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Preventing, Identifying, and Treating Concussion
Published in Paul M. Salmon, Scott McLean, Clare Dallat, Neil Mansfield, Colin Solomon, Adam Hulme, Human Factors and Ergonomics in Sport, 2020
A key tenet of each component of the CWA framework is the focus on drawing on the expertise and experiences of end-users to inform the analysis of the system. As mentioned previously, most community sports organisations administered by volunteer staff and coaches, who are ultimately the intended end-users or implementers of safety policies and programmes (Donaldson & Finch, 2012; Donaldson et al., 2012; Skille, 2008). Research has shown that policies, such as injury management protocols, need to be developed in collaboration with end-users. According to diffusion of innovations theory, the rate of adoption of an innovation depends more on the end-users’ subjective perception of the relative advantage, compatibility, complexity, trialability, and observability of an innovation than it does on the objective evidence of the innovation’s efficacy (Rogers, 2004). Therefore, SRC management strategies that have been informed by the staff and coaches who operate within the community sporting system are more likely to improve club engagement and implementation of the management protocols and maintain them consistently. The application of CWA, therefore, offers a systematic model to guide consultations with end-users and inform the revision of SRC management for optimal engagement of the community sports context.
Creativity invention and innovation
Published in Riadh Habash, Green Engineering, 2017
Diffusion of innovation is defined as the process by which innovations spread among users (Johnson et al. 2011). Rogers (2003) defines diffusion as “the process in which an innovation is communicated thorough certain channels over time among the members of a social system.” As expressed in this definition, innovation, communication channels, time, and social system are the four key components of the diffusion of innovations. In fact, much diffusion research involves technological innovations, so Rogers (2003) usually used the words “technology” and “innovation” as synonyms. For Rogers, “a technology is a design for instrumental action that reduces the uncertainty in the cause–effect relationships involved in achieving a desired outcome.” It is composed of two parts: hardware and software. While hardware is “the tool that embodies the technology in the form of a material or physical object,” software is “the information base for the tool.” Since software (as a technological innovation) has a low level of observability, its rate of adoption is quite slow.
Towards Adoption of Generative AI in Organizational Settings
Published in Journal of Computer Information Systems, 2023
The classic Diffusion of Innovation (DoI) theory described by Rogers20 outlines five key attributes of innovation within the context of technology. These characteristics encompass relative advantage, which refers to the extent to which an innovation is considered superior to its predecessor or existing alternatives; compatibility, which focuses on how well an innovation aligns with the existing business processes, practices, and values of potential adopters; complexity, which assesses the level of difficulty related with understanding and utilizing the innovation; observability, which pertains to the visibility of the results or outcomes of adopting the innovation to others, and trialability which reflects the ease with which potential adopters can experiment with the innovation before committing to its full implementation. While all five characteristics are relevant, the first three (compatibility, relative advantage, and complexity) are widely found to explain and predict the diffusion of innovations across several studies. As a result, this study recommends incorporating these three factors into the proposed research context.
Evidence-driven model for implementing Blockchain in food supply chains
Published in International Journal of Logistics Research and Applications, 2023
Nam Vu, Abhijeet Ghadge, Michael Bourlakis
Besides the phases by which an innovation is integrated into an organisation, broad categories of determinants to the implementation process are identified based on prominent theories and models from extant IA literature. These determinants can influence the propensity to adopt new technology (Zhu et al. 2006), as well as the success and adequacy of each implementation stage (Hameed, Counsell, and Swift 2012; Pichlak 2016). As observed from the literature, the influential factors to the implementation process of technology can be broadly categorised into four dimensions: technology, organisation, environment, and management. Diffusion of Innovation (DoI), proposed by Rogers (2003), suggested that certain characteristics of new technology, namely relative advantage, complexity, compatibility, trialability, and observability, can influence its adoption. Technology is also a core element in the TOE framework developed by Tornatzky, Fleischer, and Chakrabarti (1990). The other two categories in this framework are organisation characteristics (e.g. size, structure, resources, etc.) and environment characteristics (e.g. market, industry, country, etc.). Management is another important cluster of determinants, as managers possess critical roles in championing and realising the implementation of new technologies and, therefore, should be examined thoroughly (Hameed, Counsell, and Swift 2012; Pichlak 2016). These four main categories of determinants thus feature in a great number of integrative models for the implementation of technologies such as RFID (Hossain, Quaddus, and Islam 2016), IT technology (Hameed, Counsell, and Swift 2012), software as a service (Martins, Oliveira, and Thomas 2016), or business analytics software (Nam, Lee, and Lee 2019).