2 Practitioner notesWhat is already known about this topic• Technology acceptance models (TAMs) include technology use behaviour, use intention, individual believes about technology use, and several context variables as moderators.• A prominent TAM is the Unified Theory of Acceptance and Use of Technology (UTAUT), which is however insufficiently validated in culturally diverse settings.• Geert Hofstede describes culture using several dimensions that include masculinity, individualism and uncertainty avoidance.• Scarce and isolated research results suggest moderating influences of Hofstede's cultural dimensions in educational TAMs; an overall picture is missing.What this paper adds• Based on a culturally diverse sample of nearly 3000 participants, UTAUT is validated with respect to computers as learning tools, while suggesting the integration of three cultural dimensions in UTAUT.• The results confirm a positive influence of cultural masculinity on performance expectancy, and a negative influence on effort expectancy.• Cultural individualism may reduce the perceived social influence with respect to the adoption of educational technologies.• Uncertainty avoidance may increase computer anxiety, amplify the influence of effort expectancy on the use intention and the influence of computer anxiety on the use behaviour, and impede technology use.Implications for practice and/or policy• In intercultural and cross-cultural settings, learners' acceptance of educational technology may depend on their cultural background.• National culture is likely to have the strongest influence on acceptance mechanisms; nevertheless, professional culture plays also a significant role.• Depending on culture, performance and effort expectancy, social influence and computer anxiety may be different, and have different influences on educational technology acceptance.• Culture may serve as a customizing criterion for the design of technology-based educational settings.3 Examining a large sample (N = 2866) of learning technology users from Germany and Romania by means of questionnaire survey, we investigate the differences in culture and technology acceptance between sample subgroups. The collected data reveal the presence of cultural differences both between countries and between professions. In line with previous research, these differences are associated with dissimilar acceptance profiles, ie. different values of acceptance variables and of path coefficients between them. Based on the findings, this study makes headway in cross-cultural research by proposing an extended model of UTAUT -one which integrates three of Hofstede's culture dimensions. As a practical implication, national and professional culture may shape computer-based learning environments. Towards the integration of culture into the Unified Theory of Acceptance and Use of Technology

The Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003Venkatesh et al., , 2012 proposes a major model of educational technology acceptance (ETA) which has been yet validated only in few languages and cultures. Therefore, this study aims at extending the applicability of UTAUT to Turkish culture. Based on acceptance and cultural data from a large sample (N = 1723) of Turkish educational technology users of diverse profession, geographical location, age and gender, the UTAUT questionnaire displays good convergent and discriminant validity. Structural equations modeling confirms the model validity. Cross-cultural differences are explored within Turkey both between regions (Istanbul area vs. other regions) and between professional cultures (STEM, i.e. science, mathematics, engineering and mathematics, vs. non-STEM professions). The comparison uses measurement results from other European countries as a reference. Conclusions are drawn with respect to UTAUT applicability in educational practice, and to interconnections between ETA and culture.

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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.

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