James L. Morrison
University of North Carolina at Chapel Hill
Thomas V. Mecca
Piedmont Technical College
[Note: This is a re-formatted manuscript that was originally published
in J. C. Smart, (Ed), Higher Education: Handbook of Theory and Research
(Vol. 5, pp. 334-382). New York: Agathon Press, 1989. It is posted here by
permission of Agathon Press]
The external environment of institutions of higher
education can be characterized by change and turbulence. Administrators
of colleges and universities have witnessed major shifts in the
demographics of their institutions' clientele. External agencies
have tightened their control of policymaking and fiscal decisions
made by the institutions' administrations. There has been a growing
criticism of the value of the curriculum offered and the quality
of instruction provided by many institutions of higher education,
particularly in view of the importance of education in the increasingly
competitive environment of the global economy. Less obvious, but
no less significant there has been a pervasive, spread of electronic
technologies through American society, challenging the dominant
instructional and managerial paradigm found in the majority of
American higher education institutions. In short, the accelerating
rate, magnitude, and complexity of change occurring in all sectors
of American society have created vulnerabilities and opportunities
across the higher education "tableau" (Keller, 1983).
The rapidity and volume of changes have resulted
in less lead time for administrators to analyze changes in their
institutions' external environment and to formulate appropriate
strategies. In addition, the risks and uncertainty involved in
implementing a particular strategy or set of strategies have intensified.
In summary, the turbulence in higher education's external environment
challenges the capability of decision makers to effectively anticipate
This phenomenon of rapid environmental shifts led
to a recognition among administrators and organizational theorists
of the need for a comprehensive approach to institutional planning
that empl1asizes sensitivity to the effects of environmental shifts
on the strategic position of the institution (Ellison, 1977; Cope,
1988). An administrator's analysis of the organization's environment
is critical in accurately assessing the opportunities and threats
that the environment poses for the institution and in developing
the strategic policies necessary to adapt to both internal and
All organizations, including colleges and universities,
are perceived by contemporary organizational theorists as social
systems existing in and interacting with their environment (Aldrich,
1979; Scott, 1981). An organization's environment is essentially
all those external factors that affect it or are perceived to
affect it. Hall (1977) divides an organization's environmental
factors into two categories: the limited number of factors that
directly affect it (the task environment) and the almost unlimited
number of factors that influence all organizations in the society
(the general societal environment). In essence, the task environment
is composed of the set of factors that are unique to organization,
while the general societal environment includes environmental
factors that are the same for all organizations.
Factors in the task environment are readily apparent
to college and university administrators (e.g., clients/students,
revenue sources, and government educational policies and regulations).
However, the distinction between the organization's task environment
and the general societal environment is not always clear. Particularly
under turbulent conditions, factors in the general societal environment
"break through" into the organization's task environment
(Kast and Rosenzweig, 1979). Consequently, changes in the general
societal environment can, and often do, have significant effects
on the organization, effects well documented in the literature
of organizational analysis (Osborne and Hunt, 1974; Hall, 1977;
Kast and Rosenzweig, 1979; Scott, 1981).
The uncertainty faced by a decision-maker in planning
strategically is compounded by an increasingly dynamic and uncertain
environment (Emery and Trist, 1965). Terreberry (1968) concluded
that organizations must be prepared to adapt even more to the
influence of external forces. Most environments are dynamic and,
consequently, rich in possible opportunities as well as possible
threats to the organization. Therefore, the strategic planner
and policy maker cannot analyze the condition of the future environment
by assuming that it will remain in a readily predictable state
(i.e., in an orderly and incremental progression into the future).
Contingency approaches to organizational theory have
focused upon the effect of environmental change in creating uncertainty
for policy makers lorn1uhllillg organizational strategy (Anderson
and Paine, 1975; Lindsay and Rue, 1980; Boulton et al., 1982;
Miller and Friesen, 1980; Jauch and Kraft, 1986; Kast and Rosenzweig, 1984). Duncan (1972) describes three
factors that contribute to this sense of uncertainty: (a) a lack
of information about environmental factors that would influence
a given decision-making situation; (b) a lack of knowledge about
the effects of an incorrect decision; and (c) the inability of
the decision-maker to assess the probability that a given environmental
factor will affect the success (or failure) of the organization
or one of its subsystems in fulfilling its mission. In a later
study, Leblebici and Salancik (1981) also found that the uncertainty
experienced by a decision-maker arises from his or her inability
to predict the outcomes of certain actions.
This inability to predict decision outcomes is derived from two
sources. The first is the nature of the world in which we live-multivariate,
complex, and interrelated. The second is the probabilistic quality
of our world-an event can occur tomorrow, next week, or next year
that could affect the interrelationships of variables, trends,
and issues. In essence, the more turbulent and complex the organization's
environment appears, the less able an administrator is to anticipate
the probability of success in implementing a particular strategy.
Traditional planning models are weak in identifying
environmental changes and in assessing their organizational impact.
In his analysis of the approaches to planning exhibited by American
educational institutions, Ziegler (1972) identified two primary
assumptions that characterize the weakness of these models: (a)
the organization's environment will remain essentially static
over time; and (b) the environment is composed of only a few variables
that impact education. In essence, the underlying assumption of
most current educational planning is that environmental change
will be a continuation of the rate and direction of present (and
past) trends. These trends are manifested in the "planning
assumptions" typically placed in the first part of an institution's
strategic or long-range plan. Therefore, many administrators implicitly
expect a "surprise-free'' future for the institutions. We
know, however, that change, not continuation, will be the trend,
and the further we go out into the future, the more true this
will be. An approach is needed that enables administrators to
detect signals of change in all sectors of the environment and
to link environmental information to the organizations strategic
management (1975; Weber, 1984; Chaffee, 1985; Levy and Engledow,
1986; McConkey, 1987; Dutton and Duncan, 1987; Hearn1988).
The purpose of this chapter is to describe an approach
to environmental analysis and forecasting that educational policymakers
can employ in dealing with the level of uncertainty associated
with strategic decision-making. Unlike traditional models of planning,
such an approach does not lead decision-makers to conclude that
the uncertainty they perceive in the external environment has
been reduced. Rather, the focus of this approach is to enhance
their capability to deal with a changing environment by making
the perceived uncertainty in that environment explicit (Fahey,
King, and Narayanan, 1981). This is accomplished through the analysis
and evaluation of possible alternative future states of organization's
environment and the sources of change within it. In this chapter,
we will explain one model of this approach and demonstrate its
application in a case study. We conclude with an examination of
the issues and questions posed by the application of this model
to educational institutions, and we suggest directions
for future research in this emerging methodological domain.
Environmental analysis and forecasting are based
upon a number of assumptions, among them the following (Boucher
and Morrison, 1989):
- The future cannot be predicted, but it can be forecasted
probabilistically lakh1g explicit account of uncertainty.
- Forecasts are virtually certain to be useless or
misleading if they do not sweep widely across possible future
developments in such areas as demography, values and lifestyles,
technology, economics, law and regulation, and institutional change.
