Network Linkages and Financial Leverage－The Group Governance
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Dan Lin
Assistant Professor
Department of Banking and Finance
Takming University of Science and Technology
56, Sec. 1, Huanshan Rd., Neihu, Taipei 11451, Taiwan
Tel: 886-2-26585801; Fax: 886-2-26588448
E-mail: mcylin@takming.edu.tw
Hsien-Chang Kuo
Professor
Department of Banking and Finance
Takming University of Science and Technology
56, Sec. 1, Huanshan Rd., Neihu, Taipei 11451, Taiwan
Tel: 886-2-26585801; Fax: 886-2-26588448
E-mail: hckuo@takming.edu.tw
Lie-Huey Wang
Associate Professor
Finance Department
Ming Chuan University
250, Zhong Shan N. Rd., Sec. 5, Taipei 111, Taiwan
Tel: 886-2-28824564 ext.2191
E-mail: lhwang@mail.mcu.edu.tw
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This work was fully supported by the National Science Council in Taiwan, under contract no.: NSC
96-2416-H-147-009-MY2.
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Network Linkages and Financial Leverage－The Group Governance
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Abstract
This study examines capital structures from the perspective of network linkages.
Based on a panel of business groups listed on the Taiwan Stock Exchange (TSE) and
in the over-the-counter (OTC) market from 2006 to 2008, we test the relationship
between capital structures and network linkages. Specifically, we use the linkages
between related party purchases and sales transactions as the proxy for network
linkages and employ a panel data linear regression model to examine the relationship
between financial leverage and network linkages after considering the family
governance in Taiwan’s business groups.
Overall, the results show that the shareholdings of family members and the
divergence between seat control rights and voting rights are negatively correlated with
the related party purchases and sales network linkages. For information technology
(IT) family firms, we have observed the following relationships: (1) higher sales to
related parties are associated with higher debt ratios; (2) higher purchases from related
parties are associated with higher long-term debt ratios; and (3) the greater the number
of related purchasers, the higher the short-tem debt ratios. Opposite results are found
for non-information technology (NIT) family firms. Finally, both IT and NIT family
firms use more short-term debt when seat control rights diverge from voting rights.
Keywords: capital structure, related party purchases and sales network linkages,
business groups, family governance, panel data linear regression model
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1. Introduction
Since the concept of network linkages has been used by Johanson and Mattsson
(1987) to explain the basis for corporate cooperation, network linkages have been
widely discussed and applied to important issues such as strategic organizations and
overseas investments. Firms construct effective value chain linkages through network
structures to generate comparative profits (Porter, 1980) and develop value-adding
partnerships (Johnston and Lawrence, 1988). Therefore, a firm can increase its firm
value through cooperative network relationships (Prahalad and Hamel, 1990;
Blankenburg et al., 1999). In addition, from the viewpoint of the company’s resources,
the cooperative relationship between firms can be considered to be a resource of the
firm. Therefore, network linkages and firm value are expected to be positively
correlated, while firm value may exhibit a positive or negative relationship with
capital structure. In other words, network linkages and capital structure may be
negatively correlated (Vicente-Lorente, 2001; Kuo, 2006) or positively correlated
(Kuo et al., 2003). Kuo (2006) examines the changes in capital structure from the
perspective of network linkages, and studies the evolution of capital structure from a
new angle, namely, that of corporate cooperation. Kuo (2006) arrives at an interesting
finding which indicates that network linkages and financial leverage are negatively
correlated. The evidence suggests that network linkages can help increase firm value
and subsequently reduce the use of debt financing.
The supply chain effects formed by the economic links between the firm, its
suppliers and its clients will affect the firm’s capital structure decisions and
competitive behavior in the market. Here, the supply chain effects refer to the credit
financing and the long-term trade agreement with the suppliers, the quality guarantee
demanded by customers and the ongoing trade relationship with the clients. In recent
years, with the rise of the Chinese economy and its structural impact on international
economic development, global and inter-industry competition has made the
cooperative relationship between firms more crucial and important. Without a doubt,
the multiple supply chain network linkage has become an important direction for
development. In deciding on the capital structure, firms can incorporate the idea of a
network structure, which can provide further understanding not only of the firms’
financial leverage but also its optimal leverage level. More importantly, this opens a
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new research field in studies on capital structure. The influence of network linkages
on the capital structure during the development process of a firm has hardly been
discussed by scholars. Thus, with the increasing importance of the issue of supply
chain management, the effect of network linkages on a firm’s capital structure
constitutes a valuable new direction for further research.
With the trend towards globalization, firms are facing more intense competition
in global markets and transitions in the global economic environment. To cope with
these changes, corporations are seeking cooperative opportunities through, for
example, mergers, joint ventures, reinvestments, and cross-shareholdings, or else
forming strategic alliances or business groups in order to achieve resource sharing,
economies of scale, business growth and competitive edges. Since the subsidiaries of
business groups are linked through shares, business groups can be considered to be a
network collectivity. In Taiwan, large business groups are mostly founded and
operated by families or relationship networks (Wong, 1985; Claessens et al., 2000;
Yeh et al., 2001) and possess the characteristic of overlapping ownership and control
(Yeh et al., 2001; Yeh, 2005). The benefits of family business groups include the
consolidation of power, the centralized execution of commands and increased
company performance and value (Gerlach, 1992; Keister, 1998; James, 1999;
Schneider, 2000; Khanna and Palepu, 2000; Khanna and Rivkin, 2001; Carney and
Gedajlovic, 2002; Chang, 2003; Joh, 2003; Anderson et al., 2003; Anderson and Reeb,
2003; Burkart et al., 2003; Luo and Chung, 2005). However, family business groups
can increase conflicts of interest (Granovetter, 2005; Berkman et al., 2009) and are
more likely to allow the expropriation of minority shareholders by management to
take place (Mahaffy, 1998; La Porta et al., 1999; Gilles, 1999; Johnson et al., 2000;
Hutchinson, 2002). The empirical findings show that monitoring by large shareholders
can decrease the agency problem caused by choosing sub-optimal capital structures
(Fosberg, 2004) and can restrain the controlling shareholders from increasing debt to
reduce the dilution of control. Furthermore, monitoring by large shareholders can
mitigate the threat of being taken over (Harris and Raviv, 1988; Stulz, 1988; Du and
Dai, 2005) and the reduce-debt-for-tunneling effects (Du and Dai, 2005).
The reasons for the formation and the characteristics of business groups in
Taiwan are drastically different from those of such groups in other countries. This is
because, in Taiwan, the business groups are typically family businesses, while other
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business groups are established by forming the parent company first and the
subsidiaries later. Therefore, the scale of network linkages in family groups must be
substantially different from the scale of network linkages formed by individual
businesses. Since the subsidiaries are linked with each other through a “shareholding
relationship network” and the characteristic of being family-related in Taiwanese
business groups highlights the importance of network relationships, this study
contributes to the literature by examining business groups from the network linkage
perspective and analyzing the effect of business group supply chain network linkages
on company capital structures.
Although network relationships exist in all economic activities, they are not
easily observable. In a departure from the previous literature, this study analyzes
network linkage from the business transactions perspective. Since the purchases and
sales relationships between a firm and its trading partners form a network, we can use
trading data to examine the interactive relationships between firms. The number of
cooperating companies and the trade volume in dollar amounts then represent the
strength of a network linkage. This study adopts a sample of business groups listed on
the Taiwan Stock Exchange (TSE) and in the over-the-counter (OTC) market over the
period 2006-2008 and is based on the idea of a purchases and sales network linkage
between related parties in the cooperative industrial system. In addition to analyzing
the relationship between the corporate governance of Taiwanese business groups and
the network linkage strategies, we incorporate the effect of family governance in the
panel data linear regression model when examining the relationship between the
capital structure and the related party purchases and sales network linkage. Our results
show that family shareholdings and the seats-shareholding divergence ratio are
negatively correlated with the purchases and sales involving related parties. The effect
of the related party purchases and sales network linkage on the capital structure is also
different for family and nonfamily companies in different industry sectors. In the
information technology (IT) industry, family companies tend to provide financial
support through debt financing to related parties with a sales network linkage.
However, for non-family IT companies, the related party purchase network linkage
will increase the maturity risk in a capital structure. Finally, this study finds that the
seats-shareholding divergence ratios of IT and non-information technology (NIT)
family companies are positively and negatively correlated with debt (both short-term
and long-term debt), respectively, indicating that the seats-shareholding divergence
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ratios will influence the risk-taking tendency in companies’ capital structure decisions.
The remainder of this paper is organized as follows. Section 2 provides a
literature review on capital structures, network linkages, and corporate governance in
family firms and business groups, which form the foundation of the study. Section 3
presents the methodology and explains the data sources, research variables and
empirical models. Section 4 analyzes and discusses the empirical results. The final
section is the conclusion.
2. Literature Review
2.1 Network linkage and capital structure decisions
Since Modigliani and Miller (1958), Jensen and Meckling (1976), Myers (1977),
Ross (1977) and Myers and Majluf (1984) proposed different models for capital
structures, subsequent studies have analyzed corporate capital structures from other
perspectives, including agency theory (Jensen and Meckling, 1976; Haugen and
Senbet, 1979), bankruptcy costs (Myers, 1977; Haugen and Senbet, 1979; Titman,
1984), debt tax shields (DeAngelo and Masulis, 1980; Dammon and Senbet, 1988),
pecking order theory (Myers and Majluf, 1984), profitability (Myers and Majluf,
1984), signaling theory (Ross, 1985), growth opportunities (Myers, 1977; Titman and
Wessels, 1988) and product uniqueness (Titman and Wessels, 1988).
