Educational Technology & Society 2(1) 1999
ISSN 1436-4522

Contingent innovation-decisions, infrastructure, and information technologies

Kay A. Persichitte
Associate Professor, Department of Educational Technology
College of Education, University of Northern Colorado
Greeley, CO 80639 USA
TEL: +1 (970) 351-2913
Email: Persi@edtech.UNCo.edu

Donald D. Tharp
Assistant Professor, United States Air Force Academy

Edward P. Caffarella
Professor, University of Northern Colorado


ABSTRACT

This paper details a study of 465 schools, colleges, and departments of education (SCDEs) regarding the adoption of information technologies contingent upon the availability of the necessary infrastructure. Three SCDE groups were investigated: SCDE faculty, SCDE students, and the SCDE (combined faculty and student users). Hypothesis testing resulted in statistically significant differences for the SCDE faculty user groups and the SCDEs, but not for the SCDE student user groups. For both cases of statistical difference, group use was significantly greater for institutions which provided adequate infrastructure when compared to group use at institutions without adequate infrastructure. Discussion is included for the no significant difference group, SCDE students, and recommendations for further research are provided.

Keywords: technology adoption; information technology utilization; contingent innovation-decisions


* Manuscript received Aug. 29, 1998; revised Nov. 19, 1998

Introduction

The use(s) of computer-based technologies and the significant financial requirements associated with hardware, software, and infrastructure development continue to be high stakes considerations for institutions of higher education. According to Green and Gilbert (1995), by the end of 1994 college administrators had allocated sufficient moneys to connect 63% of all US higher education faculty and administrators to campus networks. By the end of 1995, US institutions of higher education had established a 6.5 to 1 ratio of students to computers (Green, 1996b). Current administrations of higher education institutions and other external constituents are seeking evidence of the benefit(s) of these investments (DeLoughry, 1993; McCandless, 1995; Resmer, 1997).

This paper summarizes a large-scale research study conducted with US higher education teacher preparation institutions regarding contingent innovation-decisions and the availability of adequate information technologies infrastructure. "Contingent innovation-decisions are choices to adopt or reject that can be made only after a prior innovation-decision" (Rogers, 1995, p. 30). In this study, the researchers investigated preservice teacher faculty and preservice student adoption of information technologies based on the prior institutional innovation-decision related to the development of an infrastructure that provided access to such information technologies. The specific information technologies investigated in this study included computers, e-mail, the World Wide Web (WWW), the Internet, and other interactive communication media. The technologies were loosely grouped, for data collection purposes, into e-mail and Web technologies and analyzed collectively as interactive information technologies.


Problem/Significance

To be adopted for use by faculty, technology must be available, pervasive, non-intrusive, easy to use, and reliable (Graves, 1993). Recent research, which has targeted technology use in higher education (Green, 1997, 1996a, 1996b; Green & Eastman, 1994), has focused on the university as a composite of all colleges and disciplines. These authors believe that technology use is particularly important to institutions of higher education which prepare future educators. Schools, Colleges, and Departments of Education (SCDEs) must assess whether adequate infrastructure is in place for the use of information technologies (specifically, e-mail and Web technologies) as well as the impact of existing institutional infrastructure on adoption decisions by faculty and students. This study also investigated the possibility that preservice student or preservice faculty use of information technologies might be contingent upon the other group's adoption decision.

Literature Base

According to Rogers (1983, 1995), SCDE faculty and student adoption of information technologies is contingent upon the administration of the institution providing the infrastructure necessary to access and use information technologies. While that might seem to be a given in our technological society, some question whether the investment in such infrastructures reaps adequate benefit. Green and Gilbert (1995) state that administrators in higher education settings require data to support decisions they make in the budgeting of billions of dollars for technology. For SCDEs, the importance of such technology planning is heightened as they attempt to meet National Council for Accreditation of Teacher Education (NCATE) standards for teacher preparation (NCATE, 1993). The adoption, utilization, and subsequent integration of information technologies by SCDE faculty and students is, consequently, contingent upon funding the necessary infrastructure (Cummings, 1995).

However, once such infrastructure is in place, the quality and quantity of the use(s) of information technologies remains an additional consideration for institutional planners. The concept of critical mass of adopters provides some orientation for decision making about further investment in information technologies. Critical mass theory lays the foundation for understanding the size of adoption audience necessary for the adoption rate to become self-sustaining (Rogers, 1995; Markus, 1990; Oliver, Marwell, & Teixeira, 1985).

