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Technology 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.

Purpose To develop and test a complex model that captures the individuals’ general well-being and the specific oral-health-related well-being. We were specifically interested, as a specific research question, if self-esteem, dental fear, and the oral health-related well-being are credible predictors for the general well-being. Patients and methods A one-time associative research design measured dental-specific anxiety, self-esteem, oral-health-related specific well-being, and general well-being in 281 participants, 3rd and 6th year dental students ( M Age =22.59 years, SD Age =3.13; 55% females), which completed a battery of relevant questionnaires: the Dental Fear Survey, the Rosenberg Self-Image Scale, the short form of Oral Health Impact Profile, and the Flourishing Scale. The data were subject to structural equation modeling in order to validate potential pathways of influence hypothesized based on previous evidence from the literature. Results We developed and tested a complex structural equations model, in which dental fear influences both the specific oral-health-related well-being and the persons’ self-esteem. In turn, self-esteem mediates the influence pathways between dental fear and oral-health-specific well-being, on the one hand, and the overall well-being, on the other hand. Conclusion Our research contributes directly to strengthening the theoretical basis for future interdisciplinary research, by providing, first, a tested and replicable model that surpasses the simple correlation or prediction, and second, empirical evidence for the significant mutual interdependence between psychological experiences, eg, self-esteem, and the two main aspects of well-being, ie, specific and general. From a practical, clinical viewpoint, our research provides further insights and justification for the importance of educating the patient, on all levels, from the individual clinical practice to community programs and public oral health policies, with respect to the importance of oral health.

Dental education includes the development of manual skills to perform tasks needed to train students to drill for the elimination of caries or for restorative purposes. 1 Historically, cadaver teeth have been used to train dental students in these drilling procedures but, for ethical, legal and biological reasons, 2 they have been replaced by artificial teeth made of plastic. 1,3 Contemporary technology allowed the development of haptic virtual simulators (HVS); in recent years, their use has been growing exponentially in medical and dental education. 4-6 High-fidelity simulators can be an interactive didactic tool that allows students to perform certain professional tasks in a safer and more controlled environment. This performance requires the mobilisation and integration of their knowledge and skills in order to solve simulated clinical situations. 1-3 Also, the combined use of both HVS and conventional Phantom-head systems improves spatial