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Abstract:COVID-19 has caused a devastating impact on public health and made the development of the COVID-19 vaccination a top priority. Herd immunity through vaccination requires a sufficient number of the population to be vaccinated. Research on factors that promote intention to receive the COVID-19 vaccination is warranted. Based on Diffusion of Innovations Theory, this study examines the association between the perceived efficacy of the COVID-19 vaccination, use of social media for COVID-19 vaccine-related information, openness to experience and descriptive norm with the intention to receive the COVID-19 vaccination, and the moderating role of openness to experience among 6922 university students in mainland China. The intention to receive the free and self-paid COVID-19 vaccination is 78.9% and 60.2%, respectively. Results from path analyses show that perceived efficacy of the COVID-19 vaccination, use of social media for COVID-19 vaccine-related information, and openness to experience and descriptive norm are all positively associated with the intention to receive COVID-19 free and self-paid vaccination. The association between the perceived efficacy of the COVID-19 vaccination and descriptive norm with the intention to receive the COVID-19 vaccination is stronger among those with a lower level of openness to experience. Our findings support the usefulness of Diffusion of Innovations Theory and the moderating role of openness of experience in explaining intention to receive the COVID-19 vaccination.Keywords: COVID-19 vaccination; diffusion of innovations theory; perceived efficacy of vaccine; social media; openness to experience; descriptive norm
The Internet is the most common communication and research tool worldwide. Perusal of the World Wide Web quickly reveals the variety of information available. Internet adoption can be considered the late 20th century's most important event. In academic environments today, Internet use among faculty members has been widely expanded, with professors now integrating Internet technology into classroom activities. Imam Muhammad Bin Saud Islamic University (IMSU) is a pioneering public university in Saudi Arabia. Until recently, some faculty members at IMSU were unable to access the Internet through the university. It is important to study the effects of this delay on faculty members regarding research and academic activities. This study identified the statistically significant differences in demographic characteristics of Internet adopters and non-adopters among faculty members at IMSU, examined whether faculty members' perceptions of the Internet affected adoption, determined if the university administration's decisions impacted faulty members' decisions to adopt the Internet, identified factors motivating faculty members to adopt the Internet, identified obstacles influencing faculty members' decisions to use the Internet, and determined whether innovation characteristics as perceived by faculty members predicted Internet adoption. Using Rogers' diffusion of innovation theory, the influence of eight attributes were examined regarding Internet adoption among IMSU faculty members. Multiple regression and chi-square techniques were conducted to analyze the data and answer research questions. Statistically significant differences were identified among Internet adopters and non-adopters regarding gender, age, academic rank, discipline, and English proficiency. The data revealed 54.7% of IMSU faulty members used the Internet for research and academic activities twice a month or less, indicating a low Internet adoption rate. Statistically significant differences were noted among adopters and non-adopters relative to income level and English proficiency. Multiple regression analysis showed that all attributes of innovation individually predicted Internet adoption. The combination of all attributes indicated the model could predict Internet adoption among faculty.
Another factor that can also have an effect on the rate of adoption of innovation is the communication channel. This refers to the way through which messages about the innovation are transmitted from one person to another (Chakrabarti, Feineman & Fuentevilla, 1983). Individuals often assess an innovation not based on scientific research by experts, but through the subjective evaluations of near peers who have adopted the innovation. Such near peers represent a role model, whose innovation behaviour tends to be imitated by other individuals in their system. A distinguishing aspect of diffusion is that at least some degree of heterophily is usually present in communication about innovations. Heterophily represents the extent to which two or more individuals are different in certain characteristics, such as beliefs, education, and social status. The opposite of heterophily is homophily - the extent to which two or more individuals are similar in certain traits. The level of similarity among group members across which an innovation diffuses tends to accelerate the ease and speed with which the diffusion occurs. For instance, innovations spread faster among homophilous groups than among heterophilous groups (Cain, 2002).
