’s financial health. The rest of two values of beta coefficients are also highlighted the
inverse relationship.
Jensen (1986) stated that debt financing is a monitoring device that aligns the
interest of managers with shareholders. High debt ratio also induces firm managers to be
efficient, restrain from unitization of funds and evade them from over investment. In
addition, servicing obligation of debt lessens the cash flow accessible to mangers to
expend at their discretion (Jensen, 1989). In a similar manner, spending behavior of
managers towards discretionary earnings is also contingent upon the level of product
market competition (Jermias, 2008). Intense product market competition is another
influential instrument that refrains the managers from maneuver the operations of the
business for their personal objectives (Laksmana & Yang, 2015). If managers waste the
corporate resources under high competitive environment, firms become incapable to
successfully compete in the market and may exit from the competition or become
insolvent (Hou & Robinson, 2006). Baggs and Bettignies (2007) also indicated that high
product market competition reduces the firm’s cost, improves the product quality, makes
employees more efficient and is consistent with the alignment of interests argument.
Therefore, high competition can be used as a substitute of debt financing to limit the
discretionary resources of the firm managers. In this scenario, asset financing through
debt cannot create real benefits for firms.
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Table 4.18: Capital Structure, Competitive Intensity and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Assets (ROA)
Variables Fixed Random OLS Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 0.15 -0.09 -0.04 -0.08 -0.24 -0.19 -0.13 -0.26 -0.22 (1.97)*** (-1.22)*** (-0.51)*** (-2.62)*** (-9.16)*** (-6.67)*** (-6.67)*** (-15.42)** (-12.14)**
TDR -0.22 -0.19 -0.16
(-10.51)** (-11.74)*** (-12.01)**
INT -4.74 -7.77 9.52
(-0.26)*** (-0.80)** (-1.21)** TDR*INT 0.09 7.04 9.94
(1.32)** (1.17)** (0.78)*** LDR -0.05 -0.03 -0.06
(-1.66)*** (-2.03)*** (-2.72)*** INT 0.08 -0.08 -0.02
-(5.76)** (-7.79)* (-11.02)** LDR*INT 0.11 0.04 0.05
(7.25)* (8.37)** (11.08)* SDR -0.08 -0.08 -0.07 (-5.39)*** (6.34)*** (-6.46)***
INT 0.05 -0.05 -0.05 (-5.26)** (-6.76)* (-9.70)** SDR*INT 0.08 0.04 0.03 (5.92)*** (6.74)* (9.18)*** Size 0.07 0.03 0.04 0.03 0.01 0.01 0.04 0.02 0.07 (0.76)*** (0.22)** (0.24)*** (6.56)*** (7.93)*** (6.92)*** (12.96)*** (14.19)*** (13.13)***
Liquidity 0.04 0.02 0.02 0.04 0.01 0.02 0.03 0.06 0.05 (2.93)*** (4.87)*** (2.53)*** (2.26)*** (5.22)*** (2.62)* (1.11)*** (4.76)*** (2.30)*** Growth 0.05 0.03 0.05 0.03 0.01 0.01 0.02 0.03 0.03 (6.12)*** (6.00)*** (5.49)*** (6.02)** (6.08)*** (5.39)** (4.74)*** (5.02)** (3.97)** Age 0.03 0.02 0.03 0.01 0.01 0.02 0.02 0.01 0.03
136
(0.27)* (0.56) (0.24) (1.61) (1.42) (1.50) (2.31) (2.00) (1.96)* Risk 0.04 0.05 0.05 0.04 0.05 0.05 0.05 0.05 0.05 (10.19)*** (11.88)*** (12.35)*** (15.42)*** (16.27)*** (17.22)*** (20.66)*** (19.62)*** (21.37)*** F value 73.82 56.67 60.86 887.41 702.72 753.01 168.71 141.74 148.53 Adjusted R2 0.32 0.30 0.33 0.40 0.37 0.38 0.41 0.37 0.38 Hausman test 0.0031 0.0033 0.0089 0.0031 0.0033 0.0089 - - - Note: t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. INT is a product market competition which is measured through Herfindahl index (an inverse measure of product market competition i.e. higher value of HHI indicates low product market competition or competitive intensity and vice versa). The moderating variable TDR*INT is the interaction term between total debt ratio and firms competitive intensity while interaction terms between long and short term debt ratios with product market competition are depicted through LDR*INT and SDR*INT respectively. Model 1 includes total debt ratio (TDR) and product market competition of the firms (INT) as an explanatory variables and TDR*INT is a moderating variable whereas long term debt ratio (LDR), product market competition (INT) and the moderating variable LDR*INT are included in the model 2. Model 3 contains the SDR*INT as a moderating variable along with the short term debt ratio (SDR) and product market competition (INT). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models.
