|Australasian Journal of Educational Technology
2008, 24(5), 611-626.
Relative ranking of conditions that facilitate innovation implementation in the USA
David C Ensminger
Loyola University Chicago
Daniel W Surry
University of South Alabama
This study compared how people working in three different types of organisations in the United States (K-12 schools, higher education, and business) rank the importance of eight conditions that have been shown to facilitate the implementation of innovations. The study also sought to determine if the nature of the innovation (i.e., technology or process innovation) affected the rankings. Technology innovations are those that require the use of a new tool or product such as an innovative communications device or new piece of manufacturing equipment. Process innovations are those that require a new method or system such as a new method for performance evaluations or new budget approval process. A total of 635 participants completed an online instrument to determine their individual ranking of the eight conditions, 315 participants responded to questions specific to technology innovation while 320 responded to process specific questions. Analysis of variance was used to compare differences between the groups. Significant differences were found on five of the eight conditions in the technology sample and on seven of the eight conditions in the process sample. In addition, there were differences within groups based on the nature of the innovation. Change agents must adapt their strategies to account for the different rankings of the eight implementation conditions based on type of organisation and the nature of the innovation. The results of this study provide a framework for understanding and accounting for the group differences. This is the first study to address the prescriptive value of the eight implementation conditions.
A well designed implementation plan is essential to the success of any innovation or organisational change (Smith & Mourier, 1999; Voss, 1992). Only one out of every three change efforts in the business sector will be successful (Murhrcke, 1999). Approximately 75% of companies fail to see a return on investment when implementing an innovation (Day, 1999), while less than half of executives report that change efforts within their organisation were successful (Schiemann, 1992). About 50% of technology innovations do not achieve their intended goals and more than 40% are abandoned before completion (Griffith, Zammuto & Aiman-Smith, 1999).
Implementation is also a major concern for educational organisations. Berman (1981) reports that the efforts during the implementation phase of the educational change process are the most critical for producing successful outcomes. Berman goes so far as to suggest that successful outcomes are as much a result of the implementation process as the innovation itself. Burkman (1987) notes that in many cases the failure of an innovation is not due to the quality of the product, but results from those responsible for its successful implementation failing to consider variables other than the product itself.
Even after decades of research, the implementation phase remains a misunderstood, but critical, part of change process in both educational and business settings (Ely, 1999). Given that success of an innovation is directly tied to its successful implementation, organisations must not only be aware of variables that facilitate implementation, but need a means for determining which variables are most important to their organisation, given a specific innovation. To fully understand the need for assessing variables, we need first to become familiar with some of the main models and strategies related to change.
Rogers also describes a number of other theories and ideas relevant to the change process. Perhaps the two most widely cited of his remaining theories relate to the attributes of an innovation and adopter categories. Briefly stated, Rogers believes potential adopters perceive an innovation in regard to five general attributes: Relative advantage, compatibility, complexity, trialability, and observability. The concept of adopter categories states that potential adopters tend to be normally distributed within an organisation along a continuum of innovativeness from "innovators" to "laggards". Taken together, the concepts of adoptor categories and innovation attributes suggest that the adoption of an innovation results, at least in part, from the interaction between the individual's innovativeness and their perceptions of the innovation. However, the decision to adopt an innovation does not guarantee successful implementation.
For implementation to be successful, other variables need to be addressed, particularly when implementation affects individuals who were not actively involved in the initial decision to adopt the innovation. Rogers' concepts related to the adoption and diffusion process are useful in outlining the broad range of those variables. His work comes closer than anyone in providing a single, comprehensive, macro level model of change. However, his work provides little information about the specific actions that occur in the implementation step, and does not provide any guidelines for how organisations can work to facilitate the implementation of innovations. Both business and education have change models and strategies that inform the implementation of innovations in their field.
