Technology Acceptance of Augmented Reality and Wearable Technologies

Augmented Reality and Wearables are the recent media and computing technologies, similar, but different from established technologies, even mobile computing and virtual reality. Numerous proposals for measuring technology acceptance exist, but have not been applied, nor fine-tuned to such new technology so far. Within this contribution, we enhance these existing instruments with the special needs required for measuring technology acceptance of Augmented Reality and Wearable Technologies and we validate the new instrument with participants from three pilot areas in industry, namely aviation, medicine, and space. Findings of such baseline indicate that respondents in these pilot areas generally enjoy and look forward to using these technologies, for being intuitive and easy to learn to use. The respondents currently do not receive much support, but like working with them without feeling addicted. The technologies are still seen as forerunner tools, with some fear of problems of integration with existing systems or vendor-lock. Privacy and security aspects surprisingly seem not to matter, possibly overshadowed by expected productivity increase, increase in precision, and better feedback on task completion. More participants have experience with AR than not, but only few on a regular basis.WEKIT (grant agreement no. 687669

INVESTIGATING THE IMPACT OF ECONOMIC, POLITICAL, AND SOCIAL FACTORS ON AUGMENTED REALITY TECHNOLOGY ACCEPTANCE IN AGRICULTURE (LIVESTOCK FARMING) SECTOR IN DEVELOPING COUNTRIES

The discussion of the factors affecting the tendency to accept new technologies in developing countries is very important. Lack of use of modern technologies in developing countries, especially in the agricultural (livestock farming) sector, leads to negative effects on the quality and quantity of products and the country loses its ability to compete in the international arena. The main purpose of this study is to investigate the factors affecting on Augmented Reality technology acceptance in the agricultural (livestock) sector of Iran. In this research, the dependent variable is a qualitative variable that is classified into five levels based on the theory of experts using the SWARA method. The dependent variable indicates the ability (awareness) and capability (financially) of a person to accept AR technology. We used the Multinomial Logit model due to the dependent variable is nominal and has more than two categories. The results showed that, the variables of public participation, and education have a significant effect on the willingness to adopt Augmented Reality technology at all levels among farmers.  But variable costs and the number of family labor do not have a significant effect on the willingness to accept Augmented Reality technology. The policy recommendations of this research are that councils can play an important role in raising the level of public participation and conveying the demands of the people to the government. To do this, they must receive the necessary training in order to attract public participation. This is possible through training workshops to increase the level of farmers’ awareness. &nbsp

Methods for enhanced learning using wearable technologies. A study of the maritime sector

Maritime safety is a critical concern due to the potential for serious consequences or accidents for the crew, passengers, environment, and assets resulting from navigation errors or unsafe acts. Traditional training methods face challenges in the rapidly evolving maritime industry, and innovative training methods are being explored. This study explores the use of wearable sensors with biosignal data collection to improve training performance in the maritime sector. Three experiments were conducted progressively to investigate the relationship between navigators' experience levels and biosignal data results, the effects of different training methods on cognitive workload, trainees' stress levels, and their decision-making skills, and the classification of scenario complexity and the biosignal data obtained by the trainees. questionnaire data on stress levels, workload, and user satisfaction of auxiliary training equipment; performance evaluation data on navigational abilities, decision-making skills, and ship-handling abilities; and biosignal data, including electrodermal activity (EDA), body temperature, blood volume pulse (BVP), inter-beat interval (IBI), and heart rate (HR). Several statistical methods and machine-learning algorithms were used in the data analysis. The present dissertation contributes to the advancement of the field of maritime education and training by exploring methods for enhancing learning in complex situations. The use of biosignal data provides insights into the interplay between stress levels and training outcomes in the maritime industry. The proposed conceptual training model underscores the relationship between trainees' stress and safety factors and offers a framework for the development and evaluation of advanced biosignal data-based training systems