Clustering university teaching staff through UTAUT: Implications for the acceptance of a new learning management system

Abstract

AbstractThe unified theory of acceptance and use of technology (UTAUT) is a survey instrument used to determine technology usage intention and behaviour. UTAUT consists of four predictor variables; performance expectance, effort expectancy, social influence and facilitating conditions. This study investigates the technology acceptance profiles of university teaching staff, by using the UTAUT predictor variables as clustering variables, in the context of the implementation of a new learning management system (LMS). While students are mostly the focus of research on technology acceptance in higher education, university teaching staff are predominantly overlooked. Using a modified UTAUT questionnaire, 244 university teaching staff from a Belgian university took part in a survey focusing on their acceptance and use of the new LMS. Most studies on LMS acceptance in higher education are variable centred, whereas this research takes a person‐centred approach. This approach will shed new light on how UTAUT can provide information which can be used to interpret the professional development needs during an institution‐wide educational technology implementation. A cluster analysis with the predictor variables of UTAUT as input variables resulted in three distinct groups: a high, moderate and a low scoring cluster. These differences between the clusters were also reflected in the acceptance of the LMS. The results of this study will therefore facilitate decision making and guidelines for the design of institution‐wide professional development initiatives that are targeted towards the needs of specific groups of university teaching staff. Practitioner NotesWhat is already known about this topic Most research has focused on university students, or on general barriers to technology acceptance. The four core predictors of UTAUT are reliable determinants of intention and attitude, which in turn are direct determinants of use. An alternative to measuring actual use, self‐reported intensity of use, has been found to be a direct determinant of actual use. Previous cluster analyses in connection with technology acceptance have shown that there are meaningful differences between groups of users in the way they accept and use a technology. Clustering gives additional information about technology users that may facilitate follow‐up research or support initiatives designed to suit the needs of the groups. What this paper adds A three‐solution cluster analysis reveals three distinct groups of technology acceptance in university teaching staff: High, moderate and low. High users are most likely to innovate. Moderate and low users most likely need additional support as well as increased social influence from policy and decision makers. Implications for practice and/or policy The professional development support needs of each group can be more easily addressed and targeted separately to suit their specific needs. This is an opportunity for policy and decision makers to revise and optimise policies on LMS use as well as on the supportive initiatives needed to facilitate better and more effective use of the LMS. Innovating will require additional support. While the high usage group is clearly more inclined to use the system more innovatively, the two other groups will require additional resources and social pressure to influence their intentions regarding innovating.