A STRUCTURED FRAMEWORK FOR APPLYING CONCEPTUAL MODELS TO PROBLEM ANALYSES: HARDENING UP


Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 

 27

A STRUCTURED FRAMEWORK FOR APPLYING CONCEPTUAL MODELS TO PROBLEM ANALYSES: 
HARDENING UP 

 
John K. Butler, Jr., Clemson University 

 
ABSTRACT 

 
This paper extends a well known approach to analyzing 
interpersonal problems. It outlines a rigorous, systematic, 
“hard” framework for solving such problems. The 
framework emphasizes the usefulness of the conceptual 
model. The objectives are to improve the quality and 
consistency of managers’ decisions and to facilitate the 
process of grading students’ case and exercise analyses. The 
framework might be useful to those who are concerned that 
behavioral science is too “soft” for explaining, prescribing, 
and predicting management activities. 
 

A PROBLEM 
 
There is a pervasive problem facing those of us who would 
attempt to teach the theories and applications of behavioral 
science. We hear about it at conferences and on 
comprehensive exam committees. We read about it in the 
teaching journals. This problem concerns the “softness” of 
behavioral science and the consequent difficulty of 
evaluating students’ solutions (or any solutions) to 
interpersonal situations. The problem exists partly because 
there are often many good solutions to interpersonal 
situations, partly because we sometimes lack clarity in our 
own thinking about such situations, and partly because 
students do not structure the logic of their solutions clearly 
enough for us to follow them. 
 
One resolution to the problem is to adopt the following 
assumptions. 
1. Students begin a course already knowing a lot about 
solving interpersonal problems. 
2. Students can benefit from each other through a synergistic 
learning process. 
3. A teacher’s supportive behavior in class will be emulated 
by students when they become managers. 
4. Supportive behavior by managers is always good for 
organizations in terms of producing valued outcomes. 
 
Acceptance of these assumptions leads to the following 
conclusions: A teacher, particularly an OB teacher, should 
be a model of supportiveness. A teacher is most effective in 
the role of facilitator. A teacher should refrain from directing 
and correcting in favor of supporting students’ thoughts and 
feelings. Therefore, there are no wrong solutions to 
interpersonal problems. If there are no wrong solutions, then 
evaluation is moot; except that there might be some 
solutions that are more right than others. 
 
Several years ago, I adopted the philosophy and role of 
facilitator for three sections of a graduate OB course over a 
period of two years. I found that the students did not feel 
supported. They expressed doubt that there was any content 
at all in our discipline and charged that whatever content we 
had was, “only common sense.” Maybe I was doing it 
wrong. Maybe the students were not ready. Maybe the 
quantitatively- oriented curriculum produced a climate that 
inhibited the facilitative approach. However, my general 
perception was that the students felt frustrated by being 
allowed to flounder with only their previous knowledge and 
group norms to guide them. The anarchy led to anomie, and 
then to apathy. Informal exit interviews with graduates of 

both PhD and masters programs have indicated that the 
whole “soft” area of management is considered to be 
something of a joke. 
 
This criticism is not unique to my institution. At every 
professional meeting I have attended, sooner or later the 
conversation has turned to softness. I was originally 
sensitized to this issue at a meeting where colleagues from 
other institutions were discussing the softness criticisms of 
our discipline. 
 
In evaluating the appropriateness of softness, we need to 
question the four assumptions stated above. I believe now 
that assumption 1 was false and number 2 was questionable 
for my students. They did not begin the course knowing 
much about solving interpersonal problems and their group 
processes were not particularly synergistic. (Most of them 
were majoring in mathematics or quantitative methods. It 
seemed ironic to me that students majoring in those 
particular disciplines were criticizing OB for being “only 
common sense.” I failed to provide structure and to teach 
content, and they needed those dimensions -- especially in 
an introductory course.) Assumption 3 has considerable 
research support, although it seems somewhat presumptuous 
to expect that our students will attempt to emulate our 
classroom behavior. However, assumption 3 is moot if 
assumption 4 if false; and it is. If we believe the contingency 
leadership theories of our own discipline, we have to 
conclude that supportive (consideration, relationships-
oriented) behavior is good only sometimes. 
 