The work of Kenneth Green (1997, 1996a, 1996b) and colleagues (Green & Gilbert, 1995; Green & Eastman, 1994) has documented critical mass levels for US institutions of higher education and both student and faculty access for computer-based technologies. Access research has investigated all three groups since access at the institutional level does not necessarily result in access for faculty or students (Markus, 1990; Rogers, 1983). Other research has documented critical mass for access to computer-based technologies in SCDEs (Tharp, 1997; Persichitte, Tharp, & Caffarella, 1997).

There is substantial evidence beyond this research that institutions of higher education have reached critical mass for their adoption of computer-based technologies (DeLoughry, 1996; Denk, Martin, & Sarangarm, 1993). Yet, Valente (1993) argues that there is still insufficient data about critical mass to evaluate the adoption of information technologies and Rogers (1995) recommends that research be conducted regarding the resultant impact of complete information technologies infrastructure on contingent innovation-decisions.


Methodology

This study was an investigation of the impact of existing infrastructure (adequate or inadequate) on the adoption of e-mail and Web technologies by SCDE institutions (combined faculty and student users), SCDE faculty, and SCDE students. Adequate or inadequate infrastructure was determined by coding responses to five items from a 23-item questionnaire. These items were coded as either available (+1) or not available (0), then summed for an institutional infrastructure score. If the sum was three or greater, the institution was identified as providing adequate infrastructure for the defined information technologies.

A well-respected professional teacher education organization commissioned the design, distribution and analysis of a survey to gather data about technology use within SCDEs. This survey was mailed (by the professional organization) to 744 member institutions. These researchers received 465 (63%) useable responses from the United States, Guam, and Puerto Rico. Of the 23 questionnaire items, 15 were used to determine SCDE user institutions (combined faculty and student users), SCDE faculty user institutions, and SCDE student user institutions. Details of the coding are available if requested (Tharp, 1997). The professional teacher organization was interested in documenting technology use at the SCDE level, thus the combined faculty and student user group was analyzed. Individual group analyses, for faculty and students, were conducted to investigate weighted influences at the SCDE level as well as to consider the effect of one group's adoption decision on the other group's adoption decision.

Description and analyses of the additional data collected are available in report form (Persichitte et al., 1997). The research question and three associated hypotheses related to contingent innovation-decisions were tested using the Chi-square test of independence. The alpha level was set a priori at .05.


Limitations

The limitations of this study included the sample identification, survey instrument and distribution, and knowledge base of survey respondents. The sample was the membership of an intact United States professional organization consisting of private and public four-year schools, colleges, and departments of education. This sample places a limitation on generalizing to SCDEs that are not members of the intact organization.

The research team created the survey instrument using a process that included over fifteen iterations of development. Two recognized experts in the field of change in schools examined the survey for face and content validity. The SCDE Technology Survey was sent to member institutions of the national organization as part of a lengthy data collection instrument and submission was completely voluntary. The potential for a low response return rate with a mailed survey jeopardizes representativeness and generality due to the selective nature of the non-respondents.

The survey respondent's knowledge base is a potential limitation. An administrator within the member SCDE completed the larger data collection instrument. One potential limitation is that the administrator may not have known enough about information technologies to respond accurately to the Technology Survey. A second potential confound is that the administrator may have completed the survey based upon their personal beliefs or perceptions of the SCDE's use of information technologies and not upon actual use of information technologies within the SCDE. Inter-woven in this limitation is that the administrator answered on behalf of SCDE students and faculty with expected biases based on the administrator's personal knowledge, observation, and perceptions of information technologies use within the SCDE.


Results

The research question was: Is the number of SCDEs which have reached critical mass for their use of e-mail and Web technologies with adequate infrastructure significantly different from the number of SCDEs which have reached critical mass for their use of e-mail and Web technologies with inadequate infrastructure?

A Chi-square test of independence was conducted to determine if the proportions of users and nonusers were significantly different for SCDEs (combined faculty and student users) with adequate infrastructure compared to institutions with inadequate infrastructure. The resulting Chi-square statistic of 14.672 (p<.001) was found to be significant. These results indicate that the combined faculty and student users with adequate infrastructure (406/455; 89%) were significantly greater than the combined faculty and student users without adequate infrastructure (5/10; 50%). The practical significance of this finding lies more in the comparison of the actual number of adequate/inadequate infrastructure institutions. Only 2% of the SCDEs were identified as lacking adequate infrastructure for the use of information technologies, but half of those institutions still met the criteria for being SCDE users of information technologies. This finding indicates that contingent innovation-decisions related to the adoption of information technologies is not solely dependent on adequate infrastructure in all settings.