Klien and Sorra (1996) describe several variables that influence implementation of innovations or change. They refer to variables such as the existing skills and knowledge of employees, reinforcing the use of the innovation, the removal of implementation barriers (i.e., providing training and opportunities to practise, financial and infrastructure resources, leadership support, changes in policies and practices to encourage use of the innovation), and developing a climate for implementation. The "implementation climate" interacts with the "innovation value fit" or the perceived fit of the innovation with the organisations existing values. (p. 1062). The degree of fit and interaction between these two factors determines the level of implementation success.
The innovation process model proposed by Vrakking (1995) has four main phases, generation of ideas, initiation phase, implementation, and incorporation. In the implementation phase, several actions by the organisation can influence the implementation process. Some of these include training, employee involvement in the design and development of the innovation, communication from upper management on the need to change, and support from lower level management.
Havelock and Zlotolow (1995) present a seven step model that change agents can use to facilitate change. The CREATER model focuses the change agent's efforts on particular strategies designed to overcome obstacles related to the each of the seven steps from "Caring" to "Renew." The CREATER model provides a guide for developing implementation plans or change strategies by providing change agents with a series of steps and strategies that allow them to develop relationships, identify problems, resources and solutions, and assist in the implementation of the solution.
Stockdill and Morehouse (1992) relate successful implementation of technological innovations to five factors. These include allowing the educational need to drive the selection of technology; recognising that the technology must match the needs of the user as well as their skills and attitude; that the technology must match the academic content; the general attributes of the technology as assessed by Rogers five attributes; and organisational variables such as "staff," "linkages," "equipment," "expertise," "rewards," and the "attitudes and values of the individuals" (Stockdill & Morehouse, 1992, p. 57).
Burkman's (1987) User Oriented Instructional Development process describes four types of support that assist in the implementation of new products. These supports address specific rewards and process for encouraging the use of the product, changes in policies and procedures that communicate the backing of the product by powerbrokers, provision of specific training for indented users, and resources that support the implementation of the product (Burkman, 1987).
More recently, the RIPPLES model has been introduced and provides an instrumentalist perspective to the implementation of technology in higher education. The model identifies seven components (i.e. resources, infrastructure, people, policies, learning, evaluation, and support) that must be attended to when implementing technology (Surry, Ensminger & Haab, 2005). Although the model was developed to explain technology implementation in higher education, the seven components are applicable to a variety of academic settings.
Dissatisfaction with status quo refers to an emotional discomfort resulting from the use of current processes or technology that is perceived as inefficient, ineffective, or not competitive. This affective state is either self induced, results from organisational failure, or from leadership campaigning for the need to change (Ely, 1990, 1999; Surry & Ely, 2001). This condition is similar to relative advantage (Rogers, 1995), establishing a sense of urgency (Kotter, 1996), organisational values (Klien & Sorra, 1996), and matching product to users needs and values (Burkman, 1987). The "policies" component of the RIPPLES model discusses the need for institutions to put into place policies that compel the use of technology by organisational members (Surry, Ensminger & Haab, 2005). Others have citied concepts similar to dissatisfaction with the status quo (e.g. Herson et al., 2000; Pajo & Wallace, 2001; Vrakking, 1995).
Adequate resources refers to the availability and accessibility of resources needed to implement the innovation. Resources includes the existing infrastructure as well as an organisation's finances, hardware, software, materials, personnel, and support structures (Ely, 1999, 1990). Burkman (1987) discusses the importance of locating of the necessary equipment, materials, and facilities in order to support the implementation of a new instructional product. Klien and Sorra (1996) identify infrastructure resources as part of their factor climate for implementation. Havelock and Zlotolow (1995) emphasise change agents acquiring the necessary resources for implementation. "Resources", specifically financial resources, is a major component of the RIPPLES model, as is "infrastructure" - the existence of the needed hardware and software to support the technology integration. Additionally, the RIPPLES model stresses the need for personnel to assist the technology integration efforts as part of the "support" component (Surry, Ensminger & Haab, 2005). Other researchers who have identified resources as an important part of implementation include Benson and Palaskas (2006), Buchan and Swann (2007), Dhanarajan (2001), Ebersole and Vorndam (2003), Okumus (2001), and Pajo and Wallace (2001).