- Alternative futures including the "most likely''
future are defined primarily by human judgment, creativity, and
- The aim of defining alternative futures is to try
to determine how to create a better future than the one that would
materialize if we merely kept doing essentially what is presently
A model based upon assumptions like these is shown
in Figure 1. Basically, the model states that from our experiences
or through environmental scanning we identify issues or concerns
that may require attention. These issues/concerns are then defined
in terms of their component parts-trends and events. Univariate
forecasts of trends and events are generated and subsequently
interrelated through cross-impact analysis. The "most likely"
future is written in a scenario format from the univariate trend
and event forecasts; outlines of alternative scenarios to that
future are generated by computer simulations from the cross-impact
matrix. In turn, these scenarios stimulate the development of
policies appropriate for each scenario. These policies are analyzed
for their robustness across scenarios. The purpose of the entire
exercise is to derive a final of policies that effectively address
the issues and concerns identified in the initial stage of the
process. These policies are then implemented in action plans.
FIG. 1. Environmental Analysis/Forecasting Model
(modified from Boucher and Morrison, 1989)
A wide range of literature provides insights into
how issues are recognized by decision-makers. Included is literature
related to problem sensing and formulation (Kiesler and Sproull,
1982; Lyles and Mitroff, 1980; Pounds, 1969), normative strategy development (Nutt, 1979), decision-making
(Alexis and Wilson, 1967; Mintzberg, Raisinghani, and Theoret,
1976; Segev, 1976), and environmental scanning (Aguilar, 1967;
Kefalas and Schoderbeck, 1973; King,1982). Regardless of how issues
are identified, there is agreement that inconsistencies perceived
within the environment stimulate the decision-maker to further
examine the issue (Dutton and Duncan, 1987).
The articulation of issues/concerns is particularly
critical for effective strategic planning. A central tenet of
strategic management pervading both the literature of organizational
theory (Lawrence and Lorsch, 1967) and traditional business policy
(Andrews, 1971) is that the proper match between an organization's
external conditions and its internal capabilities is critical
to its performance. Accordingly, the primary responsibility of
the organizational strategist is to find and create an alignment
between the threats and opportunities inherent in the environment
and the strengths and weaknesses unique to the organization (Thompson,
A number of writers have recognized that the strategist's
perceptions Of the environment and the uncertainty it represents
to the organization are key to the strategy-making process (Aguilar,
1967; Anderson and Paine, 1975; Bourgeois, 1980; Hambrick, 1982).
Hatten and Schendell (1975) and Snow (1976) further suggest that
the effectiveness of the strategy an organization pursues is dependent
upon the strategist's ability to identify and evaluate major discontinuities
in the environment. This ability is dependant upon the experience
that the strategists brings to this task as well as his or her
ability to systematically scan the contemporary external environment.
A major tool to identify discontinuities in the external
environment is environmental scanning. Aguilar (1967) defined
environmental scanning as the systematic collection of external
information in order to lessen the randomness of information flowing
into the organization. According to Jain (1984), most environmental
scanning systems fall into one of four stages: primitive, ad hoc,
reactive, and proactive. In the primitive stage, the environment
is taken as unalterable. There is no attempt to distinguish between
strategic and nonstrategic information; scanning is passive and
informal. In the ad hoc stage, areas are identified for careful observation, and there are
attempts to obtain information about these areas (e.g., through
electronic data base searches), but no formal system to obtain
this information is instituted. In the reactive stage, efforts
are made to continuously monitor the environment for information
about specific areas. Again, a formal scanning system is not utilized,
but an attempt is made to store, analyze, and comprehend the material.
In the proactive stage, a formal search replaces the informal
searches characteristic of the earlier stages. Moreover, a significant
effort is made to incorporate resulting information into the strategic
Aguilar suggests that environmental assessment is
more effective where a formal search replaces the informal search
of the environment. The formal search uses information sources
covering all sectors of the external environment (social, technological,
economic and political) from the task environment to the global
environment. A comprehensive system includes specifying particular
information resources (e.g., print, TV, radio, conferences) to
be systematically reviewed for impending discontinuities. Examples
of such systems are found mainly in the corporate world (e.g.,
United Airlines, General Motors); less comprehensive systems are
now appearing in colleges and universities (Hearn and Heydinger,
1985; Morrison, 1987), although recent literature advocates establishing
formal environmental scanning systems to alert administrators
to emerging issues (Cope, 1988; Keller, 1983; Simpson, McGinty,
and Morrison, 1987).
Issues may be structured by identifying their
parts as trends or events. Trends are a series of social,
technological, economic, or political characteristics that can
be estimated and/or measured over time. They are statements of
the general direction of change, usually gradual and long-term,
and reflect the forces shaping the region,
nation, or society in general. This information may be subjective
or objective. For example, a subjective trend is the level of
support for a public college by the voters in the state. An objective
trend would be the amount of funding provided to all public institutions
in the state. An event is a discrete, conformable occurrence
that makes the future different from the past. An example would
be: ''Congress mandates a period of national service for all l
Structuring the issues involved in the planning problem
includes developing a set of trends that measure change in individual
categories, along with a set of possible future events that, if
they were to occur, might have a significant effect on the trends,
or on other events. The trend and event set is chosen to reflect
the complexity and multidimensionality of the category. Ordinarily,
this means that the trends and events will describe a wide variety
of social, technological, economic, and political factors in the
regional, national, and global environment.
Having defined the trend and event sets, the next
step is to forecast subjectively the items in each of these sets
over the period of strategic interest (e.g., the next 15 years).
For trends, the likely level over this period is projected. This
is an exploratory forecast. It defines our expectation,
not our preference. (Normative forecasts
define the future as we would like it to be with the focus on
developing plans and policies to attain that future.) Similarly,
the cumulative probability of each event over the period of interest
is estimated, again on the same assumption.
It is important to distinguish between the terms prediction and forecast. Science depends upon theoretical
explanation from which predictions can be made. With respect to
the future, a prediction is an assertion about how some element
of "the" future will, in fact, materialize. In contrast,
a forecast is a probabilistic statement about some element of
a possible future. The underlying form of a forecast statement
is, "If A occurs, plus some allowance for unknown or unknowable
factors, then maybe we can expect B or something very much like
B to occur, or at least B will become more or less probable."
It is also important to distinguish the criteria
for judging predictions and forecasts. Predictions are judged
on the basis of their accuracy. Forecasts are judged, according
to Boucher (1984, as reported in Boucher and Morrison, 1989),
on the following criteria:
1. Clarity. Are the objects of the forecast
and the forecast itself intelligible? Is it clear enough for practical
purposes? Users may, for example, be incapable of rigorously defining
"GNP" or "the strategic nuclear balance,"
but they may still have a very good ability to deal with forecasts
of these subjects. On the other hand, they may not have the least familiarity with
the difference between households and families, and thus be puzzled
by forecasts in this area. Do users understand how to interpret
the statistics used in forecasting (i.e., medians, interquartile
2. Intrinsic credibility. To what extent do
the results "make sense" to planners? Do the results
have "face validity"?
3. Plausibility. To what extent are the results
consistent with what the user knows about the world outside of
the scenario and how this world really works or may work
in the future?
4. Policy relevance. If the forecasts are
believed to be plausible, to what extent will they affect the
successful achievement of the user's mission or assignment?
5. Urgency. To what extent do the forecasts
indicate that, if action is required, time must be spent fairly
quickly to develop and implement the necessary changes?
6. Comparative advantage. To what extent do
the results provide a better foundation now for investigating
policy options than other sources available to the user today?
To what extent do they provide a better foundation now for future
efforts in forecasting and policy planning?
7. Technical quality.
Was the process that produced the forecasts technically sound?
To what extent are the basic forecasts mutually consistent?
These criteria should be viewed as filters. To reject
a forecast requires making an argument that shows that the item(s)
in question cannot pass through all or most of these filters.
