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This study aims to provide insights into human–algorithm interaction in revenue management (RM) decision-making and to uncover the underlying heuristics and biases of overriding systems’ recommendations.

Following constructivist traditions, 20 in-depth interviews were conducted with revenue optimisers, analysts, managers and directors with vast experience in over 25 markets and working with different RM systems (RMSs) at the property and corporate levels. The hermeneutics approach was used to interpret and make meaning of the participants’ lived experiences and interactions with RMSs.

The findings explain the nature of the interaction between RM professionals and RMSs, the cognitive mechanism by which the system users judgementally adjust or override its recommendations and the heuristics and biases behind override decisions. Additionally, the findings reveal the individual decision-maker characteristics and organisational factors influencing human–algorithm interactions.

Although the study focused on human–system interaction in hotel RM, it has larger implications for integrating human judgement into computerised systems for optimal decision-making.

The study findings expose human biases in working with RMSs and highlight the influencing factors that can be addressed to achieve effective human–algorithm interactions.

The study offers a holistic framework underpinned by the organisational role and expectation confirmation theories to explain the cognitive mechanisms of human–system interaction in managerial decision-making.

The tourism industry is undergoing a rapid transformation brought on by artificial intelligence (AI), which is offering a wide range of benefits to both businesses and their customers. Nevertheless, this technological advancement also poses a number of difficulties for which the sector will need to find solutions. This chapter investigates the effects that artificial intelligence (AI) has had on the tourism industry in Europe, with a particular focus on how the hospitality industry has reacted to the advent of this technology. Following an overview of the tourism and hospitality industries in Europe, this chapter begins with an introduction to artificial intelligence (AI) in the tourism industry. The section on the methodology describes the various approaches to research that were utilised in this study, and the section on the conclusion summarises the findings.

The purpose of this paper is to explore how GenAI can help companies achieve a higher level of hyper-segmentation and hyper-personalization in the tourism industry, as well as show the importance of this disruptive tool for tourism marketing.

This paper used the Web of Science and Google Scholar databases to provide updated studies and expert authors to explore GenAI in the tourism industry. Analysing hyper-segmentation and hyper-personalization modalities through GenAI and their new challenges for tourists, tourism cities and companies.

Findings reveal that GenAI technology exponentially improves consumers’ segmentation and personalization of products and services, allowing tourism cities and organizations to create tailored content in real-time. That is why the concept of hyper-segmentation is substantially focused on the customer (understood as a segment of one) and his or her preferences, needs, personal motivations and purchase antecedents, and it encourages companies to design tailored products and services with a high level of individual scalability and performance called hyper-personalization, never before seen in the tourism industry. Indeed, contextualizing the experience through GenAI is an important way to enhance personalization.

This paper also contributes to enhancing and bootstrapping the literature on GenAI in the tourism industry because it is a new field of study, and its functional operability is in an incubation stage. Moreover, this viewpoint can facilitate researchers and companies to successfully integrate GenAI into different tourism and travel activities without expecting utopian results. Recently, there have been no studies that tackle hyper-segmentation and hyper-personalization methodologies through GenAI in the tourism industry.

The present research aims to explore the drivers of generative artificial intelligence (GEN AI)-based innovation adoption in the hospitality industry in Jordan.

To address the research gap and achieve the research work objectives, the Technology-Organization-Environment (TOE) lens and the structural equation modeling (SEM) approach were employed to analyze the sample data collected (n = 221) from the hospitality industry.

The findings indicate that relative advantage, top management support, organizational readiness, organizational culture, competitive pressures, government regulations support and vendor support significantly influence the GEN-AI-based innovation adoption, while the technological complexity is negatively associated with GEN-AI-based innovation adoption. Furthermore, the results showed there is no significant effect of cost on GEN-AI-based innovation adoption.

The paper analyses the TOE framework in a new technological setting. The paper also provides information about how GEN-AI-based innovation adoption may influence hospitality industry performance. Overall, this article provides new insights into the literature concerning AI technologies and through the TOE lens.

This study aims to use big data analysis and sheds light on key hotel features that play a role in the revisit intention of customers. In addition, this study endeavors to highlight hotel features for different customer segments.

This study uses a machine learning method and analyzes around 100,000 reviews of customers of 100 selected hotels around the world where they had indicated on Trip Advisor their intention to return to a particular hotel. The important features of the hotels are then extracted in terms of the 7Ps of the marketing mix. This study has then segmented customers intending to revisit hotels, based on the similarities in their reviews.

In total, 71 important hotel features are extracted using text analysis of comments. The most important features are the room, staff, food and accessibility. Also, customers are segmented into 15 groups, and key hotel features important for each segment are highlighted.