Rewards and incentives refers to either intrinsic or extrinsic rewards that result from using the innovation. These rewards can vary significantly from user to user. Additionally, the innovation itself may be perceived as reward or the anticipated outcomes from the use of the innovation may serve an incentive (Ely 1999, 1990). Burkman (1987) discusses the use of rewards as part of "moral support" during implementation (p. 450). Stockdill and Morehouse (1992) identified rewards as a significant factor in "organisational capacity" (p. 57). The RIPPLES model discusses the use of linking technology to tenure, and merit systems as part of its "policies" component. Others citing this condition as part of implementation include Jost and Schneberger (1994), Klein and Sorra (1996), Okumus (2001), and Smith and Mourier (1999).
Knowledge and skills refers to users possessing or acquiring the needed skills and knowledge to employ the innovation. This condition also reflects users' feelings of self efficacy about using the innovation with training being a necessary part of the implementation plan (Ely 1990, 1999). The complexity of the innovation will affect implementation in that it will often require more training or skill development on the part of the users (Rogers 1995). Kotter (1996) emphasises the development of competencies in order to facilitate the change process. Varkking (1995) identifies training as part of the implementation phase. Surry, Ensminger and Habb (2005) describe knowledge and skill development as an aspect of the "people" component of the RIPPLES model. Other researchers have linked knowledge and skills or training to successful implementation (e.g. Benson & Palaskas, 2006; Dalton, 1989; Dhanarajan, 2001; Ebersole & Vorndam, 2003; Herson et al., 2000; Okumus, 2001; Pajo & Wallace, 2001).
Adequate time refers to the willingness of companies to provide paid time for users to learn the new skills or procedures in order to use the innovation, as well as the users' willingness to devote time to develop these new skills (Ely, 1990, 1999). This also represents the individual's belief that, with time, they can successfully adapt to the change. It is important to note that adequate time does not reflect a set chronological time span for implementation, but instead addresses the idea that users must be provided time to develop a sense of familiarity and comfortableness with the innovation. Klein and Sorra (1996) mention opportunity to practise or use the innovation as critical to implementation. Inadequate or insufficient time is recognised as a barrier to implementation of technology in higher education (Ebersole & Vorndam, 2003; Pajo & Wallce 2001).
Participation refers to the involvement that stakeholders have in the decision making process to adopt and implement an innovation. Participation may take the form of user group representatives if it is difficult to get feedback from all potential users (Ely 1990, 1999). This condition helps to develop ownership of the innovation and increases the stakeholders' interests in a successful implementation. Varkking (1995) states "participation in the design phase is in fact the first step of implementation" (p. 35). The RIPPLES model includes participation as part of the "people" component through its suggestion that faculty be a part of the decision making processes when it comes to technology implementation (Surry, Ensminger & Haab, 2005). Others stress participation by intended users or employees during innovation or change design as well (Dirks et al., 1996; Herson et al., 2000; Meyers et al., 1999; Sims & Sims, 2002; Smith & Mourier, 1999).
Commitment refers to the visible support by the upper level leaders or powerbrokers in an organisation. The key to this condition is how the users perceive the powerbrokers' commitment to the implementation of the innovation. Simple verbal endorsement of the innovation by leaders and powerbrokers does not constitute commitment (Ely, 1990, 1999). Visible forms of commitment include such things as personal communication, development of strategic implementation plans, changes to organisational policies, dedication of resources, and active involvement in the implementation of the innovation. Kotter (1996) discusses building a guiding coalition of powerbrokers that share the common change goal. These members must have key characteristics such as power, expertise, credibility, and leadership. Bishop-Clark and Grant (1991) describe "top down implementation as powerbrokers developing plans and committing resources" (p. 321). Dhanarajan (2001) lists lack of commitment from university administrators as a barrier to implementation. Others who have emphasised the importance of powerbrokers in the change process include Buchan and Swann (2007), Conger (2000), Jost and Schneberger (1994), Meyers et al. (1999), and Varkking (1995).