A "good" forecast is one that survives such an assault;
a "bad" forecast is one that does not (Boucher and Neufeld
1981). Boucher and Neufeld stress that it is important to communicate
to decision-makers that forecasts are transitory and need constant
adjustment if they are to be helpful in guiding thought and action.
It is not uncommon for forecasts to be criticized by decision-makers.
Common criticisms are that the forecast is obvious; it states
nothing new; it is too optimistic, pessimistic, or naive; it is
not credible because obvious trends, events, causes, or consequences
were overlooked. Such objections, far from undercutting the results,
facilitate thinking strategically. The response to these objections
is simple: If something important is missing, add it. If something
unimportant is included, strike it. If something important is
included but the forecast seems obvious, or the forecast seems
highly counterintuitive probe the underlying logic. If the results
survive, use them. If not, reject or revise them (Boucher and
A major objective of forecasting is to define alternative
futures, not just "most likely" future . The development
of alternative futures is central to effective strategic decision-making
(Coates, 1985). Since there is no single predictable future organizational
strategists need to formulate strategy within the context of alternative
futures (Heydinger and Zenter, 1983; Linneman and Klein, 1979).
To this end, it is necessary to develop a model that will make
it possible to show systematically the interrelationships of the
individually forecasted trends and events.
This model is a cross-impact model. The essential
idea behind a cross-impact model is to define explicitly and completely
the pairwise causal connections within a set of forecasted developments.
In general, this process involves asking how the prior occurrence
of a particular event might affect other events or trends in the set. When these relationships have been specified,
it becomes possible to let events "happen"-either randomly,
in accordance with their estimated probability, or in some prearranged
way-and then trace out a new, distinct, plausible and internally
consistent set of forecasts. This new set represents an: alternative
to the comparable forecasts in the "most likely" future
(i.e., the "expected" future). Many such alternatives
can be created. Indeed, if the model is computer-based, the number
will be virtually unlimited, given even a small base of trends
and events and a short time horizon (e.g., the next ten years).
The first published reference to cross-impact analysis
occurred in the late 1960s (Gordon, 1968), but the original idea
for the technique dates back to 1966, when the coinventors, T.
J. Gordon and Olaf Helmer, were developing the game FUTURES for
the Kaiser Aluminum Company. In the first serious exploration
of this new analytic approach, the thought was to investigate
systematically the ''cross correlations" among possible future
events (and only future events) to determine among other things,
if improved probability estimates of these events could be obtained
by playing out the cross-impact relationships and, more important
if it was possible to model the event-to-event interactions in
a way that was useful for purposes of policy analysis (Gordon
and Haywood, 1968).
The first of these objectives was soon shown to be
illusory, but the second was not, and the development of improved
approaches of event-to-event cross-impact analysis proceeded (Gordon,
Rochberg, and Enzer, 1970), with most of the major technical problems
being solved by the early 1970s (Enzer, Boucher, and Lazar, 1971).
The next major step in the evolution of cross-impact
analysis was to model the interaction of future events and trends.
This refinement, first proposed by T. J. Gordon, was implemented
in 1971-1972 by Gordon and colleagues at The Futures Group and
was called trend impact analysis or TIA (Gordon, 1977).
Similar work was under way elsewhere (Helmer, 1972; Boucher, 1976),
but TIA became well established, and it is still in use, despite
certain obvious limitations, particularly its failure to include
Two strands of further research then developed independently
and more-or-less parallel with the later stages in the creation
of TlA. Each was aimed primarily at enabling cross-impact analysis
to handle both event-to-event and event-to-trend interactions
and to link such a cross-impact modeling capability to more conventional
system models, so that developments in the latter could be made
responsive to various sequences and combinations of developments
in the cross-impact model. One strand led to the joining of cross-impact
analysis with a system dynamics model similar to the one pioneered
by Jay Forrester and made famous in the first Club of Rome study
(Meadows et al., 1972). This line of research-again directed by
T. J. Gordon produced a type of cross-impact model known as probabilistic system dynamics or
The second strand led to a cross-impact model known
as INTERAX (Enzer, 1979), in which the run of a particular path
can be interrupted at fixed intervals to allow the user to examine
the developments that have already occurred. The user can also
examine the likely course of developments over the next interval
and can intervene with particular policy actions before the run
is resumed. Since the development of INTERAX, which requires the
use of a mainframe computer, some work has been done to make cross-impact
analysis available on a microcomputer. The Institute for Future
Systems Research (Greenwood, SC) has developed a simple cross-impact
model (Policy Analysis Simulation System- PASS) for the Apple
II computer and an expanded version for the IBM AT. A comprehensive
cross-impact model, Bravo! will be released in mid-1989 by the
Bravo! Corporation (West Hartford, CT) for an IBM AT (Morrison,
1988, July-August). These microcomputer based models greatly enhance
the ability to conduct cross-impact analyses and, therefore, to
write alternative scenarios much more systematically.
Scenarios are narrative descriptions of possible
futures. A single scenario represents a history of the future.
The "most likely" future, for example, contains all
of the forecasts from the forecasting activity in a narrative
weaving them together from some point in the future, describing
the history of how they unfolded. Alternatives to this future
are based upon the occurrence or nonoccurrence of particular events
in the event set. Such alternatives define unique mixes of future
environmental forces that may impact on a college or university.
The range of uncertainty inherent in the different scenarios (which
are, themselves, forecasts) changes the assumption that the future
will be an extrapolation from the past (Zentner, 1975; Mandel,
1983). Within the context of an alternative future depicted by
a scenario, the decision-maker can identify causal relationships
between environmental forces, the probable impacts of these forces
on the organization, the key decision points for possible intervention,
and foundations of appropriate strategies (Kahn and Wiener, 1967;
Sage and Chobot, 1974; Martino, 1983; Wilson, 1978). By providing
a realistic range of possibilities, the set of alternative scenarios
facilitates the identification of common features likely to have
an impact on the organization no matter which alternative occurs.
It is conventional to create from three to five such histories
to cover the range of uncertainty.
Numerous approaches can be taken in writing the scenarios,
ranging from a single person writing a description of a future
situation (Martino, 1983) to the use an interactive computer model
that uses cross-impact analysis to generate outlines of the alternatives
(Enzer, 1980a,b; Mecca and Adams, 1985; Goldfarb and Huss, 1988) . A broader range of scenario writing approaches is
described by Mitchell, Tydeman, and Georgiades (1979), Becker
(1983), and Boucher 1985).
Any of a number of scenario taxonomies, each with
its own benefits and limitations, may bc used to guide the development
of a scenario logic (Bright, 78; Ducot and Lubben, 1980; Hirschorn,
1980; Boucher, 1985). The most comprehensive of the taxonomies,
however, is that of Boucher (1985) which has been updated in Boucher
and Morrison (1989). In this taxonomy there are four distinct
types of scenarios: the demonstration scenario, the driving-force
scenario the system change scenario, and the slice-of-time scenario.
The first three types are characteristic of "path-through-the
time" narratives; the fourth is a "slice
of time" narrative. The following descriptions are derived
from Boucher (1985) as updated in Boucher and Morrison (1989).
The demonstration scenario was pioneered by
Herman Kahn, Harvey DeVeerd, and others at RAND in the early days
of systems analysis. In this type scenario, the writer first imagines
a particular end-state in the future and then describes a distinct
and plausible path of events that could lead to that end-state.