A SOLUTION 
 
This paper suggests a structured approach to incorporating 
the content (theories) of our discipline into rigorous analyses 
of interpersonal problems. The short- run intent is to 
improve students’ understanding of OB theories and their 
applications, and thereby to improve students’ case and 
exercise analyses. Secondarily, the structure makes their 
analyses much easier to grade. The long-run goal is to give 
students a rigorous, systematic, structured, disciplined, 
“hard” approach to solving interpersonal problems that they 
can use to improve the quality and consistency of their 
decisions when they become managers. 
 
I require case and exercise analyses to be written in outline 
form for two reasons. First, the outline form reduces the 
likelihood that students will omit important elements of an 
analysis. Secondly, the outline greatly facilitates the grading 
process. In order to ensure (almost) those two benefits, I 
spend seven 50- minute class periods presenting the 
framework and using it to analyze a practice case. The third 
and fourth weeks of my OB course become a mini-course in 
case and exercise analysis. 
 
The following outline has evolved over several years and 
seems to have stabilized somewhat. Sections I, II, and III of 
the outline come from Homans’ work on social systems 
[2;3J as modified by Cohen, Fink, Gadon, and Willits [1] in 
their Basic Social System Conceptual Scheme. Cohen, et al. 
[1, p. 67-79] thoroughly describe these sections and I will 
just outline them, but not discuss them, here. The 
percentages at the left 



Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 

 28

show the weights I use for grading each of the sec- t ions. 
 
Outline Framework 
20% 1. Background factors (conditions preceding an 

interpersonal relationship). 
A. Personal system (set of values, needs, 

abilities, etc.). 
B. External status (social position outside the 

group). 
C. Organizational culture (socially transmitted 

beliefs, behavior patterns, and climate). 
D. Technology and layout (required skills, 

physical arrangement of people and things). 
E. Reward system (set of interrelated 

Outcomes intended to motivate). 
10% II. Required system (outcomes required or expected 

by the formal organization). 
A. Activities (behavioral acts that are supposed 

to be done). 
B. Interactions (intended transfer of 

communications or things). 
C. Attitudes (intended satisfaction or 

dissatisfaction). 
 
20% III. Emergent system (events that actually occur in 

the informal organization -- symptoms of 
possible problems). 
A. Activities (behaviors that actually occur). 
B. Interactions (communications and transfers 

that actually occur). 
C. Consequences (problems in terms of:) 

1. Productivity 
2. Satisfaction (This replaces “Attitudes” 

in the emergent system, as 
described by Cohen, et al.) 

3. Learning/development 
 
40% IV. Conceptual models (theories of OB). 

A. First model 
1. Description of model (What are the 

independent, dependent, mediating, 
and moderating variables and their 
interrelationships?) 

2. Explanation of emergent system (Why 
did the emergent system occur as it 
did? How did the background factors 
and required system produce the 
emergent system?) 

3. Recommended solutions (How should 
the independent or moderating 
variables in the model be changed to 
produce the desired changes in the 
mediating and dependent variables?) 

4. Predictions (what will result if the 
recommended solutions are 
implemented?) 

B. Second model 
 
10% V. Plan of action (Who will do what? With what 

resources? When? For how much?) 
A. Activity-based program and schedule 
B. Activity-based budget 

 
The rest of this paper discusses sections IV and V of the 
outline (primarily section IV) in an attempt to extend the 
Basic Social System Conceptual Scheme [1] of sections I, II, 
and III. 
 

Conceptual Models 
 
The purpose of section IV is to elicit a rigorous explanation 
and solution of the problem(s) that were defined in the 
consequences of the emergent system (section III, C). The 
explanation and solution must be guided by one or more 
conceptual models. 
 
Before describing section IV to students, I discuss models in 
general. I give them Kerlinger’s [4] definition of a “theory”, 
calling it “model”. Here, I emphasize that models give 
structure to problems by specifying relationships among 
variables in order to explain why the problems exist, to 
suggest solutions, and to predict possible consequences of 
those solutions. I discuss causal relationships and give brief 
examples of dependent and independent variables, such as in 
expectancy theory: 
[Motivation = ∑ (Valence x Instrumentality x Expectancy)], 
and in equity theory: [Satisfaction = f(Outcomes/In- puts)]. 
 
