The commoditization of IT: Evidence from a longitudinal text mining study

Sangno Lee, Jaeki Song, Jeff Baker, Youngjin Kim, James C. Wetherbe

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


While Information Technology (IT) has been identified by researchers as a source of strategic advantage for businesses, commentators have argued that this reality may not endure. These commentators argue that the growing ubiquity of IT makes it a commodity input rather than a scarce and valuable resource. We examine CEOs' Letters to Shareholders, one of the primary statements of corporate strategy, using both content analysis and latent semantic analysis, a text mining technique. Examining these letters allows us to investigate whether IT may be declining in strategic importance over time. We examine 160 annual reports from firms in the healthcare industry, covering a ten-year span of time, from 1997 through 2006. Our results indicate that the strategic emphasis placed on IT may be increasing, but its association with firm performance is declining. Our findings imply that as markets become more competitive, IT management capabilities and the strategic use of IT take on increasing importance. Our findings also imply that CEOs' perception of the importance of IT is a necessary but not a sufficient condition for improved firm performance. This article makes two primary contributions. First, we present an empirical examination of the issue of IT commoditization as a complement to existing anecdotal discussions. Second, we demonstrate the use of latent semantic analysis (LSA), a relatively new methodology for analyzing textual data, one that is evolving into an alternative to the well-known content analysis technique.

Original languageEnglish
Pages (from-to)221-242
Number of pages22
JournalCommunications of the Association for Information Systems
Issue number1
StatePublished - 2011


  • Annual reports
  • CEOs
  • Firm performance
  • IT commoditization
  • Longitudinal data
  • Senior management
  • Text mining


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