Australian
Agri-Food 2000 Research Forum
Melbourne August 17
Owner Characteristics And Business Planning As Determinants Of Australian Family Farm Performance
George A. Tanewskia
PhD, Claudio A. Romanob PhD, & Kosmas X. Smyrniosb PhD
AXA Australia Family Business
Research Unit
Department of Accounting & Finance
Faculty of Business and Economics
Monash University
aResearch Fellow; bFoundation
Directors of the AXA Australia Family Business Research Unit.
We wish to acknowledge the support of the Rural Industries, Research & Development Corporation (RIRDC) for providing funding support (Project No. UMO-20A).
Requests for reprints should be made to Dr George Tanewski, Research Fellow, AXA Australia Family Business Research Unit, Department of Accounting and Finance, Faculty of Business and Economics, Monash University, P.O. Box 197, Caulfield East, Victoria, 3145 Australia. Email: george.tanewski@buseco.monash.edu.au
No part of this manuscript is to be cited without the consent of the authors
Extant strategic management literature has long recognised the importance of planning in small owner-managed businesses. While there is acceptance of the notion that sophisticated planning can make firms more competitive, the applicability of planning processes to small business has not been resolved. Thus, the question of why some businesses plan and others don’t becomes especially relevant. Using a multi-method approach of focus groups and a cross-sectional survey, the present study explores pathways between antecedent and business planning variables, and farm performance.
Structural equation modelling techniques were employed to estimate the goodness of fit of a business planning and farm performance model based on the responses of Australian broadacre and dairy farmers (n = 748). Goodness of fit statistics accord with the observed data. Findings demonstrate that business planning, as well as background characteristics such as size of farm business, and farm owners’ level of education and age are associated significantly with higher farm performance.
Exogenous factors including level of farm entrepreneurship, perceptions of environmental uncertainty, and farm owner’s internal locus of control are significant predictors of business planning. These findings are in line with contingency theory, suggesting that variables such as environmental uncertainty and size of firm are important moderating variables of firm performance.
In Australia, the agricultural industry is experiencing major challenges and global economic pressures. Climatic factors, such as drought, and economic factors, such as agriculture’s shrinking share of GDP (from approximately 26% in the early 1950’s to the current 3%), increasing competition, weak commodity prices, and continuing technological change have contributed substantially to the financial pressures experienced by family farm owners .
These challenges are further exacerbated by a large number of smaller family farms being unable to increase their farm size in order to take advantage of economies of scale. This has led some policy makers to view Australian agriculture as a rapidly declining and non-viable industry.
Open systems models, including contingency theory and resource dependence, argue that a firm’s survival depends on its ability to adapt successfully to a changing environment. Strategic planning is one tool utilised by business managers to assist in the minimisation of environmental turbulence and uncertainty.
Given that strategic planning is an important tool for minimising environmental turbulence and uncertainty, extant farm-management literature reveals that there is widespread perception that farm owners are unable to prepare strategic plans . Indeed, many farm owners believe that they do not gain any immediate or real benefits from business planning, particularly longer-term planning, viewing it as an unnecessary chore that requires many hours of paperwork that could be better spent elsewhere. Thus, the issue of why some farmers plan and others don’t requires further examination.
A review of the farm management literature (e.g., Lees, 1997; Riley, 1999), particularly pertaining to the Australian agricultural sector, reveals that it is long on theory and prescription, but short on descriptions of how farmers actually manage their businesses. The issue of how Australian farmers do, as opposed to how they should, has received very little coverage. Moreover, research relating to human relationships in farm management has attracted scant interest, with articles on the changing role of women in agriculture and succession issues being given prominence only recently.
Little attention also seems to have been paid to farmers’ management practices , including management processes such as setting objectives, planning, decision making and control. This deficiency is surprising, particularly given the view that sound farm-management practices play an important role in the overall performance of a business. A number of authors describe the consequences resulting from poor business management practices. These investigations argue that poor planning contributes to an increase in the financial vulnerability of the business; decreases firm performance; lowers firm competitiveness; and leads to a lack of strategic focus.
Given the importance that business planning has in the management function of any firm, this study goes some way towards addressing the dearth of empirically-based research concerning Australian farm business management practices. Although some studies (e.g., Thune & House, provide support for a positive relationship between planning and performance, these evaluations have often been criticized on methodological grounds including measurement and replication problems, and a failure to take contextual influences into account.
Other investigations (e.g., Pearce, Freeman, & Robinson, 1987) suggest the spuriousness of the planning and performance relationship, contributing to the unresolved debate concerning the applicability of planning processes to small business, despite the importance of planning having been long recognised.
Thus, the primary objective of this research is to assess the relationship between planning and performance by taking contextual influences into account. Although this link has been empirically tested, researchers have limited their inquiry to single-equation systems. Moreover, researchers have failed to simultaneously take contextual influences such as background characteristics, environmental uncertainty, entrepreneurship, and locus of control into account.
Given these problems, the present study utilises structural equation modelling (SEM) techniques to examine the extent to which owners’ level of education and age, farm size, environmental uncertainty, entrepreneurship, internal locus of control, and sophistication of planning are related to family farm business performance. Finally, this paper also reports the measurement properties of constructs, and the extent to which the prevalence of business planning mediates farm performance.
