In order to use the fuzzy model there was built the rules database, as in figure 7.
Figure 7. The data base of rules
Based on the rules database and the membership functions defined above, there were obtained the 3D surfaces representing the dependency of the output on two of the inputs.
Figure 8. Scope-Cognitive Means-Actions relationship surface
It can be observed Minimum Effective Actions and Non Efficient Actions, with a significant risk associated to human action, even if the scope is valuable (high levels of the functioning probabilities), the lack of the cognitive means, significantly influences the situation.
Figure 9. Effects-Means-Actions relationship surface
It can be analyzed the efficiency of the actions that tend to Very Good Efficient Actions, when we have Excellent means, Minor Importance effects that do not cause long term interruptions of productive activities with impact on the all elements of the working system.
Figure 10. Effects-Cognitive Means-Actions relationship surface
It can be observed the high level of the risk associated to Non Efficient Actions when the cognitive means are Inadequate, and the effects generate malfunctions in the entire working system.
Figure 11. Scope-Effects- Actions relationship surface
Also, it can be emphasized the importance of the appropriate identification and evaluation of the effects and framing them in a class of importance depending on the peculiarities of the working system.
The evaluation of the human action performing in a working system, using fuzzy logic, generates interpretations concerning the quality improvement of the working process. The actions will concentrate, for examples, on the organization
at the working place (working groups dimensioned correctly, special intercession team), purchase of appropriate equipment, spare parts materials, necessity of collaboration regarding the improvement of the quality at the suppliers, staff- forming-training program.
References
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2. A. Ionica, S. Irimie, C. Jujan, The Influence of the Human Factor on the Underground Working System, Proceedings of the AMIREG, 2004 Advances in Mineral Resources Management and Environmental Geotechnology, Creta, Grecia, ISBN: 960-88153-0-4, pp. 635-640, (2004).
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6. M. Leba, E. Pop, A. Badea, Adaptive Software Oriented Automation for Industrial Elevator, Proceedings of the 11th International Conference on Automatic Control, Modelling & Simulation, Istanbul, Turkey, ISBN 978- 960-474-082-6, ISSN 1790-5117, pp.128-133, BLX36, (2009).
7. Yager, RR, Decision making with fuzzy probability assessments Source: IEEE TRANSACTIONS ON FUZZY SYSTEMS Volume: 7 Issue: 4 pp. 462-467 (1999)
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Modeling and Prediction, Phoenix AZ
Source: SOCIAL COMPUTING, BEHAVIORAL MODELING AND PREDICTION pp. 89-99 (2008)
MAIN FACTORS TO MAINSTREAM DEBT FOR SHAPING CAPITAL STRUCTURE IN SERVICE, TRADE,
TRANSFORMATION, CONSTRUCTION SECTORS, MINING INDUSTRY AND TELECOMUNICATION IN MEXICO
JUAN GAYTÁN CORTES*, JOSÉ SÁNCHEZ GUTIÉRREZ University Center of Economic-Administrative Sciences
Universidad de Guadalajara, Guadalajara, Jalisco, México
JOEL BONALES VALENCIA
Institute of Economic and Enterprise Investigations Universidad Michoacana de San Nicolás de Hidalgo
Morelia, Michoacán, México
ABSTRACT
The purpose of this study was to construct a mathematical model to identify key institutional factors of the country and the company, its mathematical relationship and their discrepancies, by incorporating debt, forming the capital structure of the service, trade, processing, construction and extractive industries as well as telecommunications.
The context that enabled the analysis of this phenomenon was shaped by companies in each sector that were quoted within the Mexican Stock Exchange (MSE) in the period 2000-2007.
The financial data was sorted, graphed and analyzed. Afterwards, it was used to power the E-Views version 4.1. The long-term debt was the dependent variable. The independent variables were the main factors of the country and company. The positive or negative mathematical relationship was calculated, using the statistical technique which is known as panel data. Finally, discrepancies between the sectors were shown.
Keywords: capital structure, institutional factors of the company, the country's institutional factors, sectorial differences.
* This work was to construct a mathematical model to identify key institutional factors of the country and the company, its mathematical relationship and discrepancies, by including debt, forming the capital structure of the service, trade, processing, construction, mining and telecommunications industries in Mexico.
1. Introduction
The theoretical models which were developed in the last half century have sought to validate and generalize, sometimes, the irrelevance thesis by Modigliani and Miller [20], They have tried to adapt some others as well, the maximum indebtedness by Modigliani and Miller [18] to the empirical evidence that the market limits the indebtedness capacity of the company. From the convergence of two research lines, in the 60’s came a renewed theory of capital structure, which postulates the existence of an optimal capital structure as the suitable solution to the problem.
Decisions of the capital structure are even more complicated when examined in an international context, particularly in developing countries where markets are characterized by the limitations of government institutions, Boateng [4].
We reviewed the theories that have been addressed on the factors and the relationship and influence they exert by determining the capital structure, mentioning the following ones among the others: theory of optimal capital structure, tax base theory, theory of asymmetric information, hierarchical selection theory and pecking order theory (POT). Moreira and Mesquita [21]
found evidence in favor of this theory. Fama and French [9] found a positive relationship between debt and profitability supporting the theory (POT). Brown and Lima [3], found that small firms borrow more short-term and verified a positive relationship between total debt and long term confirming the theory (POT). In the theory of agency costs and the theory of free cash flow, Faulkender and Petersen [11] developed a model to analyze the funding’s source effect for firms in determining their capital structure.
We also reviewed empirical studies that support these theories, highlighting among others the study by Rajan and Zingales [23], and the Wald’s study [31].
These studies provided empirical evidence for G7 countries, Bradley, Harrell and Kim [2] Kester [18], Van der Wijst the [30], Chung [8], Filbeck and Gorman [13]. Just as Booth, Aivazian, Demirguc-Kunt, and Maksimovic [6], who examined the determinants of capital structure in ten developing countries during 1980-1990 and providing the evidence that the determinants are similar in developed countries. Chang and Maquieira [6] replicated the study by Rajan and Zingales [24], for Latin American companies issuing American Depositary Receipt, ADR, checking the sign and significance of three out of four determinants studied: growth opportunities (-), size (+) and profitability (-).
Famá and Perobelli [10], using the study of Titman and Wessels [29], found a negative relationship between resource growth, size and profitability with the degree of short-term debt. Gomes [15], Fried and Lang [14], considering the size, growth, risk and industry, found a negative relationship between debt and profitability factors, growth and size.
In relation to the sector, empirical evidence has been presented by Gupta [16], Scott and Martin [28], Schwartz and Aronson [27] and Archer and Faerber [1], among others, showing that the kind of industry influences financial structure.
Authors Ferri and Jones [12], using data on U.S. companies re-examined this relationship and concluded that there is a definite relationship between capital structure and the sector or industry classification. Moreover, the evidence presented dissenting studies by Remmers, Stonehill, Wright and Beekhuis [23], who argue that the kind of industry is not a determinant element of capital structure.