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The development of an effective corporate vision is a necessary issue for corporate performance, and it is a key issue for corporate sustainable development as well. The recognition of questions like “what is the role of corporate vision in corporate performance” is directly related to the attitude and practice of entrepreneurs and managers toward the development of corporate vision as well as the effectiveness of the corporate vision itself. To better answer the questions concerning the role of corporate vision development and effectively guide the practice of corporations, the authors study the pathways and mechanisms by which corporate visions operate to assist businesses in achieving high performance.

The article completes the construction of indicators to measure each dimension of the corporate vision in line with social cognitive theory and analyzes the relationship between corporate vision and corporate performance by combining qualitative comparative analysis (QCA) and necessary condition analysis (NCA) research methods. The article provides insights into the logic of constructing and adjusting corporate visions from a process perspective.

The mechanisms by which corporate visions can be articulated, accepted and transformed within the organization are also the means by which corporate visions can improve corporate performance. In a dynamic environment, the corporate vision setting and acceptance process integrates the requirements of various stakeholders, leading to the adjustment and acceptance of the corporate vision. As a result, the vision has continuous validity in a changing environment. Both start-ups and non-start-ups can benefit from the guidance provided by a strong corporate vision in overcoming a variety of issues and obstacles to produce strong business performance.

This is the first study that shows the relationship between corporate vision and corporate performance from a process perspective. The authors are interested in understanding which characteristics for building a corporate vision are more accepted by organizational members and, in turn, create high corporate performance. The authors also explore the conditions for corporate vision acceptance. This research has positive implications for shedding some light on the mechanisms by which corporate visions improve corporate performance.

This study aims to investigate how preservice teachers’ stages of concern, beliefs, confidence and interest in AI literacy education evolve as they deepen their understanding of AI concepts and AI literacy education.

AI literacy lessons were integrated into a technology integration course for preservice teachers, and the impacts of the lessons were evaluated through a mixed-methods study. The Concerns-Based Adoption Model was employed as the analytical framework to explore participants’ specific concerns related to AI.

Findings revealed that participants initially lacked AI knowledge and awareness. However, targeted AI literacy education enhanced preservice teachers’ awareness and confidence in teaching AI. While acknowledging AI’s educational benefits, participants expressed ongoing concerns after AI literacy lessons, such as fears of teacher displacement and the potential adverse effects of incorporating generative AI on students’ critical learning skills development.

Despite the importance of providing preservice teachers with AI literacy skills and knowledge, research in this domain remains scarce. This study fills this gap by enhancing the AI-related knowledge and skills of future educators, while also identifying their specific concerns regarding the integration of AI into their future classrooms. The findings of this study offer valuable insights and guidelines for teacher educators to incorporate AI literacy education into teacher training programs.

Despite the potential of artificial intelligence (AI) systems to increase revenue, reduce costs and enhance performance, their adoption by organisations has fallen short of expectations, leading to unsuccessful implementations. This paper aims to identify and elucidate the factors influencing AI adoption at both the organisational and individual levels. Developing a conceptual model, it contributes to understanding the underlying individual, social, technological, organisational and environmental factors and guides future research in this area.

The authors have conducted a systematic literature review to synthesise the literature on the determinants of AI adoption. In total, 90 papers published in the field of AI adoption in the organisational context were reviewed to identify a set of factors influencing AI adoption.

This study categorised the factors influencing AI system adoption into individual, social, organisational, environmental and technological factors. Firm-level factors were found to impact employee behaviour towards AI systems. Further research is needed to understand the effects of these factors on employee perceptions, emotions and behaviours towards new AI systems. These findings led to the proposal of a theory-based model illustrating the relationships between these factors, challenging the assumption of independence between adoption influencers at both the firm and employee levels.

This study is one of the first to synthesise current knowledge on determinants of AI adoption, serving as a theoretical foundation for further research in this emerging field. The adoption model developed integrates key factors from both the firm and individual levels, offering a holistic view of the interconnectedness of various AI adoption factors. This approach challenges the assumption that factors at the firm and individual levels operate independently. Through this study, information systems researchers and practitioners gain a deeper understanding of AI adoption, enhancing their insight into its potential impacts.

The rapid evolution of artificial intelligence (AI) is revolutionizing organizational operations and altering competitive landscapes. This study examines the influence of organizational resources on AI adoption in recruitment, focusing on their role in achieving competitive advantage through effective implementation.

This research utilizes a cross-sectional quantitative approach, applying partial least squares structural equation modeling (PLS-SEM) to data from 290 human resource (HR) professionals. It is grounded in the resource-based view (RBV) and dynamic capability framework (DCF).

The results reveal that HR competencies and open innovation significantly influence dynamic capabilities, which are essential for AI integration, supported by financial support and information technology (IT) infrastructure. These capabilities enable effective AI adoption, leading to a competitive advantage.

