Journal of the Knowledge Economy, Journal Year: 2024, Volume and Issue: unknown
Published: June 25, 2024
Language: Английский
Journal of Open Innovation Technology Market and Complexity, Journal Year: 2024, Volume and Issue: 10(2), P. 100261 - 100261
Published: April 4, 2024
This study proposes and evaluates a novel approach utilizing ensemble machine learning techniques for personalized meal services to address critical gap in understanding AI-powered decision-making within the food delivery restaurant industry. We draw inspiration from diverse fields, including non-traditional simulation methodologies open innovation dynamics, create framework that leverages combined strengths of individual algorithms. Three algorithms – decision trees, logistic regression, naïve Bayes are rigorously evaluated their efficacy classifying assigning an model new service. A simulated dataset, informed by expert tagging, is training ground, ensuring practical relevance. employ voting probability metric on held-out test set provide robust measure accuracy this task. Our findings reveal significant potential services. Ensemble models demonstrate high accuracy, showcasing collaboration combining originality lies applying business case with far-reaching implications management societal well-being. Beyond technical success, we explore technology's broader impact. recommendations can enhance accessibility individuals dietary needs, promote healthier lifestyles through nutritious suggestions, generate job opportunities. Acknowledging limitations future research avenues, invite further exploration applications across various domains.
Language: Английский
Citations
16
Technological Forecasting and Social Change, Journal Year: 2024, Volume and Issue: 200, P. 123187 - 123187
Published: Jan. 11, 2024
Manufacturers are facing increased competitive pressure to strengthen their business ecosystems in order adapt ongoing twin transitions toward digitalization and sustainability associated trends. Yet, managing such complex inter- intra-organizational settings requires a profound, yet little understood, shift the role capabilities of internal organizational functions. This study aims investigate composition ecosystem management capabilities. We employ an in-depth single case leading global solution provider automotive transport industry, based on 51 interviews across function, various functions, actors. Our analysis, rooted dynamic perspective, highlights three sets crucial capabilities: foresight, integration, governance. further detail underlying routines micro-foundational activities enabling these By illuminating key capabilities, routines, context, this makes significant contributions strategy research rapidly evolving landscapes.
Language: Английский
Citations
14
Technological Forecasting and Social Change, Journal Year: 2024, Volume and Issue: 201, P. 123160 - 123160
Published: Jan. 25, 2024
The business landscape has significantly changed in the last few years due to unforeseen incidents such as recent pandemic and war Europe. While have interrupted or even impaired economies businesses, they also accelerated digitalization. Parallel this, many countries started moving towards sustainability realized importance of circularity on a large scale. Thus, international industrial firms must deal with new regulations. Despite growth research digitalization circular economy, our understanding manufacturers their models, particular, is limited. this aims study digitally enabled models. A case approach covered company, six partners, three sister companies, parent company. findings reveal characteristics model its outcomes, including resource utilization, supply chain, sustainable efficiency. Theoretical managerial implications are discussed.
Language: Английский
Citations
14
Applied Sciences, Journal Year: 2024, Volume and Issue: 14(14), P. 5994 - 5994
Published: July 9, 2024
The maritime industry, responsible for moving approximately 90% of the world’s goods, significantly contributes to environmental pollution, accounting around 2.5% global greenhouse gas emissions. This review explores integration artificial intelligence (AI) in promoting sustainability within sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, enhancing operational efficiency, AI offers transformative potential reducing industry’s impact. highlights application fuel optimization, predictive maintenance, route planning, smart management, alongside its role autonomous logistics management. Case studies from Maersk Line Port Rotterdam illustrate successful implementations, demonstrating significant improvements emission reduction, monitoring. Despite challenges such as high implementation costs, data privacy concerns, regulatory complexities, prospects industry are promising. Continued advancements technologies, supported by collaborative efforts public–private partnerships, can drive substantial progress towards a more sustainable efficient industry.
Language: Английский
Citations
14
Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 450, P. 142078 - 142078
Published: April 1, 2024
Language: Английский
Citations
11
Technovation, Journal Year: 2024, Volume and Issue: 133, P. 103013 - 103013
Published: April 17, 2024
Language: Английский
Citations
10
Transportation Research Part E Logistics and Transportation Review, Journal Year: 2024, Volume and Issue: 188, P. 103625 - 103625
Published: June 20, 2024
Language: Английский
Citations
10
Sustainable Production and Consumption, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 1, 2025
Language: Английский
Citations
1
International Journal of Production Economics, Journal Year: 2025, Volume and Issue: unknown, P. 109550 - 109550
Published: Jan. 1, 2025
Language: Английский
Citations
1
Industrial Management & Data Systems, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 31, 2025
Purpose Generative artificial intelligence (GenAI) can potentially improve supply chain management (SCM) processes across levels and verticals. However, despite its promise, the implementation of GenAI for SCM remains challenging, mainly due to lack knowledge regarding key drivers. To address this gap, study examines factors driving in an environment how these optimize performance. Design/methodology/approach A thorough literature review was followed identify The resultant model from drivers validated using a quantitative based on partial least squares structural equation modeling (PLS-SEM) that used responses 315 expert respondents field SCM. Findings results confirmed positive effect performance expectancy, output quality reliability, organizational innovativeness commitment usage. Further, they showed successful usage improved through transparency, better decision-making, innovative design, robust development responsiveness. Practical implications This reports potential contemporary highlights action plan GenAI’s optimal findings suggest by increasing rate implementation, organizations continuously their strategies practices Originality/value establishes first step toward empirically testing validating theoretical
Language: Английский
Citations
1