Download Managerial risk preference and its influencing factors: analysis of

Original Article
Managerial risk preference and its
influencing factors: analysis of
large state-owned enterprises
management personnel
in China
Yingyu Zhanga,*, Hui Luana, Wei Shaoa and Yingjun Xub
a
School of Management, Qufu Normal University, No. 80 Yantai Road,
Rizhao 276826, Shandong, China.
E-mail: [email protected]
b
Research Center for Food Safety Governance Policy, Qufu Normal University,
No. 80 Yantai Road, Rizhao 276826, Shandong, China.
*Corresponding author.
Abstract Using the managerial risk preference scale, the managerial risk preferences
of 308 state-owned enterprises (SOEs) management personnel in China are investigated.
The results show that older management personnel are more risk averse. Management
personnel who have higher positions are more risk averse. The highest degree of risk
pursuit across different industries is management personnel in the agriculture industry
followed by manufacturing, energy, transportation, trade, and construction. No significant differences were observed in the managerial risk preference distribution of management personnel with respect to gender, and level of education.
Risk Management (2016). doi:10.1057/s41283-016-0001-9
Keywords: managerial risk preference; managerial risk preference scale;
influencing factors; China; state-owned enterprise management personnel
Introduction
S
tate-owned enterprises (SOEs) are an important component of
China’s economy. Reforms over the last 3 decades have divided
China’s SOEs into two groups: those supervised by local governments, and those supervised by the central government (Li et al, 2015). In
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March 2003, the State-Owned Assets Supervision and Administration
Commission (SASAC) was established as a shareholder representative of the
central government in order to better manage central enterprises (Sam, 2013).
Subsequently, local governments also set up asset supervision and administrative institutions to manage local SOEs. In general, SOEs supervised by local
or central government are large entities in key industries such as national
security and national economic lifelines. These industries are the basis of
healthy development for the entire country. SOEs under the supervision of
central government contribute approximately 10% of GDP. From 2003 until
the end of 2013, the assets of central enterprises rose from RMB 7.13 trillion
to RMB 35 trillion. Operating revenue rose from RMB 3.36 trillion to RMB
24.4 trillion. Profit increased from RMB 240.5 billion to RMB 1.3 trillion.1
SOEs usually play more important roles in developing countries than in
developed countries (Andrew and Smith, 2001). However, in mainstream
economics, empirical evidence shows that SOEs, rather than correcting market
failures (Xu and Gui, 2016), generally yield negative results (Andrew and Smith,
2001). From 2001 to 2011, China’s economy had an average annual growth rate
of 10.4%, while from 2012 to 2015, the growth rates were 7.7, 7.7, 7.3, and
6.9%, respectively.2 This is the ‘‘new normal,’’ and China’s economy no longer
sees double-digit growth (Huang, 2014; Jin, 2015; Li, 2015; Wang, 2014).
Under the ‘‘new normal’’ economic background, there are many difficulties and
challenges for China’s SOEs. In 2014, the total profit of national state-owned
and state holding enterprises (excluding financial enterprises) showed slowed
growth, increasing only by 3.4%. The losses of large SOEs have become
disastrous, and the deteriorating trend is increasingly disquieting (Wang, 2015).
In 2015, the total revenue of national SOEs was RMB 45.47 trillion, down by
5.4%; total profit was RMB 2.3 trillion, down by 6.7%.3 Investigations showed
that a large number of ‘‘zombie firms’’ comprise large SOEs, typified by
discontinuation, semi-cut, continued loss, insolvency, and operations maintained by relying on government subsidies and bank credit (Yang, 2016).
Managing risk is fundamental in today’s dynamic, uncertain environment
(Gordon et al, 2009). Companies use enterprise risk management (ERM) to
assess, control, and monitor risks to increase organizations’ short- and longterm values, and it is important for an effective corporate governance system
(COSO, 2004). Since the 1990s, more and more companies are moving from
traditional risk management, which managed different corporate risks
individually, toward ERM, which takes an integrated view of corporate
risks (Beasley et al, 2005; Hoyt and Liebenberg, 2010; Nocco and Stulz,
2006; Mikes, 2005, 2008). Effective risk management is viewed as a crucial
competitive advantage that determines the success of an enterprise in an
uncertain environment (Bartram, 2000; Beasley et al, 2005). In September
2004, the Committee of Sponsoring Organizations of the Treadway
Commission (COSO) issued Enterprise Risk Management – Integrated
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Managerial risk preference and its influencing factors
Framework, to provide a model framework for ERM. In July 2006, SASAC
issued Central Enterprise Risk Management Guidelines (hereinafter referred
to as Guidelines), which served as the prelude to central enterprise risk
management (ERM). The Guidelines were favorably received by central
enterprises, and ERM gained the attention of SOEs (Zhang, 2009; Zhang
and Li, 2009).