In the branch-point version of this type of scenario, attention
is called to decisive events along the path (i.e., events that
represent points at which crucial choices are made-or not-thus
determining the outcome). Thus the branch points, rather than
the final outcome, become the object of policy attention. As Kahn
and Wiener (1967) point out, they answer two kinds of questions:
(a) How might some hypothetical situation come about, step by
step? and (b) What alternatives exist at each step for preventing,
diverting, or facilitating the process?
The major weakness of the demonstration scenario,
as Boucher (1985) points out, is that it is based upon "genius''
forecasting and is, therefore, dependent upon the idiosyncrasies
and experiences of individuals. However, this type of scenario
(like all methods and techniques in this field) is useful in both
stimulating and disciplining the imagination.
The driving-force scenario, perhaps the most
popular type of scenario in governmental and business planning
(Goldfarb and Huss, 1988; Ashley and Hall, 1985; Mandel, 1983),
is exemplified by Hawken, Ogilvy, and Schwartz's Seven Tomorrows
(1982). Here the writer first devises a "scenario space"
by identifying a set of key trends, specifying
at least two distinctly different levels of each trend, and developing
a matrix that interrelates each trend at each level with each
other. For example, two driving forces are GNP growth and population
growth. If each is set to "high," "medium,"
and ''low,'' there are nine possible combinations, each of which
defines the scenario space defining the context of a possible
future. The writer's task is to describe each of these futures,
assuming that the driving-force trends remain constant.
The purpose of the driving-force scenario is to clarify
the nature of the future by contrasting alternative futures with
others in the same scenario space. 1t may well be that certain
policies would fare equally well in most of the futures, or that
certain futures may pose problems for the institution. In the
latter case, decision-makers will know where to direct their monitoring
and scanning efforts.
The major weakness of the driving-force scenario
is the assumption that the trend levels, once specified, are fixed-an
assumption that suffers the same criticism directed to planning
assumptions in traditional long-range planning activities (i.e.,
they ignore potential events that, if they occurred, would affect trend levels). The advantage of this type of scenario,
however, is that, when well executed, the analysis of strategic
choice is simplified-a function of considerable value at the beginning
of an environmental or policy analysis when the search for key
variables is most perplexing.
The system-change scenario is designed to
explore systematically, comprehensively, and consistently the
interrelationships and implications of a set of trend and event
forecasts. This set, which may be developed through scanning,
genius forecasting, or a Delphi, embraces the full range of concerns
in the social, technological, economic and political environments.
Thus, this scenario type varies both from the demonstration scenario
(which leads to a single outcome and ignores most or all of the
other developments contemporaneous with it) and from the driving-force
scenario (which takes account of a full range of future developments but assumes that the driving trends
are unchanging), in that there is no single event that caps the
scenario, and there are no a priori driving forces.
The system-change scenario depends upon cross-impact
analysis to develop the outline of alternative futures. The writer
must still use a good deal of creativity to make each alternative
intriguing by highlighting key branch points and elaborating on
critical causal relationships. However, this scenario suffers
from the same criticisms that may be leveled at driving-force
and demonstration scenarios although everything that matters is
explicitly stated, all of the input data and relationships are
judgmental. Moreover, the scenario space of each trend projection
is defined by upper and lower envelopes as a consequence of the
cross-impacts of events from the various scenarios that are run.
Although it is valuable to know these envelopes, this information
by itself provides no guidance in deciding which of the many alternative
futures that can be generated should serve as the basis for writing
scenarios. This choice must be made using such criteria as "interest,"
"plausibility," or "relevance."
The slice-of-time scenario jumps to a future
period in which a set of conditions comes to fruition, and then
describes how stakeholders think, feel, and behave in that environment
(e.g., 1984, Brave New World). The objective is to summarize a
perception about the future or to show that the future may be
more (or less) desirable, fearful, or attainable than is now generally
thought. If the period within the "slice of time" is
wide, say from today to the year 2000, it is possible to identify
the macro-trends over this period (e.g., Naisbitt's Megatrends,
1982). In this sense, a slice-of-time scenario is the same as
the "environmental assumptions" found in many college
and university plans. The weakness of this approach is that there
is no explanation as to the influences on the direction of these
trends, no plausible description of how (and why) they change
Variations in these types of scenarios occur according
to the perspective brought to the task by scenario writers. Boucher
(1985) points out that writers using the exploratory perspective
adopt a neutral stance toward the future, all appearing to be
objective, scientific, impartial. The approach is to have the
scenario begin in the present and unfold from there to the end
of the period of interest. The reader "discovers" the
future as it materializes. The most common version of this mode,
"surprise-free," describes the effects of new events
and policies, although only likely events and policies are used.
A second version, the play-out" version, assumes that only
current forces and policy choices are allowed to be felt in the
future (i.e., no technological discoveries or revolutions are
Writers using the normative perspective focus
on the question, "What kind of future might we have?"
They respond to this question from a value-laden perspective,
describing a "favored and attainable" end-state (a financially
stable college and the sequence of events that show how this could
be achieved) or "feared but possible" end-state (merger
with another institution).
In the hypothetical or what-if? mode, writers experiment
with the probabilities of event forecasts to "see what might
happen." In this mode, the writer explores the sensitivity
of earlier results to changes in particular assumptions. Many
"worst case" and "best case" scenarios are
of this sort.
Boucher (1985) maintains that ail scenarios may be
placed in a particular type/mode combination. The current business-planning
environment, for example, with its emphases on multiple-scenario
analysis (Heydinger and Zenter, 1983), places a "most likely"
future (exploratory, driving-force) surrounded by a
"worst case" (normative-feared but possible, driving-force)
and a "best case" (normative-desired and attainable,
driving-force) scenario. Unfortunately, such a strategy ignores
potentially important alternative futures from such type/mode
combinations as the exploratory system change or exploratory driving-force
scenarios. The choice of which scenario to write must be made
Policy analysis is initiated when the sneers are
completed. Since a scenario represents a type of forecast, it
is evaluated by the same criteria described earlier (i.e., clarity,
intrinsic credibility, plausibility, policy relevance, urgency,
comparative advantage, and technical quality). Once these criteria
are satisfied, each scenario is reviewed for explicit or implied
threats and opportunities, the objective being to derive policy
options that might be taken to avoid the one and capture the other.
It is here that the value of this approach may be judged, for
the exercise should result in policies that could not have been
developed without having gone through the process.
Action plans are directly derived from the policy
options developed through reformulating each option as a specific
institutional objective. Responsibilities for developing detailed
action plans and recommendations for implementation may be assigned
members of the planning team. Typically, these staff members have
knowledge, expertise, and functional responsibilities in the area
related to and/or affected by the implementation of the strategic
option. The resulting action plans are incorporated into the institution's
annual operational plan as institutional objectives assigned to
appropriate functional units with projected completion dates (Morrison
and Mecca, 1988).
A Case Study
The brief case study that follows illustrates the
application of this approach to the strategic planning process
of a two-year college. The institution, a public technical college
located in the southeastern United States, is charged with offering
a comprehensive program of technical and continuing education
in concert with the economic and industrial development needs
of its seven county service area. Like most two-year colleges,
the institution's mission, role and program scope are greatly
determined by the totality of its external relationships (Gollattscheck,
Several years ago, recognizing the institution's
sensitivity to external change, the administration adopted a strategic
planning process, ED QUEST, which incorporates the external analysis
and forecasting approach described in this chapter . The participants
in the process were drawn from across the college's administrative
and instructional staff. The 15 members of the institution's planning
team represented many of the functional areas of the college (e.g.,
instruction, continuing education, finance, and student services).