For the first of many times in the course, I point out how 
models can be useful to managers. They can help managers 
make sense out of a chaotic world by giving structure to 
their thinking. Models can suggest courses of action for 
managers. Primarily, models can improve the quality and 
especially the consistency of a manager’s decisions, and 
thereby improve the manager’s track record, and thereby 
lead to better jobs, more responsibility, more money, and .... 
I suspect that only the most gullible and docile students 
believe all of this the first time I tell them. Nevertheless, 
most of them get much of it into their notes and start 
thinking about it. The promise of relevance seems to be a 
magnet that holds their attention while I talk about how to 
choose the right model to solve the problem. 
 
Students often find it difficult to choose the “appropriate” 
models for making decisions. They feel unfamiliar with 
applying models; and, therefore, they feel anxious. I remind 
them that the skill is not new to them. Ever since junior high 
school, they have been applying models to physics, 
geometry, and statistics problems. Familiar examples, to 
convince them of this point, include the “formulas” for time-
speed-distance problems [d = st], the area of a circle [A = 
πr2], and the arithmetic mean [X = ∑Xi/n]. 
 
Clearly, the primary criterion for choosing a model to 
explain a situation and solve a problem is relevance. The 
variables in the model must be the same as those in the 
problem defined in the consequences of the emergent system 
(section III, C). It is nonsensical to apply the equation for the 
area of a circle to the time- speed-distance problem. It is 
equally nonsensical to try to fit a theory of group dynamics 
to a situation that was defined in the emergent system as a 
problem of low productivity of a single individual. None of 
the group dynamics variables are relevant to the problem. 
Yet, students often try this in their early attempts. 
 
The number of attempts to apply models in a seemingly 
random manner is appalling. I have found, from discussing 
this with students, that much of the reason for the 
inconsistency is that case teams have delegated tasks by 
section of the outline, forgetting to coordinate efforts among 
the members to ensure consistency. Since the internal 
consistency of an analysis is a primary criterion for grading, 
students quickly learn the value of intragroup coordination. 
Specifically, all the sections of an analysis must be mutually 
consistent. The conceptual models have to show how the 
background factors and required system caused the emer-



Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 

 29

gent system and what can be done about the undesirable 
aspects, if any, of the emergent system. Thus, group 
members learn to work interdependently. 
 
Plan of Action 
 
An additional consistency check comes with section V, the 
plan of action. Section V shows how to implement the 
recommendations of the conceptual models in section IV, A, 
3; B, 3; etc. Therefore, those recommendations, and only 
they, must be included in the plan of action. The plan of 
action comprises an activity- based program and schedule 
and an activity-based budget. The program must list the 
specific activities that were implied by the recommendations 
of the conceptual models, and must specify the resources 
needed to accomplish these activities. The schedule must be 
expressed on a Gantt chart that shows when the activities 
will be done. The budget must list all the activities in the 
program and on the Gantt chart, and cost them out by 
multiplying the quantity of each resource (usually man-
hours) by its cost per unit. For example: 
 
(2 managers)(30 hours)($20/mgr. hr.)  $1200.00 
 
(15 workers)(100 hours)($9/skr. hr.) = 13500.00 

 $14700.00 
I require neither extensive budgets nor documented figures. I 
assure students that it can be very time consuming and 
expensive to collect complete, accurate information for the 
budget. Instead, I emphasize the process of building 
programs, schedules, and budgets rather than accuracy and 
completeness. I am usually satisfied if students program, 
schedule, and budget for four or five activities, even though 
a complete plan might call for hundreds of activities. 
Budgets vary drastically from one group’s analysis to 
another’ s. 
 
A Sample Application of a Conceptual Model 
 
After discussing the use of models and developing a plan of 
action, I spend five class periods on a sample case analysis. 
For example, the emergent system in the Slade Company 
case [1, p. 716-7261 concerns an extremely cohesive group 
whose members restrict productivity by embracing a norm 
that violates company rules. They are highly satisfied, they 
refrain from challenging the group’s norms, and they share 
information freely within the group; but they tend to reject 
information coming from outside the group. 
 
The cohesiveness model [1, p. 87-96] is very appropriate for 
explaining the emergent system in the Slade Company. 
Section IV, A, 1 of the outline calls for a description of the 
model. The following brief description of the cohesiveness 
model would be appropriate for this section. 
 