Decision making constitutes the start of all human economic behavior (Liu,
2008). Any model attempting to explain the behavior of the decision maker
has a basic element: risk preference, which has significant impact on outcomes
in saving, migration, technology adoption, and risk-taking (Brown and Pol,
2015; Chuang and Schechter, 2015; Wang, 2005). Empirical evidence suggests
that risk preference varies considerably among individuals (Barsky et al,
2010), and a number of studies have researched methods of predicting it. One
approach to theoretical development is through understanding the consistency
of risky decision making (Blais and Weber, 2006; Weber et al, 2002). Despite
increased focus on the importance of risk in decision making, empirical studies
on enterprise managerial risk preference – the attitude of enterprise management personnel toward risk management, risky decision making, and facing
enterprise risks – remain limited (Slovic et al, 1982; Vlek and Stallen, 1980;
Zhang, 2009). Until the 1980s, this problem was explored only in its
theoretical aspects. A large number of studies found that the traits of
management personnel, such as risk aversion, lack of patience, and optimism,
were significantly related to business-associated decision making (Graham
et al, 2013; Lv, 2014; Zhang et al, 2016).
One of the basic premises of effective ERM is to establish an appropriate
risk preference for individuals and firms. For firms, risk preference refers to
the amount of risk generally accepted while pursuing value. Risk preference is
the overall risk impact that firms will undertake to realize strategic goals.
Thus, risk preference directly reflects the operations of an enterprise. Risk
preference differs among enterprises as well as individuals. The 2008 global
financial crisis was partially caused by failures and weaknesses of corporate
governance, and in many cases, risk management systems, rather than
traditional risk management techniques (Feng, 2010; Isaksson and Kirkpatrick, 2009). Managerial risk preference also differs among management
personnel. The risk preference of management personnel directly influences
the effect of various risk-based decisions, as well as the effectiveness and
process of ERM. With the current economic development of China,
determining the risk preference and contributing factors of management
personnel in large SOEs has gained considerable attention from regulators,
stakeholders, management personnel, and researchers.
The management personnel of large SOEs in China have a dual identity. On
one hand, they are enterprise staff members; on the other hand, they are
‘‘government officials.’’ This paper examines the risk preference of large SOE
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management personnel when they face enterprise risk-based decisions. Using
the managerial risk preference scale, the managerial risk preferences of 308
large SOE management personnel in China are investigated. The paper focuses
on two main issues: (1) will China SOE management personnel present
features characteristic of enterprise staff, government officials, or both? For
various reasons, the studies of domestic and foreign scholars will not be
referenced regarding this question, so this work will initiate a new discussion.
(2) what factors affect the managerial risk preference of China’s SOE
management personnel, including personal characteristics and organizational
characteristics.
Literature Review and Hypotheses
Gender and risk preference
Gender is one of the factors that have gained the attention of researchers. A
large number of psychology and behavioral science studies have shown that
women were more cautious during uncertainty. Byrnes et al (1999) conducted
a literature review on 150 studies on risk preference from 1967 to 1997. These
studies involved more than 100,000 people, and the results showed, in general,
that men pursued more risks than women. Based on an analysis of 50 studies,
Arch (1993) also found that women were more risk averse than men. Eckel
and Grossman (2008) pointed out that although women were more risk averse
than men, the results of the laboratory experiment were ambiguous.
According to Hambrick and Chen (1996), the gender composition of the
senior management is one of the most important factors that influence
organizational risk preference from the angle of gender difference in risk
aversion. Zhu et al (2012) conducted an empirical study that considered
investment level and long-term loan as indicators of risk aversion, and
discovered that companies with a high proportion of female directors
demonstrated a rapid decline in investment level during a crisis. These
companies also tended to reduce long-term borrowing. Cheng and Zhao
(2014) revealed that an increase in the number of female supervisors in
Chinese listed financial institutions would not result in an improvement in
organizational risk preference, whereas the increase in the number of female
directors could improve the risk preference level of the organization.
Therefore, the following hypothesis is proposed.
Hypothesis 1:
138
Female management personnel are more risk averse than male
management personnel (H1).
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Age and risk preference
Lauriola and Levin (2001) used an experimental method to compare the riskbased decisions of three age groups (21–40, 41–60, 61–80 years old), and found
that under the condition of income, the group with younger members was more
willing to take risks than the other age groups; however, under the condition of
loss, the adventurous feature of the younger group (21–40 years) was lower than
that of the other two groups. Gardner and Steinberg (2005) conducted a
questionnaire survey on the risk preference of people in three different age
stages, namely, puberty (13–16 years), youth (18–22 years), and adult (24+
years). The results indicated that the younger the individual, the lesser the risk
taking and risky decision making. The influence of the same age group on
adolescent and young people in risky decision making was larger than on adults.
