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Theoretical optimisation of IT/IS investments: A research note
The justification of Information Technology (IT) is inherently fuzzy, both in theory and practice. The reason for this is due to the largely intangible dimensions of IT projects. In view of this, this research note presents the results of on-going research, in the application of Fuzzy Cognitive Mapping (FCM), as a tool to identify complex functional interrelationships associated with the justification of IT. This paper presents a theoretical functional model which describes these relationships, and by using an FCM, further interrelationships are developed in the context of justifying IT projects. A procedure which would address the optimisation of these intangible relationships in the form of a Genetic Algorithm (GA) is proposed as a process for Investment Justification
Graph ambiguity
In this paper, we propose a rigorous way to define the concept of ambiguity in the domain of graphs. In past studies, the classical definition of ambiguity has been derived starting from fuzzy set and fuzzy information theories. Our aim is to show that also in the domain of the graphs it is possible to derive a formulation able to capture the same semantic and mathematical concept. To strengthen the theoretical results, we discuss the application of the graph ambiguity concept to the graph classification setting, conceiving a new kind of inexact graph matching procedure. The results prove that the graph ambiguity concept is a characterizing and discriminative property of graphs. (C) 2013 Elsevier B.V. All rights reserved
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A revised perspective on the evaluation of IT/IS investments using an evolutionary approach
On-going research into the evaluation of Information Technology (IT) / Information Systems (IS) projects has shown that aerospace and supply chain industries are needing to address the issue of effective project investment in order to gain technological and competitive advantage. The evaluative nature of the justification process requires a mapping of interrelated quantities to be optimised. Earlier work by the authors (Irani and Sharif 1997) has presented a theoretical functional model that describes these relationships in turn. By applying a fuzzy mapping to these variables, the optimisation of intangible relationships in the form of a Genetic Algorithm (GA) is proposed as a method for investment justification. This paper revises and reviews these key concepts and provides a recapitulation of this optimisation problem in terms of long-term strategy options and cost implications.
Glossary of terms : DC = Direct Costs, FA = Financial Appraisal, FR = Financial Risks, FUR = Functional Risks, HC = Human Costs, IC = Indirect Costs, IR = Infrastructural Risks, OB = Operational Benefits, OC = Organisational Costs, PB = Project Benefits, PC = Project Costs, RF = Risk Factor, SB = Strategic Benefits, SM = Strategic medium-term benefit, SR = Systemic Risks, TB = Tangible Benefits, TC = Tangible Costs, TL = project lead time, TR = Technological Risks, V= Project Value
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