INTERNATIONAL JOURNAL OF ORGANIZATION THEORY AND
BEHAVIOR, 7(4), 42-65
SPRING 2004
STRATEGIC DECISION MAKING IN THE QUASIGOVERNMENTAL SECTOR: THE ILLINOIS SOYBEAN
PROGRAM OPERATING BOARD
Donna K. Fisher, Steven T. Sonka and Randall E. Westgren*
ABSTRACT. This paper reports on an intervention for improving the strategic
decision making and strategic planning in a specific quasi-public organization:
the Illinois Soybean Program Operating Board and on how decision support
systems can alter perceptions of the decision making environment, which in turn
affect strategic planning. The study hypothesis is that if the use of a
sophisticated 3-D modeling tool, the Protein Consumption Dynamics (PCD)
model, broadens perspectives to include a more global and long-term outlook,
then the quality of planning should be enhanced.
Before and after
questionnaires are used to capture the changes in 121 soy industry decisionmakers’ perceptions of the decision making environment. The perceptions of
soybean industry decision makers change to reflect more long-term thinking
about the industry, indicating that the PCD model’s visualized presentation of
complex information did influence strategic behavior.
INTRODUCTION
This paper reports on an intervention for improving
decision making in a specific quasi-public organization:
Soybean Program Operating Board (ISPOB).1 ISPOB
-----------------------* Donna K. Fisher, Ph.D., is Assistant Professor, School
the strategic
the Illinois
is a quasi-
of Economic
Development, Georgia Southern University. Her research interest is in strategic
leadership, specifically in economic development organizations. Steven T.
Sonka, Ph.D., is Director, National Soybean Research Lab; and the Soybean
Industry Chair in Agricultural Strategy, University of Illinois at UrbanaChampaign. His research interest is in strategic change and decision making in
the food and agribusiness sector. Randall E. Westgren, Ph.D., is Associate
Professor of Agribusiness Management, University of Illinois at UrbanaChampaign. His research interest is in strategic management in food industry
firms and supply chains.
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
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Copyright © 2004 by PrAcademics Press
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
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43
governmental sector organization that invests in soybean research and
market development for Illinois soybean producers. The board is part of
a system in which soybean producers voluntarily tax themselves to fund
efforts in domestic and international market development and research
(similar to other quasi-governmental organizations). As the government
research budget diminished, organizations such as the ISPOB were
created.
Although established through Congressional action and
administered through the U.S. Department of Agriculture (USDA), the
state organizations such as ISPOB, and their national federations, operate
with considerable autonomy.
Nationally, total funding for the
organizations varies between $60 and $80 million annually. ISPOB
funding ranges between $12 and $14 million each year. Budget
allocation decisions are the primary responsibility of an elected board of
18 soybean producers who serve without compensation.2
The ISPOB has a mixture of characteristics typical of both for-profit
and nonprofit boards. Like many for-profit boards, the ISPOB is subject
to long run market influences (Bowen, 1994). Changes in the global
market for soybeans can vitiate ISPOB investment decisions. Similar to
most nonprofit boards, the ISPOB members are constituency
representatives (Ward, 2000), who serve voluntarily, for short periods of
time, limited to 6 years in this case (Andringa & Engstrom, 1997).
Strategic decision making is a particular challenge in this setting for
several reasons. Although committed to enhancing ISPOB’s success,
each board member has a unique set of operational and tactical
challenges that preoccupy the bulk of their attention on a daily basis. All
board members are producers and therefore have a more natural affinity
for soybean production-related issues, such as funding for breeding
programs and disease control. Further, short-term pressures and
challenges tend to demand attention and responses from the board itself,
i.e., current low soybean prices and the uncertainty about societal
acceptance of biotechnology are urgent concerns. Although immediate,
it is not clear that these issues should distract the board’s strategic
direction over the long run.
Complexity and pluralism add to the difficulty of strategic decision
making for the ISPOB members. Most strategic decisions are addressed
in a group setting. In these situations each member brings a different set
of perspectives and understandings to the decision (Bessler, 1984). Not
least among these are their roles as constituency representatives—
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FISHER, SONKA & WESTGREN
geographic, large vs. small farms, commodity vs. specialty market
orientation.