In this research, the number of repetitions of words was used to identify key hotel features, whereas sentence-based analysis or group analysis of adjacent words can be used.

This study highlights key hotel features that are crucial for customers’ revisit intention and identifies related market segments that can support managers in better designing their strategies and allocating their resources.

By using text mining analysis, this study identifies and classifies important hotel features that are crucial for the revisit intention of customers based on the 7Ps. Methodologically, the authors suggest a comprehensive method to describe the revisit intention of hotel customers based on customer reviews.

The coronavirus disease (COVID-19) has impacted all economies, businesses and societies. The purpose of this paper is to analyze and present a case for corporate social responsibility (CSR) in terms of its relevance amidst the turmoil caused by the pandemic.

The authors use a directed content analysis approach to retrieve relevant information from news articles using Thomson Reuters’ Eikon^® and Bloomberg^® databases. Based on stakeholder theory, the authors evaluate some of the CSR initiatives undertaken by organizations around the world. The authors then undertake a systematic literature review using the preferred reporting items for systematic reviews and meta-analyses standard to provide possible implications for organizations.

The findings suggest that in response to the pandemic, corporations from both developed and developing countries have been pursuing CSR measures for stakeholder engagement. The systematic literature review signals positive outcomes that companies might expect at the organizational level. The paper concludes by suggesting research propositions that indicate effective CSR at a time of crisis like COVID-19 encourages stakeholder partnerships and helps to gain a competitive advantage.

The authors present an overview of the CSR responses taken by firms globally in response to the pandemic by way of stakeholder engagement. The authors analyze the stakeholders targeted through such initiatives and report possible implications based on the extant literature. The findings of the study can be used to understand the various transitions that happen in an unprecedented situation like COVID-19 at all levels of business and society.

The significance of smart mobility practices in shaping cities from a smart perspective has grown in recent years, influencing policies and the choices made by inhabitants. This transformation has led to the emergence of novel services and strategies, creating a new, vibrant and highly personalised urban environment that caters to the needs and preferences of both local residents and visitors. The purpose of this study is to evaluate the influence of smart mobility practices on tourism flows in cities, considering the moderating effect of airport activities on this relationship.

Using a generalised method of moments estimation and focusing on 20 Italian cities over an eight-year period, the authors highlight the current relationship between smart mobility practices and tourism flows. Moreover, the authors demonstrate that the yearly advancement of airports positively moderates this relationship.

The findings indicate a significant relationship between smart mobility practices in modern cities and tourism inflows because they influence the development of tourism services and emerging trends such as smart tourism and smart destinations. Furthermore, airport activities as a proxy for city openness play a crucial role in this link. The study shows that airports have an incremental impact on tourism and on the relationship between tourism and sustainable practices.

The limitations of this quantitative approach include the focus on a single country, the challenge of measuring the development of smart mobility practices due to a lack of standardised variables and the need for future research to expand the sample to different countries in relation to tourism inflows.

This study has practical implications for policymakers and governance in their task of effectively coordinating internal smart mobility practices and managing incoming tourism flows.

This study has social implications, highlighting the need for policymakers and governance to address the societal impacts of smart mobility practices and tourism inflows, ensuring inclusive and sustainable outcomes for local communities.

This study contributes to the existing literature as one of the first attempts to examine the interplay between smart mobility practices in smart cities and tourism flows. Furthermore, it emphasises the role of airports in this relationship, highlighting how the interaction between these variables benefits both stakeholders.

This study addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones.

This research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings.

The study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total.

This study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.

Tourist destinations are constantly changing products, evolving as per the controls exerted by the stakeholders. The study aims to map the pattern of tourism development and identify the strategies formed at the destination over a seven-decade period for a state as a unit of analysis.

The paper evaluates tourism development through the tourism area life cycle (TALC) model and uses Mintzberg's strategy analysis process to identify strategies. The study involves time series analysis, pattern matching and explanation-building techniques. The TALC is plotted for the number of tourist arrivals from 1947 to 2019, and strategies are mapped for each stage.

The TALC shows a cycle-recycle pattern of tourism development. The research revealed several strategies at different stages. Both the central and state governments and entrepreneurs, distinctively and in conjunction, have formed strategies. The pattern shows the period of piecemeal and global strategic changes contributing to tourism development.

The research unearths the strategies that drive the development curves of TALC, emphasising the integration of TALC with other theories. The research also assesses the strategy formed in the pre-tourism stage.

The research brings to light the use of TALC as a strategic road-mapping tool. In addition, the study emphasises the significance of global and piecemeal strategic periods and stakeholder's regulatory and operational roles.

The research uses a unique methodology that maps the strategies, periods of strategic changes and incremental strategies for each stage of TALC, along with identifying the stakeholders.