137
Table 4.19: Capital Structure, Competitive Intensity and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Return on Equity (ROE)
Variables Fixed Random OLS Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 0.25 -0.11 0.09 -0.29 -0.53 -0.40 -0.40 -0.56 -0.50 (1.29)*** (-0.57)*** (0.45)*** (-3.99)*** (-8.23)*** (-5.84)*** (-7.99)*** (-13.33)** (-10.99)** TDR -3.35 -0.29 -0.21
(-6.29)*** (-6.46)*** (-5.84)***
INT -0.23 -0.16 -8.04
(-0.92)** (-1.49)** (-0.41)***
TDR*INT 0.12 0.12 0.43
(2.90)** (2.55)* (1.40)*** LDR -0.06 0.05 -0.04
(-1.41)*** (-0.39)*** (-1.17)*** INT -0.22 0.12 -0.15
(-3.26)* (-3.37)** (-4.33)** LDR*INT 0.35 0.54 0.27
(2.41)* (2.82)* (3.29)**
SDR -0.23 -0.17 -0.09 (-5.80)*** (-5.15)*** (-3.37)***
INT -2.13 -0.27 -0.35 (-1.76)** (-1.98)** (-3.50)*** SDR*INT 5.80 0.76 0.65 (0.12)** (0.67)*** (2.22)** Size 0.03 0.01 0.03 0.03 0.03 0.03 0.04 0.03 0.03 (0.92)*** (0.52)*** (0.70)*** (6.19)*** (6.90)*** (6.04)*** (11.76)*** (12.38)*** (11.69)*** Liquidity 0.05 0.02 0.03 0.01 0.01 0.02 0.04 0.01 0.03 (2.69)*** (3.33)*** (1.77)*** (1.91)*** (3.39)*** (1.72)*** (1.25)** (3.17)*** (1.85)*** Growth 0.04 0.03 0.03 0.03 0.03 0.03 0.02 0.02 0.02 (5.62)** (5.20)** (5.05)*** (5.30)** (4.99)*** (4.70)** (3.72)*** (3.70)*** (3.21)*** Age 0.03 0.02 0.02 0.02 0.01 0.01 0.03 0.01 0.02
138
(0.59)* (0.87) (0.39)* (1.36)* (1.26) (1.27)* (2.13)** (2.00)* (1.99)** Risk 0.20 0.11 0.11 0.09 0.10 0.10 0.09 0.09 0.09 (8.97)*** (10.58)*** (10.42)*** (13.25)*** (14.21)*** (14.61)*** (16.97)*** (16.51)*** (17.40)*** F value 37.35 31.87 36.49 427.80 378.86 408.42 75.77 70.42 72.05 Adjusted R2 0.14 0.14 0.15 0.24 0.23 0.23 0.25 0.23 0.24 Hausman test 0.0004 0.0003 0.0000 0.0004 0.0003 0.0000 - - - Note: t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. INT is a product market competition which is measured through Herfindahl index (an inverse measure of product market competition i.e. higher value of HHI indicates low product market competition or competitive intensity and vice versa). The moderating variable TDR*INT is the interaction term between total debt ratio and firms competitive intensity while interaction terms between long and short term debt ratios with product market competition are depicted through LDR*INT and SDR*INT respectively. Model 1 includes total debt ratio (TDR) and product market competition of the firms (INT) as an explanatory variables and TDR*INT is a moderating variable whereas long term debt ratio (LDR), product market competition (INT) and the moderating variable LDR*INT are included in the model 2. Model 3 contains the SDR*INT as a moderating variable along with the short term debt ratio (SDR) and product market competition (INT). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models.
