A decision making model for
management executive planned
behaviour in higher education
by
Laurentiu David M.Sc.Eng., M.Eng., M.B.A.
Doctoral student at the Ontario Institute for Studies in Education
University of Toronto, CANADA
AGENDA
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Statement of purpose
Introduction
Brief literature review
Linear programming model
Method
Case Study
Research model
Theory of planned behaviour
Competing Value Framework
Mathematical application of the model
Data
Conclusion
The End
Statement of purpose

The purpose of this paper is two fold:
to develop a linear programming
solution for decision making
processes
 to offer a possible justification for the
existing gap between an agent
intention to pursue a particular
behaviour and the actual behaviour

Introduction
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In the past, the decision making processes were based on
intuition, experience and/or a mix of the two.
Even today, in spite of the development of a large array of
mathematical planning methods, business – planning
processes include subjective judgements making decisions
frequently vague.
As a result, it can be claimed that since a decision can be
vague it can be represented on fuzzy numbers.
Dyson (1980, p.264) purported that fuzzy programming
models should not be seen as a new contribution to multiple
objective decision making methods, but rather as a lead to
new conventional decision methods.
The present paper builds its structure on an existing linear
programming technique developed by Li and Yang (2004,
p.271) that takes into consideration a multidimensional
analysis of preferences in multiattribute group decision
making under fuzzy environments.
Brief Literature Review
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According to Treadwell (1995, p.93) who claimed that” the
dialogue between the human sciences and fuzzy set theory
has been scattered, unsystematic, and slow to develop” the
fuzzy set is not the panacea for dealing with the world of
uncertainty in certain terms, but it is a strong contender.
Smithson (1987, p.11) noted the fact that the principal value
he found in fuzzy set theory is that it generates alternatives
to traditional methods and approaches, thereby widening
the range of choices available to researchers.
According to Lazarevic and Abraham (2004, p.1) decision
processes with multiple criteria are dealing with human
judgement.
The human judgement element is in the area of preferences
defined by the decision maker (Chankong, & Haimes, 1983).
Kaufmann and Gupta (1998, p.7) considered that classical
social system models are suited for simple and isolated
natural phenomena.
Linear programming model
 

max   kl 
 1 ( k , l )  
   a 
m
1
3
j
j 1
( k , l ) 
2
ljL

 
 
 







 a kjL
 aljM
 a kjM
 aljM
 a kjM
 aljR
 a kjR
1
1
2
2
2
2
2
2
2
2
2

  m
m
m
m








 23   jL  aljL
 a kjL
  jM 1  aljM
 a kjM
  jM 1  aljM
 a kjM
  jR  aljR
 a kjR
1
1
2
2




 1 
j

1
j

1
j

1
j

1
( k , l ) 
( k , l ) 
( k , l ) 
( k , l ) 


m
1
3

j 1
j
a

2
kjL
 
2

Sk  Sl  kl  0

j 1
j
2
 
j  
m
1
 jL   j a *jL
 jM   j a *jM
1
 jM   j a
2
 jM  0
1
 jM  0
2
 jR  0
1
*
jM 2
 jR   j a *jR
kl  0
 jL  0
 


 








 aljL
 akjM
 aljM
 akjM
 aljM
 akjR
 aljR
1
1
2
2
 m


 jL akjL  aljL 
 j 1
kl  0
2
3


2
m
j 1
jM 1
a
kjM 1
2
2
 

 aljM

1
m
j 1
jM 2
2
a
kjM 2
2


  h
   a


 aljR



 