Hambrick and Mason (1984), Bantel and Jackson (1989), Barker and Mueller
(2002), Chen et al (2010) believed that the age of management personnel
affected their attitudes toward risks because young management personnel
possess more recent education and are better acquainted with modern and
advanced technologies than older management personnel. Thus, young management personnel are more capable of learning and integrating all kinds of
information. They are also more confident and tend to choose riskier strategies
when called to exercise their decision making because their financial and career
securities are not related to the recent situation of the enterprise, but to its longterm status. Therefore, the following hypothesis is proposed.
Hypothesis 2:
Older management personnel are more risk averse than younger
management personnel (H2).
Position and risk preference
Regarding the influence of position on risk preference, scholars have
concluded that, in general, management personnel with higher positions had
more propensity toward risk taking than personnel holding lower positions
(Fishburn and Kochenberger, 1979; Dan and Crum, 1980; MacCrimmon and
Wehrung, 1986; Li and Xu, 1996). Graham et al (2013, 2015) also found that
CEOs were more optimistic than general management personnel, and that
companies with optimistic management personnel had higher leverage.
Western management scholars widely believed that one of the most common
psychological characteristics of entrepreneurs is high ambiguity (risk) tolerance because the physical capital of entrepreneurs usually account for only a
small proportion, and they have more reasons to take more risks (Zhu and Li,
2000). March and Shapira (1987) found that middle management personnel
tended to believe that the difference in risk preference would disappear when
they are promoted to a higher position, whereas top management personnel
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stated the necessity of educating new office management personnel to help
them understand the importance of taking risks. Therefore, the following
hypothesis is proposed.
Hypothesis 3: Lower position management personnel are more risk averse
than higher position management personnel (H3).
Education and risk preference
The influence of professional experience and level of education of management
personnel on their decision-making process have been discussed over the last 25
years (Barker and Mueller, 2002). The discussion focused on whether the
preference of management personnel in decision making reflected their training.
Different study fields have different purposes and methods that facilitate
management personnel to adapt and strengthen their training to collect and
interpret information. Many studies have also shown that management
personnel make different decisions because of different levels of education,
and better education resulted in better strategic decisions that would benefit the
company. Thus, the company would enjoy profit and growth (Tihanyi et al,
2000; Bosma et al, 2004). Jianakoplos and Bernasek (1998) showed that level of
education was closely related to risk preference, and that the lower the level of
education, the higher the degree of risk aversion. By contrast, Blais and Weber
(2006) investigated the risk preference of 359 British and French individuals,
and found no significant difference in the distribution of risk preference among
different education levels. Wang and Zhou (2013) used a sample of listed
companies in Shanghai and Shenzhen stock exchanges from 2003 to 2012, and
determined that the correlation between education level and risk preference of
top decision makers was significant and negative. Corporations managed by
more highly educated decision makers were found to have lower earnings
volatility and asset-liability ratio as well as higher risk management and
tolerance capacity. Therefore, the following hypothesis is proposed.
Hypothesis 4:
Lower education management personnel are more risk averse
than higher education management personnel (H4).
Industries and risk preference
Different industries have different characteristics. For example, some industries
are technology intensive, while some industries are capital intensive; some
industries are mainly concentrated in the domestic market, while some
industries are focused on foreign market. Scholars have relatively little research
on risk preference in different industries. Dan and Crum (1980) found that
industry has a significant effect on risk preference, and that the management
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personnel of airline companies pursued the most risks, followed by personnel
from the food chain, construction, and chemical industries, whereas a bank’s
manager was the most risk averse. Levy (1994) found that the degree of risk
aversion of management personnel in food, steel, and real-estate industries
displayed an increasing trend. John et al (2004) study showed that the
relationship between corporate risk-taking behaviors and industry growth rate
was statistically significant. Therefore, the following hypothesis is proposed.
Hypothesis 5:
Management personnel of different industries will demonstrate different levels of risk preference (H5).
Method
Procedure and samples
This part of study successively conducted two large-scale surveys with the help
of SASAC. One is from June to August in 2010 in 231 large SOEs in 30
provinces, municipalities, and autonomous regions; the other one is in July
2010 in 37 Beijing SOEs. It is necessary to specially emphasize that a series of
questionnaires used in this study are supported by SASAC and local SASAC,
thus ensuring the credibility of the questionnaire. Table 1 provides the relative
information of the respondents. For these two investigations, 500 questionnaires were issued, among which 359 were recycled and 308 were valid. The
recovery rate was 71.80%, and the effective rate was 61.60%.
Measures
Management personnel will inevitably meet and deal with risks in the process
of operation management. Obviously, their attitude toward risks will also
differ. Previous studies have pointed out that the attitude of enterprise
management personnel toward industrial risks differed from that of individual
risks. Studies on enterprise managerial risk preference should not simply
continue to use the traditional risk preference research method. Therefore, this
research employed the concept of risk preference and used managerial risk
preference to replace the traditional risk preference. Dan and Crum (1980),
McKenna and Yong (1986), and Grambo (1978) referred to risk preference as
managerial risk preference. Smith (2004) referred to it as corporate risk
attitude, whereas David and Ruth (2008) referred to it as corporate risk
attitude and organization risk attitude. The literature did not provide a clear
and systematic definition of managerial risk preference. Using relevant
theories and concepts, this paper defines managerial risk preference as the
behavior of managerial personnel in the risk management process and the
decisions they make toward the risks confronting their companies.