Finally, change agents aim to affect the innovation adoption decisions of individuals in the system in a direction considered desirable by the agent. There are 7 functions performed by change agents: creating a need for change on the part of clients; developing an information exchange relationship; diagnosing problems; developing an intent to change in the client; translating intentions into action; stabilising adoption and preventing discontinuance; and attaining a terminal relationship with clients. Change agents operate interventions, as actions with a coherent goal to bring about behaviour change with the purpose of generating identifiable outcomes. Targeting, which is based on customising the design and delivery of a communication program on the basis of the characteristics of an intended audience segment, is one way of segmenting a heterogeneous audience. Through this aforementioned approach, customised messages that fit each individual's situation are delivered. In terms of a change agent's relative success in ensuring the adoption of innovations by clients, it is positively related to factors such as the extent of the change agent's effort in contacting clients, a client orientation, rather than a change agency orientation, the level to which the diffusion program complies with clients' needs, and increasing clients' capability to assess innovations (Rogers, 2003).
In particular, there is a typical shape for a diffusion curve when innovations are developed successfully and stay undisturbed in a social system. At the outset, the adoption rate is low, but it then increases gradually and decreases again towards the end. If it is presented graphically as a curve of percentages, it normally takes the form of an S-curve (Figure 1 below). If the rates of adoption are taken as an absolute number of adopters per unit of time rather than in percentages, the outcome is a bell-shaped or wave curve, similar to a normal distribution (Figure 2 below).
When discussing the limitations in relation to the DOI theory, it is worth discussing first the limitations or shortcomings of the diffusion research itself, based on which some limitations of the theory itself can be understood and explained, as below. The four major criticisms of diffusion research discussed by Rogers are:
Ardis and Marcolin (2017) state that researchers have to carefully identify the complex, networked, and learning intensive aspects of technology. They should also understand the role of institutional regimes, putting emphasis on process aspects (involving histories) and the main players in the diffusion area. They have to create multi-layered research designs that factor out mappings between different layers and locales. Also, they have to utilise different viewpoints involving political models, institutional models and theories of team behaviour. They have to use different time scales when crafting accounts of what happened and what the reason behind it is. DOI theory does not provide specific variables to deal with collective adoption behaviours (e.g., the critical role of standards, critical mass, network externalities, sunk costs, path dependence). The Diffusion of Innovations researchers have to be cautious when examining the role of institutional policies and regimes, the effect of the industrial policies and strategies, and the significance of the installed base and learning inertia.
Our lessons learned are limited by the fact that the implementation of the two research projects has recently ended, and therefore it is difficult to draw final conclusions about scale-up, spread, and diffusion of innovations. Building on the work of their predecessor projects, MANEST and MANIFEST have catalyzed the adoption of certain interventions, which the community, district, and national level stakeholders found acceptable. By examining the projects retrospectively, we can draw some lessons about the historic and contextual factors that facilitated the inception and implementation of the MANEST and MANIFEST projects. We are limited in any future-looking/prospective analysis, as the diffusion of the current interventions is yet to reach its full potential.
Our short report highlights the added value of adapting the model of diffusion of innovations for understanding barriers and facilitators to implementing health systems interventions, such as the ones implemented by the MANEST and MANIFEST projects. Implementing interventions through a PAR approach facilitates stakeholder engagement and feeding back of monitoring and evaluation information throughout the implementation period. Furthermore, this approach facilitated the support for strong local leadership through both dissemination and active decision-making about the project, building on the relationships that the teams had developed locally over many years. Designing interventions to support existing processes enhance the likelihood that they will be compatible with the system, though entrenched social norms and customs at the community level need to be understood and appreciated early in the process as they might pose barriers to future adoption and diffusion. Health systems research projects would benefit from analyses beyond the implementation period, in order to better understand how adoption and diffusion happen, or not, over time, after the external catalyst departs. Finally, blending innovations and implementation research adds value and further reflection on the frameworks, tools, and processes needed to facilitate the synthesis of findings and their feedback into decision-making around scaling up key health interventions would be useful. 2b1af7f3a8