Currently, both the funding and the agenda setting processes for
research are done in a complex decision environment. The Federal
government, under the auspices of the USDA, funds public research on
agronomy, new products, and markets. Private sector firms fund
traditional breeding programs, crop chemical development, new product
development, and other forms of research and development (R&D). The
producer boards are mandated to fund research and market development
using the funds from the voluntary tax for the benefit of producers
(Wright, 1996). Thus, public, quasi-public, and private sector decision
makers are making uncoordinated, overlapping long-term R&D
decisions. This turbid environment exacerbates the problems of pluralism
in the strategic decision making of ISPOB. There is no natural vehicle
for articulating a shared vision of research agendas or funding priorities.
This paper reports on how decision support systems can alter
perceptions of the decision-making environment in a specific setting—
long range R&D funding in the soybean industry. To do so, the study
reports on the development of a sophisticated modeling framework that
includes three-dimensional visualized presentation of information, and
evaluates the effect of experience with this tool on decision makers’
perceptions of the decision environment. The research hypothesis is that
if the use of this tool broadens perspectives to include a more global and
long-term outlook, then the quality of decision making should be
enhanced.
Scientists in psychology and organizational behavior struggle with
how to measure the decision-making processes both for groups and
individuals (Goldstein & Hogarth, 1997). Decisions are based in part on
the decision maker’s perception (cognitive map) of the decision
environment. Group decision making is affected by the perceptions of
the individual group members. Methods are developed and employed in
this research to measure how exposure to the visualization model of
information changes the cognitive maps of decision makers. Ultimately,
the research analyzes the shift in strategic issues identification, and how
this shift can improve decision making.
The research design is founded on developing a strategic decision aid
for the quasi-public ISPOB. However, to make the results more
generalizable, a broader demographic group within the industry is also
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
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45
used. A total of 121 decision makers from the soy industry value chain
participated in the study. Results indicate that decision makers’
perceptions changed after exposure to information presented in the
visualization model. The strategic issues identified by participants
before exposure to the visualization model focus on localized production
issues, the European market, and new uses for soybeans. However the
participants shift emphasis to more global, long-term issues, such as new
markets for existing products. After a brief look at supporting literature,
the remainder of the paper describes the underlying system dynamics
model, the visualization model, the experimentation and results, and
some summary remarks.
LITERATURE REVIEW
The objective of this research is to determine whether group strategic
decision making processes, specifically those of the ISPOB, can be
improved by using computerized decision support tools. Various
literature streams are exploited in the design of this research. Among
these are the literatures on strategic issue identification, scenario
analysis, system dynamics modeling, and data visualization.
Nonprofit Boards of Directors
Nonprofit organization boards of directors share a number of similar
challenges. Board members serve the community interests at large, as
well as those specific to the organization (Duca, 1996). Members are
volunteers, with limited terms of office (Andringa & Engstrom, 1997).
Performance is difficult to measure as nonprofits focus more with
service-related activities rather than on the bottom line (Bowen 1994).
Therefore, it is easier for board members to concentrate on the more
gratifying day-to-day activities that provide instant feedback, at the
expense of developing strategies around an ambiguous future (Duca,
1996). The model in this research is designed to help ISPOB members to
focus on the strategic decisions for the uncertain future.
Boards of directors, whether for- or nonprofit, have a fundamental
duty to establish and maintain a successful organization (Bowen 1994).
However, nonprofit organizations differ from their for-profit counterparts
in that they are not businesses, and tend to operate differently. There are
a plethora of board structures for the many different types of nonprofit
46
FISHER, SONKA & WESTGREN
organizations (Bowen, 1994; Carver, 1990). The ISPOB, while primarily
representing a nonprofit organization, grapples with similar R&D
funding allocation issues that are faced by for-profit organizations. The
primary difference is that the end result is not an increase in profitability
for the ISPOB, but increased and sustainable profits for the soybean
industry as a whole. Moreover, while most nonprofits gain their
revenues through formal fundraising activities (Andringa & Engstrom,
1997), the ISPOB’s funding is generated through the taxation of soybean
producers.
Strategic Issue Identification and Management
Strategic issues are circumstances, internal or external (Ansoff,
1980), an organization must face, “that involve:
(a) possible outcomes that are important to, or of possible high impact
on, the organization’s overall performance;
(b) controversy, in that it is likely that reasonable people may take
different positions concerning the impact of the issue; and
(c) strategy consequences, in that the various possible outcomes
implied by the issue would prescribe that different strategies
should be implemented” (King, 1982, p. 45).