Theoretical Rationale and Model Development
Given the paucity of empirical evidence related directly to the effects of strategic management on farm operations, this study primarily reviews the organisational literature based on non-agricultural operations. For the purposes of this research, farm management is viewed no different to management in non-agricultural operations. Indeed, Giles and Renborg argued that … in managerial terms, the farmers’ job is not as different … from those who manage other kinds of businesses (pp. 400-1). Thus, this review examines the effects of business planning on firm performance from the perspective of organisational literature and discusses the contributions of variables such as environmental uncertainty, the level of farm entrepreneurship, internal locus of control, and owner and farm background characteristics to planning and farm performance.
Planning Definition.
The organisational literature (e.g., Matthews & Scott, 1995; Mintzberg,
1981; Snyder, 1982) emphasises the multi-dimensional nature of business
planning, contributing to no one clear-cut operational and conceptual definition
of business "planning". This lack of conceptual and operational
clarity prompted Mintzberg to evaluate four definitions of planning: as future
thinking; an integrated decision; a formalised procedure; and, as programming.
The first definition seems tautological as most, if not all, decisions take the future into consideration. The second definition, which Mintzberg argued as being too broad, encompasses a conscious attempt to integrate decisions across different points of time. The third definition, which captures planning as an orientation toward analysis, presents planning as a formalised procedure that articulates results. Finally, Mintzberg argued that planning is an already conceived programming procedure that justifies and elaborates consequences of an intended strategy of a company.
Snyder suggested that these four definitions of planning are related and comprise the entire strategic planning process. He further argued that a necessary condition of this process is evaluation, determining whether strategic choice matches the objectives of an organisation .
Owing to the contentious nature of the debate and the multi-faceted nature of planning, this study characterises planning as a process of formalising, implementing, and evaluating goals and objectives. This definition takes the approach that a firm’s strategic planning process involves the explicit systematic procedures used to gain the involvement and commitment of those principal stakeholders affected by the plan . Thus, farm business planning is deciding in advance what should be done, how tasks should be accomplished, when tasks should be undertaken, and who will be responsible for completing them.
Planning and Performance.
The organisational literature presents empirical tests of the
planning-performance relationship in several industry sectors including small
electronics firms , manufacturing , retail , and banking . Although in general
the literature suggests that planners outperform nonplanners, research continues
to remain divided about this relationship.
As noted earlier, the applicability of planning processes to small business has not been resolved, even though the importance of planning in small owner-managed businesses has been long recognised. It is noteworthy that Robinson and Pearce described small business planning as unstructured, irregular and uncomprehensive (p.129). Robinson and Pearce also noted that formal strategic planning is a conceptual activity suited solely to large organisations. Nonetheless, Bracker and Pearson argued that sophistication of strategic planning is an important factor in the performance of firms.
For example, planning has been shown to increase success rates and levels of performance of small enterprises . As well, the nature of the strategic planning process has been shown to have a positive effect on firm performance and efficiency .
However, Pearce, Freeman, and Robinson argued that empirical support for the normative suggestions that all small firms should engage in formal strategic planning has been inconsistent and often contradictory. In a meta-analysis of 14 studies examining formal strategic planning-performance relationship, Schwenk and Shrader found that planning did not necessarily improve performance, arguing against the assertion that strategic planning is only appropriate for large companies.
Despite these research efforts there is surprisingly little empirical work examining planning methods employed by family farm businesses, as well as the relationship between planning sophistication and farm performance. The present study fills this void and examines the relationship between sophistication of planning and farm performance. Accordingly, the following hypothesis is presented:
H1: Sophisticated business planning is associated positively with farm performance.
Risk and Uncertainty. Research shows that environmental uncertainty impacts on decision making and planning. For example, Lindsay and Rue found that large firms increase planning in the face of turbulent environments. Shrader et al. reported a positive correlation between perceived uncertainty and operational and strategic planning in small firms. Similarly, Bracker and Pearson suggested that entrepreneurs who employ formal planning procedures are better prepared to develop a framework for anticipating and coping with future change.
The literature also suggests that when uncertainty is zero, planning might be a deterministic means of scheduling business activities. Matthews found that small and entrepreneurial firms prefer to plan under low uncertainty. However, under circumstances of high uncertainty, and when forecasting is difficult in the short-term, long-term planning appears redundant. Under such conditions, entrepreneurs need to be alert and flexible and the rigidity of formalised plans sometimes work against them.
Correspondingly, Robinson and Pearce argued that owing to resource constraints and limited strategic options, small enterprises are less likely to plan, particularly in turbulent times. These findings suggest that environmental uncertainty is an influential factor during the strategic planning process, and that this factor warrants further investigation, especially in agricultural sectors where uncertainty plays a large role in the current social and economic climate. Thus, the following hypothesis is proposed
H2: Farm owners who perceive increasing certainty in their business environment will be associated positively with sophistication of business planning.
Internal Locus of Control and Entrepreneurship.
Early theorists such as Cole (1946) and McClelland (1961) argued that the level
of entrepreneurship is determined by personality traits such as need for
achievement. Indeed, one personality characteristic that has been demonstrated
to have associations with entrepreneurship is locus of control (Rotter, 1966).