Similarly, a Chi-square test of independence was conducted to determine if the proportions of faculty users and nonusers were significantly different for SCDEs with adequate infrastructure compared to faculty users and nonusers at SCDEs with inadequate infrastructure. The resulting Chi-square statistic of 34.929 (p<.001) was found to be significant. In this sample, faculty use was significantly greater for institutions that provided adequate infrastructure (432/455; 95%) when compared to SCDE faculty use at institutions without adequate infrastructure (5/10; 50%). It is interesting to note that the faculty user percentage at institutions with adequate infrastructure is higher than the comparative SCDE percentage (95% vs. 89%), but the faculty user percentage at institutions without adequate infrastructure is the same (50%).

Lastly, a Chi-square test of independence was conducted to determine if the proportions of SCDE student users and nonusers were significantly different for institutions with adequate infrastructure compared to SCDE student users and nonusers at institutions with inadequate infrastructure. The resulting Chi-square statistic of .04551 (p<.001) was not found to be significant. For this sample, student use was not significantly greater for institutions that provided adequate infrastructure (418/455; 92%) when compared to student use at institutions without adequate infrastructure (9/10; 90%). This data indicates that student use of information technologies is much more common than faculty use within institutions that lack adequate infrastructure. For the preservice students in this study who lacked institutional infrastructure, their adoption decision was clearly not contingent on access to infrastructure in the institutional setting. Also of interest is that the number of faculty user institutions does exceed the number of student user institutions (432 vs. 418) in a time when attitudes toward technology use are perceived to be positively skewed toward students.


Discussion

For combined SCDE faculty and student user groups and for the SCDE faculty user groups, a significantly greater proportion of users were identified at institutions that provided adequate infrastructure for the use of e-mail and Web technologies. This supports previous research which indicates that if we are to facilitate technology integration within education programs and courses, access to adequate infrastructure is essential to increasing technology use within teacher preparation settings (Arms, 1992; Awbrey, 1996, Boettcher, 1995; Cummings, 1995; DeLoughry, 1993).

The proportion of SCDE student user groups for institutions with adequate infrastructure for the use of e-mail and Web technologies was not statistically different from the proportion of SCDE student user groups for institutions without adequate infrastructure. Results of these statistical analyses did not support the hypothesized contingent innovation-decision as dependent on infrastructure. It should be noted, though, that cell sizes for the "inadequate infrastructure" student user groups were extraordinarily low for the sample size of 465; only one nonuser with inadequate infrastructure and nine users with inadequate infrastructure. The lack of statistical support for differences in SCDE student users may be explained by the cell sizes for the "no infrastructure" groups being extraordinarily low for the sample size of 465 (Marascuilo & McSweeney, 1977). An alternative explanation might lie in the combination of the innovation selected (interactive information technologies) and SCDE student user profiles. Interactive information technologies are relatively new innovations which are quickly diffusing (with considerable publicity) into American society and educational environments (Geoghegan, 1994). According to MacKnight (1995) and McCandless (1995), the students' awareness of the innovation through publicity, but lack of exposure to, or experience using, relatively new technologies may result in the ideal conditions for the majority of student adoption to be correlated with a contingent innovation-decision that depends on access to information technologies, but not necessarily institutional infrastructure.


Conclusion

While it would seem 'common sense' today to expect that the use of information technologies is contingent on adequate infrastructure, the results of this study provide a snapshot of technology adoption that has implications for infrastructure planning at the institutional level. This study confirms that contingent innovation-decisions occur as part of the faculty adoption process for information technologies (e-mail and Web technologies) and the requisite infrastructure for their use must be in place for adoption to increase. It is unclear, however, whether student adoption of these technologies is dependent on institutional infrastructure or simply dependent on access to the technologies. Essentially all SCDEs have provided the necessary infrastructure; those with infrastructure in place have greater proportions of users for combined SCDE student and faculty use, as well as for SCDE faculty use.