External issues (competitors, government, industry, customer
groups) are somewhat more controversial than internal issues, and are
therefore more strategic in nature (King, 1982).
Strategic issue
management (Dutton & Ottensmeyer, 1987) or analysis (King, 1982)
decreases uncertainty through information and issue interpretation, and
defines issues for ease of problem resolution.
Strategic issues
management can focus on output or process, and assists organizations by
reducing ambiguity around key issues, (Dutton & Ottensmeyer, 1987). It
comprises the following steps: issue identification, formal issue
definition, preliminary issue model development, model revision, and
data collection. This research focuses on issue identification of the
decision makers in the US soybean value chain.
System Dynamics Modeling
System dynamics modeling has principles rooted in electrical
engineering analogs applied as a problem solving methodology to
managed systems. Forrester (1961) led the initial work in the area. In
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
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the early 1990s, Senge’s (1990) emphasis on system thinking as a key to
learning organizations renewed academic and popular interest in system
dynamics. In recent years, system dynamics models have been
extensively employed to represent and examine complex management
problems in a variety of business settings (Morecroft & Sterman, 1994;
Roberts, et al., 1994).
System dynamics modeling uses mathematical equations to describe
the causal relationships between variables in the system (Nelson, 1998).
Stock variables accumulate or deplete depending on flows (change rates)
across time. This type of modeling moves beyond typical quantitative
techniques as it can incorporate time lags, as well as the influences of
both endogenous and exogenous factors.
A system dynamics simulation is the core of a decision support tool
for this complex, pluralistic decision environment. The tool simulates
future global protein consumption scenarios. It builds on secondary data
for population and income growth to examine future world food needs.
Both quantitative data and qualitative intelligence from experts are
incorporated to describe variables that explicitly relate to the market
development and research mission of the ISPOB. Through the use of
scenarios, this sophisticated modeling tool assists decision makers to
focus on and better anticipate the future. The ultimate goal of the
research is to improve decision makers’ confidence about where to invest
research dollars so as to positively affect future success.
Scenarios
Models and scenarios assist decision makers to narrow the scope and
therefore better comprehend the complexity of their environment.
Scenario analysis differs from forecasting in that it is more descriptive,
qualitative and contextual; and that it identifies plausible possible
futures. “Scenarios also provide a common means for everyone … to
think about the future that takes into account many uncertain factors
(some of which are qualitative) in a flexible, although estimative, way,”
(Mason, 1994, p. 66). Scenarios lend themselves to environments where
there are only a limited number of important unpredictable variables,
(Schnaars, 1987). By focusing on only a small number of potential
futures, decision makers are able to more fully explore the implications
of decisions they make today in relationship to these various potential
futures.
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FISHER, SONKA & WESTGREN
Scenarios help get people thinking about the future (Georgantzas &
Acar, 1995). The scripts behind the scenario descriptions are designed to
alleviate anchoring-and-adjustment problems that decision makers have
in placing themselves outside the norms of their immediate, present
situation. Georgantzas and Acar (1995) also set the precedent for using
script-based computerized scenarios. The computer-driven scenario
allows for flexibility in exploring the breadth and depth of decision
context that does not exist in text-based scenarios.
Visualization
“Visualization—combining computer graphics, computation,
communication, and interaction—is invaluable for changing data into
information, designing products and supporting complex decision
making” (Brown, 1997, p. 1; also see Rheingans & Landreth, 1995).
Visualization enables understanding and communicating research results
to other researchers and the general public. It helps shape public policy
by improving understanding regarding potential outcomes and the
relationships between multiple variables (Orland et al., 1997).
Computer technology in general allows system dynamics modelers
to use a more visual approach for data representation (West, 1992). This
combination of the power of system dynamics and visualization should
aid in understanding the interrelationships of the simulation model
variables (Richardson, 1996). The three-dimensional representation
highlights the relationships between multiple variables simultaneously.
The understanding gained from seeing the interrelationships among
variables should enable soybean decision makers to more fully
comprehend their environment. However, as West (1992, p. 93) points
out, “despite the obvious importance of visual images in human
cognitive activities, visual representation remains a second-class citizen
in both the theory and practice of mathematics.”