The president and the three vice-presidents of the college were
also members of the planning team. In addition to the 15 members
of the planning team, 16 other staff members were selected based
upon their expertise in a particular curriculum content area (e.g.,
business, engineering technology, industrial crafts) or for the
"boundary-spanning" nature of their institutional role
(e.g., admissions, job placement, financial aid, management development
programs). Together, these individuals participated in environmental
scanning and constituted a Delphi panel tasked to forecast relevant
trends and events. The membership of this panel represented as
broad a range of functional areas and organizational specialties
Scanning the External Environment
The information and forecasts about environmental
trends, issues, and developments that might have impact on the
college's future were drawn from a variety of sources. Materials
were obtained not only from education sources (e.g., Chronicle
of Higher Education, Change, Community College Journal), but
- General sources (e.g. US News and World Report, Newsweek, New York Times,
- "Fringe" publications (e.g. Mother
Jones, New Ages);
- Periodicals covering four major areas- social,
technical, economic, and political (e.g., Working Women, American Demographics, High Technology,
Week, Computer World);
- Future-focussed journals/newsletters (i.e., The
Futurist, What's New, and the Issue Management Newsletter);
- Additional information obtained from the college's
task environment (e.g., college-going rates of high school graduates,
state revenues, demographic profile of state region).
The intent of this information was to stimulate readers
to identify possible future changes in the environment (i.e.,
trends, events, or issues) that would affect the college's future.
The material was selected to provide an "information gestalt,"
within which members of the Delphi panel could begin to see patterns
of change in the external environment. Using this material and
personal experience, the members of the Delphi panel completed
an open-ended questionnaire. This represents Round One (Rl) of
the Delphi survey. The questionnaire asked each respondent to
identify several trends that would have major consequences for
the college during the period of the next 11 years and to identify
several events believed to have both a high likelihood of occurring
at some time during the same period and, it occurring, a significant
impact on the institution.
Forecasting External Changes
The Rl responses were used to develop the second-round
(called R2) questionnaire. Typically, Rl responses reflected a
general concern, "The demographics of our student body are
changing rapidly.'' This concern needed to be restated into measurable
trend statements, such as "the percentage of black students,"
"the percentage of Asian students," and "the percentage
of those students older than 25 years of age." A related
potential event statement was "The percentage of minority
first graders in our area is greater than 50%."
The R2 questionnaire provided the Delphi panel members
with the opportunity to forecast the set of trends (N = 78) and
events (N = 60) over the period of the next 11 years (e.g., 1987
to 1997.) Representative trends on this questionnaire were as
- Annual number of manufacturing jobs moving to
the developing countries (e.g., Mexico, Korea) from the U.S.;
- Number of new jobs annually created by industrial
development and expansion in the state;
- Number of industries in the southern U.S. using
- Number of four-year colleges in the U.S. offering
technical programs at the baccalaureate level.
Representative events on this questionnaire were
- A national opinion poll reveals that over 40%
of the public believe that a general/liberal arts education is
the best preparation for entering the job market.
- The federal government requires an 800 SAT or
comparable ACT score for persons to be eligible to receive federal
- The state legislature mandates articulation policies
and procedures among two-year colleges and four-year colleges.
- A major depression occurs in the U.S. (unemployment exceeds
15% for two consecutive years).
Panel members forecasted the level of each trend
at two points in the future, 1992 and (1997), and estimated the
probability that each event would occur at some time between 1987
and 1997. In order to relieve their anxiety about forecasting,
they were instructed to provide their "best guess,"
and to indicate their first impressions. The purpose of requesting
their forecasts as opposed to relying solely on forecasts of experts
was to obtain the thinking of the chief decision-makers of the
college as to their version of the "most likely" future.
It is certainly possible that when faced with making these forecasts
they may turn to the information initially provided, or they may
seek other information. The assumption is that by having the decision-makers
participate in the analysis, they "own" the analysis
and, therefore, will find it creditable for developing policy
options in the basis of the analysis.
In addition, panel members assessed the positive
and negative consequences of each trend and event. This latter
information was used to reduce the size of the trend and event
set by eliminating those variables with lesser impact upon the
Refining the Forecast
The forecasts of trends represented the panel's view
of the "most likely" future of the college. In order
to develop alternative scenarios to this future, it was necessary
to conduct a third round (R3) Delphi, which focused on refining
the probability estimated from the previous round (R2). This refinement
was conducted using small groups from the Delphi panel. Initially,
it was planned to use the Delphi panel to make these estimates
as well as those estimates required to develop the cross-impact
model (see below). This required each member of the Delphi panel
to potentially make an enormous number of estimates. Although
having the entire membership of the Delphi panel make all the
estimates would have resulted in a single vision of the future
of the group, it was decided that this task would be overwhelming
to the individuals on the panel and would lead to panel "dropouts,"
a recurring problem in a large Delphi.
Therefore, to refine the forecast of events, the
panel was divided into smaller groups, each being assigned a set
of events and required to complete several estimates for each
event: the earliest year the event's probability would first exceed
zero and the event's probability of occurring by 1990 and by 1994.
The procedure was for team members to (a) review R2 estimates
for the median and interquartile range; (b) make a decision if,
on the bases of earlier discussion, these estimates needed revision;
(c) discuss the rationale for reestimation with other members
of the group; and (d) make individual reestimations.
Developing the Cross-Impact Model
These groups were used to develop a cross-impact
model that defined the interrelationships of events-on-trends
and events-on-events. The events-on-trends model required the
group to determine the impact of an event on the level of each
trend. This was accomplished by the group providing both estimates
of the magnitude of the event's maximum and "steady-state''
impact on the trend's forecasted level (i.e., how long the maximum
impact would remain to affect the trend level). In addition, group
members estimated the number of years it would take from the initial
occurrence of the event until it affected the trend, how long
it would take for the effect of the event to reach its maximum
effect, how long the maximum effect would last, and how long it
would take for the impact of the event to decline until the trend
reached a "steady static." For example, one event in
the set was "voice-activated microcomputers available in
the U.S." The impact of this event on the level of automation
in U.S. offices was as follows: It would be five years before
voice-activated microcomputers would begin to influence the level
of office automation, and another two years before the maximum
impact of a 40% increase in office automation would be reached.
It was estimated that the maximum impact would continue for four
years after which the impact would decrease over a three-year
period to 30% steady-state impact.
The process of making these estimates was initially
slow. After panel members grasped the concept of cross-impact
analysis, however, the process proceeded at a smooth pace. The
estimates from all teams were then reviewed by selected panel
members. This step was necessary to ensure that there was consistency
in the vision of the future represented by the cross-impact model's
Development of Alternative Scenarios
Once the cross-impact model was completed, a series
of scenarios showing possible alternative future environments
of the college were developed. The first scenario developed represented
the college's "most likely" future. It described the
content of the "expected futures" as defined by those
trends identified as critical to the college's future. The specific
character of this future was represented by the forecasted level
of the trend based upon the impact assumptions of each member
of the panel. In this sense, the "most likely" future
was a compilation of the planning assumptions used in most planning
models, written in the form of a scenario.
