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This paper explores the intricate interdependencies among core components of airline business models (BMs). In the airline industry, where BMs are complex systems, a successful BM requires an orchestrated configuration of various components. However, there is a paucity of research in BM literature pertaining to the interrelationships among key components of airline BMs.

Employing interpretive structural modelling, we gathered input from experts in Iran to assess the driving power and dependency of elements within airline BMs.

Our findings highlight the significance of operating environment conditions and competitive market dynamics as pivotal external components shaping the foundational structure. Value proposition, customer relationship management, and process monitoring are crucial linkage components that drive power and dependency. Notably, capturing value is positioned with the highest dependency.

We utilised the ISM technique to visualize interdependencies within airline business models, aiding strategic decision-making. Our findings suggest aligning business and operational strategies with market needs ensures effective value creation and capture, maintaining competitive advantage in the airline industry. In addition, our research reveals critical factors affecting value creation and capture, emphasising monitoring the operating environment and competitive market, and strategically managing value propositions and customer relationship initiatives in the airline industry. We advise adapting business models to external changes for sustained growth and recommend regular monitoring of industry trends and customer expectations.

Framed within complexity theory, these insights offer valuable perspectives on identifying and situating critical BM components in the airline industry. The practical implications derived from this study serve as strategic tools for airline managers and potential investors to optimise the design of their airline BMs.

High-skilled employees are crucial for sustained competitive advantage of organisations. In the “war for talent”, organisations must position themselves as attractive employers. This study aims to introduce a unified framework to systematically identify and prioritise organisational attractiveness (OA) components, focusing on the extreme context of the airline industry.

Treating OA as a multi-criteria decision-making (MCDM) situation, this study uses the Fuzzy Delphi Method to validate key OA factors and the Fuzzy Analytical Hierarchy Process to prioritise them based on experts’ judgements.

This study identifies 5 criteria and 22 sub-criteria for OA, with job characteristics and person–job fit as most critical. These elements signal employment quality and skill–job alignment, reducing information asymmetry and attracting talent.

This research provides a practical framework for airline managers to identify and prioritise key aspects of OA to enhance their value proposition and attract and retain qualified employees. For policymakers, applying the OA framework supports informed policy decisions on employment standards and workforce development.

This research introduces a fuzzy OA index and a framework that enhances OA. By incorporating signalling theory into a fuzzy MCDM approach, it systematically addresses key OA components, offering a strategic method to boost OA.

ChatGPT has been receiving mounting research attention recently. However, its application and challenges to adopt for tourism and hospitality businesses remain relatively unexplored. To address this research gap, this study aims to systematically assess the application of ChatGPT and its challenges within the domain of tourism and hospitality.

This study conducts bibliometric and content analyses of papers retrieved from Web of Science and Scopus. Particularly, it systematically reviewed the tourism and hospitality research to identify critical applications of ChatGPT in the context of tourism and hospitality. In addition, this study identified challenges associated with the application of ChatGPT in this context.

It has been revealed that the use of generative artificial intelligence (AI), such as ChatGPT, in tourism and hospitality research is ascending, with an opportunity to advance the existing knowledge in customer service research. In addition, the results suggest an ongoing interest in assessing the role of AI and language modeling for tourism education and human resource management.

The results are constrained by the used search keywords and electronic databases. Additionally, this study covered only papers published in English. However, the findings shed light on existing knowledge concerning ChatGPT’s transformative potential, identify areas for further exploration and offer guidelines for practice in the tourism and hospitality industry. The findings also revealed various challenges that various stakeholders should keep a closer eye on to ensure the effective and safe use of ChatGPT accordingly.

This study initiates a discussion on ChatGPT’s role in tourism and hospitality and underscores the importance of comprehensive AI integration within the sector.

近年来, ChatGPT受到了越来越多的研究关注。然而, 它在旅游和酒店业中的应用及其面临的挑战仍然相对未被探索。为填补这一研究空白, 本研究系统评估了ChatGPT在旅游和酒店业中的应用及其挑战。

本研究通过对从Web of Science（WoS）和Scopus检索的文献进行文献计量分析和内容分析。特别是, 系统回顾了旅游和酒店业的研究, 以识别ChatGPT在这一背景下的关键应用, 并识别了与其应用相关的挑战。

研究揭示了生成式人工智能（如ChatGPT）在旅游和酒店业研究中的应用日益增多, 为推动客户服务研究的现有知识提供了机会。此外, 研究结果表明, 对人工智能和语言建模在旅游教育和人力资源管理中的作用存在持续的兴趣。

本研究开启了对ChatGPT在旅游和酒店业中作用的讨论, 并强调了在该行业中全面整合人工智能的重要性。

本研究受限于所用的搜索关键词和电子数据库。此外, 本研究仅涵盖了英文论文。然而, 研究结果揭示了关于ChatGPT变革潜力的现有知识, 确定了进一步探索的领域, 并为旅游和酒店业实践提供了指导。研究还揭示了各利益相关者应密切关注的各种挑战, 以确保ChatGPT的有效和安全使用。