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A Methodology for Constructing Collective Causal Maps *
Author(s) -
Scavarda Annibal José,
BouzdineChameeva Tatiana,
Goldstein Susan Meyer,
Hays Julie M.,
Hill Arthur V.
Publication year - 2006
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.2006.00124.x
Subject(s) - computer science , construct (python library) , causal model , brainstorming , causal loop diagram , field (mathematics) , data science , mind map , concept map , knowledge management , management science , risk analysis (engineering) , artificial intelligence , system dynamics , engineering , business , medicine , mathematics , pathology , pure mathematics , programming language
This article develops a new approach for constructing causal maps called the Collective Causal Mapping Methodology (CCMM). This methodology collects information asynchronously from a group of dispersed and diverse subject‐matter experts via Web technologies. Through three rounds of data collection, analysis, mapping, and interpretation, CCMM constructs a parsimonious collective causal map. The article illustrates the CCMM by constructing a causal map as a teaching tool for the field of operations management. Causal maps are an essential tool for managers who seek to improve complex systems in the areas of quality, strategy, and information systems. These causal maps are known by many names, including Ishikawa (fishbone) diagrams, cause‐and‐effect diagrams, impact wheels, issue trees, strategy maps, and risk‐assessment mapping tools. Causal maps can be used by managers to focus attention on the root causes of a problem, find critical control points, guide risk management and risk mitigation efforts, formulate and communicate strategy, and teach the fundamental causal relationships in a complex system. Only two basic methods for creating causal maps are available to managers today—brainstorming and interviews. However, these methods are limited, particularly when the subject‐matter experts cannot easily meet in the same place at the same time. Managers working with complex systems across large, geographically dispersed organizations can employ the CCMM presented here to efficiently and effectively construct causal maps to facilitate improving their systems.