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Impact of Decision Makers Divergence in Risk Attitudes in Canada on Co-
operative Management

Getu Hailu, PhD Candidate,
Department of Rural Economy
University of Alberta
Edmonton, Alberta, Canada
T6G 2H1
Tel: (780)-492-2265
Fax: (780)-492-0268
E-mail: [email protected]

Ellen Goddard, Professor and Chair,
Department of Rural Economy
University of Alberta
Edmonton, Alberta, Canada
T6G 2H1
Tel: (780) 492-4596
Fax: (780)-492-0268
E-mail: [email protected]

Scott Jeffrey, Associate Professor,
Department of Rural Economy
University of Alberta
Edmonton, Alberta, Canada
T6G 2H1
Tel: (780)-492-5470
Fax: (780)-492-0268
E-mail: [email protected]




Paper presented at 15
th
International Cooperatives Forum 2004 in Muenster: Competitive
Advantage of Cooperative’ Networks, September 7-9, 2004, Germany

(Please Do Not Quote)

____________________________________________________________________________
Authors acknowledge the financial support of Co-operative Program in Agricultural
Marketing and Business, Department of Rural Economy, University of Alberta.
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Abstract: Economic and financial theories predict that decision makers’ risk attitudes
influence capital structure and firm performance. In this study we scrutinize the impact of
difference in risk attitudes and decision making power between managers and directors of co-
operative agribusiness firms on capital structure and members’ welfare. Preliminary results
indicated that differences in decision makers’ attitude and their relative decision making
power matter in influencing members’ welfare and capital structure of the co-operatives.

1. Motivation
Risk can be defined as imperfect knowledge where the probabilities of the possible
outcomes are known (Hardaker, Huirne, and Anderson 1997; Johnson and Boehlje 1981).
Risk attitude
1
refers to the decision maker’s general or consistent tendency towards risks. Risk
attitudes are commonly modeled within an expected utility framework (von Neumann and
Morgenstern 1947; Schoemaker 1982; Fishburn 1988) or using psychometrics/ Fishbeins’s
multi-attribute attitude models (MacCrimmon and Weherug 1986). Risk perception reflects
the decision maker’s interpretation of the likelihood of risk exposure and is defined as the
decision maker’s assessment of the risk inherent in a particular situation.
Within the finance literature, decision maker’s risk attitude and perception assessment
are assumed to be important factor in ensuring successful business management. In this
regard, any information concerning risk attitudes and perceptions of managers and the Boards
of Directors (BODs) could be useful for co-operative businesses in making decisions
regarding training, personnel selection, and placement. Furthermore, assessment of managers’
and BODs’ risk attitudes has important implications for the designing and choice of
alternative financial risk management strategies and the performance/success of co-operative
businesses. Among other things, the process of risk management
2
may be affected by the risk
attitude and risk perception of decision makers of the business.
One of the issues in co-operative finance concerns the capital constraints facing the
user-owned organization under the financial risks associated with the various sources of
capital. In Canada, some co-operative agribusinesses are in financial distress as a result of too
much debt leverage (Goddard 2002). According to Robison and Barry (1987), optimal debt
for a business depends, among other things, on the decision maker’s risk attitude. For
example, a risk averse decision maker would tend to hold less debt (MacCrimmon and
Weherug 1986), ceteris paribus. Thus, in developing risk-based ranges of optimal debt
policies, the extent to which managers or BODs exhibit risk taking or risk avoiding behaviour
when making decisions with a variety of financial data is of specific interest.
Since the objective of the co-operative business is the maximization of its members’
welfare (Bateman, Edwards, and LeVay 1979; Enke 1945), efficient allocation of the co-
operative resources will be critical to whether the sector is competitive nationally and
internationally. Theoretical evidence suggests that co-operative businesses are less efficient
than investor-owned firms (Sexton, Wilson, and Wann 1989), due mainly to director lack of
business expertise as compared to directors of investor-owned firms (Helmberger 1966) and
the lack of an incentive structure in co-operatives to induce management to run the
association efficiently (Caves and Petersen 1986). These problems may be related to risk

1
An attitude is a mental or neural state of readiness, organized through experience, exerting a directive or
dynamic influence on the individual's response to all objects and situations to which it is related (Allport 1935).
2
Risk management may be defined as choosing among alternative strategies to reduce risks.
3
attitude differentials between managers and the board of directors leading to differing
opinions regarding investment, consolidation, and borrowing and ultimately firm financial
risk exposure and implemented risk management strategies. No previous study has attempted
to empirically scrutinize the impact of risk attitude differentials on co-operative business
performance. Arguably, the variation in debt leveraging risk could be due to the increase in
the transaction costs associated with efforts to resolve conflicts and costs of time taken to
arrive at consensus. Thus, risk attitude incompatibility may impede overall efficiency of
resource use.
In this study we (i) construct a latent risk attitude based on observed variables, (ii)
investigate whether risk attitude differs between managers and boards of directors of co-
operative agribusiness firms and (iii) explore the impact of the differences in risk attitude, if
any, on co-operative business financial risk exposure (e.g., debt policy) and performance.
2. Literature Review
Several empirical studies have investigated risk attitudes for a variety of different
classes of decision makers, using a variety of methods, examining a number of different
issues (Chavas and Holt 1990; Antle 1987; Saha, Shumway, and Talpaz 1994; Pennings and
Smidts 2001; Pennings and Leuthold 2000; Lence 2000; Pennings and Garcia 2001; Roosen
and Hennessy 2003; Meuwissen, Huirne, and Hardaker 1999; Brockhaus 1980). For example,
Brockhasu (1980) studies the relation between entrepreneurial decisions and risk. Johnson and
Powell (1994) and Olen and Cox (2001) examine the relationship between risk attitudes and
gender. Pennings and Smidts (2001) assess the relationship between risk attitude and market
behavior. Thus far, no study has explicitly explored the impact of divergence in risk attitudes
of managers and BODs on business management such as selection of financial risk
management strategies and capital structure decision. For example, risk-averse managers are
expected to borrow less as compared to risk-taking managers. Managers’ /directors’ degree of
risk-aversion has important implication on the level of debt financing risk exposure. Different
attitudes will affect negotiations between directors and managers and potentially lead to
conflict.
3. A Behavioural Conceptual Framework
3.1. Stochastic Dominance
One of the empirical applications of EU involves the Stochastic Dominance (SD)
theory. To systematically analyze the risk attitude of DMs, the theory of SD (Hardar and
Russell, 1969; Hanoch and Levy, 1969; Rochschild and Stiglitz, 1970; Levy 1992) is applied
in this study. The theoretical attractiveness of SD lies in its nonparametric orientation in that it
does not requires a full parametric specification of DM preferences. It relies on general
preference assumptions. SD has been developed to identify conditions under which one risky
outcome would be preferable to another (Hardar and Russell, 1969; Hanoch and Levy, 1969;
Rochschild and Stiglitz, 1970; Levy 1992). The basic approach of SD is to resolve risky
choices while making the weakest possible assumptions. Generally, SD assumes an individual
is an EU maximizer and then adds further assumptions relative to preference for wealth and
risk aversion (e.g., two alternatives are to be compared and these are mutually exclusive).
SD theory has been developed to identify conditions under which one risky outcome
would be preferable to another. The SD theory provides a systematic conceptual framework
for assessing economic behaviour under uncertainty (Hadar and Russell, 1969; Hanoch and
Levy, 1969; Rothschild and Stiglitz, 1970; Levy 1992). The application of SD appears in
various areas of economics and finance. According to Levy (1992), the theory of SD and its
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many application in economics and finance only developed within the past three decades
(Hadar and Russell, 1969; Hanoch and Levy, 1969; Rothschild and Stiglitz, 1970;Whitemore,
1970). The SD theory has the advantage that it does not require a parameterized utility
function, or a specification of the return distribution. This theory allows to check whether a
DM’s behaviour is dominated by risk taking or risk averting behaviour. Associated with
different preference assumptions, the Stochastic Dominance literature involves a multitude of
different criteria: First-Order SD (FSD), Second Order SD (SSD), and Thirds Order-SD
(TSD) (Levy). FSD assumes more over less, or non-satiation. SSD adds the assumption of
risk aversion or convex utility function, and the TSD assumes DMs exhibit decreasing
absolute risk aversion. In this study, the SSD criterion is used to obtain managers and
directors risk attitudes.
3.2. Theory of Planned Behaviour (TpB)
With its foundation in social psychology literature, the theory of planned behaviour
(TpB) is the most widely used model to describe and measure DM’s attitude towards an
object, behavioural intention and behaviour. In this study the use of the TpB allows the
incorporation of DMs perception, preference, experience, belief, facilitating conditions and
social pressure in the measurement of attitudes towards debt and its impact on the resulting
capital structure (Matthews et al 1994). The theory of planned behaviour has been applied to
predict behaviour in diverse contexts from managerial performance benchmarking (Hill et al
1996), consumer purchasing (Brinberg and Cummings 1983), cigarette use (Budd 1986) and
effects of advertising on attitude (Berger and Mitchell 1989) to capital strucure decision
making process (Matthews et al 1993), among others.
The TpB states that an individual’s behaviour can be predicted if observers know (1)
his/her attitude towards a particular behaviour, (2) his/her intention to perform the behaviour,
(3) his/her beliefs with respect to the consequences of performing that behaviour and, (4) the
social norms which govern that behaviour (Ajzen 1991). Behaviour is a function of intention
to perform and perceived behavioural control (or ability to perform the behaviour). Figure 1
depicts the relationship between intention and behaviour.

