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This paper explores the convergence of Education 4.0 and Industry 4.0 and presents a Twin Peaks model for their seamless integration.

A high-level literature review is conducted to identify and discuss the important challenges and opportunities offered by both Education 4.0 and Industry 4.0. A novel Twin Peaks model is devised for the convergence of these domains and to cope with the challenges effectively.

The proposed Twin Peak model for the convergence of Education 4.0 and Industry 4.0 suggests that the development of these two domains is interdependent. It emphasizes ethical considerations, inclusivity and understanding the concerns of stakeholders from both education and industry. We have also explained how continuous incremental adaptation within the proposed Twin Peaks model might assist in addressing concerns of one sector with the opportunities of the other.

First, Education 4.0 and Industry 4.0 are reviewed in terms of opportunities and challenges they present. Second, a novel Twin Peaks model for the convergence of Education 4.0 and Industry 4.0 is presented. The proposed discovers that the convergence is adaptive, iterative and must be ethically sound while considering the broader societal implications of the digital transformation. Third, this study also acts as a torch-bearer for the necessity for more research of this kind to guarantee that our educational ecosystem is adaptable and capable of producing the skills required for success in the era of IR4.0.

Business models (BMs) are becoming increasingly crucial for value creation in the healthcare sector. The study explores the conceptualization and application of BM concepts within the healthcare sector and investigates their evolution in emerging economies (EEs) and developed economies (DEs). This study aims to uncover these two contexts' shared characteristics and unique variances through a comparative analysis.

The paper systematically investigates and consolidates the literature on healthcare by employing the antecedents, decisions and outcomes (ADO) framework and finally examines 71 shortlisted articles published between 2003 and 2022.

The recognition of the BM within healthcare is increasing, both in EEs and DEs. EEs prioritize value creation and capture through cost efficiency, while DEs focus on innovation. Key theories employed include a resource-based view, the network theory and the theory of innovation. Case studies are commonly used as a methodology. Further research is needed to explore the decisions and outcomes of BMs.

The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

Beyond comparing and highlighting gaps in BMs between EEs and DEs, benchmarking DE's healthcare business models (HBMs) helps healthcare organizations in EEs align their practices, mitigate risks and establish efficient healthcare systems tailored to their specific contexts. The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

The study analyzes HBMs using an SLR framework perspective and provides practical implications for academicians and practitioners to enhance their decision-making.

Challenges of conducting site visit activities, a vital component of architecture learning during the recent pandemic have proved our unreadiness in facing the digital future. The lack of understanding of learning technology has affected the education experience. Thus, there is a need to investigate immersive learning technology such as immersive virtual reality (IVR) to replace students’ concrete experience in the current learning setting. This study aims to answer: (1) What is the influence of IVR in experiential learning (EL) in enhancing the personal spatial experience? (2) Does IVR in EL influence students' approach to learning during the architecture design process?

The research was conducted as an action research design approach. Action research was employed in the first-year architecture design studio by the lecturer as a practitioner-researcher. The personal spatial experience survey was performed in the earlier phase to identify the students’ prior spatial experience. Architectural Spatial Experience Simulation (ASES) a learning tool was implemented and assessed with Architecture Design Learning Assessment (ADLA) rubric, which was developed to evaluate EL and student’s approach to learning during the architecture design learning process.

The outcomes revealed that ASES as a learning tool in EL could improve the participants’ spatial experience, particularly those with minimal prior personal spatial experience. ASES was recognized to enhance the participants’ EL experience and encourage changes in student’s approach to learning from surface to deep learning.

This research benefits the architecture design learning process by offering a learning tool and a framework to resolve challenges in performing site visit activities and digital learning. It also contributes by expanding the EL theory and students’ approach to learning knowledge in the architecture education field.

Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions.

The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data.

The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs.

The proposed approach explores the possibility of using machine learning for SDG classifications in scale.

The rapid advancement of Education Technology (EdTech) offers promising opportunities for educational institutions to integrate sustainable business practices into their operations and curriculum. The integration of EdTech into sustainability education has emerged as a powerful tool to promote environmental awareness, foster sustainable behavior, and address the pressing challenges of climate change and resource depletion. This chapter explores the growing significance of EdTech in sustainability education, analyzing its potential to cultivate a generation of environmentally conscious and responsible global citizens. It also aims at identifying and examining the most prominent emerging EdTech tools specifically designed to promote sustainability in educational settings. Furthermore, it aims to comprehend the institutional elements that have successfully incorporated and expanded the utilization of EdTech tools to promote enduring business practices. Additionally, the chapter addresses the challenges and obstacles faced by educational institutions in adopting and implementing these technologies and propose strategies to overcome these barriers.

Open educational resources (OERs) are internet-based digital content that is used for academic purposes by instructors, students and researchers in the era of the information economy. Hence, this study aims to systematically review the literature, focusing on OERs’ benefits and challenges in the academic world.

