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Relating information technology investment to organizational performance: a causal model analysis

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  • Byrd, T. A.
  • Marshall, T. E.

Abstract

Corporations have invested billions of dollars in information technology (IT) over the last 20 years. There is much debate regarding the benefits accruing from these expenditures. Direct linkage between technology investment and increases in organizational performance and productivity has been extremely elusive. This research investigates the relationship between IT investment and organizational performance so that managers may better evaluate IT expenditures. With data on IT investment and organizational performance from 350 public companies over 4 years, this study uses structural equation analysis to empirically test a theoretical model composed of five IT investment variables and five organizational performance variables. The study found that the variable used to measure the extent to which users have access to IT was significantly and positively related to sales by employee, an organizational measure of labor productivity. Two other IT investment variables, the value of supercomputers, mainframes, and minicomputers and the percentage of IT budget spent on IT staff, were significantly and negatively associated with the sales by employee measure. Another IT variable, the IT budget as a percentage of revenue, was significantly and negatively associated with sales by total assets, a traditional measure of capital productivity. The last IT variable, the percentage of IT budget spent on IT staff training, was not related to any performance variable. Implications of these findings are discussed and, from a management perspective, postulations relating IT investment to organizational performance are stated. Researchers are provided with suggestions and encouraged to use these results to probe deeper into the relationship between IT investment and organizational performance.

Suggested Citation

  • Byrd, T. A. & Marshall, T. E., 1997. "Relating information technology investment to organizational performance: a causal model analysis," Omega, Elsevier, vol. 25(1), pages 43-56, February.
    • Handle: RePEc:eee:jomega:v:25:y:1997:i:1:p:43-56
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    References listed on IDEAS

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    Cited by:

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    2. Rajiv Kohli & Sarv Devaraj, 2003. "Measuring Information Technology Payoff: A Meta-Analysis of Structural Variables in Firm-Level Empirical Research," Information Systems Research, INFORMS, vol. 14(2), pages 127-145, June.
    3. António Madureira & Nico Baken & Harry Bouwman, 2011. "Value of digital information networks: a holonic framework," Netnomics, Springer, vol. 12(1), pages 1-30, April.
    4. Stefan Schweikl & Robert Obermaier, 2020. "Lessons from three decades of IT productivity research: towards a better understanding of IT-induced productivity effects," Management Review Quarterly, Springer, vol. 70(4), pages 461-507, November.
    5. Bozeman, Barry & Youtie, Jan & Jung, Jiwon, 2020. "Robotic Bureaucracy and Administrative Burden: What Are the Effects of Universities’ Computer Automated Research Grants Management Systems?," Research Policy, Elsevier, vol. 49(6).
    6. Martinez-Lorente, Angel R. & Sanchez-Rodriguez, Cristobal & Dewhurst, Frank W., 2004. "The effect of information technologies on TQM: An initial analysis," International Journal of Production Economics, Elsevier, vol. 89(1), pages 77-93, May.
    7. Tembi Maloney Tichaawa & Oswald Mhlanga & Sisa Sicwebu, 2017. "The Impact of Information Communication Technologies (ICTs) on Tourism Businesses in East London, South Africa," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 13(3), pages 18-29, JUNE.
    8. Kumar, Ram L., 1999. "Understanding DSS value: an options perspective," Omega, Elsevier, vol. 27(3), pages 295-304, June.

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