Artificial intelligence (AI) as a strategic driver of tourism innovation is widely recognized; however, its effect on small and medium-sized enterprises’ (SMEs) innovation performance (in developing countries) remains unknown. The current study proposes an integrated conceptual model that shows how human-centered organizational processes mediate the relationship between AI capability and innovation performance, and under context-specific conditions. Using a mixed-methods approach based on a bibliometric and systematic literature review (SLR), the study attempts to integrate these different streams of research on AI, digital transformation, and tourism innovation. Based on the Dynamic Capabilities Theory (DCT) and Technology-Organization-Environment framework, this study represents AI capability as a conglomerate factor (e.g., AI knowledge, data quality, AI infrastructures, and digital skills), influencing innovation performance (product, services, process, and marketing innovations) indirectly through digitally grounded self-efficacy, epistemic curiosity, and knowledge integration. Additionally, the study shows that the mediating effects of self-efficacy, curiosity, and knowledge integration in the relationship between AI capabilities and tourism innovation performance are moderated by institutional support and environmental limitations. The study contributes to the literature by introducing a socio-technical, context-specific perspective on AI-driven innovation at the macro level rather than technology-based perspectives. Beyond that, innovation can also be stimulated by the interaction of technology, dynamic organizational capabilities, and the external environment. Therefore, a valid empirical investigation may be based on the theoretical lens provided by the current framework and shed light on practical applications for tourism managers and practitioners in emerging countries, thereby enhancing AI capacity and boosting tourism innovation.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
This paper proposes adaptive sampling with approximate expected futures (ASAp), a decoding algorithm that guarantees the output to be grammatical while provably producing outputs that match the conditional probability of the LLM's distribution conditioned on the given grammar constraint.
Kanghee Park, Jiayu Wang, Taylor Berg-Kirkpatrick et al.· Neural Information Processin...· 70 citations· ⚡5
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
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