Figure 1: Theory of Planned Behaviour (Ajzen 2002)
The individual’s intention to perform a given behaviour (e.g., intention to increase
debt capital) is a central construct in the theory of planned theory; and reflects how
5
individuals are motivated to try to perform the behaviour in question (Ajzen 1991). Basically,
the TpB (Fishbein and Ajzen, 1975) states that human behaviour is determined by the
formation of prior intentions, and that intentions are formed on the basis of a weighted
combination of attitudinal (A) and normative factors (SN). According to Ajzen (1991),
individual DM’s behavioural intention is affected by the attitude towards the behaviour,
subjective norm and perceived behavioural control. Attitude is the individual’s feeling and
belief about the behaviour. Subjective norm refers to approval of a person’s important
referents with regard to the consequences of performing the behaviour or not. Perceived
behavioural control refers to the degree to which a person feels that his or her performance or
non-performance of the behaviour is under his or her control (Ajzen 1991). Perceived
behavioural control is hypothesized to have an impact both on the behavioural intention to
perform the behaviour and the behaviour per se.
Empirically, attitudes toward actions (e.g., debt leveraging) are determined by and can
be measured as the sum of evaluative salient behavioural belief, where behavioural beliefs are
beliefs held about the consequences of the action in question. The basic form of the Fishbein
multi-attribute attitude model can be expressed as:
∑ =
=
n
1 i
i ij j
a b A (1)
where Aj is an individual’s attitude towards an object j (e.g., debt leveraging); b
ij
is the
individual’s belief, expressed as a subjective probability that object j is associated with some
attribute i; a
i
is the evaluative aspect (i.e., judged goodness or badness) of attribute i; and n is
the number of salient beliefs. Equation [1] represents a model of attribute measurement
wherein strength of an individual’s beliefs about particular attributes are weighted and
summed to yield an index of overall attitude. It is assumed that a person’s attitude towards the
behaviour is proportional (∝) to this summative index (Ajzen 1991).
Subjective norm (SN) is obtained by summing the products of the strength for each
normative belief (NB
i
) and the motivation to comply (MC
i
) with the referent in question, over
the m normative beliefs. Normative belief is a belief about what a specific referent person
thinks one should or should not do regarding borrowing. Individuals who believe that most
referents with whom they are motivated to comply think they should endorse borrowing will
perceive social pressure to do so. It is assumed that a person’s subjective norm is proportional
(∝) to the resulting summative index. Thus, subjective norm can be expressed as:

i
m
1 i
i
MC NB SN ∑ =
=
[2]
where NB
i
is the DM’s normative belief that the salient reference thinks he/she should
(or should not) perform the behaviour and MC
i
is the DM’s motivation to comply with that
referent (Ajzen and Fishbein, 1980).
To obtain a measure of perceived behavioural control (PBC) each control belief (CB
k,
the assessment as to whether or not a given control factor – e.g., decision making power-
makes it harder or easier to endorse additional borrowing) is multiplied by perceived
behavioural facilitation (PF
k,
the assessment of the strength of the given control factor – e.g.,
decision making power- in actually affecting borrowing) of the particular control factor to
facilitate or inhibit performance of behaviour, and the resulting products are summed across
the r salient control beliefs to produce the perception of behavioural control (PBC); that is,
k
r
1 k
k
PF CB PBC ∑ =
=
[3]
6
Overall, the motivational factors that influence behaviour are assumed to be captured
by intention to perform a given behaviour. Intentions are the indications of how much of an
effort the DMs are planning to exert in order to perform the behaviour. Behavioural intention
represents the person’s motivation to perform the behaviour in question.
The above theoretical constructs are latent variables in that they cannot be directly
observed but must be inferred from observable responses. The theory of planned behaviour
can be used to organize the key concepts of behaviour and to predict behaviour. In this case,
the behaviour in question is debt financing. Once the information on attitude towards risk or
debt capital, subjective norm, and perceived behavioural control is obtained, the next step is to
investigate which of the three is the best predictor of intention to increase/decrease debt
capital; that is, PBC w SN w A w BI
3 2 1
+ + = , where the w’s are parameters to be estimated.
In the empirical literature, the TpB has been modified to include individuals’ previous
habit or behaviour and socio-demographic variables. For example, using the Fishbein and
Ajzen approach, Bentler and Speckart (1979) modeled attitudes, subjective norms, intentions
and past behaviour and subsequent behaviour. The behavioural model is also versatile in
accommodating socio-demographic variables. Identifying differences in attitudes attributable
to DMs’ gender, age, manager-director, income, education, awareness of risk management
practices, is an important outcome of the study.
4. Data and Methods
A questionnaire was constructed according to the TpB. Survey questionnaires were
sent to 139 managers and directors of agribusiness co-operative firms. Of these, 30 completed
questionnaires were returned for a response rate of 22%. The respondents included 2 females
and 28 males. Fourteen of the respondents were managers and while the other sixteen were
directors. Approximately 67% of the respondents had more than high school education. 30%
of the respondents were above the age of 54 years, and 50% of the respondents had before tax
household income in 2003 of at least CAN $100,000. More than 80% of the respondents were
from agribusiness supply co-operatives while the rest of the respondents were from feed mills,
fruit and flower co-operatives. Besides the responses considered in the current analysis
company background, awareness of different risk management strategies, frequency of
previous gambling activities, and perceptions of importance and effectiveness of risk
management strategies were also elicited
3
.
4.1. Stochastic Dominance
Second-order stochastic dominance
4
approach is implemented to assess whether the
shape of the utility function is concave or convex. To do so, individuals were asked questions
regarding choices between alternatives in which both positive and negative outcomes are
possible. This enables one to “experimentally” elicit whether individuals are generally
characterized by risk aversion (Levy and Levy 2001). As discussed in Levy and Levy (2001)
and McCord and de Neufville (1986), all the investments are uncertain, to avoid the effect of
‘certainty effect.’ To circumvent the problem of subjective probability distortion, which
occurs for small probabilities, all probabilities are fairy large (Levy and Levy 2001).
In this approach the elicitation of the risk attitude is based on the answers to the
following questions:

3
This information was used in other analysis not dealt with in this paper.
4
Not that the survey method used in this study differs from the interactive method used by most researchers to
obtain risk attitudes of DMs.
7
Table 1
5
: Suppose that you have decided to invest $10,000 in either Business A or Business
B. For the following two scenarios, indicate the Business that you would choose (A or B)
given the information provided:
Scenario I
6
: Would you prefer A or B if the potential dollar gain or loss one month from now for
each is as follows?
Business A Business B
Gain (+) or loss (-) Likelihood of
occurrence
Gain (+) or loss (-) Likelihood of occurrence
-$500 ⅓ -$500 ½
+$2500 ⅔ +$2500 ½
Please circle A or B

Scenario II: Would you prefer A or B if the potential dollar gain or loss one month from now
is as follows:
Business A Business B
Gain (+) or loss (-) Likelihood of
occurrence
Gain (+) or loss (-) Likelihood of occurrence
-$500 ¼ $0 ½
+$500 ¼
+$1000 ¼ +$1500 ½
+$2000 ¼
Please circle A or B

The response for Scenario I is used to test the degree of respondents rationality in the
sense that they prefer more to less (Figure 5). Figure 5 depicts the cumulative distribution
corresponding to the two business alternatives in Scenario I. In Scenario I, A dominates B by
FSD. Responses for Scenario II are used to directly assess the risk attitudes of the
respondents. By second-degree stochastic dominance, any risk-averse individual should prefer
B to A (Figure 6).