Johanson and Mattson (1987) propose the concept of a network linkage to
explain the basis for corporate cooperation. This idea has been widely discussed and
applied to important issues such as strategic organizations and overseas investment. A
“network” refers to the horizontal or vertical cooperation between supply chains,
through which companies combine their financial, production, marketing, personnel,
and R&D resources to form a cooperative support network, share resources, and
increase their competitive advantages (Thorelli, 1986; Jarillo, 1988). The network
structure can also help construct an effective value chain, create comparative
advantages (Porter, 1980) and develop a value-adding partnership (Johnston and
Lawrence, 1988). Therefore, industrial network linkages can increase the efficiency in
resource usage, construct a complete supply chain, and increase the company’s
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resource flexibility and adaptability. Moreover, companies can benefit from
economies of scale, economies of scope and, most importantly, increases in firm value
(Prahalad and Hamel, 1990; Blankenburg et al., 1999; Elfring and Hulsink, 2003).
There have been many studies on strategic networks, such as supply chain
linkages (Jarillo, 1988; Dyer and Singh, 1998) and network resources (Gulati, 1999).
From the strategic alliance perspective, there have been discussions on the
resource-based view, such as the close cooperative relationship between construction
material suppliers (Wernerfelt, 1984; Dierickx and Cool, 1989; Barney, 1991), the
discussions on the cooperation based on R&D (Powell et al., 1996), and on the trade
information linkages between managerial teams (Gulati and Westphal, 1999). In
addition, from the resource-based perspective, the cooperative relationship between
companies can be a form of company resources, which can increase firm value
(Thorelli, 1986; Jarillo, 1988; Johnston and Lawrence, 1988; Prahalad and Hamel,
1990; Blankenburg et al., 1999). In particular, the network linkages based on financial
resources can increase the capacity of spontaneous financing, raise the overall
company’s financial flexibility and thus reduce the company’s debt level
(Vicente-Lorente, 2001; Kuo, 2006). Kuo et al. (2003) examine the corporate capital
structure decisions from the perspective of international network linkages and find
that the level of working capital in the US IT industry has a positive relationship with
the capital structures in the Taiwanese IT industry. Therefore, during the development
of a company, the network relationship does indeed affect the capital structure. This is
a completely new issue and deserves further study.
2.2 Corporate governance and capital structure decisions
Even though debt financing can reduce agency conflicts and increase firm value
(Grossman and Hart, 1980; Jensen, 1986), the opportunistic behavior of management
in making financing decisions will be influenced by the structure of equity ownership
(Demsetz, 1983; Shleifer and Vishny, 1986; Agrawal and Mandelker, 1990). The
difference between the goals of the managers and of the shareholders is an important
factor influencing a firm’s capital structure decisions (Morellec, 2004).
Fosberg (2004) finds that a firm’s debt ratio is positively correlated with the
director shareholding ratio. Fosberg (2004) argues that the monitoring by large
shareholders can effectively reduce the agency problem of choosing the sub-optimal
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capital structure by the company. The empirical finding of Arslan and Karan (2006)
shows that the degree of ownership concentration is correlated with the debt maturity.
Harris and Raviv (1988), Stulz (1988) and Du and Dai (2005) all suggest that
controlling shareholders, based on entrenchment motives, prefer debt financing. This
is because increasing debts can avoid the dilution of control and reduce the takeover
risk. Du and Dai (2005) propose another view and suggest that a higher debt level will
result in higher pressure to pay off the debt. Therefore, more of the company’s
earnings will be used for paying debts. Consequently, controlling shareholders are
restrained from misappropriating firm’s assets through self-dealing. However,
controlling shareholders may choose to reduce the firm’s financial leverage to cover
up their misappropriating behavior and this is the “reduced-debt-for-tunneling effect”
hypothesis.
Mahaffy (1998) and Gilles (1999) find that concentrated ownership is
detrimental to firm performance, especially when controlling shareholders are
individuals or family members. However, James (1999) and Schneider (2002) both
suggest that as family shareholders are more concerned about the companies’ future
growth and longevity, family firms tend to have longer horizons for the companies’
operations. Anderson et al. (2003) find that family control can provide more resource
aids to a company and increase firm value. Anderson and Reeb (2003) also find that
family ownership can provide more stringent monitoring and long-term attention to
the firm’s operations. Moreover, family involvement in a firm’s operations can
increase the firm’s competitive advantages, thereby increasing its long-term and
short-term performance. Carney and Gedajlovic (2002), Chang (2003), and Joh (2003)
all find that firms with controlling family ownership have higher operational
performance. Anderson and Reeb (2004) suggest that family shareholders will adopt
the maximization of firm performance as their goals. However, Hutchinson (2002)
finds that when a board of directors is dominated by company insiders, it is more
likely that there will be opportunistic behavior. Filatotchev et al. (2005), however, do
not find a significant relationship between family ownership and firm performance.
The studies by Yeh et al. (2001) and Yeh (2005) find that large family shareholders in
Taiwanese corporations tend to utilize pyramidal structures, cross-shareholdings,
indirect shareholdings, the intervention of management, board of director seats and
important managerial positions to meet the government requirement of separation of
ownership and control.
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Since family shareholders often use cross-shareholdings within the business
groups to secure the management ownership or use board of director seats or voting
rights to strengthen the rights of management and decision-making, it is likely that
there will be a divergence between control rights and cash flow rights. Grossman and
Hart (1988) and Harris and Raviv (1988) suggest that when voting rights diverge from
cash flow rights, there will be a negative entrenchment effect on firm values. Du and
Dai (2005) further propose that the divergence of shareholder control rights and cash
flow rights may result in the non-dilution entrenchment effect, debt signaling effect,
and reduce-debt-for-tunneling effect. Du and Dai (2005) find that the divergence of
shareholder control rights from cash flow rights is positively correlated with the firm’s
financial leverage. That is, in order to avoid the dilution of control, large controlling
shareholders tend to increase a firm’s financial leverage, thereby increasing the
risk-taking tendency in the firm’s capital structure decisions.
Overall, based on the study by Banerjee et al. (2008) who argue from the related
party perspective that capital structure decisions are affected by the buyer-supplier
relationship, this study examines a sample of Taiwanese business groups. In Taiwan,
companies affiliated to a business group often engage in related party purchases and
sales as a form of supply chain cooperation. Therefore, this study uses related party
purchases and sales linkages to proxy for the network linkage, and at the same time
takes into consideration the family governance in Taiwanese business groups.
Accordingly, this study will incorporate the influence of family governance in
business groups on network linkage strategies and use a panel data linear regression
model to test the relationship between network linkages and capital structure
decisions.
3. Methodology
3.1 Data
So far, there has still not arisen a consistent and definitive definition for
“business groups” in the literature (Leff, 1978; Granovetter, 1995; Collin, 1998;
Guillen, 2000; Chung, 2001; Khanna and Rivkin, 2001). Therefore, this study adopts
the definition of business groups used in the Taiwan Economic Journal (TEJ) database;
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that is, companies with the same ultimate controller that meet the following
requirements: (1) the primary shareholders are members of the same family (the
primary shareholders refer to the top-ten large shareholders or shareholders with more
than 5% shareholdings); (2) at least one third of the directors on the board of directors
are the same; (3) primary management is the same; the chairperson or CEO is the
same; (4) there is a controlling or dependency relationship with real controls; and (5)
there is a mutual investment relationship. This study is based on Taiwanese listed and
OTC companies, and covers the period from 2006 to 2008. The empirical data are
obtained from the corporate financial statement database and corporate governance
database of the TEJ, the Market Observation Post System of the Taiwan Stock
Exchange (MOPS), and companies’ annual reports.
This study follows the methodology used in Benerjee et al. (2008) who construct
the list of buying and selling firms for each company based on the buyer-supplier list
released by firms. In this study, our sample is based on the core companies of the
business groups in the TEJ database. In order to obtain the information on related
party purchases and sales (i.e., the amount of related party transactions exceeding
NT$100 million or firms with paid-in capital of over 20%) for the core companies, we
go through each related party transaction one by one as disclosed in the Market
Observation Post System of the Taiwan Stock Exchange and companies’ annual
reports. This is the first-level purchases and sales linkage. To construct the
second-level purchases and sales linkage, we obtain information on firms that have a
purchasing or sales relationship with the first-level firms. We treat all related buyers
and sellers as the same group and continue doing so until no further purchase and
sales linkages can be constructed. While compiling this information, we have
observed duplicated information. Thus, during the process of constructing purchasing
and sales information, if a firm’s data linked to a specific company have appeared
once already, we do not repeat the process. In addition, if a firm linked to a particular
company is also the core company of another business group, we do not continue with
the process. Thus, after excluding companies with incomplete sales and purchases
data, banks and holdings companies, we obtain the related party purchases and sales
data for 323 companies (or 969 observations). Moreover, we exclude 8 companies
with incomplete financial statement data, and the final sample includes 315
companies or a total of 945 observations.
