In the last few years, artificial intelligence has grown rapidly and has become increasingly popular in daily life. As a powerful branch of artificial intelligence, generative AI (Gen AI) offers immense potential for application within the new product development (NPD) process. In this study, we investigate the impact of Gen AI on NPD by utilizing a closed-form analytical model within a single-product monopoly. The results show that the profit and productivity levels of a company adopting Gen AI technology are lower than those of a non-Gen AI company. However, the company that adopts Gen AI technology achieves better product quality than a company without Gen AI implementation. Furthermore, by increasing design costs by a small amount, the Gen AI adopting company improves its product quality level, while non-Gen AI companies experience only a slim increase in product quality. Additionally, under lower fixed costs, outcomes improve for both Gen AI and non-Gen AI companies. Our findings suggest that a company which has already adopted Gen AI technology should not reduce the cost of quality. Instead, it should focus more on increasing the cost of quality to realize greater benefits from Gen AI implementation.

By building a game model between brands and outsourced anchors, this article systematically studies the incentive design, opportunistic behavior and blockchain governance mechanism in live streaming. We found that while promoting the sales efforts of anchors, commission incentives will also induce opportunistic behaviors such as AI rhetoric exaggeration, traffic manipulation and channel encroachment. Secondly, blockchain technology can effectively suppress opportunism by improving the traceability of information and the verifiability of behavior: on the one hand, it increases the marginal cost of implicit violations, On the other hand, contractible punishment is imposed on explicit sexual encroachment. The key threshold conditions in the study show that when the sum of the incentive intensity and the blockchain punishment exceeds the encroachment of personal interests, the encroachment can be completely suppressed. The study further reveals the three-state equilibrium model of the optimal blockchain governance intensity: strong governance, weak governance and precise governance. Its choice depends on the trade-off of product characteristics, incentive costs and governance benefits. At the same time, the governance effect is affected by the power structure of the platform, and the platform, as a provider of governance infrastructure, may have the problem of insufficient motivation. Based on this, this article proposes that the sustainable development of live ecology requires building a comprehensive system of incentive contracts, technical governance and institutional environment coordination. It also provides a theoretical framework for understanding the problem of entrusted agencies in digital marketing and a decision-making basis for the governance practice of the platform economy.

This paper provides a bibliometric analysis of AI and cloud computing in product revolution and development. It attempts to make sense of its evolution and styles of research to date. A systematic analysis based on the PRISMA protocol was regulated over journals rescued from Web of Science Core Current Contents Connect (CCC) and Web of Science Core Collection (WoSCC) related to 118 articles published between 2009 and 2025. Python-based Bibliometric performance through analytical tools and visualization using VOSviewer statistics and science mapping. The findings reveal an authoritative yearly growth trend and rising academic attraction in the subject, and especially since 2018. The big players were universal in co-occurrence analysis of keywords. AI-focused Product Design, Cloud-based Collaborative engineering, digital twin, data analytics, generative AI and open innovation ecosystems. Furthermore, temporal overlay analysis shows a progressive transition from the pioneer articles of digital infrastructures to intelligent innovation systems. The results increase existing research on innovation management literature by aiming at how AI technologies, cloud programs and digital innovation ecosystems converge, revealing rich avenues for further cross-disciplinary research.

Generative artificial intelligence (GenAI) has reshaped organizational work since the release of ChatGPT in late 2022, yet research on the cultural dimensions of its adoption remains fragmented. This study synthesizes the emerging field through a bibliometric review combining performance analysis, science mapping, and the Theory–Context–Characteristics–Methodology (TCCM) framework. A Scopus search executed on 5 May 2026 yielded 257 records, refined to 128 by automatic filters, and reduced to 69 articles after PRISMA 2020 screening. Analysis employed bibliometrix (R 4.3.0) and four VOSviewer 1.6.20 networks. The field exhibits an annual growth rate of 114.07%, with publications rising from one in 2022 to 36 in 2025 and 21 in early 2026 across 65 sources and 23 countries (China leads with 12 articles), anchored by Ng et al. (2025), Jo and Bang (2023), and Lee and Park (2024). The keyword map yields four thematic clusters; the co-citation map reveals three intellectual sub-structures (Technology Acceptance, Methodological and Applied GenAI, and GenAI in Management). Both layers converge on a technology-acceptance dominant, methodologically homogeneous, and culturally thin configuration: classical culture references (Schein, Cameron, and Quinn) and dedicated AI-ethics works do not reach the co-citation core. To redress this asymmetry, we propose an integrative framework: Leadership Vision → AI Literacy and Trust → Psychological Safety → Ethical Climate → GenAI Adoption Culture → Productivity, Innovation, and Well-being. The agenda outlines five priorities, including cross-cultural instruments, longitudinal multi-wave designs, and emerging-market studies foregrounding Indonesia and ASEAN.

In contemporary medicine, two trends are of particular significance: the establishment of Telehealth as a fundamental component of healthcare delivery, and the emergence of Large Language Models (LLMs) as a transformative category of artificial intelligence. The convergence of these domains has created a new, dynamic, and rapidly expanding research frontier. Despite this escalating interest, a comprehensive map of the field's intellectual foundations, key contributors, and thematic progression has yet to be established. This study addresses this critical knowledge gap by employing a quantitative scientometric methodology to systematically map the intellectual structure and evolutionary trajectory of research at the intersection of Telehealth and LLMs. We conducted a comprehensive bibliometric analysis of 670 scientific documents extracted from the Web of Science (WoS) Core Collection database, spanning the period from 1997 to Oct 2025. The analysis utilized performance metrics and network mapping tools to identify publication dynamics, influential actors, and core conceptual themes. The findings reveal a field in a state of “Hypergrowth“, characterized by an exponential increase in scientific production, particularly after 2021. The United States and China are identified as the dominant leaders in research output. Thematic analysis demonstrates a clear paradigm shift within the literature: an evolution from a broad focus on general artificial intelligence and machine learning applications toward a more specialized and intense concentration on the capabilities and implications of LLMs and Generative AI. This research provides the first large-scale quantitative map of the Telehealth and LLM landscape. It documents a field that is maturing at an accelerated rate, creating an urgent need for scholarly and practical frameworks that bridge the gap between rapid technological innovation and the slower-moving, yet critical, domains of clinical validation, regulatory oversight, and ethical considerations. The insights provided herein offer a data-driven foundation for researchers, policymakers, and practitioners to navigate and contribute to this critical and rapidly evolving field.

The emergence of large language models (LLMs), like ChatGPT, has caused a major shift in all academic and professional fields; education being the one impacted the most deeply. The first 200 most cited articles from Scopus on the topic of "large language models" and "education" were selected for a scientometric analysis of this survey. The aim is to map the intellectual landscape, specifying dominant research themes, key contributors, methodological trends, and the emergent challenges that researchers faced during this initial, explosive phase of research. This review systematically analyzes the publication trends, authorship patterns, geographical and institutional contributions, and the conceptual structure of the literature to provide a quantitative and qualitative snapshot of a field that is rapidly changing. The analysis uncovers a domain where exploratory studies, conceptual debates, and early empirical validations dominate, and where the focus of research is mainly on higher education and medical training. The major issues being discussed are academic integrity, assessment redesign, and the ethical integration of AI, while promising applications are personalized learning, automated feedback, and content generation. This survey is a reference for a better understanding of the roots and forefronts of LLM-related educational research as it passes from the initial reaction to systematic integration.