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Technological innovations play an important role in modern higher education. In response to growing importance of technological innovations, different aspects of adoption to innovation have been studied intensively. Rogers' theory of the diffusion of innovations is one of the most researched and dominant model in the study of innovation adoption behavior. The purpose of this study was to investigate the factors that influence students' intention to use online registration such as attitude, innovativeness and social support based on Rogers' theory of the diffusion of innovations. Data were collected from 241 undergraduate students at Trakya University Education Faculty. Models were tested using LISREL 8.3 with maximum likelihood estimation. Overall, the fit statistics indicate that models provide an adequate fit to the data. According to result of the study, relative advantage, compatibility, trialability, and observability of online registration had positive and a significant effect on attitude, complexity had a negative significant effect. While innovativeness and social support had a significant effect on adoptive intention with exception of the attitude in the former model, the results of the second model demonstrated that attitudes have a significant effect on intentions, by influencing innovativeness. Summary Technological innovations play an important role in modern higher education. In response to growing importance of technological innovations, adoption behavior has been studied intensively. These research efforts have examined different aspects of adoption to innovation using a variety of theoretical perspectives. Rogers' theory of the diffusion of innovations is one of the most researched and dominant model in the study innovation adoption behavior. Diffusion of innovation theory also provided a useful perspective about one of the most continuous and challenging issues to improve technology adoption and utilization (Park, 2004, Berger, 2005). Although the overall theory is rich and complex, its essence views the innovation adoption process as one of information gathering and uncertainty reduction (Agarwal, Ahuja, Carter ve Gans, 1998). According to Rogers (1995) an innovation is “an idea, practice or object that is perceived as new by an individual or another unit of adoption”. The newness of the innovation does not just involve new knowledge but also new ways to approach the perceived problem or need. The innovation is not necessarily new in its concept or design; however, it is new to the individual or organization utilizing it. (Berger, 2005). Diffusion is a special type of communication concerned with the spread of messages that are perceived as dealing with new ideas, and necessarily represent a certain degree of uncertainty to an individual or organization (Rogers, 1995). Rogers (1995) identified four element of diffusion: innovation, communication channels, time and the social systems. Innovation consists of five stages: knowledge, persuasion, decision, implementation, and confirmation. The innovation-decision process is a slow process that happens over a period of time in a series of actions and decisions. The second element of the diffusion process, communication channel is essential in the diffusion and adoption of an innovation. Communication is the process that individuals use to create and share information to achieve a mutual understanding. The third element of the diffusion process is time. The element of time refers to the process of adopting an innovation and the rate of adoption. There are many different types of innovations and, as a result, they are not equivalent in their adoption rate by potential users. The rate of adoption is the speed that a social system's members adopt an innovation. Innovations have five characteristics that help to explain the speed that individuals adopt a new idea. These characteristics are relative advantage, compatibility, complexity, trialability, and observability. Innovations that are perceived by individuals as having greater relative advantage, compatibility, trialability, observability, and less complexity will be adopted more rapidly than other innovations. The fourth element of the diffusion process is the social system or "a set of interrelated units that are engaged in joint problem-solving to accomplish a common goal". While there are differences among individual rates of adoption, there are also differences between the rates of adoption for the same innovation within social systems. Rogers (1995) categorized adopters into five categories of a social system based on their degree of innovativeness: innovators, early adopters, early majority adopters, late majority adopters, and laggards. Each category contains dominant characteristics that help to set each apart from the next. These characteristics are intended for theoretical formulation of the adoption or rejection of an innovation. The purpose of this study was to investigate the factors that influence students' intention to use online registration based on Rogers' Diffusion Theory. Data were collected from 241 students in an undergraduate programs at Trakya University Education Faculty. 