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3.2 Variables
This study uses long-term debt ratios and short-term debt ratios as the proxy
variables for capital structure. In terms of how to measure the degree of network
linkages between companies, the previous literature has not provided a consistent and
definitive definition. Since this study focuses on Taiwanese business groups, we need
to take into consideration their unique characteristics, such as the family network,
organizational network and human relations network. In Taiwan, companies affiliated
to business groups are often involved in related party purchasing and sales
transactions. In other words, the network linkages in Taiwanese business groups are
achieved through supply chain cooperation and the division of labor. Therefore, in
contrast to the definition of network linkages used in the previous literature,2 this
study defines network linkages from the commercial transactions perspective. Using
the financial data of business groups, we adopt two measures for the strength of the
network linkages in business groups: (1) the number of related party purchases and
sales (i.e., the number of related firms purchased from and sold to); and (2) the ratio
of related party purchases and sales in dollar amounts.3 In the first measure, the
amounts of related party purchases and sales are defined so as to include only the
purchasing or selling firms with related party transactions exceeding NT$100 million
or with paid-in capital of over 20%. The second measure (i.e., the ratio of related
party purchases and sales in dollar amounts) is defined as the ratio of purchases from
(or sales to) related parties with transactions exceeding NT$100 million or with
paid-in capital of over 20% to the total amount of purchases and sales.
2
Previous studies have discussed many different aspects of the network relationship, for example, the
human relations network which is based on the interactions between people, the knowledge network
formed by exchanges of knowledge and experience, the alliance network created by partner
relationships, the social network, and the business network created by interdependent corporations
(Johanson and Mattsson, 1987; Coleman, 1990; Uzzi, 1997).
3
This is based on the Statements of Financial Accounting Standards No. 6 “Related Party Disclosures”,
which defines a related party as follows: “a party is related to an entity if one party can exercise control
or significantly influence the financing policies of the other party. Entities under common ownership or
control are also deemed as related parties.” “An entity that satisfies one of the following conditions is
referred to as a related party of an enterprise (except when it can prove that there is no control or
significant influence): (a) an investee accounted for by the equity method; (b) an investor who uses the
equity method to account for the investment in the enterprise; (c) the director or the president is the
same person as the chairman or the president of another company, or is the spouse or second immediate
family of other companies; (d) a non-profit organization of which the funds donated from the enterprise
exceeds one third of the non-profit organization’s total funds; (e) a director, supervisor, president,
vice-president, assistant vice-president and the head of department; (f) the spouse of a director,
supervisor or president of the enterprise; and (g) the immediate and second immediate families of the
enterprise’s chairman and president.”
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Controlling shareholders of pyramidal business groups tend to use related party
transactions, for example, through buying assets at high prices and selling them at low
prices, to obtain personal benefits (Almeida and Wolfenzon, 2005; Riyanto and
Toolsema, 2008; Cheung et al., 2009). In addition, as family shareholders hold large
cash flow rights, opportunistic behavior is likely to occur. Family shareholders may
sacrifice firm performance for the benefit of family members (Yammeesri and Lodh,
2004). Furthermore, with the divergence of voting rights from cash flow rights, large
shareholders or family shareholders may use pyramidal and cross-shareholdings to
misappropriate company assets (Bertrand et al., 2002; Faccio and Lang, 2002; Lins,
2003; Morck et al., 2005; Bozec and Laurin, 2008). Therefore, to take into account the
family governance of business groups, we include two proxy variables for corporate
governance, namely, the shareholding ratio by controlling family members and the
seats-shareholding divergence ratio.
Capital structures are also influenced by other factors, such as firm size
(Remmers et al., 1974; Scott and Martin, 1975; Warner, 1977; Ferri and Jones, 1979;
Aggarwal, 1981; Titman and Wessels, 1988; Friend and Lang, 1988; Moh’d et al.,
1998; Smith and Watts, 1992; Kuo et al., 2000), profitability (Myers and Majluf, 1984;
Kester, 1986; Friend and Lang, 1988; Baskin, 1989; Titman and Wessels,1988; Moh’d
et al., 1998; Kuo et al., 2000), and long-term investments. Therefore, this study uses
the number of affiliated companies in a business group and total assets as proxy
variables for group size. Moreover, the return on assets and the long-term investment
to equity ratio are used as proxy variables for profitability and long-term investments,
respectively. Table 1 presents the operational definitions of research variables.
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Table 1 Operational definitions of research variables
Variables
Capital structure
Network
Linkage
Corporate
Governance
Firm
Characteristics
Abbreviation
Definitions
Long-term debt
ratio
LD
Long-term debt ratio = (Long-term debts /
Total assets)*100%
Short-term debt
ratio
SD
Short-term debt ratio = (Short-term debts /
Total assets)*100%
Number of related
firms sold to
Sale_No
The number of related firms sold to which
have trade amounts exceeding NT$100
million or have paid-in capital of over 20%.
Number of related
firms purchased
from
Supply_No
The number of related firms purchased from
which have trades amounts exceeding
NT$100 million or have paid-in capital of
over 20%.
Related party sales
ratio
Sale_ratio
Related party sales ratio = (Dollar amount of
sales to related parties with transactions
exceeding NT$100 million or with paid-in
capital of over 20% / Total dollar amount of
sales) *100%
Related party
purchase ratio
Supply_ratio
Related party purchase ratio = (Dollar amount
of purchases from related parties with
transactions exceeding NT$100 million or
with paid-in capital of over 20% / Total dollar
amount of purchases) *100%
Family member
shareholding ratio
FMown
Family member shareholding ratio =
(Shareholdings of ultimate controlling
shareholder in the family / Total shares of the
firm)*100%
Seats-shareholding
divergence ratio
Diverge
Group size
GpSize
Number of affiliated companies in a business
group.
Total assets
LnTA
Log of total assets.
Return on assets
ROA
Return on assets = (Net income after tax /
Average total assets)*100%
Long-term
investment ratio
LIratio
Long-term investment ratio = (Long-term
investments / Shareholder equity)*100%
Seats-shareholding divergence ratio = Family
member seats ratio / Family member
shareholding ratio; where the family member
seats ratio = (Seats held by ultimate
controlling shareholder in the family / Total
seats of the firm)*100%; family member
shareholding ratio = (Shareholdings of
ultimate controlling shareholder in the family
/ Total shares of the firm)*100%
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3.3 Empirical models
As this study uses cross-sectional and time-series panel data, we adopt the panel
data linear regression model for the empirical analysis. The advantage of using
cross-sectional and time-series data is that the problem of estimation errors caused by
insufficient observations can be solved or reduced, thereby increasing the degrees of
freedom for test statistics, reducing the multicollinearity between explanatory
variables, and most importantly, increasing the efficiency of model estimation (Hsiao,
1985). In addition, the panel data allow for multiple observations of subjects (firms)
or time periods. Therefore, the potential relationship between unobserved variables
and explanatory variables can be more fully incorporated in the behavioral models.
The mutual learning behavior between subjects (or firms) can also be analyzed. Hence,
we are able to perform a more in-depth analysis of important economic issues that
have been neglected by conventional tests of cross-sectional or time-series data.
In general, based on the characteristics of the intercept items, the models can be
classified into fixed effects models and random effects models (Hsiao, 2003). The
fixed effects model can be further categorized into two forms: the ordinary least
squares model (OLS) that has fixed constant intercepts, and the fixed effects model
(or the least squares dummy variable model) where the intercept item can reflect the
heterogeneity between individual subjects (or firms). The second model is the random
effects model, also known as a variance components model, which has random
intercepts. Therefore, in order to avoid the bias of OLS which ignores the
cross-sectional self-discrepancy effect of individual firms, this study uses the F-test,
the Lagrange multiplier test and Hausman’s (1978) test to determine the choice of
panel data linear regression model and follow-up empirical analyses.4
First, use the F test, F  [(RRSS  URSS ) / N  1] [URSS /( NT  ( N  K  1))] ~ F(N-1, NT-N-K+1), to test
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the null hypothesis  0 : 1   2     n (i.e., accepting the least squares model) and the alternative
hypothesis H1:  i not all equal (i=1,…,N) (i.e., accepting the fixed effects model), where RRSS is the
residual sum of squares of the least squares model; URSS is the residual sum of squares of the fixed
effects model; N indicates the number of observations; T indicates the time periods; and K is the
number of independent variables. Then, use the Lagrange multiplier test (LM test) to test the null
hypothesis
0
: constant intercept,  2  0 (i.e., accepting the least squares model), and the
alternative hypothesis  1 : intercept as a random variable,  2  0 (i.e., accepting the fixed effects
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First, we test the effect of family governance of a business group on its related
party purchases and sales network linkages. Then, we analyze the relationship
between the related party purchases and sales network linkages and the capital
structure.
3.3.1 The model on the relationship between the family governance of business
groups and network linkages
This study uses the related party purchases and sales network linkage (NL) as the
dependent variable of the model, which is measured by the number of related firms
sold to (Sale No.), the number of related firms purchased from (Supply No.), the ratio
of related party sales (Sale ratio), and the ratio of related party purchases (Supply
ratio). The independent variables include the family member shareholding ratio
(FMown) and the seats-shareholding divergence ratio (Diverge), with group size
(GpSize) as the control variable. The empirical model is as follows:
NLit   i  1 FMownit   2 Divergeit   3GpSizeit   it
(1)
3.3.2 The model on the relationship between related party purchases and sales
network linkages and capital structures
This study uses the capital structure (Debt), measured by the short-term debt
ratio (SD) and the long-term debt ratio (LD), as the dependent variable. The related
party purchases and sales network linkage (NL), measured by the number of related
firms sold to (Sale No.), the number of related firms purchased from (Supply No.), the
ratio of related party sales (Sale ratio), and the ratio of related party purchases (Supply
ratio), and the seats-shareholding divergence ratio (Diverge) are included as
model) for the test.
