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There has recently been much interest in analysing the creation of personalised tourism services and studying their effect on organisations. However, there still needs to be more work in analysing their effect on happiness and the role that emotions play in these processes. This paper aims to analyse, in the context of personalised and innovative tourism services, which factors can encourage and improve managers' happiness.

A model of analysis is presented with five variables: tourism managers' happiness (TMH), innovative personalised tourism services, internal factors: emotions (IFE), organisational factors (OF) and personal factors (PF). Eight hypotheses are proposed and tested with a structural equation model.

The results allow the authors to affirm that personalised technological advances applied to tourism services not only contribute to improving the happiness of hotel managers but also in generating emotions that contribute to improving their attitude towards the company.

This scientific work has some limitations. Firstly, this study was carried out exclusively in Spain due to the relevance of this country in the international tourism sector, according to the World Tourism Organisation. The results achieved in this research should be contrasted with other studies in other territories. Secondly, the interviews and surveys were carried out at specific time intervals. It has not led to problems of significant bias in the variance of the standard method. Therefore, it is desirable to undertake longitudinal or cross-sectional studies for future research. Thirdly, it is interesting to develop theoretical models that include other psycho-directive or leadership style constructs to determine whether they holistically enhance the subjective well-being of hospitality managers. Moreover, other types of factors of a social or strategic nature can be considered, which can positively or negatively impact the analysed variables. Finally, future research can deepen the empirical analysis of the relationship between managerial competencies and digital innovation from the perspective of happiness management. These findings would contribute to a greater cognitive understanding of the implications of personalised and innovative tourism services on hotel establishments' happiness and economic benefits.

This paper shows the fundamental role of a happy leadership style in creating responsible, green and innovative environments in today's digital society. Furthermore, the happiness of tourism managers can contribute to the generation of high quality and excellent services that are in line with the principles of sustainable development.

Personalised technological advances applied to tourism services not only contribute to improving the happiness of hotel managers but also to generating emotions that contribute to improving their attitude towards the company. On the other hand, it has been observed that personalised and innovative tourism services generate positive effects at organisational, internal and personal levels. The following reflections are advanced: The development of internal factors such as the emotions of awe and gratitude or the generation of trust can enhance the happiness of tourism managers. The happiness of tourism business managers can be enhanced by developing OF such as smart-personalised tourism services and data protection. The happiness of tourism managers can be enhanced by the development of PF such as travellers' desires, expectations and needs, or other factors such as disposable income, health status or family situation.

This is the first empirical study that focuses on investigating how personalised and innovative tourism services affect managing happiness.

This study aims to investigate the engagement gap between Metaverse and in-person travel, the influence of Metaverse tourism on tourists and the industry and the challenges and responses associated with Metaverse technology. The study presents practical cases and highlights the implications of this research for practice, society and future research.

This study uses a literature review to explore concerns about Metaverse technology in tourism. It analyzes the difference between in-person travel and Metaverse tourism, the impact on tourists and the industry and challenges and responses to Metaverse. The review shows a rising trend in Metaverse tourism research.

These findings suggest differences between Metaverse tourism and in-person travel. By providing personalized travel options, social interaction, immersive experiences and soliciting visitor feedback, it is possible to enhance the tourist experience. Additionally, the study highlights the opportunities and challenges that Metaverse tourism presents to the tourism industry. The study provides practical cases in the tourism industry and implications for practice, society and future research.

The study’s implications for Metaverse tourism are practical, societal and future research-related. Metaverse technology can enhance the tourist experience through personalized options, social interaction, immersive experiences and feedback. This inclusivity can promote social equity and cultural exchange. Further research is needed to explore the social effects of Metaverse tourism and its long-term impacts on local communities, economies and the environment.

This study contributes by exploring the impact of Metaverse tourism, supporting academic research and practice. It fills a knowledge gap by analyzing the application of Metaverse technology in tourism, providing insights for researchers and practitioners. It offers practical guidance by identifying opportunities and challenges in Metaverse tourism, fostering industry innovation. Additionally, it informs policymakers about the impact of Metaverse tourism on development.