33.6% of subjects were first year students, 34.4% second year students, and 32% third year students. In this research study, innovation diffusion theory is empirically tested via a questionnaire to create new models that depict the determinants of the adoption of online registration. According to model, relative advantage, compatibility, complexity, trialability, and observability will have a significant direct effect on attitude, and attitude, innovativeness and social support will have a significant direct effect on adoptive intention. Research questionnaire was developed based on the review of related research. Each item was based on Likert-type 7-point scale ranging from “strongly disagree” to “strongly agree”. Internal consistency was measured by using Cronbach Alpha method. Alpha values ranged from 0.68 and 0.86. Therefore, the internal consistency of the survey instrument was reliable at an acceptable level. In this study, latent variables were defined as relative advantage, compatibility, complexity, trialability, and observability, attitude, innovativeness and social support. Construct validity was examined by assessing the standardized factor loading of items hypothesized in the measurement model with Confirmatory Factor Analysis. The result of Confirmatory Factor Analysis was observed that all factor loadings were between 0.30 and 0.40 for three items and 0.40 or higher for all remaining items, and all loadings are significant (p<0.001). The corrected item total correlations also ranged from 0.27 and 0.85. Models were tested using LISREL with maximum likelihood estimation. All factors are significantly correlated with adoptive intention. Overall, the fit statistics indicate that the first model provides an adequate fit to the data (χ2=746.85, df=499, p=0.00, CFA=0.93, NFI= 0.83, RMR=0.06, RMSEA=0.04, GFI=0.86, AGFI=0.82). Chi-square divided by degrees of freedom is 1.50; it can be interpreted that model has an acceptable fit. Path coefficients in the first model were significant, with the exception of the path from attitude to adoptive intention. It was found that relative advantage (β=0.37, p<0.05), compatibility (β=0.33; p>0.10), and observability (β=0.25, p<0.05) of online registration had positive and a significant effect on attitude, complexity had negative and a significant effect (-0.42, p<.01). Innovations characteristics accounts for 29% of the variance in attitude. Innovativeness (β=0.81; p<.001) and social support (β=0.21; p<.01) had a significant effect on adoptive intention, except attitude (β=0.03; p>.05) in the model. The model accounts for 74% of the variance in adoptive intention. While innovativeness accounts for 82% of the variance in adoptive intention, attitude accounts for only 0.4% of the variance. The fit statistics indicate that the second model also provides an adequate fit to the data (χ2=799.57, df=511, p=0.00, CFA=0.91, NFI= 0.81, RMR=0.08, RMSEA=0.049, GFI=0.84, AGFI=0.80). Chi-square divided by degrees of freedom is 1.56, it can be interpreted that second model also has an acceptable fit. All path coefficients in the second model were significantly related to adoptive intention. It was found that relative advantage (β=0.32, p<0.05), compatibility (β=0.38; p>0.05), and observability (β=0.28, p<0.05) of online registration had a positive significant effect on attitude, complexity had a negative significant effect (-0.39, p<.01). Innovation characteristics all together accounts for 30% of the variance in attitude. Innovativeness (β=0.34; p<.001), social support (β=0.21; p<.01), and attitude (β=0.15; p<.05) had a significant effect on adoptive intention in the second model. Innovativeness accounts for 42% of the variance in adoptive intention. Social support accounts for 25% of the variance in adoptive intention and attitude explained 18% of the variance. The overall model accounts for 31% of the variance in adoptive intention. These results indicate that relative advantage, compatibility, and observability of online registration had a positive significant effect on attitude, complexity had a negative significant effect. While innovativeness and social support had a significant effect on adoptive intention, except the attitude in the former model, the results of the second model demonstrated that attitudes have significant effects on intentions, by influencing innovativeness. Perceived characteristics of online registration collectively explain a considerable degree of variance in the attitude. In addition, the findings of study indicated that relative advantage, compability, complexity, and observability are significant factors in predicting the adoption of innovations. These findings were consistent with diffusion of innovation theory's assertion that perceived characteristics of innovations play an important role in forming an attitude. The results present that relative advantage was most important characteristic of online registration. In addition, the respondents reported that compability, relative advantage, and observability as the next highest in importance, respectively. But, the strongest variable in predicting attitude among significant characteristics was complexity. The results from the current study are generally consistent with the expectations of innovation diffusion theory that certain innovation factors are important in encouraging an individual to adopt an innovation. Contrary to the expectation, attitude had not a significant effect on adoptive intention in the first model. It may be interpreted that attitudes may not predict behavior in mandatory settings. In mandatory settings, it can be harder to act on one's attitudes than the others. In addition, the influence of personal character can be influence relationship between attitude and behavior. For this reason, another model was developed, which included the path from attitude to innovativeness. The second model indicated that attitudes have significant effects on intentions, by influencing innovativeness. This study also shows that among the significant factors, innovativeness was the strongest predictor of adoptive intention. This result supports that the students were encouraged to adopt and use online registration by their degree of innovativeness and perceived social support. In summary, it has been suggested that failure of innovations is often not attributable to innovation characteristics, but to deficiencies in its implementation.

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