THE PROTEIN CONSUMPTION DYNAMICS MODEL
In order to cope with the complexity and pluralism issues, a
sophisticated decision aid is developed to provoke better understanding
among volunteer ISPOB members and other stakeholders in the strategic
issues that affect the future of the soy industry. A system dynamics
model is developed to explore future scenarios for global protein
consumption. The model projects the estimated human appetite for six
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
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agricultural commodities (beef, pork, poultry, fish, fats and oils, and
vegetable protein), on a global basis (with the world divided into eight
geographic regions), and annually for the years 2001 to 2025. The model
also tracks malnutrition by region. Output from the model is presented
using three-dimension dynamic information display software.
System Dynamics Model Characteristics
The modeling approach employed is system dynamics, a powerful
tool for analyzing complex settings subject to change. The Protein
Consumption Dynamics (PCD) Model is developed using PowerSim
software. Figure 1 illustrates the relationships made explicit in the PCD
model. The PCD relates population and income growth by cohorts to
regional protein needs and malnutrition. On a region-by-region level, the
model determines the per capita appetite for various commodities based
upon income elasticities for consumption and cultural factors (e.g.
religious proscriptions against pork consumption). The model then
aggregates across income and population cohorts to obtain the total
potential demand for each region: China, East Asia, Transition
Economies (former Soviet Union and Eastern Europe), Latin America,
Middle East and North Africa (MENA), Organization of Economic
Cooperation and Development (OECD), South Asia, and Sub-Saharan
Africa. These regions are consistent with those used in policy analyses
by both the World Bank and the UN Food and Agriculture Organization
(FAO).
The model is based upon historic relationships, linking food consumption
and malnutrition to per capita income by region as seen in Figure 1. One
of the basic assumptions is that individual consumption of agricultural
commodities is primarily driven by per capita income, at least at low and
modest income levels. For each region, econometric relationships are
estimated between the consumption of each agricultural commodity and
per capita income.
The model is tested against the dynamics of historic consumption,
for the years 1971 through 1995. The purpose of the Historic Scenario is
threefold. First, we use historical data in the modeling process to
externally validate the model parameters. The average error for any
commodity “backcast” for the region was 3.34%. The second use of the
Historic Scenario is in presenting the model and scenario exercises to
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FISHER, SONKA & WESTGREN
decision makers. As managers in the soy industry have already lived the
FIGURE 1
Relationships Underlying the Protein Consumption Dynamics Model
Total Population
Income
Distribution
Per Capita
Incidence
of
Malnutritio
n
Total Appetite
Per Capita
Appetite
Total
Income
Cultural
Influences
past, the model output allows participants to become comfortable with
the model. Thirdly, the historic situation is a sharp contrast to the future
opportunities, so as to be a point of departure for exploring new
landscapes.
The potential to enhance learning through the use of a tool such as
the PCD model is not fully achieved by performing one set of
computations based upon the most likely set of parameters. Indeed, a
key reason for the development of this modeling capability is to explore
a range of parameter values. Therefore, the capability to compare the
effects of alternative assumptions on the desire and need for protein
across a range of parameter values is important. To illustrate this
capability, three future scenarios are defined.3 These are:
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
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- Base Case employs population growth projections consistent with
World Bank and United Nations Food and Agricultural Organization
(FAO) medium-level projections and the income growth projections.
- Lower Population Case incorporates population growth
projections consistent with World Bank and FAO low growth
projections and the same income growth projections as in the
Base Case.
- Lower Income Case uses the population projections of the Base
Case and income growth rates that are 50 % smaller than those
of the Base Case.
An example of the model output for the Base Case Scenario is
provided in Table 1. Globally, the appetite for animal protein has
increased by 82% and vegetable protein by 38%. China, East Asia, and
South Asia show large increases in the appetite for animal protein, 154%,
156%, and 506% respectively. Consumption growth in the OECD is
relatively modest, 5% for animal protein and –17% for vegetable protein.
This reflects that the OECD countries, in general, will not devote
additional income to expenditures for food. Note however, that even
though the OECD had the smallest level of change in the appetite for
animal protein, the region still accounts for nearly 20% of the global total
(Fisher 2000).
The Visualization Tool
The preceding discussion identifies a potential problem for decision
makers. The capability to produce large amounts of data is both a
TABLE 1
Base Case Scenario: Changes from 2001 to 2025
Base Case
Region
China
East Asia
Trans. Econ.
