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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.

Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.

Through critically reflecting on existing research on information and communication technology (ICT) in hospitality, the purpose of this study is to propose recommendations for future research to further narrow the theory-practice gap.

Personal experiences along with evidence from the literature provide a foundation for discussion, which is further enriched by integrating industry practitioners’ points of view.

Single-perspective and technology adoption studies have dominated ICT research in the hospitality literature. Technology effectiveness has often been measured indirectly. Oversimplifying technological issues has limited the generalizability of research findings.

Future studies are suggested to go beyond examining technology adoption, embrace multi-perspective approaches and incorporate a wider range of situational and contextual factors.

Through a unique perspective, this study highlights the limitations of previous ICT research in the hospitality literature and provides suggestions for future research to better meet the needs of practitioners. The arguments presented are not purely from an academic standpoint, as they have been endorsed by senior industry executives.

This study social based on cognitive theory (SCT), aims to better understand how transformational leadership affects team-level knowledge sharing and absorptive ability in the construction industry. It also examines the moderating influence of the AI-based virtual assistant on the indirect relationship between transformational leadership and team innovation through knowledge sharing and absorptive ability at the team level.

This study used a simple random sample approach to gather data from several small and medium-sized construction firms in Anhui Province, China. A total of 407 respondents, including 89 site engineers and 321 team members, provided their responses on a five-point Likert scale questionnaire.

The findings showed that AI-based virtual assistants significantly moderated the direct and indirect association between transformational leadership and knowledge sharing, and subsequently with team innovation. Unexpectedly, the findings showed that AI-based virtual assistant did not moderate the direct relationship between transformational leadership and team-level absorptive capacity.

This study adds a fresh perspective to the literature on construction management by examining team innovation driven by transformational leadership through an underlying mechanism. It is unique in that it uses the team adaptation theory to investigate the understudied relationship between transformational leadership and team innovation in the construction industry.

The COVID-19 pandemic has generated unprecedented public fear, impeding both individuals’ social life and the travel industry as a whole. China was one of the first major countries to experience the COVID-19 outbreaks and recovery from the pandemic. The demand for outings is increasing in the post-COVID-19 world, leading to the recovery of the ride-sharing industry. Integrating protection motivation theory and the theory of reasoned action, this study aims to investigate ride-sharing customers’ self-protection motivation to provide anti-pandemic measures and promote the resilience of ride-sharing industry.

This study followed a two-phase mixed-methods design. In the first phase, the authors executed a qualitative study with 30 interviews. In the second phase, the authors used the results of the interviews to inform the design of a survey, with which 272 responses were collected. Both studies were conducted in China.

The present results indicate that customers’ perceived vulnerability of COVID-19 and perceived COVID protection efficacy (self-efficacy and response efficacy) are positively correlated with their attitude toward self-protection, thus leading to their self-protection motivation during the rides. Moreover, subjective norms and customers’ distrust appear to also impact their self-protection motivation during the ride-sharing service.

The present research provides one of the first in-depth studies, to the best of the authors’ knowledge, on customers’ protection motivation in ride-sharing services in the new normal. The empirical evidence provides important insights for ride-sharing service providers and managers in the post-pandemic world and promote the resilience of ride-sharing industry.

This study, rooted in affordance-actualization theory and communication theory, aims to critically examine how ChatGPT influences users’ transition from new adopters to loyal advocates within the context of travel decision-making. It incorporates constructs including communication quality, personalization, anthropomorphism, cognitive and emotional trust (ET), loyalty and intention to adopt into a comprehensive model.

This study used quantitative methods to analyze data from 477 respondents, collected online through a self-administered questionnaire by Embrain, a leading market research company in South Korea. Lavaan package within R studio was used for evaluating the measurement model through confirmatory factor analysis and using structural equation modeling to examine the proposed hypotheses.

The findings reveal a pivotal need for enhancing ChatGPT’s communication quality, particularly in terms of accuracy, currency and understandability. Personalization emerges as a key driver for cognitive trust, while anthropomorphism significantly impacts ET. Interestingly, the study unveils that in the context of travel recommendations, users’ trust in ChatGPT predominantly operates at the cognitive level, significantly impacting loyalty and subsequent adoption intentions.

The findings of this research provide valuable insights for improving Generative AI (GenAI) technology and management practices in travel recommendations.

As one of the few empirical research papers in the burgeoning field of GenAI, this study proposes a highly explanatory model for the process from affordance to actualization in the context of using ChatGPT for travel recommendations.

In recent decades, a significant number of research contributions have been made to the intersection of digital technologies and the tourism industry. However, a thorough examination of digitalization and digital transformation in the tourism industry has not been given sufficient consideration. This study aims to provide a bibliometric review of digitalization and digital transformation research in the tourism industry and devise future research agendas to advance the research field.