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Table 1: Managerial risk preference respondent situation
Project
Amout
Percentage
(%)
Remarks
Gender
Male
205
75.93
38 valid questionnaires with item on
gender not filled out
Age
Fmale
Total
40
65
270
135
24.07
100.00
47.37
[40
Total
Junior college and
under
Undergraduate
Master or above
Senior
150
285
25
52.63
100.00
8.31
161
115
89
75.93
38.20
30.07
121
37
49
296
72
40.88
12.50
16.55
100.00
24.41
110
38
45
37.29
12.88
15.25
30
10.17
295
100.00
Education
Position
Yearly
operating
Income of
Enterprise
Department head
Office head
General manager
Total
Under RMB 1
billion
RMB 1–5 billion
RMB 5–10 billion
RMB 10–20
billion
RMB 20 billion
and above
Total
23 valid questionnaires with item on age
not filled out,
Average age was 40.01 years old
7 valid questionnaires with item on
gender not filled out
12 valid questionnaires with item on
gender not filled out
12 valid questionnaires with item on
gender not filled out
From the traditional microeconomic theory perspective, the measurement
and characterization of risk preference became controversial because the
assumption of risk preference was too simple. Studies on managerial risk
preference cannot utilize the same research method used to investigate risk
preference in individual decision making. Therefore, we used the managerial
risk preference scale of Zhang (2009) to investigate and measure the
managerial risk preference. Empirical validation results showed that the scale
had good content validity and structure validity. This scale has 5 dimensions
and 16 items. Table 2 lists the items included in each dimension.
Results
In this paper, managerial risk preference is divided into two types: individual
managerial risk preference, which refers to the preference level of management personnel toward certain risk; and overall risk preference, which refers
to the overall response of management personnel toward the overall
industrial risks.
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Table 2: Item and information on managerial risk preference scale
Dimension
Indicator
Entry information
Strategic
risk
Merger risk
Risk of personnel placement, non-performing assets, and
cultural conflict because of merger andacquisition
Going out to face the risk of politics, economics, and society
of the host country
Risk of negative effects of changes in national industries and
fiscal policy on companies
Risk of external environment, technical level, and tight
money.
Risk of enterprise assets safety because of associated business,
guaranty, and entrusted financing
Risk of failure to fulfill the payment obligation or promise to
pay because of the financial gap
Risk of fluctuating corporate profitability
‘‘Go out’’ national
risk
National policy risk
Financial
risk
Major projects
investment risk
Asset security risk
Liquidity risk
Market risk
Operational
risk
Legal risk
Yield fluctuation
risk
Account receivable
risk
Customer demand
risk
Remit interest rate
risk
Supplier product
quality risk
Information system
risk
Major natural
disaster risk
Environmental risk
Contract law risk
Financial report
compliance risk
Risk of companies cannot recycle all kinds of accounts in time
Risk of company products that cannot satisfy the market
need, which is increasingly personalized and customized
Risk of turmoil in international financial structure, market
interest rate, and exchange rate fluctuation
Risk of quality problems of company product because of the
quality of supplier products
Risk of inaccurate and leaked information as well as attacks
from hackers and viruses
Risk due to major natural disasters (such as typhoons,
earthquakes, and floods)
Risk from various environment problems arising from the
enterprise production process
Risk of business contract legal disputes in enterprise
production and operation process
Risk of financial report without compliance
Reliability test
In practice, the most commonly used method to test the reliability is
Cronbach’s coefficient technique. In general, Cronbach’s coefficient above 0.7
can be considered the scale with higher reliability. The scale score in each item
converts to scoring matrix, obtaining the total score variance of all items, and
the sum of the correlations between each item then calculates the Cronbach’s
coefficient of each item (results shown in Table 3). The Cronbach’s
coefficients of all items range between 0.85 and 0.92, which indicates
managerial risk reference scale with a satisfactory reliability.
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Table 3: Cronbach’s coefficient
Item
Cronbach’s
Merger risk
‘‘Go out’’ national risk
National policy risk
Major projects investment risk
Asset security risk
Liquidity risk
Yield fluctuation risk
Account receivable risk
Customer demand risk
Remit interest rate risk
Supplier product quality risk
Information system risk
Major natural disaster risk
Environmental risk
Contract law risk
Financial report compliance risk
0.8740
0.8713
0.8674
0.8833
0.9139
0.8770
0.8849
0.8904
0.8974
0.8862
0.8890
0.9148
0.9066
0.9024
0.9209
0.9089
Validity test
Scholars have proposed a variety of methods to test the validity of scale. In this
paper, the content validity and construct validity were tested. Content validity
refers to whether or not the design items represent the content or subject
matter to be measured. Content validity tests are often used in the methods of
logical analysis and statistical analysis. In general, logical analysis is used by
the developer to judge whether the entry ‘‘looks’’ consistent with the purpose
and requirements of measurement. Statistical analysis is mainly used for the
relationship analysis between single item and all items to obtain the test
results. First, the data were normalized; then, using SPSS, the Spearson
coefficients between single item and all items were calculated (results in
Table 4). Statistical analysis shows that, in 16 items, there are significant
correlations between the score of each item and the total score at 0.01 level,
which indicates that the scale possesses a satisfactory content validity.