 aljM
2
m
j 1
jR
kjR

Method
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1. Evaluate the parameters of the decision maker
2. Determine the decision maker’s order of preferences
3. Determine the linguistic ratings of the variables (roles)
4. Map the decision maker opinion using the linguistic rating
for each of the variables (roles) under each attribute
(parameter)
5. Construct the fuzzy decision matrix and normalize the
positive trapezium fuzzy number decision matrix
6. Construct the linear programming formulation
7. Solve the system of equations
8. Obtain the weights vectors and the fuzzy positive ideal
solution
9. Calculate the distance of each variable (role)
10. The determine the ranking order of each variable (role)
Case Study
Place: Higher education institution
 Position: Management
 Decision Maker(s): 1
 Assumptions:
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•
•
•
Research model
Theory of Planned Behavior
Competing Value Framework
RREEMM
Research Model
CONTENT
DOMAINS
____________
ACADEMIC
INPUT
ADMINISTRATIVE
ACCOUNTABILITY
Background
Factors
_______________
Individual
Personality
Mood, emotion
Intelligence
Values,
Stereotypes
General Attitudes
Experience
Behavioral
Beliefs
Attitude
Toward the
Behavior
INNOVATOR
BROKER
Personal
Interest
__________
Stress
Provost – Related
Faculty Chair
Time/Personal
Scholarship
Salary/Recognition
Fundraising
PRODUCER
DIRECTOR
Normative
Beliefs
Subjective
norm
Intention
Behavior
__________
MENTOR
TECHNOLOGICAL
ADVANCEMENT
Social
Education
Age, gender
Income
Religion
Race, ethnicity
Culture
Information
Knowledge
Media
Intervention
Circumstances
FACILITATOR
_________
Institutional
Interest
Control
Beliefs
Perceived
Behavioral
Control
Actual
Behavioral
Control
MONITOR
COORDINATOR
OUTPUT
Theory of Planned Behaviour

The original linear formulation of the theory of planned behavior in its
simplest form is expressed by the following mathematical function:
BI  W1 ABb  e  W2 SN n  m  W3 PBC c    p 
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BI – behavioral intention,
AB – attitude toward behavior
b – the strength of each belief
e – the evaluation of the outcome or attribute
SN – social norm
n- the strength of each normative belief
m – the motivation to comply with the referent
PBC – perceived behavioral control
c- the strength of each control belief
p – the perceived power of the control factor
W – empirically derived weights
Competing Value Framework
FLEXIBILITY
Human Relations Model
Open System Model
Mentor
Innovator
Facilitator
Broker
INTERNAL
EXTERNAL
Monitor
Producer
Coordinator
Director
Internal Process Model
Rational Goal Model
CONTROL


  kl 
k ,l  
max
subject to:
   a
m
1
3
j
j 1
2
ljL
 
 

 
2
2
2
2
2
2
2
 a kjL
 aljM
 a kjM
 aljM
 a kjM
 aljR
 a kjR

1
1
2
2
m
m
m
m

 23  jL  aljL  a kjL    jM1  aljM1  a kjM1    aljM2  a kjM 2    aljR  a kjR   h
j 1
 k ,l 
j 1  k ,l 
j 1  k ,l 
 j 1 k ,l 


  a
m
1
3
j 1
j
2
kjL
 

 



 
2
2
2
2
2
2
2
 aljL
 a kjM
 aljM
 a kjM
 aljM
 a kjR
 aljR

1
1
2
2
m
m
m
m

 23  jL a kjL  aljL    jM1 a kjM1  aljM1   jM 2 a kjM 2  aljM2   jR a kjR  aljR   kl
j 1
j 1
j 1
 j 1


m

j 1
j
1
 jL   j a *jL
 jM   j a *jM
1
 jM   j a
2
 jR   j a *jR
kl  0
 jL  0
 jM  0
1
 jM  0
2
 jR  0
1
*
jM 2



Data
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A linear programming problem was developed once the data from
the trapezium fuzzy number matrix was introduced into the new set
of equations.
The objective function was then configured to be as it follows:
The objective function was subjected to a set of over 20 equations
containing over 30 distinct variables.
max 14  45  57  73  38  82  26  15 
Because of the lengthy aspect of equations the mathematical
calculus has been omitted from the paper.
Solving the linear equations using the Simplex method helped with
obtaining the and vectors.
The ranking order of the possible roles was obtained by calculating
the distances from the generated fuzzy positive ideal solution.
The generated ranking order places R4 – director role at the best
choice as the outcome maximizing executive behaviour when it
comes to offer a solution to the low enrolment situation since:
R4  R1  R5  R7  R3  R8  R2  R6
Conclusion
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The numerical example showed the fact that even
though the actor had a particular pre-action set of
roles’ preferences when it came to solve a particular
problem the final role choice differed from the
expected one.
The gap between the agents’ intentions and
behaviours can be even better exemplified when the
number of actors is increased.
In the envoi, the biggest assumption of the model is
that the agent final choice will correspond to the
mathematical solution found by employing the herein
proposed fuzzy logic anchored method of calculus.
THE END
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Thanking you for attending this
presentation I am inviting you in the
next 10 minutes to address your
questions!