That is,
N T
 N T

LM  ( NT / 2(T  1)) [  (  uˆ it ) 2 /   uˆ it2 ]  1 ~  2 (1)
n 1t 1
 n 1 t 1

where ûit is the
residual estimated using least squares;  2 (1) is a Chi-square test with 1 degree of freedom. Finally,
use Hausman’s (1978) test, i.e.,


  ˆ *  ˆ ' Var ( ˆ * )  Var ( ˆ ' )
 ˆ
1
*

 ˆ ' ~  2 k  to test
the null hypothesis  0 : E  i 1it ,..., kit    (i.e., accepting the random effects model), and the
alternative hypothesis  1 : E  i 1it ,..., kit    (i.e., accepting the fixed effects model) for the test,
in which ˆ * is the regression coefficient matrix estimated using the least squares model; ̂ ' is the
regression coefficient matrix estimated using the fixed effects model; and  2 (k ) is a Chi-square test
with k degrees of freedom.
15
independent variables. Total assets (LnTA), the return on assets (ROA) and the
long-term investment ratio are the control variables. The empirical model is as
follows:
p
p
k 1
k 1
Debt it   i    k NLkit  Divergeit    k Contolkit   it
(2)
4. Empirical Results
4.1 Sample data analysis
The descriptive statistics of each variable are presented in Table 2. We find that
based on a sample of 315 firms, the short-term debt ratio, the ratios of the related
party sales and purchases, the family member shareholding ratio and the
seats-shareholding divergence ratio all exhibit greater differences in maximum values,
minimum values and standard deviations. Therefore, this study further categorizes the
sample into groups based on the type of corporations, the degree of seats-shareholding
divergence and the industry sectors. Then, we use a two-sample t-test to see if there
are any significant differences between groups.
Schneider (2000) suggests that family shareholders are more concerned about the
firm’s growth and longevity. James (1999) also argues that family firms hold longer
horizons for the operation of the firms. Anderson et al. (2003) further suggest that
family firms place stronger emphasis on sustained operations and reputations. On the
whole, there are more incentives for family shareholders to monitor the firms, thereby
increasing firm performance and reducing the operational risks. Hence, this study
classifies the sample firms according to whether they are run by families. If a
company meets one of the following requirements, then it is classified as a “family
firm”: (1) the positions of chairman of the board of directors and CEO are held by
members of the same family; (2) the family controls more than 50% of the board seats
(excluding friendly seats) while the friendly director seats ratio and the external
director seats ratio are both lower than 33%; (3) the family controls more than 33% of
the board seats while at least 3 directors or managers are from the ultimate controlling
family; and (4) the family controlled shareholding ratio is higher than the necessary
16
controlling shareholding ratio (i.e., with the controlling shareholding ratio higher than
20%). Under this principle of classifications, there are 177 family firms and 138
non-family firms.
Table 2 Descriptive statistics of research variables
Variables
Min
Max
Average
Std. Dev.
Capital
Long-term debt ratio
0.00
51.71
7.96
9.32
structure
Short-term debt ratio
2.20
86.79
26.95
14.34
Sale No.
0
29
2.68
3.54
Network
Supply No.
0
17
1.74
2.08
Linkage
Sale Ratio
0
100
21.98
23.43
Supply Ratio
0
100
29.81
31.92
.00
100.00
18.65
21.86
.00
16.37
1.61
2.20
186
648663
31300
78428
-47.38
53.10
5.22
9.61
.00
197.23
48.37
32.69
Corporate
Family member shareholding
Governance
ratio
Seats-shareholding divergence
ratio
Firm
Total assets (Million NT$)
Characteristics
ROA
Long-term investment ratio
Note: Long-term debt ratio is (long-term debts/total assets)* 100%. Short-term debt ratio is (short-term debts/ total assets)*100%.
Sale No. is the number of related firms sold to which have trade amount exceeding NT$100 million or have paid-in capital of
over 20%. Supply No. is the number of related firms purchased from which have a trade amount exceeding NT$100 million or
have paid-in capital of over 20%. Sale ratio is (dollar amount of sales to related parties with transactions exceeding NT$100
million or with paid-in capital of over 20% / total dollar amount of sales) *100%. Supply ratio is (dollar amount of purchases
from related parties with transactions exceeding NT$100 million or with paid-in capital over 20% / total dollar amount of
purchases) *100%. Family member shareholding ratio is (shareholdings of ultimate controlling shareholder in the family / total
shares of the firm)*100%. Seats-shareholding divergence ratio is (family member seats ratio / family member shareholding ratio),
in which the family member seat ratio = (seats held by the ultimate controlling shareholder in the family / total seats of the
firm)*100%; the family member shareholding ratio = (shareholdings of ultimate controlling shareholder in the family / total
shares of the firm)*100%. Total assets is the total amount of assets. ROA is (net income after tax / average total assets) *100%.
Long-term investment ratio is (long-term investment / shareholder equity)*100%.
The studies by Grossman and Hart (1988) and Harris and Ravivi (1988) both find
that the divergence between voting rights and cash flow rights can cause a negative
entrenchment effect. Du and Dai (2005) also find that the divergence between
shareholders’ voting rights and cash flow rights can increase the risk-taking tendency
in a firm’s capital structure decisions. Therefore, this study uses a seats-shareholding
divergence ratio of 1 as the point of division. That is, if a firm’s seats-shareholding
divergence ratio is larger than 1, it is classified as “high divergence”, totaling 160
firms. If the seats-shareholding divergence ratio is smaller than or equal to 1, then it is
classified as “low divergence”, totaling 155 firms.
Many studies support the view that the industrial group is an important factor to
capital structures (Scott and Martin, 1975; Errunza, 1979; Ferri and Jones, 1979;
17
Bowen et al., 1982; Aggarwal and Baliga, 1987; Sekely and Collins, 1988), while
only a small number of studies hold a different view (Belkaoui, 1974; Remmers et al.,
1974; Stonehill et al., 1974; Naidu, 1983). In Taiwan, the information technology
industry has far exceeded the traditional industry since the 1990s, and has become the
main player in the development of the Taiwan economy. It also provides the direction
for Taiwan’s future development. In fact, the information technology with a
comparatively high degree of internationalization has led the steady growth in
Taiwan’s GNP. Therefore, this study uses SIC codes for categorizing the industry
sectors. Specifically, firms are classified into the information technology (IT) industry
and non-information technology (NIT) industry. The IT industry, which has a total of
186
firms,
includes
semiconductors,
computer
peripherals,
optoelectronics,
communications networks, components, electronic access, information services, and
other electronics. The NIT industry, which has a total of 129 firms, includes cement,
food, plastics, textiles, electrical goods, chemicals, biotechnology, steel, rubber,
automobiles, building materials, construction, shipping, trade, department stores, and
others.
The t-test results presented in Panel A and Panel B of Table 3 show that family
or high divergence firms have significantly higher long-term debt ratios, family
member shareholding ratios, seats-shareholding divergence ratios, and long-term
investment ratios than non-family or low divergence firms. Non-family or low
divergence firms exhibit significantly higher short-term debt ratios and related party
purchases and sales in both the number of related firms and the ratio, as well as the
return on assets than family or high divergence firms. Furthermore, the t-test results in
Panel C show that the IT industry has a significantly higher short-term debt ratio and
related party sales and supply ratio than the NIT industry, while the NIT industry has
a significantly higher long-term debt ratio, family member shareholding ratio,
seats-shareholding divergence ratio, and long-term investment ratio than the IT
industry.
Overall, the t-test results show that the shareholding control and seat control of
family firms have a higher degree of divergence. Family firms also prefer long-term
debt financing and exhibit a certain relationship with long-term investments.
Non-family firms tend to use short-term debt financing and may be influenced by
related party purchases and sales. In addition, the short-term debt financing of the IT
18
industry may be influenced by the amount of sales and purchases by related parties,
while the long-term debt financing of the NIT industry may be influenced by family
governance and long-term investments.
Table 3 Test on differences between cluster samples
Panel A: Firm type
Family
Variables
Long-term debt ratio
Short-term debt
ratio
Sale No.
Nonfamily
t-test
Panel B: Seats-shareholding
divergence
High
divergence
Low
divergence
t-test
Panel C: Industry sector
IT
NIT
t-test
8.69
7.01
2.767***
8.88
7.00
3.125***
6.66
9.82
-5.131***
25.42
28.91
-3.714***
24.93
29.04
-4.441***
28.45
24.79
3.936***
2.39
3.05
-2.763***
2.44
2.92
-2.092**
2.60
2.79
-0.744
Supply No.