This study indicates that faculty adoption of information technologies is contingent on adequate institutional infrastructure, but that is not necessarily the case for student adoption. The findings portend a difficult situation, then, for SCDEs that must help higher education preservice faculty increase adoption if preservice preparation programs are to meet contemporary requirements for technology integration. Preservice students are using information technologies with or without adequate institutional infrastructure, but SCDE faculty adoption is contingent.

Additional research is recommended in the exploration and documentation of the extent to which SCDE students adopt information technologies contingent on faculty modeling and classroom integration and, similarly, the extent to which SCDE faculty adopt information technologies contingent on the type and amount of student use. Further, given the perception that students have greater enthusiasm for technology use in general, additional investigation is needed to determine other contingencies (e.g., prior attitudes, ethical positions, reliability of the infrastructure) that may be influencing faculty adoption decisions.


Acknowledgements

The authors wish to acknowledge the American Association of Colleges for Teacher Education (AACTE) and the joint AACTE/NCATE Research and Information Committee for their support of this research effort.


References

  • Arms, C. R. (1992). The impact of information technology on universities in the United States. Higher Education Management, 4(3), 293-307.
  • Awbrey, C. R. (1996, Winter). Successfully integrating new technologies into the higher education curriculum. Educational Technology Review, 5, 7-17.
  • Boettcher, J. V. (1995). Technology classrooms, teaching and tigers. Syllabus, 9(2), 10-12.
  • Cummings, L. E. (1995, Autumn). Educational technology--A faculty resistance view: Part II: Challenges of resources, technology, and tradition. Educational Technology Review, 5, 18-20.
  • DeLoughry, T. J. (1996). Reaching a critical mass. The Chronicle of Higher Education, 47(20), A17-A20.
  • DeLoughry, T. J. (1993). More colleges eye outside companies to run their computer operations. The Chronicle of Higher Education, 39(19), A19-A20.
  • Denk, J., Martin, J., & Sarangarm, S. (1993). Not yet comfortable in the classroom: A study of academic computing at three land-grant universities. Journal of Educational Technology Systems, 22(1), 39-55.
  • Geoghegan, W. H. (1994). What ever happened to instructional technology? Norwalk, CT: International Business Schools Computing Association.
  • Green, K. C. (1997). Campus computing 1996. Encino, CA: Campus Computing.
  • Green, K. C. (1996a). Campus computing 1995. Encino, CA: Campus Computing.
  • Green, K. C. (1996b). The coming ubiquity of information technology. Change, 28(2), 24-31.
  • Green, K. C., & Gilbert, S. W. (1995). Content, communications, productivity and the role of information technology in higher education. Change, 27(2), 8-18.
  • Green, K. C., & Eastman, S. (1994). Campus computing 1993: The USC national survey of desktop computing in higher education. Los Angeles: University of Southern California.
  • Graves, W. H. (1993). The educational ecosystem of information and computation medium and message. EDUCOM Review, 28(5), 22-30.
  • MacKnight, C. B. (1995). Managing technological change in academe. Cause/Effect, 8(1), 29-39.
  • Marascuilo, L. A., & McSweeney, M. (1977). Nonparametric and distribution-free methods for the social sciences. Monterey, CA: Brooks Cole.
  • Markus, M. L. (1990). Toward a "Critical Mass" theory of interactive media. Communication Research, 14(5), 491-511.
  • McCandless, G. (1995). Paying for technology on campus: You're not Santa Claus. Syllabus, 9(3), 26-29.
  • National Council for Accreditation of Teacher Education. (1993). Proposed refinements of NCATE's standards for the accreditation of professional education units. Washington, DC: National Council for Accreditation of Teacher Education.
  • Oliver, P., Marwell, G., & Teixeira, R. (1985). A theory of critical mass: Interdependence, group heterogeneity, and the production of collective action. American Journal of Sociology, 91(3), 552-556.
  • Persichitte, K. A., Tharp, D. D., & Caffarella, E. P. (1997). The use of technology by schools, colleges, and departments of education: Fall, 1996. Washington, DC: American Association of Colleges for Teacher Education.
  • Resmer, M. (1997). Universal student access to information resource technology. Syllabus, 10(6), 12-14.
  • Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York: Free Press.
  • Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). New York: Free Press.
  • Tharp, D. D. (1997). Documenting critical mass for the use of interactive information technologies in schools, colleges, and departments of education. Published dissertation, University of Northern Colorado, Greeley, CO.
  • Valente, T. W. (1993). Diffusion of innovations and policy decision-making. Journal of Communication, 43(1), 30-41.

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