Generative Artificial Intelligence in Adaptive Social Manufacturing: A Pathway to Achieving Industry 5.0 Sustainability Goals DOI Open Access
Parisa Jourabchi Amirkhizi, Siamak Pedrammehr, Sajjad Pakzad

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(4), P. 1174 - 1174

Published: April 12, 2025

As manufacturing transitions from Industry 4.0 to 5.0, a critical challenge emerges in integrating Generative Artificial Intelligence (GAI) into adaptive social achieve sustainability goals. This transition reflects paradigmatic shift technology-centric model focused on automation and efficiency toward more holistic framework that embeds human-centricity environmental responsibility industrial systems. Whereas emphasizes digital innovation productivity, 5.0 seeks align technological advancement with broader ecological societal objectives. Despite advancements digitalization, existing frameworks lack structured approach leveraging GAI for environmental, social, economic sustainability. study explores the transformative role of manufacturing, addressing gap frameworks. Employing multi-method research design, including content analysis, expert-driven validation, system dynamics modeling, identifies nine key dimensions maps them 17 functions. The findings reveal significantly enhances by optimizing resource efficiency, promoting inclusivity, supporting ethical governance. System analysis highlights complex interdependencies between GAI-driven functions outcomes, underscoring need balance human values. provides novel industries seeking implement sustainable production systems, bridging theoretical insights practical applications. Additionally, it offers actionable strategies address challenges such as workforce adaptation, AI governance, adoption barriers, ultimately facilitating 5.0’s

Language: Английский

Advancing Sustainability Through Artificial Intelligence: Implications for Firm Value in Indonesia DOI Creative Commons

Dendi Mulyana,

Aristanti Widyaningsih,

Rozmita Dewi Yuniarti Rozali

et al.

Jurnal Akuntansi, Journal Year: 2025, Volume and Issue: 29(1), P. 148 - 170

Published: Jan. 31, 2025

This research seeks to explore the influence of AI adoption on ESG performance and further assess mediation effect in relation between firm value. The was carried out from 2020 2023 companies Indonesia, yielding 288 observational data points. A multivariate analysis performed utilising partial least squares structural equation modelling (PLS-SEM) hypothesis. findings hypothesis testing demonstrate that has a significant favourable impact performance. Similarly, significantly enhances Additionally, indirect effects reveals effectively mediates positive relationship by serving as strategic resource, improving efficiency, advancing sustainability meet stakeholder expectations, enhancing corporate encourages government support, managerial integration, standardised policies for AI-driven business sustainability.

Language: Английский

Citations

0
Generative Artificial Intelligence in Adaptive Social Manufacturing: A Pathway to Achieving Industry 5.0 Sustainability Goals DOI Open Access
Parisa Jourabchi Amirkhizi, Siamak Pedrammehr, Sajjad Pakzad

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(4), P. 1174 - 1174

Published: April 12, 2025

As manufacturing transitions from Industry 4.0 to 5.0, a critical challenge emerges in integrating Generative Artificial Intelligence (GAI) into adaptive social achieve sustainability goals. This transition reflects paradigmatic shift technology-centric model focused on automation and efficiency toward more holistic framework that embeds human-centricity environmental responsibility industrial systems. Whereas emphasizes digital innovation productivity, 5.0 seeks align technological advancement with broader ecological societal objectives. Despite advancements digitalization, existing frameworks lack structured approach leveraging GAI for environmental, social, economic sustainability. study explores the transformative role of manufacturing, addressing gap frameworks. Employing multi-method research design, including content analysis, expert-driven validation, system dynamics modeling, identifies nine key dimensions maps them 17 functions. The findings reveal significantly enhances by optimizing resource efficiency, promoting inclusivity, supporting ethical governance. System analysis highlights complex interdependencies between GAI-driven functions outcomes, underscoring need balance human values. provides novel industries seeking implement sustainable production systems, bridging theoretical insights practical applications. Additionally, it offers actionable strategies address challenges such as workforce adaptation, AI governance, adoption barriers, ultimately facilitating 5.0’s

Language: Английский

Citations

0