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Diffusion of innovations

About: Diffusion of innovations is a research topic. Over the lifetime, 2139 publications have been published within this topic receiving 191397 citations. The topic is also known as: diffusion of innovation & diffusion of innovations theory.


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Dissertation
01 Jan 1997
TL;DR: In this paper, the authors used social network analysis to study the diffusion of two computer-based administrative innovations within a university faculty network and found no significant difference between network nominations for advice, friendship, and discussion identified at the beginning and at the end.
Abstract: Identifying predictors of computer use such as attitude, anxiety, and receptivity to change have been the primary area of interest in instructional technology. Research relating to the diffusion of innovations in education has been based primarily on looking at these individual characteristics as predictors of use. This dissertation proposes to use social network analysis to study the diffusion of two computer-based administrative innovations within a university faculty network. Methodology issues concerning time of adoption and network nominations were examined as well as the relationship of time of adoption and the number of network nominations received, spatial proximity, and organizational unit proximity. Finally, the diffusion of the innovations was to be analyzed using the dual-classification and T/CM models. Subjects were 66 faculty members in a College in Education from a southwestern university during the 1996-1997 academic year. At the beginning of the study subjects were introduced to the innovations and asked to provide demographic information and to identify communication partners in the areas of advice, friendship, and discussion. At the conclusion of the study subjects were asked to provide feed back related to the innovations and to once again identify their communication partners in the areas of advice, friendship, and discussion. Results indicated that there was no significant difference between adopters recall time of adoption and actual time of adoption. In addition, there was no significant difference between network nominations for advice, friendship, and discussion identified at the beginning and at the end of the study. The number of network nominations received was found to be negatively correlated with the time of adoption. No correlation was found between time of adoption and spatial and organizational unit proximity. The diffusion process could not be studied, because the necessary threshold and critical mass levels were not reached. The innovations did not diffuse through the network. The lack of diffusion could be explained by the negative correlation between the number of network nominations received and the time of adoption as well as by comments faculty submitted related to the innovations and a graphical representation of the social network with the nodes of adopters shaded.
01 Jan 2019
TL;DR: In this article, the power of Facebook is explained by describing each entity (person or organization) within a network as a node connected to many other nodes by virtue of various relationships that allow information to spread.
Abstract: Facebook continues to dominate the social media landscape with 68% of American adults using the social networking site and – among those users – 74% accessing the site daily (Pew Research Center, 2018). Like consumers, businesses have adopted Facebook in droves. According to Facebook, Inc. (2017), more than 70 million businesses around the world use Facebook business pages each month. Given the overwhelming, generation-spanning adoption and impressive usage statistics of the social networking site, there is little question as to whether a brand should have a presence on Facebook, but many questions remain concerning how brands should manage their Facebook brand page presence for maximum return on investment. Social network theory helps explain the power of Facebook, describing each entity (person or organization) within a network as a node connected to many other nodes by virtue of various relationships that allow information to spread (Marin & Wellman, 2010). Information shared via word of mouth (WOM) is generally considered to significantly impact consumer purchasing decisions (Richins, 1983) and positive or negative statements issued by a consumer to multiple nodes on the internet are considered electronic world of mouth (eWOM) (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004). On Facebook, relationships allow information to spread from node to node via electronic word of mouth (eWOM). Unlike advertising, a highcontrol/low-credibility promotional tool, eWOM is a low-control/high-credibility tool (Solomon, 2014). While marketers have little control over what is said about their brand, consumers are more receptive to the information shared via eWOM as consumers trust credible peers more than paid sponsors. For social media marketers, it is important to understand what types of brand page content spark eWOM in the form of sharing, which is among a set of higher-involvement, visible consumer online brand related activities (COBRAs) (Muntinga, Moorman, and Smit, 2011). These COBRAs enable a brand to leverage the consumer’s relationships/credibility with other consumers as information is transmitted from node to node. This may be especially useful to marketers of innovative brands working to reduce perceived risk associated with purchase and increase rate of adoption, as WOM has a greater effect on purchasing decisions when consumers perceive higher levels of risk associated with the purchase (Still, Barnes, & Kooyman, 1984).
DOI
22 Sep 2021
TL;DR: In this article, the authors analyze the diffusion of innovation models to increase the use of information technology so that every application developed in higher education can run optimally following the design objectives.
Abstract: The rapid and massive development of technology is a challenge for Higher Education to continue adapting and innovating to maintain academic quality. Therefore, this study aims to analyze the diffusion of innovation models to increase the use of information technology so that every application developed in higher education can run optimally following the design objectives. The method used in this study is an innovation diffusion model, which includes an analysis of the essence of communication channel innovation, the reception process, and social systems. Technology acceptance is assessed based on the innovation-decision process stage, which includes the degree of knowledge, persuasion, decision-making process, implementation, and confirmation of the benefits of using technology. The results show that the process of innovation diffusion can run effectively when paying attention to the profile of human resources (brain-ware), technological conditions (software and hardware), organizational policies (orga-ware), documentation (info-ware), and environmental dynamics (social systems).

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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202310
202236
202172
202078
201977
201898