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Innovation is key to the economic performance of firms. However, several types of risks and uncertainties often lead to high failure rates in innovation projects, to the extent that failure is now considered a typical step in an innovation process. If innovation failure was provoking stress in the past, the current socio-economic contexts makes if it something to regularly deal with and learning from. However, there still is a dilemma on whether innovation failure should be considered as an opportunity or a trauma for the firm. Exploring the three steps of an innovation failure, that is identifying, analyzing and experimenting, the present research offers new insights on the nature of innovation failure at an individual and team levels. In particular, via an inductive approach this study provides fours key approaches to understanding innovation failure. First, we have uncovered a wide range of approaches to organizational management and ‘Spaghetti’ organizational models of innovation that perceive failure as an opportunity for the innovation process. Secondly, our study identified what seems to be a widespread perception of innovation failure as a trauma, which encourages the culture of openness to failure and to share knowledge of failing innovations. Such views apply not only at organizational but also at individual and team levels. Third, our results serve to encourage individuals, teams and organizations to find new ways to learn from failures in innovation projects, and to have a less centralized management and control over innovation. Fourth, in our perception of the domain, failure could become the ‘new normal’ and a mean to generate new knowledge. This stimulates new directions in the decision-making process, towards novel pathways to innovation.

This study aims to identify and assess the key ethical challenges associated with integrating Artificial Intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making processes within organisations. The research explores the ethical dimensions of AI-driven KS, such as privacy and data protection, bias and fairness, and transparency and explainability. The study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and to assess their impact on decision-making processes. The findings reveal that challenges related to privacy and data protection, bias and fairness, and transparency and explainability are particularly significant in AI-driven decision-making. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the decision-making process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation, and global governance and regulation are found to be less central to the decision-making process. This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management and decision-making within organisations. By providing insights and recommendations for researchers, managers, and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies while mitigating their associated risks.

Artificial intelligence (AI) technologies are rapidly transforming the landscape of the tourism sector, offering innovative solutions to enhance customer experiences, operational efficiency, and strategic decision-making. AI, including machine learning, natural language processing (NLP), and computer vision, are revolutionizing customer engagement in the tourism sector. AI-driven chatbots and virtual assistants enable personalized customer interactions, responding to inquiries, providing recommendations, and facilitating bookings with human-like efficiency. Additionally, AI-powered analytics empower tourism businesses to harness vast amounts of data, deriving actionable insights to optimize pricing strategies, inventory management, and resource allocation. In destination management, AI plays a pivotal role in promoting sustainable tourism practices and enhancing visitor experiences. For instance, AI-based predictive analytics facilitate dynamic crowd management and infrastructure planning, ensuring optimal resource utilization while minimizing environmental impact. Moreover, AI-driven personalization enables tailored marketing campaigns, delivering relevant content and offers based on individual preferences and behavioural patterns. Examples of AI applications in tourism encompass a wide range of areas, from intelligent itinerary planning and real-time translation services to predictive maintenance for transportation fleets. This paper delves into specific use cases to illustrate how AI technologies reshape the tourism sector, foster innovation, and drive business growth. By examining successful AI implementations and their impacts on different facets of the tourism industry, this study contributes to a deeper understanding of AI's transformative potential in shaping the future of travel and hospitality.

Information technology (IT) is tied to any plans to enhance knowledge management (KM) in organisations. This tight connection is rooted form a revolution in deep learning that has improved the capacity for algorithms to simulate human capabilities in managing knowledge, from “seeing” (image recognition) and “hearing” (voice recognition, natural language processing), to more complicated abilities such as “deciding”. Therefore, these capabilities, which are connected with human social behaviour, as in artificial intelligence (AI) tools, have increasingly found their way into knowledge management procedures. Moreover, knowledge and learning are the cross points in AI and KM, so recent advances in AI have provided new foundations for transforming KM in organisations, and an organised KM system has increased AI capabilities. However, despite the pros of AI roles in KM, ethical challenges have been at the core of concern among academic scholars and digital experts. On the other hand, the current literature has also discussed just some lines and fragmented problems, which led to a big gap. This research seeks to identify the key indicators of AI ethical challenges in the KM in the tourism industry. We follow rigorous research using a combined exploratory and confirmatory approach to identify determinant factors and indicators of ethical challenges in applying AI in KM and measure the importance of each element. Therefore, first, we applied the Delphi method to explore and forecast the main items and factors. Then, we analysed the conformity of first-step outputs by Confirmatory Factor Analysis for the next step. Experts who participated in the first round are 18 professionals and academic professors in Tourism and AI in Iran. For the second study, we applied surveys from 210 employees with different levels of responsibilities in the Tourism industry. Our results concluded six critical factors and 24 indicators of ethical challenges associated with AI in KM: procedure value