5
Adapted from Levy and Levy (2001).
6
All DMs with non-decreasing utility functions (concave, convex, or with both concave and convex segments)
prefer A to B.
8
0
0.25
0.5
0.75
1
-1000 -500 0 500 1000 1500 2000 2500 3000

Figure 2: Cumulative Distribution of Scenario I: A dominates B by FSD
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-1000 -500 0 500 1000 1500 2000 2500 3000 3500

Figure 3: Cumulative Distribution of Scenario II: B dominates A by SSD

Table 2 summarizes the outcomes of the two business investment scenarios in terms of
expected value and variance. In Scenario I, although both A and B have equal variances,
business A’s expected payoff is higher than that for B. Under Scenario II, both A and B have
equal expected payoffs, but business B has higher expected volatility (i.e., more risky).
Table 2: Expected Value and Variance of a Random Outcome of Investment in Two
Alternative Businesses
Scenario I Business A Business B
X P(X=x) X P(X=x)
-500 0.33 -500 0.50
2500 0.67 2500 0.50
E(x)
1500 1000
A
B
A
9
V(x)
2312500 2312500

Scenario II Business A Business B
X P(X=x) X P(X=x)
-500 0.25 0 0.50
500 0.25 1500 0.50
1000 0.25
2000 0.25
E(x)
V(x)
750
812500
750
562500

4.2. Theory of Planned Behaviour (TpB)
For comparison purpose, in addition to the above approach, a social psychological
approach is also adopted to investigate individual risk attitude, intention and behavior. This
approach extends the Fishbein’s multi-attribute model by including intention and behaviour,
in addition to attitude. The social psychological approach following the theory of planned
behaviour (TpB) is used to elicit DMs’ attitudes towards financing investment expansion
using debt. As mentioned in the conceptual model section, the TpB states that human
behaviour/intentions are guided by attitude towards the behaviour (debt), subjective norm
(perceived social pressure), perceived behavioural control (ability to affect company
decisions) [Figure 1]. In the socio-psychological approach, attitude towards risks is a latent
variable whose “value” is inferred by answers to multi-scale questions.
To obtain sample respondents’ attitudes towards the impact of increase in long-term
borrowing on financial risk exposure, the following hypothetical business situation is framed.
A company that is planning to expand by 10% over the next two years in order to survive
competitive pressure is defined as activities to be performed (Table 4). The expansion should
be financed by either debt capital or equity, or both over the same period. Based on this
scenario TpB based questions are designed.
Table 3: Assume a company with the following characteristics:
Assets: $200.4 million.
Total liabilities: $150 million
Existing Long-Term debt: $100 million.
Proposal: To ensure survival it is necessary to expand the current
capacity by 10% over two years.
Costs of expansion: The expansion is expected to cost approximately $50.4
million.

The above hypothetical business expansion plan is designed to provide insights into
co-operative DMs’ attitudes towards financial risk exposure (i.e, debt leveraging risk
exposure) and perception of appropriate or ‘optimal’ capital structure. Ex ante and ex post
expansion financial situation of this hypothetical business with different financing scenarios
are given below. A debt to equity ratio in excess of one is considered to be risky. Given the
initial situation of this hypothetical business, any additional borrowing will definitely
aggravate the financial risk exposure.
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Table 4: Impacts of Various Business Expansion Financing Sources on Company Risk
Exposure

Ex ante
Expansion
Situation
Ex post Expansion Situation

% Debt for Expansion
Assets Initial 25% 50% 75% 100% All Equity
200.4 250.8 250.8 250.8 250.8 250.8
Total Liabilities 150 162.6 175.2 187.8 200.4 150
Long-term Debt 100 112.6 125.2 137.8 150.4 100
Total Equities 50.4 88.2 75.6 63 50.4 100.8
Liabilities/Equity Ratio 2.98 1.84 2.32 2.98 3.98 1.49
Long-tern Debt to Equity
Ratio 1.98 1.28 1.66 2.19 2.98 0.99

More than 63% of sample respondents indicated that they would ‘possibly’ approve a
100% increase in additional borrowing for the purpose of the proposed expansion. When the
sample respondents were asked as to the ‘appropriate’ proportion of additional borrowing for
the business expansion, 43%, 30% and 27% of the respondents recommended a 25%, 50%
and 75% of long-term debt to finance the new investment, respectively. In terms of co-
operative DMs’ structure, 50% of the managers and 38% of the directors would like to
approve a 25% long-term borrowing for the proposed business expansion. The above
descriptive results may indicate that there are difference in terms of financial risk exposure
among co-operative DMs, in general, and managers and directors, in particular.
Based on this information the attitudinal index is derived for each individual as in
Table 1. Individuals are asked to respond to a series of statements, such as “Increasing
expected returns to members equity is …”, using a seven point Likert scale response from
“very bad” to “very good”. Their response is indexed from -3 to +3 and used as the outcome
evaluation measure. Individuals are then asked to respond to another series of statements,
such as “If I approve 100% long-term debt financing of expansions it will increase returns to
members equity”, again using a seven point Likert scale response from “very unlikely” to
“very likely”. Their response is indexed numerically from 1 to 7, and used as the belief
strength measure. The products of Outcome Evaluation and Belief Strength are summed over
all of the statements to obtain an overall attitudinal index. Table 1 provides a summary of the
statements from the survey and numerically indexed responses for a sample respondent, to
illustrate the method used. His/Her overall attitude index value is 13. This person’s attitude
towards increasing borrowing is, then, predicted to be positive.
Table 5: Decision makers’ belief about long-term debt financing of business
expansions

Outcome
Evaluation
Belief
Strength Product
1. Increasing expected returns to shareholder/member equity 1 5 5
2. Overcoming capital constraints problems 3 5 15
3. Benefiting from the tax deductibility of interest charge -2 4 -8
4. Increasing likelihood of bankruptcy 1 6 6
11
5. Increasing profit -1 1 -1
6. Increasing financial risk exposure -1 2 -2
7. Reducing future flexibility -1 2 -2
8. Making a safe investment 0 6 0
Sum 13

5. Empirical Model
First, to investigate if there are differences in risk attitudes between managers and
directors various methods are applied (e.g., non-parametric test and regression analysis).
First, t-test, Fisher’s exact test
7
, and Mann-Whitney tests
8
are used to assess if there are any
significant differences between the attitudes of managers and directors for different types of
risk attitude constructs.
Second, to investigate the relationship between risk attitude measures obtained from
SD and exogenous variables (e.g., age, income, age, and education), multiple regressions are
applied. For risk elicitation based on stochastic dominance, since the dependent variable is a
discrete random variable, the appropriate way to model factors explaining risk attitudes is to
define the probability of RA=1, not the value of RA itself, as a function of the exogenous
variables. Thus, a probability model that defines the probability of risk aversion as a function
of the exogenous variables is:
( ) ( ) β = = = ; x P 1 RA Pr P
i i

If one is interest in working with discrete choice models with continuous variables on the
right-hand side, logit and probit models provide a valuable framework (Maddala 1989). Based on the
risk attitude information obtained using stochastic dominance approach, the following model
is specified:
ε + ∑η + η =
=
n
1 j
j j 0
Demo * RA [4]
where Demo’s are explanatory variables (age, income, education and manager-director
dummy). For each decision maker we have observed the binary dependent variable RA:

¹
´
¦
≤ µ
>
=
) averse risk More ( * RA if 1
) averse risk Less ( * RA 0 if 0
RA
1
[5]
where the probabilities (normal distribution or logistic distribution) are given as
( )
¦
¦
¹
¦
¦
´
¦
=
|
.
|

\
|
∑η + η Φ −
=
|
.
|

\
|
∑η + η Φ
=
=
=
1 RA , Demo 1
0 RA , Demo
Demo , RA p
n
1 j
j j 0
n
1 j
j j 0
[6]
The above probability model is estimated using maximum likelihood approach.


7
McKinney, W.P., Young, M.J., Hartz A., and Lee M.B. (1989). The inexact use of Fisher's Exact Test in six
major medical journals. Journal of American Medical Association, 16:261(23):3430-3.

8
Non-parametric procedures are recommended when sample size is small or the distribution of the population
from which the data is obtained is uncertain (Hollander and Wolfe 1973).
12
For debt leveraging risk attitude measure based on the theory of planned behaviour,
the following linear regression equation is specified.
] 7 [
1
0
ε α α + + =

=
n
j
j j
Demo A

where A is attitude towards behaviour, Demo are demographic characteristics (age, manager-
director dummy variable, age, income), α’s are parameters to be estimated and ε’s is i.i.d
disturbance term. The above equations are estimated independently. Equation [7] is estimated
using ordinary least square.







