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Planning Information Technology--Knowledge Worker Systems

Author

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  • Cheryl Gaimon

    (DuPree School of Management, Ivan Allen College of Management, Policy, and International Affairs, Georgia Institute of Technology, Atlanta, Georgia 30332-0520)

Abstract

A model is introduced to examine the long-term planning associated with the purchase and implementation of information technologies (IT) that indirectly contribute to output through enhancement of an organization's knowledge workers. For example, architectural firms in the service industry and manufacturers developing new or modified products invest in computer-aided design systems (CAD) to increase the volume of output generated by the engineering specialist. A critical element of the IT-knowledge worker model introduced is the inclusion of implementation-related dynamics. In particular we model both the short-term disruption to output that typically occurs at the start of the implementation period and the effect of worker learning during implementation. The ability of the organization to generate output is examined in relation to the levels of workforce, information technology, worker skill, and a variety of features of the IT. Seven production function attributes are introduced to quantify the manner in which output is generated for the IT-knowledge worker system. The production function is embedded into an optimization model in which profit is maximized. The formulation is dynamic in order to capture important elements of the decision-making environment, including the learning process, technological change, increasing wages, and changes in the labor supply. Optimal policies are obtained depicting the manner in which an organization should hire and fire its workforce and acquire information technology over time. A fundamental result is obtained establishing the existence of a complementary relationship between IT and knowledge workers. It is shown that a greater level of effective IT increases the desirability of hiring additional workers. In addition, as the ability of the IT to enhance knowledge worker productivity improves (reflecting IT features such as ease of use, functionality, connectivity, etc.), the desirability of hiring additional workers increases. Similarly, due to a high volume of workforce or a high level of worker skill, the desirability of acquiring IT increases. It is also shown that if an organization ignores the nature of the lagged effect of workforce learning, or if the impact of workforce learning on output is underestimated, then the projected volume of output over time is largely exaggerated. These results have important implications to management decision making with respect to effective implementation practices, technology choice issues, and workforce selection. Numerical results are generated to explore the effect of organizational size. It is shown that a smaller organization (as measured by the size of its workforce) relies more heavily on information technology to enhance worker productivity and generate output. Lastly, in response to higher wages, analytic results are derived demonstrating that an organization should reduce its level of hiring and its purchase of IT. Furthermore, numerical results show that an organization paying higher wages should allocate a greater amount of IT per worker to enhance productivity.

Suggested Citation

  • Cheryl Gaimon, 1997. "Planning Information Technology--Knowledge Worker Systems," Management Science, INFORMS, vol. 43(9), pages 1308-1328, September.
  • Handle: RePEc:inm:ormnsc:v:43:y:1997:i:9:p:1308-1328
    DOI: 10.1287/mnsc.43.9.1308
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    Citations

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    Cited by:

    1. D. J. Wu & Min Ding & Lorin M. Hitt, 2013. "IT Implementation Contract Design: Analytical and Experimental Investigation of IT Value, Learning, and Contract Structure," Information Systems Research, INFORMS, vol. 24(3), pages 787-801, September.
    2. 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.
    3. Bordoloi, Sanjeev & Guerrero, Hector H., 2008. "Design for control: A new perspective on process and product innovation," International Journal of Production Economics, Elsevier, vol. 113(1), pages 346-358, May.
    4. Meadows, Maureen & Merendino, Alessandro & Dibb, Sally & Garcia-Perez, Alexeis & Hinton, Matthew & Papagiannidis, Savvas & Pappas, Ilias & Wang, Huamao, 2022. "Tension in the data environment: How organisations can meet the challenge," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    5. I. Robert Chiang & Vijay S. Mookerjee, 2004. "A Fault Threshold Policy to Manage Software Development Projects," Information Systems Research, INFORMS, vol. 15(1), pages 3-21, March.
    6. 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.
    7. 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.
    8. Alessandro Arlotto & Stephen E. Chick & Noah Gans, 2014. "Optimal Hiring and Retention Policies for Heterogeneous Workers Who Learn," Management Science, INFORMS, vol. 60(1), pages 110-129, January.
    9. Rajiv D. Banker & Robert J. Kauffman, 2004. "50th Anniversary Article: The Evolution of Research on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science," Management Science, INFORMS, vol. 50(3), pages 281-298, March.
    10. Janice E. Carrillo & Cheryl Gaimon, 2000. "Improving Manufacturing Performance Through Process Change and Knowledge Creation," Management Science, INFORMS, vol. 46(2), pages 265-288, February.
    11. Cheryl Gaimon & Manpreet Hora & Karthik Ramachandran, 2017. "Towards Building Multidisciplinary Knowledge on Management of Technology: An Introduction to the Special Issue," Production and Operations Management, Production and Operations Management Society, vol. 26(4), pages 567-578, April.
    12. Joglekar, Nitin R. & Ford, David N., 2005. "Product development resource allocation with foresight," European Journal of Operational Research, Elsevier, vol. 160(1), pages 72-87, January.
    13. Linda Argote & Sunkee Lee & Jisoo Park, 2021. "Organizational Learning Processes and Outcomes: Major Findings and Future Research Directions," Management Science, INFORMS, vol. 67(9), pages 5399-5429, September.
    14. Sulin Ba & Jan Stallaert & Andrew B. Whinston, 2001. "Optimal Investment in Knowledge Within a Firm Using a Market Mechanism," Management Science, INFORMS, vol. 47(9), pages 1203-1219, September.

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