“I am fine with any technology, as long as it doesn’t make trouble, so that I can concentrate on my study”: A case study of university students’ attitude strength related to educational technology acceptance

Abstract

AbstractTechnology acceptance models presuppose that technology users have clearly defined attitudes toward technology, which is not necessarily true. Complementary, social‐psychological research proposes attitude strength (AS), a construct that has been so far insufficiently examined in the context of technology acceptance. Attitudes toward technology might become weaker after frequent changes in the used technology. This study examines the relationships between AS and educational technology acceptance predictors. In the case of N = 225 German undergraduate students of Educational Sciences, “millennials” using the learning management system Moodle, and based on structural equations modeling (SEM) and fuzzy set qualitative comparative analysis (fsQCA), we found significant relationships between AS and acceptance predictors. Further results suggest two situations leading to technology acceptance, one in which students are performance‐oriented and comply with faculty recommendations; the other in which students are technically experienced and will accept any technology, but avoid technical problems and effort. While the latter situation is only vaguely suggested by SEM, it is much clearly indicated by fsQCA. For acceptance research, we conclude that current acceptance models should be extended by AS, and employ fsQCA. For educational practice, we recommend using fsQCA to assess acceptance predictors when educational technology is implemented in higher education. Practitioner NotesWhat is already known about this topic Educational technology acceptance is mainly represented by the use intention of that technology, further predicted by attitudes operationalized as performance and effort expectancy and social influence. Attitude strength (AS) can differ interindividually, and be directly related to attitudes or moderate the relationships between them. Little is known about the relationships between AS and technology acceptance models. Technology acceptance models are currently verified by regression and structural equations (SEM); fuzzy sets qualitative comparative analysis (fsQCA) additionally informs about factor configurations leading to an outcome. What this paper adds In a sample of over 200 German undergraduate students of Educational Sciences using Moodle as a learning management system, UTAUT could be partially verified. In one situation leading to technology acceptance, students are performance‐oriented and comply with faculty recommendations. In another situation leading to acceptance, students are technically experienced and will accept any technology, but avoid technical problems and effort. While the latter situation is only vaguely suggested by SEM, it is much clearly indicated by fsQCA Implications for practice and/or policy AS may interindividually differ in response to frequent technology changes, and play a significant role for educational technology acceptance. Technology acceptance models used to assess the implementation of educational technologies in higher education can be extended by fsQCA, in order to identify configurations of acceptance factors leading to technology acceptance. Elaborating on different patterns, or situations, of use can lead to targeted educational interventions that enhance acceptance behaviors and, in the case of educational technology, that enhance learning.