Performance, not climate concern, drive green AI adoption in Industry 4.0

Industry 4.0 has promised smarter, faster and more connected manufacturing systems, but it has also intensified the energy demands of industrial production. As artificial intelligence (AI) becomes the backbone of predictive maintenance, collaborative robotics and data-driven operations, concerns about the digital carbon footprint of advanced manufacturing are gaining urgency. Sustainable AI is increasingly viewed as a strategic necessity rather than a secondary goal.

In response to this shift, the study Toward a Sustainable Digital Footprint in Industry 4.0: Predicting Green AI Adoption Among Gen Z Manufacturing Technicians, published in Information, examines how the next generation of technical professionals approaches Green AI. The author assesses the factors that influence adoption intentions among Gen Z students training for Industry 4.0 environments.

Performance over values: What really drives green AI adoption

The author’s research applies an extended version of the Unified Theory of Acceptance and Use of Technology, widely known as UTAUT, to assess Green AI adoption intentions. Rather than proposing a new theory, the study adapts UTAUT to reflect the specific operational and sustainability conditions of Industry 4.0.

The framework integrates traditional predictors such as performance expectancy and technology influence with new constructs tailored to manufacturing and sustainability contexts. These include Industry 4.0 eligibility, digital manufacturing competence, sustainability conditions, Green AI recognition and green manufacturing concern.

Data were collected from 1003 Generation Z students aged 18 to 23 enrolled in technical disciplines in Saudi Arabia. The participants represented nine developing countries and were preparing for manufacturing-oriented careers. None were employed as technicians at the time of data collection, meaning the study captures pre-workforce intention formation rather than workplace behavior.

Using Partial Least Squares Structural Equation Modelling, the study tests how each construct influences behavioral intention to adopt Green AI in collaborative manufacturing systems. The results are striking.

The model explains 62 percent of the variance in adoption intention, indicating strong predictive power. The dominant driver is performance expectancy. Students are significantly more likely to adopt Green AI when they believe it will improve learning outcomes, technical performance and professional readiness. The stronger the perceived productivity benefit, the stronger the adoption intention.

Industry 4.0 eligibility emerges as the second most influential factor. This construct reflects perceptions of infrastructural readiness, system compatibility and technical support within educational institutions. It directly influences adoption intention and strongly predicts performance expectancy. In other words, students are more willing to adopt Green AI when they believe their institutions are technologically prepared and capable of supporting its integration.

Digital manufacturing competence also plays a central role. Students who perceive themselves as skilled in advanced digital and AI technologies show higher intention to adopt Green AI tools. Competence reduces uncertainty and builds confidence, reinforcing the link between technological capability and behavioral readiness.

Technology influence, representing encouragement from instructors, peers and institutions, has a positive but comparatively smaller effect. Social and organizational endorsement matters, but it does not outweigh performance-based motivations.

In contrast, sustainability-related constructs do not directly drive adoption. Sustainability conditions, Green AI recognition and green manufacturing concern fail to show significant direct effects on behavioral intention. Although students demonstrate awareness of environmental issues and concern for responsible manufacturing, these values alone do not translate into stronger adoption intentions.

The findings suggest a clear hierarchy. Performance gains, infrastructure readiness and digital competence form the primary behavioral pathway. Environmental awareness and sustainability concern function as contextual influences rather than immediate motivators.

Industry 4.0, digital carbon and the limits of awareness

Advanced manufacturing systems rely on artificial intelligence, machine learning, Internet of Things (IoT) networks and cloud computing. While these technologies enhance productivity and reduce waste, they also increase computational load and associated carbon emissions.

The concept of the digital carbon footprint refers to the environmental impact generated by digital technologies throughout their lifecycle. In Industry 4.0 ecosystems, continuous data processing and AI training cycles can be energy-intensive. Green AI attempts to mitigate this impact by prioritizing efficiency, reducing unnecessary computation and encouraging responsible AI design.

The author operationalizes the idea of a green digital footprint as a perceptual indicator rather than a direct measure of emissions. Students’ awareness of computational efficiency and their intention to avoid redundant AI usage reflect an attitudinal orientation toward sustainability. However, the results indicate that such awareness does not independently motivate adoption decisions in performance-driven learning environments.

This outcome has important implications. It challenges assumptions that sustainability messaging alone can drive behavioral change among digitally native cohorts. Generation Z students are widely described as environmentally conscious and socially aware. Yet in the context of Industry 4.0 technical education, instrumental considerations dominate.

The extended UTAUT model demonstrates that readiness-related and competence-based variables carry more explanatory weight than value-oriented motivations. Performance expectancy exerts the strongest direct effect. Industry 4.0 eligibility exerts both direct and indirect effects, shaping expectations and intentions simultaneously. Digital manufacturing competence reinforces adoption by equipping students with the skills necessary to operationalize Green AI tools.

Sustainability conditions and environmental concern remain relevant, but as background orientations rather than decisive drivers. The study underscores that in technologically intensive environments, tangible utility and system readiness overshadow abstract sustainability ideals.

Education, readiness and the future of sustainable manufacturing

If green AI adoption among future technicians is primarily performance-driven, institutions must focus on demonstrating clear productivity benefits. Curriculum design should integrate applied digital manufacturing skills and emphasize real-world AI use cases that enhance efficiency while reducing computational waste.

Strengthening Industry 4.0 eligibility is equally critical. Students’ perceptions of infrastructural maturity and technical support strongly influence both performance expectancy and behavioral intention. Educational institutions seeking to promote sustainable AI practices must ensure compatibility between systems, provide accessible technical resources and embed AI tools into collaborative learning environments.

The study also highlights the importance of digital manufacturing competence. As Industry 4.0 ecosystems become more complex, adoption depends on individual capability. Building advanced digital literacy, AI fluency and technical self-efficacy among students will likely yield stronger long-term engagement with sustainability-oriented technologies.

The study has some limitations. The cross-sectional design relies on self-reported data and captures intention rather than observable behavior. Participants were students preparing for manufacturing careers, not practicing technicians facing real production pressures and cost constraints. The findings therefore reflect pre-labor-market readiness rather than confirmed workplace adoption patterns.

Future research is needed to test whether these intention patterns translate into operational behavior in industrial settings. Incorporating objective sustainability metrics such as energy consumption, computational load and carbon emissions would deepen understanding of Green AI’s real-world impact. Additional variables including trust in AI systems, ethical risk perception and organizational sustainability culture may further refine predictive models.