6. Results and Discussion
6.1. Manager-Director Differences in Attitudes
Tests for differing attitudes towards increased long-term borrowing and risk attitudes
towards risk investment, lottery and general business situation between directors and
managers are conduced using t-test and Mann-Whitney tests. Measures for risk-aversion (risk
attitudes) are obtained based the SSD criterion. Willingness to pay for risk is elicited based on
lottery “experiment.” Respondents’ attitudes towards increased long-term borrowing are
obtained using TpB. General business risk attitudes are obtained based on Fishbein’s multi-
attributes attitude scales. Results from these methods are presented independently below; and
finally summarized.
6.2. Differing attitudes towards Risky Business Investment
Two scenarios of SD are presented in Table 1. In Scenario I, alternative A dominates
alternative B by FSD. That is, alternative A is a rational choice for any individual who prefers
more to less. Only three out of the thirty sample managers and directors selected alternative
B. Out of the fourteen managers only two of them selected alternative B whereas only one out
of sixteen directors chose alternative B. From the results in Scenario I, it can be concluded
that the majority of the DMs conform to the monotonicity axiom.
Scenario II is important for testing differing risk-aversion or risk attitudes of managers
and directors. By SSD, B dominates A in Scenario II. Any risk-averse individual should
prefer B to A. The survey results indicated that, two out of the fourteen managers and eight
out of the sixteen directors selected alternative B. Putting together the two Scenarios, it can
be inferred that i) 90% of the DMs (i.e, managers and directors) selected alternative that is
consistent with FSD (i.e., U’(w)>0) and ii) only 33 % of the DMs selected alternative that is
consistent with SSD (i.e., alternative B). Thus, the majority of the respondents (67%) are not
risk-averse.
The core of this study is to explore if there are any divergences in risk attitudes
between managers and directors of co-operative businesses. Of the sixteen directors and the
13
fourteen managers, respectively, eight directors and two managers are “risk-averse.” From the
survey results the majority of the managers appear to be less risk averse. The question is: “Do
the risk attitudes of DMs correspond to whether or not they are directors or managers?” Or, do
managers and directors show the same risk propensity? To answer this question, Fisher’s
exact test
9
is conducted using survey responses. The null hypothesis is that directors and
managers have equivalent risk attitudes. For Fisher’s exact test, the estimated one-tail p-value
equals 0.045 suggesting that directors and managers have different risk attitudes. Table 1
summarizes Fisher’s exact test of DMs risk attitudes divergence.




Table 6: A contingency table for DMs risk attitudes towards Alternative Risky Business
Investment (N=30)
Directors Managers Total
“Risk-averse” 8 2 10
“Risk-taking” 8 12 20
Total 16 14 30
Fisher’s Exact Test P - value = 0.045

6.3. Attitudes towards Long Term Borrowing
The test for discrepancies in attitude towards long-term borrowing is conducted based
on the information gathered using TpB procedures. For each individual, the index for attitudes
towards long-term borrowing is constructed as in Table 5. And then, both t-test and Mann-
Whitney tests are applied to assess if there are any divergences in attitudes towards debt
between managers and directors of co-operative firms (Table 9). Results suggest that
managers and directors differ in their attitudes towards long-term borrowing. The existence of
divergences in attitudes based on TpB approach is consistent with the finding from SSD,
although the qualitative implication of the divergence is different. Demsentz and Lehn (1985)
and Jensen and Meckling (1976) stated that if managers' holdings are substantial, their
motivations become aligned with those of shareholders and the agency problem is reduced. In
the case of co-operative business, where managers have no equity holdings in the business,
the motivations of managers and directors may not be very well aligned. Thus, difference in
risk attitudes may be expected.
Table 7: Tests for Differing Attitudes towards Additional Long-term Borrowing (N=30)
T-test for Equality of Means Nonparametric Test

9
Fisher’s exact test is a non-parametric statistical test used to determine if there are nonrandom association
between two categorical variables (risk-averse –non-risk averse, managers –directors). This test uses frequency
data to detect group differences (Source).
14
Mean Difference -1.923 Mann-Whitney U 57.5
t-statistics -2.424 Wilcoxon W 162.5
Degrees of freedom 28 Z -2.269
P-value 0.022 P-value 0.023

6.4. Determinants of Risk Attitudes towards Risky Investment
In the previous section co-operative managers and directors are found to differ in their
attitudes towards risky investment. As opposed to directors, managers are ‘less risk averse’
when considering alternative risky investments. In this section, the impact of individual
characteristics on risk attitudes towards risky investment of co-operative DMs is examined.
The measurement of the dependent variable is based on the responses for the SSD question
which is a binary variable: ‘less risk averse’ and ‘more risk averse.’ Probit model is
implemented. The explanatory variables in this model include respondents’ age, income,
education and manager-director variables. All the explanatory variables are dummy variables.
Due to multicollinearity problem between age dummy and manager-director dummy three
different models are estimated. Parameter estimates of these models are summarized in Table
8. The explanatory variables explain about 37% of the variation in the probability of risk
aversion. In addition, the probability of correct prediction for this model is about 77%.
Table 8: Determinants of Risk Attitudes towards Risky Alternative Business Investment
(N=30)
Variables
Marginal
Effects
Marginal
Effects
Marginal
Effects
Intercept 0.268 (0.432) --- -0.047 (-0.088) --- 0.600 (1.105) ---
Manager
-0.634 (-1.037) -0.154 --- --- --- -0.951* (-1.727) -0.272
Age Old
1.211* (1.884) 0.294 1.409** (2.313) 0.365 --- --- ---
Income High
-0.361 (-0.612) -0.088 -0.304 (-0.533) -0.079 -0.091 (-0.173) -0.026
Education High
-1.064* (-1.809) -0.258 -1.096* (-1.918) -0.284 -0.961* (-1.789) -0.274
Scaled R
2
0.371 0.338 0.254
S.B.I.C 21.762 20.609 21.968
LLF -13.259 -13.807 -15.166
FCP 0.767 0.767 0.767
Note: * and ** refers to 90% and 95 % confidence level, respectively. PCP: Fraction of Correct Predictions.
Figures in parentheses are t-statistic. Manager = 1, if a manager, 0 otherwise; Age old = 1, if age > 54, 0
otherwise; Income High =1, if income > $100,000, 0 otherwise; and Education high =1, if > high school, 0
otherwise.

First, for the model that includes both age and manager-director dummy variable the
coefficient of age is statistically significant and positive. This may indicate that for co-
operative leaders above the age of 54 years old the probability of being ‘more risk averse’ is
higher. Or, older co-operative DMs are more risk averse as compared to those under the age
of 55. The coefficient of education is statistically significant at the 10% significance level
suggesting that better education is negatively associated with the probability of being ‘more
risk averse.’ Put differently, those respondents with education above high school tend to be
‘less risk averse.’ When the variable age is dropped from the probit model, manager-director
15
dummy variable is found to have statistically significant effect on the probability of being
‘more risk averse.’
6.5. Determinants of Attitudes towards Increased Borrowing
One of the objectives of this study is to investigate factors that influence DMs’
attitudes towards long-term borrowing and their behavioural intention to borrow more in
order to finance business expansion. The point is that are DMs attitudes towards long-term
borrowing and their behavioural intentions to approve additional borrowing related to their
personal characteristics and social psychological factors? Factors that are believed to have an
effect on attitudes, subjective norms and perceived behavioural control are investigated using
multiple regressions. Furthermore, the impacts of attitudes, subjective norm, perceived
behavioural control, frequencies of previous gambling behaviour, and individual
characteristics on behavioural intentions are investigated using ordered probit model. The
parameters estimates of equation [7] are obtained using least-square procedures in TSP 4.5.
Results from multiple regression analysis indicated that 37.3%, 17.6%, and 21.7%,
respectively, of the variation in attitude, subjective norm and perceived behavioural control
are explained by respondents’ characteristics. Being a manager had a negative impact on the
values (indices) of attitudes, subjective norm and perceived behavioural control. As opposed
to directors, managers may have unfavourable feeling towards increase in long-term
borrowing to finance business expansion. Age has statistically significant relationship with
attitude, subjective norm and perceived behavioural control. Sample DMs who are older than
54 years of age have unfavourable feelings towards increase in long-term borrowing.

Table 9: Multiple Regression Estimates of Determinants of Attitude, (N=30)
Variable

Intercept 19.734*** (4.124)
Manager -14.008*** (-2.980)
Age Old -16.676*** (-3.225)
Income High -1.903 (-0.392)
Education High -1.980 (-0.437)
R
2
0.373
Note that Manager = 1, if a manager, 0 otherwise; Age old = 1, if age > 54, 0 otherwise; Income High =1, if
income > $100,000, 0 otherwise; and Education high =1, if > high school, 0 otherwise. Figures in parentheses
are t-statistic. ***, **, &*, represent 99%, 95% and 90% confidence level, respectively.

7. Case Studies of Plant Automation of a Co-operative Firms
From co-operative business decision makers’ point of view, knowledge of the
relationship between financing decisions, profitability and financial risk are critical to ensure
long term prosperity of the sector and to avoid financial distress. As well, an improved
understanding the impact of differences in the risk preferences of managers and directors and
differences in their relative decision making power on the choice of capital structure is
equally important. This section briefly demonstrates the effects of differences in risk attitudes
between managers and directors on the choice of capital structure, profitability and risk
exposure of co-operative agribusiness firms.
16
The co-operative sector, like its investor-owned counterpart, is going through some
major changes in terms of expansion, automation, upgrading, etc. Contingent upon the current
situation of co-operatives firms, these decisions likely involve a significant amount of capital
expenditures. By their very nature, co-operatives are characterized by the overall capital
constraint due to equity capital constraints (Cheddad 2001). As co-operative business
operations get expanded, automated, and upgraded they have a propensity to take on
additional debt that results in declines in the proportion of equity. The basic economic derive
behind their business operations expansion, upgrading and/or automation are the desire to
capture economies of scale, or improvement in efficiency and productivity that may result.
For co-operative business, where equity capital is limiting, this decision involves a trade-off
between improved efficiency and profitability associated with the automation or larger
expansion verses the increase in financial risk exposure that may results from the use of
additional debt to finance the capital expenditures.