139
Table 4.20: Capital Structure, Competitive Intensity and Firm Performance
Fixed effect models, Random Effect Models and Ordinary Least Square Models of the Predictors of Tobin’s Q
Variables
Fixed Random OLS Model1 Model 2 Model 3 Model1 Model 2 Model 3 Model 1 Model 2 Model 3
Constant 1.44 2.24 2.11 0.71 1.33 1.14 0.20 0.60 0.54 (8.17)*** (12.54)*** (11.89)*** (6.97)*** (14.09)*** (11.64)*** (3.09)*** (10.92)*** (9.25)*** TDR -0.69 0.68 0.51
(14.67)*** (15.34)*** (12.02)***
INT -0.25 -0.17 -0.23
(-6.10)** (-6.23)* (-4.72)** TDR*INT 0.46 0.26 0.56
(1.45)*** (0.55)*** (0.72)*** LDR 0.09 0.09 0.18
(2.50)*** (2.52)*** (5.18)*** INT -0.21 -0.19 -0.31
(-1.33)* (-3.24)** (-2.51)** LDR*INT 0.54 0.43 0.76
(10.23)* (11.99)* (9.95)** SDR 0.24 0.26 0.14 (7.16)*** (7.90)*** (4.05)***
INT -0.07 -0.08 -0.16 (-0.49)** (-2.06)* (-3.03)** SDR*INT 0.12 0.34 0.13 (8.39)** (10.02)*** (9.96)*** Size 0.05 0.08 0.08 0.02 0.03 0.03 0.02 0.03 0.05 (4.04)*** (5.31)*** (5.58)*** (3.52)*** (5.29)*** (4.39)*** (4.46)*** (2.57)*** (3.25)*** Liquidity 0.02 0.01 0.02 0.01 0.02 0.02 0.02 0.02 0.04 (3.85)*** (0.73)*** (3.76)*** (3.61)*** (0.06)*** (3.23)*** (3.68)*** (0.19)** (1.67)*** Growth 0.03 0.02 0.01 0.02 0.04 0.02 0.03 0.01 0.03 (0.57)*** (0.65)** (0.09)** (0.14)** (0.34)** (0.42)** (0.09)** (0.69)* (0.40)*** Age 0.01 0.01 0.01 0.01 0.02 0.01 0.03 0.01 0.01 (6.07)* (6.00)* (4.93)** (4.82)* (4.69) (4.44)* (3.55)* (3.71) (3.70)*
140
Risk 0.05 0.03 0.02 0.05 0.03 0.02 0.07 0.07 0.05 (5.51)*** (2.57)*** (2.25)*** (5.90)*** (2.95)*** (2.62)*** (8.48)*** (8.27)*** (6.68)*** F value 96.72 62.45 69.83 668.40 385.57 455.15 79.56 61.29 59.66 Adjusted R2 0.09 0.06 0.06 0.20 0.15 0.15 0.25 0.20 0.20 Hausman test 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 - - - Note: t-values are in parentheses (t-statistics) while ***, **, and * designate the level of significance at 1%, 5% and 10% respectively. INT is a product market competition which is measured through Herfindahl index (an inverse measure of product market competition i.e. higher value of HHI indicates low product market competition or competitive intensity and vice versa). The moderating variable TDR*INT is the interaction term between total debt ratio and firms competitive intensity while interaction terms between long and short term debt ratios with product market competition are depicted through LDR*INT and SDR*INT respectively. Model 1 includes total debt ratio (TDR) and product market competition of the firms (INT) as an explanatory variables and TDR*INT is a moderating variable whereas long term debt ratio (LDR), product market competition (INT) and the moderating variable LDR*INT are included in the model 2. Model 3 contains the SDR*INT as a moderating variable along with the short term debt ratio (SDR) and product market competition (INT). The values of VIF of each variable are less than 4; ensure the absence of Multicolinearity in the regression models.
140
Moreover, high debt capital is not suitable for the firms working in the
environment of intense competition as debt becomes more expensive due to the business
uncertainty and high risk (Jensen & Meckling, 1976; Botosan & Plumlee, 2005; Jermias,
2008). Numerous studies also illustrated that the firms with high debt ratio may undergo
a significant competitive disadvantage in the presence of intense product market
competition (Poitevin, 1989; Chevalier & Scharfstein, 1996; Bolton & Scharfstein, 1990;
Dasgupta & Titman, 1998). A similar argument is also presented by Bolton and
Scharfstein (1990), who stated that debt contract (formulated to support the interests of
debt providers and the firms’ managers) creates an opportunity for the less leveraged
rival firms to grow more as the restrictive covenants may not only require the fixed
periodic payments to creditors but also refrain firms from the high risk but profitable
investments. Therefore, the benefits of debt decreases under the more competitive
business environment and this interaction negatively influences the firm’s performance.
Among the control variables, the impact of size, growth, liquidity and risk on
firm’s performance is positive and statistically significant. In addition, almost consistent
results have been found in OLS regression models. Tables 4.19 also illustrates that high
product market competition positively impacts both the measures of firm performance i.e.
ROE. Moreover, competitive intensity positively and significantly moderates the
relationship between capital structure and firm’s performance as all the beta coefficients
(β= 0.12; β= 0.35; β= 5.80) are positive and statistically significant, implies that a unit
change in the moderating variables (TDR*INT, LDR*INT and SDR*INT) significantly
declines the firms ROE i.e. in the presence of high product market competition. The
results indicate that high competitive intensity negatively moderates the relationship
between capital structure and ROE (performance).