This study uses the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol and a bibliometric analysis to examine the research progress and scientifically map the research domain of digitalization and digital transformation in the tourism industry from 2002 to 2023 using bibliographic data retrieved from the Scopus and Web of Science.

This study presents the trends in publications and citations within the digitalization and digital transformation research domain in tourism. The findings also provide insights into the four primary clusters of the research field: digital innovation, smart tourism ecosystem, eTourism and smart destination experience. To further augment the application of digital transformation, this study offers several recommendations for future research on digitalization and digital transformation of the tourism industry.

This study provides valuable implications to researchers, managers and policymakers seeking to understand the current state and future research directions in tourism’s digitalization and digital transformation research field.

This study advances the research field of digitalization and digital transformation in the tourism industry by thoroughly examining the primary research clusters in the research corpus of the past two decades. Furthermore, it guides future research, thereby setting the stage for further progress in this domain.

近几十年来, 数字技术与旅游业的交叉领域做出了大量研究贡献。 然而, 对旅游业数字化和数字化转型的深入审视尚未得到充分考虑。本研究旨在对旅游业数字化和数字化转型研究进行文献计量回顾, 并制定未来的研究议程以推进该研究领域的发展。

本研究利用系统文献综述的科学程序和原理（SPAR-4-SLR）协议和文献计量分析来检验研究进展并科学地绘制旅游业数字化和数字化转型的研究领域 使用从 Scopus 和 Web of Science (WOS) 检索到的书目数据从 2002 年到 2023 年。

本研究呈现了旅游业数字化和数字化转型研究领域出版物和引用的趋势。 研究结果还提供了对该研究领域的四个主要集群的见解：数字创新、智能旅游生态系统、电子旅游和智能目的地体验。 为了进一步增强数字化转型的应用, 本研究为旅游业数字化和数字化转型的未来研究提出了几点建议。

本研究通过深入研究过去二十年研究语料库中的主要研究集群, 推进了旅游业数字化和数字化转型的研究领域。 此外, 它指导了未来的研究, 从而为该领域的进一步进展奠定了基础。

本研究为寻求了解旅游业数字化和数字化转型研究领域现状和未来研究方向的研究人员、管理者和政策制定者提供了有价值的启示。

En las últimas décadas, se ha realizado un número significativo de contribuciones de investigación a la intersección de las tecnologías digitales y la industria del turismo. Sin embargo, no se ha prestado suficiente atención a un examen exhaustivo de la digitalización y la transformación digital en la industria del turismo. Este estudio tiene como objetivo proporcionar una revisión bibliométrica de la investigación sobre digitalización y transformación digital en la industria del turismo y diseñar futuras agendas de investigación para avanzar en el campo de la investigación.

Este estudio utiliza el protocolo de Procedimientos y fundamentos científicos para revisiones sistemáticas de la literatura (SPAR-4-SLR) y un análisis bibliométrico para examinar el progreso de la investigación y mapear científicamente el dominio de investigación de la digitalización y la transformación digital en la industria del turismo. de 2002 a 2023 utilizando datos bibliográficos recuperados de Scopus y Web of Science (WOS).

Este estudio presenta las tendencias en publicaciones y citas dentro del dominio de investigación sobre digitalización y transformación digital en turismo. Los hallazgos también brindan información sobre los cuatro grupos principales del campo de investigación: innovación digital, ecosistema de turismo inteligente, turismo electrónico y experiencia de destino inteligente. Para aumentar aún más la aplicación de la transformación digital, este estudio ofrece varias recomendaciones para futuras investigaciones sobre la digitalización y la transformación digital de la industria turística.

Este estudio avanza en el campo de investigación de la digitalización y la transformación digital en la industria del turismo al examinar en profundidad los principales grupos de investigación en el corpus de investigación de las últimas dos décadas. Además, orienta la investigación futura, sentando así las bases para mayores avances en este ámbito.

Este estudio proporciona implicaciones valiosas para los investigadores, administradores y formuladores de políticas que buscan comprender el estado actual y las direcciones futuras de la investigación en el campo de la digitalización y la transformación digital del turismo.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This study aims to develop the alleviating bullwhip effects framework (ABEF) replenishment rules, and bullwhip, inventory fluctuations and customer service fulfilment rates were examined. In addition, automated smoothing and replenishment rules can alleviate supply chain bullwhip effects. This study aims to understand the current artificial intelligence (AI) implementation practice in alleviating bullwhip effects in supply chain management. This study aimed to develop a system for writing reviews using a systematic approach.

The methodology for the present study consists of three parts: Part 1 deals with the systematic review process. In Part 2, the study applies social network analysis (SNA) to the fourth phase of the systematic review process. In Part 3, the author discusses developing research clusters to analyse the research state more granularly. Systematic literature reviews synthesize scientific evidence through repeatable, transparent and rigorous procedures. By using this approach, you can better interpret and understand the data. The author used two databases (EBSCO and World of Science) for unbiased analysis. In addition, systematic reviews follow preferred reporting items for systematic reviews and meta-analyses.