Three other scenarios were created showing the alternative
futures that could occur, should specific events happen in the
future. Each of these scenarios described the changes in the level
of the trends resulting from the impacts of a particular sequence
of events over the period of the future which defined the strategic
planning horizon for the college (10 years). In essence the alternative
futures depicted in these scenarios represented a variation of
the external environment described in the "most likely"
scenario. The alternative scenarios were generated using PASS,
an event-to-event and event-to-trend cross-impact model implemented
on a personal computer. Within PASS, the "hits" for
the event-to-event and event-to-trend sections of the model were
determined from the cross-impact estimates made by the analysis
teams. These estimates represented how the probability of a particular
event would change, given the prior occurrence of an impacting
event and how the level of a trend would change given the impact
of a particular sequence of events. The result outlined a single
path of development over time. Such paths were instructive to
the planning teams not only because they integrated the input
estimates of the cross-impact model, but also because they described
the alternative paths of developments that were in fact, possible
and redefined the context of the "most likely" future
as represented by the changes in the levels of the impacted trends.
Conducting the Policy Analysis
The analysis of the implications of the four scenarios
represented the policy analysis phase of the process. The planning
team first evaluated the scenarios using the criteria previously
mentioned in this chapter for judging forecasts. These criteria
allowed the team to maintain the perspective that no scenario
was to be viewed as a prediction of a future state of affairs
of the college. Instead, there were an infinite number of possible
alternative futures, each varying because of interactions among
human choice, institutional forces, natural processes, and unknowable
chance events. Each scenario, therefore, represented a probabilistic
statement about some element of a possible future (i.e., forecast).
After the group had rigorously examined the scenarios,
they assessed how the institution would be affected if the particular
future described by the scenario materialized. This step was a
critical part of the team's strategic planning process, because
forecasts are of little or no value unless decision-makers estimate
the degree and nature of the impact of change on the organization
(Halal, 1984). Team members assessed the consequences of the scenario
for the current and future mission of the organization. Also explored
was the impact of the scenario on the institution's key indicators-factors
that were perceived to make the difference between institutional
success or failure (Rockart, 1979).
Once all scenarios had been reviewed, a list of implications
was developed. These implications, common to all scenarios, represented
those of critical importance to the establishment of institutional
strategy (e.g., the demand for the college to develop more and
varied outreach services, to provide both technical education
and technology transfer activities, to adapt a core approach to
its engineering curriculum, to demonstrate quality and excellence,
and to operate in a context of more centralized governance at
the state level). Those implications unique to a particular scenario
represented possible conditions for which contingency strategies
might have to be developed should the future described in the
particular scenario emerge.
From these implications the planning team developed
a list of institutional strategies. To ensure that strategies
were appropriately focused, team members were directed to think
of strategy as defining the relationship of the college to its
external environment and as providing guidance to the institution's
staff in carrying out their administrative and operational activities
in six key decision areas: (a) basic mission; (b) array of programs
and services; (c) types of students served; (d) geographic area
served; (e) educational goals and objectives; and (f) competitive
advantage(s) over competitors (e.g., low tuition, location). A
strategy that affected one or more of these decision areas or
the relationship between the college and the environment was considered
a good candidate for adoption by the planning team. The potential
of each strategy was assessed as to the degree it enhanced or
inhibited institutional strengths and weaknesses previously identified
by the planning team.
Those strategies estimated to enhance strengths or
reduce weaknesses were examined as to their effectiveness across
scenarios and then categorized with respect to the external implications
they address. For example, a number of strategies focussed on
the issue of educational excellence. Members of the team believed
this issue would continue to grow as a public concern based upon
the analysis of several of the scenarios; consequently, it was
deemed important to make the college's community and staff perceive
"quality" and "excellence" as important institutional
values. Specific strategies identified by the planning team to
accomplish this included:
- Publicizing institutional and faculty awards,
honors, and innovative projects;
- Publicizing student achievements;
- Establishing a task force on institutional excellence
to examine and make appropriate recommendations for improving
any aspect of those educational programs and operations deemed
"less than excellent";
- Expanding the number of major national conferences
and meetings annually hosted by the college;
- Encouraging greater faculty participation in
regional and national professional associations;
- Improving the quality of the college's adjunct
faculty through increased salaries and involvement in the college's
- Establishing an endowment fund to expand professional
development opportunities available to the college's faculty and
staff to ensure that all personnel remain current in their field
- Establishing an instructional resource center
in the college to provide support and training for all part-time
and full-time faculty to maintain their instructional skills.
Another category of strategies was intended to reaffirm
the institution's role as a catalyst for regional economic development.
- Expanding the capability of the college's continuing
education program to provide start-up and ongoing job training
and technical assistance for small business and service industries;
- Establishing a technology transfer consortium
to assist businesses and industries in the region to improve their
productivity through the application of new technologies for existing
- Establishing an ongoing program of conferences
and workshops for local and community groups to foster regional
economic and community activities;
- Establishing an advanced technology education
center for the "factory of the future" to provide technical
training and technology transfer services to industries in the
Incorporating the Strategies into the College's
The planning team was asked to discuss these strategies
with members of their staffs. The vice president for planning
circulated this list of strategies and their corresponding objectives
to all members of the planning team. At a half-day meeting, the
team reviewed suggested objectives for each strategy and selected
those objectives they believed the college should emphasize in
its annual operational plan, allocating appropriate resources.
Periodically during the year the president and the vice presidents
reviewed the progress made in accomplishing the objectives.
Benefits and Limitations
An evaluation of the process by the members of the
planning team indicated that the planning process was successful
in producing information describing changes in the external environment
relevant to the future of the college and in stimulating strategies
that would not have been developed without going through the process.
More specifically, team members felt that the process provided
a systematic approach to the identification and analysis of external
information. This viewpoint was best summarized by several members
of the team who said that the process caused the team "to
look at the future in an organized manner," and it "gave
order to all the data that are out there" by helping the
college's planning team to "structure the data so they can
be matched with what we are about and what we are trying to do."
Overall, the team members thought that this planning
approach increased their awareness and ability to assess the implications
of external changes for the institution's future. Several members
of the team said that it, "forces members [i.e., the planning
team] to look at issues which would be overlooked and . . . aids
in broadening the participant's perspective." Members of
the planning team also indicated that the alternative scenarios
were useful in developing a number of strategies and that the
process provided a systematic approach for identifying those strategies
that were to be given priority for implementation.
The incorporation of the strategies selected for
implementation into the college's ongoing management activities,
however, did not go smoothly. This was not surprising in that
Gray (1986) found that the difficulties encountered in the implementation
of strategic plans were the source of the greatest discontent
among corporate executives (p. 90). In this case, planning-team
members felt that there was a gap between the college's strategic
planning process and its operational planning. The perception
of a number of members was that the results of the process were
not used in their entirety. Members also noted that the strategies
were added to previously determined priority assignments of staff,
thus increasing work loads and resulting in incompatible demands.
In other words, the new strategies were implemented without work
assignments being "uncoupled" from strategies previously
developed by the administration (Hobbs and Heany, 1977).
The problem of implementation was also related to
what team members viewed as another problem-the lack of wider
participation in the process among other members of the faculty
and staff. While team members believed that the process facilitated
the development of a consensus regarding the strategic directions
of the institution among members of the planning team, they generally
did not perceive this consensus reaching other members of the
faculty and staff. Consequently, the results of the strategic
planning process were perceived to be mandated by some staff.
The importance of this problem is supported by the conclusion
that Cleland and King (1974) draw that an organization's success
in strategic planning is more sensitive to the overall organizational
culture within which the planning is accomplished than the planning
techniques and processes used (p. 70).
