Investment Location Selection based on Economic Intelligence and Macbeth Decision Aid Model

Investment Location Selection based on Economic Intelligence and Macbeth Decision Aid Model

Mostafa Bachrane (Mohammadia Engineering School, Mohamed V University, Rabat, Morocco), Abdelilah Khaled (Ministry of Economy and Finances, Rabat, Morocco), Jamila El Alami (Mohammadia Engineering School, Mohamed V University, Rabat, Morocco) and Mostafa Hanoune (Laboratory of Modeling and Information Processing, University of Ben m'sick Casablanca, Casablanca, Morocco)
Copyright: © 2016 |Pages: 12
DOI: 10.4018/JITR.2016070103
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In this paper, the authors present a case study that aims to apply some sound MCDM techniques in the case of Economic Intelligence (EI) and show how the use of strategic information may help deciders to choose among geographic locations in which they could settle their investments. In this regard, the authors propose a new method that uses the multi-criteria decision support of MACBETH to tackle this issue. This method is used to rank thirteen countries likely to be chosen for location in order of preference from good to unfavourable. The integration of the MCDM in Economic Intelligence (EI) permits to rank countries of the Mediterranean according to their territorial competitiveness obtained through the global scores computed by the aforementioned technique of MACBETH. The results obtained allow the authors to affirm that France and Morocco have favourable strategic assets to attract foreign investment.
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Economic intelligence is a multifaceted concept, which is according to researchers, encompasses a modern vision of processing and information management aiming to intervene in unexpected incidents and to cope with the uncertainty of the environment. Several collective and individual authors have explored this broad concept, providing many definitions. The theme is therefore rich and wavers between the decision-making control of an entity and information systems.

We consider EI to be the component of business intelligence aimed at gaining strategic advantage, as proposed by Porter (1998). EI includes competitor intelligence as well as intelligence collected on customers, suppliers, technologies, environments, or potential business relationships (Guyton, 1962; Fair, 1966; Grabowski, 1987; Gilad, 1989). The Society of Competitive Intelligence Professionals (SCIP, 2008) defines EI as “a systematic and ethical process for gathering, analyzing and managing external information that can affect the company’s plans, decisions and operations.”

The concept of an “Economic Intelligence System” as developed by Luhn in 1958, was the first to define the need for updated observation of storage and filing for decision making and business' conduct. He argues that “Any system of communication for the conduct of business in the broadest sense can be considered an intelligent system ...” This definition is echoed in the works of (Simon1960) in management strategy to designate the process of environment's exploitation with the purpose to identify situations requiring action.

A thorough reading of the concept of intelligence highlights the work of Wilensky, author of the concept of organizational intelligence. Fundamentally, Wilensky's idea is that decision making requires collection, processing, interpretation and reporting. In the year (1967 Agular) developed the term “scanning business environment,” giving rise to the concept of strategic vigil. We have observed that the first interesting definitions revolving around EI go back to the 70s by (Albaum, 1964), under the appellation of “Environment scanning” while the first glimmering concept of EI appeared in Britain where corporate banking, insurance companies and financial institutions have adopted what is called “smart marketing” in the conduct of business. This term has been translated in Britain into business intelligence (Audrey Knauf 2010).

With the development of computer science, this versatile term has taken on a new dimension, as the “Marketing information system,” supportive of intelligent information systems in decision making. Prescott names this stage 1995 “Competitive data collection.” Most distinguished authors are (Clelond & King, 1975), (Montgomery & Weinberg, 1979).

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