Organisational research (e.g., Begley & Boyd, 1987) has focused on the
relationship between owner-managers locus of control and firm entrepreneurship.
Locus of control describes the degree of control people believe they have over their environment and refers to a set of beliefs about behaviour and success or failure. Thus, persons who score high on internal locus of control believe that the consequences of their behaviour stem from their efforts. These individuals have been found to be more activity-oriented and to possess entrepreneurial qualities (Brockhaus, 1980). Miller, Kets de Vries, and Toulouse (1982) also indicated that entrepreneurs with high internal locus of control were especially likely to employ strategies of product-market innovation. Consistent with these earlier findings, Miller and Toulouse (1986) reported that small business CEOs, with high internal locus of control qualities, pursue more product innovation, are more future oriented, and tailor their approaches to the circumstances facing their firms.
These findings suggest that personality is closely related to organisational strategy and structure. Studies of entrepreneurship as an organisational level construct have found that firm entrepreneurship (This study measures level of farm entrepreneurship) consists of three dimensions: innovation, proactivity, and risk taking. Research (e.g., Lefcourt, 1982) also indicates that individuals with high locus of control are more action oriented and perform better in ambiguous situations.
Given that agriculture is especially vulnerable to production risk, where farmers are subject to vagaries of weather and uncertainty about performance of crops or livestock, the relevance and type of business planning conducted by farmers will depend on the extent to which they believe that they can influence the performance of their farm businesses through their own actions.
These beliefs are also a function of the degree to which farmers believe that they can predict or anticipate changes in the operating environments of their farms. Thus it can be expected that entrepreneurial farms, led by individuals with high internal locus of control, will be associated positively with business planning. On the basis of this evidence, the following two hypotheses are proposed.
H3(a): Locus of control is associated positively with sophistication of business planning.
H3(b): Entrepreneurial farms are associated positively with sophistication of business planning.
Background Characteristics and Planning.
Owner and firm characteristics commonly analysed in strategy research are
owner-managers’ level of education and age; and firm size. These
characteristics are regarded as important and they are discussed in relation to
operational and strategic planning, as well as farm performance.
Owner-managers’ level of education.
Planning formality and sophistication involves the ability to be thorough and
comprehensive in information gathering, to integrate decisions, and to deal with
uncertainty. Research indicates that more educated owner-managers exhibit
several qualities that are helpful in the planning process. These
characteristics include the ability to discriminate among a variety of stimuli,
higher capacity for information processing, higher tolerance for ambiguity, and
higher integrative complexity.
These findings suggest that level of education is associated positively with operational and strategic planning sophistication. Indeed, extant agriculture literature demonstrates that education positively influences farmers’ efficiency, and is positively associated with formal planning . However, research also indicates that a large proportion of small business owners, and in particular farm owners, have no professional or formal qualifications. Consistent with this view, Stanworth and Gray found that size of firm is associated negatively with owner-managers’ level of education. Thus, the following two hypotheses are advanced:
H4(a): Farm owners’ level of education is associated positively with sophistication of business planning.
H4(b): Farm owners’ level of education is associated positively with farm performance.
Age of owner. As noted previously, business planning requires a high degree of comprehensiveness and integration. Older owner-managers are expected to be less sophisticated in their planning endeavours as they tend to do less well in integrating information and in evaluating a variety of options while arriving at a decision .
However, relationships between owners’ age and farm performance results is not clear as a number of studies have reported positive associations between age and efficiency, whereas others have noted non-significant relationships. Given that older owner-managers do less well in integrating information and that age is expected to be negatively associated with business planning, we also propose that older farm owner will be associated with lower farm performance. On the basis of this evidence, the following hypotheses are proposed
H5(a): Age of owner is associated negatively with sophistication of business planning.
H5(b): Age of owner is associated negatively with farm business performance.
Size of farm. Literature (e.g., Aram and Cowen, concurs on the inverse relation between firm size and planning: When compared with large business, smaller firms spend less time on planning. As firms grow, there is a greater need for coordination, integration, control, and planning sophistication. In line with this observation, Robinson and Pearce found that most small firms do not plan owing to lack of time, expertise, trust, and openness. Aram and Cowen reported that strategic planning in smaller firms differ significantly from planning practices of larger firms.
These differences are primarily related to owner-managers of larger firms having a relatively higher personal stake in their firms’ future, compelling owner-managers to direct and control the planning process. Similarly, Shrader et al. observed that smaller firms use operational planning more than strategic planning. In respect to performance, however, a number of studies (e.g., Bracker & Pearson, ; Bracker et al., ; Robinson & Pearce, 1983; Shrader et al., 1989) have concluded that there is little or no significant relationship between strategic planning and performance of small firms. Accordingly, the following hypotheses are proposed:
H6(a): Larger farm businesses are associated positively with sophistication of business planning.
H6(b): Size of farm business is positively associated with farm performance.