There are nine independent variables that cause cohesiveness 
(frequent required interactions, common attitudes and 
values, a common enemy, etc.). If these conditions exist at 
high levels, the result is cohesiveness, which is a mediating 
variable because it is caused by the nine independent 
variables yet it influences productivity, satisfaction, and 
learning. Cohesiveness causes high productivity if the 
group’s norms support productivity goals; low productivity 
[f the norms resist those goals. Cohesiveness always leads to 
high satisfaction. Cohesiveness enhances learning to the 
extent that group members support norms that allow 
members to (a) confront one anothers’ weaknesses or (b) to 
accept information from external sources. Thus, the model 
contains three moderating variables: (a) the extent to which 
the group supports productivity moderates the cohesiveness-
productivity relationship, (b) the extent to which the group 
allows intermember confrontation moderates the 
cohesiveness-learning relationship, as does (c) the extent to 

which the group accepts outside information. 
 
Section IV, A, 2 calls for an explanation of the emergent 
system. The following explanation would fit there. 
 
The nine independent variables all have high values in the 
background factors and required system of the Slade Co. 
group. Therefore, cohesiveness is high for that group. The 
group norms resist productivity during weekdays but they 
support it on weekends. Therefore, the cohesiveness leads to 
low productivity during the week but high productivity on 
weekends. Also, no group member confronts the group with 
the impropriety of the “punch-out system”, whereby group 
members take turns punching everyone else out on the time 
clock at 5 p.m., long after everyone else has split for the day. 
The high cohesiveness, interacting with the lack of inter- 
member confrontation and with the lack of acceptance of 
outside information, leads to groupthink, which allows the 
group to justify the illegal practice. 
 
The recommendations (section IV, A, 3), must be guided by 
the relationships among the variables of the model. A 
desired change in the dependent variables must be produced 
by a management-induced change in one or more of the 
independent or moderating variables. 
 
In the Slade Co. case, desires to ensure reasonable pay, 
security, and autonomy were the causes of the norm to 
restrict productivity. Therefore, it is reasonable to assume 
that the norm would change to support productivity if the 
group members were to participate in the solution, and if 
they were reasonably certain that their needs and values 
would continue to be fulfilled. These needs and values 
would have been listed earlier in the analysis under 
“Personal System” (section 1, A of the outline). They 
include security, affiliation, achievement, autonomy, 
altruism, and creativity. The model suggests taking 
advantage of the already-high cohesiveness while attempting 
to change the moderating variables, such as the norm of 
slowing down during the week. If this approach fails, then 
the manager might attempt to lower the cohesiveness by 
lowering the levels of one or more of the independent 
variables (for example, changing the shop floor layout so 
that group members find it more difficult to interact with 
each other, or requiring additional external interactions). 
 
Section IV, A, 4 of the outline calls for prediction of the new 
emergent system, which will result if the recommended 
solutions are implemented. This new emergent system can 
be predicted in terms of three conditions. 
 
1. If the plant manager were to “blow the lid off” as he has 
suggested, the group would have another common enemy, 
the manager. Therefore, cohesiveness would increase, the 
moderating group norm of resisting productivity would 
become even stronger, and productivity would decrease even 
further. 
 
2. If group norms can be altered so that they support 
productivity during the week, as well as on the weekends, 
the high cohesiveness would lead to high productivity rather 
than low productivity, and satisfaction would remain high. 
(Other models specify relationships between dissatisfaction 
and several dysfunctional outcomes such as unionism, 
grievances, and many forms of work avoidance. However, 
the cohesiveness model does not specify these relationships.) 
 
3. If group members can be convinced that their behavior is 
reducing company profits, possibly reducing their own 
wages, and causing anxiety for the plant man-



Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 

 30

ager, then someone in the group is likely to confront the 
offending norm. If so, the groupthink might cease and 
members would no longer feel that their “punch-out system” 
is justified. 
 
It is likely that the latter two conditions would follow if the 
manager shared the problem and his anxieties with all group 
members, through the informal group leader, and involved 
them in the solution. 
 
A Short Outline for Exercise Analyses 
 
I require the foregoing framework for analyzing cases. 
However, recommendations, predictions, and plans of action 
are not usually relevant for exercise analyses. Therefore, for 
exercise analyses, I require only a brief description of the 
emergent system, and descriptions of models and 
explanations of the emergent system (section III and sections 
IV, A, 1 & 2; B, 1 & 2; etc.). An exercise analysis needs to 
describe, briefly, the emergent system and to explain why 
the activities and interactions of the emergent system 
occurred as they did in a given exercise. As in case analyses, 
students must use conceptual models to show why the 
background factors and the required system led to the 
emergent system. However, for exercise analyses, they need 
neither to list background factors, nor to describe the 
required system, nor to develop a plan of action. The task is 
to use conceptual models to explain why the emergent 
system occurred the way it did. 
 