Consistent with the previous results, table 4.20 shows that market performance (Q
ratio) of the firms also enhances with the increase in product market competition whereas
all the coefficients of moderating variables i.e. TDR*INT, LDR*INT and SDR*INT are
significantly positive. It implies that debt financing is also harmful for the market value
of the firms in the presence of high product market competition. Moreover, the outcomes
of the OLS regression models validate the outcomes of fixed effect models in terms of
relationship directions and significance level.
141
Overall, the results describe that product market competition has a positive impact
on both accounting and market measures of performance of non-financial firms of
Pakistan. Moreover, debt financing has no real benefits in the presence of high product
market competition as competitive intensity positively moderates the relationship
between capital structure and firm performance. It implies that debt financing high
product market competition can be acted as a substitute of debt financing as it aligns the
interests of shareholders and firm managers.
142
Chapter 5
Conclusion and Policy Recommendations
143
The current research contributes to the existing corporate finance literature in
three ways. Firstly, it investigates the impact of capital structure on firm’s performance
by incorporating comprehensive measures of both capital structure and firm’s
performance. Secondly, it explores the moderating role of business strategy between
capital structure and firm’s performance. Lastly, it examines the extent to which
leverage-performance relationship depends upon the competitive intensity of the sample
firms. Both book value and market value based financial data of listed 333 non-financial
firms of Pakistan over the period of 8 years i.e. 2006 to 2013 was collected to draw the
inferences. The estimated results of the study showed that debt ratio negatively affects the
accounting performance of the non-financial firms of Pakistan but positively impacts
market performance of the sample firms. This positive relationship may persist because
debt capital shows a signal to the investors that the firm has positive cash flow to pay the
debt obligations (Ross, 1977) and have better future prospects.
Based on the moderating analysis, the current study revealed that 104 non-
financial firms of Pakistan were following cost leadership strategy while 93 firms were
implementing product differentiation strategy during the sample period. It suggests that
non-financial firms of Pakistan mostly pursue cost leadership strategy and try to become
cost efficient by reducing their R&D and other relevant expenditures. In addition, the
outcome also indicates that 63 firms were implementing both cost leadership and product
differentiation strategy simultaneously while 73 non-financial firms of Pakistan were
unable to choose any singly strategy and were considered “stuck in the middle”. It
describes that almost 22% selected non-financial firms of Pakistan are running their
business operations without pursuing any single strategy.
Moreover, the estimated results of the regression analysis revealed that cost
leadership strategy, product differentiation strategy and hybrid strategy enhance the
performance of the selected non-financial firms of Pakistan whereas stuck in the middle
firms face poor financial conditions. In addition, empirical evidence also specified that
the cost leadership strategy positively moderates the relationship between capital
structure and firm performance. It suggests that debt financing enhances the performance
of the cost leadership firms due to the monitoring role of lenders.
144
On the other hand, when the firms maintain high debt ratio while attempting to
pursue a strategy based on product differentiation, they incur a significant performance
penalty. As the product differentiation strategy of the firm comprises the creation of the
products and spends heavily in R&D activities, so restrictive covenants attached with the
debt financing may limit the firm’s R&D and other relevant expenditures required to
become product differentiators. It suggests that debt financing is harmful for the firms
pursuing product differentiation strategy.
The current study also examined the extent to which the relationship between
capital structure and firm performance depends upon the hybrid strategy of the firms. The
results showed that hybrid strategy of the firms negatively moderates the leverage-
performance relationship. It implies that debt financing is not beneficial for the firms
pursuing both strategies simultaneously. Another strategic situation of the firms is when
the firms are unable to pursue any singly strategy and get “stuck in the middle”. The
results indicate that the firms with unclear strategic situation are also unable to get
financial benefits through debt financing; however this moderated relationship is
statistically insignificant. Therefore, it is concluded that debt financing is financially
viable for cost leadership firms whereas asset financing through debt is financially
harmful for the firms pursuing product differentiating strategy and hybrid strategy. In
addition, performance of the “stuck in the middle” firms also declines with high debt
capital.
The results of the current study also showed that product market competition
positively relates with firm’s performance. It is argued that high competition reduces
agency problem by enforcing a discipline between shareholders and managers and as a
result, mangers work for the maximization of shareholders wealth. With respect to the
moderating analysis, the results implied that leverage significantly harm financial
performance of the firms in the presence of competitive intensity. It suggests that
competitive intensity can be acted as a substitute of debt financing as it also play a
monetary role and align the interests of firms’ managers and owners.