Leadership refers to the level of ownership and support given by the leaders who will manage the daily activities of those implementing the innovation. (Ely, 1990, 1999). The enthusiasm of these leaders directly affects the motivation of the users of the innovation. Immediate supervisors must provide support and encouragement, answer questions, address concerns, and serve as role models for using the innovation. Kotter (1996) indicates that supervisors must actively support the change and communicate their support to employees. Kelin and Sorra (1996) note the value of leadership support implementation. Support, or championing of the innovation by supervisors, is often cited as a critical part of the change process (e.g., Buchan & Swann, 2007; Ebersole & Vorndam, 2003; Meyers, et al., 1999).
Surry and Ensminger (2003) attempted to measure the relative importance of Ely's eight conditions across two occupational groups (education and business) using scenario based questions. Their results showed significant differences between the two groups on four of the eight conditions: dissatisfaction with the status quo, time, participation, and commitment. This study supported the concept of using the eight conditions as a framework for assessing the relative ranking of the conditions for a particular group and using that information to develop organisation specific implementation plans.
Ensminger, Surry, Porter and Wright (2004) compared the relative ranking of the eight conditions for three broadly defined occupational groups (higher education, K-12 education, and business-industry). The results of their study indicated that each group perceived the relative importance of the eight conditions differently. They also found differences in the relative ranking of the conditions based on several demographic variables such as age, gender, ethnicity, and technical ability. This again supported the hypothesis that the eight conditions could be used as a framework to determine the relative ranking of the conditions, called an "implementation profile", for a specific population. Once an organisation's implementation profile was determined, an implementation plan could be tailored to account for the profile. The study described in this paper was designed to build upon and expand the findings of the two prior studies.
The participants in the technology sample were comprised of three occupational groups (K-12, higher education, business and industry). The K-12 technology group (n=124) consisted of 104 females, 18 males, and 2 who did not report gender. Forty participants in the K-12 technology group had undergraduate degrees, 62 possessed master's degrees, 12 possessed educational specialist degrees, eight had doctoral degrees. The predominate ethnic group was Caucasian, which represented 91.6% of the group. Minority populations made up 6.4% of the group and 1.6% did not report their ethnicity.
The higher education technology group (n=126) consisted of 84 females and 41 males; one respondent in this group did not report gender. The educational make up of the group was diverse, 13 held undergraduate degrees, 53 possessed master's degrees, 7 held educational specialist degrees, and 53 held doctoral or professional degrees. Caucasians represented 85.7% of the group population with minority groups representing 12.8% of the population, 1.6% of the population did not report their ethnicity.
The business/industry technology group (n=65) consisted of 37 females, 26 males, and 2 who did not report their gender. Seven reported their highest degree earned being a high school diploma or equivalent, 26 hold undergraduate degrees, 20 held master's degrees, and 11 held doctoral or professional degrees. Caucasians represented 84.6% of the population while non-Caucasians represented 13.8% of the population, 1.5% did not report their ethnicity.
The participants in the process sample were also comprised of three occupational groups (K-12, higher education, business and industry). The K-12 process group (n=135) consisted of 108 females, 24 males, and 3 participants who did not report their gender. Three reported their highest educational degree as high school diploma or equivalent, 27 held undergraduate degrees, 70 held master's degrees, 18 held educational specialist degrees, and six held doctoral or professional degrees. African Americans represented 20% of the group, Caucasians represented 68.9%, non-Caucasians represented 6.6 % of the group, and 4.4 % did not report their ethnicity.
The higher education process group (n=109) consisted of 67 females, 41 males, and one participant did not report gender. Three participants reported their highest degree earned as a high school diploma or equivalent, 19 held undergraduate degrees, 52 possessed master's degrees, one held an educational specialist degree, and 34 held doctoral or professional degrees. Caucasians represented 89.9 % of the group, while minority groups comprised the remaining 10.1 percent.
