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Do Arbitrators Use Just Cause Standards in Deciding Discharge and Discipline Cases? A Test

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  • David Dilts
  • James Moore
Abstract
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Suggested Citation

  • David Dilts & James Moore, 2009. "Do Arbitrators Use Just Cause Standards in Deciding Discharge and Discipline Cases? A Test," Journal of Labor Research, Springer, vol. 30(3), pages 245-261, September.
  • Handle: RePEc:spr:jlabre:v:30:y:2009:i:3:p:245-261
    DOI: 10.1007/s12122-009-9065-6
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    References listed on IDEAS

    as
    1. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    2. Carbonneau, Real & Laframboise, Kevin & Vahidov, Rustam, 2008. "Application of machine learning techniques for supply chain demand forecasting," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1140-1154, February.
    3. Chi Mint Tam & Thomas Tong & K. K. Chan, 2006. "Rough set theory for distilling construction safety measures," Construction Management and Economics, Taylor & Francis Journals, vol. 24(11), pages 1199-1206.
    4. Robert J. Thornton & Perry A. Zirkel, 1990. "The Consistency and Predictability of Grievance Arbitration Awards," ILR Review, Cornell University, ILR School, vol. 43(2), pages 294-307, January.
    5. Srinivasan Ragothaman & Bijayananda Naik & Kumoli Ramakrishnan, 2003. "Predicting Corporate Acquisitions: An Application of Uncertain Reasoning Using Rule Induction," Information Systems Frontiers, Springer, vol. 5(4), pages 401-412, December.
    6. Eisenbeis, Robert A, 1977. "Pitfalls in the Application of Discriminant Analysis in Business, Finance, and Economics," Journal of Finance, American Finance Association, vol. 32(3), pages 875-900, June.
    7. Geng Cui & Man Leung Wong & Hon-Kwong Lui, 2006. "Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming," Management Science, INFORMS, vol. 52(4), pages 597-612, April.
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