Among the selected control variables, firm’s size, liquidity, growth and risk are
the significant determinants of firm’s performance. Although firm’s age also positively
relate with firm performance but the relationship is statistically insignificant in most
145
cases. On the basis of the results, the present research suggests various implications for
the policy makers and other stakeholders.
Policy Recommendations:
The empirical findings of the current research reveal the following implications
for the policy makers and other stakeholders:
The policy makers and the managers of the non-financial firms of Pakistan are suggested
to discourage the high level debt financing while formulating the capital structure.
Lenders and Investors should consider the capital structure of firms before making
lending and investment decisions.
The firms should select and pursue at-least single business strategy suggested by the
porter for the successful attainment of the business goals.
The firms should maintain high debt ratio if they pursue the cost leadership strategy.
The firms pursuing the product differentiation strategy or hybrid strategy should not
employ high debt in their capital structure.
The firms are also advised to discourage high debt financing in the presence of high
product market competition prevailing the industry.
Limitations of the Study:
The current research attempted to address the objectives of the research by
considering the comprehensive measures of the selected variables. However, there are
certain limitations of the research. Firstly, the data was only collected from listed
non-financial firms of Pakistan which limits the generalizability of the results in case of
listed financial firms of Pakistan. Secondly, the non-availability of the financial data or
annual reports of non-financial firms of Pakistan restricts the firm-year observations.
Thirdly, certain variables i.e. capital structure and firm performance with the
comprehensive market based measures were not considered due to non-availability of the
financial data. Lastly, limited measurements of moderating variables were selected due to
the non-availability of data.
Direction of Future Research:
The future researchers can consider more firms from various other sectors
especially from the financial sector of Pakistan to generalize the results. In future, larger
time series data along with advanced econometric techniques can be utilized to draw the
146
inferences. As the present study used the limited measures of moderating variables, future
researchers can select more comprehensive proxies to compute the moderating variables.
Academic researchers can also select the sample from the Pakistan’s alike economies to
conduct the cross-country analysis. The current study selected business strategy and
competitive intensity as moderating variables, so future studies may choose different
managerial characteristics which may plays a role as moderators between leverage-
performance relationship.
147
Chapter 6
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Appendix-A
Cost Leadership Sample Firms:
1. Agriauto Industries Ltd.
2. Ahmed Hassan Textile Mills Ltd.
3. Al-Abbas Sugar Mills Ltd.
4. Al-Ghazi Tractors Ltd.
5. Al-Noor Sugar Mills Ltd.
6. Al-Qadir Textile Mills Ltd.
7. Allawasaya Textile & Finishing
Mills Ltd
8. Aruj Garment Accessories Ltd.
9. Atlas Honda Ltd.
10. Attock Refinery Ltd.
11. Bata Pakistan Ltd.
12. Bhanero Textile Mills Ltd.
13. Bilal Fibres Ltd.
14. Blessed Textiles Ltd.
15. Bolan Castings Ltd.
16. Buxly Paints Ltd.
17. Byco Petroleum Pakistan
18. Chakwal Spinning Mills Ltd.
19. Cherat Packaging Ltd.
20. Clover Pakistan Ltd.
21. Colony Sugar Mills Ltd.
22. Crescent Cotton Mills Ltd.
23. Dar-es-Salaam Textile Mills Ltd.
24. Dewan Farooque Spinning Mills
25. Dewan Mushtaq Textile Mills
26. Din Textile Mills Ltd.
27. Ellcot Spinning Mills Ltd.
28. Faisal Spinning Mills Ltd.
29. Faran Sugar Mills Ltd
30. Gadoon Textile Mills Ltd.
31. Gatron Industries Ltd.
32. Ghazi Fabrics International Ltd.
33. Glamour Textile Mills Ltd.
34. Globe Textile Mills (OE) Ltd.
35. Habib Sugar Mills Ltd.
36. Highnoon Laboratories Ltd.
37. Hinopak Motors Ltd.
38. Honda Atlas Cars (Pakistan) Ltd.
39. Husein Sugar Mills Ltd.
40. ICC Textiles Ltd.
41. Ideal Spinning Mills Ltd.
42. Indus Dyeing & Manufacturing
Co. Ltd.
43. Indus Motor Company Ltd.
44. International Industries Ltd.
45. Ishaq Textile Mills Ltd.
46. Ishtiaq Textile Mills Ltd.
47. J. A. Textile Mills Ltd.
48. JDW Sugar Mills Ltd.
49. Khalid Siraj Textile Mills Ltd.
50. Kohat Textile Mills Ltd.
51. Kohinoor Energy Ltd.
52. Kohinoor Power Company Ltd.
176
53. Kohinoor Spinning Mills Ltd.
54. Kot Addu Power Company Ltd.
55. Lotte Pakistan PTA Ltd.
(Pakistan PTA Ltd.)