Education 4.0 (E 4.0) represents a new paradigm in the field of education, which emphasizes a student-centric approach that allows learners to access education anytime, anywhere, tailored to their individual needs through modern-day technologies. The purpose of the study was to unearth the critical success factors (CSFs) essential for the successful implementation of E 4.0.

The CSFs were unearthed using a literature review and further the interrelationships were analysed using multi-criteria decision making (MCDM) approach.

The study unearthed 15 CSFs for the successful implementation of E 4.0. The most important factor for the successful implementation of E 4.0 was personalized learning which was found to be the casual factor. The other causal CSFs were clear vision and leadership for E 4.0, stakeholder involvement, data analytics in teaching and learning, inter-disciplinary learning and blended learning environments. The effect factors were digital citizenship-based education, teacher training and development for E 4.0, supportive environment, curriculum redesign for E 4.0, open educational resources, digital technologies, formative assessments, infrastructure for E 4.0 and sustainability in education.

This is the first study which unearthed the CSFs and found the interrelationships among them, thus contributing to the theory of technology organization environment.

This study represented a pioneering effort in understanding the CSFs underpinning the successful adoption of E 4.0, paving the way for a more personalized, tech-savvy and effective education system.

This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.

A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.

Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.

This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

This viewpoint paper aims to explore the adoption of artificial intelligence (AI) in personalized learning for employees and its potential, challenges and ethical consideration to transform the landscape of employee’s performance improvement.

A review of relevant research papers, articles and case studies is done to highlight developments in research and practice.

Personalised learning powered by AI will transform employee education and training as AI evolves, helping individuals and organisations succeed in an ever-changing, knowledge-driven world. Concerns like data privacy, algorithmic bias and human engagement must be addressed to achieve ethical and fair implementation.

This paper discusses the overall influence of AI adoption on personalised learning for employees. It examines how AI technologies have the ability to disrupt traditional learning methodologies, modify organisational learning cultures and encourage an environment that fosters ongoing learning and innovation.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

Advances in technology have given rise to an increased demand by small businesses for personalised e-government services. Given the importance of small businesses to the Australian economy, it is vital to deliver small business-centric services that offer potential to generate value. To do that effectively, government departments need to understand factors that affect small business. The purpose of this study is to explore how preferences for personalised services and the use of intermediaries affect small business participation behaviour and, in turn, stimulate positive outcomes that are of interest to the government.

This study draws on secondary data from a survey of 800 Australian small businesses about the digital services offered by a large government department. Structural equation modelling was used to empirically test the model.

The findings demonstrate that whilst preference for personalisation has a positive relationship with participation behaviour, reliance on an intermediary does not. While such behaviour fosters emotional connection and perceptions of partner quality, the results of this study show no significant impact on satisfaction.

This study advances knowledge about how small businesses can gain value from personalised support services. Importantly, it focuses on participation behaviour and small business – both of which are largely absent from existing studies. The findings can assist government departments to design personalised services that are valued by small businesses.

Experiential learning is considered to be a crucial factor in students' perceived learning outcomes. This study aims to explore the development of a mechanism in hospitality education and to analyze the learning outcomes (i.e. perceived quality, perceived value and learning satisfaction) using experiential learning (i.e. previous learning experience and personalized learning environment).

The course design consisted of two activities (instructing and learning activities) and three phases (before, during and after class) using the Moodle version 3.5 online platform as the educational and training site to sustain e-learning archives and activities. A longitudinal survey using a sample of 207 hospitality students in blended e-learning environment indicates positive relationships among the previously mentioned factors.

Results indicated that both previous learning experience and personalized learning environment have positive direct effects on perceived quality and perceived value, while both perceived quality and perceived value have positive direct effects on learning satisfaction. Furthermore, both perceived quality and perceived value mediate the relationship between previous learning experience and learning satisfaction, as well as the relationship between personalized learning environment and learning satisfaction.