1.55
1.98
-2.934***
1.59
1.89
-2.205**
1.73
1.75
-.144
Sale ratio
19.59
25.06
-3.534***
20.12
23.91
-2.488**
24.25
18.71
3.688***
Supply ratio
27.45
32.84
-2.545***
26.39
33.35
-3.361***
34.36
23.25
5.529***
Family member
shareholding ratio
Seats-shareholding
divergence ratio
Return on assets
33.20
0
39.896***
29.98
6.96
19.020***
11.22
29.36
-13.005***
2.88
0
29.552***
3.10
0.08
29.251***
1.30
2.06
-5.496***
4.82
5.73
-1.398
4.65
5.81
-1.846*
5.17
5.30
-0.228
Long-term
investment ratio
Number of firms
(observations)
53.76
41.45
5.842***
56.21
40.28
7.714***
44.68
53.68
-4.204***
177
138
160
155
186
129
(531)
(414)
(480)
(465)
(558) (387)
Note: Long-term debt ratio is (Long-term debts/total assets)* 100%. Short-term debt ratio is (short-term debts/ total
assets)*100%. Sale No. is the number of related firms sold to which have trade amounts exceeding NT$100 million or have
paid-in capital of over 20%. Supply No. is the number of related firms purchased from which have trade amounts exceeding
NT$100 million or have paid-in capital over 20%. Sale ratio is (dollar amount of sales to related parties with transactions
exceeding NT$100 million or with paid-in capital of over 20% / total dollar amount of sales) *100%. Supply ratio is (dollar
amount of purchases from related parties with transactions exceeding NT$100 million or with paid-in capital of over 20% / total
dollar amount of purchases) *100%. Family member shareholding ratio is (shareholdings of ultimate controlling shareholder in
the family / total shares of the firm)*100%. Seats-shareholding divergence ratio is (family member seats ratio / family member
shareholding ratio), in which the family member seat ratio = (seats held by the ultimate controlling shareholder in the family /
total seats of the firm)*100%; the family member shareholding ratio = (shareholdings of ultimate controlling shareholder in the
family / total shares of the firm)*100%. ROA is (net income after tax / average total assets) *100%. Long-term investment ratio
is (long-term investment / shareholder equity)*100%. This study classifies observations based on whether they are corporations
run by families; if a company fulfills any of the following requirements, it is then classified as a family firm: (1) the positions of
chairman of the board of directors and of the CEO are held by members of the same family; (2) the family controls more than
50% of the board seats (excluding friendly seats) while the friendly director seats ratio and the external director seats ratio are
both lower than 33%; (3) the family controls more than 33% of the board seats, while at least 3 directors or managers are from
the ultimate controlling family; (4) the family-controlled shareholding ratio is higher than the necessary controlling shareholding
ratio (i.e., with the controlling shareholding ratio higher than 20%) . If a firm’s seats-shareholding divergence ratio is larger than
1, it is classified as high divergence; if the seats-shareholding divergence ratio is smaller than or equal to 1, it is then classified as
low divergence. Semiconductors, computer peripherals, optoelectronics, communications networks, components, electronic
access, information services, and other electronics are classified as IT firms. Cement, food, plastics, textiles, electrical, chemicals,
biotechnology, steel, rubber, automobiles, building materials, construction, shipping, trade, department stores, and other firms are
classified as NIT firms. ***, ** and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
4.2 Analysis of the relationship between the family governance of business
groups and the related party purchases and sales network linkage
The t-test results of Table 3 show that there may be a certain degree of
relationship between the related party purchases and sales network linkage, firm type
and the seats-shareholding divergence ratio. Therefore, this study further analyzes
19
how the family governance of business groups affects the related party purchases and
sales network linkage. The results presented in Table 4 show that the ratio of related
party sales and the number of related firms sold to are mainly influenced by the family
member shareholding ratio. Specifically, the higher the family member shareholding
ratio is, the lower the related party sales ratio and related party sales number will be.
In addition, the related party supply ratio is negatively related to the
seats-shareholding divergence ratio. Therefore, the greater the divergence is, the lower
the related party supply ratio will be. On the whole, the family member shareholding
ratio and the seats-shareholding divergence ratio are negatively correlated with the
related party sales and supply ratios. Therefore, there is no evidence that family
shareholders will exhibit opportunistic behavior when the controlling seats diverge
from the controlling shareholdings.
Table 4 Results on related party purchases and sales and corporate governance
Variable
Related party sales
Related party sales
Related party
ratio
sales number
Related party purchases
Related party
Related party
supply ratio
supply number
Family member
shareholding ratio
-0.091*
-0.015***
-0.050
-0.006
(-1.863)
(-3.234)
(-0.785)
(-0.865)
Seats-shareholding
divergence ratio
-0.452
-0.003
-1.175**
-0.046
(-1.112)
(-0.072)
(-2.370)
(-0.976)
Group size
0.358**
0.180***
0.463**
-1.700***
(2.318)
(13.645)
(2.163)
(-10.537)
21.974***
1.747***
29.474***
(11.099)
(9.942)
(10.912)
F-test
18.18***
38.19***
29.74***
27.90***
LM-test
680.21***
1.46
771.92***
702.74***
0.78
0.01
0.40
123.29***
0.8522
0.164
0.9061
0.900
Intercept
Hausman-test
Adj R2
Note: Related party purchases and sales number are the number of related firms purchased from and sold to with transactions
exceeding NT$100 million or with paid-in capital of over 20%. Sale ratio is (dollar amount of sales to related parties with
transactions exceeding NT$100 million or with paid-in capital of over 20% / total dollar amount of sales) *100%. Supply ratio is
(dollar amount of purchases from related parties with transactions exceeding NT$100 million or with paid-in capital of over 20%
/ total dollar amount of purchases) *100%. Family member shareholding ratio is (shareholdings of ultimate controlling
shareholder in the family / total shares of the firm)*100%. Seats-shareholding divergence ratio is (family member seats ratio /
family member shareholding ratio), in which the family member seats ratio = (seats held by the ultimate controlling shareholder
in the family / total seats of the firm)*100%; the family member shareholding ratio = (shareholdings of ultimate controlling
shareholder in the family / total shares of the firm)*100%. Group size is the number of affiliated companies in a business group.
***, ** and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
20
4.3 Analysis of the relationship between related party purchases and sales
network linkages and capital structure
The t-test results for firm types and industry sectors in Table 3 indicate that the
differences between family and non-family firms and between IT and NIT industries
in relation to related party purchases and sales network linkages may have
heterogeneous influences on capital structure decisions. In addition, the results from
Table 4 show that the family member shareholding ratio and the seats-shareholding
divergence ratio are significantly negatively correlated with the related party
purchases and sales. Therefore, by taking the family governance of business groups
into consideration, this study further analyzes the relationship between related party
purchases and sales network linkages and corporate capital structures. In particular,
we classify 315 sample firms into four subsample groups based on firm types
(family/non-family) and industry sectors (IT/NIT industry) for further panel data
linear regression analysis. The number of firms in each subsample group is as follows:
75 IT family firms, 111 IT non-family firms, 102 NIT family firms and 27 NIT
non-family firms. The empirical results are presented in Table 5 and Table 6.
21
Table 5 IT industry panel data linear regression analysis results
Variables
Related party sales
no.
Related party
supply no.
Network
Linkage
Related party sales
ratio
Related party
supply ratio
Corporate
governance
Seats-shareholding
divergence ratio
Total assets (LnTA)
Firm
characteristics
Return on assets
(ROA)
IT family firms
Short-term
Long-term
debt ratio
debt ratio
0.058
-0.719*
(0.094)
(-1.712)
(2.809)
(0.194)
0.680
0.359
0.777
-0.0663**
(1.019)
(0.629)
(1.547)
(-2.319)
0.109*
0.065*
-0.113***
-0.010
(1.782)
(1.803)
(-3.219)
(-0.501)
-0.079
0.015
0.001
-0.001
(-1.614)
(0.607)
(0.026)
(-0.041)
0.591
-0.578**
(0.993)
(-1.978)
8.802***
4.197***
-1.091
1.976***
(3.869)
(5.725)
(-1.244)
(4.069)
-0.071
-0.169***
-0.008
-0.098***
(-1.353)
(-3.509)
(-0.148)
(-2.909)
Long-term
investment ratio
Intercept
Panel data
Model tests
IT non-family firms
Short-term
Long-term
debt ratio
debt ratio
1.045***
0.041
0.029
0.022
(1.184)
(1.349)
-59.245***
45.497***
-24.184***
(-5.271)
(3.380)
(-3.258)
F-test
LM-test
23.82***
7.68***
9.40***
7.86***
136.94***
76.43***
161.37***
141.73***
Hausman-test
41.35***
6.03
9.38
5.78
0.8919
0.7098
9.38
5.78
2
Adj R
Number of firms (observations)
75 firms (225)
111 firms (333)
Note: Long-term debt ratio is (long-term debts/total assets)* 100%. Short-term debt ratio is (short-term debts/ total assets)*100%.
Sale No. is the number of related firms sold to which have trade amounts exceeding NT$100 million or have paid-in capital of
over 20%. Supply No. is the number of related firms purchased from which have trade amounts exceeding NT$100 million or
have paid-in capital of over 20%. Sale ratio is (dollar amount of sales to related parties with transactions exceeding NT$100
million or with paid-in capital of over 20% / total dollar amount of sales) *100%. Supply ratio is (dollar amount of purchases
from related parties with transactions exceeding NT$100 million or with paid-in capital of over 20% / total dollar amount of
purchases) *100%. Family member shareholding ratio is (shareholdings of ultimate controlling shareholder in the family / total
shares of the firm)*100%. Seats-shareholding divergence ratio is (family member seats ratio / family member shareholding ratio),
in which the family member seat ratio = (seats held by the ultimate controlling shareholder in the family / total seats of the
firm)*100%; the family member shareholding ratio = (shareholdings of ultimate controlling shareholder in the family / total
shares of the firm)*100%. Total assets is the log of total assets. ROA is (net income after tax / average total assets) *100%.
Long-term investment ratio is (long-term investment / shareholder equity)*100%. IT industry includes semiconductors, computer
peripherals, optoelectronics, communications networks, components, electronic access, information services, and other
electronics; in addition, if a firm fulfills any of the following requirements, it is classified as a family firm: (1) the positions of
the chairman of the board of directors and the CEO are held by members of the same family; (2) the family controls more than
50% of the board seats (excluding friendly seats), while the friendly director seats ratio and the external director seats ratio are
both lower than 33%; (3) the family controls more than 33% of the board seats while at least 3 directors or managers are from the
ultimate controlling family; (4) the family-controlled shareholding ratio is higher than the controlling shareholding ratio (i.e.,
with the controlling shareholding ratio higher than 20%). ***, ** and * denote statistical significance at the 1%, 5%, and 10%
levels, respectively.