In summary, empirical evidence concerning the relationships between business planning and farm performance, and environmental uncertainty, entrepreneurship, locus of control, and background characteristics has been documented extensively and hypotheses proposed. However, it seems that research has failed to simultaneously take a number of contextual variables into account. Within a multivariate context, this study examines the extent to which owner background variables, farm characteristics, and owners’ perceptions of environmental uncertainty influence business planning and farm performance. Figure 1 provides a summary of hypothesized relationships between exogenous and endogenous variables. Appendix A provides operational definitions of variables.
Figure
1. Hypothesised Model of Business Planning and Farm Performance
This study involved a multi-method approach. This approach was adopted for two principal reasons: First, it is recognized that current knowledge of Australian farm management practices, particularly in the areas of strategic and operational planning, is not well articulated. Thus, focus groups were used to gain an overall understanding of perceptions regarding strategic planning and growth on family-owned farms. Second, a national survey of broadacre and dairy farmers permitted us to undertake a more detailed analysis of business planning practices and issues, as well as achieve the overall objectives of this study.
Initially, data were collected using six focus group interviews conducted on Australia’s eastern seaboard. Focus group interviews comprised three mixed grain grower groups, one sheep farming group, and two dairy farmer groups. Interviews were semi-structured, often free-flowing in nature, with approximately 10 to 12 participants in each group. Interviews provided a forum that encouraged farmers to discuss whether they conducted strategic and operational planning on their farms, benefits of such planning, problems associated with business planning, and efficient measures of farm performance. The interview guide was designed with the dual aim of avoiding bias, and ensuring adequate reporting within the frame of reference of the present study. This guide did not require that questions be addressed in a particular order, whereas the prespecification of questions and probes on each theme assisted in maintaining a nondirective stance.
A structured self-report questionnaire was developed following focus group interviews. Use of focus group interviews followed by a national survey aimed to derive the benefits of quantitative and qualitative methods, and to apply appropriate methods to questions of interest. The sample was selected on the basis of Australian Bureau of Statistics (ABS) 1996-97 annual listing of key characteristics and industry information for Australian broadacre and dairy farm establishments. The broadacre (N=71,944) and dairy farm (N=13,714) industry estimates in this study cover establishments with an estimated value of agricultural operations of $22,500 or more in 1996-97. Utilising Axiom Australasia Pty Ltd, an independent national agriculture industry database provider, 4,080 farmers stratified on state location and industry were selected.
Questionnaires were distributed during the spring of 1999 over a two-month period (i.e.., September and October). Seasonal economic and weather conditions in Australia were generally favourable, except for Victoria and South Australia, where farmers were experiencing drought weather conditions. 748 broadacre farmers responded, reflecting a response rate of 19% after allowing for return to sender, those who retired, sold their farm, deceased, and refusals. 57 respondents were excluded from the analysis as they indicated that they were not broadacre farmers.
A breakdown of respondents by state location demonstrates that the characteristics of our participants are comparable to those reported by the ABS (1996). However, either under- or over-representation occurred in some states. Broad acre industry breakdowns reveal that respondents are over-represented in the mixed livestock/crops and sheep-beef industries, but are under-represented in the wheat & other crops and beef industries. While the response rate to this study was low, which places constraints on generalisability, these results nonetheless suggest that findings are comparable to the ABS population statistics for five of the six states and for four of the six industries. Further comparisons of the present sample against other ABS distributional data, such as education, age, and gender, suggest comparability. Furthermore, comparisons of average total income and average asset value of farm figures with those compiled by Australasian Agribusiness Services (1997) are comparable (see Appendix B).
The typical family farm business owner is 51 years of age, with almost 23% under the age of 40 and 20% over 60 years. Females represent approximately 18% of respondents. Almost 44% of farm owners have tertiary qualifications (31.5% TAFE/college, 12% university qualifications) compared with 47.1% who have only secondary-level education.
Over 88% of respondents view their farm as a family farm, and 92.4% indicated that more than 50% of the farm’s share capital is owned by the family. Similarly, 88.7% of respondents indicated that they make more than 80% of farm management decisions. Of the 7.6% non-family owned farm enterprises, 0.2% are publicly owned. The typical family farm business has been owned for 58 years, with the age of farms ranging from one to 170 years. Median size of farms is 781.5 hectares, with the primary business activity being mixed livestock (35.7%), followed by sheep-beef producers (14.7%), beef (14.0%), and dairy farmers (13.6). Reported median value of properties is $0.8 million, with total farm income averaging around $284,000. Table 1 shows information on background characteristics.