These findings sustain the value of experiential learning, particularly the curriculum, student and faculty development in sustainable development education for hospitality.

The paper contributes to the discussion on valuation in organization studies and strategic management literature. The nascent literature on valuation practices has examined established markets where producers and consumers are known and rivalry in the market is a given. Furthermore, previous research has operated with a narrow meaning of value as either a financial profit or a subjective consumer preference. Such a narrow view on value is problematic and insufficient for studying the interlacing of innovation and value creation in emerging technoscientific business domains.

The authors present an empirical study about value creation in an emerging technoscience business domain formed around personalized medicine and digital health data.

The results of this analysis show that in a technoscientific domain, valuation of innovations is multiple and malleable, entails pursuing attractiveness in collaboration and partnerships and is performative, and due to emphatic future orientation, values are indefinite and promissory.

As research implications, this study shows that valuation practices in an emerging technoscience business domain focus on defining the potential economic value in the future and attracting partners as probable future beneficiaries. Commercial value upon innovation in an embryonic business milieu is created and situated in valuation practices that constitute the prospective market, the prevalent economic discourse, and rationale. This is in contrast to an established market, where valuation practices are determined at the intersection of customer preferences and competitive arenas where suppliers, producers, service providers and new entrants to the market present value propositions.

The study findings extend discussion on valuation from established business domains to emerging technoscience business domains which are in a “pre-competition” phase where suppliers, customers, producers and their collaborative and competitive relations are not yet established.

As managerial implications, this study provides insights into health innovation stakeholders, including stakeholders in the public, private and academic sectors, about the ecosystem dynamics in a technoscientific innovation. Such insight is useful in strategic decision-making about ecosystem strategy and ecosystem business model for value proposition, value creation and value capture in an emerging innovation domain characterized by collaborative and competitive relations among stakeholders. To business managers, the findings of this study about valuation practices are useful in strategic decision-making about ecosystem strategy and ecosystem business model for value proposition, value creation and value capture in an emerging innovation domain characterized by collaborative and competitive relations among stakeholders. To policy makers, this study provides an in-depth analysis of an overall business ecosystem in an emerging technoscience business that can be propelled to increase the financial investments in the field. As a policy implication, this study provides insights into the various dimensions of valuation in technoscience business to policy makers, who make governance decisions to guide and control the development of medical innovation using digital health data.

This study's results expand previous theorizing on valuation by showing that in technoscientific innovation all types of value created – scientific, clinical, social or economic – are predominantly promissory. This study complements the nascent theorizing on value creation and valuation practices of technoscientific innovation.

Technology-enhanced learning (TEL), particularly in science, technology, engineering, arts, and math (STEAM), revolutionizes educational approaches by fostering active, transformative learning and expediting the learning process. TEL employs various tools like online courses, artificial intelligence (AI) technologies, virtual reality (VR), simulations, makerspaces, visual learning, and project-based learning, all contributing to accelerated learning in STEAM. A notable TEL innovation is the emergence of Large Language Models (LLMs) and AI chatbots, exemplified by the release of GPT-3 in December 2022. These tools utilize extensive parameters to generate natural language and perform tasks such as classification and prediction, thereby offering personalized and collaborative learning experiences essential for STEAM education. The generative pre-training transformer (GPT), a leading model in natural language processing (NLP), excels in generating human-like text and handling complex tasks like translation, summarization, and question answering. This chapter explores TEL environments that support transformative learning in STEAM, focusing on AI models. It reviews research on TEL’s impact on STEAM education, discussing the constructionism theory and emphasizing TEL’s role in creating engaging, student-centered learning experiences. However, challenges like technology access, instructor training, infrastructure, internet connectivity, and hardware resources are crucial. Additionally, the rise of AI brings ethical concerns regarding privacy, security, and potential biases in AI algorithms. Despite these hurdles, TEL’s potential to enhance STEAM learning experiences and accelerate the educational process is significant. By effectively implementing TEL strategies and leveraging LLMs and AI tools, educators can substantially improve learning outcomes in STEAM education.