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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 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。

This paper aims to empirically examine the impact of psychological factors (i.e. privacy and intrusiveness concerns) on user intentions regarding artificial intelligence (AI)-enabled social commerce applications at their core through perceived usefulness. The theoretical model is supported by the theory of planned behaviour (TPB).

Data was gathered from 488 social media users in Saudi Arabia.

Privacy concerns significantly affect perceived usefulness. Furthermore, the link between privacy concerns and behavioural intentions was mediated by perceived usefulness.

Business leaders should raise users’ awareness about the effectiveness of AI-powered tools that can influence their behavioural intentions. Furthermore, managers must be aware of the regulations that protect user privacy, track online activity and offer secure communication channels.

This paper expands on TPB by bridging the theoretical and practical divide. It further develops a theoretical framework for practitioners to better understand customers’ physiological aspects of using AI-powered social commerce platforms.

Este artículo examina empíricamente el impacto de los factores psicológicos (es decir, preocupaciones de privacidad e intrusión) en las intenciones de los usuarios con respecto a las aplicaciones de comercio social habilitadas con inteligencia artificial (IA) en su núcleo a través de la utilidad percibida. El modelo teórico se sustenta en la teoría del comportamiento planificado (TPB).

Los datos se recopilaron de 488 usuarios de redes sociales en Arabia Saudita.

Las preocupaciones sobre la privacidad afectan significativamente la utilidad percibida. Además, el vínculo entre las preocupaciones por la privacidad y las intenciones de comportamiento estuvo mediado por la utilidad percibida.

Los líderes empresariales deberían concienciar a los usuarios sobre la eficacia de las herramientas impulsadas por la IA que pueden influir en sus intenciones de comportamiento. Además, los gerentes deben conocer las regulaciones que protegen la privacidad de los usuarios, rastrear la actividad en línea y ofrecer canales de comunicación seguros.

Este artículo amplía el TPB cerrando la brecha teórica y práctica. Además, desarrolla un marco teórico para que los profesionales comprendan mejor los aspectos fisiológicos de los clientes al utilizar plataformas de comercio social impulsadas por IA.

本文透過實證研究了心理因素 (即隱私和侵入性問題) 對人工智慧 (AI) 驅動的社交商務應用程式的使用者意圖的影響, 其核心是透過感知有用性。 此理論模型得到計劃行為理論 (TBP) 的支持。

資料收集自沙烏地阿拉伯的 488 名社群媒體用戶。

隱私問題顯著影響感知的用處。 此外, 隱私問題和行為意圖之間的連結是透過感知有用性來調節的。

企業領導者應該提高使用者對人工智慧工具有效性的認識, 這些工具可以影響他們的行為意圖。 此外, 管理人員必須了解保護用戶隱私、追蹤線上活動並提供安全通訊管道的法規。

本文透過彌合理論和實踐鴻溝，對 TPB 進行了擴展。 它進一步為從業者開發了一個理論框架，以便更好地了解使用人工智慧驅動的社交商務平台的客戶的生理方面。

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.

This study aims to investigate whether cloud-based accounting information system (AIS) usage contributes to AIS effectiveness, with firm size acting as a moderator. Furthermore, the role of AIS effectiveness as a mediator in the relationship between cloud-based AIS usage and organizational performance (OP) is further evaluated. In this context, the study is a bridge to show that small and medium-sized enterprises (SMEs) try to apply cloud accounting to improve profitability, thereby funding more social-environmental activities on the path to sustainability.

The study conducted an online survey of chief accountants in Vietnam’s SMEs. Data from 193 responses were gathered and analyzed using partial least squares structural equation modeling.

The findings show that cloud-based AIS usage considerably contributes to AIS effectiveness and OP and that AIS effectiveness has a beneficial influence on OP. Furthermore, the study shows that firm size moderates the relationship between cloud-based AIS usage and AIS effectiveness. Further findings show that cloud-based AIS usage influences OP via AIS effectiveness.

The findings of this study expand the existing body of knowledge on cloud-based AIS usage and benefit managers when formulating their business information models. In practice, SMEs need to increase the use of cloud-based AIS to better manage AIS. Enhancing profitability through cloud accounting also determines the ability to finance sustainability activities in SMEs.

