Rogers` Theory of the Diffusion of Innovations and Online Course Registration

Yıl 2006, Cilt: 47 Sayı: 47, 367 - 391, 01.08.2006

Kamile Demir

Öz

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.

Anahtar Kelimeler

diffusion of innovations , innovation adoption intention , online registration

Rogers`ın Yeniliğin Yayılması Teorisi ve İnternetten Ders Kaydı

Öz

Teknolojik yenilikler çağdaş üniversitelerde önemli bir rol oynar. Teknolojik yeniliklerin artan önemi karşısında yeniliğe uyumu etkileyen etkenleri ortaya koymayı amaçlayan çeşitli teoriler ve modeller geliştirilmektedir. Bunlar arasında “Rogers'ın Yeniliğin Yayılması Teorisi” bilgi sistemleri uygulaması araştırmalarında yaygın olarak kabul gören bir teoridir. Bu araştırma Eğitim Fakültesi öğrencilerinin internetten ders kaydına uyum kararını etkileyen etkenlerin Rogers'ın Yeniliğin Yayılması Teorisi temel alınarak incelenmesini amaçlamaktadır. Araştırmanın örneklemini, Trakya Üniversitesi Eğitim Fakültesinde öğrenim görmekte olan 241 sınıf öğretmenliği öğrencisinden oluşmaktadır. Modeller Maksimum Olabilirlik Yaklaşımı kullanılarak LISREL 8.3 programı ile test edilmiştir. Uygunluk istatistikleri, yeniliğin yayılması modellerinin veriler ile uyumlu olduğunu göstermektedir. Araştırmanın sonucunda, her iki modelde de internetten ders kaydı yaptırmanın göreli avantajı, görülebilirliği ve uygunluğunun tutuma etkisinin pozitif ve anlamlı, karmaşıklığın etkisinin ise negatif ve anlamlı olduğu görülmektedir. Yeniliğe açıklık ve yardım almanın yeniliğine uyum kararlarına etkisi pozitif ve anlamlıdır. Öğrencilerin internetten ders kaydı yaptırmaya tutumlarının ise, öğrencilerin yeniliğine uyum kararlarına etkisi birinci modelde anlamlı değilken, tutumun yeniliğe etkisini de içeren ikinci modelde anlamlıdır.

Anahtar Kelimeler

yeniliğin yayılması , yeniliğe uyum kararı , yeniliğe uyum davranış

Kaynakça

• Agarwal, R; Ahuja, M.; Carter, P.M.; Gans, M. (1998). Early and Late Adopters of IT Innovations: Extensions to Innovation Diffusion Theory. http://discnt.cba.uh.edu/chin/digit98/panel2.pdf
• Agarwal, R.; Prasad, J. (1998). The antecedents and consequents of user perceptions in information technology adoption. Decision Support Systems, 22,15-29.
• Agarwal, R.; Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9 (2), 204–215.
• Ajzen, I.; Fishbein, M. (2005). The influence of attitudes on behavior. Handbook of Attitudes. (Ed. Dolores Albarracin), 173-215. USA: Lawrence Erlbaum Associates.
• Argabright, G.C. (2002). An investigation of the relationship between technology acceptance and technological stress on consumer behavior. Doctor of Business Administration Thesis. University of Sarasota, Florida.
• Au, K.A.; Enderwick, P. (2000). A cognitive model on attitude towards technology adoption. Journal of Managerial Psychology, 15 (4), 266-282.
• Berger, J.I. (2005). Perceived consequences of adopting the internet into adult literacy and basic education classrooms. Adult Basic Education, 15(2), 103-121.
• Bradford, M.; Florin, J. (2003). Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. International Journal of Accounting Information Systems, 4, 205–225.
• Carter, L.; Belanger, F. (2005). The utilization of e-government services: citizen trust, innovation and acceptance factors. Information Systems Journal 15, 5–25.
• Cegielski. (2001). A model of the factors that affect the integration of emerging information technology into corporate technology into corporate strategy. PhD Thesis. The University of Mississippi.
• Chakravarty, S.; Dubinsky, A. (2005). Individual investors’ reactions to decimalization: Innovation diffusion in financial markets. Journal of Economic Psychology, 26, 89–103.
• Chapman, B.F. (2003). An Assessment Of Business Teacher Educators. Adoption Of Computer Technology. Phd Thesis. Faculty of the Virginia Polytechnic Institute and State University Blacksburg, Virginia.
• Christopher, J.C. (2003). Extent of decision support information technology use by principals in Virginia public schools. Doctorate Thesis. Virginia: Virginia Commonwealth University.
• Curan, J.M.; Meuter, M.L. (2005). Self-service technology adoption: comparing three technologies. Journal of Services Marketing 19 (2), 103–113
• Davis, F. D. (1985). A technology acceptance model for empirically testing new end user information systems: theory and results. PhD Thesis. Sloan School Of Management, Massachusetts Institute Of Technology..
• Jansma, A. P. (2003). Innovation and diffusion of information technology in noncompetitive environments as typified by county and local governments. PhD Thesis. Faculty of Graduate School of the University of Minnesota.
• Jarrett, S. M. (2003). Factors affecting the adoption of e-business in the aerospace industry. Doctorate Thesis in Business Administration. Wayne Huizenga Graduate School of Business and Entrepreneurship Nova Southeastern University.
• Heller, D.E. (2001). The states and public higher education policy: Affordability, Access and accountability. USA: The John Hopkins University Pres.
• Hoerup, S. L. (2001). Diffusion of an Innovation: Computer Technology. Integration and the Role of Collaboration. PhD Thesis. Faculty of the Virginia Polytechnic Institute and State University, Blacksburg, Virginia.
• Huang, Z. (2003). Toward a deeper understanding of the adoption decision for Interorganizational information systems. Phd Thesis. The University of Memphis. Kim, S. (2003). Exploring factors influencing personal digital assistant adoption. Master Thesis. University of Florida.
• Mao, E. (2001). Organizational use and diffusion of information technology in china and an international comparative assessment. PhD Thesis. The University of Memphis.
• Oskamp, S. (2004). Attitudes and Opinions. USA: Lawrence Erlbaum Associates. Park, S. (2004). Factors that Affect Information Technology Adoption by Teachers. Faculty of The Graduate Collage, University of Nebraska, Nebraska.
• Pegler, G. (1992). Perspectives for school information systems. Australian Journal of Educational Technology, 8(2), 161-171. http://www.ascilite.org.au/ajet/ajet8/pegler.html
• Vishwanah, A.; Goldhaber, G.M. (2003). An examination of the factors contributing to adoption decisions among late-diffused technology products. New Media & Society, 5(4), 547–572.