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 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 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 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

Method 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: Research model Theory of Planned Behavior Competing Value Framework RREEMM

Research Model

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 – 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

max subject to:

Data 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 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:

Conclusion 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.

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