Lat. America
MENA
Animal Protein
Percent change
154%
156%
25%
46%
27%
Vegetable Protein
Percent change
57%
44%
23%
28%
58%
Fats and Oils
Percent change
170%
125%
19%
38%
47%
52
OECD
South Asia
SS Africa
World
FISHER, SONKA & WESTGREN
5%
506%
64%
82%
-17%
43%
67%
38%
9%
109%
66%
60%
strength and a weakness of the simulation approach. In the case of the
PCD model, estimates are available for eight regions, for each of 25
years, for six agricultural commodities, and for two measures of
malnourishment. In addition, annual regional data are generated for per
capita incomes and population. Visualization provides a sophisticated
means of characterizing information to enable decision makers to more
easily perceive the interrelationships between the model drivers, and the
resulting appetite for the various commodities. Because of recent
advances in computing power, data visualization applications that were
only available on supercomputers just a few years ago are now becoming
available for users of workstations and personal computers.
Visualization should make it easier to see and understand the
interrelationships between the variables typically produced in simulation.
Figure 2 is a video image of the three-dimensional visualization
model. Clearly a single, static photograph cannot completely convey the
impact of the dynamic visualization. However, the following brief
discussion highlights a few of the features of the tool. The visualization
screen shown in Figure 2 is comprised of four sections.
1. The regional population and GDP totals are positioned on the
“back wall” of the visualization. These can remain visible as
output values are displayed to allow the observer to continually
link back to the driving forces in the simulation.
2. Each region of the world is shown on the center area of the
“floor” of the visualization. Color-coding of the regions is
linked to the population and GDP totals and to the comparison
section of the visualization.4
3. The front-right area of the “floor” displays a list of the years
from 2001 to 2025.
4. The left part of the “floor” contains comparison bar charts where
the user can compare output results across time or across
scenarios for the six commodities or for the malnutrition
variables.
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
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53
A key feature of this visualization is animation. Each region on the
world map contains a tri-colored bar which represents the potential
demand for the various commodity groups. The lower segment of each
bar shows the total for meat and fish; the middle segment shows fats and
oils, and the top segment indicates vegetable protein. As the model
animates through time, the size of the bars changes to reflect how the
specific population and income growth scenario affects potential appetite
on a region-by-region basis.
Movement through time is shown by highlighting the list of years at
the front of the world map. The presenter of the visualization controls
the animation by selecting the scenario and clicking on the appropriate
arrow at the front of the floor. By selecting the box between the arrows,
the simulation can be stopped at any year. In Figure 2, results for the
Base Case scenario are being shown and the highlighting of the year
2025 denotes that the levels of the bars indicate values for that year of
the simulation.
The animation can be controlled to show explicit differences among
regional consumption projections across scenarios, between regions, or
between pairs of years (e.g. between the beginning and end of a
particular scenario. Additionally, the 3-D image can be rotated on any
axis and zoomed to highlight any particular data display. This capability,
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FISHER, SONKA & WESTGREN
FIGURE 2
Base Case Protein Consumption Dynamics Model Visualization
as well as the controllability of animation sequences, permits decision
makers to explore cause-effect relationships and data comparisons more
fully.
EXPERIMENTATION AND ANALYSIS
Following Doyle’s (1997) suggestion, before and after
questionnaires are used to capture the changes in soy industry decision
makers’ perception of the decision making environment.
The
questionnaires include open-ended questions as well as a budget
allocation question. The responses from the 121 participants are
transcribed and then coded by the senior author using NVIVO
software. The evaluation uses content analysis—a technique that enables
the interpretation and inferences to be made from text passages through
the use of data coding schema.
The coding categories emerge from the data. The data are coded two
times to ensure consistent assignment to the appropriate strategic issues.
The final strategic issues coding categories are the result of preliminary,
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
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initial and final coding efforts, and include demand, supply, other,
decision motivation, market relationships, and research issues.
Table 2 describes demographic information related to the subjects in this
experiment. The subjects were selected as they all play a unique role in
the soybean value-chain. The researchers were included as they are an
integral part of the check-off board research process. The American
Soybean Association (ASA) regional marketing directors work in various
parts of the world to promote the use of U.S. grown soybeans and
soybean products. ASA also administers the national check-off program.
Agribusiness students were included for two reasons. First, many of the
students are from farms where soybeans are produced. Second, many of
these students will be future decision makers of the industry. The other
soybean industry personnel included members of the Illinois Farm
Bureau’s Young Leaders Committee, most of whom are soybean
producers. The committee members are under the age of 34 and show
leadership potential in Illinois agriculture. The participants from the
Illinois Association of Farm Managers and Rural Appraisers are a
combination of farm managers who manage soybean-producing farms,
and rural appraisers who work in association with banks and other
organizations that provide capital and services to soybean producers.