The managerial risk preference scale has five dimensions: the dimension of
strategic risk preference, the dimension of financial risk preference, the
dimension of market risk preference, the dimension of operational risk
preference, and the dimension of legal risk preference , which are characterized by 16 items. According to this idea, the scale can be visually presented as
structural equation modeling (SEM). By SEM operation, free parameter
estimates and model evaluation index values can be obtained, which can test
the rationality of the hypothetical model. Maximum likelihood estimation
method is used to estimate the free parameters. The results are shown in
Table 5 and Figure 1. Taken together, the findings indicate the scale with a
satisfactory construct validity.
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Table 4: Spearson coefficient
Item
Spearson coefficient
Merger risk
‘‘Go out’’ national risk
National policy risk
Major projects investment risk
Asset security risk
Liquidity risk
Yield fluctuation risk
Account receivable risk
Customer demand risk
Remit interest rate risk
Supplier product quality risk
Information system risk
Major natural disaster risk
Environmental risk
Contract law risk
Financial report compliance risk
0.240*
0.363**
0.367**
0.478**
0.306**
0.482**
0.530**
0.309**
0.395**
0.363**
0.463**
0.459**
0.160*
0.405**
0.365**
0.450**
* P \ 0.05.
** P \ 0.01.
Table 5: Fit indices of the managerial risk reference scale
v2
v2/df
NFI
CFI
IFI
GFI
AGFI
RMSEA
437.30
4.70
0.74
0.92
0.94
0.96
0.94
0.026
Gender, education, and managerial risk preference
No obvious differences were observed in the Mann-Whitney U test, distribution of all individual risk preferences, all kinds of managerial risk preferences,
and overall managerial risk preference of different genders (specific data
omitted), implying that gender was not an influencing factor of managerial
risk preference. In this study, level of education was divided into three
categories, namely, college degree and under, undergraduate, and graduate
and above. No obvious difference was found in the results of the KruskalWallis test on individual risk preference, all kinds of managerial risk
preferences, and overall managerial risk preference of management personnel
with different educational levels except for contract law risk preference, which
had 0.01 significant differences (specific data omitted). Therefore, no obvious
difference was found in the distribution of the managerial risk preference of
management personnel with different levels of education. In taking all the
results together, H1 and H4 are not supported.
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Figure 1:
Free parameter estimation.
Age and managerial risk preference
The age of respondents was divided into two age groups ( 40, [40 years).
The result of Kruskal-Wallis test revealed an obvious difference of 0.01 in the
distribution of the merger risk preference, significant investment risk preference, customer demand change risk preference, supplier product quality risk
preference, and financial reporting compliance risk preference; an obvious
difference of 0.05 was found between the distribution of the yield fluctuation
risk and asset security risk preferences (see Table 6), whereas no obvious
difference was found in the other individual managerial risk preference
distribution (specific data omitted). Further study revealed that one of the
characteristics of the different items, management risk aversion, showed an
increasing trend as age increased. The result of the Kruskal-Wallis test on the
distribution of the overall managerial risk preference of management
personnel at different age groups showed an obvious difference. Figure 2
provides the average of the overall managerial risk preference of management
personnel of different age groups. Management personnel who belong to
40-year-old age group tend to manifest more risk pursuit than[40-year-old
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Table 6: Individual managerial risk preference related to age
Item
Merger and acquisition risk preference
Customer demand change risk preference
Supplier product quality risk preference
Financial reporting compliance risk preference
Major investment risk preference
Yield fluctuation risk preference
Asset security risk preferences
B40
[40
Asymp. Sig.
2.7968
2.1578
2.5397
1.4363
2.3783
2.2513
1.8062
2.4921
1.9434
2.1593
1.2807
2.0773
2.0566
1.6819
0.007**
0.001**
0.000**
0.001**
0.006**
0.040**
0.044**
* P \ 0.05.
** P \ 0.01.
60
56.15
50
45.55
Score
40
30
20
10
0
≤40
>40
Years
Figure 2:
The overall management of risk preference of different age groups.
group risks. The overall risk pursuit of management personnel was on a
declining curve as age increased, and older people were more risk averse than
younger ones. In taking all the results together, H2 is supported.
