Adoption and impact of generative artificial intelligence on blockchain-enabled supply chain efficiency

Journal of Systems and Information Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 14, 2025

Purpose The purpose of this study is to investigate the primary determinants influencing acceptance generative artificial intelligence (GAI) adoption within Blockchain-enabled environments. Further research will examine impact GAI on supply chain efficiency (SCE) through enhancement Blockchain. Design/methodology/approach Drawing innovation diffusion theory (IDT), used partial least square structural equation modelling (PLS-SEM) look into hypotheses. data were gathered via online questionnaires from employers Chinese enterprises that have already integrated Findings findings demonstrate relative advantages (RAs), compatibility, trialability and observability a significant positive effect adoption, while complexity harms adoption. Above all, has significantly enhanced Blockchain, thus effectively improving SCE. Practical implications outcomes furnish organizations with valuable insights proficiently integrate Blockchain capability, optimize management bolster market competitiveness. Also, help accelerate successful integration business processes attain Sustainability Development Goals 9, industrial growth diversification. Originality/value To extent author’s knowledge, current status remains largely exploratory, there limited empirical evidence integrating capability GAI. This bridges knowledge gap by fully revealing optimal these two transformative technologies leverage their potential in management.

Language: Английский

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Impact of artificial intelligence and knowledge management on proactive green innovation: the moderating role of trust and sustainability

Asia-Pacific Journal of Business Administration, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 25, 2024

Purpose In this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil gas organizations. It also aims investigate moderator role trust sustainability these relationships. Design/methodology/approach This paper employs a quantitative analysis. Surveys have been gathered from middle-line managers twenty-four government organizations evaluate perceptions towards AI, KM processes, trust, measures toward innovation. Analytical statistical tools that were employed study, including structural equation modeling with SmartPLSv3.9, used analyze data examine measurement models study. Findings The study results reveal significant positive impact AI utilization, PGI Furthermore, turn out be viable moderators affecting, influencing strength direction particular, higher levels more substantial commitments enhance on outcomes. Practical implications Understanding KM, offers valuable insights for organizational leaders policymakers seeking promote industry. Thus, can increase efficiency sustainable product development, process improvement environmental by using robust technologies effective systems. fostering among stakeholders embedding principles into culture amplify effectiveness initiatives driving Originality/value extends current assessing effect while accounting as moderators. Utilizing methods nuanced understanding complex interactions between variables, thereby advancing theoretical fields management, behavior. Additionally, identification specific mechanisms contextual factors enriches practical practitioners striving dynamics complexities an AI-driven era.

Language: Английский

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Trends and Opportunities in Sustainable Manufacturing: A Systematic Review of Key Dimensions from 2019 to 2024

Sustainability, Journal Year: 2025, Volume and Issue: 17(2), P. 789 - 789

Published: Jan. 20, 2025

Purpose: This systematic literature review analyzes trends, key findings, and research opportunities in manufacturing sustainability from 2019 to 2024, with a focus on the integration of emerging technologies socio-economic dimensions. Methodology: 181 publications was conducted, emphasizing technological advancements, gaps, influence global events sustainable manufacturing. Findings: highlights: (1) shift towards advanced like AI-driven circular economy solutions, digital twins, blockchain, which have demonstrated potential reduce energy consumption by 30% decrease material waste 20%, significantly enhancing outcomes; (2) persistent gaps addressing social, policy, regulatory dimensions; (3) role COVID-19 pandemic accelerating transformation reshaping priorities. Key findings also include PT Indocement achieving cumulative 35% reduction natural gas through sustained optimization initiatives 12% increase adoption among SMEs developing regions. Practical implications: strategic recommendations are provided for industry, policymakers, academics address regional disparities, ensuring 50% rates inclusive within regions over next five years, align efforts contexts. Originality: this presents comprehensive analysis current actionable insights, critical areas future research, highlighting that organizations adopting AI blockchain report up 25% improvement operational sustainability.