One of the practical values of this study is the impact on Vietnam’s socioeconomic growth and sustainability. With cloud-based AIS, SMEs may enhance information and system quality, boost system usage frequency, gain satisfaction and increase performance. Furthermore, the comprehensiveness of AIS from cloud-based AIS usage is also a condition for SMEs to enhance accountability for social-environmental information in future sustainable reporting. These advantages improve the efficiency of strategic decision-making, hence increasing SMEs’ competitiveness and social-environmental performance. These benefits will work directly or indirectly toward fostering broader socioeconomic and environmental sustainability in developing economies.

To open a bright perspective of cloud-based AIS usage for AIS effectiveness as well as OP in SMEs toward sustainability in a developing economy, the authors conducted an exploratory study because this topic is quite new in these firms, especially in a developing economy such as Vietnam. These discoveries partly support SMEs to quickly achieve sustainable development goals in the future.

This paper aims to systematically review the application of artificial intelligence (AI) in the tourism industry. By integrating human–computer interaction, machine learning, big data and other relevant technologies, the study establishes a comprehensive research framework that explores the systematic connections between AI and various facets of tourism.

This paper conducts a keyword co-occurrence analysis of 4,048 articles related to AI in tourism. The analysis identifies and classifies dominant topics, which are further refined through thematic literature review and manual coding for detailed discussion.

The analysis reveals five main topics: AI’s impact on tourist experience, AI in tourism marketing and prediction, AI in destination management, AI’s role in tourism enterprises and AI integration in strategic and regulatory framework. Each topic is reviewed to construct an integrated discussion that maps the current landscape and suggests directions for future research.

This paper transcends the fragmented discourse commonly found in the literature by establishing a unified framework that not only enhances understanding of the existing methodologies, theories and applications of AI in tourism but also identifies critical areas for breakthroughs, aiming to inspire a more humane and sustainable integration of AI in the tourism industry.

本文旨在系统回顾人工智能（AI）在旅游业中的应用。通过整合人机交互、机器学习、大数据和其他相关技术, 本研究建立了一个全面的研究框架, 探索人工智能与旅游业各方面之间的系统联系。

本文对4048篇与旅游业人工智能相关的文章进行了关键词共现分析。分析确定了主要议题并对其进行了分类, 然后通过主题文献梳理和手动编码对其进行了进一步完善, 以便进行详细讨论。

分析揭示了五个主要主题：人工智能与旅游体验、人工智能与旅游营销和预测、人工智能与目的地管理、人工智能与旅游企业, 以及人工智能在战略和监管框架中的整合。每个主题都进行了回顾, 以构建一个综合讨论, 勾勒出当前的研究格局, 并提出了未来的研究方向。

研究力图突破目前关于旅游与人工智能的碎片化讨论, 建立了一个统一的框架, 旨在加强对旅游业中人工智能现有方法、理论和应用的理解, 还点明了需要突破的关键领域, 以助力旅游业与人工智能共同创造更加人性化和可持续发展的前景。

Este artículo pretende revisar sistemáticamente la aplicación de la inteligencia artificial (IA) en el sector turístico. Mediante la integración de la interacción humano-ordenador, el aprendizaje automático, big data y otras tecnologías relevantes, el estudio establece un marco de investigación exhaustivo que explora las conexiones sistemáticas entre la IA y diversas facetas del turismo.

Este trabajo realiza un análisis de co-ocurrencia de palabras clave de 4.048 artículos relacionados con la IA en el turismo. El análisis identifica y clasifica los temas dominantes, sobre los que se profundiza mediante una revisión temática de la literatura y una codificación manual para su discusión detallada.

El análisis presenta cinco temas principales: El impacto de la IA en la experiencia turística, la IA en el marketing y la predicción turística, la IA en la gestión de destinos, el papel de la IA en las empresas turísticas y la integración de la IA en el marco estratégico y normativo. Cada tema se revisa para construir un debate integrado que trace el panorama actual y sugiera direcciones para futuras investigaciones.

Este artículo expande el análisis fragmentado que suele encontrarse en la bibliografía al establecer un marco unificado que no sólo mejora la comprensión de las metodologías, teorías y aplicaciones existentes de la IA en el turismo, sino que también identifica las áreas críticas para los avances, con el objetivo de inspirar una integración más humana y sostenible de la IA en la industria turística.

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.