22
From Table 5, we see that, for IT family firms, the long-term debt ratio and
short-term debt ratio are significantly positively correlated with the related party sales
ratio. The long-term debt ratio is also significantly negatively correlated with the
related party sales number and the seats-shareholding divergence ratio. As for the IT
non-family firms, the short-term debt ratio is significantly positively correlated with
the related party sales number and significantly negatively correlated with the related
party sales ratio. Furthermore, the long-term debt ratio is significantly negatively
correlated with the related party supply number.
Table 6 shows that, for NIT family firms, the short-term debt ratio is
significantly positively correlated with the related party supply number and
significantly negatively correlated with the related party sales ratio. The long-term
debt ratio is significantly negatively correlated with the seats-shareholding divergence
ratio. In addition, while the long-term investment ratio is significantly positively
correlated with the long-term debt ratio of NIT family firms, it is significantly
negatively correlated with the long-term debt ratio of NIT non-family firms.
In addition, this study finds that the influence of related purchases and sales
network linkages on capital structures is more evident in the IT industry (including
family and non-family firms) and NIT family firms. For IT family firms, when the
related party sales are more diverse (i.e., when the number of related firms sold to is
high), the firm’s long-term debt financing is significantly lower. For NIT family firms,
when the related party supply is more diverse (i.e., when the number of related firms
purchased from is high), the firm’s short-term debt financing is significantly higher.
Moreover, the related party sales ratio increases with the level of long-term and
short-term debt financing levels for IT firms, but decreases with the short-term debt
financing for NIT family firms. This shows that IT family firms tend to use debt to
finance operational capital requirements arising from related party sales.
23
Table 6 NIT industry panel data linear regression analysis results
Variables
Related party sales no.
Corporate
governance
Firm
characteristics
NIT non-family firms
Short-term debt
Long-term debt
ratio
ratio
0.360
(1.327)
1.181**
(1.973)
-0.102**
(-2.541)
-0.095
(-0.491)
-0.249
(-0.595)
-0.002
(0.936)
-1.351
(-1.231)
-1.032
(-1.337)
0.035
(0.246)
1.169
(1.360)
0.551
(0.912)
-0.129
(-1.083)
Related party supply ratio
0.003
(0.070)
-0.022
(-0.826)
-0.058
(-0.501)
0.113
(1.169)
Seats-shareholding
divergence ratio
Total asset (LnTA)
0.450
(1.265)
-0.478*
(-1.735)
-4.747***
(-4.816)
2.162***
(3.352)
33.984***
(3.745)
5.033
(0.682)
-0.097
(-1.139)
-0.115
(-1.636)
0.094***
(4.883)
-0.566***
(-3.988)
-0.283*
(-1.774)
-0.346**
(-2.268)
99.018***
(6.418)
-27.230***
(-2.766)
Related party supply no.
Network
linkage
NIT family firms
Short-term debt
Long-term debt
ratio
ratio
Related party sales ratio
Return on Assets (ROA)
Long-term investment
ratio
Intercept
Panel data
Model tests
F-test
16.76***
8.29***
15.29***
17.09***
LM-test
195.59***
106.13***
43.09***
30.60***
Hausman-test
10.33
11.83
18.51***
17.94**
Adj R2
0.8480
0.7226
0.8510
0.8690
Number of firms (observations)
102 firms (306)
27 firms (81)
Note: Long-term debt ratio is (long-term debts/total assets)* 100%. Short-term debt ratio is (short-term debts/ total assets)*100%.
Sale No. is the number of related firms sold to which have trade amounts exceeding NT$100 million or have paid-in capital of
over 20%. Supply No. is the number of related firms purchased from which have trade amounts exceeding NT$100 million or
have paid-in capital of over 20%. Sale ratio is (dollar amount of sales to related parties with transactions exceeding NT$100
million or with paid-in capital of over 20% / total dollar amount of sales) *100%. Supply ratio is (dollar amount of purchases
from related parties with transactions exceeding NT$100 million or with paid-in capital of over 20% / total dollar amount of
purchases) *100%. Seats-shareholding divergence ratio is (family member seats ratio / family member shareholding ratio), in
which the family member seats ratio = (seats held by the ultimate controlling shareholder in the family / total seats of the
firm)*100%; the family member shareholding ratio = (shareholdings of ultimate controlling shareholder in the family / total
shares of the firm)*100%. Since the family member shareholding ratio is close to 0 in some of the NIT non-family firms, we do
not include the seats-shareholding divergence ratio in the model. Total assets is the log number of total assets. ROA is (net
income after tax / average total assets) *100%. Long-term investment ratio is (long-term investment / shareholder equity)*100%.
NIT industry includes cement, food, plastics, textiles, electrical, chemicals, biotechnology, steel, rubber, automobiles, building
materials, construction, shipping, trade, department stores, and others; in addition, if a firm fulfills any of the following
requirements, it is classified as family firm: (1) the positions of chairman of the board of directors and the CEO are held by
members of the same family; (2) the family controls more than 50% of the board seats (excluding friendly seats), while the
friendly director seats ratio and the external director seats ratio are both lower than 33%; (3) the family controls more than 33%
of the board seats while at least 3 directors or managers are from the ultimate controlling family; (4) the family controlled
shareholding ratio is higher than the necessary controlling shareholding ratio (i.e., with the controlling shareholding ratio being
higher than 20%). ***, ** and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
24
Moreover, the seats-shareholding divergence ratio is positively correlated with
the short-term debt ratio for IT and NIT family firms, but is negatively correlated with
the long-term debt financing. The results suggest that when the seats-shareholding
divergence ratio is high, IT and NIT family firms prefer to use short-term debt
financing, similar to the findings of Du and Dai (2005). That is, high divergence
increases the risk-taking tendency in a firm’s capital structure. Finally, this study finds
that NIT family firms prefer to use long-term debt financing for long-term
investments. Whether this will result in agency problems where the family
shareholders use debt financing as a way to engage in opportunistic behavior needs to
be further studied.
Based on the results in Table 5 and Table 6, we summarize the findings on the
relationship between network linkages, industries, family governance and capital
structures (short-term debt ratio, SD or long-term debt ratio, LD) on a relationship
diagram as shown in Figure 1. The horizontal and vertical axes are family governance
and the industry sectors, respectively. On the whole, the related party purchases and
sales network linkages give rise to opposite results in terms of their influences on the
capital structures in the family/non-family firms of different industries. For IT/NIT
family firms, the related party sales ratio and long-term debt of IT family firms are
positively correlated, while the related party sales ratio and long-term debt of NIT
family firms are negatively correlated. In addition, the related party sales ratio has a
negative effect on the short-term debt of IT family firms, while it has a positive
influence on long-term debt, with the opposite results being found for NIT family
firms. The results show that IT family firms tend to use debt financing to provide
financial support to related parties through the sales network linkage. Consequently,
we need to take note of whether this behavior will create agency problems in IT
family shareholders. In terms of IT/NIT non-family firms, the related party supply
number and supply ratio have positive effects on the short-term debt of IT non-family
firms and negative effects on their long-term debt, with the opposite results being
found for NIT non-family firms. Thus, the results show that the related party
purchases network linkage increases the maturity risk in capital structures for IT
non-family firms.
25
IT
High related party sale no. – high SD*, high LD
High related party supply no. – high SD, low LD*
High related party sale ratio – low SD*, low LD
High related party supply ratio – high SD, low LD
High related party sale no. – high SD, low LD*
High related party supply no. – high SD, high LD
High related party sale ratio – high SD*, high LD*
High related party supply ratio – low SD, high LD
Non-family
Family
High related party sale no. – low SD, high LD
High related party supply no. – low SD, high LD
High related party sale ratio – high SD, low LD
High related party supply ratio – low SD, high LD
High related party sale no. – high SD, low LD
High related party supply no. – high SD*, low LD
High related party sale ratio – low SD*, low LD
High related party supply ratio – high SD, low LD
NIT
Note: * represents a statistically significant relationship. SD is the short-term debt ratio. LD is the long-term debt ratio.
Figure 1 Relationship diagram for network linkages, industries, family governance
and capital structures
5. Conclusion
In recent years, with the trend toward globalization and liberalization,
corporations have faced increasingly fierce competition in the global market and a
rapidly changing global economic environment. This has forced companies to develop
closer working relationships with each other through network linkages in order to
achieve resource sharing, business growth, and enhanced competitiveness. A network
relationship is an important resource for constructing an effective value chain during a
company’s development process as it can enhance the company’s competitive
advantage and firm value, which is achieved through reduced debt financing levels.
Therefore, capital structure issues can be examined from the perspective of network
linkages. This can not only enable one to further analyze the firm’s financing leverage
and its optimal financing level, but can also lead studies on capital structure to a new
field. Therefore, this study contributes to the literature by examining the changes in
26
capital structure from the perspective of business transaction network linkages. This
issue is under-researched by scholars and provides an area for innovative research.