56. Maqbool Textile Mills Ltd.
57. Mehran Sugar Mills Ltd.
58. Merit Packaging Ltd.
59. Millat Tractors Ltd.
60. Mirpurkhas Sugar Mills Ltd
61. N. P. Spinning Mills Ltd.
62. Nadeem Textile Mills Ltd.
63. Nagina Cotton Mills Ltd.
64. National Refinery Ltd.
65. Olympia Spinning & Weaving
Mills Ltd.
66. Pakistan Synthetics Ltd.
67. Philip Morris (Pakistan) Ltd.
(Lakson Tobacco Company Ltd.)
68. Premium Textile Mills Ltd.
69. Prosperity Weaving Mills Ltd.
70. Quality Textile Mills Ltd.
71. Quetta Textile Mills Ltd.
72. Rafhan Maize Products Co. Ltd.
73. Reliance Cotton Spinning Mills
74. Reliance Weaving Mills Ltd.
75. Resham Textile Industries Ltd.
76. Rupali Polyester Ltd.
77. S. S. Oil Mills Ltd.
78. Safe Mix Concrete Products Ltd.
79. Saif Textile Mills Ltd.
80. Sakrand Sugar Mills Ltd.
81. Sally Textile Mills Ltd.
82. Salman Noman Enterprises Ltd.
83. Sana Industries Ltd.
84. Sanghar Sugar Mills Ltd
85. Sapphire Textile Mills Ltd.
86. Sargodha Spinning Mills Ltd.
87. Saritow Spinning Mills Ltd.
88. Sazgar Engineering Works Ltd.
89. Searle Pakistan Ltd.
90. Service Industries Textile Ltd.
91. Shadab Textile Mills Ltd.
92. Shadman Cotton Mills Ltd.
93. Shahtaj Textile Ltd.
94. Shahzad Textile Mills Ltd.
95. Shams Textile Mills Ltd.
96. Sitara Energy Ltd.
97. Sunrays Textile Mills Ltd.
98. Suraj Cotton Mills Ltd.
99. The National Silk & Rayon Mills
100. The Thal Industries
Corporation Ltd.
101. TRG Pakistan Ltd.
102. Tri-Pack Films Ltd.
103. Yousaf Weaving Mills
104. Zahidjee Textile Mills
177
Appendix-B
Product differentiation Sample Firms
1. Adil Textile Mills Ltd.
2. Agritech Ltd.
3. Al Abbas Cement Industries Ltd.
(Essa Cem. Ind. Ltd.)
4. Al-Abid Silk Mills Ltd.
5. Amtex Ltd.
6. Artistic Denim Mills Ltd.
7. Azgard Nine Ltd. (Legler-Nafees
Denim Mills Ltd.)
8. Baluchistan Glass Ltd
9. Bannu Woollen Mills Ltd.
10. Bawany Air Products Ltd.
11. Cherat Cement Company Ltd.
12. Crescent Jute Products Ltd.
13. D. G. Khan Cement Company
Ltd.
14. D.M. Textile Mills Ltd.
15. Dandot Cement Company Ltd.
16. Data Agro Ltd.
17. Dawood Hercules Chemicals
Ltd.
18. Descon Oxychem Ltd.
19. Dewan Cement Ltd. (Pakland
Cem. Ltd.)
20. Dewan Farooque Spinning Mills
Ltd.
21. Dreamworld Ltd.
22. Emco Industries Ltd.
23. Engro Corporation Ltd. (Engro
Chemical Pakistan Ltd.)
24. Engro Polymer & Chemical Ltd.
25. Fateh Textile Mills Ltd.
26. Fatima Fertilizer Co. Ltd.
27. Fauji Fertilizer Bin Qasim Ltd.
28. Feroze 1888 Mills Ltd.
29. Flying Cement Company Ltd
30. Frontier Ceramics Ltd..
31. Ghandhara Industries Ltd..
32. Ghandhara Nissan Ltd.
33. Ghani Automobiles Industries
Ltd.
34. Ghani Glass Ltd.
35. Gharibwal Cement Ltd.
36. Gulshan Spinning Mills Ltd.
37. Haji Muhammad Ismail Mills
Ltd.