Some planning-team members were critical about the
techniques and procedures used during the process. Several individuals
believed that the scope of the environmental scan was too narrow
and concentrated too heavily on technological and economic changes
in the environment. There was far from unanimity on this point.
One team member's sole criticism of the process was that the information
from the scan was of little value and should rather have concentrated
on the economic and employment data reflective of the local economy
of the college's service area. Most team members thought, however,
that the environmental scan and the trend and event statements
contained on the Delphi's R2 questionnaire reflected changes in
all sectors of the environment affecting the college .
Lastly, team members thought the procedures followed
for evaluating the robustness and probable effectiveness of the
strategies needed to be strengthened. More specifically, it was
pointed out that short of a subjective assessment the impact on
college expenditures, the completed financial implications of implementing a particular strategy would not be known
until after it was projected. Also, several individuals believed
that in addition to assessing the strategies impact on the institution's
strengths and weaknesses, it would have been useful if the strategies
had also been assessed as to their impact on the college's key
indicators. With the availability of the PASS model, such an assessment
was technically feasible, as it allows the user to incorporate
policies (.i.e., strategies) and trend data for each indicator
into the cross-impact model of the institution's future environment.
Problems, Issues, and Needed Research
This approach to planning and associated research
methods and techniques is derived from the development of technological
forecasting by military planners during the years that followed
World War II in an attempt to avoid being unprepared for future
wars. Technological forecasting differed from traditional planning
methods in that findings were based upon judgments about the future
and were intended to develop complex scenarios (as opposed to
identifying only the next operation of military-related breakthroughs).
However, according to Enzer (1983), it was not until the mid-1960s
that technological forecasting was placed within an analytical
framework with such supporting methods as the Delphi, scenario
writing, cross-impact analysis, and system dynamics, through the
work Gabol (1964), Jantsch (1967), Kahn and Wiener (1967), and
de Jouvenel (1967).
As one might expect with such a newly developing
field, there are a variety of problems and
issues associated with external analysis and forecasting. Indeed,
Boucher ( 1977) identified some 300 unique problems and issues
in this emerging arena in a survey of the literature and of leading
researchers in the futures field; Coates (1985) identified almost
as many in a survey of issues managers. Space permits only a limited
description of the most pressing issues for further research this
Forecasting the "most likely" and alternative
futures using the approach ascribed here is based on soft, judgmental
data, data based upon intuitive, often theoretically unstructured
insights into real-world phenomena. Indeed, one of the major problems
in this area of inquiry is the inadequacy of current theories
of social change. Boucher (1977) found that none of the competing
theories existing hen or now (personal communication, August 1988)
had predictive value. If our understanding of social change were
more highly developed, forecasting the future would be much less
Improving methods of forecasting involves the question
of how the validity of results obtained by the construction of
a simulation model about the future can be measured. Of course,
the concept of validity is difficult to apply to the study of
the future. For this reason, many forecasters emphasize that accuracy
is not a criterion for evaluating forecasts, for it is impossible
to identify and assess the impact of all future events. Therefore,
the best criteria we can develop at present are that forecasts
be credible, plausible and internally consistent given the information
we have as a result of our scan and given our state of knowledge
vis-a-vis social change.
Reliability and validity are also problems in judgmental
forecasting. There has been some research on the extent to the
same methods produce the same results. Martino (l983) reviewed
a number of studies that reported a similarity of results across
different Delphi studies. However, Sackman (1974) found that the
similarity of forecasted median dates for events from some of
these Delphi studies were statistically low. The effect of expert
and nonexpert panels on the potential validity of judgmental'
forecasting has also been difficult to assess. Proponents argue
that there is evidence that the more expertise panel members have,
the better the forecast. Sackman (1974) reviewed a variety of
Delphi studies comparing forecasts of experts with those of nonexperts
and found that there was no difference. Studies by Campbell (1966)
and Salancik, Weger, and Helfer (1971) came to essentially the
same conclusion. Unfortunately, there has been little recent research
on reliability and validity in judgmental forecasting.
Moreover, there has been little research on the relative
advantages of different methods of eliciting forecasts from a
group (e.g., questionnaires, interviews, computer terminals, face-to-face
discussion), and on the extent to which forecasts derived through
the use of these different techniques differ (Boucher, personal
communication, August 1988). Perhaps one reason for the lack of
research on these questions is the paucity of university-based
programs that incorporate a responsibility for developing the
concepts and methodology of forecasting. Another reason may be
due to the pragmatic use of this approach to planning. That is,
a major function of this approach to planning is to involve decision-makers
in thinking about the future in ways that they have not thought
previously. Ideally, they should be involved in all forecasting
activities so that they "own" the products of the analysis
and, therefore, are comfortable in using this analysis to stimulate
the development of policy options that can be implemented in action
plans. They use forecasts by experts (as reported in the literature
or through personal communications) to assist them in making their
own forecasts. In so doing, they become "smart" about
current and forecasted changes and use this increasing alertness
to conduct their managerial and planning responsibilities. The
process of scanning, forecasting, and planning, therefore, may
be more important to the future of the organization than the product
of any particular round of forecasting or planning. Consequently,
the validity of the analysis is not as crucial as it would be
in other research activity. '
There are a number of questions related to one of
the major tools used by forecasters-the Delphi. Olaf Helmer (1983),
one of the developers of the Delphi technique, has posed the following
research questions (p 118): What degree of anonymity is most helpful
to the performance of a panel? How should the questioning process
be structured? How can information from a variety of individuals
from a variety of disciplines be best used? How stable is a panel's
judgement over time? What is the optimal panel size? How can the
performance of forecasters be calibrated? Be enhanced? What data,
data-processing facilities, simulations, communication devices
or models would be most helpful to forecasters'? How can control
for the systematic bias of forecasters be obtained?
There are also a number of issues related to a tool
essential to forecasting alternative scenarios-cross-impact analysis.
For example, the cross-impact matrix is constructed in a bivariate,
first-order impact fashion (if Event A occurs, does it affect
the probability of Event B occurring and, if so, to what extent?).
It is too unwieldy and complex for this technique to handle the
possibility of two or more events jointly affecting the probability
of another event. Too, Helmer ( 1983) notes the problem of "double-accounting";
i.e., "if event A has a direct impact on event C but also
has an indirect impact on it via another event, B, how can we
make sure that this indirect impact is not also reflected in the
direct impact of A on C and thus counted twice?" (p. 120).
Most educational leaders can readily identify pressing
concerns and issues facing colleges and universities on the basis
of their reading, experience in managing issues, and discussions
with colleagues, both at home and around the world. Frequently,
however, this identification is limited without the benefit of
a comprehensive environmental scan of critical trends and potential
events in the social, technological, economic, or political environments
from the local to the global levels. Moreover, a systematic and
continuous scanning process is crucial to the successful implementation
of an external analysis/forecasting approach to planning in order
to reevaluate the forecasts to determine if they need to be reestimated
on the basis of new information generated in the scan.
