J
Jan Dul
Researcher at Erasmus University Rotterdam
Publications - 112
Citations - 6622
Jan Dul is an academic researcher from Erasmus University Rotterdam. The author has contributed to research in topics: Human factors and ergonomics & Creativity. The author has an hindex of 34, co-authored 107 publications receiving 5529 citations. Previous affiliations of Jan Dul include Vanderbilt University.
Papers
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Journal ArticleDOI
A strategy for human factors/ergonomics: developing the discipline and profession
Jan Dul,Ralph Bruder,Peter Buckle,Pascale Carayon,Pierre Falzon,William S. Marras,John R. Wilson,Bas van der Doelen +7 more
TL;DR: A strategy for the HFE community to strengthen demand and application of high-quality HFE, emphasising its key elements: systems approach, design driven, and performance and well-being goals is presented.
Book
Case Study Methodology in Business Research
TL;DR: A review of case studies in business research can be found in this article, where the authors present a case study of the influence of urban time access windows on a retailer's distribution costs.
Posted Content
Ergonomics Contributions to Company Strategies
Jan Dul,W. Patrick Neumann +1 more
TL;DR: Conceptual models are presented and examples are given to illustrate the present situation in which ergonomics is not part of regular planning and control cycles in organizations to ensure business performance and the desired situation, which is an integrated part of strategy formulation and implementation.
Journal ArticleDOI
Dutch Musculoskeletal Questionnaire: description and basic qualities.
TL;DR: It appears that most indices and factors show significant associations with low back and/or neck shoulder symptoms and can be used as a simple and quick inventory for occupational health services to identify worker groups in which a more thorough ergonomic analysis is indicated.
Journal ArticleDOI
Necessary Condition Analysis (NCA) Logic and Methodology of “Necessary but Not Sufficient” Causality
TL;DR: The necessary condition analysis (NCA) as discussed by the authors is a general and straightforward methodology for identifying necessary conditions in data sets, and it can be used to test or induce necessary but not sufficient statements.