Table 1. Family Farm Business and Owner Characteristics
|
Farm Business Characteristics |
% (n = 748) |
Farm Owner Characteristics |
% (n = 748) |
|
Industry Wheat & Other Crops Mixed Livestock Sheep-Beef Sheep Beef Dairy Other |
7.0 35.7 14.7 5.3 14.0 13.6 9.6 |
Marital status married single divorced other |
86.9 5.3 2.5 5.3 |
|
State NSW Victoria Queensland Western Australia South Australia Tasmania |
23.7 26.6 12.9 12.9 17.6 6.3 |
Educational level Primary School Secondary School TAFE/Agricultural College University Other Courses |
4.4 47.1 31.5 12.0 5.0 |
|
Family Business Yes No |
88.4 11.6 |
Gender male owner female owner |
82.4 17.6 |
|
Ownership of Farm Share Capital 0% 1-20% 21-50% 51-75% 76-100% |
2.5 1.5 2.5 5.4 88.5 |
Generation of ownership first generation second generation third generation fourth generation fifth generation + |
25.1 31.9 27.3 10.6 5.2 |
|
Farm Management Decision-Making Conducted by Owner 0% 20% 40% 60% 80% 100% |
0.7 1.5 2.8 6.3 11.9 76.8 |
Age (years) of business owner Mean Median |
50.8 51.0 |
|
Farm Business Subject to Annual Financial Report Audit Yes No |
38.3 61.7 |
Years Farm Owned by Family Mean Median |
58.2 50.0 |
|
Total Area of Property (Hectares) Mean Median |
2,526.8 781.5 |
Current Value of Property 1999 Mean Median |
2,526.8 781.5 |
|
Total Area Crops Grown (Hectares) Mean Median |
582.6 242.8 |
% Change in Total Assets 1995-1999 Mean Median |
19.9 20.0 |
|
Head of Cattle Mean Median |
411.6 200.0 |
Farm’s Total Income 1999 Mean Median |
$284,134.3 $180,000.0 |
|
Head of Sheep Mean Median |
3,379.3 2,000.0 |
% Change Farm Income 1995-1999 Mean Median |
11.5 10.0 |
Measures
Strategic management and family business literature and information derived from focus group interviews was used to develop the self-report questionnaire. In this way, the present research instrument approaches the notion of ‘gestalt’ as proposed by Labaw and others. The questionnaire comprises eight sections: Strategic and operational planning; risk and uncertainty; farm business objectives; business and life outcomes; entrepreneurship; family functioning; background of farm business; and, farm owner characteristics.
Strategic and Operational Planning.
The Sophistication of Strategic Planning and Operational Planning measure was
adapted from Matthews and Scott . Our 18-item instrument assesses the extent to
which family farm owners use specific types of strategic, operational,
benchmarking, and succession plans. Sophistication was assessed by using 6-point
Likert scales (0=No; 1=In My Head (Unwritten) to 5=Formal Written).
Matthews and Scott’s original item Budgets are developed for different courses of action was changed into two items: Budgets are developed for cashflow and Budgets are better developed for equipment purchases to better reflect the farming communities needs. Five further items (e.g., My farm has specific personal/lifestyle objectives) were added to the present measure. Overall level of internal consistency is a = .93. Strategic, operational, benchmarking, and succession planning scales reflect reliability estimates of a = .90, a = .87, a = .92, and a = .80, respectively, and are comparable to Matthews and Scott’s reliability estimates.
Sophistication of Strategic and Operational Planning was subjected to confirmatory factor analyses techniques through LISREL (7.2). Polychoric correlation and asymptotic covariance matrices were produced through PRELIS (a LISREL pre-processor) and the weighted-least squares method was employed to estimate the business planning model (this method was also employed to estimate and assess all exogenous latent variables in the SEM). As data are ordinal, Muthén (1984), and Jöreskog and Sörbom’s (1989, pp.192-193) suggestion to use a weighted-least squares procedure was followed.
Matthews and Scott’s (1995) reported that their 12-item measure yielded a two factor model comprising strategic and operational planning. First, their initial 12-items were subjected to confirmatory factor analysis, and their two factor model provided good fit and yielded the following results: c 2 (64, N=556) = 672.25, p<.000, GFI = .957, AGFI = .939. However, as two benchmarking items Farm industry data are used to compare/benchmark farm performance and Farm specific data are used to compare/benchmark farm performance indicated high correlations with each other, but had lower correlations with other items, it was decided to use these items in a separate dimension.
Three additional succession planning items (not included in the Matthews and Scott measure) were included in a separate latent variable. Thus, the four factor business planning model revealed good fit to data and yielding the following results: c 2 (129, N=556) = 469.62, p<.000, GFI = .974, AGFI = .965. Table 2 provides LISREL confirmatory factor results for subconstructs of business planning, entrepreneurship, environmental uncertainty, and internal locus of control. Table 3 shows cross-correlations, reliability coefficients (Cronbach alpha), means, standard deviations.
Table 2. Summary of Confirmatory Factor Analyses Results
|
Model |
c 2 |
df |
p |
GFI |
AGFI |
RMSR |
|
Business Planning (3 Factors, 18 items) |
469.62 |
129 |
.000 |
.974 |
.965 |
.058 |
|
Entrepreneurship (3 Factors, 18 items) |
70.69 |
32 |
.000 |
.984 |
.973 |
.048 |
|
Environmental Uncertainty (3 Factors, 18 items) |
250.69 |
116 |
.000 |
.969 |
.959 |
.052 |
|
Internal Locus of Control (1 Factor, 7 items) |
55.87 |
14 |
.000 |
.979 |
.959 |
.058 |
Table 3. Pearson Correlation Coefficients Among Exogenous Variables
|
Exogenous Variable |
1 |
2 |
3 |
4 |
5 |
6 |
|
Entrepreneurship (1) |
1.00 |
|
|
|
|
|
|
Environmental Uncertainty (2) |
.19** |
1.00 |
|
|
|
|
|
Size of Farm Business (3) |
.21** |
.33** |
1.00 |
|
|
|
|
Farm Owner’s Locus of Control (4) |
.15** |
.50** |
.25** |
1.00 |
|
|
|
Farm Owner’s Level of Education (5) |
.25** |
.07 |
.10** |
.09* |
1.00 |
|
|
Farm Owner’s Age (6) |
-.27** |
-.17** |
-.08* |
-.08* |
-.24 |
1.00 |
*
*p<.05; **p<.01Entrepreneurship.
