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Determinants of the Relative Advantage of a Structured SDM During the Adoption Stage of Implementation

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Abstract

This research investigates a new theoretical model for examining the relationships between user perceptions during innovation adoption. We have taken several innovation-related variables and constructed a framework for assessing the ability of a technology to improve worker performance. Prior research has not addressed the appropriate relationship between innovation adoption-related variables as applied to information systems development methodologies (SDM). This study attempts to use innovation-related variables created by Moore and Benbasat (Information Systems Research 2(3) (1991) 192–222) and Davis (MIS Quarterly 13(3) (1989) 319–340) to propose a framework useful by project managers in designing innovations that will successfully support the efforts of technology users. A framework is proposed, tested, and modified in the context of using an SDM to govern large systems development operation. Forty-seven users within a military software development organization were surveyed about their perceptions of a recently implemented structured SDM. A proposed model of innovation adoption perceptions was tested using correlation and partial least squares regression. Findings suggest a model for predicting the perceived relative advantage of SDMs in the adoption stage of their implementation which is useful in designing techniques in the IS development organizational function.

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References

  1. D.A. Adams and R.R. Nelson, Perceived usefulness, ease of use, and usage of information technology: A replication, MIS Quarterly 16(2) (1992) 227–247.

    Google Scholar 

  2. I. Ajzen and M. Fishbein, Understanding Attitudes and Predicting Social Behavior (Prentice-Hall, Englewood Cliffs, NJ, 1980).

    Google Scholar 

  3. O. Arndt and R. Sternberg, Do manufacturing firms profit from intraregional innovation linkages? An empirical based answer, European Planning Studies 8(4) (2000) 465–485.

    Google Scholar 

  4. E. Brown, New medical technology diffusion key element in health cost increases, Physician Executive 22(1) (1996) 56–57.

    Google Scholar 

  5. E.P. Bucy, Social access to the Internet, Harvard International Journal of Press/Politics 5(1) (2000) 50–61.

    Google Scholar 

  6. D.F. Burton and K.M. Hansen, German technology policy: Incentive for industrial innovation, Challenge 36(1) (1993) 37–47.

    Google Scholar 

  7. E. Cale, Jr., J. Kanter and D.B. Stoddard, Developing Applications with the User in Mind (Auerbach Publications, 1997).

  8. M. Carroll and B. Phillips, Survey on Assistive Technology Abandonment by New Users (Cooperative Agreement No. H133E0016) (National Institute on Disability and Rehabilitation Research, Washington, DC, 1993).

    Google Scholar 

  9. W.W. Chin and P.R. Newsted, Structural equation modeling analysis with small samples using partial least squares, in: Statistical Strategies for Small Sample Research, ed. R.H. Hoyle (Sage Publications, London, 1999) pp. 307–341.

    Google Scholar 

  10. R.B. Cooper and R.W. Zmud, Information technology implementation research: A technological diffusion approach, Management Science 36 (1990) 123–139.

    Google Scholar 

  11. L.J. Cronbach, Test validation, in: Educational Measurement, 2nd edn., ed. R.L. Thorndike (American Council on Education, Washington, DC, 1971).

    Google Scholar 

  12. F.D. Davis, A technology acceptance model for empirically testing new end-user information systems: Theory and results, Doctoral dissertation, Sloan School of Management, MIT, Cambridge, MA (1986).

    Google Scholar 

  13. F.D. Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly 13(3) (1989) 319–340.

    Google Scholar 

  14. F.D. Davis, R.P. Bagozzi and P.R. Warshaw, User acceptance of computer technology: A comparison of two theoretical models, Management Science 35(8) (1989) 982–1003.

    Google Scholar 

  15. S.M. Dekleva, The influence of the information systems development approach on maintenance, MIS Quarterly 16(3) (1992) 355–372.

    Google Scholar 

  16. B. Dhar and C.N. Rao, Trade relatedness of intellectual property rights, Science Communication 17(3) (1996) 304–325.

    Google Scholar 

  17. G.W. Downs and C.B. Mohr, Conceptual issues in the study of innovation, Administrative Science Quarterly 21 (1976) 700–714.

    Google Scholar 

  18. V.A. Durrington, J. Repman and T.W. Valente, Using social network analysis to examine the time of adoption of computer-related services among university faculty, Journal of Research on Computing in Education 33(1) (2000) 16–27.

    Google Scholar 

  19. J. Eaton and S. Kortum, Measuring technology diffusion and the international sources of growth, Eastern Economic Journal 22(4) (1996) 401–410.

    Google Scholar 

  20. J. Eaton and S. Kortum, International technology diffusion: Theory and measurement, International Economic Review 40(3) (1999) 537–569.

    Google Scholar 

  21. R.G. Fichman and C.F. Kemerer, Adoption of software engineering process innovations: The case of object orientation, Sloan Management Review (Winter 1993) 7–22.

