Early adopters versus the majority: Characteristics and implications for academic development and institutional change

Abstract

The diffusion of innovation curve has become common parlance when considering the adoption of technology-enhanced learning in higher education. Innovators, early adopters, early majority, late majority and laggards indicate the different rates of technology adoption. This study explored the views and experiences of staff at one research-led institution within one of four colleges, to determine whether there were differences between the characteristics and digital academic practices of teachers, based on their self-identification as one of the five types in a questionnaire. Subsequently, two focus groups allowed comparisons between early adopters and early majority.
 The questionnaire found differences in terms of using a VLE to foster deep thinking through discussion, teachers’ digital practices and attributes, previous engagement with developmental TEL opportunities, perceived level of support from management, and perceived usefulness of CPD events. Focus groups revealed qualitative differences in terms of the amount of time participants invested in learning new technologies, sources of TEL support, preference for different types of academic development, and how they engaged with exemplars in their own or other disciplines.
 In addition to recommendations for different types of academic development for different groups, the study highlighted the importance of early adopters in leading digital practice. Institutions need to recognise and support the unique contribution made by early adopters. They contribute to the resilience, agility and digital capabilities of an institution in responding to rapid changes, such as the Covid19 pandemic, in terms of supporting and leading other staff, and prompting the institution to expand its digital education infrastructure.

Understanding Uganda’s early adoption of novel differentiated HIV treatment services: a qualitative exploration of drivers of policy uptake

“…Secondly, Uganda is often considered a model of DSD delivery and uptake and countries such as Ghana have undertaken visits to the country to learn from its DSD implementation experience [ 30 , 31 ]. The HIV Policy Lab data of 2022 reveals that Uganda is one of the countries which has adopted all three salient features of differentiated HIV treatment services which include community ART distribution, maximum flexibility for clinic visits and multi-month dispensing [ 18 ]. Select providers in Uganda piloted select DSD models (such as FTDR) prior to their formal endorsement by the WHO in 2016 [ 32 ].…”

“…We utilize an implementation research lens [ 17 ] to understand drivers of ‘early adoption’ [ 18 ] of differentiated HIV treatment services, with Uganda as a country case study. The implementation science literature distinguishes between ‘early adopters’ and ‘late adopters’ of health care innovations [ 18 ]. Rogers (2003) introduced the notion of five categories of adopters (Innovators, Early Adopters, Early Majority, Late Majority, and Laggards) [ 19 ].…”

“…This is valuable to major global HIV funding agencies such as PEPFAR and the Global Fund, who are interested in strategies for the further scale-up of DSD globally. For nationallevel HIV program managers in high burden countries, evidence is needed on drivers of DSD uptake for policy planning and programmatic interventions as well as in understanding demand-side factors from the perspective of recipients of HIV care (RoC) [1][2][3][4][5]10] We utilize an implementation research lens [17] to understand drivers of 'early adoption' [18] of differentiated HIV treatment services, with Uganda as a country case study. The implementation science literature distinguishes between 'early adopters' and 'late adopters' of health care innovations [18].…”

“…For nationallevel HIV program managers in high burden countries, evidence is needed on drivers of DSD uptake for policy planning and programmatic interventions as well as in understanding demand-side factors from the perspective of recipients of HIV care (RoC) [1][2][3][4][5]10] We utilize an implementation research lens [17] to understand drivers of 'early adoption' [18] of differentiated HIV treatment services, with Uganda as a country case study. The implementation science literature distinguishes between 'early adopters' and 'late adopters' of health care innovations [18]. Rogers (2003) introduced the notion of five categories of adopters (Innovators, Early Adopters, Early Majority, Late Majority, and Laggards) [19].…”

The impact of word-of-mouth (WOM) on attitudes, behavioural intentions, and actual usage of non-pharmaceutical interventions (NPIs) among early and late adopters

“…Technology adoption (or diffusion) is the process by which the market for a new technology transform over time (Stoneman and Battisti, 2010). Previous research has suggested the existence of five types of adopters, including innovators, early adopters, early majority, late majority and laggards (Dale et al, 2021). Rogers defined diffusion as a "special form of communication" (1995, p. 5) where new ideas are spread from individual to individual over time (Rogers, 2003;Straub, 2009), indicating the critical importance of communication during the technology adoption process between the various types of technology adopters.…”