The business process group (n=76) consisted of 34 females and 40 males; two did not report gender. Three reported having a high school education or equivalent, 22 possessed master's degrees, 3 held educational specialist degrees, and 10 held doctoral or professional degrees. Asian/Pacific islanders comprised 18.4% of the group, Caucasians made up 69.7% of the group, other minority group comprised 6.6% of the group, and 5.3% of the group did not report their ethnicity.
While this article only presents data analysis that utilises the occupational group variable, the other demographic data indicate that the technology and process samples had similar demographic characteristics and appear to be homogenous. Future research should be conducted to examine differences in implementation profiles that may be found for demographic variables other then occupational group (e.g., age, gender, technical proficiency).
|Dissatisfaction with status quo||7.15||3.99||8.17||4.01||8.22||3.96|
|Skills and knowledge *||9.06||3.85||7.89||3.46||7.98||3.53|
|* significant at p < .05|
** significant at p < .01
Post hoc comparisons using the Bonferroni's correction were conducted on the five significant conditions. Results of post hoc comparisons showed that the K-12 group valued the condition resources significantly more (p < .01) than either the higher education group or the business group. Additionally, the higher education group valued resources significantly more (p < .05) than the business group. The results indicate that the K-12 group considers the availability of resources as more important to the implementation of a technology based innovation that do either the higher education or the business groups.
Overall, the condition of resources was the most valued condition among those in the K-12 group and the higher education group while it was fourth overall for the business group (see Table 2). Business settings are often thought of as having more abundant technological resources than educational settings. This may explain the difference between the scores and importance that the K-12 and higher education placed on resources. This result supports previous research on technology innovations in academic settings that indicated resources were a critical component to implementation (e.g., Bauder 1993; Dalton, 1989; Ebersole & Vorndam 2003; Herson, et al., 2000; Ravitz, 1999; Rogers, 2000).
Post hoc comparison for the condition skills and knowledge resulted in a significant difference, p < .05 between the K-12 and the higher education group. The K-12 group appeared to value this condition more than the higher education group. Skills and knowledge ranked second for the K-12 group and was ranked fourth for the higher education group (see Table 2). Where it appears both groups value this condition when implementing a technology innovation, those in K-12 settings consider the condition more important than do those in higher education and business settings. These findings support earlier research on implementation of technologies in K-12 settings (e.g., Bauder, 1993; Dalton, 1989; Ebersole & Vorndam, 2003; Herson, et al., 2000; Ravitz, 1999). Individual group comparisons for the condition participation indicated a significant difference between the K-12 group and the business group (p < .05). This result suggests that the business group valued involvement in the decision making process for technology implementation more than did the K-12 group.
|Skills and knowledge||Participation||Dissatisfaction|
|Time||Dissatisfaction||Skills and knowledge|
|Dissatisfaction||Skills and knowledge||Resources|
Prior research indicates the importance of employee participation when implementing technology innovations in business settings (e.g., Dirks et al., 1996; Sims & Sims, 2002; Varkking, 1995). Additionally, post hoc comparisons between the three groups for the condition time resulted in significant difference between the K-12 and the higher education group, p < .01, as well as the business group p < .05. Time ranked as the third most important condition for the K-12 group, but was sixth for both the higher education group and the business group. This indicates that time to become familiar with and develop the skills to implement technology is critical for the K-12 group. Those in K-12 may not feel that adequate time to develop skills and become familiar with technology is provided and they are responsible for the using personal time to develop skills and determine how to integrate technology into the curriculum. Earlier research on implementation of technologies in K-12 settings also supports the value of time (e.g., Bauder, 1993; Dalton, 1989; Ebersole & Vorndam, 2003; Herson, et al., 2000; Ravitz, 1999).