This study is based on a sample of TSE-listed and OTC business groups in
Taiwan from 2006 to 2008. The sample is categorized into four subsample groups
based
on
the
industry
sectors
(IT/NIT
industry)
and
business
types
(family/non-family). The related party purchases and sales linkages in the industrial
cooperation system are used to proxy for network linkages. In addition to analyzing
the relationship between family governance and network linkages in Taiwanese listed
and OTC business groups, we construct the panel data linear regression model for
network linkages and capital structure decisions. We include the effect of family
governance on business groups in the model and analyze the relationship between the
related party purchases and sales network linkages and capital structures of Taiwanese
listed and OTC business groups.
Overall, this study finds that shareholdings of family members and the
seats-shareholding divergence ratio are negatively correlated with the related party
sales and supply number. Therefore, the results indicate that when the seat control
rights diverge from the voting rights, there is no clear evidence that family
shareholders or large shareholders will use related party transactions to obtain
personal benefits. Secondly, the effects of related party purchases and sales network
linkages on capital structures have opposite results for family/non-family firms and
different industries. The related party sales ratio is positively correlated with the debt
of IT family firms, while it is negatively correlated with the debt of NIT firms. In
addition, the related party supply ratio is negatively and positively correlated with the
short-term and long-term debts of IT family firms, respectively. However, the results
are reversed for NIT family firms. Moreover, the related party supply number and
supply ratio have positive and negative effects on the short-term and long-term debt of
IT non-family firms, respectively. The influences are again reversed for NIT
non-family firms. Thus, the results indicate that IT family firms tend to use debt
financing to fund the capital needs required by related party sales network linkages.
However, we need to take note of whether this behavior will create agency problems
for IT family shareholders and whether the related party purchases network linkages
will increase the maturity risk in the capital structures of IT non-family firms. That is,
for IT non-family firms, we need to take note of whether there will be an increase in
27
short-term debt financing when the number of related firms purchased from or the
related purchases in dollar amounts is high. Finally, this study finds that the
seats-shareholding divergence ratios of IT and NIT family firms are positively and
negatively correlated with the short-term and long-term debt ratios, respectively,
indicating that the seats-shareholding divergence ratios will influence the risk-taking
tendency in firms’ capital structure decisions. To sum up, this study finds that network
linkages do indeed influence firms’ capital structures. However, in analyzing the
relationship between the related party purchases and sales network linkages and the
capital structure, we need to take into consideration the industrial differences and the
influence of family involvement in corporate governance.
Future studies could examine the effect on companies’ capital structures by
integrating the dynamic adjustments of network linkages and director linkages using
the latent variable structural equation modeling approach and discuss the relationship
between manifest variables, latent variables and disturbance variables. The
simultaneous equations model could be used to estimate the regression coefficients
between endogenous variables and exogenous variables, thereby enabling analyses to
be performed on the direct effects, indirect effects and total effects on capital
structures under dynamic adjustments to network linkages and director linkages.
28
References
Aggarwal, R. (1981). “International differences in capital structure norms: An
empirical study of large European countries.” Management International Review
21: 75-88.
Aggarwal, R. and G. Baliga (1987). “Capital structure among Latin American
companies.” Managerial Finance 13: 3-11.
Agrawal, A. and C. Mandelker (1990). “Large shareholders and the monitoring of
managers: The case of antitakeover charter amendments.” Journal of Financial
and Quantitative Analysis 25: 143-161.
Almeida, H. and D. Wolfenzon (2005). “A theory of pyramidal ownership and family
business groups.” Journal of Finance 61: 2637–2680.
Anderson, R., S. Mansi, and D. Reeb (2003). “Founding family ownership and the
agency costs of debt.” Journal of Financial Economics 68: 263-285.
Anderson, R. and D. Reeb (2003). “Founding-family ownership and firm performance:
Evidence from the S&P 500.” Journal of Finance 53: 1301-1328.
Anderson, R. and D. Reeb (2004). “Board composition: Balancing family influence in
S&P 500 firms.” Administrative Science Quarterly 49: 209-237.
Arslan, O. and M. B. Karan (2006). “Ownership and control structure as determinants
of corporate debt maturity: A panel study of an emerging market.” Corporate
Governance 14: 312-324.
Banerjee, S., S. Dasgupta, and Y. Kim (2008). “Buyer-supplier relationships and the
stakeholder theory of capital structure.” Journal of Finance 63: 2507-2554.
Barney, J. B. (1991). “Firm resources and sustained competitive advantage.” Journal
of Management 17: 99-120.
Baskin, J. (1989). “An empirical investigation of the pecking order hypothesis.”
Financial Management 18: 26-35.
Belkaoui, A. (1974). “A Canadian survey of financial structure.” Financial
Management 4: 74-79.
Berkman, H., R. A. Cole, and L. J. Fu (2009). “Expropriation through loan guarantees
to related parties: Evidence from China.” Journal of Banking and Finance 33:
141-156.
Bertrand, M., P. Mehta, and S. Mullainathan (2002). “Ferreting out tunneling: An
application to Indian business groups.” The Quarterly Journal of Economics
117: 121–48.
29
Blankenburg H. D., K. Eriksson, and J. Johanson (1999). “Creating value through
mutual commitment to business network relations.” Strategic Management
Journal 20: 467-486.
Bowen, R. M., L. A. Daley, and C. C. Hubert, Jr. (1982). “Leverage measures and
industrial
classification:
Review
and
additional
evidence.”
Financial
Management 11: 10-20.
Bozec, Y. and C. Laurin (2008). “Large shareholder entrenchment and performance:
Empirical evidence from Canada.” Journal of Business Finance and Accounting
35: 25-49.
Burkart, M., F. Panunzi, and A. Shleifer (2003). “Family firms.” Journal of Finance
58: 2167-2201.
Carney, M. and E. Gedajlovic (2002). “The coupling of ownership and control and the
allocation of financial resources: Evidence from Hong Kong.” Journal of
Management Studies 39: 123-146.
Chang, S. (2003). “Ownership structure, expropriation, and performance of
group-affiliated companies in Korea.” Academy of Management Journal 46:
238-254.
Cheung, Y. L., Y. Qi, P. Raghavendra Rau, and A. Stouraitis (2009). “Buy high, sell
low: How listed firms price asset transfers in related party transactions.” Journal
of Banking and Finance 33: 914-924.
Chung, C. N. (2001). “Markets, culture and institutions: The emergence of large
business groups in Taiwan.” Journal of Management Studies 38: 719-745.
Claessens S., S. Djankov, and L. H. P. Lang (2000). “The separation of ownership and
control in East Asian corporations.” Journal of Financial Economics 58: 81-112.
Coleman, J. S. 1990. Foundations of Social Theory. Belknap Press of Harvard
University Press: Cambridge, MA.
Collin, S. O. (1998). “Why are these islands of conscious power found in the ocean of
ownership? Institutional and governance hypotheses explaining the existence of
business groups in Sweden.” Journal of Management Studies 35: 719-746.
Dammon, R. M. and L. W. Senbet (1988). “The effect of taxes and depreciation on
corporate investment and financial leverage.” Journal of Finance 43: 357-374.
DeAngelo, H. and R. Masulis (1980). “Optimal capital structure under corporate and
personal taxation.” Journal of Financial Economics 8: 3-29.
Demsetz, H. (1983). “The structure of ownership and the theory of the firm.” Journal
of Law and Economics 26: 375-393.
30
Dierickx, I. and K. Cool (1989). “Asset stock accumulation and the sustainability of
competitive advantage.” Management Science 35: 1504-1511.
Du, J. and Y. Dai (2005). “Ultimate corporate ownership structures and capital
structures: Evidence from East Asian economies.” Corporate Governance 13:
60-71.
Dyer, J. and H. Singh (1998). “The relational view: Cooperative strategies and sources
of interorganizational competitive advantage.” Academy of Management Review
23: 660-679.
Elfring, T. and W. Hulsink (2003). “Networks in entrepreneurship: The case of high
technology firms.” Small Business Economics 21: 409-422.
Errunza, V. R. (1979). “Determinants of financial structure in the central American
common market.” Financial Management, 8: 72-77.
Faccio, M. and L. H. P. Lang (2002). “The ultimate ownership of Western European
corporations.” Journal of Financial Economics 65: 365–95.
Ferri, M. G. and W. H. Jones (1979). “Determinants of financial structure: A new
methodological approach.” Journal of Finance, 34: 631-644.
Filatotchev, I., Y. C. Lien, and J. Piesse (2005). “Corporate governance and
performance in publicly-listed, family-controlled firms: Evidence from Taiwan.”
Asia Pacific Journal of Management 22: 257-283.
Fosberg, R. H. (2004). “Agency problems and debt financing: Leadership structure
effects.” Corporate Governance 4: 31-38.
Friend, I. and L. H. P. Lang (1988). “An empirical test of the impact of managerial
self-interest on corporate capital structure.” Journal of Finance, 43: 271-281.
Gerlach, M. L. (1992). “The Japanese corporate network: A blockmodel analysis.”
Administrative Science Quarterly 36: 88-105.
Gilles, P. L. (1999). “A fresh look at incentive plans for privately-held companies.”
Compensation and Benefits Review 31: 61-72.
Granovetter, M. (1995). “Coase revisited: Business groups in the modern economy.”
Industrial and Corporate Change 4: 93-130.
Granovetter, M. (2005). “Business group and social organization.” In N. Smelser and
R. Swedberg (eds.), Handbook of Economic Sociology, 429-450. Princeton, NJ:
Princeton University Press.
Griliches, Z. and J. A. Hausman (1986). “Error-in-variables in panel data.” Journal of
Econometrics 31: 93-118.
Grossman, S. and O. Hart (1988). “Corporate governance: Voting rights and majority
31
rules.” Journal of Financial Economics 20: 203-235.