Entrepreneurship is assessed using 10-items (e.g., Our farm strongly emphasises
the marketing of true and tried produce) Entrepreneurship comprises three
dimensions: Innovation (a =.52); proactivity (a =.69); and risk taking (a =.80).
Constructs reliability is a = .77. These reliability estimates are comparable to
Boone and de Brabander . Table 2 shows confirmatory factor results.
Risk and Uncertainty.
This 18-item measure, adapted from Matthews and Scott , assesses farm owners’
perceptions of environmental or state uncertainty. That is, the ability of farm
owners to understand or to predict the state of the environment owing to a lack
of information. Items (e.g., obtaining resources such as equipment) are measured
on 6-point Likert scales ranging from 0=N/A; 1=Very Low Certainty to 5=Very High
Certainty.
Matthews and Scott reported four environmental uncertainty dimensions: Input/output uncertainty (a = .71), government uncertainty (a = .93), competitor uncertainty (a = .83), and financial market uncertainty (a = .62). Even though uncertainty was modified to suit the Australian farming community, our measure (overall a = .87) revealed three dimensions similar to those reported by Matthews and Scott : Operational uncertainty (a = .82); landcare uncertainty (a = .73); and competitor uncertainty (a = .79). Table 2 shows confirmatory factor results.
Internal Locus of Control.
This 7-item instrument (e.g., Working out the strengths of my farm business in
some detail can often give me useful leads for the future) was adapted from
Kaine et al. . This measure was specifically designed for mixed-farming
operations such as broadacre and dairy farming. Items are measured on 6-point
Likert scales ranging from 1=Totally Disagree to 6=Totally Agree, and the
reliability coefficient for this measure is a = .73 (see Table 2 for
confirmatory factor analysis results and Appendix A for operationalization of
variables).
Model Evaluation and Assessment
The hypothesized structural equations model was examined using covariance matrices and both LISREL’s (7.2) and AMOS’s (4) maximum likelihood procedures. Owing to its extensive goodness of fit indices, AMOS was employed to complement the LISREL output. Covariances, using listwise deletion of missing data, were computed. Relationships were examined between entrepreneurship, a latent variable with three indicators (ie., innovation, proactivity, risk); environmental uncertainty, a latent variable with three indicators (ie., operational uncertainty, landcare uncertainty, competitor uncertainty); farm business size, a latent variable with two indicators (total area of property and asset value of farm); locus of control (a latent variable with seven indicators); owners’ age; level of education; business planning, a latent variable with four indicators (ie., strategic planning, operational planning, benchmarking, succession planning); and farm performance (see Figure 1 for hypothesized model of business planning and farm performance and Appendix A for operationalization of variables). Relationships between exogenous variables and farm performance are mediated by business planning.
Three criteria (ie. absolute, incremental, & parsimonious fit measures) were used to assess the acceptability of the hypothesized model. The independence model that tests the hypothesis that the variables are uncorrelated with one another was rejected, c 2 (253, N=556) = 34,347.48, p<.000. A chi-square difference test indicated a significant improvement in fit between the independence model and the hypothesised model, with the hypothesised model yielding the following results: c 2 (187, N=556) = 624.49, p<.000, GFI = .936, AGFI = .909, indicating acceptable fit to the observed data.
Approximately 69% of the variance in business planning, 40% of the variance in entrepreneurship, 63% of the variance in environmental uncertainty, 73% of the variance in farm size, and 47% of the variance in internal locus of control was accounted for by latent variable indicators. Table 4 provides goodness of fit statistics for the model and Figure 2 shows results for the hypothesized structural equations model.
Table 4. Summary of Business Planning and Farm Performance Model Results
|
SEM Model |
Hypothesised Model |
Independence Model |
|
Chi-Square (c 2) |
624.49 |
34,347.48 |
|
Degrees of Freedom (df) |
187 |
253 |
|
Number of Parameters |
88 |
22 |
|
Chi-Square/df |
3.34 |
135.76 |
|
Goodness of Fit Index (GFI) (LISREL) |
.938 |
|
|
Adjusted Goodness of Fit Index (AGFI) (LISREL) |
.912 |
|
|
Normed Fit Index (NFI) |
.982 |
|
|
Relative Fit Index (RFI) |
.975 |
|
|
Incremental Fit Index (IFI) |
.987 |
|
|
Tucker-Lewis Index (TLI) |
.983 |
|
|
Comparative Fit Index (CFI) |
.987 |
|
|
Parsimony Ratio |
.739 |
|
|
Noncentrality Parameter Estimate |
437.49 |
34,094.48 |
|
RMSEA |
.065 |
.493 |
Figure
2. Accepted Model of Business Planning and Farm Performance - Here
Direct and Indirect Effects
As shown in Figure 2, business planning is moderately and positively associated with farm performance (standardized coefficient = .17, p<.000), thus providing support to H1 that higher levels and business planning sophistication enhance farm performance. Size of business is also associated positively with farm performance (standardized coefficient = .62, p<.000), providing no support to H6b that there will be no association between size and performance.