  22. C. Finkelstein and P. Aiken, Building Corporate Portals Using XML (McGraw-Hill, New York, 1999).

    Google Scholar 

  23. J.S. Fiorelli and H. Margolis, Managing and understanding large systems change: Guidelines for executives and change agents, Organization Development Journal 11(3) (1993) 1–13.

    Google Scholar 

  24. S. Globerman, A. Kokko and F. Sjoholm, International technology diffusion: Evidence from swedish patent data, Kyklos 53(1) (2000) 17–38.

    Google Scholar 

  25. S.D. Gregor, Explanations from knowledge-based systems for human learning and problem solving, Doctoral dissertation, University of Queensland, Brisbane (1996).

    Google Scholar 

  26. S.D. Gregor and I. Benbasat, Explanations from intelligent systems: A review of theoretical foundations and empirical work, MIS Quarterly 23(4) (1999) 497–530.

    Google Scholar 

  27. V. Grover and M.K. Malhotra, Business process reengineering: A tutorial on the concept, evolution, method, technology, and application, Journal of Operations Management 15 (1997) 193–213.

    Google Scholar 

  28. J. Grudin, Systematic sources of suboptimal interface design in large product development organizations, Human–Computer Interaction 6 (1991) 147–196.

    Google Scholar 

  29. M. Hammer and J. Champy, Reengineering the Corporation (Harper Business, New York, 1993).

    Google Scholar 

  30. J. Hartwick and H. Barki,Explaining the role of user participation in information systems use, Management Science 40(4) (1994) 440–465.

    Google Scholar 

  31. M.T. Hill, Tax incentives and manufacturing profitability in Puerto Rico, Atlantic Economic Journal 18(2) (1990) 66–73.

    Google Scholar 

  32. H. Hotelling, The economics of exhaustible resources, Journal of Political Economics, 39(April) (1931) 137–175.

  33. P.J. Hu and P.Y.K. Chau, Examining the technology acceptance model using physician acceptance of telemedicine technology, Journal of Management Information Systems 16(2) (1999) 91–112.

    Google Scholar 

  34. M. Igbaria, T. Guimaraes and G.B. Davis, Testing the determinants of microcomputer usage via a structural equation model, Journal of Management Information Systems 11(4) (1995) 87–114.

    Google Scholar 

  35. M.F. Iyigun, Public education and intergenerational economic mobility, International Economic Review 40(3) (1999) 697–710.

    Google Scholar 

  36. H.K. Jacobsen, Technology diffusion in energy-economy models: The case of danish vintage models, Energy Journal 21(1) (2000) 43–71.

    Google Scholar 

  37. J. Kanter, Have we forgotten the fundamental it enabler: Ease of use, Information Systems Management 17(3) (2000) 70–77.

    Google Scholar 

  38. E. Karahanna, D.W. Straub and N.L. Chervany, Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs, MIS Quarterly 23(2) (1999) 183–213.

    Google Scholar 

  39. K. Koschatzky, Firm innovation and region: The role of space in innovation process, International Journal of Innovation Management2(4) (1998) 383–408.

    Google Scholar 

  40. T.H. Kwon and R.W. Zmud, Unifying the fragmented models of information systems implementation, in: Critical Issues in Information Systems Research, eds. Boland and Hirschheim (Wiley, New York, 1987).

    Google Scholar 

  41. K.A. Lieber, Grasping the technological peace, International Security 25(1) (2000) 71–104.

    Google Scholar 

  42. C.H. Loch and B.A. Huberman, A punctuated-equilibrium model of technology diffusion, Management Science 45(2) (1999) 160–177.

    Google Scholar 

  43. A. Malmberg and P. Maskell, Towards an explanation of regional specialization and industry agglomeration, European Planning Studies 5(1) (1997) 25–41.

    Google Scholar 

  44. J.-Y. Mao and I. Benbasat, The use of explanations in knowledge-based systems: Cognitive perspective and a process-tracing analysis, Journal of Management Information Systems 17(2) (2000) 153–179.

    Google Scholar 

  45. C. Mar-Molinero, Gender differences in undergraduate university admissions in the United Kingdom, Educational Studies 17(1) (1991) 49–63.

    Google Scholar 

  46. J. Martin, Information Engineering (Prentice-Hall, Englewood Cliffs,NJ, 1990).

    Google Scholar 

  47. T.E. McNamara, Rethinking proliferation in the post-cold war era: The challenge of technology, U.S. Department of State Dispatch 6(50–52) (1995) 928–931.

    Google Scholar 

  48. G.C. Moore, An examination of the implementation of information technology by end-users: A diffusion of innovations perspective, unpublished doctoral dissertation (University of British Columbia, 1989).

  49. G.C. Moore and I. Benbasat, Development of an instrument to measure the perceptions of adopting an information technology innovation, Information Systems Research 2(3) (1991) 192–222.

    Google Scholar 

  50. F.P. Morgeson, The structure and function of collective constructs: Implications for multilevel research and theory development, Academy of Management Review 24(2)(1999) 249–265.

