Early vs. late adoption of radical information technology innovations across software development organizations: an extension of the disruptive information technology innovation model

  • Jessica Luo Carlo
    Department of Advertising, Public Relations, & Retailing Michigan State University East Lansing Michigan 48824 USA
  • James Gaskin
    Marriott School of Management Brigham Young University Provo Utah 785 TNRB USA
  • Kalle Lyytinen
    Department of Information Systems, Weatherhead School of Management Case Western Reserve University Cleveland Ohio 44106 USA
  • Gregory M. Rose
    College of Business Washington State University 14204 NE Salmon Creek Ave. Vancouver Washington 98686 USA

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<jats:title>Abstract</jats:title><jats:p>This paper extends the disruptive information technology innovation model (DITIM) by exploring the impact of adoption timing on innovation outcomes within software development organizations during a disruptive innovation cycle. The DITIM suggests that radical changes in computing platforms result in pervasive and radical innovations in software development organizations across three innovation types: base technologies adopted, services produced and processes adopted. Upstream attributes (amount and radicalness) of the base innovations impact effects in‐kind downstream (a.k.a., strong order effects) on services and processes. Extending these tenets of the DITIM, we posit that during disruptive information technology (IT) innovation, the temporal stage of innovation activity (early vs. late) by software development organizations will significantly impact four innovation characteristics: (1) adoption rate of radical IT innovations, (2) strong order effects on downstream innovations related to the amount of innovation, (3) perceived radicalness of innovations and (4) strong order effects on downstream innovations related to the amount of perceived radicalness of innovation. We examine these impacts by reanalysing a cross‐sectional study of 121 software development organizations that adopted internet computing as reported in the original data analysis of the DITIM. By splitting the data into early and late adopter groups, our moderation analysis shows significant differences between early and late adopting groups in each of the four hypothesized impacts. Specifically, the adoption rate of radical IT innovations, strong order effects on the amount of innovation, perceived radicalness of innovations and strong order effects on perceived radicalness were each found to differ between early and late adopters. However, it is also important to consider innovation type as three significant effects were in the opposite direction for process innovations. These findings suggest that IT‐innovation scholars and practitioners should carefully consider innovation timing and type when studying or managing radical IT innovation.</jats:p>

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