Grading 
 
Grading is quite straightforward and easy to justify to 
students (I think). The major grading criteria focus on the 
quality of analysis. 
1. Are the elements of the analysis accurately classified into 
their correct categories? 
2. Are the conceptual models appropriate for explaining the 
emergent system. In case analyses, are the conceptual 
models appropriate for recommending solutions and for 
predicting consequences of these solutions. 
 
3. How internally consistent is the entire analysis, both 
within and among sections. Are the specified background 
factors and required system incorporated in the conceptual 
models when the models are used to explain the emergent 
system? Does the plan of action specify an activity-based 
program, schedule, and budget for all the solutions 
recommended by the conceptual models? 
 

CONCLUSIONS 
 
Limitations of Models 
 
Models do not help us to diagnose problems nor do they give 
us specific courses of action. In fact, it is possible that 
models can interfere with diagnoses. If we become 
dogmatically attached to a limited number of models, we 
might easily fall victim to the hammer-and- nail disease, “If 
the only tool I have is a hammer, then I see every problem as 
a nail.” Models can be useful tools, but we need a broad 
selection of them to fit the broad selection of problems. 
Given a diagnosis, a model can structure our thinking by 
systematically specifying relationships among independent 
and dependent variables (causes and consequences). 
Knowledge of these relationships enables us to explain why 
an emergent system has occurred the way it has, to 
recommend ways to change the emergent system as desired, 
and to predict the new emergent system that will result if 

those recommendations are implemented. 
 
The specific solution of the Slade Co. manager’s problem 
depends on the outcome of his talk with the group. Feasible 
solutions would include (a) formalizing the flextime that the 
group has implemented informally through its “punch-out 
system”, (b) MBO, or (c) incentive pay. 
 
It is clear that the cohesiveness model is not sufficient to 
provide a full explanation of the emergent system, nor does 
it suggest the complete design of a solution to the problem 
(any more than Bernoulli’s theorem is sufficient for the 
complete design of an airplane). Equity theory would be 
very useful for explaining the slowdowns during weekdays, 
when pay is low, and the speedups on the weekends, when 
pay is relatively high. It also predicts higher productivity and 
satisfaction if pay is increased. Expectancy theory would be 
useful for contrasting the manager’s motivation for “blowing 
the lid off” vs. taking a more participative approach. To 
calculate the manager’s motivation to follow each of the 
courses of action, we need to determine: (a) the valences of 
all possible outcomes, (b) the instrumentalities of 
eliminating the “punch-out system” for each of these 
outcomes, and (c) the manager’s expectancy that each course 
of action will lead to eliminating the “punch-out system.” 
Contrasting the two levels of motivation for the two courses 
of action enables us to predict which course the manager 
will take. 
 
The process of analyzing a problem by breaking it down into 
its components can clarify managers’ thinking about why 
they should choose a particular action. The variables of a 
conceptual model are these components. Knowledge of 
models can improve managers’ decisions. If we teach the 
models but not how to apply them, then we are wasting 
students’ time. If we do not teach the models, then we are 
wasting a vast amount of research. In either case, we deserve 
to be called “soft”. 
 

REFERENCES 
 
[1] Cohen, A. R., Fink, S. L., Gadon, H., and Willits, R. 

D., Effective Behavior in Organizations (Home- wood, 
IL: Irwin, 1984). 

 
[2] Homans, G. C., The Human Group (New York: 

Harcourt, Brace & World, 1950). 
 
[3] Homans, G. C., Social Behavior:  Its Elementary 

Forms (New York: Harcourt, Brace & World, 1961). 
 
[4] Kerlinger, F. N., Foundations of Behavioral Research 

(New York: Holt, Rinehart and Winston, 1973). 