Demographic
Category
Gender
TABLE 2
Respondent Demographics
Sub-category
Male
Female
Number of
Participants
93
28
Education
Age
Occupation
High School
Vocational/Associates
Some College
Bachelor’s Degree
Master’s Degree
Doctoral Degree
Average
ISPOB & Illinois Soybean Association5
University Researchers
4
13
26
23
26
29
38.5
14
28
56
FISHER, SONKA & WESTGREN
American Soybean Association
Agribusiness Students
Other Soy Industry Personnel
Total
21
34
24
121
The gender mix is male-dominated, which is representative of the
industry. However, there are no hypothesized gender effects. Most of
the subjects have at least some post-secondary education, with nearly 65
percent of the respondents having at least a bachelor’s degree. The data
are analyzed in terms of the five occupation groups in Table 2, and for
the combination of all groups. Individual responses are aggregated to
give the cognitive map of a given occupation group.
Table 3 provides the response frequency (the number of respondents who
identified the given strategic issue) levels for a subset of the strategic
issues categories identified in the before and after questionnaire
responses. The issues categories are listed in a hierarchical fashion and
include demand, supply, other, market relationship and research at the
top level (underlined). Issues related to the top level categories are listed
under the primary categories.
Three of the more significant
subcategories are included in Table 3.6 The remaining issues are
aggregated into the additional subcategories. Demand related strategic
issues more than double between the before and after questionnaire.
Specifically, decision makers focus more on global issues after being
exposed to the visualization model. At the same time, the emphasis on
the short run issues such as consumer resistance to biotechnology
decreases, indicating that the model helps decision makers focus on more
long-term global issues.
TABLE 3
Frequency of Key Strategic Issues in Before and After Questionnaire
Responses
Strategic Issues
Before Frequency After Frequency
Demand Issues
233
508
Global Issues
62
172
Additional Demand Issues
171
336
232
197
Supply Issues
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
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Competition
67
48
Additional Supply Issues
165
149
Other Issues
62
47
Market Relationship Issues
209
311
Research Issues
185
136
Biotechnology
146
95
Additional Research Issues
39
41
The 89 strategic issues subcategories are numerically ranked
according to decision maker response frequency. Table 4 includes a
subset of the strategic issues identified by the ISPOB. The before and
after response frequency is provided for each issue. A rank is assigned
based on the frequency. The issue with the highest frequency, i.e., the
highest number of respondents that identified the issue in the
questionnaires, receives a rank of 1.7 The issue with the next highest
frequency receives a rank of 2, and so on. For example, in Table 4
Global has a frequency of 4 (4 respondents listed global issues) in the
before column and this was the 5th most frequent issue category
identified. Note that Trade Policy also has a frequency of 4 and a rank of
5. Malnutrition went from being ranked 32nd in the before questionnaire
TABLE 4
Subset of Numerical Ranking of Strategic Issues from ISPOB Respondents
Before
Strategic Issue
Malnutrition
Developing countries
Global
Income levels
Marketing and promotion
Regional influences
Population levels
Animal Feed
Trade policy
Substitutes for soybeans and
soybean products
Frequency
1
2
4
0
2
0
0
1
4
0
After
Rank
32
17
5
52
17
52
52
32
5
52
Frequency
10
9
6
5
5
4
3
3
3
2
Rank
2
3
6
8
8
10
15
15
15
22
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FISHER, SONKA & WESTGREN
Before
Strategic Issue
Consumer education
China
East Asia
US
Distribution
Changes in Demand
Health benefits
Brazil
Frequency
1
0
0
1
1
2
3
2
Rank
32
52
52
32
32
17
11
17
After
Frequency
2
1
1
1
1
1
1
0
Rank
22
28
28
28
28
28
28
62
to 2nd in the after questionnaire. The ranking is done for both the before
and after responses at the occupation group level as well as for the entire
respondent pool. For detailed comparisons of issues between the ISPOB
and the other groups, see Fisher (2000).
A key comparison for this analysis is the change in the before and
after responses to the questionnaires. This section discusses a more
descriptive evaluation of the strategic issues. Table 5 lists 17 frequently
mentioned strategic issues. These are the top 10 ranking strategic issues
identified in either the before or after questionnaire responses. The
issues are sorted into three categories: an increase in rank, stable rank, or
a decrease in rank between the before and after questionnaires.