This finding suggests that larger farm businesses, in terms of area size and value of assets, are more likely to have higher growth in income, providing some support to Robinson and Pearce’s (1984) contention that small firms are less likely to plan. Age and level of education of farm owner are both negatively associated with farm performance (standardized coefficients = -.14, p<.000; -.15, p<.000, respectively), providing support for H5b, but refuting H4b. These findings suggest that younger farm owners with lower levels of education are more likely to attain higher growth in income than older farm owners with higher levels of education.
As expected, farm owners with higher internal locus of control (see H3a) and those who perceive increasing certainty in their business environment are significantly more likely to use business planning procedures (standardized coefficients = .40, p<.001; .18, p<.001, respectively), suggesting that farm owners who view themselves as having control over changes in their operating environments and hold high levels of certainty, are more likely to conduct sophisticated business planning. This proposition is also supported by the high positive correlation (r = .50) between environmental uncertainty and internal locus of control (see Table 3), providing further evidence that owner-managers with high internal locus of control are more future oriented and possess entrepreneurial qualities.
Similarly, entrepreneurial farms are more likely to utilize sophisticated business planning, providing support for H3b. Indeed, entrepreneurial characteristics are significantly and positively correlated with environmental uncertainty, farm size, internal locus of control, level of education, but negatively related to age (see Table 3). These correlations suggest that younger, more educated, and larger farm owners, who have higher internal locus of control and environmental certainty, are more likely to conduct sophisticated business planning.
These findings are further supported by negative associations between owners’ age and business planning (standardized coefficient = -.14, p<.001), providing support to H5a. However, demonstrate a lack of support for H4a, which proposed a positive and significant association between level of farm owner’s education and business planning (standardized coefficient = .05, p>.05). Finally, our study did not reveal significant indirect effects between exogenous variables and farm performance, and exogenous variables and business planning, suggesting that business planning is not a mediating variable.
This study demonstrates that business planning has a positive, though moderate, influence on farm performance. Farm background characteristics and owner characteristics are also important predictors of both business planning and farm performance. Findings further suggest that younger entrepreneurial owners of larger farms, and who have a strong belief about their success and who perceive greater environmental certainty are significantly more likely to utilize sophisticated business planning processes and to be associated with better performing farms.
It is surprising, though, that level of education is significantly associated with farm performance but not business planning sophistication, suggesting that personal motivation and higher internal locus of control is a better predictor of business planning.
For the present study, four planning constructs were assessed independently as extant literature (e.g., Matthews & Scott, 1995) suggests that low uncertainty might be a deterministic means of scheduling business activity. Similarly, findings reveal that sophistication of strategic, operational, and succession planning increased with decreasing environmental uncertainty, in particular operational and competitor uncertainty, whereas benchmarking was not significantly associated with environmental uncertainty.
These results support Matthews and Scott’s (1995) suggestion that an increase in planning, particularly strategic planning, is congruent with greater certainty. Entrepreneurship was moderately associated with business planning, providing support to Keats and Bracker’s (1988) proposition that entrepreneurs tend to use more sophisticated planning processes compared with nonentrepreneurs.
While this investigation indicates that business planning has a positive effect on farm performance, background characteristics, internal locus of control, environmental uncertainty, and entrepreneurship also impact on performance. This finding is in line with contingency theory, suggesting that variables such as environmental uncertainty and firm size are important intervening variables of firm performance. Indeed, in conjunction with business planning, these factors contribute to enhancing farm performance.
While our study provides evidence that farm and owner background characteristics, and environmental uncertainty play an important role in farm business planning, nonetheless, the antecedent conditions of farm business planning remain poorly understood.
We recommend that further research into farm business planning should include an in-depth examination of the internal processes of farm enterprises such as owners’ individual and business skills, core human resource competencies or capabilities, communication characteristics between generations, and the attitudes, values, and goals of significant other family members.
Rigorous evaluation of these factors will enable the development of fine-grained benchmarking and best-practice resource-based models, which farm owners and professionals can use for competitive advantage.
Findings should be considered in the light of the following limitations. Approximately 4,080 farmers stratified on state location and industry were selected for this study. 748 owner managers responded to the survey, reflecting a response rate of only 19%. However, this response rate is consistent with mail surveys of the farm sector. Notwithstanding, this relatively low response rate places constraints on the generalisability of findings. Replication studies are necessary to validate the present findings.
