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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.

This research was developed to identify artificial intelligence (AI) opportunities and challenges in higher education.

This qualitative research was developed using the six-step thematic analysis method (Braun and Clark, 2006). Participants in this study were AI PhD students from Tehran University in 2022–2023. Purposive sampling was used to select the participants; a total of 15 AI PhD students, who were experts in this field, were selected and interviews were conducted.

The authors considered the opportunities that AI creates for higher education in eight secondary subthemes (for faculty members, for students, in the teaching and learning process, for assessment, the development of educational structures, the development of research structures, the development of management structures and the development of academic culture). Correspondingly, The authors identified and categorized the challenges that AI creates for higher education.

Concerning the intended research, several limitations are significant. First, the statistical population was limited, and only people with characteristics such as being PhD students, studying at Tehran University and being experts in AI could be considered the statistical population. Second, caution should be exercised when generalizing the results due to the limited statistical population (PhD students from Tehran University). Third, the problem of accessing some students due to their participation in research grants, academic immigration, etc.

The innovation of the current research is that the authors identified the opportunities and challenges that AI creates for higher education at different levels. The findings of this study also contribute to the enrichment of existing knowledge in the field regarding the effects of AI on the future of higher education, as researchers need more understanding of AI developments in the future of higher education.

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.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

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.

To support achieving sustainable development goals (SDGs), we integrated science, technology, engineering and math (STEM) and extended reality technologies into an artificial intelligence (AI) learning activity. We developed Feature City to facilitate students' learning of AI concepts. This study aimed to explore students' learning outcomes and behaviors when using Feature City.

Junior high school students were the subjects who used Feature City in an AI learning activity. The learning activity consisted of 90-min sessions once per week for five weeks. Before the learning activity, the teacher clarified the learning objectives and administered a pretest. The teacher then instructed the students on the features, supervised learning and unsupervised learning units. After the learning activity, the teacher conducted a posttest. We analyzed the students' prior knowledge and learning performance by evaluating their pretest and posttest results and observing their learning behaviors in the AI learning activity.

(1) Students used Feature City to learn AI concepts to improve their learning outcomes. (2) Female students learned more effectively with Feature City than male students. (3) Male students were more likely than female students to complete the learning tasks in Feature City the first time they used it.

Within SDGs, this study used STEM and extended reality technologies to develop Feature City to engage students in learning about AI. The study examined how much Feature City improved students' learning outcomes and explored the differences in their learning outcomes and behaviors. The results showed that students' use of Feature City helped to improve their learning outcomes. Female students achieved better learning outcomes than their male counterparts. Male students initially exhibited a behavioral pattern of seeking clarification and error analysis when learning AI education, more so than their female counterparts. The findings can help teachers adjust AI education appropriately to match the tutorial content with students' AI learning needs.

The adoption of artificial intelligence (AI) in frontline service encounters is a growing phenomenon in service marketing, which can lead to positive and negative results. In this context, this paper aims to review the literature on value cocreation and codestruction in AI-enabled service interactions.

A systematic literature review was carried out using the PRISMA protocol. Data were retrieved from the Web of Science and Scopus databases, from which 48 articles were selected for review. Data analysis, presentation of results and the research agenda followed the theory, context, characteristics and methodology (TCCM) framework.

The review especially revealed that: publications on AI-enabled value cocreation and codestruction are in the early stages of development; few articles have addressed value codestruction, and the main research emphasis is on value cocreation; interactions between human actors and AI-enabled autonomous nonhuman actors are resulting in value cocreation or value codestruction, or both, and these phenomena are also likely to occur when AI replaces more than one human actor in the service encounter; and AI is considered an increasingly independent nonhuman actor that integrates resources and interacts with other actors, yet prudence is necessary for its adoption.

This review fills a gap by jointly exploring the value cocreation and codestruction in the context of AI, presents an overview of the issues discussed and provides a research agenda with directions for future studies.

La adopción de la inteligencia artificial (IA) en los encuentros de servicio en primera línea es un fenómeno creciente en el marketing de servicios, que puede llevar a resultados positivos y negativos. En este contexto, el objetivo de este artículo es revisar la literatura sobre la cocreación y codestrucción de valor en las interacciones de servicio habilitadas por IA.

Se realizó una revisión sistemática de la literatura utilizando el protocolo PRISMA. Los datos se obtuvieron de las bases de datos Web of Science y Scopus, de las cuales se seleccionaron 48 artículos para su revisión. El análisis de los datos, la presentación de resultados y la agenda de investigación siguieron el marco de teoría, contexto, características y metodología (TCCM).

La revisión reveló especialmente que: (1) las publicaciones sobre la cocreación y codestrucción de valor habilitadas por IA están en las primeras etapas de desarrollo; (2) pocos artículos han abordado la codestrucción de valor, y el principal énfasis de la investigación está en la cocreación de valor; (3) las interacciones entre actores humanos y actores no humanos autónomos habilitados por IA están resultando en cocreación o codestrucción de valor, o ambas, y es probable que estos fenómenos también ocurran cuando la IA reemplaza a más de un actor humano en el encuentro de servicio; (4) la IA es considerada un actor no humano cada vez más independiente que integra recursos e interactúa con otros actores, pero se requiere prudencia en su adopción.

Esta revisión llena un vacío al explorar conjuntamente la cocreación y codestrucción de valor en el contexto de la IA, presenta una visión general de los temas discutidos y proporciona una agenda de investigación con direcciones para estudios futuros.

人工智能（AI）在前线服务接触中的应用已成为服务营销中的一个日益增长的现象, 这可能带来正面和负面的结果。在这一背景下, 本文旨在回顾关于人工智能驱动的服务互动中价值共创与共损的文献。

采用PRISMA协议进行了系统文献综述。数据从Web of Science和Scopus数据库中提取, 共选择48篇文章进行审阅。数据分析、结果呈现及研究议程遵循理论、背景、特征与方法论（TCCM）框架。

综述特别揭示了以下几点：(1) 关于AI驱动的价值共创与共毁的出版物尚处于发展初期; (2) 针对价值共损的文章较少, 主要研究重点集中在价值共创上; (3) 人类参与者与AI驱动的自主非人类参与者之间的互动, 可能导致价值共创或价值共损, 甚至同时发生, 特别是在AI替代多个服务接触中的人类参与者时; (4) AI被视为越来越独立的非人类参与者, 它整合资源并与其他参与者互动, 但在采用过程中需谨慎。

本综述填补了在AI背景下共同探讨价值共创与共损的空白, 概述了相关问题, 并提供了未来研究方向的议程。