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This study explores how emotional intelligence, customer orientation, deep acting and surface acting influence job satisfaction among middle managers in their interactions with customers, colleagues and business partners. By examining these factors, we aim to provide insights into their collective impact on job satisfaction and interpersonal dynamics within organizational contexts.

By involving 95 middle managers at Indonesian Internet service providers as respondents, this research used a questionnaire to collect data. Next, the data were analyzed using the partial least square-structural equation modeling (PLS-SEM) technique, which evaluated measurement models and structural models. A total of twelve hypotheses were tested in this study.

This study found that customer orientation does not have a significant effect on deep acting, thereby nullifying its indirect effect on job satisfaction. Conversely, it's demonstrated that both deep acting and surface acting serve as partial mediators in the relationship between emotional intelligence and job satisfaction. Furthermore, surface acting emerges as a partial mediator in the connection between customer orientation and job satisfaction.

By exploring the relationship between customer orientation, emotional intelligence and job satisfaction among employees, this study seeks to reveal novel insights. The study examines the impact of these critical elements, which are necessary for middle managers to effectively manage their emotions and cultivate significant connections, on their overall job satisfaction and interpersonal dynamics in their diverse responsibilities.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

Climate change poses risks to society and the demand for carbon literacy within small and medium-sized enterprises is increasing. Skills and knowledge are required for organizational greenhouse gas accounting and science-based decisions to help businesses reduce transitional risks. At the University of Copenhagen and the University of Northern British Columbia, two carbon management courses have been developed to respond to this growing need. Using an action-based co-learning model, students and business are paired to quantify and report emissions and develop climate plans and communication strategies.

This paper draws on surveys of businesses that have partnered with the co-learning model, designed to provide insight on carbon reductions and the impacts of co-learning. Data collected from 12 respondents in Denmark and 19 respondents in Canada allow for cross-institutional and international comparison in a Global North context.

Results show that while co-learning for carbon literacy is welcomed, companies identify limitations: time and resources; solution feasibility; governance and reporting structures; and communication methods. Findings reveal a need for extension, both forwards and backwards in time, indicating that the collaborations need to be lengthened and/or intensified. Balancing academic requirements detracts from usability for businesses, and while municipal and national policy and emission targets help generate a general societal understanding of the issue, there is no concrete guidance on how businesses can implement operational changes based on inventory results.

The research brings new knowledge to the field of transitional climate risks and does so with a focus on both small businesses and universities as important co-learning actors in low-carbon transitions. The comparison across geographies and institutions contributes an international solution perspective to climate change mitigation and adaptation strategies.

This study aims to investigate the integration of heritage language and culture in technology-enhanced bilingual education and examine the dominance of the English language and culture in computer-assisted language learning settings.

This research used a narrative inquiry methodology. The data came from semi-structured interviews with 25 bilingual teachers in the Kurdistan region of Iraq and Texas.

The study found a significant bias in the use of technology toward the target language, often at the expense of heritage language and culture. The curricula analyzed were predominantly focused on superficial cultural elements of the target language, leading to a neglect of deeper cultural engagement.

This research highlights the phenomenon of cultural cringe within bilingual education and the skewed use of technology toward the target language.

The challenges facing 21st-century society are becoming increasingly complex, requiring the development of new citizen competencies. This study aims to validate an educational model focused on developing complex thinking in higher education students. Current educational models lack future-ready competencies, necessitating the emergence of new models to guide future generations toward the common good.

This was an adaptation of the causal-layered analysis (CLA) applied to 415 participants from higher education institutions in Mexico, Panama and Spain. Sessions were designed to present the proposed educational model and explore participants’ perceptions of its significance and contributions to future education.

Key findings include the following: participants perceived complexity as difficult and challenging; causes of problems were linked to outdated educational models requiring replacement by those that develop students’ competencies; participants envisioned changes that would develop individuals capable of understanding and transforming society; and participants recognized the model’s transformative potential, offering a novel proposal for 21st-century education.

This research sought to gather opinions from different stakeholders using the CLA methodology, providing a deep understanding of participants’ perspectives on the proposed solution.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.

Guided by Habermas’s three cognitive interests, this paper reviews the studies on school leaders’ emotional labor. It seeks to provide a typology of how researchers inquire about school leaders’ emotional labor by focusing on different understandings, topics and characteristics.

This is a narrative review with 38 studies finally selected for analysis. Guided by Habermas’s three cognitive interests, all the studies were examined carefully and were found to fall into different clusters of understanding of school leaders’ emotional labor.

The review revealed three understandings of school leaders’ emotional labor, namely instrumental understanding, practical understanding and emancipatory understanding. The instrumental understanding treats school leaders’ emotional labor as a tool to effectively control the schools; the practical understanding regards emotional labor as a way to build and maintain relationships and as the process of meaning-making; the emancipatory understanding perceives emotional labor as a site for school leaders’ reflection and action for achieving a more just and self-determined leadership.

This review contributes to the growing literature on school leadership and emotional labor by providing a theory-guided typology and synthesis of the existing understanding of school leaders’ emotional labor, which lays a knowledge base and points out directions for future scholarly inquiries. It also provides practical suggestions for educational policy, school leaders’ practice and leadership training.

This chapter contributes to the scholarship of teaching and learning in extracurricular enterprise and entrepreneurship education. It draws on research from two annual ‘Business Challenge Weeks’ (BCW) held at Oxford Brookes University in 2021 and 2022, in which teams of postgraduate students from three faculties worked on external client projects, supported by an academic mentor. It presents and discusses findings derived from a survey and interviews conducted after the second of these years. The chapter takes a transdisciplinary perspective, after Budwig and Alexander (2020), Piaget (1972) and Klein et al. (2001) and explores the relationship between this and the enterprise and entrepreneurship development pipeline set out by QAA (2018). It analyses the experiences of the three main participating groups engaged in the challenge weeks – students, external clients and academic mentors – and explores the organising challenges inherent in multiparty pedagogical initiatives. The chapter contributes to knowledge in this area by revealing and reflecting on the motivations and expectations of the three participant groups, the roles they played during the week and the outcomes they reported. It also expands understanding of transdisciplinary enterprise pedagogy.

Self-regulation is the level of learning where the learner becomes an active agent in their learning process in terms of activity and aspects of motivation and metacognition. The current paper mostly deals with the metacognitive aspect. The purpose of this study is to gain insight into self-regulation of learning in the context of modern technology in higher education. This study also aims to highlight the direction, tendencies and trends toward which self-regulation of learning is moving in relation to modern technologies.

The review study was compiled via searches in three databases: Scopus, Web of Science and ERIC. A filter was used to search for empirical studies solely in English, published over the past decade on the topics of self-regulation of learning and technology in higher education.

The findings clearly show a correlation between self-regulation of learning and modern technology, especially after a significant event such as the Covid-19 pandemic. However, in the wake of this change, the field of education has seen the emergence of methods and new platforms that can provide support for the development of self-regulated learning strategies.

The originality of the study lies in the fact that it focuses on the link between self-regulation of learning and modern technologies in higher education, including some predictions of the future direction of self-regulation of learning in this context.