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Appendix A. Operationalisation of Variables
|
Endogenous Variables |
No. Items |
Operational Definition |
|
Business Planning (h 1) (Second-order factor made up of 4 constructs) |
|
Business planning is the process by which organisational goals and the means to achieve those goals are formulated and implemented |
|
Strategic Planning (Y1) |
8 |
Strategic plans cover at least one-year into the future and include specification of objectives, allocation of resources, and selection of long-range strategies |
|
Operational Planning (Y2) |
5 |
Operational plans cover a 12-month basis and involve the functional areas of a farm business such as budgeting and equipment purchases |
|
Benchmarking (Y3) |
2 |
Farm industry and farm specific data are used to compare farm performance |
|
Succession Planning (Y4) |
3 |
Assesses retirement, ownership, and management succession plans |
|
Farm Performance (h 2) (Observed variable) |
|
|
|
Growth in farm income (Y5) |
1 |
Over the last 5 years (ie., 1994/95) what has been your farm’s annual average change in total income or loss (ie., farm income plus non-farm income less all relevant costs that might include overheads, variable costs, finance costs, depreciation, etc.) |
|
Exogenous Variable |
|
|
|
Entrepreneurship (x 1) (Second-order factor made up of 3 constructs) |
|
"Firms with entrepreneurial postures are risk taking, innovative and proactive. They are willing to take on high-risk projects with chances of very high returns, and are bold and aggressive in pursuing opportunities. Entrepreneurial organisations often initiate actions to which competitors respond, and are frequently first-to-market with new product offerings." (Covin & Slevin, 1991, p.7) |
Appendix A. Operationalisation of Variables (cont.)
|
Innovation (X1) |
3 |
The degree to which the farm business innovates new produce and processes |
|
Proactivity (X2) |
2 |
The degree to which the farm business challenges competitors |
|
Risk Taking (X3) |
5 |
The degree of risk-taking propensity in the farm business |
|
Environmental Uncertainty (x 2) (Second-order factor made up of 3 constructs) |
|
Uncertainty is the inability to predict the state of the farm business environment owing to a lack of information |
|
Operational Uncertainty (X4) |
8 |
Certainty regarding functional areas of the farm business such as being able to obtain working capital, equipment, skilled labour, fertiliser, and water |
|
Landcare Uncertainty (X5) |
5 |
Certainty regarding disease control, product quality, changing climatic conditions, and health and safety |
|
Competitor Uncertainty (X6) |
4 |
Certainty regarding the farm owner’s ability to compete locally and internationally, to deal with emerging technologies, and to comply with changing regulation |
|
Exogenous Variables |
No. Items |
Operational Definition |
|
Size of Farm Business (x 3) (First-order factor made up of 2 items) |
2 |
Includes total area of property in hectares and asset value of farm property |
|
Internal Locus of Control (x 4) (First-order factor made up of 7 items) |
7 |
Internally oriented individuals believe that achieving success or avoiding failure depends on their own efforts and actions |
|
Farm Owner’s Level of Education (X16) (Observed variable) |
1 |
An interval item: 1=Completed Primary School; 2=Completed Secondary School; 3=Completed TAFE/Agricultural College; 4=Completed University |
|
Age of Farm Owner (X17) (Observed variable) |
1 |
A continuous variable |
Appendix B. Comparison of Sample with Population Data
B1. Comparison of Farm Owner Manager’s Highest Education Qualifications
|
|
1999a |
1994-95b |
||
|
Completed Primary School |
5% |
11% |
||
|
Completed Secondary School/TAFE/Trade Apprenticeship/Technical/ Vocational |
84% |
80% |
||
|
Completed University/Other Tertiary |
11% |
10% |
||
|
|
1999a |
1994-95b |
||
|
|
Male |
Female |
Male |
Female |
|
Completed Primary School |
5% |
3% |
11% |
10% |
|
Completed Secondary School/TAFE/Trade Apprenticeship/Technical/ Vocational |
85% |
79% |
82% |
48% |
|
Completed University/Other Tertiary |
10% |
18% |
9% |
42% |
a
Figures from this study.bFigures are 1994-95 ABS estimates (see Garnaut & Lim-Applegate, 1998).
Table B2. Comparison of Average Total Farm Income by Industry
|
|
1999 |
1996 |
|
Wheat and other crops (ANZSIC 121) |
$460,000a |
$456,000b |
|
Mixed livestock / crops (ANZSIC 122) |
$309,000 |
$198,000 |
|
Sheep – Beef (ANZSIC 123) |
$174,000 |
$115,000 |
|
Sheep (ANZSIC 124) |
$127,000 |
$88,000 |
|
Beef (ANZSIC 125) |
$160,000 |
$119,000 |
|
Dairy (ANZSIC 130) |
$344,000 |
$210,000 |
a
Figures from this study. (Defined as total farm income plus non-farm income, less all relevant costs including overheads, variable costs, finance costs, and depreciation).bFigures based on 1996 estimates (See Australasian Agribusiness Services,1997).
Table B3. Comparison of Average Current Value of Farm by Industry
|
Average Current Value of Farm |
|
|
Wheat and other crops (ANZSIC 121) |
$1.84 milliona ($1.29 million)b |
|
Mixed livestock / crops (ANZSIC 122) |
$1.54 million ($1.07 million) |
|
Sheep – Beef (ANZSIC 123) |
$1.44 million ($1.04 million) |
|
Sheep (ANZSIC 124) |
$1.04 million ($0.92 million) |
|
Beef (ANZSIC 125) |
$1.19 million ($1.28 million) |
|
Dairy (ANZSIC 130) |
$1.38 million ($1.07 million) |
a
Figures from this study.bFigures based on 1996 estimates (See Australasian Agribusiness Services,1997).
