The fourth industrial revolution, or Industry 4.0, introduced a new level of production and transformed the use of digital integration and intelligent engineering. The essence of such initiatives is that manufacturers are able to satisfy a constantly changing demand by using flexible "smart" machines. However, the implementation of all the technologies used by the Industry 4.0 in production systems is not easy due to significant challenges. Therefore, it is necessary to identify and examine the challenges faced by manufacturing companies when trying to apply Industry 4.0. Occupational safety is certainly one of the most important challenges of the new production paradigm, to which adequate attention should be paid. This challenge is becoming even more pronounced in the project environment because of the temporary nature of the projects. In order to improve the level of occupational safety, its adequate analysis seems necessary. One way to do this is to compare the subject area in different contexts. In this paper, using the PROMETHEE tool, a multicriterial analysis of the effect of Industry 4.0 on occupational safety factors at industrial projects in Serbia was performed, compared to the previous industrial paradigm.
Research question: The aim of the study is to examine the inclination of engineering students in Serbia to adopt M-learning. Motivation: Mobile technologies provide new solutions in the current concept of learning. Students today have a new approach to learning since they live in the era of digital technology. The use of mobile technology in higher education came along with the technological enlightenment of professors and students, eradicating geographical boundaries and enabling cooperative learning. Idea: Mobile learning (M-learning) is unthinkable without the use of mobile devices and it plays an increasingly important role in the development of teaching methods in higher education. The research refers to the analysis and assessment of M-learning in higher education with the aim to identify problems and weaknesses related to the application of M-learning in order to set alternatives and criteria for the proposed model. Data: The survey was conducted through a questionnaire, and 341 correctly filled out questionnaires were collected. The obtained data were processed by the Visual Promethee software package. Tools: The research methodology was based on the Multi-criteria decision analysis (MCDA) method which was used in the form of PROMETHEE II/GAIA technique. In order to rank the student programmes from different study areas, groups of questions were used as criteria, and study programmes were used as alternatives. Findings: The acquired results indicated that M-learning was mostly used by the students of Engineering Management, since they frequently used mobile technology in certain required courses, as well as for obligatory business practice. It was also concluded that the study programme Metallurgical Engineering had the lowest performance concerning the implementation of M-learning among all other observed study programmes. Contribution: The conclusions of this study can add to the awareness-raising of the higher education institutions' interested parties and alert them to improve the quality of M-education by pointing out that there are differences in the adoption of M-learning depending on the type of engineering sciences.
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