7.1. Capital Expenditure and Members’ Welfare
The key to understanding the impact of capital investment using debt financing is the
determination of the desired level of capital stock. Suppose for the following members’
welfare maximizing marketing co-operatives, the producers’ welfare may be defined as:
PS MW
c
+ π = [8]
where MW is producers’ welfare,
c
π is co-operative firm’s profit and PS is producer surplus
defined as:
I w x w x w Py
I
i i j j c
− − − = π [9]

− =
j
X
0
j j j
dx ) x ( w x w PS [10]
where P is price of co-operative’s output, ) K , x , x ( f y
j i
= is the quantity of co-operative
output, w
j
is price of raw materials from members, x
j
is quantity of raw material from
members, w
i
is a vector of prices of other variable inputs, x
i
is a vector of quantities of other
variable inputs, w
k
is price for a unit of capital and I is investment.
A co-operative that maximizes the present value of its members’ welfare stream (or
the stream of value added), and substituting equations (9) and (10) in (8), would solve the
following optimization problem:
dt I w x w dx x w Py e MW
k
i i
X
j rt
j
∫ ∫


|
|
.
|


\
|
− − − =
0
0
) ( (4)
subject to ) , , ( K x x f y
j i
= and
t t t
K I K δ − =
+1

The integral

j
X
j
dx x w
0
) ( can be interpreted as the variable costs of producing x
j
. In steady state,
the solution to the above problem is: ( ) k , w , p f MW = . In this formulation, k is positively
related to MW, suggesting that capital expenditure may enhance members’ welfare under
17
some conditions. The other theoretical results from the first order condition of the above
problem may be given as:
K k k k
MC ) r ( p w
K
y
P MRP = δ + = =


=
where MC
k
is user cost of capital. In this context, any increase in the price of capital p
k
, in the
depreciation rate δ, or in the interest rate r, tends to increase the user cost MC
k
, and thus
reduce capital demand. This is called the cost of capital model (without tax expenses)
(Jorgenson and Siebert, 1968) which states that that factors of production are employed until
the point where their marginal value product equals their cost. Now, if MRP
k
> MC
k
, then
business expansion, automation or upgrading via acquiring additional unit of capital increases
profit or the contribution of additional unit of capital to revenue exceeds its contribution to
costs. On the other hand, if MRP
k
< MC
k
, acquiring additional capital for expansion or
automation or upgrading leads to a decline in profit resulting in financial distress. The change
in interest rate would increase the rental price of capital. In this situation, economic theory
suggests that firms should react to this cost increases by using less capital in production. Thus,
debt financed capital expenditures targeted at boosting co-operative firm’s profitability (or
members’ welfare) and growth through economies of size may result in negative returns and
hence increases risks of financial distress. As a result of that co-operative companies may
announce downsizing or closing their businesses. The other downside to excessive borrowing
is the fact that bankers do not like to lend to unhealthy businesses where loan repayment is not
assured which suggest that further financing may be a problem, and if possible at higher costs
of borrowing. In conclusion, even though capital investment is expected to enhance the
members’ welfare, the resulting risk exposure should also be taken into account. The next
section, presents the impact of differing risk attitudes towards debt financing on members
welfare and financial risk exposure.
7.2. Impacts of differing risk attitudes
Co-operative firms make a two-stage decision. At the beginning of the period co-
operatives decision makers choose how much total capital to be employed and determine the
optimal mix of debt to equity ratio depending on: costs of borrowing, tax benefits of
borrowing, risk attitude differences between managers (agency costs).
Once capital structure is chosen, at the second stage, firms choose different real
variable to produce and sell products to maximize members’ welfare at the end of the period.
Important decision variables for maximization of members’ welfare include: sales (output),
gross investment, proportion of fixed to total assets, plant automation and upgrading, R&D
expenditures and patronage payment. Among others, these choice variables are expected to be
affected by firms’ debt to equity mix and the structure of debt (short and long term debts).
7.3. Differing Risk Attitudes and Decision Makers Power: A Case Study
7.3.1. Risk Attitudes and Capital Structure
According to cognitive literature differing attitude refers to “variability concerning
relatively unobservable … attitudes …” (Kilduff et al., 2000: 22). In previous empirical
studies the impact of differing risk attitudes on group decision-making has not been
investigated. In addition, implication of differing risk attitudes is unknown. That is, the
relationship between difference in attitudes and firm performance remain unclear, particularly
18
because most studies focus on direct measurable attributes on individuals (Pfeffer 1983) such
as age, gender, education, etc., and tended to neglect the impact of differing attitudes on firm
performance (Kidluff et al 2000). How does the difference in risk attitudes affect capital
structure? What are the effects of divergence in attitudes on performance? We explore these
questions in a simulation of manager-director decision making process. In this study, to
demonstrate the relationship between differing risk attitudes and optimal capital structure, a
method proposed by Nelson and Escalante (2003) is adopted. Based on their approach, for a
single decision maker, a firm’s optimal debt level (D) is given by the following expression:
( )( )
( )
2 2
2
1
r r
r r r
i
i
E D
λσ µ
λσ µ µ
− −
+ + −
=
where D is optimal level of debt, E is initial equity, i is the
known interest rate on debt, µ
r
is the mean rate of return on assets, σ
r
is standard deviation of
return on assets, and λ is the coefficient of relative risk aversion. Taking into account the
condition that causes the debt level to be positive and decreasing in λ give the following
bounds, which provide a reasonable range of representing different degrees of risk aversion,
for the value of λ (Nelson and Escalante, 2003):
( ) ( )( )
2 2
2
1
r
r r
r
r
i i
σ
µ µ
λ
σ
µ + −
< <

.
For the case co-operative agribusiness firm, µ =0.0761, σ = 0.00346, i = 0.07, then the
range of admissible value of λ is [0.0108, 1.8967]. Within the framework of group decision-
making, this range may suggest decision makers should acknowledge the differences in
individuals’ preferences and its implication for decision making process
10
. Thus, if managers
and directors differ in their level of risk attitudes, they may estimate different optimal level of
debt. For example, results from TpB model suggest that managers have less intention to
increase borrowing to finance business expansion implying that in terms of borrowing
managers are more risk averse.
Table 10: Risk Attitudes and Optimal Capital Structure
Relative Risk
Aversion
D-E Total Borrowing
(CAN $)
Members’
Welfare ($)
Co-op Profits
(CAN $)
0.954 1.000 28,046,425 1,342,349,724 28,798,732
0.765 1.500 42,059,243 1,430,879,491 27,134,421
0.639 2.000 56,069,169 1,519,390,990 25,470,455
0.550 2.499 70,078,139 1,607,896,451 23,806,601

For instance, assuming an initial $28,039,247 equity capital, the co-operative directors
may believe that $70,078,139 (λ=0.550) should be borrowed for financing plant automation,
while the manager may believe that $42,059,243 (λ=1.500) for financing plant automation
(Table 10). In this case, the manager may not approve debt level above $42,059,243 unless
he/she is not sure about substantial benefits from additional debt while the directors think
higher debt level adds more to the welfare of the co-operative members. It is clear that
managers and directors will each have their own motivations and, hence, will be in conflict on
certain issues. This conflicts of interest among decision makers may delay the process of

10
Risk avers individuals will sacrifice some level of expected return to reduce the probability of loss. Risk taking
individuals prefer alternative with some probability of high return. Risk neutral individuals would prefer
alternative with higher expected return regardless of the associated probabilities.
19
decision-making and, hence, the actual automation of the plant. There must be trade-offs
between goals of managers and directors in this group decision-making process. The final
decision may also depend on the individual decision maker’s power and influence, and the
degree of group consensus. Therefore, it is important that differences in preferences and
decision making power be understood and that mechanisms and procedures for describing and
handling them be developed and applied. Analytical hierarchy process (AHP) is one of the
techniques that can be used to resolve this type of group decision problem (Saaty 1980). The
AHP enables decision-makers to structure a complex problem in the form of a hierarchy of its
elements and capture managerial preferences through pair-wise comparisons of the relevant
factors or criteria.