	Table of Contents
	Volume 14, 1987
	Simulation Gaming as An Experimental Context for the Study of Multicriteria Decision Making
	How to Multiply Your Management Game
	Grading the Personnel In-Basket Exercise
	Simulation: The Players™ Perspective
	The Acute Susceptibility of Nominal Grouping to Negativity
	The Relative Value of the National ABSEL Meetings: An Analysis of Perceptions by Faculty and Deans
	A Survey of Behavioral Labs Used by American Business Schools
	A Structured Framework for Applying Conceptual Models to Problem Analysis: Hardening Up
	An Expert System Approach for Teaching Marketing Case Analysis
	From Theory to Practice: A Model for Teaching Beginning Advertising
	The Use of a Simple Forecasting Technique During an Interactive Computerized Business Game
	An Investigation of the Relationship Between Formal Planning and Simulation Team Performance and Satisfaction
	The Exponential Logarithm as an Algorithm for Business Simulations
	Learning macroeconomic Theory and Policy Analysis Via Microcomputer Simulation
	Laptop: A Principles of Marketing Simulation
	Teaching About Sampling in a Marketing Research Class
	Integrating Decision Support Systems and Business Games
	Demand Generation in a Service Industry Simulation: An Algorithmic Paradox
	ABSEL - At a Crossroads?
	Research on Predicting Performance in the Simulation
	An Expert System for Financial Planners
	"Developing Various Student Learning Abilities Via Writing, the Stock market Game, and Modified Marketplace Game in Beginning Macroeconomics"
	Professional Activity Reports: Getting back to Basics ΠA Preliminary Investigation
	"A Comparison of Performance, Attitudes, and behaviors of MBA and BBA Students in a Simulation Environment: A Preliminary Investigation"
	Decision Styles and Student Simulation Performance: A Replication
	Personal Power Strategies: An Experiential Exercise
	LTL I: A Trucking Simulation Game
	Total Enterprise Business Games: An Evaluation
	Can the Skill of Management be Taught?
	Developing Critical Thinking and Effective Communication
	Meaningful Notebooks and Enhanced Learning Using a Totally Menu-Driven Data base and Decision Support (DSS) Program
	Using a Computerized Menu-Driven Data Entry and fiWhat-Iffl Program as a Pedagogical Enhancement for Management Simulation
	Using a Joint Project Involving MBA Marketing Management and Undergraduate Marketing Research Students to Teach Marketing Research
	Testing the PAGE Technique: Results and Further Developments
	A Comparison of Student Perceptions with Accepted Expectations for Business Simulations
	"Conflict Management in Action: Verbal Strategies, Nonverbal behaviors and Conflict Styles"
	Dramatic Monologues as Surrogates for Experiential Learning
	Influencing One™s Superior: An Assessment and Exercise
	Personality Traits and Organizational Cultures: Two One-Hour Experiential Learning Exercises
	Computype: A Strategic Marketing Game
	Open System Simulations and Simulation Based Research
	"Teaching Forecasting to the Masses: Little Decision Support Training, But a lot of Answers"
	Goal Setting and Performance Evaluation with Different Starting Positions ΠThe Modeling Dilemma
	The Gamesmanship of Pricing: Building Pricing Strategy Skills Through Spreadsheet Modeling
	Teaching MPR Experientially Through the Use of Lotus 1-2-3
	Business Simulation Through Activities of a Manufacturing Company
	Simulated Stock Broker
	Teaching Employee Counseling Skills to Management Students: A Simulation
	Airline: A Strategic Management Simulation
	Developing Simulations and Experiential Exercises on the Personal Computer: Some Critical Issues
	A Powerful Tool ΠFor What?
	An Integrative Systems Approach to Simulating Free Enterprise in a Small Business Course in a Business School
	Profits: The False Prophet
	The Use of Expert Systems to Develop Strategic Scenarios: An Experiment Using a Simulated Market Environment
	An Interdisciplinary Workshop: Spreadsheet Modeling and Case Analysis Techniques for Beginning Executive MBA Students
	An Innovative Approach to an O. D. Class ΠThe Creative Interaction
	The Application of Spreadsheet Software Technology to Complete Taxpayer Elections
	Experiential Learning as the Cornerstone of an Industry Management Course
	"Identifying, Exercising and Enhancing Right Brain Skills in the Business Classroom"
	Utilizing Envisionary Technology in the Business Policy Course: An Experiential Pedagogy
	Business Simulation in the Policy Course: A Survey of American Assembly of Collegiate Schools of Business
	The Moving Van: Vehicle to Success of Disaster for the Two-Gender Work Force
	Oil and Gas Well Investment Analysis Using the Lotus 1-2-3 Decision Support System
	Team Cohesion Effects on Business Game Performance