38. Husein Industries Ltd.
39. Ideal Energy Ltd
40. Ismail Industries Ltd.
41. J. K. Spinning Mills Ltd.
42. Javedan Corporation Ltd.
43. Johnson & Philips (Pakistan)
Ltd.
44. K-Electric (formerly KESC)
178
45. Khairpur Sugar Mills Ltd.
46. Kohat Cement Company Ltd.
47. Kohinoor Mills Ltd.
48. Kohinoor Sugar Mills Ltd.
49. Kohinoor Textile Mills Ltd.
50. Lafarge Pakistan Cement
Ltd.(Pak. Cem. Ltd.)
51. Lucky Cement Ltd.
52. MACPAC Films Ltd.
53. Maple Leaf Cement Factory Ltd.
54. Mian Textile Industries Ltd.
55. Mohammad Farooq Textile Mills
Ltd.
56. Murree Brewery Company Ltd.
57. Nakshbandi Industries Ltd.
58. Netsol Technologies Ltd.
59. Nimir Industrial Chemicals Ltd.
60. Nishat Mills Ltd.
61. Oil & Gas Development
Company Ltd. (Pub.)
62. Packages Ltd.
63. Pak Datacom Ltd. (Pub.)
64. Pak Elektron Ltd.
65. Pakistan Engineering Company
Ltd. (Pub.)
66. Pakistan International Container
Terminal Ltd.
67. Pakistan National Shipping
Corporation (Pub.)
68. Pakistan Oilfields Ltd.
69. Pakistan Paper Products Ltd.
70. Paramount Spinning Mills Ltd.
71. Pioneer Cement Ltd.
72. Power Cement
73. Quice Food Industries Ltd.
74. Ravi Textile Mills Ltd.
75. Sardar Chemical Industries Ltd.
76. Security Papers Ltd. (Pub)
77. Shabbir Tiles & Ceramics Ltd.
78. Shakarganj Mills Ltd. (Al-Jadeed
Textile Mills Ltd.)
79. Shifa International Hospital Ltd.
80. Siemens (Pakistan) Engineering
Co. Ltd.
81. Singer Pakistan Ltd.
82. Sitara Chemical Industries Ltd.
83. Sitara Peroxide Ltd.
84. Taha Spinning Mills Ltd.
85. Tata Textile Mills Ltd.
86. Telecard Ltd.
87. The Climax Engineering
Company Ltd.
88. The Crescent Textile Mills Ltd.
89. The Premier Sugar Mills & Dist.
Co. Ltd.
90. Towellers Ltd.
91. Transmission Engineering
Industries Ltd.
92. Treet Corporation Ltd.
93. Unilever Pakistan Foods
Ltd.(Rafhan Bestfoods)
179
Appendix-C
Sample Firms Pursuing Hybrid/Combine strategy:
1. Abbott Laboratories Ltd.
2. Ados Pakistan Ltd.
3. Al-Khair Gadoon Ltd.
4. Archroma Pak Ltd
5. Ashfaq Textile Mills Ltd.
6. Atlas Battery Ltd.
7. Attock Cement Pakistan Ltd.
8. Baluchistan Wheels Ltd.
9. Berger Paints Pakistan Ltd.
10. Biafo Industries Ltd
11. Burshane LPG (Pakistan) Ltd.
12. Cherat Packaging Ltd.
13. Colgate - Palmolive (Pakistan)
Ltd.