7.3.2. Analytical Hierarchy Process (AHP)
Analytical Hierarchy process (AHP) is a decision-aiding technique developed by
Saaty (1980; 1990). AHP helps in quantifying relative priorities or weights for a give set of
alternatives on ratio scale based on the judgment of the decision makers (Saaty 1980). The
AHP has been applied to evaluate alternative projects and business strategies in diverse
contexts from merger and acquisition process evaluation (Arbel and Orgler 1990), choosing
the best house to buy (Saaty 1990), project management (Al-Harbi, 2001), capital budgeting
(Kwak et al 1996), to resource allocation problem (Ramanathan and Ganesh 1995), among
others. The strength of AHP is that it organizes tangible and intangible factors in a systematic
way, and provides a structured yet relatively simple solution to decision-making problems
(Saaty 1980).
There are four steps required in applying the AHP (Saaty 1980; 1990): (i) Define the
problem and determine its goal; (ii) structure the problem as a hierarchy from the top (e.g.,
members’ welfare maximization in the case of co-operatives) to the lowest level (e.g.,
alternative debt policies); (iii) elicit a set of pair-wise comparison judgment by using the
relative scale measurement depicted in Table 26. This scale has been validated for
effectiveness both empirically and theoretically (Saaty 1990). The pair-wise comparisons are
done in terms of which element dominates the other (e.g., a scale of 9 if the manager
extremely dominates the director in the decision making process). There are n(n-1) judgments
required to develop the set of matrices. Reciprocals are automatically assigned in each pair-
wise comparison; and (iv) having made all the pair-wise comparisons, the consistency pair-
wise comparison matrix is determined by using the eigenvalue, λ
max
, as follows: CI=(λ
max
-
n)/(n-1), where n is the dimension of the matrix. Judgment consistency can be checked by
using the consistency ratio (CR) of CI with the appropriate value. The CR is acceptable, if it
does not exceed 0.01. If it is higher, the judgment matrix is inconsistent. To obtain a
consistent matrix judgment should be reviewed and improved.
AHP technique enables one to form a pair-wise comparison matrix A to determine the
relative importance of the criteria in achieving the goal (Table 11). The element in the i-th raw
and j-th column of A gives the relative importance of the criteria i as compared to j. Saaty
(1980) suggested a scale from 1-9 with a
ij
=1 if i and j are equally important, a
ij
= 9 if i is
extremely more important than j (Table 10).
Table 11: Pair-wise comparison of scale for AHP preference
Numerical rating Verbal judgments of preference Explanation
1 Equally preferred Equally contribute to the objective
2 Equally to moderately
20
3 Moderately preferred Moderately Favour one over the other
4 Moderately to strongly
5 Strongly preferred Strongly Favour one over the other
6 Strongly to very strongly
7 Very strongly preferred Very Strongly Favour one over the other
8 Very strongly to extremely
9 Extremely preferred Extremely Favour one over the other

The matrix A =[a
ij
] has positive entries everywhere and satisfies the reciprocal
property a
ji
=1/a
ij
. A pair-wise comparison matrix for n items can be given as:
|
|
|
|
|
.
|





\
|
=
|
|
|
|
|
.
|





\
|
=
1 a / 1 a / 1
a 1 a / 1
a a 1
a a a
a a a
a a a
A
n 2 n 1
n 2 12
n 1 12
nn 2 n 1 n
n 2 22 21
n 1 12 11
L
M O M M
L
L
L
M O M M
L
L

where a
ij
is the relative importance of criteria i as compared to criteria j; a
ij
=1 ∀ i=j; and
a
ij
=1/a
ji
∀ i≠j. For example, if the number of decision makers to be compared in terms of their
decision making power is equal to 2, A will be a 2x2 matrix with 1s along the main diagonal
depicting comparison of a decision maker with itself. In this case, one comparison must be
made. In general, if there are n decision makers to be compared, a total of
( )
2
1 n n −

comparisons are required.

Financing Strategies
Basically, the AHP approach gathers input judgments of managers and directors in the
form of a matrix by pair-wise comparison of criteria (e.g., debt levels). The relative
importance of alternative capital structures can be structured in a hierarchy as in Figure 4. In
this formulation, the overall goal of the co-operative business is specified as maximization of
members’ welfare which appears at the top of the hierarchy. Next to the overall goal of the
co-operative business, decision makers are identified to investigate their relative power in
influencing the financing strategies. The final level in the hierarchy deals with the specific
debt policy to be evaluated and implemented.
21
Figure 4: AHP Hierarchy for Alternative Debt Policy

Level 1: Members’ Welfare Maximization

Level 2: Decision makers Relative Power
Decision makers’ relative power appears on the second level in the hierarchy: managers,
and directors. The decision makers are compared with respect to their degree of relative
power in influencing the overall company goal. Questions such as “Which of the following
two actors has more relative power in shaping the capital structure of the co-operative at this
point in time?” may be asked to assess their relative power in shaping and directing debt
policy/strategy of the co-operative. The assessment of the relative power in influencing
strategic decision making of the co-operative firm may be gathered from this pair-wise
comparison. The matrix giving the relative decision-making power that is useful to compute
the relative weight in decision-making process is given as:
|
|
|
|
|
.
|





\
|
+
+
=
) p 1 (
1
) p 1 (
p
iority Pr
1
p
1
Director
p 1 Manager
Director Manager
P
12
12
12
12
12
where p
ij
∈ [1/9, 9]
For example if p
12
equals 1/5, suggesting that the director have a stronger power over
the manager in influencing strategic decision making.
|
|
|
.
|



\
|
=
833 . 0
167 . 0
iority Pr
1 5 Director
5 / 1 1 Manager
Director Manager
P
D

The priority column in the above matrix indicates that the director is dominant in
influencing and shaping the debt policy for co-operative agribusiness firm with priority of
0.833, and the “manager” has a priority of 0.167. This outcome may reflect the fact that the
director may be deeply involved in shaping the debt policy of the co-operative. In strategic
management literature this type of directors are referred to as “proactive boards” (Pearce and
Members’
Welfare
Managers Directors
D/E = 1.00 D/E = 1.50 D/E = 2.00 D/E = 2.50

Goal



Power




Financing
22
Zahra 1991). Proactive boards are characterized by relative decision-making power that
surpasses those of their managers. Other types of directors include “caretaker board”,
“statutory boards” and “participative boards”. Caretaker boards are ‘characterized by low
board power… [and] are usually dominated by company managers’ (Perace and Zahra
1991:137). In our case example, the value of p
12
may be assumed as 3 suggesting that
managers have moderately higher domination over the directors. Statutory boards are ‘often
function as ‘rubber stamps’ of managerial decisions, and do not thoroughly examine
managerial decisions because of the lack of expertise or interest’ (Perace and Zahra, 1991:
137). In the case of statutory board type, the company is characterized by powerful managers.
In statutory board type case the value of p
12
may be assumed to be 7 indicating very strong
managerial domination over the board. Finally, participative boards are ‘characterized by
discussion, debate, and disagreement. Differences of opinion are resolved by a vote, a
majority vote prevailing’ (Vance, 1983:9). The participative boards style may be thought of as
the situation where the board and the managers are characterized by equal or balanced power
(i.e, p
12
=1).

Level 3: Financing Strategies
The lowest in the hierarchy is the specific long-term debt financing strategies. The
alternative debt policies to be considered are labelled as D’s at the lowest level (Figure). The
alternative capital structures are compared regarding the extent to which they are important to
each decision maker. For a single decision maker, other things being equal, based on his/her
financial risk attitudes different long-term debt financing policies can be adopted in choosing
the optimal capital structure. The question that arises is as to what weight to assign to
individual with diverse risk attitudes that are involved in a group decision-making process.
The following matrices indicate the preference of directors and managers for four debt
policies, where D
1
, D
2
, D
3
, and D
4
are debt to equity ratio of 1.00, 1.50, 2.00 and 2.50,
respectively. Matrix D illustrates two individual decision makers’ preferences of four debt to
equity ratios to compute the relative weights.
|
|
|
|
|
|
|
|
.
|








\
|
=
1
d
1
d
1
d
1
D
d 1
d
1
d
1
D
d d 1
d
1
D
d d d 1 D
D D D D DM
D
34 24 14
4
34
23 13
3
24 23
12
2
14 13 12 1
4 3 2 1
where d
ij
∈ [1/9, 9]
Suppose, for director a debt to equity ratio of 2.5 is absolutely preferred to a debt to equity
ratio of 1 based on his/her risk preference. The pair-wise comparison is given in the following
matrices for hypothetical decision makers.
|
|
|
|
|
|
.
|






\
|
=
1 5 / 1 5 / 1 5 D
4 1 3 / 1 7 D
4 3 1 9 D
5 / 1 7 / 1 9 / 1 1 D
D D D D Director
D
4
3
2
1
4 3 2 1
D

|
|
|
|
|
|
.
|






\
|
=
1 5 / 1 5 / 1 9 / 1 D
5 1 3 / 1 7 / 1 D
5 3 1 5 / 1 D
9 7 5 / 1 1 D
D D D D Manager
D
4
3
2
1
4 3 2 1
M