Position and managerial risk preference
The positions of the respondents in this study were divided into four types:
general manager, head of the office, department head, and senior executives.
The results of the Kruskal-Wallis test on the distribution of investment risk
preference of management personnel of different positions revealed an obvious
difference at 0.01 significance in all single managerial risk preference
distributions, whereas asset-safety risk preference, information-system risk
preference, and environmental risk preference had an obvious difference at
0.05 significance (as shown in Table 7), implying that the position of
management personnel in the company was a factor that would influence
some individual managerial risk preferences.
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Table 7: Individual managerial risk preference related to position
Item
Asset security risk preferences
Information system risk
preference
Environmental risk preference
Investment risk preference
Senior
executive
Office
Department
General
Asymp.
Sig.
1.6629
1.2921
1.7025
1.4545
1.8378
1.4865
1.8980
1.4082
0.032*
0.044*
1.7303
2.0337
1.7521
2.3140
1.9730
2.2432
1.8776
2.4490
0.045*
0.005**
* P \ 0.05.
** P \ 0.01.
The result of Kruskal-Wallis test on the distribution of the overall
management of risk preference of management personnel with different
positions revealed an obvious difference at 0.05 significance. Figure 3
compares the average overall management of risk preference of management
personnel with different positions. General management personnel pursued
the most risks, followed by office and department heads. Senior management
personnel were the most risk averse, indicating that overall level of risk pursuit
tended to decrease as the position became higher, and thus management
personnel in higher positions were more risk averse than those in lower
positions. In taking all the results together, H3 is not supported.
Industries and managerial risk preference
We selected six industries including construction, transportation, energy,
agriculture, trade, and manufacturing, which had more samples. The result of
the Kruskal-Wallis test on the distribution of remitted interest rate risk of
management personnel from different industries revealed an obvious difference at 0.01 significance among all distributions of individual managerial risk
preference. The venturing-out national risk preference had an obvious
difference at 0.05 significance, although no obvious relations were found
among customer demand change risk preference, contract law risk preference,
yield fluctuation risk preference, and supplier managerial risk preference. The
value was also generally small, indicating that individual risk preference of
management from different industries had some differences (see Table 8). The
results indicated that industry is a factor that influences some individual
managerial risk preferences.
The result of the Kruskal-Wallis test on the distribution of the overall
management of risk preference of management personnel from different
industries revealed an obvious difference at 0.05. Figure 4 compares the
average of overall management risks of different industries. Agriculture had
the highest level of risk pursuit, followed by manufacturing, energy,
transportation, trade, and construction. In taking all the results together,
H5 is supported.
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Figure 3:
The overall managerial risk preference of different positions.
Discussion and conclusions
In studies of risk preference in decision making, gender difference has always been
an important factor. A considerable number of studies on gender differences in
risk preference have been conducted (Byrnes et al, 1999; Arch, 1993; Eckel and
Grossman, 2008). One general conclusion is that men tended to display greater
tendency toward risk pursuit compared with their women counterparts. No
obvious differences in managerial risk preference of management personnel
between genders was observed because both men and women industrial
management personnel were adventurous in operation decision making (Li and
Xu, 1996). However, gender could also, to some extent, predict the risk
propensity of an individual (Xie, 2003). The results of the analysis show that the
understanding of management personnel toward enterprise risk problem depends
on their external environment and their own practical experience. A high level of
education may give rise to differences in the understanding of management
personnel on some issues (such as contract law risk preference). Overall,
education level would not have a significant effect on managerial risk preference.
Our statistical analysis results were consistent with the conclusion of
Gardner and Steinberg (2005) who stated that risk-taking behavior would
decrease along with age. The differences in the risk preference of the various
age groups could be attributed to the varying mental abilities of the age groups
(Steinberg and Cauffman, 2010). Young people would take more risks because
they can be easily influenced by similar risk propensities of their peers
(Zimring, 1998). In addition to the above explanations, we also believe that
the cognitive level toward various industrial risks of management personnel
from different age groups varied because of the differences in social and
management experiences. The difference in risk cognitive level would cause
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150
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** P \ 0.01.
* P \ 0.05.
Customer demand change risk
Risk of ‘‘venturing out’’
Remitted interest rate risk
Contract law risk
Yield fluctuation risk
Supplier product quality risk
Customer demand change risk
Individual managerial risk preference
2.0000
2.0000
1.9375
1.6875
1.8125
2.2500
2.0000
Construction
2.1667
2.6111
1.4444
1.7222
2.1111
2.0556
2.1667
Transportation
2.2273
2.2727
1.4545
1.8182
2.0000
2.4091
2.2273
Energy
Table 8: Individual managerial risk preference related to industries and Asymp. Sig
2.4375
2.5625
2.0000
1.8750
2.4375
2.6875
2.4375
Husbandry
1.8636
2.4091
1.5909
1.8636
1.9545
2.2727
1.8636
Trade
2.1667
2.6667
1.7778
2.1111
2.2778
2.5556
2.1667
Manufacturing
0.058
0.020*
0.009**
0.084
0.091
0.083
0.058
Asymp. Sig.
