Open Access

Student academic success plays an important role in higher education institutions, as it is often used as a core measure of university performance. Internationally, there is an extensive literature on the predictors of academic success. In addition to intellectual variables, the effects of psychological factors have received increased attention. However, there is little literature on cross-cultural differences in the predictors of academic success. This study examines three psychological predictors (hope, self-efficacy, and motivation) and investigates differences between the scores of business students at a university in Germany and a university in Finland. The results indicate differences in students’ scores on the predictor hope. In addition, some differences were found in relation to the students’ demographics. Regarding the predictive power of the factors examined, for hope and self-efficacy this was higher in Finland than in Germany.

Dynamic personalization of learning trajectories that integrate different perspectives and variable scenarios is a viable way to improve the effectiveness and efficiency of training and education. Serious games offer a designated platform for this by aggregating learner interactions and using these to dynamically configure, adjust, and tailor the game to individuals and contexts. An architecture is presented to support the creation of serious games for specific scenarios in a faster, more effective, and efficient manner. Following a research-by-design approach, the architecture is simultaneously developed and applied in case studies, with the experiences infused as enhancements for subsequent design iterations.

Vergleicht man die Corona-bedingten Schulschließungen in Deutschland mit sechs ausgewählten Ländern Europas im Zeitraum von Januar 2020 bis Mai 2021, liegt Deutschland auf dem zweiten Platz der längsten Schulschließungen mit insgesamt 183 Tagen. Länger schlossen nur die Schulen in Polen mit insgesamt 273 Tagen. Bei der Herausforderung, auf Distanzlehre umzusteigen, ist als Ausgangslage der Digitalisierungsstand der Schulen vor Corona ein wichtiger Faktor: Deutschland liegt in Bezug auf Online-Lernplattformen und Ressourcen bzw. Vorbereitungszeit für die Nutzung digitaler Geräte im Unterricht auf den letzten Rängen.

The scheduling problem of distributed permutation flow shop with limited buffer aiming at production efficiency measures has attracted widespread attention due to its closer alignment with real manufacturing environments. However, the energy efficiency metric is often ignored. The Energy-efficient Scheduling of Distributed Permutation Flow shop with Limited Buffer (EEDPFSP-LB) with the objectives of Makespan (Cmax) and Total Energy Consumption (TEC) is studied, and a Cooperative Fruit Fly Optimization Algorithm (CFOA) is proposed in this paper. First, the critical path of EEDPFSP-LB is identified, and energy-efficient operation are applied to non-critical paths to reduce the system’s energy consumption. Second, five acceptance criteria for multi-objective optimization are introduced to enhance the diversity of the population. Third, to select a superior next-generation population, a new congestion calculation method is introduced to resolve the issue of indeterminate positional relationships among non-dominated solutions with identical crowding distances at the same dominance level. Finally, CFOA is extensively tested and compared with state-of-the-art algorithms across 360 instances, demonstrating CFOA’s strong competitiveness in solving EEDPFSP-LB.

Purpose: Competency models are widespread in entrepreneurship and help develop educational offerings. Although existing models cater to specific sub-disciplines, the field of Industry 4.0 startups still needs a tailored competency. Therefore, this study aims to bridge this gap by developing a specific competency model to address the unique challenges in Industry 4.0 entrepreneurship. Design/methodology/approach: The research approach involved a content analysis and interview study in compiling and categorizing the necessary competencies to succeed in the Industry 4.0 domain. The developed model was subjected to different forms of validation using the Content Validity Index and inter-rater reliability incorporating expert feedback. Findings: The described multi-methodological approach resulted in the proposed “CompEntre 4.0” model, which contains 23 crucial competencies for Industry 4.0 startups. The results of this model validation demonstrate that it meets the necessary threshold values, establishing its reliability and potential for future use and further improvement. Practical implications: By providing a structured framework tailored to the specific demands of this domain, the competency model has the potential to guide and empower entrepreneurs, improving their prospects for success in the rapidly evolving landscape of Industry 4.0. Originality/value: While there are specific competency models for the entrepreneurship field and for specific sub-disciplines of entrepreneurship, there is, despite numerous specifics, no competency model for Industry 4.0 entrepreneurship yet.