Developing and institutionalizing a systematic, comprehensive
environmental scanning function requires a commitment of time
and resources that at present only major corporations (e.g., General
Motors), trade associations (e.g., American Council of Life Insurance),
think tanks (e.g., Standford Research Institute) and some philanthropic
organizations (e.g., United Way of America) have been willing
to do. A number of colleges (e.g., St. Catherine) and universities
(e.g., Arizona State, Colorado, and Minnesota) have conducted
period scans, but the only comprehensive, ongoing system reported
in the literature is at the Georgia Center for Continuing Education
(Simpson, McGinty, and Morrison, 1987). There may be several reasons
for this state of affairs. One is the resource commitment required
in (a) obtaining sufficient readers to regularly scan a variety
of information sources, (b) maintaining the files manually and
electronically, and (c) obtaining time of busy administrators
and faculty members to review, discuss, and use the pertinent
information developed in the process. Pflaum (1985) argues, for
example, that many scanning processes do not survive because of
the time and energy required to sustain them by volunteers. Ptaszynski,
in applying the ED QUEST model in the School of Management at
Wake Forest University, reported that their planning team thought
that they were wasting valuable professional time scanning irrelevant
material, time that detracted from the more important analysis
phase (personal communication, May 28, 1988).
There are attempts under way to develop environmental
scanning consortia. United Way of America, for example, encourages
colleges and universities to participate in its electronic environmental
scanning network, although it has not yet established a separate
subnet for higher education (Morrison, 1987). Even with such assistance
in maintaining a shared data base, however, the question of how
to best use the scarce time available for the major decision-makers
remains an issue.
In addition to the research implications of the discussion
above in the advancement of this important area of inquiry, there
are a number of specific studies needed. For example, the general
approach to external analysis and forecasting advanced here has
been applied only in a small two-year technical college. How applicable
is this model to other types of educational organizations and
units (e.g., academic departments, four-year colleges, research
universities, state systems of higher education)? A number of
case studies are under way that apply this approach to a learning
resources center in a dental school (Raney, personal communication,
August 1988), to the admissions program of a school of management
(Ptaszynski, 1988), to a department of training and development
in a university hospital (Clay, personal communication, August
1988), to a consortium of church-related colleges (May, 1987),
and to a doctoral-degree-granting university (Porter, personal
communications May, 1988). More are needed. Such studies could
include forecasting, and planning activities. Others could focus
on comparisons of effectiveness (as measured by outcomes) of those
institutions using this approach to those not using the approach,
controlling on relevant third variables (e.g., selectivity, type
of control, institutional size, financial support).
Winkler (1982) identified several promising research
directions when considering modeling decision making problems
under uncertainty that are relevant to the approach described
in this chapter. First, the link between the creative process
and the model-formulation stage of decision-making under uncertainty
has not been explored, although Mendell (1985a) has developed
a set of rules for improving an individual's ability to create
mental scenarios of the future and a framework of questions designed
to stimulate consciousness of the future implications or current
Winkler (1982) also suggested the development of
decision-aids involving user-friendly computer software for modeling
decision-making problems under uncertainty, preferably in an interactive
mode. The cross-impact models noted in this chapter such as PAS
and Bravo! are designed to enable users to generate outlines
or scenarios of future environments and of organizational performance
simultaneously. It is possible to examine each alternative scenario
for developments that give the future its special character and,
thereby, to identify those events that are particularly "bad"
or "good." Policy options may then be designed to increase
the probability of "good" events and decrease the probability
of "bad" events. By including these policies in the
cross-impact model it is possible to treat them analytically in
the same manner as events (i.e., estimate their effects on the
events and trends in the model), and to rerun the computer simulation
to create alternative scenarios that contain policies as well
as events and trends. This is known as policy-impact analysis
(Renfro, 1980). Although such decision-aids are available, there
is no evidence in the literature that they are being used in external
analysis and forecasting in colleges and universities. As Norris
and Poulton (1987) note, there is a dire need for case studies
to illuminate the applicability of this approach to educational
The applicability of catastrophe theory to sociopolitical
forecasting is another direction for possible research. Catastrophe
theory defines sudden changes and discontinuities in the behavior
of natural and social systems (Woodcock and Davis, 1978). Zeeman
(cited in Smith, 1980) points out that catastrophe theory ''call
be applied with particular effectiveness in those situations where
gradually changing forces or motivations lead to abrupt changes
in behavior" (p. 26). Although a relatively young science,
catastrophe theory is beginning to be applied in planning. For
example, analysts at a major corporation adapted the approach
for modeling alternative "catastrophes" of discontinuous
and divergent change in the motivational forces of growth and
profit that control business behavior (Smith, 1980). One can only
speculate as to the value in the decision-making process of alternative
scenarios generated by computerized cross-impact
models incorporating the mathematical modeling approach of catastrophe
There are dozens of other research possibilities
to improve this approach to academic planning, of which only two
additional ones will be mentioned here. First, there is a need
for a current handbook on external analysis and forecasting that
can guide college and university institutional researchers and
planners in this promising methodology. The only published guides
(Fowles, 1978; Henckley and Yates, 1974), although good, are dated.
Second, there is a need for a national research effort on the
future of higher education, with corresponding implications for
academic planning in America's diversified system of colleges
and universities. This effort should include an environmental
scanning/ forecasting data base, housed either with the U.S. Department
of Education or at one of the major professional associations
(American Association for Higher Education, American Council on
Education, Association for Institutional Research, or the Society
for College and University Planning) . This data base should be
electronically accessible to the higher education research and
planning community. Moreover, portions of the annual meetings
of professional associations could focus on the implications of
this evolving data base for academic planning and provide professional
development opportunities in current techniques of external analysis
The purpose of environmental analysis/forecasting
in academic planning is to provide college and university administrators
information that can facilitate better decision-making, particularly
in making decisions affecting the long-range future of their institutions.
Given that we live in an age of "future shock," when
changes in the educational environment occur with ever-increasing
rapidity, educational leaders are faced with a future that most
assuredly will be different from the present. This chapter has
reviewed the salient literature describing a basic approach used
to manage this uncertainty-identifying issues/concerns based upon
experience and upon environmental scanning, structuring issues
in the form of trends and events, forecasting the "most likely"
future of these trends and events, assessing the interrelationships
of these trends and events through cross-impact analysis, and
producing alternative scenarios of plausible futures that stimulate
the development of viable and robust strategic options that can
be incorporated in specific institutional plans. This approach
varies from a traditional long-range planning approach based upon
a single set of environmental assumptions about the future in
recognizing that, although the future is a continuation of existing
trends, it is subject to modification by events that have some
probability of occurrence. Indeed, environmental uncertainty is
caused by potential events. We cannot predict the future, because
uncertainty is a product of our incomplete understanding of trends,
potential events and their interrelationships. However, by using
the best available information we have, we can anticipate plausible
alternative futures and, thereby, limit the number of unanticipated
possibilities to the smallest possible set.
of the ideas expressed in this chapter were developed in earlier
field work by the first author with Wayne 1. Boucher, who continued
to provide advice, and encouragement while this manuscript was
being prepared. In particular, the sections on cross-impact analysis
and scenarios draw heavily on his work as reported in Boucher
and Morrison, 1989. In addition, the authors would 1ike to express
appreciation to Blanche Arons, Carol Binzer, Maria Clay, Joseph
Coates, Roben Cope, Cay Davis, David Dill, Christopher Dede, Willaim
Held, Lee May, Elizabeth Markham, Sherry Morrison, and James Ptaszynski
for their helpful comments on earlier versions of the manuscript.
Of course, the views expressed here, and any errors are solely
the responsibility of the authors.
1. This view is not shared by everyone, however.
James Ptaszynski (personal communication, May 28, 1988) and David
Snyder (personal communication, January 29, 1989) argue that college
and university planing teams should not engage in forecasting,
but rely solely on forecasts produced by experts.
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