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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: hgetu@ualberta.ca

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: ellen.goddard@ualberta.ca

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: scott.jeffrey@ualberta.ca

Paper presented at 15th 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(Please Do Not Quote)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 attitude1 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 management2 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.

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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

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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

1iiijj abA (1)

where Aj is an individual’s attitude towards an object j (e.g., debt leveraging); bij is the individual’s belief, expressed as a subjective probability that object j is associated with some attribute i; ai 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 (NBi) and the motivation to comply (MCi) 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

1iiMCNBSN ∑=

= [2]

where NBi is the DM’s normative belief that the salient reference thinks he/she should (or should not) perform the behaviour and MCi 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 (CBk, 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 (PFk, 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

1kk PFCBPBC ∑=

= [3]

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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, PBCwSNwAwBI 321 ++= , 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 elicited3. 4.1. Stochastic Dominance

Second-order stochastic dominance4 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.

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Table 15: 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 I6: 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.

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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

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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 Product1. 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

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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 test7, and Mann-Whitney tests8 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=== ;xP1RAPrP ii 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

1jjj0 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:

≤µ>

=)averseriskMore(*RAif1

)averseriskLess(*RA0if0RA

1

[5]

where the probabilities (normal distribution or logistic distribution) are given as

( )

=

∑η+ηΦ−

=

∑η+ηΦ

=

=

=

1RA,Demo1

0RA,DemoDemo,RAp

n

1jjj0

n

1jjj0

[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).

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For debt leveraging risk attitude measure based on the theory of planned behaviour, the following linear regression equation is specified.

]7[1

0 εαα ++= ∑=

n

jjj DemoA

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

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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 test9 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).

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Mean Difference -1.923 Mann-Whitney U 57.5t-statistics -2.424 Wilcoxon W 162.5Degrees of freedom 28 Z -2.269P-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 R2 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) R2 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:

PSMW c +π= [8] where MW is producers’ welfare, cπ is co-operative firm’s profit and PS is producer surplus defined as:

IwxwxwPy Iiijjc −−−=π [9]

∫−=jX

0

jjj dx)x(wxwPS [10]

where P is price of co-operative’s output, )K,x,x(fy ji= is the quantity of co-operative output, wj is price of raw materials from members, xj is quantity of raw material from members, wi is a vector of prices of other variable inputs, xi is a vector of quantities of other variable inputs, wk 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:

dtIwxwdxxwPyeMW kii

Xjrt

j

∫ ∫∞ −

−−−=

00

)( (4)

subject to ),,( Kxxfy ji= and ttt KIK δ−=+1

The integral ∫jX

jdxxw0

)( can be interpreted as the variable costs of producing xj. In steady state,

the solution to the above problem is: ( )k,w,pfMW = . 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:

Kkkk MC)r(pwKyPMRP =δ+==

∂∂

=

where MCk is user cost of capital. In this context, any increase in the price of capital pk, in the depreciation rate δ, or in the interest rate r, tends to increase the user cost MCk, 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 MRPk > MCk, 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 MRPk < MCk, 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:

( )( )( ) 22

21

rr

rrr

ii

EDλσµ

λσµµ−−

++−=

where D is optimal level of debt, E is initial equity, i is the known interest rate on debt, µr is the mean rate of return on assets, σr 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): ( ) ( )( )

22

2 1

r

rr

r

r iiσµµ

λσ

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 process10. 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, λmax, as follows: CI=(λmax-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 aij =1 if i and j are equally important, aij = 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 =[aij] has positive entries everywhere and satisfies the reciprocal

property aji=1/aij. A pair-wise comparison matrix for n items can be given as:

=

=

1a/1a/1

a1a/1aa1

aaa

aaaaaa

A

n2n1

n212

n112

nn2n1n

n22221

n11211

L

MOMM

L

L

L

MOMM

L

L

where aij is the relative importance of criteria i as compared to criteria j; aij=1 ∀ i=j; and aij=1/aji ∀ 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

1nn −

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:

+

+=

)p1(1

)p1(p

iorityPr

1p1Director

p1ManagerDirectorManager

P

12

12

12

12

12 where pij ∈ [1/9, 9]

For example if p12 equals 1/5, suggesting that the director have a stronger power over the manager in influencing strategic decision making.

=

833.0167.0iorityPr

15Director5/11Manager

DirectorManagerPD

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 p12 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 p12 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, p12=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 D1, D2, D3, and D4 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.

=

1d1

d1

d1D

d1d1

d1D

dd1d1D

ddd1DDDDDDM

D

3424144

342313

3

242312

2

1413121

4321

where dij ∈ [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.

=

15/15/15D413/17D4319D5/17/19/11D

DDDDDirector

D

4

3

2

1

4321

D

=

15/15/19/1D513/17/1D5315/1D975/11D

DDDDManager

D

4

3

2

1

4321

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

=

161.0065.0D374.0077.0D325.0248.0D140.0609.0D

ManagerDirector

D

4

3

2

1P

The results in DP indicate that D4 is expected to contribute the most to the overall members’ welfare maximization objective. In terms of individual objective, D1 is the most important debt policy for the directors while D3 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.

( )

=

145.0324.0312.0218.0

161.0374.0325.0140.0065.0077.0248.0609.0

167.0833.0

The result shows that D3 has been given an overall weight of 0.324 while D4 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,

( )

145.0324.0312.0217.0

68313020546710854086047727060137 = 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 Style11

Scenario I Scenario II Scenario III Scenario IV Debt Level $37,356,955 $49,079,038 $49,079,038 $60,801,122Net 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 IVDebt Level 37356955 58456705 39701371 60801122Net 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 IVDebt Level 37356955 39701371 58456705 60801122Net 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

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