23
Priority vectors derived for each debt policy matrix are given as [0.609, 0.248, 0.077,
0.065] for director and [0.140, 0.325, 0.374, 0.161] for manager. That is
|
|
|
|
|
|
.
|






\
|
=
161 . 0 065 . 0 D
374 . 0 077 . 0 D
325 . 0 248 . 0 D
140 . 0 609 . 0 D
Manager Director
D
4
3
2
1
P

The results in D
P
indicate that D
4
is expected to contribute the most to the overall
members’ welfare maximization objective. In terms of individual objective, D
1
is the most
important debt policy for the directors while D
3
is the most important debt policy for the
managers of the co-operatives.
The next step in AHP is to obtain the aggregate weights of the four alternative debt
policies by mathematically tying together the decision-making power priority matrix and
financing strategies priority matrix.
( )
|
|
|
|
|
.
|





\
|
=
|
|
.
|


\
|
145 . 0
324 . 0
312 . 0
218 . 0
161 . 0 374 . 0 325 . 0 140 . 0
065 . 0 077 . 0 248 . 0 609 . 0
167 . 0 833 . 0
The result shows that D
3
has been given an overall weight of 0.324 while D
4
has been
given an overall weight of only 0.145. Thus, the optimal debt level that simultaneously
incorporates managers’ and directors’ risk preference and their relative decision making
power for this case co-operative firm is $ 49,551,310. That is,
( )
|
|
|
|
|
.
|





\
|
145 . 0
324 . 0
312 . 0
217 . 0
68313020 54671085 40860477 27060137 = 46,309,816
This solution reflects the preferences/judgments and influence of multiple decision
makers and different ranges of risk aversion. If the DMs are not satisfied with the solution,
new global weights may be computed.
The next important step in the AHP technique is to perform sensitivity analysis to find
out if the final recommendations are sensitive to certain judgments, assumptions, or
operational environments assumed to be valid during the course of the analysis (Arbel and
Orgler 1990). The sensitivity analysis may include, among others, the following: (i) changing
the relative power of DMs and observe what effect, if any, can be traced to the bottom level
(debt policy options); (ii) Introducing environmental scenario as an additional hierarchy (e.g.,
expanding economy with strong competition, stable economy with strong competition, etc.)
and members’ welfare; and (iii) Modeling and changing the relative risk aversion of decision
makers.
Since the objective of this study is to investigate the impact of divergence in the
attitudes of decision makers on business performance sensitivity analysis on relative power of
DMs and degree of divergences in risk attitudes are carried out. In Table 11, four different
degrees of divergence/similarities in risk attitudes are defined. Note that no debt is assumed to
24
be the status quo. In scenario it is assumed that both directors and managers are risk averse;
Scenario II assumes managers are risk averse and directors are risk taking; Scenario III
assumes risk taking manager and risk averse director; and Scenario IV assumes both
managers and directors are risk taking. All the four Scenarios assume participative board style
is assumed. Scenarios II and III show the case where managers and directors diverge in terms
of risk attitudes. Results indicate that, for participative board style, the degree of risk aversion
does matter rather than the divergence in risk attitudes (Table 12).
Table 12: Percentage Changes in Net Profits, Producer Surplus, Total Welfare and Return on
Equity attributed to borrowing for Participative Board Style
11

Scenario I Scenario II Scenario III Scenario IV
Debt Level $37,356,955 $49,079,038 $49,079,038 $60,801,122
Net Profit (patronage) -25.01% -35.65% -35.65% -48.28%
Producer Surplus 16.80% 20.96% 20.96% 24.73%
Total Welfare 16.02% 20.04% 20.04% 23.70%
ROE Volatility 6.30% 9.04% 9.04% 12.31%

Table 13 depicts the impacts of divergence/similarities in risk attitudes between
managers and directors for proactive board style. As can be seen from Table 12, divergence in
risk attitude does matter when there are differences in decision-making power between
managers and directors. The more powerful and the more risk taking the decision maker is,
the higher the debt level, the lower the net profit, the higher the producer surplus, and the
higher the total welfare are. In this case, the final decision-making is dominated by the
influence from directors.
Table 13: Percentage Changes in Net Profits, Producer Surplus, Total Welfare and Return on
Equity attributed to borrowing for Proactive Board Style
Scenario I Scenario II Scenario III Scenario IV
Debt Level 37356955 58456705 39701371 60801122
Net Profit (patronage) -25.01% -45.57% -27.00% -48.28%
Producer Surplus 16.80% 24.01% 17.67% 24.73%
Total Welfare 16.02% 22.99% 16.86% 23.70%
ROE Volatility 6.30% 11.60% 6.82% 12.31%

Table 14 presents the situation whereby managers dominate directors. In this case results are
the same as the situation whereby the board is proactive except that the value of Scenario II
and III interchanges.

Table 14: Percentage Changes in Net Profits, Producer Surplus, Total Welfare and Return on
Equity attributed to borrowing for Caretaker/Statutory Board Style

11
Note that no debt is assumed to be the status quo. Scenario I assumes both directors and managers are risk
averse; Scenario II assumes either managers or directors are risk averse (risk taking); Scenario III assumes risk
taking manager and risk averse director; and Scenario IV assumes both managers and directors are risk taking.
All the three Scenarios assume participative board style.
25
Scenario I Scenario II Scenario III Scenario IV
Debt Level 37356955 39701371 58456705 60801122
Net Profit (patronage) -25.01% -27.00% -45.57% -48.28%
Producer Surplus 16.80% 17.67% 24.01% 24.73%
Total Welfare 16.02% 16.86% 22.99% 23.70%
ROE Volatility 6.30% 6.82% 11.60% 12.31%



8. Concluding Remarks
For the sample respondents, there are statistically significant differences in attitudes
towards long-term borrowing between managers and directors. These differences may result
in agency problems emanating from conflicting preferences. These differences, if not
resolved, may result in significant costs of resolving conflicts (agency costs), or may hamper
the success of the co-operative business. The conflicts of preference among decision makers
may delay the process of decision-making and, hence, may negatively affect the actual
business performance.
Findings from this study have several managerial implications. First, given results
from other studies (e.g., agency costs; Hailu et al, 2004), differences in DM’s attitude towards
debt and risk may affect corporate financial risk management. Tufano (1998) found that the
level of managerial risk aversion affected corporate risk management policy in the North
American gold mining industry. Demsentz and Lehn (1985) and Jensen and Meckling (1976)
stated that if managers' holdings are substantial, their motivations become aligned with those
of shareholders and the agency problem is reduced. In the case of co-operative business,
where managers have no equity holdings in the business, the motivations of managers and
directors may not be very well aligned. Thus, differences in risk attitudes may be expected.
Second, acknowledging and aligning differing DMs’ attitudes through technical support may
facilitate the optimization of the overall co-operative goals. Hence, evidence from the survey
may suggest a need for technical support for co-operative decision makers in the area of
financial risk management.
Although the results from this study may not be conclusive due to the small sample
size, yet, it may provide some direction and suggestions for future research. Further research
is warranted to assess the degree to which manager-director differences in attitude towards
long-term borrowing affect the success of the business. As well, does this result extend to a
larger and diversified sample of managers and directors? By using larger sample size from
diverse co-operative types and structure, more confidence may be placed on how
representative are the results
In order to explore the implication of divergence in risk preference and attitude
towards long-term borrowing, simulation based on a multiple criteria and multiple DMs
models is carried out. Simulation results suggest that oprimal capital structure depends on
decision makers’ risk attitude, divergence in DMs’ risk attitudes and relative decision making
power of DMs. Thus, differences in decision makers’ attitude and their relative decision
making power matter in influencing members’ welfare and co-operative risk exposure.