The business group rated commitment as more important to implementation that did the higher education group or the K-12 group. These differences were significant for both for the K-12 group (p < .01) and for the higher education group (p < .05). Commitment ranked as the least important condition for the all three groups. Although there were significant differences between the scores on this condition, it appears that all three groups consider powerbroker support as the least essential to the implementation of technology innovations.
|Dissatisfaction with status quo **||10.06||3.29||11.41||2.86||10.79||3.42|
|Skills and knowledge||5.97||3.38||5.61||3.73||5.36||3.44|
|* significant at p < .05|
** significant at p < .01
Post hoc comparisons using the Bonferroni's correction were conducted on the seven significant conditions. For the condition dissatisfaction with the status quo the higher education group mean was significantly greater than the mean score for the K-12 group. Although statistical significance was determined between the two groups, the question remains of the practical value of the significance given that all three groups consider dissatisfaction with status quo as being the most important condition when implementing a process innovation. The results suggest that all three groups want to be sure the current process or program is ineffective prior to deciding to implement a new process or program.
For the condition resources, the K-12 group mean score was greater than both the higher education group (p < .05) and the business group (p < .01). Resources ranked second in overall importance for those working in K-12 while for those working in higher education and business it ranked fourth and fifth, respectively (see Table 4). Similar to the technology form results, those working in K-12 are often asked to implement new programs but are not provided with adequate resources to successfully meet the implementation goals.
For the condition participation, the K-12 group's mean score was significantly lower than the means scores for higher education group and the business group (p < .01). For the K-12 group the overall importance of participation ranked fifth, while the higher education group and business group rated the overall importance of participation as second and third respectively. These results are similar to the findings on the technology form and seem to support more the cultural aspects of these environments, with individuals in both environments seeking direct involvement in the change process.
For the condition commitment, the business group (p < .01) and higher education group (p < .05) means were significantly higher than the mean score for K-12. Additionally the mean score for the business group was significantly greater than the higher education group (p < .05). This indicates the business group considered commitment to be an important condition when implementing process innovations, placing this condition second in overall importance while the K-12 group ranked the condition fourth and the higher education group ranked it third (see Table 4). Although the rankings vary little between the three groups, the significant difference between the means score suggests that the perceived value of commitment by each group may differ more than is suggested by the overall ranking. The results seem to suggest that visible indication of powerbroker support is critical to implementation of process innovations for those in business settings and those in higher education with those in business placing a greater significance on the condition than the other two groups.
|Skills and knowledge||Leadership||Time|
|Leadership||Skills and knowledge||Skills and knowledge|
For the condition time, the K-12 mean score was significantly different from the mean scores of both the business and the higher education groups (p < .01). The K-12 group considered time to be the third most important condition. This suggests that those in K-12 settings consider having adequate time to become familiar and comfortable with process innovations is critical. However, this is often not the case in K-12 settings where change is often introduced at the start of an academic year and teachers must implement the change immediately without fully understanding the innovation or having the necessary time to adapt their practice to the innovation. Time was ranked fifth for higher education and sixth for business.
The post hoc comparisons for the condition leadership indicated that the mean score of the business group was significantly higher than the means score of the K-12 group. The business group placed relatively more importance on leadership, ranking it fourth overall, while the higher education group ranked it sixth and the K-12 group ranked it 8th. This finding seems to reflect the nature of the organisations in that supervisors in business settings typically are directly involved in the use of the innovation along with other end product users.
The post hoc comparison for the condition rewards indicate that the K-12 group considered rewards to be more important that did the business group. The results indicate the K-12 group values incentives or rewards for implementing new process as more important that do those in business settings. However, all three groups consider this condition to be the least important of the eight and any significant difference between the groups may have not direct practical or observable impact.
Future research in this area should focus on the affect of specific implementation strategies for each condition. For example, if participation is found to be the most important condition for members of an organisation, what modes of participation are most effective in facilitating implementation? Additional research in this area could also focus on differences in the relative importance of the eight conditions based on demographic variables such as age, gender, ethnicity, or educational experience. Also, while this study showed there were differences between occupational groups and that the occupational groups differed between process and technology based innovations, the underlying causes for these differences remain unknown. Future researchers could employ qualitative designs, such as case study, to explore the reasons for the differing implementation profiles. Finally, because a majority of participants in this study were from the United States, it would be useful to determine if the relative importance of the conditions differed from country to country.