14. Descon Chemicals (Pvt.) Ltd.
15. Dewan Farooque Motors Ltd.
16. Dynea Pakistan Ltd.
17. Eco. Pak. Ltd.
18. Engro Foods Ltd.
19. Exide Pakistan Ltd.
20. Fauji Fertilizer Company Ltd.
21. Fecto Cement Ltd.
22. Gul Ahmed Textile Mills Ltd.
23. Glaxo Smithkline (Pakistan) Ltd.
24. General Tyre & Rubber Co. of
Pak Ltd
25. Habib ADM Ltd.
26. HUM Network Ltd.
27. Husein Sugar Mills Ltd.
28. IBL Health Care Ltd.
29. ICI Pakistan Ltd.
30. International Knitwear Ltd.
31. Island Textile Mills Ltd
32. Karam Ceramics Ltd
33. Khyber Tobacco Company Ltd.
34. KSB Pumps Company Ltd.
35. Leiner Pak Gelatine Ltd.
36. Linde Pakistan Ltd.
37. Mahmood Textile Mills Ltd.
38. Masood Textile Mills Ltd.
39. Mitchell’s Fruit Farms Ltd.
40. National Foods Ltd.
41. Nestle Pakistan Ltd.
42. Noon Pakistan Ltd.
43. Noon Sugar Mills Ltd.
44. Olympia Textile Mills Ltd.
45. Otsuka Pakistan Ltd.
46. Pak Leather Crafts Ltd.
47. Pakistan Cables Ltd.
48. Pakistan Tobacco Company Ltd.
49. Safa Textiles Ltd.
50. Sanofi-Aventis Pakistan Ltd.
51. Service Industries Ltd.
52. Shaheen Cotton Mills Ltd.
180
53. Shell Gas LPG (Pakistan) Ltd.
54. Shezan International Ltd.
55. Shield Corporation Ltd.
56. Sindh Abadgar’s Sugar Mills Ltd
57. Sui Northern Gas Pipelines Ltd.
58. Sui Southern Gas Co. Ltd. (Pub.)
59. Tariq Glass Industries Ltd.
60. Thal Ltd. (Thal Jute Mills Ltd.)
61. Thatta Cement Company Ltd.
62. Wah Nobel Chemicals Ltd .
(Pub.)
63. Zil Ltd. (Zulfeqar Industries Ltd.)
181
Appendix-D
“Stuck in the Middle” Sample Firms:
1. Abdullah Shah Ghazi Sugar
Mills Ltd.
2. Adam Sugar Mills Ltd.
3. Ali Asghar Textile Mills Ltd
4. Altern Energy Ltd.
5. Ansari Sugar Mills Ltd.
6. Apollo Textile Mills Ltd.
7. Attock Petroleum Ltd.
8. Ayesha Textile Mills Ltd.
9. Azam Textile Mills Ltd.
10. Baba Farid Sugar Mills Ltd
11. Babri Cotton Mills Ltd.
12. Bestway Cement Ltd.
13. Century Paper & Board Mills
Ltd.
14. Chashma Sugar Mills Ltd.
15. Colony Mills Ltd. (Colony
Textile Mills Ltd.)
16. Crescent Steel & Allied Products
Ltd.
17. Dadabhoy Cement Industries
Ltd.
18. Dadabhoy Sack Ltd.
19. Dadex Eternit Ltd.
20. Dawood Lawrancepur Ltd.
(Dawood Cotton Mills)
21. Dewan Farooque Spinning Mills
Ltd.
22. Dewan Khalid Textile Mills Ltd.
23. Dewan Salman Fibre Ltd.
24. Dewan Sugar Mills Ltd.
25. Dewan Textile Mills Ltd.
26. Diamond Industries Ltd.
27. Fatima Enterprises Ltd.
28. Fauji Cement Company Ltd.
29. Fazal Cloth Mills Ltd.
30. Fazal Textile Mills Ltd.
31. Ferozsons Laboratories Ltd.
32. Gammon Pakistan Ltd.
33. Ghani Gases Ltd.
34. Goodluck Industries Ltd.
35. Gulistan Spinning Mills Ltd.
36. Gulistan Textile Mills Ltd.
37. Haseeb Waqas Sugar Mills Ltd.
38. Hashimi Can Company Ltd.
39. Hira Textile Mills Ltd.
40. Huffaz Seamless Pipe Industries
Ltd.
41. Husein Industries Ltd.
42. Ibrahim Fibres Ltd.
43. Idrees Textile Mills Ltd.
44. International Steels Ltd.
182
45. Janana De Malucho Textile Mills
Ltd.
46. Jubilee Spinning & Weaving
Mills Ltd.
47. Kohinoor Industries Ltd.
48. Leather Up Ltd.
49. Mari Gas Company Ltd.
50. Media Times Ltd.
51. Mehr Dastgir Textile Mills Ltd.
52. Metropolitan Steel Corporation
Ltd.
53. Mustehkam Cement Ltd.
54. Pace (Pakistan) Ltd.
55. Pakistan Hotels Developers Ltd.
56. Pakistan Petroleum Ltd. (Pub.)
57. Pakistan Refinery Ltd.
58. Pakistan State Oil Company Ltd.
(Pub.)
59. Redco Textiles Ltd.
60. Ruby Textile Mills Ltd.
61. Salfi Textile Mills Ltd.
62. Samin Textiles Ltd.
63. Sapphire Fibres Ltd.
64. Shaffi Chemicals Industries Ltd
65. Shahmurad Sugar Mills Ltd.
66. Shahtaj Sugar Mills Ltd.
67. Siddiqsons Tin Plate Ltd.
68. Southern Electric Power Co. Ltd.
69. Suhail Jute Mills Ltd.
70. The Hub Power Company Ltd.
71. United Distributors Pakistan Ltd.
72. Zeal Pak Cement Factory Ltd.
73. Zephyr Textile Ltd.