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Figure 4:
The overall managerial risk preference of different industries.
management personnel from different age groups to form diverse judgments
regarding the risk level in the same risks. Moreover, this kind of difference
may be attributed to personality traits. Personality is associated with different
risk-based decisions, and young people are more extroverts in nature and more
liberal than older people, and thus, they are more prone to taking more risks.
Previous results of studies on the risk preference of management personnel
with different positions indicated that management personnel with higher
positions had higher risk pursuit levels (Li and Xu, 1996; Xie, 2000). The
explanation of this result in Xie (2000) is that when the power is in the range of
prescribed, decision-making risk is relatively small; while the power is beyond the
prescribed scope, the decision-making risk is relatively large. In order to avoid
excessive risk, the managers of lower position take decisions in their relatively
small power range, with relatively strong risk aversion. We believe that this
explanation has its rationality, but cannot be used for the interpretation of our
results. The ‘‘abnormal’’ phenomenon of managerial risk preference of management personnel of China’s large SOEs can be explained from two aspects.
Chinese traditional thought is based on the ‘‘Official Standard.’’ Enterprise
management officials and entrepreneurs of Chinese state-owned businesses
differ significantly. Although both types of management personnel engage in
management work under the framework of principal-agent, entrepreneurs
allocate resources and provide innovative combination, whereas management
officials of SOEs are the owners of the enterprise or main management cadres
appointed by the ruling party. At present, senior management in state-owned
business in most provinces and cities manage the business according to
standards set by the administrative level. Chinese traditional thought on
Official Standard, on the one hand, has enabled industrial officials of all levels
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to maintain a low-key style and sound approach to decision making,
particularly in making decisions at various levels of risks. On the other hand,
officials with lower positions would attempt various methods to earn
promotions. Although all kinds of risky decision-making problems would
bring considerable risks, these problems also offer opportunities. Thus,
officials in lower positions would take more risks in decision making.
Results of the risk income differences. In the compensation system series of
current SOEs, the compensation for senior management is based on the
performance assessment of the state-owned assets regulatory institution at the
corresponding level, and deputies take a free rider on the principal that is
multiplied by a fixed coefficient based on the total salary of principals. The
salary of industrial officials can be described as cap-on-top and guarantee-atthe-bottom, whereas the salary of other management personnel is decided
internally based on performance level (Zhang, 2013). Therefore, the risk
motivation of the enterprise in general and middle management personnel to
some extent would be higher than senior executives. In their view, the
undertaking of some risks would bring more benefits, including a rise in
position and fame, as well as an improvement in their internal status, among
others.
We draw the following conclusions based on the risk preference scale
and our investigation on the managerial risk preferences of management
personnel of large state-owned enterprise in China. A difference exists
among the distribution of the managerial risk preference of management
personnel of different age, positions, and industries. The results of our
statistical analysis indicated that age, position, and industry are the factors
that influence managerial risk preference. As management personnel of
large SOEs grew older, their level of risk pursuit decreased, and older
management personnel became more risk averse than their younger
counterparts. As the position of management personnel became higher,
their level of risk pursuit decreased. Management personnel with higher
positions were more risk averse than their younger counterparts. In the six
industries analyzed in this study, the overall management of risk preference
in the agriculture, manufacturing, energy, transportation, commerce, and
construction industries tended to exhibit an increasing trend toward risk
aversion. The managerial risk preferences of management personnel of
different genders, education level, and firm scale had no significant
differences. Gender and level of education do not influence managerial
risk preference. However, firm scale should be analyzed further because
only large-scale firms were included in this study.
In 1957, the Mean-Variance Model was proposed by Markowitz. Since
then, a number of mathematical finance experts from developed economies
has sought to measure and manage risk using quantitative statistical
techniques, excessive superstition, and exaggerated features of mathematical
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models, and, ultimately, risk management has been led astray. Blind trust and
overreliance on these models contributed to Wall Street’s confused means and
goals, leading to a loss of rationality in the process of pursuing rationality
(Liu, 2009). In the 2008 global financial crisis, with many well-known
international financial institutions being pushed to a state of distress or even
bankruptcy, the fundamental lesson was that risk preference had undergone
significant ‘‘deviation.’’ In the face of immediate interests and market
temptation, risk preference had become radical (Zhang, 2009). Although risk
management failed in the global financial crisis, the importance of risk
management remains.
The basic principle of Samuelson’s Revealed Preference Theory is that
consumer purchase behavior under certain price conditions exposes his/her
inherent preferences. Thus, purchasing behavior can be used to derive
consumers’ preference, which is not logical thinking based on ‘‘preference
(utility function)-select,’’ but the opposite, namely, ‘‘select-preference.’’ Managerial risk preference is based on logical ‘‘select preference’’ thinking
combined with risk management or risky decision-making behavior, under
certain conditions, to derive managerial risk preference. Therefore, managerial
risk preference is a revealed preference. In expected utility theory, the body of
risk preference is ‘‘economic man,’’ whose rationality is the basic assumption,
and whose individual risk preferences are determined through utility function.