The cross-sectional data in this study captures the current landscape of AI adoption in recruitment, providing a snapshot of the present scenario in a rapidly evolving technological environment.

This study offers HR professionals and managers strategic guidance on effectively integrating AI into recruitment processes. By enhancing HR competencies, fostering collaboration and ensuring sufficient financial and infrastructural support, organizations can navigate AI adoption challenges and secure a competitive advantage in a rapidly evolving technological landscape.

The adoption of AI in recruitment can reduce biases, enhance diversity and improve fairness through standardized assessments. However, as AI technologies evolve, continuous human oversight is essential to ensure ethical use and to modify AI systems as needed, further reducing biases and addressing societal concerns in AI-driven recruitment processes.

This research introduces a novel framework that underscores the importance of integrating human expertise with advanced technological tools to ensure successful AI implementation. A key contribution is that HR professionals not only facilitate AI integration but also ensure accuracy, accountability and configure the most suitable AI tools for recruitment by collaborating with AI developers to meet the specific needs of the organization.

The study aims to synthesize findings from over two decades of research, highlighting key trends, progress, innovations, methodologies and challenges in bioclimatic design strategies and their interconnection with building environmental performance across the world.

This systematic review examines advancements in bioclimatic design strategies aimed at enhancing the environmental performance of buildings from 2000 to 2023 (n = 1,069). The methodology/approach involves a comprehensive analysis of literature from the SCOPUS database using bibliometric analysis, identifying trends, thematic evolution, keyword clusters and pivotal strategies such as passive solar design, natural ventilation, green roofs and thermal mass utilization.

The review highlights significant progress in several areas, including improved simulation/modeling tools for passive solar design, advanced computational fluid dynamics models for natural ventilation optimization, and the integration of green roofs with photovoltaic systems for increased building energy efficiency. Additionally, the use of phase change materials and high-performance glazing has reduced heating and cooling loads, while real-time optimization technologies have enhanced building performance and led to energy savings.

The study recognizes limitations where the effectiveness of bioclimatic strategies varies across different climates. For example, passive solar design is highly effective in temperate climates but less so in tropical regions. Global differences in design preferences and building types and practices impact the applicability of bioclimatic strategies and traditional building methods in some cultures may not easily integrate with modern approaches, affecting their implementation and effectiveness. Furthermore, practical implications highlight the potential for reduced reliance on artificial heating, cooling and lighting systems, while social implications underscore the role of bioclimatic design in promoting sustainable construction practices.

Practical implications highlight the potential for reduced reliance on artificial heating, cooling and lighting systems.

Social implications underscore the role of bioclimatic design in promoting sustainable construction practices.

This review offers a detailed analysis of bioclimatic design evolution, highlighting trends such as adaptive building designs and smart materials. This study serves as a crucial resource for architects, engineers and policymakers, advocating for innovative, climate-responsive design solutions to mitigate the environmental impact of the built environment and address challenges related to climate change and urbanization.

Artificial Intelligence (AI) is revolutionizing the world. Despite the numerous advantages of AI in terms of faster processing and higher efficiency, AI hasn’t been widely accepted by humans yet. This study aims to shed light on this phenomenon by exploring the Dunning–Kruger Effect in AI knowledge and examining how AI knowledge affects AI acceptance through AI-related self-efficacy.

By collecting data from 179 managers, we examined the Dunning–Kruger Effect in AI knowledge and used mediation analysis to explore the mechanisms by which AI knowledge leads to AI acceptance.

Our findings indicated the presence of the Dunning–Kruger Effect in AI knowledge. Furthermore, our results revealed that AI knowledge has a nonlinear effect on AI acceptance through AI-related self-efficacy.

In contrast to previous research that posited a linear link between knowledge and acceptance of technology, this study offers a new framework for the nonlinear relationships between AI knowledge, AI-related self-efficacy and AI acceptance by extending the Dunning–Kruger effect to the AI field.

This study aims to explore how engagement with AI mediates the relationship between AI adoption in tourism and the resulting smart experiences, which ultimately foster both smart tourism love and perceived happiness among tourists.

Data were collected from 622 domestic Thai tourists aged 19 and above who visited Khon Kaen Province within the past three months and used AI-powered tools for tourism. The hypothesised model was tested using structural equation modelling (SEM) through a two-step approach.

The study reveals that the successful integration of AI in tourism largely depends on the depth of tourists’ engagement with these technologies. Greater engagement fosters enriched smart experiences and stronger emotional bonds.

This study utilises the stimulus-organism-response (S-O-R) model to examine the impact of AI adoption on tourist behaviour, making substantial theoretical contributions to the expanding body of literature on AI in tourism.