TABLE 5
Significant Strategic Issues Identified in the Before and
After Questionnaires
Category A. Increase in Rank
Rank
Change in rank
Percent of
respondents
identifying the issue
After Questionnaire
Frequency
Rank
Percent of
respondents
identifying the issue
Frequency
Strategic
Issues
Before Questionnaire
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
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Income
1
0%
Population
2
2%
Malnutrition
6
5%
Meeting demand
8
7%
Developing
countries
9
7%
New markets
18
15%
Category B. Stable Rank
Global
22
18%
Marketing
28
23%
New products
29
24%
Soy foods
40
33%
General research
22
18%
Health benefits
25
21%
Category C. Decrease in Rank
New uses1
45
37%
Biotechnology
acceptance
44
36%
Soybean Prices
22
18%
Biotech impacts
27
22%
GMO
37
31%
1
Note: The United Soybean Board
nonfood, non-feed.
76
68
55
44
33
35
41
37
27%
29%
34%
31%
10
9
6
8
35
14
38
49
31%
41%
7
2
9
6
5
3
9
8
47
48
48
64
29
24
38%
40%
40%
53%
24%
20%
5
3
3
1
11
13
4
3
2
2
-2
-5
1
22
18%
18
-17
2
18
15%
21
9
13
11%
32
7
12
10%
36
4
15
12%
29
and ISPOB definition for new
66
59
49
36
28
12
-19
-21
-23
-25
uses is
Category A issues (Table 5) are identified by many more
respondents in the after questionnaire than in the before version.
Category B issues are considered to be relatively important in both the
before and after questions. Conversely, the five issues in Category C are
listed by substantially fewer respondents for the after questionnaire than
for the before version. The composition of Categories A and C is of
particular interest. Issues that fall in Category A relate to demandenhancing factors: building demand in developing nations (not currently
emphasized), and malnutrition. Conversely, issues among Category C
tend to be more production-oriented. This suggests that exposure to the
simulation model results and the visualization successfully raised the
participants’ awareness and sensitivity to demand enhancing strategic
issues. Thus, model exposure did in fact change the respondents’
cognitive maps.
60
FISHER, SONKA & WESTGREN
The after questionnaire responses for the population and income
categories are considerably large, probably a function of the visualized
information’s emphasis on the two as drivers of consumption patterns.
The strong focus on soy foods can also be attributed, in part, to the nature
of the modeling exercise, i.e., the dynamics of global protein
consumption. Moreover, the fact that soy foods and new products are
both ranked highly reinforces the validity of the responses as they are
related issues.
A final set of results examines the participants’ preference as to
research allocation decisions after seeing the visualization model (Table
6). In the before questionnaire, respondents focus more on new product
development and developing new markets. In the after questionnaire, the
group directs even more resources toward developing new markets and
shifts away from new product development and genetics research. In the
after questionnaire, the subjects still recognize the importance of the
local issues, but this perspective expands to include more global and
long-term issues. The decreases in the research budget allocation for
new product development and genetics research are significant at the
99.9% level, while the increase in allocation towards new market
development was significant at the 99.95% level. (A negative t statistic
signifies that the budget allocation increased between the before and
after questionnaires).
We also look at the budget allocation question results for each of the
five respondent groups. New product development and genetics research
decrease between the before and after questionnaire for all groups, as
was indicated in Table 6. The other categories are mixed across the
groups, but indicate a shift by most groups away from the short-term
issues of new product development and genetics research into more longterm issues of market development.
TABLE 6
After Questionnaire responses to Question 3 on Research Budget
Allocation
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
SECTOR
61
Most groups increase in confidence regarding budget allocation
Research Area
Before
Mean
After
Mean
t-test
Significant
Production Research
16.01
15.92
0.133
0.895
New Product Development
21.89
19.44
2.592
0.011
Strengthen Existing Markets
17.87
18.93
-1.007
0.316
Develop New Markets
21.70
25.29
-3.671
0.000
Genetics Research
18.68
16.36
3.303
0.001
3.87
4.08
- 0.307
0.760
Other
decisions. As previously stated, the ASA group works in an environment
where they contend with many of the issues presented in the
visualization on a daily basis. Therefore, the visualization of complex
information serves to reinforce their cognitive maps (which change the
least).