Not only must change agents be aware of the theories and strategies related to change but they must also have a more contextualised understanding of their specific organisation. By measuring the value of these eight conditions, change agents can develop specific implementation plans designed to address the most valued conditions first, thereby building momentum for the change while addressing the remaining conditions later in the change process.
Benson, R. & Palaskas, T. (2006). Introducing a new learning management system: An institutional case study. Australasian Journal of Educational Technology, 22(4), 548-567. http://www.ascilite.org.au/ajet/ajet22/benson.html
Berman, P. (1981). Educational change an implementation paradigm. In R. Lehming & M. Kane (Eds), Improving schools using what we know. Beverly Hills, CA: Sage Publications.
Bishop-Clark, C. & Grant, R. (1991). Implementing computer technology in educational settings. Journal of Educational Technology Systems, 19(4), 313-326.
Buchan, J. F. & Swann, M. (2007). A bridge too far or a bridge to the future? A case study in online assessment at Charles Sturt University. Australasian Journal of Educational Technology, 23(3), 408-434. http://www.ascilite.org.au/ajet/ajet23/buchan.html
Burkman, E. (1987). Factors affecting utilization. In R.M. Gagne (Ed.), Instructional technology: Foundations. Hillsdale, NJ: Lawrence Erlbaum.
Conger, J. A. (2000). Effective change begins at the top. In M. Beer & N. Mohria (Eds.), Breaking the code of change. Boston, MA: Harvard Business School Press.
Dalton, D. W. (1989). Computers in schools: A diffusion/adoption perspective. Educational Technology, 11, 20-27.
Day, G. S. (1999). The market driven organization. Direct Marketing, 62(9), 32-33.
Dhanarajan, G. (2001). Distance education: Promise, performance and potential. Open Learning, 16(1), 61-68.
Dirks, K. T., Cummings, L .L. & Pierce, J. L. (1996). Psychological ownership in organizations: Conditions under which individuals promote and resist change. In R.W. Woodman, & W.A. Pasmore (Eds), Research in organizational change and development. Greenwich, CT: JAI Press.
Ebersole, S. & Vorndam, M. (2003). Adoption of computer based instructional methodologies: A case study. International Journal of E-Learning, 2(2), 15-20.
Ely, D. P. (1990). Conditions that facilitate the implementation of educational technology innovations. Journal of Research on Computing in Education, 23(2), 298-305.
Ely, D. P. (1999). Conditions that facilitate the implementation of educational technology innovations. Educational Technology, 39, 23-27.
Ensminger, D. C., Surry, D. W., Porter, B. E. & Wright, D. (2004). Factors contributing to the successful implementation of technology innovations. Educational Technology & Society, 7(3), 61-72. http://www.ifets.info/others/download_pdf.php?j_id=1&a_id=7
Griffith, T. L., Zammuto, R. F. & Aiman-Smith, L. (1999). Why new technologies fail. Industrial Management, 41(3), 29-34.
Hall, G. E. & Hord, S. M. (1987). Change in schools: Facilitating the process. New York, NY: State University of New York Press.
Havelock, R. G. & Zlotolow, S. (1995). The change agents guide (2nd ed.). Englewood Cliffs, NJ: Educational Technology Publications.
Herson, K, Sosabowski, M., Lloyd, A., Flowers, S., Paine, C. & Newton, B. (2000). Implementation strategies for educational intranet resources. British Journal of Educational Technology, 31(1), 47-55.
Johnson, R. B. & Christensen, L. (2008). Educational research: Quantitative, qualitative, and mixed approaches, 3rd ed. Thousand Oaks, CA: Sage.