Tourism operators and policymakers should prioritise fostering meaningful engagement with AI technologies to enhance tourist experiences and strengthen emotional connections to destinations, aligning with Thailand’s digital transformation initiatives.

This study emphasises the crucial role of engagement with AI – rather than mere adoption – in shaping smart tourism experiences and emotional outcomes, thereby contributing to the literature on AI in tourism.

Generative Artificial Intelligence (Gen-AI) has provided new opportunities and challenges in using educational environments for students’ interaction and knowledge acquisition. Based on the expectation–confirmation theory, this paper aims to investigate the effect of different constructs associated with Gen-AI on engagement, satisfaction and word-of-mouth.

We collected data from 508 students in the UK using Qualtrics, a prominent online data collection platform. The conceptual framework was analysed through structural equation modelling.

The findings show that Gen-AI expectation formation and Gen-AI quality help to boost Gen-AI engagement. Further, we found that active engagement positively affects Gen-AI satisfaction and positive word of mouth. The mediating role of Gen-AI expectation confirmation between engagement and the two outcomes, satisfaction and positive word of mouth, was also confirmed. The moderating role of cognitive processing in the relationship between Gen-AI quality and engagement was found.

This paper extends the Expectation-Confirmation Theory on how Gen-AI can enhance students’ engagement and satisfaction. Suggestions for future research are derived to advance beyond the confines of the current study and to capture the development in the use of AI in education.

Using VOSviewer software, this research delves into the various implications of ethical artificial intelligence (AI) within the retail industry. We explored the latest research trends using bibliometric analysis unveiling the journals, organisations, sources, articles, and documents that topped the chart. To shed light on the critical areas, we leveraged a citation analysis approach to explore the numerous trending research areas that were associated with fostering trust and transparency in AI-based retail applications. The research recognised the most influential areas by investigating the highly cited works. This research insight works as a guiding roadmap to navigate the complexities related to the ethical use of AI and direct towards fostering trust.

The purpose of this paper is to introduce the artificial intelligence (AI) Citizenship Framework, a model that equips teachers and school library professionals with the tools to develop AI literacy and citizenship in students. As AI becomes increasingly prevalent, it is essential to prepare students for an AI-driven future. The framework aims to foster foundational knowledge of AI, critical thinking and ethical decision-making, empowering students to engage responsibly with AI technologies. By providing a structured approach to AI literacy, the framework helps educators integrate AI concepts into their lessons, ensuring students develop the skills needed to navigate and contribute to an AI-driven society.

This paper presents a theoretical framework, developed from the author’s experience as an information and digital literacy coach and teacher librarian across Asia, the Middle East and Europe. The AI Citizenship Framework was created without following specific empirical methodologies, drawing instead on practical insights and educational needs observed in diverse contexts. It outlines a scope and sequence for integrating AI literacy into school curricula. The framework’s components build on existing pedagogical practices while emphasising critical, ethical and responsible AI engagement. By providing a structure for AI education, it serves as a practical resource for school librarians and educators.

While no empirical data was collected for this theoretical paper, the AI Citizenship Framework offers a structured approach for school librarians and educators to introduce and develop AI literacy. It has the potential to influence AI education by fostering critical and ethical awareness among students, empowering them to participate responsibly in an AI-driven world. The framework’s practical application can be expanded beyond school librarians to include classroom teachers, offering a comprehensive model adaptable to various educational settings. Its real-world implementation could enhance students’ readiness to engage with AI technologies, providing long-term benefits for both educational institutions and the broader society.

One limitation of the AI Citizenship Framework is that it has not yet been empirically validated. Future research could focus on testing its practical effectiveness in real-world settings, offering insights that may inform refinements and adaptations to better support school librarians and educators in fostering AI literacy and AI citizenship.

The practical implication of the AI Citizenship Framework is its application in educational settings to equip students with AI literacy and responsible citizenship skills. School library professionals and teachers can use the framework to integrate AI concepts into curricula, fostering critical thinking, ethical understanding and informed decision-making about AI technologies. The framework provides ready-to-use curriculum plans, enabling educators to prepare students for an AI-driven world. Its adaptability also allows classroom teachers to lead AI literacy initiatives, making it a versatile tool for embedding AI education across subjects and promoting responsible use and engagement with AI technologies in real-world contexts.

The originality and value of the AI Citizenship Framework lie in its approach to integrate AI literacy into educational contexts, specifically tailored for teacher librarians and school librarians. To the best of the authors’ knowledge, it is the first framework that comprehensively addresses the need for AI literacy from an ethical, critical and societal perspective, while also promoting active participation and leadership in AI governance. The framework equips educators with practical tools and curriculum plans, fostering responsible AI use and engagement. Its adaptable structure ensures it can be implemented by classroom teachers as well, adding significant value to AI education across disciplines and age groups.