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An Empirical Investigation of the Malcolm Baldrige National Quality Award Causal Model
Author(s) -
Wilson Darryl D.,
Collier David A.
Publication year - 2000
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.1540-5915.2000.tb01627.x
Subject(s) - quality (philosophy) , set (abstract data type) , test (biology) , structural equation modeling , process (computing) , causal model , quality management system , management science , empirical research , management , operations research , computer science , marketing , psychology , process management , knowledge management , operations management , quality management , business , management system , engineering , mathematics , economics , statistics , machine learning , paleontology , philosophy , epistemology , biology , programming language , operating system
The objective of this research is to test the theory and causal performance linkages implied by the Malcolm Baldrige National Quality Award (MBNQA). The survey instrument used a comprehensive set of 101 questions that were directly tied to specific criteria in the 1995 MBNQA Criteria. Results reported here represent the first published article that tests the MBNQA performance relationships and causal model using comprehensive measurement and structural models. In general, our research concludes that (1) The underlying theory of the MBNQA is supported that “leadership drives the system that causes results”; (2) Leadership is the most important driver of system performance; (3) Leadership has no direct effect on Financial Results but must influence overall performance “through the system”; (4) Information and Analysis is statistically the second most important Baldrige category; (5) the Baldrige category, Process Management, is twice as important when predicting customer satisfaction as when predicting financial results; and (6) a modified “within system” set of five Baldrige causal relationships is a good predictor of organizational performance.