Jost, K. L. & Schneberger, S. L. (1994). Educational technology adoption and implementation: Learning from information systems research. Canadian Journal of Educational Communication, 23(3), 213-230.
Klien, K. J. & Sorra, J. (1996). The challenge of innovation implementation. Academy of Management Review, 21(4), 1055-1080.
Kotter, J. (1996). Leading change. Cambridge, MA: Harvard Business School Press.
Meyers, W. P., Sivakumar, K. & Nakata, C. (1999). Implementation of industrial process innovations: Factors, effects, and marketing implications. Journal of Product Innovation Management, 16(2), 295-311.
Muehrcke, J. (1999). Meeting the test of time. Nonprofit World, 17(7), 2-3.
Okumus, F. (2001). Towards a strategy implementation framework. International Journal of Contemporary Hospitality, 13, 327-338.
Pajo, K. & Wallace, C. (2001). Barriers to the uptake of web based technology by university teachers. Journal of Distance Education, 16(1), 70-84. [verified 31 Oct 2008] http://www.jofde.ca/index.php/jde/article/view/171/127
Porter, B. E., Surry, D. W. & Ensminger, D. C. (2003). Reliability test results for an implementation profile instrument. ERIC Documentation Reproduction Services No. 481230. [verified 31 Oct 2008] http://www.eric.ed.gov:80/ERICDocs/data/ericdocs2sql/content_storage_01/0000019b/80/1b/72/ab.pdf
Ravitz, J. L. (1999). Conditions that facilitate teacher internet use in schools with high internet connectivity: A national survey. Dissertation Abstracts International, 60(4) 1094A. (UMI No. 9925992).
Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York, NY: The Free Press.
Rogers, P. L. (2000). Barriers to adopting emerging technologies in education. Journal of Computing Research, 22(4), 455-472.
Schiemann, W. A. (1992). Why change fails. Across the Board, April, 53-54.
Sims, S. J. & Sims, R. R. (2002). Employee involvement is still the key to successfully managing change. In R. R. Sims (Ed.), Changing the way we manage change. Quorum Books, Westport, CT.
Smith, M. E. & Mourier, P. (1999). Implementation: Key to organizational change. Strategy & Leadership, 27(6), 37-41.
Stockdill, S. H. & Morehouse, D. L. (1992). Critical factors in the successful adoption of technology: A checklist based on TDC findings. Educational Technology, 1, 57-58.
Surry, D. W. & Ely, D. P. (2002). Adoption, diffusion, implementation, and institutionalization of instructional design and technology. In R. A. Reiser & J. V. Dempsey (Eds.), Trends and issues in instructional design and technology, Upper Saddle River, NJ: Pearson Education.
Surry, D. W. & Ensminger, D. C. (2003). Perceived importance of conditions that facilitate implementation. E-Journal of Instructional Science and Technology, 6(1). http://www.ascilite.org.au/ajet/e-jist/docs/Vol6_No1/surry.html
Surry, D. W. & Ensminger, D. C. (2004). Development of implementation profile instrument. British Journal of Educational Technology, 34(4), 503-504.
Surry, D. W., Ensminger, D. C. & Haab, M. (2005). Strategies for integrating instructional technology into higher education. British Journal of Educational Technology, 36(2), 327-329.
Varkking, W. J. (1995). The implementation game. Journal of Organizational Change Management, 8(3), 31-46.
Voss, C. (1992). Successful innovation and implementation of new processes. Business Strategy Review, 3(1), 29-44.
|Authors: David C. Ensminger|
Loyola University Chicago, School of Education
820 N Michigan Ave, Chicago Illinois 60611, USA
Daniel W. Surry
University of South Alabama, College of Education
3906 University Commons, Mobile, AL 36688, USA
Please cite as: Ensminger, D. C. & Surry, D. W. (2008). Relative ranking of conditions that facilitate innovation implementation in the USA. Australasian Journal of Educational Technology, 24(5), 611-626. http://www.ascilite.org.au/ajet/ajet24/ensminger.html