26

Reference

Allport, G. W. 1935. Attitudes. A Handbook of Social Psychology. (pp. 798-844). Worcester,
MA: Clark University Press.ed. C. Murchison .
Antle, J. M. 1987. Econometric Estimation of Producers' Risk Attitudes. American Journal of
Agricultural Economics 69: 507-22.
Arbel, A. and Y.E. Orgler. (1990). An Application of the AHP to bank Strategic Planning:
The Mergers and Acquisitions Process. European Journal of Operational Research, 48:
27-37.
Bateman, D. I., J. R. Edwards, and C. LeVay. 1979. Problems of Defining a Cooperative as an
Economic Organization. Oxford Agrarian Studies 8: 53-62.
Berger, I. E. and A. A. Mitchell. 1989. The Effect of Advertising on Attitude Accessibility,
Attitude Confidence, and the Attitude-Behaviour Relationship. The Journal of
Consumer Research, 16(3):269-279.
Brockhaus, R. H. 1980. Risk-taking Propensity of Enterpreneurs. Academy of Management
Journal 23: 509-20.
Caves, R. E., and B. C. Petersen. 1986. Cooperatives' Shares in Farm Industries:
Organizational and Policy Factors. Agribusiness: An International Journal 2: 1-19.
Chaddad, F. R. 2001. "Financial Constraints in U.S. Agricultural Cooperatives-Theory and
Panel Data Econometric Evidence."
Chavas , and Holt. 1990. Acreage decisions under risk. American Journal of Agricultural
Economics 72: 529-38.
Demsetz, H., and K. Lehn. 1985. “The Structure of Corporate Ownership: Causes and
Consequences.” Journal of Political Economy, 93:1155–1177.
Enke, S. 1945. Consumer Cooperatives and Economic Efficiency. American Economic
Review 35, no. 1: 148-55.
Fishburn, P. C. 1988. Non-Linear Preference and Utility Theory. Baltimore: Johns Hopkins
University Press.
Gloy, Brent A., and Timothy G. Baker. The Importance of Financial Leverage and Risk
Aversion in Risk-Management Strategy Selection. American Journal of Agricultural
Economics, November 2002, V. 84, Iss. 4, Pp. 1130-43 .
Goddard, E. 2002. Factors Underlying the Evolution of Farm-Related Co-operatives in
Alberta. Canadian Journal of Agricultural Economics 50, no. 4: 473-96.
Hadar, J., and W.R. Russell .1969. Rules for Ordering Uncertain Prospects, American
Economic Review 59, 25-34.
Hailu, G., S.R. Jeffrey, E.W. Goddard and D. Ng. (2004). Incentive Incompatibility in Co-
operative Agribusiness Firms in Canada: Does Supply Management Matter? Journal
of Food Distribution Research, XXXV(1):110-111.
Hanoch, G., and H. Levy .1969. The Efficiency Analysis of Choices Involving Risk, Review
of Economic Studies 36, 335-346.
Hardaker, J. B., R. B. M. Huirne, and J. R. Anderson. 1997. Coping With Risk in Agriculture.
27
New York: CAB International .
Helmberger, P. 1966. Cooperative Enterprise as a Structural Dimension of Farm Markets.
Journal Fo Farm Economics 46: 1427-35.
Hill, M., L. Mann and A. J. Wearing. 1996. The Effects of Attitude, Subjective Norm and
Self-Efficacy on Intention to Benchmark: A Comparison between Managers with
Experience and No Experience in Benchmarking. Journal of Organizational Behavior,
17(4):313-327.
Hill, M., L. Mann and A. J. Wearing. 1996. The Effects of Attitude, Subjective Norm and
Self-Efficacy on Intention to Benchmark: A Comparison between Managers with
Experience and No Experience in Benchmarking. Journal of Organizational Behavior,
17(4):313-327.
Hogan, K. and G.T Olson. (1999). Evaluating Potential Acquisitions Using the Analytic
Hierarchy Process. Advances in Mathematical Programming and Financial Planning.
5: 3-17.
Jensen, M. C. and W. H. Meckling. (1976). Theory of the firm: Managerial behaviour, agency
costs and ownership structure. Journal of Financial Economics, 3: 305-60.
Jensen, M. C. and W. H. Meckling. 1976. Theory of the firm: Managerial behaviour, agency
costs and ownership structure. Journal of Financial Economics, 3: 305-60.
Johnson, D. A., and M. D. Boehlje. 1981. Mimizing Mean Standard Deviations to Exactly
Solve Expected Utility Problems. American Journal of Agricultural Economics 63:
:28-29.
Johnson, J. E. V., and P. L. Powell. 1994. Decision Making, Risk and Gender: Are Managers
Different? British Journal of Management 5: 123-38.
Jorgenson, D., and C. Siebert. “Optimal Capital Accumulation and Corporate Investment
Behavior.” Journal of Political Economy 76, no. 6(1968): 1123-1151.
Jorgenson, D., and J. Stephenson. “Investment Behavior in U.S. Manufacturing, 1947-1960.”
Econometrica 35, no. 2(1967): 169-220.
Kaplan, M.F., and C.E. Miller. 1987. Group decision making and normative versus
informational influence: Effects of type of issue and assigned decision rule. J.
Personal. Soc. Psych. 53:306–313.
Kerkel, P., R.B. Holcomb and E.W. Ac Bol. 2003. Attitudes of Cooperative Managers and
Board Members Toward Value Added Enterprises and New Generation Cooperative
Structures. Food Technology Research Report, Oklahoma State University, P-997: 1-
8.
Kwak, W., Y. Shi, H. Lee and C.F. Lee. 1996. Capital Budgeting with Multiple Criteria and
Multiple Decision Makers. Review of Quantitative Finance and Accounting, 7: 97-
112.
Lence, S. H. 2000. Using Consumption and Asset Return Data to Estimate Farmers' Time
Preferences and Risk Attitudes. American Journal of Agricultural Economics 82, no.
2: 934-47.
Levy, H. 1992. Stochastic Dominance and Expected Utility: Survey and Analysis,
Management Science 38(4), 555-593.
Levy, H. 1998. Stochastic Dominance, Kluwer Academic Publishers, Norwell, MA.
28
Levy, Moshe, and Haim Levy. Testing for Risk Aversion: A Stochastic Dominance
Approach. Economics Letters, May 2001, V. 71, Iss. 2, Pp. 233-40 .
MacCrimmon, K. R., and D. A. Weherug. 1986. Taking Risks: The Management of
Uncertainty. New York: Free Press.
Matthews, C.H., D.P. Vasudevan, S.L. Barton and R. Apana .1994. Capital Structure Decision
Making in Privately Held Firms: Beyond the Finance Paradigm. Family Business
Review, 7(4): 349-367.
Meuwissen, M. P. M., R. B. M. Huirne, and J. B. Hardaker. 1999. Perceptions of risks and
risk management strategies: and analysis of Dutch Livestock Farmers. AAEA Annual
Meetings, August 8-11, 1999, Nashville, Tennessee.
Neslon, C.H. and Escalante, C.L. (2003). Optimal Farm Debt under Decreasing Absolute Risk
Aversion and Constant Relative Risk Aversion: An Application of the Location-Scale
Condition. D
Olen, R. A., and C. M. Cox. 2001. The Influence of Gender on the Perception and Response
to Investment Risk: The Case of Professional Investors. Journal of Psychology and
Financial Markets 2: 29-39.
Pearce II, J.A. and S.A. Zahra. 1991. The Relative Power of CEOs and Boards of Directors:
Associations with Corporate Performance. Strategic Management Journal, 12(1): 135-
153.
Pennings, J. M. E., and A. Smidts. 2001. Assessing the Construct Validity of Risk Attitude.
Management Science 46, no. 10: 1337-48.
Pennings, J. M. E., and P. Garcia. 2001. Measuring Producers' Risk Preferences: A global
Risk-Attitude Construct. American Journal of Agricultural Economics 83: 993-1009.
Pennings, J. M. E., and R. M. Leuthold. 2000. The Role of Farmers' Behavioral Attitudes and
Heterogeneity in Futures Contracts Usage. American Journal of Agricultural
Economics 82, no. 4: 908-19.
Robison, L. J., and P. J. Barry. 1987. The Competitive Firm's Response to Risk. New York:
MacMillan Publishing Company.
Roosen, J., and D. A. Hennessy. 2003. Tests for the Role of Risk Aversion on Input Use.
American Journal of Agricultural Economics 85, no. 1: 30-43.
Rothschild, M., and J.E. Stiglitz .1970. Increasing Risk I: A Definition, Journal of Economic
Theory 2, 225-243.
Saaty, T.L. 1980. The Analytic Hierarchy Process, McGraw-Hill, New York, NY.
Saaty, T.L. 1990. Multicriteria Decision Making: The Analytic Hierarchy Process, RWS
Publications, Pittsburgh, PA.
Saaty, T.L. 1994. Fundamentals of Decision Making and Priority Theory with the Analytic
Hierarchy Process, RWS Publications, Pittsburgh, PA.
Saha, A., C. R. Shumway, and H. Talpaz. 1994. Joint Estimation of Risk Preference Structure
and Technology Using Expo-Power Utility. American Journal of Agricultural
Economics 76: 173-84.
Schoemaker, Paul J. H. 1982. The Expected Utility Model: Its Variants, Purposes, Evidence
29
and Limitations. Journal of Economic Literature 20, no. 2: 529-63.
Sexton, R. J., B. M. Wilson, and J. J. Wann. 1989. Some Tests of Economic Theory of
Cooperatives: Methodology and Application to Cotton Ginning. Western Journal of
Agricultural Economics 14, no. 1: 56-66.
Tufano, P. 1998. The Determinants of Stock Price Exposure: Financial Engineering and the
Gold Mining Industry. Journal of Finance, 53(3): 1015-1052.
von Neumann, J., and O. Morgenstern. 1947. Theory of Games and Economic Behaviour.
Princeton: Princeton University Press.


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