SUMMARY
ISPOB’s decision making environment is complex and pluralistic
due both to the long-run nature of the decisions they make; the
competing research agendas in the public, quasi-public, and private
sectors; and the multiple perspectives that various board members bring
to the table. This research focuses on the use of a sophisticated
visualization model (PCD) designed to foster a common research vision.
Strategic issues identification is used to evaluate the effectiveness of the
visualization model in changing the decision makers’ perceptions of their
decision making environment. The results indicate that perceptions are
affected by exposure to the visualized presentation of complex
information.
The perceptions of soybean industry decision makers change to
reflect more long-term thinking about the industry, indicating that the
PCD model’s visualized presentation of complex information did
influence strategic behavior. The ex post focus on more demandoriented issues such as the marketing of existing products in expanded
62
FISHER, SONKA & WESTGREN
markets is indicative of some of the changes in cognitive maps held by
study participants. Likewise, the use of soy products to alleviate
malnutrition becomes a more important strategic issue, where human
nutrition needs in the past were overshadowed by interest in animal
nutrition products/markets. This change in focus represents a need for
redirection of both research and market development agendas for ISPOB
planning.
The PCD model addresses stakeholders’ need to better understand
the strategic decision making environment by allowing decision makers
to explore the future without having lived it (Boehlje, 1999). Moreover,
this research reinforces that people are affected by the visual
representation of complex information (West, 1992). As Orland et al
(1997) point out, tools such as visualization of dynamic systems improve
understanding and communicate research results more easily to the other
researchers and the general public.
Implications for Managers
The visualization of complex and voluminous information is an
effective means of communicating with managers and other industry
decision makers. The focus on malnutrition is of particular interest here
for a number of reasons. Historically, the soybean industry has not
invested heavily in serving the developing regions. The PCD was a
driving force in securing funding of $1 million a year for three years to
study the use of soybeans as a human protein source, with emphasis on
their use in humanitarian aid. Following this impetus, in early 2000 the
USDA agreed to purchase 420,000 tons of soybeans for humanitarian aid
purposes.
The PCD model has been instrumental in developing a shared
perspective in the research budget allocation-decision setting of decisionmakers with disparate backgrounds. Recently the model was used at a
meeting of representatives from state and federal government agencies,
universities, corporations, and private voluntary organizations to discuss
future directions of humanitarian aid in Africa. The visualized scenarios
permitted the group to gain a common perspective for agenda-building.
Future Research
The data from this experimentation have barely just begun to be
explored. The results herein only address the frequency of key issues
STRATEGIC DECISION MAKING IN THE QUASI-GOVERNMENTAL
SECTOR
63
mentioned, even though respondents assign priorities to the issues in the
questionnaires. The inferences need to be expanded and tested further
both with the existing data and through different experimental designs.
For example, research to test the differences between using the 3-D
model and a tabular representation of the same information is already
underway.
Visualization of information is an effective way to improve
understanding of complex, voluminous data not only for nonprofit
boards, but for participants across the value chain. We would like to
continue to explore the use of this and other techniques to develop more
effective ways to communicate information to decision makers and
policy makers in all levels of the food industry value chain, including,
but not limited to other similar boards to verify the generalizability of the
results.
NOTES
1. For further information on the ISPOB see its website at:
www.ilsoy.org.
2. Antecdotal evidence indicates that the board members are generally
motivated to serve voluntarily as they see the ISPOB as a means to
positively influence the success of the soybean industry.
3. These scenarios are projections as defined by Ferris (1998). For our
purposes, the probability of each scenario’s occurring is not
important. We are more concerned with getting decision makers to
consider alternative potential futures, than in predicting the future.
4. The regions are color coded as follows: China is light blue, East
Asia is dark yellow, Transition Economies is light pink, Latin
America is green, MENA is orange, OECD is light yellow, South
Asia is dark pink, and Sub-Saharan Africa is dark blue.
5. Illinois Soybean Association (ISA) is the sister organization to
ISPOB. The board structure is similar, however they are focused on
legislative issues as opposed to the allocation of research funds.
6. Study participants identified a total of 89 strategic issues. However,
inclusion here would take up significant space and not help to
illustrate the point. See Fisher (2000) for a complete list of strategic
issues identified by the study participants.
64
FISHER, SONKA & WESTGREN
7. The category “new uses” ranked first in the before questionnaire,
while the category “soy foods” ranked first in the after questionnaire
responses.
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