Grossman, S. and O. Hart (1980). “Takeover bids, the free-rider problem and the
theory of the corporation.” Bell Journal of Economics 11: 42-64.
Guillen, M. F. (2000). “Business groups in emerging economies: A resource base
view.” Academy of Management Journal 43: 362-380.
Gulati, R. (1999). “Network location and learning: The influence of network
resources and firm capabilities on alliance formation.” Strategic Management
Journal 20: 397-420.
Gulati, R. and J. Westphal (1999). “Cooperative or controlling? The effect of
CEO-based relations and the content of interlocks on the formation of joint
ventures.” Administrative Science Quarterly 44: 473-506.
Harris, M. and R. Raviv (1988). “Corporate governance: Voting rights and majority
rules.” Journal of Financial Economics 20: 203-235.
Haugen, R. A. and L. W. Senbet (1979). “New perspectives on informational
asymmetry and agency relationships.” Journal of Financial and Quantitative
Analysis 14: 671-694.
Hausman, J. A. (1978). “Specification test in econometrics.” Econometric Society
46(6): 1251-1271.
Hsiao, C. (1985). “Benefits and limitations of panel data.” Econometric Reviews 4:
121-174.
Hsiao, C. (1991). “Identification and estimation of latent binary choice models using
panel data.” Review of Economic Studies 58: 717-731.
Hsiao, C. (2003). Analysis of panel data, Cambridge: Cambridge University Press.
Hsiao, C. and G. Taylor (1991). “Some remarks on measurement errors and the
identification of panel data models.” Statistica Neerlandica 45: 187-194.
Hutchinson, M. (2002). “An analysis of the association between firms’ investment
opportunities, board composition, and firm performance.” Asia-Pacific Journal
of Accounting and Economics 9: 17-38.
James, H. (1999). “Owner as manager, extended horizons and the family firm.”
International Journal of the Economics of Business 6: 41-55.
Jarillo, J. C. (1988). “On strategic networks.” Strategic Management Journal 9:
31-41.
Jensen, M. (1986). “Agency costs of free cash flow, corporate finance, and
takeovers.” American Economic Review 76: 323-329.
32
Jensen, M. C. and W. Meckling (1976). “Theory of the firm: management behavior,
agency costs and ownership structure.” Journal of Financial Economics 3:
305-360.
Joh, S. (2003). “Corporate governance and firm profitability: Evidence from Korea
before the economic crisis.” Journal of Financial Economics 68: 287-322.
Johanson, J. and L. G. Mattsson, 1987. “Interorganizational relations in industrial
systems: A network approach compared with the transaction-cost approach.”
International Studies of Management and Organization 17: 34-48.
Johnston, R. and P. R. Lawrence (1988). “Beyond vertical integration – The rise of the
value-adding partnership.” Harvard Business Review 88: 94-101.
Johnson, S., F. La Porta, A. Shleifer, and R. Vishny (2000). “Tunneling.” American
Economic Review 90: 22–27.
Keister, L. A. (1998). “Engineering growth: Business group structure and firm
performance in China’s transition economy.” American Journal of Sociology
104: 404-440.
Kester, W. C. (1986). “Capital and ownership structure:
A comparison of United
States and Japanese manufacturing corporations.” Financial Management 15:
5-16.
Khanna, T. and K. Palepu (2000). “The future of business in emerging markets:
Long-run evidence from Chile.” Academy of Management Journal 43: 268-285.
Khanna, T. and J. W. Rivkin (2001). “Estimating the performance effects of business
groups in emerging markets.” Strategic Management Journal 22:45-74.
Kuo, H. C., L. H. Wang, and S. Wu (2000). “The determinants of capital structure for
small and medium enterprises in Taiwan.” Advances in Pacific Basin Business,
Economics, and Finance 4: 23-36.
Kuo, H. C. (2006). “Network linkage and financial leverage.” Journal of Financial
Studies 14: 95-123.
Kuo, H. C., Li, Y. and H. S. Ho (2003). “Financial network linkages on capital
structure.” The 11th Annual Conference on Pacific Basin Finance, Economics,
and Accounting, Taipei, R.O.C.
Leff, N. H. (1979). “Entreprencurship and economic development: The problem
revisited.” Journal of Economic Literature 17: 46-64.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A. (1999). “Corporate ownership around
the world.” The Journal of Finance 54: 471–517.
Lins, K. V. (2003). “Equity ownership and firm value in emerging markets.” Journal
33
of Financial and Quantitative Analysis 38: 159–84.
Luo, X. and Chung, C. (2005). “Keeping it all in the family: The role of particularistic
relationships in business group performance during institutional transition.”
Administrative Science Quarterly 50: 404-439.
Mahaffy, P. A. (1998). “Shotguns, puts and calls: The advantages of buy/sell
agreements for closely-held companies.” CMA Magazine 71: 23-25.
Modigliani, F. and M. Miller (1958). “The cost of capital, corporation finance and the
theory on investment.” American Economic Review 48: 261-297.
Moh’d, M. A., L. G. Perry, and J. N. Rimbey (1998). “The impact of ownership
structure on corporate debt policy: A time-series cross-sectional analysis.” The
Financial Review 33: 85-98.
Morellec, E. (2004). “Can managerial discretion explain observed leverage ratios?”
The Review of Financial Studies 17: 257-294.
Morck, R., D.Wolfenson, and B. Yeung (2005). “Corporate governance, economic
entrenchment and growth.” Journal of Economic Literature 43: 655–721.
Myers, S. C. (1977). “The determinants of corporate borrowing.” Journal of Financial
Economics 5: 147-175.
Myers, S. C. and N. S. Majluf (1984). “Corporate financing and investment decisions
when firms have information that investors do not have.” Journal of Financial
Economics 13: 187-221.
Naidu, G.N. (1983). “Country and industry norms of capital structure: Asian
evidence.” Management International Review 23: 64-70.
Porter, M. (1980). Competitive Strategy. Free Press: New York.
Powell, W. W., K. Koput, and L. Smith-Doerr (1996). “Interorganizational
collaboration and the locus of innovation: Networks of learning in
biotechnology.” Administrative Science Quarterly 41: 116-145.
Prahalad, C. K. and G. Hamel (1990). “The core competence of the corporation.”
Harvard Business Review 68(3): 79-91
Remmers, L., A. Stonehill, R. Wright, and T. Beekhuisen (1974). “Industry and size as
debt
ratio
determinants
in
manufacturing
internationally.”
Financial
Management 3: 24-32.
Riyanto, Y. E., Toolsema, L. A. (2008). “Tunneling and propping: A justification for
pyramidal ownership.” Journal of Banking and Finance 32: 2178–2187.
Ross, S. A. (1985). “Debt and taxes and uncertainty.” Journal of Finance 40: 637-656.
Ross, S. (1977). “The determinants of financial structure: The incentive signaling
34
approach.” Bell Journal of Economics 8: 23-40.
Schneider, M. (2000). “When financial intermediaries are corporate owners: An
agency model of institutional ownership.” Journal of Management and
Governance 4: 207-237.
Scott, D. F. and J. D. Martin (1975). “Industry influence on financial structure.”
Financial Management 4: 67-73.
Sekely, W. and J.M. Collins (1988). “Cultural influences on international capital
structure.” Journal of International Business Studies, 19: 87-100.
Shleifer, A. and R. Vishny (1986). “Large shareholders and corporate control.”
Journal of Political Economy 94: 461-488.
Smith, C. W. Jr. and R. L. Watts (1992). “The investment opportunity set and
corporate financing, dividend, and compensation policies.” Journal of Financial
Economics 32: 263-292.
Stonehill, A., T. Beekhuisen, R. Wright, L. Remmers, N. Toy, A. Pares, A. Shapiro, D.
Egan, and T. Bates (1974). “Financial goals and debt ratio determinants: A
survey of practices in five countries.” Financial Management 4: 27-41.
Stulz, R. (1988). “Managerial control of voting rights: Financing policies and the
market for corporate control.” Journal of Financial Economics 20: 25-54.
Thorelli, H. B. (1986). “Network: Between markets and hierarchies.” Strategic
Management Journal 7: 37-51.
Titman, S. (1984). “The effect of capital structure on a firm’s liquidation decision.”
Journal of Financial Economics 13: 137-151.
Titman, S. and R. Wessels (1988). “The determinants of capital structure choice.”
Journal of Finance 43: 1-19.
Uzzi, B. (1997). “Social structure and competition in interfirm networks: The paradox
of embeddedness.” Administrative Science Quarterly 42: 35-67.
Vicente-Lorente, J. D. (2001). “Specificity and opacity as resource-based
determinants of capital structure: Evidence for Spanish manufacturing firms.”
Strategic Management Journal 22: 157-177.
Warner, J. B. (1977). “Bankruptcy costs: Some evidence.” Journal of Finance 32:
337-347.
Wernerfelt, B. (1984). “A resource-based view of the firm.” Strategic Management
Journal 5: 171-180.
Wong, S. L. (1985). “The Chinese family firm: A model.” The British Journal of
35
Sociology 36: 58-72.
Yammeesri, J. and S. C. Lodh (2004). “Is family ownership a pain or gain to firm
performance?” Journal of American Academy of Business 4: 263-270.
Yeh, Y. H., T. S. Lee, and T. Woidtke (2001). “Family control and corporate
governance: Evidence for Taiwan.” International Review of Finance 2: 21-48.
Yeh, Y. H. (2005). “Do controlling shareholders enhance corporate value?” Corporate
Governance 13: 313-325.
36