    Google Scholar 

  51. J. Neter, W. Wasserman and M.H. Kutner, Applied Linear Statistical Models,3rd edn. (Irwin, Burr Ridge, IL, 1990).

    Google Scholar 

  52. New Republic, Let's make a deal, editorial 208(12) (1993) 9.

  53. J. Nunnally, Psychometric Theory (McGraw-Hill, New York, 1978).

    Google Scholar 

  54. R. O'Callaghan and P.J. Kaufmann, Adoption correlates and share effects of electronic data interchange systems in marketing channels, Journal of Marketing 56(2) (1992) 45–56.

    Google Scholar 

  55. H.P. Parette, Jr. and A. VanBiervliet, Tentative findings of a study of the technology needs and use patterns of persons with mental retardation, Journal of Intellectual Disability Research 36(1) (1992) 7–27.

    Google Scholar 

  56. M. Parthasarathy and A. Bhattacherjee, Understanding post-adoption behavior in the context of online services, Information Systems Research 9(4) (1998) 362–389.

    Google Scholar 

  57. M. Prescott and S. Conger, Information technology innovations: A classification by IT locus of impact and research approach, The Data Base for Advances in Information Systems 26(2/3) (1995) 20–41.

    Google Scholar 

  58. K. Ramamurthy, G. Premkumar and M.R. Crum, Organizational and interorganizational determinants of EDI diffusion and organizational performance: A causal model, Journal of Organizational Computing and Electronic Commerce 9(4) (1999) 253–285.

    Google Scholar 

  59. H.R. Rao, The technology transfer of expert systems, Information Systems Management 8(3) (1991) 42–47.

    Google Scholar 

  60. M.L. Riemer-Reiss and R.R. Wacker, Factors associated with assistive technology discontinuance among individuals with disabilities, Journal of Rehabilitation 66(3) (2000) 44–50.

    Google Scholar 

  61. E.M. Rogers, Diffusion of Innovations, 3rd edn. (The Free Press, New York, 1983).

    Google Scholar 

  62. N. Sakurai and G. Papaconstantinou, Impact of R&D and technology diffusion on productivity growth: Empirical evidence for 10 OECD countries, Economic Systems Research 9(1) (1997) 81–110.

    Google Scholar 

  63. G.R. Salancik and J. Pfeffer, A social information processing approach to job attitudes and task design, Administrative Science Quarterly 23 (1978) 224–252.

    Google Scholar 

  64. F.A. Shah and D. Zilberman, Technology adoption in the presence of an exhaustible resource: The case of groundwater extraction, American Journal of Agricultural Economics 77(2) (1995) 291–299.

    Google Scholar 

  65. A. Shampine, Compensating for information externalities in technology diffusion models, American Journal of Agricultural Economics 80(2) (1998) 336–337.

    Google Scholar 

  66. P.E. Spector, Summated Rating Scale Construction: An Introduction, Quantitative Applications in the Social Sciences Series, Sage University Paper 82 (Sage, Beverly Hills, CA, 1992).

    Google Scholar 

  67. R. Sternberg, Technologiepolitik und High-Tech Regionen – Ein Internationaler Vergleich, 2nd edn. (Munster, Hamburg, 1998).

    Google Scholar 

  68. G.H. Subramanian, A replication of perceived usefulness and perceived ease of use measurement, Decision Sciences 25(5/6) (September–December 1994) 863–874.

    Google Scholar 

  69. P. Todd and I. Benbasat, The use of information in decision-making: An experimental investigation of the impact of computer-based decision aids, MIS Quarterly 16(3) (1992) 373–393.

    Google Scholar 

  70. P. Todd and I. Benbasat, Evaluating the impact of DSS, cognitive effort, and incentives on strategy selection, Information Systems Research 10(4) (1999) 357–375.

    Google Scholar 

  71. L.G. Tornatzky and L. Klein, Innovation characteristics and innovation implementation: A metaanalysis of findings, IEEE Transactions on Engineering Management 29(1) (1982) 28–45.

    Google Scholar 

  72. R.W. Vossen, Relative strengths and weaknesses of small firms in innovation, International Small Business Journal 16(3) (1998) 88–94.

    Google Scholar 

  73. H.J. Watson, D.A. Annino, B.H. Wixom, K.L. Avery and M. Rutherford, Current practices in data warehousing, Information Systems Management 18(1) (2001) 47–55.

    Google Scholar 

  74. R.W. Zmud, An examination of ‘push-pull’ theory applied to process innovation in knowledge work, Management Science 30(6) (1984) 727–738.

    Google Scholar 

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Authors and Affiliations

  1. Management and Information Systems Department, Mississippi State University, P.O. Box 9581, MS, 39762, USA

    Gary F. Templeton

  2. Department of Management, College of Business, Auburn University, Auburn, AL, 36849, USA

    Terry Anthony Byrd

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  1. Gary F. Templeton
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Templeton, G.F., Byrd, T.A. Determinants of the Relative Advantage of a Structured SDM During the Adoption Stage of Implementation. Information Technology and Management 4, 409–428 (2003). https://doi.org/10.1023/A:1025186302598

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