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A review of the predictors, linkages, and biases in IT innovation adoption research

  • Research Review
  • Published:
Journal of Information Technology

Abstract

We present a review and analysis of the rich body of research on the adoption and diffusion of IT-based innovations by individuals and organizations. Our review analyzes 48 empirical studies on individual and 51 studies on organizational IT adoption published between 1992 and 2003. In total, the sample contains 135 independent variables, eight dependent variables, and 505 relationships between independent and dependent variables. Furthermore, our sample includes both quantitative and qualitative studies. We were able to include qualitative studies because of a unique coding scheme, which can easily be replicated in other reviews. We use this sample to assess predictors, linkages, and biases in individual and organizational IT adoption research. The best predictors of individual IT adoption include Perceived Usefulness, Top Management Support, Computer Experience, Behavioral Intention, and User Support. The best predictors of IT adoption by organizations were Top Management Support, External Pressure, Professionalism of the IS Unit, and External Information Sources. At the level of independent variables, Top Management Support stands as the main linkage between individual and organizational IT adoption. But at an aggregate level, two collections of independent variables were good predictors of both individual and organizational IT adoption. These were innovation characteristics and organizational characteristics. Thus, we can consistently say that generic characteristics of the innovation and characteristics of the organization are strong predictors of IT adoption by both individuals and organizations. Based on an assessment of the predictors, linkages, and known biases, we prescribe 10 areas for further exploration.

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Notes

  1. Individual: Research that examines adoption and diffusion of IT-based innovations by individuals.

  2. Organizational: Research that examines adoption and diffusion of IT-based innovations by organizations or organizational units (such as IS Departments).

  3. Since its founding in 1989, this interest group has been at the forefront of innovation diffusion research. DIGIT meets annually as a pre-conference event at ICIS (International Conference in Information Systems) and has consistently attracted the top diffusion researchers and explored the most promising advances in diffusion research.

  4. For clarity and readability, we use the convention of bolding and italicizing Independent Variables and capitalizing DEPENDENT VARIABLES.

  5. The publication years of the 99 studies in the review are: 1992 (9), 1993 (8), 1994 (8), 1995(18), 1996 (12), 1997 (14), 1998 (7), 1999 (3), 2000 (7), 2001 (5), 2002 (5), and 2003 (2).

  6. We use the term ‘significant’ to capture both the statistical meaning of significance (at P<0.05) for quantitative studies as well as the broader definition of ‘indicating importance’ for qualitative studies.

  7. We selected .80 as a cut-off because it is a reasonable indicator of a best predictor. While the specific cut-off could be contested, researchers could easily re-do the analysis with a more lenient or more stringent condition because all the data is in Appendix B.

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Acknowledgements

We thank the attendees and reviewers at DIGIT 2004 for their recommendations on an early version of this paper. We also thank Roy Schmidt and the two anonymous JIT reviewers for their insightful comments that significantly improved the paper.

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Authors and Affiliations

  1. College of Business Administration, University of Missouri-St. Louis, One University Boulevard, St. Louis, MO, USA

    Anand Jeyaraj, Joseph W Rottman & Mary C Lacity

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  1. Anand Jeyaraj
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  2. Joseph W Rottman
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  3. Mary C Lacity
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Correspondence to Mary C Lacity.

Appendices

Appendix A

Table A1 lists the citations of the final 99 studies.

Table a1 Description of 99 studies in the review
Full size table

Appendix B

Table B1 shows the independent variables used to examine the IT adoption in individuals and organizations. Here, IT adoption is at the aggregate (comprehensive) level for all dependent variables. The columns indicate:

  1. a)

    the number of times an independent variable was examined in individual IT adoption;

  2. b)

    the number of times an independent variable was found to be significant in individual IT adoption;

  3. c)

    the weight, calculated by (b)/(c) for individual IT adoption (predictive power);

  4. d)

    the number of times an independent variable was examined in organizational IT adoption;

  5. e)

    the number of times an independent variable was found to be significant in organizational IT adoption;

  6. f)

    the weight, calculated by (d)/(e) for organizational IT adoption (predictive power).

Table a2 Independent variables used to examine the IT adoption in individuals and organizations
Full size table

Appendix B is sorted by (a) to present the first findings. The independent variables that were well-utilized (examined 5 or more times) are italicized. Independent variables that were negatively related to IT adoption are indicated by a (−).

Appendix C

Table C1 shows the independent variables used to examine the dependent variable of PERCEIVED SYSTEM USE in individuals studies. The columns indicate:

  1. a)

    the number of times an independent variable was examined as a predictor to PERCEIVED SYSTEM USE;

  2. b)

    the number of times an independent variable was found to be significant;

  3. c)

    the weight, calculated by (b)/(c) for PERCEIVED SYSTEM USE (predictive power).

Table a3 Independent variables used to examine the dependent variable of PERCEIVED SYSTEM USE in individuals studies
Full size table

Appendix C is sorted by (a) to present the first findings. The independent variables that were well-utilized (examined 5 or more times) are italicized. Independent variables that were negatively related to IT adoption are indicated by a (−).

Appendix D

Table D1 shows the independent variables used to examine the dependent variable of INTENTION TO USE in individual studies. The columns indicate:

  1. a)

    the number of times an independent variable was examined as predictor of INTENTION TO USE;

  2. b)

    the number of times an independent variable was found to be significant in individual IT adoption;

  3. c)

    the weight, calculated by (b)/(c) for INTENTION TO USE (predictive power).

Table a4 Independent variables used to examine the specific dependent variable of INTENTION TO USE in individual studies
Full size table

Appendix D is sorted by (a) to present the first findings. The independent variables that were well-utilized (examined 5 or more times) are italicized. Independent variables that were negatively related to IT adoption are indicated by a (−).

Appendix E

Table E1 shows the independent variables used to examine the dependent variable of ADOPTION in Organizational Studies. The columns indicate:

  1. a)

    the number of times an independent variable was examined as predictor of ADOPTION;

  2. b)

    the number of times an independent variable was found to be significant;

  3. c)

    the weight, calculated by (b)/(c) for ADOPTION (predictive power).

Table a5 Independent variables used to examine the specific dependent variable of ADOPTION in organizational studies
Full size table

Appendix E is sorted by (a) to present the first findings. The independent variables that were well-utilized (examined 5 or more times) are italicized. Independent variables that were negatively related to IT adoption are indicated by a (−).

Appendix F

Table F1 shows the independent variables used to examine the dependent variable of DIFFUSION in Organizational Studies. The columns indicate:

  1. a)

    the number of times an independent variable was examined as a predictor of DIFFUSION;

  2. b)

    the number of times an independent variable was found to be significant;

  3. c)

    the weight, calculated by (b)/(c) for DIFFUSION (predictive power).

Table a6 Independent variables used to examine the specific dependent variable of DIFFUSION in organizational studies
Full size table

Appendix F is sorted by (a) to present the first findings. Independent variables that were negatively related to IT adoption are indicated by a (−).

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Jeyaraj, A., Rottman, J. & Lacity, M. A review of the predictors, linkages, and biases in IT innovation adoption research. J Inf Technol 21, 1–23 (2006). https://doi.org/10.1057/palgrave.jit.2000056

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