The body of managerial risk preference is based on ‘‘administrative man,’’
whose basic assumption is bounded rationality, and whose managerial risk
preference is determined through exhibited behavior in the process of
management and risky decision making. The meaning of ‘‘economic man’’
and ‘‘administrative man’’ are fundamentally different: ‘‘economic man’’
considers man to be completely rational, makes choice that maximize his/her
own interests; ‘‘administrative man,’’ due to scarcity of resources, does not
meet rational requirements, and chooses the principle of satisfaction to replace
the principle of optimal self-utility.
Based on expected utility theory, the research objective of risk preference is
to determine risky behavior in the individual decision-making process using
lottery method; while the research objective of managerial risk preference is
risky behavior in a company’s decision-making process. Individual decision
making can be regarded as a single objective whose purpose is to satisfy a
single requirement: personal utility maximization. Company decision making
is a multiobjective or multiattribute decision-making problem, which determines that managers consider not only personal gain, but other targets, such as
national interests, social interests, corporate interests, and so on, when making
business decisions. Managers are not isolated individuals, and their behavior is
influenced by internal and external factors. Managerial decision making needs
to adapt risk management objectives, making a greater contribution to the
survival and development of the enterprise, and deriving greater benefit from
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it. Enterprise risk management objectives are adjusted according to changes in
the environment, which is a dynamic process. Similarly, the managerial risk
preference is adjusted, with adaptive features.
A basic premise of effective risk management is to establish an appropriate
risk preference, including individuals and organizations. Risk preference affects
the risk management process at every stage (David and Ruth, 2008), including
start-up, risk identification, risk assessment, risk analysis, and risk response.
For example, in the risk identification stage, risk-averse managers express more
pessimism than risk-seeking managers facing the same risk. Risk-averse
managers identify many risks, including risks otherwise considered small,
unworthy of attention, or uncertain fuzzy events, ignoring opportunities; while
risk-seeking managers usually do not focus on threats, even treating them as
‘‘normal business activity,’’ with a tendency to downplay risk. At present, ERM
is used in China’s large SOEs. The study of managerial risk preference and its
influencing factors has positive implications. Through horizontal or vertical
comparison of the managerial risk preferences among members of enterprises,
decision makers can ‘‘intervene’’ in the managerial risk preference of individuals
and groups according to the enterprises risk management objectives, and
optimize managerial risk preference through micro-regulation and scientific
management, therefore guaranteeing the achievement of ERM objectives. In
addition, managerial risk preference plays an important role in human resource
management (HRM) activities. In recruitment and selection, candidates with
appropriate managerial risk preference may be selected. For example, when a
company is in the start-up period, a moderate risk-seeking candidate should be
considered for general manager. In contrast, risk-averse managers may be more
appropriate for financial management positions.
Limitations of this study
Several methodological limitations of the present study should be acknowledged. One limitation of the research is that the method is too simple. Lottery
method is a common method to research the individual risk preference.
Individual risk preference can be viewed as the degree of risk aversion in the
face of personal decision problem. Managerial risk preference can be viewed
as the degree of risk aversion in the face of enterprise decision problem.
Obviously, in the processing of personal decision and enterprise decision,
management personnel will show different degrees of risk aversion. The
method of individual risk preference can not be directly applied to the study of
the managerial risk preference. But so far, we have not found one good tool
for measuring managerial risk preference. The Managerial Risk Preference
Scale (Zhang and Li, 2009) is relatively a too simple tool. In addition, the
reliability and validity of the scale also need large-scale test.
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The research results highlighted the existence of an obvious difference
among the managerial risk preferences of management personnel from
different industries. As for individual managerial risk preference, a large
manufacturing firm generally encounters international problems, and thus
pursues a higher level of national risk of ‘‘venturing out.’’ However, we cannot
provide a convincing explanation for the difference in the overall management
of risk preference. This question can be investigated in future studies.
Acknowledgments
This work was supported by Major project of the National Social Science
Foundation of China (Project ID: 14ZDA069), National Natural Science
Foundation of China (Project ID: 11426143, 11501320, 71203122), Humanities and Social Sciences in Colleges and Universities of Anhui provincial key
projects (Project ID: SK2013A040), and Natural Science Foundation of
Shandong Province, China (Project ID: ZR2013GL002, ZR2014AP008). We
sincerely thank anonymous reviewers who help in improving this paper.
Notes
1 http://www.sasac.gov.cn/n86302/index.html.
2 http://www.stats.gov.cn/tjsj/ndsj/2015/indexch.htm.
3 http://www.money.163.com/16/0126/08/BE8978KP00253B0H.html.
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