Language: Английский

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ANALYSIS OF A COMPREHENSIVE APPROACH TO THE FORMATION OF TEST DATASETS FOR TRAINING UAV SWARMS UNDER DYNAMIC CONDITIONS

Випробування та сертифікація, Journal Year: 2025, Volume and Issue: 4(6), P. 69 - 78

Published: Jan. 20, 2025

The article addresses the issue of generating test datasets for training swarms unmanned aerial vehicles (UAVs) under complex and dynamic operational conditions, which are in constant change. study emphasises necessity considering various factors, including presence obstacles, terrain features, challenges associated with lack a stable GPS signal. Proper dataset formation ensures swarm reliability combat effectiveness by enabling algorithms to pre-emptively account diverse scenarios. analysis existing methods highlights three main directions. Firstly, clustering techniques (e.g. K-means, DBSCAN) enable automatic grouping numerous potential scenarios, identification typical rare avoidance data duplication that does not contribute broader scenario coverage. Secondly, application genetic facilitates search globally optimal parameter configurations, taking into multidimensional nature problem (simultaneous changes UAV positioning, variability weather types obstacles). This approach helps identify critical combinations factors often overlooked other methods. Thirdly, machine learning (including neural networks, support vector machines, multi-agent reinforcement learning) equip ability adaptively 'learn' from historical data, respond new threats, predict future developments. proposes comprehensive integrates advantages clustering, algorithms, learning. Initially, is employed structure broad range categorising them simplest most conditions. At next stage, analyse each cluster, identifying key parameters could reduce performance. Simultaneously, development adaptive models capable promptly adjusting their behaviour based on obtained results. balanced encompasses both non-trivial cases, thereby facilitating more flexible informed configuration control systems. practical significance this lies substantial enhancement readiness swarms. These able learn perform effectively predictable conditions acquire necessary skills operate scenarios limited resources. Future research will focus improving process forming ensure high substantially mitigate risks during missions maximise challenging rapidly changing environments.

Language: Английский

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Future of Productivity Management

Advances in business strategy and competitive advantage book series, Journal Year: 2025, Volume and Issue: unknown, P. 89 - 112

Published: Jan. 31, 2025

In today's competitive business environment, optimizing employee productivity is crucial for organizational success. Traditional methods of management often fall short in effectively leveraging the vast amount data available. This chapter explores application Generative AI as a transformative tool enhancing practices. It discusses fundamental concepts and its diverse applications across industries, highlighting potential to revolutionize traditional approaches enhancement. Key benefits using are examined, including case studies real-world examples illustrate successful implementations AI, demonstrating tangible impact on efficiency performance. The also addresses challenges limitations associated with use emerging trends future directions, predicting how will continue evolve shape optimization.

Language: Английский

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Gen-Optimizer: A Generative AI Framework for Strategic Business Cost Optimization

Computers, Journal Year: 2025, Volume and Issue: 14(2), P. 59 - 59

Published: Feb. 10, 2025

Strategic cost optimization is a critical challenge for businesses aiming to maintain competitiveness in dynamic markets. This paper introduces Gen-Optimizer, Generative AI-based framework designed analyze and optimize business costs through intelligent decision support. The employs transformer-based model with over 140 million parameters, fine-tuned using diverse dataset of cost-related scenarios. By leveraging generative capabilities, Gen-Optimizer minimizes inefficiencies, automates analysis tasks, provides actionable insights decision-makers. proposed achieves exceptional performance metrics, including prediction accuracy 93.2%, precision 93.5%, recall 93.1%, an F1-score 93.3%. perplexity score 20.17 demonstrates the model’s superior language understanding abilities. was tested real-world scenarios, demonstrating its ability reduce operational by 4.11% across key functions. Furthermore, it aligns sustainability objectives, promoting resource efficiency reducing waste. highlights transformative potential AI management, paving way scalable, intelligent, cost-effective solutions.

Language: Английский

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Environmental footprint of GenAI – Changing technological future or planet climate?

Journal of Innovation & Knowledge, Journal Year: 2025, Volume and Issue: 10(3), P. 100691 - 100691

Published: March 17, 2025

Language: Английский

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Generative AI Techniques and Models

Lecture notes on data engineering and communications technologies, Journal Year: 2025, Volume and Issue: unknown, P. 45 - 64

Published: Jan. 1, 2025

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Addressing Rights on Responsible AI in Digital Companies

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 109 - 138

Published: March 7, 2025

This study examined the top five reference companies for Generation Z, in relation to bias and responsible Artificial Intelligence (AI). Through a literature analysis on of Law 15/2022, July 12 (15917/2022), comprehensive equal treatment non-discrimination, key factors are detected determine whether or not comply from an ethical point view. The findings confirmed that all items analyzed complied with except seals algorithms officially. These results essential guide market towards more transparent business models, which helps increase trust they transmit society. offers implications, limitations future lines research, focus algorithmic literacy.

Language: Английский

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Generative artificial intelligence and the challenges to adding value ethically

Technovation, Journal Year: 2025, Volume and Issue: 144, P. 103235 - 103235

Published: April 8, 2025

Language: Английский

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