The study uses UCINET6 software to analyse the data. The study found that specific topics received high centrality (more attention) from scholars when it came to the study topic. Contrary to this, others experienced low centrality scores when using NETDRAW visualization graphs and dynamic capability clusters. Comprehensive analyses are used for the study’s comparison of clusters.

This study used a journal publication as the only source of information. Peer-reviewed journal papers were eliminated for their lack of rigorousness in evaluating the state of practice. This paper discusses the bullwhip effect of digital technology on supply chain management. Considering the increasing use of “AI” in their publications, other publications dealing with sensor integration could also have been excluded. To discuss the top five and bottom five topics, the author used magazines and tables.

The study explores the practical implications of smoothing the bullwhip effect through AI systems, collaboration, leadership and digital skills. Artificial intelligence is rapidly becoming a preferred tool in the supply chain, so management must understand the opportunities and challenges associated with its implementation. Furthermore, managers should consider how AI can influence supply chain collaboration concerning trust and forecasting to smooth the bullwhip effect.

Digital leadership and addressing the digital skills gap are also essential for the success of AI systems. According to the framework, it is necessary to balance AI performance and accountability. As a result of the framework and structured management approach, the author can examine the implications of AI along the supply chain.

The study uses a systematic literature review based on SNA to analyse how AI can alleviate the bullwhip effects of supply chain disruption and identify the focused and the most important AI topics related to the bullwhip phenomena. SNA uses qualitative and quantitative methodologies to identify research trends, strengths, gaps and future directions for research. Salient topics for reviewing papers were identified. Centrality metrics were used to analyse the contemporary topic’s importance, including degree, betweenness and eigenvector centrality. ABEF is presented in the study.

This paper aims to explore the intricate relationship between artificial intelligence (AI) and health information literacy (HIL), examining the rise of AI in health care, the intersection of AI and HIL and the imperative for promoting AI literacy and integrating it with HIL. By fostering collaboration, education and innovation, stakeholders can navigate the evolving health-care ecosystem with confidence and agency, ultimately improving health-care delivery and outcomes for all.

This paper adopts a conceptual approach to explore the intricate relationship between AI and HIL, aiming to provide guidance for health-care professionals navigating the evolving landscape of AI-driven health-care delivery. The methodology used in this paper involves a synthesis of existing literature, theoretical analysis and conceptual modeling to develop insights and recommendations regarding the integration of AI literacy with HIL.

Impact of AI on health-care delivery: The integration of AI technologies in health-care is reshaping the industry, offering unparalleled opportunities for improving patient care, optimizing clinical workflows and advancing medical research. Significance of HIL: HIL, encompassing the ability to access, understand and critically evaluate health information, is crucial in the context of AI-driven health-care delivery. It empowers health-care professionals, patients and the broader community to make informed decisions about their health and well-being. Intersection of AI and HIL: The convergence of AI and HIL represents a critical juncture, where technological innovation intersects with human cognition. AI technologies have the potential to revolutionize how health information is generated, disseminated and interpreted, necessitating a deeper understanding of their implications for HIL. Challenges and opportunities: While AI holds tremendous promise for enhancing health-care outcomes, it also introduces new challenges and complexities for individuals navigating the vast landscape of health information. Issues such as algorithmic bias, transparency and accountability pose ethical dilemmas that impact individuals’ ability to critically evaluate and interpret AI-generated health information. Recommendations for health-care professionals: Health-care professionals are encouraged to adopt strategies such as staying informed about developments in AI, continuous education and training in AI literacy, fostering interdisciplinary collaboration and advocating for policies that promote ethical AI practices.

To enhance AI literacy and integrate it with HIL, health-care professionals are encouraged to adopt several key strategies. First, staying abreast of developments in AI technologies and their applications in health care is essential. This entails actively engaging with conferences, workshops and publications focused on AI in health care and participating in professional networks dedicated to AI and health-care innovation. Second, continuous education and training are paramount for developing critical thinking skills and ethical awareness in evaluating AI-driven health information (Alowais et al., 2023). Health-care organizations should provide opportunities for ongoing professional development in AI literacy, including workshops, online courses and simulation exercises focused on AI applications in clinical practice and research.

This paper lies in its exploration of the intersection between AI and HIL, offering insights into the evolving health-care landscape. It innovatively synthesizes existing literature, proposes strategies for integrating AI literacy with HIL and provides guidance for health-care professionals to navigate the complexities of AI-driven health-care delivery. By addressing the transformative potential of AI while emphasizing the importance of promoting critical thinking skills and ethical awareness, this paper contributes to advancing understanding in the field and promoting informed decision-making in an increasingly digital health-care environment.