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MINDS: A Flexible Interpretive Structural Modeling System

Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 2005
MINDS is a computer-assisted interpretive structural modeling (ISM) system which effectively and flexibly supports systems structural modeling. A description of a flexible ISM process, algorithms employed in MINDS and a system overview are given. The Procedures operate without the requirement of an a priori knowledge of MINDS structure.
A. Ohuchi, S. Kase, I. Kaji
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Hypothesis Formulation, Model Interpretation, and Model Equivalence: Implications of a Mereological Causal Interpretation of Structural Equation Models

Multivariate Behavioral Research, 2008
One can distinguish statistical models used in causal modeling from the causal interpretations that align them with substantive hypotheses. Causal modeling typically assumes an efficient causal interpretation of the statistical model. Causal modeling can also make use of mereological causal interpretations in which the state of the parts determines the
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Interpretive structural modelling: a methodology for structuring complex issues

Transactions of the Institute of Measurement and Control, 1988
This paper discusses the nature of Interpretive Structural Modelling (ISM) as methodology for dealing with complex issues. Aspects of managing complexity relating particularly to the use of ISM with a group of participants are explored. These include the interrelations between the issue, group and methodology, and between content, context, process and
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Tissue structural organization: measurement, interpretation, and modeling

SPIE Proceedings, 2010
Analysis of the first and second order statistical properties of light is a powerful means of establishing the properties of a medium with which the light has interacted. In turn, the first and second order statistical properties of the medium dictate the manner in which light interacts with the medium.
Donald Duncan   +3 more
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Interpretative Structural Modeling for Ceramic Grindability System

Key Engineering Materials, 2007
Based on systematic engineering theory and method, interpretative structural model of ceramic grindability system was set up. Property parameters, grinding parameters and grindability indexes were selected as system elements. The adjacency matrix between system elements was constructed.
Ai Bing Yu   +3 more
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An interpretive structural model of corporate governance

International Journal of Business Governance and Ethics, 2007
Corporate Governance (CG) issues have driven organisations to set their house right. There is a continual effort by organisations to build on a good framework of policies, not only as an undertaking enforced by a regulatory body, but also to sustain and win. However, these organisations are facing a dilemma in terms of their focus priority.
Dimple Grover   +2 more
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Interpretive Structural Modelling Using Fuzzy Linguistic Information

2019 4th International Conference on Information Systems and Computer Networks (ISCON), 2019
In this work a fuzzy set based interpretive structural modelling (ISM) has been proposed. The proposed technique can be assumed to be an extension to the Boolean logic-based ISM. In the existing ISM technique, the influence of one criterion on other criteria is Boolean in nature i.e.
Sanjay K. Tyagi   +2 more
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APLIKASI KAEDAH INTERPRETIVE STRUCTURAL MODELING UNTUK PEMBINAAN MODEL INTERGENERASI

The Asia Pacific Journal of Curriculum and Teaching
An intergeneration gap resulted from extended families being geographically separated, as well as age-segregation within communities which may be caused by residential planning, recreational facilities and educational institutions. This segregation has resulted in a serious breakdown of interaction and communication among the generations affected.
Ngu Kee Shing   +2 more
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Total Interpretive Structural Modelling: Evolution and Applications

2020
Total Interpretive Structural Modelling (TISM) is an approach that is used for theory building as it helps the researchers to answer the fundamental research questions of what, how and why. The method helps identify and define the variables, the relationship between them and the reason for causality between variables.
Shalini Menon, M. Suresh
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