The relevant literature systematically reviewed following the preferred reporting items for systematic literature reviews and meta-analyses (PRISMA) guidelines. The pertinent literature was obtained from four main scholarly databases, and finally, 21 papers that met the inclusion criteria were included in this study.

The findings revealed that the key benefits of OERs include expanded access to knowledge, supporting lifelong learning, pedagogical benefits and enhancing students’ learning outcomes. However, the key challenges include lack of time to find appropriate resources, lack of awareness about the usage and copyrights, quality assurance and technological limitations and lack of organizational support.

The practical and policy implications highlight the joint venture of academia and library professionals to help the students evaluating OERs, quality assurance, copyright issues and lifelong learning.

Earlier studies missed few significant insights of OERs, such as they did not address the quality assurance of OERs; the issue of understanding of copy right (creative common license) challenges related with OERs; and lack of time for finding suitable resources. Hence, this study identified significance insights related with OERs.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether gamification and personalization as environmental stimuli to learners’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs) and, in turn, their learning outcomes in MOOCs.

Sample data for this study were collected from learners who had experience in taking gamified MOOCs provided by the MOOCs platform launched by a well-known university in Taiwan, and 331 usable questionnaires were analyzed using structural equation modeling.

This study demonstrated that learners’ perceived gamification and personalization in MOOCs positively influenced their cognitive LE and emotional LE elicited by MOOCs, which jointly explained their LP in MOOCs and, in turn, enhanced their learning outcomes. The results support all proposed hypotheses and the research model, respectively, explaining 82.3% and 65.1% of the variance in learners’ LP in MOOCs and learning outcomes.

This study uses the S-O-R model as a theoretical base to construct learners’ learning outcomes in MOOCs as a series of the psychological process, which is influenced by gamification and personalization. Noteworthily, while the S-O-R model has been extensively used in prior studies, there is a dearth of evidence on the antecedents of learners’ learning outcomes in the context of MOOCs, which is very scarce in the S-O-R view. Hence, this study enriches the research for MOOCs adoption and learning outcomes into an invaluable context.

We wanted to find out how infant multinationals originating from Poland enact opportunities in Sub-Saharan African (SSA) countries.

We conducted a comparative case study of four Polish firms operating in SSA.

We found that when entering SSA, studied firms employed effectual decision-making logic. Thus, their internationalization was means-driven, serendipitous, partnership-oriented, based on the “affordable loss” principle and focused on shaping opportunities in SSA, rather than predicting, analyzing and planning any firm-specific assets or capabilities.

We illuminated the nature of the means employed in effectual internationalization and the role of partners (“effectual stakeholders”) in this process. Thus, we contribute to a deeper understanding of how infant multinationals navigate extreme uncertainty in the emerging SSA markets.

This study explores resilience learning from uncertainty, taking a holistic view by considering individual, firm and contextual factors. Resilience development is understood by focusing on how uncertainty is related to entrepreneurs and their environment, suggesting that developing resilience needs to be a continuous learning process.

This qualitative study explores factors related to entrepreneurial uncertainty, resilience and learning. Evidence is drawn from interviews with rural entrepreneurs in two regions of Indonesia, and analyzed using a rigorous approach to generate codes, second-order themes and aggregate dimensions for the theoretical contributions.

Uncertainty readiness, uncertainty response and uncertainty opportunity for resilience emerge as the key learning areas from this study. They are related to resilience on a personal, community and systemic level. The proposed framework relates learning from uncertainty to the process of developing resilience for entrepreneurs and their communities.

This study proposes a framework based on resilience motivation and learning from uncertainty as usual. It explores the relationships between uncertainty readiness, responses and opportunities with personal, relational and systemic resilience factors. This contributes to entrepreneurship behavior research at the intersection of organization studies and management in the socio-economic and often informal context of developing countries.

This study aims to predict customer churn in resort hotels by calculating the churn probability of repeat customers for future stays in the same hotel brand.

Based on the recency, frequency, monetary (RFM) paradigm, random forest and logistic regression supervised machine learning algorithms were used to predict churn behavior. The model with superior performance was used to detect potential churners and generate a priority matrix.

The random forest algorithm showed a higher prediction performance with an 80% accuracy rate. The most important variables were RFM-based, followed by hotel sector-specific variables such as market, season, accompaniers and booker. Some managerial strategies were proposed to retain future churners, clustered as “hesitant,” “economy,” “alternative seeker,” and “opportunity chaser” customer groups.

This study contributes to the theoretical understanding of customer behavior in the hospitality industry and provides valuable insight for hotel practitioners by demonstrating the methods that facilitate the identification of potential churners and their characteristics.

Most customer retention studies in hospitality either concentrate on the antecedents of retention or customers’ revisit intentions using traditional methods. Taking a unique place within the literature, this study conducts churn prediction analysis for repeat hotel customers by opening a new area for inquiry in hospitality studies.