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Managing Knowledge-Based Resource Capabilities Under Uncertainty


  • Janice E. Carrillo

    () (Warrington College of Business, University of Florida, Gainesville, Florida 32611-7169)

  • Cheryl Gaimon

    () (DuPree College of Management, Georgia Institute of Technology, Atlanta, Georgia 30332-0520)


A firm's ability to manage its knowledge-based resource capabilities has become increasingly important as a result of performance threats triggered by technology change and intense competition. At the manufacturing plant level, we focus on three repositories of knowledge that drive performance. First, the physical production or information systems represent knowledge embedded in the plant's technical systems. Second, the plant's workforce has knowledge, including diverse scientific information and skills, to effectively operate the technical systems. Third, the firm's managerial systems embody knowledge in the form of goals, reward systems, and control and coordination systems. Taken together, we consider the technical systems, workforce knowledge, and the managerial systems as the plant's knowledge-based resource capability. Two normative models are introduced offering insight on how plant performance is impacted by investments in workforce knowledge (training) or the technical systems (process change). The models explicitly recognize that the outcome of investments in knowledge-based change is uncertain due to factors including technical problems, worker resistance, and limited financial resources. Also, we recognize that workforce knowledge may be deployed to mitigate the outcome uncertainty encountered with process change. Investments in knowledge-based change cannot be fully understood in isolation of the managerial systems. In one model, the plant manager is motivated by an incentive system that rewards the realization of a threshold goal, whereas in the other model the incentive system emphasizes the realization of meeting a particular target goal. We also investigate the impact of the manager's view of uncertainty (her willingness to absorb risk), which is influenced by the managerial systems. Results show that different characterizations of the managerial systems have a profound effect on managerial behavior and plant-level performance.

Suggested Citation

  • Janice E. Carrillo & Cheryl Gaimon, 2004. "Managing Knowledge-Based Resource Capabilities Under Uncertainty," Management Science, INFORMS, vol. 50(11), pages 1504-1518, November.
  • Handle: RePEc:inm:ormnsc:v:50:y:2004:i:11:p:1504-1518

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    References listed on IDEAS

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    Cited by:

    1. Cheryl Gaimon & Gülru F. Özkan & Karen Napoleon, 2011. "Dynamic Resource Capabilities: Managing Workforce Knowledge with a Technology Upgrade," Organization Science, INFORMS, vol. 22(6), pages 1560-1578, December.
    2. repec:eee:proeco:v:204:y:2018:i:c:p:411-420 is not listed on IDEAS
    3. Cuihong Li, 2013. "Sourcing for Supplier Effort and Competition: Design of the Supply Base and Pricing Mechanism," Management Science, INFORMS, vol. 59(6), pages 1389-1406, June.
    4. Vörös, József, 2013. "Multi-period models for analyzing the dynamics of process improvement activities," European Journal of Operational Research, Elsevier, vol. 230(3), pages 615-623.
    5. Cai, Shaohan & Yang, Zhilin, 2014. "On the relationship between business environment and competitive priorities: The role of performance frontiers," International Journal of Production Economics, Elsevier, vol. 151(C), pages 131-145.
    6. Yimin Wang & Wendell Gilland & Brian Tomlin, 2010. "Mitigating Supply Risk: Dual Sourcing or Process Improvement?," Manufacturing & Service Operations Management, INFORMS, vol. 12(3), pages 489-510, September.
    7. Martin-Tapia, Inmaculada & Aragon-Correa, Juan Alberto & Senise-Barrio, Maria Eugenia, 2008. "Being green and export intensity of SMEs: The moderating influence of perceived uncertainty," Ecological Economics, Elsevier, vol. 68(1-2), pages 56-67, December.
    8. Lai, Yung-Lung & Hsu, Maw-Shin & Lin, Feng-Jyh & Chen, Yi-Min & Lin, Yi-Hsin, 2014. "The effects of industry cluster knowledge management on innovation performance," Journal of Business Research, Elsevier, vol. 67(5), pages 734-739.
    9. Izlem Gozukara & Osman Yildirim, 2016. "Exploring the link between Distributive Justice and Innovative Behavior: Organizational Learning Capacity as a Mediator," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 6(2), pages 61-75, April.
    10. Larry J. Menor & M. Murat Kristal & Eve D. Rosenzweig, 2007. "Examining the Influence of Operational Intellectual Capital on Capabilities and Performance," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 559-578, May.
    11. Gülru F. Özkan-Seely & Cheryl Gaimon & Stylianos Kavadias, 2015. "Dynamic Knowledge Transfer and Knowledge Development for Product and Process Design Teams," Manufacturing & Service Operations Management, INFORMS, vol. 17(2), pages 177-190, May.
    12. White, Sheneeta W. & Badinelli, Ralph D., 2012. "A model for efficiency-based resource integration in services," European Journal of Operational Research, Elsevier, vol. 217(2), pages 439-447.


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