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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 (DM) processes within organisations.

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 assess their impact on DM processes.

The findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM 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 DM process.

This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM 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 whilst mitigating their associated risks.

Purpose: The COVID-19 pandemic is the most severe threat we have faced since World War II. So far, there have been about 5 million recorded cases, with over 300,000 fatalities globally. The epidemic is also wreaking havoc on the corporate world. People are losing their jobs and money, and no one knows when normalcy will return. So, addressing the VUCA Leadership Strategies Model is important to get more insight into this topic.

Need for the Study: According to the International Labor Organization, the pandemic might cost 195 million jobs. Even when the immediate impacts wear off, the long-term economic impact will reverberate for years. All four volatile, unpredictable, complex, and ambiguous (VUCA) characteristics apply to the issues we confront due to the coronavirus.

Methodology: Changes caused by COVID-19 occur daily, and are unpredictable, dramatic, and quick. No one can predict precisely when the epidemic will end or when a treatment or immunisation will be available. The pandemic impacts many parts of society, including health care, business, the economy, and social life. There is no ‘best practice’ that enterprises may utilise to tackle the pandemic’s issues. The VUCA leadership strategy models will be discussed and compared in this research study.

Findings: In this moment of transition, leaders must adhere to their fundamental values, core purpose, and ambition for big, hairy, and audacious goals.

Practical Implications: In this chapter, VUCA leadership strategy models will be discussed in detail for pre- and post-pandemic scenarios and their impact on different sectors, which will be very important for researchers in the same field.

Machine learning (ML) analytical tools are increasingly being considered as an alternative quantitative methodology in management research. This paper proposes a new approach for uncovering the antecedents behind product and product–service innovation (PSI).

The ML approach is novel in the field of innovation antecedents at the country level. A sample of the Equatorian National Survey on Technology and Innovation, consisting of more than 6,000 firms, is used to rank the antecedents of innovation.

The analysis reveals that the antecedents of product and PSI are distinct, yet rooted in the principles of open innovation and competitive priorities.

The analysis is based on a sample of Equatorian firms with the objective of showing how ML techniques are suitable for testing the antecedents of innovation in any other context.

The novel ML approach, in contrast to traditional quantitative analysis of the topic, can consider the full set of antecedent interactions to each of the innovations analyzed.

This study aims to apply the appreciative inquiry approach (AI) to develop a tourism strategy for poverty alleviation in marginalised communities. The focus is to provide practical insights for leveraging tourism to drive positive socio-economic change for the impoverished, using Rosetta, a port city in Egypt with cultural and historical significance, as a case study.

This qualitative applied study uses the four-D phases of AI and thematic analysis to strategise tourism development in Rosetta. Through interviews, focus groups and field visits, the study identifies tourism potential, stakeholder aspirations and actionable strategies for sustainable development. The approach prioritises a bottom-up, community-centric and stakeholder-involved process, aiming for inclusive and equitable growth.

The study revealed Rosetta’s underutilised tourism potential, emphasising heritage tourism. Although tourism offers some economic benefits, its impact on alleviating poverty in Rosetta remains limited. A holistic strategy for tourism development in Rosetta is proposed for economic growth and poverty reduction, focusing on sustainable management, local empowerment, enhanced marketing, improved infrastructure and diversified tourism offerings.

While AI is not new in qualitative studies, the novelty of this study lies in its application to tourism planning for poverty alleviation in a marginalised community like Rosetta, introducing a comprehensive tourism strategy with an original framework applicable to comparable destinations. The study’s significance is emphasised by providing actionable strategies for policymakers, valuable insights for practitioners and enriching the discourse and methodology on pro-poor tourism for academics, representing a step towards filling the gap between theoretical concepts and practical strategies.

In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation and the business model revolution. This paper aims to investigate whether and how the application of AI enhances the ESG performance of enterprises.

This study uses panel data from Chinese A-share listed companies spanning the period from 2012 to 2022. Through a multivariate regression analysis, it examines the impact of AI on the ESG performance of enterprises.

The findings suggest that the application of AI in enterprises has a positive impact on ESG performance. Internal control systems within the organization and external information environments act as mediators in the relationship between AI and corporate ESG performance. Furthermore, corporate compliance plays a moderating role in the connection between AI and corporate ESG performance.

This paper underscores the pivotal role played by AI in enhancing corporate ESG performance. It explores the pathways to improving corporate ESG behavior from the perspectives of internal control and information environments. This discussion holds significant implications for advancing the application of AI in enterprises and enhancing their sustainable governance capabilities.

This study aims to empirically examine accounting information system (AIS) success at the organizational level during COVID-19 era.

Based on the information system success model, this paper developed its model and proposed a total of nine hypotheses. This paper gathered the required data via a questionnaire from Yemeni small and medium enterprises (SMEs) owners and managers. To test the proposed research model paths, SmartPLS software, which is known as partial least squares structural equation modeling, was used.

The results showed that the quality dimensions (information quality and system quality) positively affected the use of AIS and satisfaction; user satisfaction positively affected the use of AIS. Management support positively affected the AIS users' usage and satisfaction. Finally, the use dimensions (user satisfaction and usage) positively impacted the net benefits in terms of gaining a competitive advantage, productivity enhancement and saving time and cost. In all, this research has succeeded in providing support for DeLone and McLean's IS success model at the organizational level during the COVID-19 era.

AIS is becoming increasingly important for SMEs in low-income countries like Yemen, particularly in the present pandemic conditions (COVID-19 era). By using AIS, users can access the enterprise's data and conduct transactions without being limited by distance. Indeed, AIS proved its ability in enhancing the net benefits at the organizational level in the COVID-19 era in terms of gaining a competitive advantage, productivity enhancement and saving time and cost. However, AIS can only be considered useful to the enterprise if it is effective/successful.

This study is one of the first to have assessed the impact of AIS success at the organizational level in the era of COVID-19 pandemic, the context of Yemeni SMEs.

The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.