A taxonomy of factors affecting attitudes towards educational technologies for use with technology acceptance models

Abstract

The aim of this theoretical review was to identify the important factors shown to affect attitudes towards use of educational technologies by students or educators in higher education institutions and organise them into broad, intermediate and narrow groupings. This was done to assist the construction of more objective measurement instruments used in the evaluation of educational technologies. A qualitative review of the influential factors that affect user attitudes, intentions and motivations to use educational technologies was conducted, first by interrogating the fundamental behavioural theories underpinning technology acceptance models, and then by exploring the findings of later and contemporary empirical research conducted in the educational context. Identified factors were grouped to produce an ordered taxonomy of measurement constructs. This taxonomy provides each construct’s lineage back through tertiary, secondary and primary taxonomic groups and provides a greater scope of measurement than commonly used models. Seven primary and twenty two secondary and tertiary taxonomic groups were defined, which collectively comprise sixty one measurement constructs. The taxonomy is designed to reduce measurement bias within studies and also acts as a basis for consistent and objective benchmarking within and across institutions. Practitioner NotesWhat is already known about this topic Technology acceptance models are derived from a number of foundational behavioural and motivational theories. The TAM and UTAUT are validated models that appraise attitude and/or behavioural intent to use an educational technology, which nonetheless do not cover the entire scope of what has been shown to be important in various studies. There is little consistency from study to study of measurement constructs used in technology acceptance models. What this paper adds Collection and organisation of the salient measurement constructs into a flexible taxonomy. Establishment of a consistent measurement scope that is specifically suited to educational technology research. Establishment of construct lineage that clearly shows similarities and differences between the various constructs. Implications for practice and/or policy The taxonomy supports robust instrument construction to improve both convergent and discriminant validity of measurement models. The taxonomy provides a recommended scope for higher education institutions to measure factors affecting use of various educational technologies. Consistent use of the taxonomy will provide an objective standard that can be used to compare across institutions or within institutions over time, which assist with benchmarking and management decisions. The taxonomy can be used as a framework for meta‐analyses or to collate ‘prior’ data to use in Bayesian‐type technology evaluation.

College students’ use and acceptance of emergency online learning due to COVID-19

“…Technology, if used effectively, allows students and teachers to mutually engage and collaborate ( Bower, 2019 , GarciaBotero et al, 2018 ; Gonzalez et al, 2020 ). The more successful transitions to online learning are influenced by the user's intention and the usefulness of the technology ( Yakubu & Dasuki, 2019 ; Kemp et al, 2019 ). Online learning's effectiveness highly depends on the degree of acceptance of the user (Tarhini et al, 2017).…”

“…Technology acceptance models explain the determinants of computer acceptance among user populations ( Abdullah & Ward, 2016 ; Chan, 2013; Kemp et al, 2019 ; Teran-Guerrero, 2019 ). The first technology acceptance model (TAM) was based on cognitive theories that explain the process of adopting a behavior.…”

“…Researchers use TAM to understand the use and acceptance of mobile learning; however, the first model has limitations and has been redefined many times ( GarciaBotero et al, 2018 ; Teran-Guerrero, 2019 ). Kemp et al (2019) analyzed different technology acceptance models and developed a taxonomy of factors that affect attitudes towards the use of educational technologies by students or educators in higher education institutions. The taxonomy included seven primary categories: a) attitude, affect, and motivation; b) social factors; c) usefulness and visibility; d) instructional attributes; e) perceived behavioral control, f) cognitive engagement, and g) system attributes.…”

“…The first group of factors refers to attitudes, affect, and motivation ( Kemp et al, 2019 ). Attitudes towards a behavior refer to the individual's positive or negative evaluation of the behavior ( GarciaBotero, 2018 ; Kemp et al, 2019 ). Students’ attitudes towards educational technology directly affect their learning process ( Ali, 2020 ).…”

“…Another consideration is the user's affect toward the learning experience. Affect includes the user enjoyment and satisfaction with the prior use of an information system, the affect toward the use of technology, and the individual's emotional state ( Kemp et al, 2019 ). Findings suggest that there are negative correlations between negative emotions and cognitive processes and learning outcomes ( Heckel & Ringeisen, 2019 ).…”