Harnessing artificial intelligence to strengthen green innovation capacity in pursuit of sustainable development goals: Evidence from Taiwan’s manufacturing sector

Abstract

Research background: Artificial Intelligence (AI) is becoming a revolutionary ability that can speed up the shift towards sustainable production through re-source efficiency, optimization of processes, and low-carbon innovations. Consistent with the United Nations Sustainable Development Goals (SDGs), SDG 9 (sustainable industrialization), SDG 12 (responsible consumption and production), and SDG 13 (climate action), AI is becoming a driver of green innovation, as well as a facilitator of the same.

Purpose of the article: This paper examines how AI applications affect organizational performance (OPE) in the Taiwanese manufacturing industry with a special emphasis on the mediating effect of GIC. Based on the Dynamic Capabilities Theory (DCT), the paper constructs and empirically validates a structural model that elucidates how AI adoption increases sustainable competitiveness through the development of innovation-oriented capabilities.

Methods: The research used a cross-sectional, quantitative study design and gathered data on 270 professionals in the Taiwanese manufacturing sectors. The AI applications, GIC, and OPE were measured using a structured questionnaire to measure them using multi-item Likert scales. Hypotheses were tested using the Partial Least Squares Structural Equation Modeling (PLS-SEM).

Findings & value added: The study shows that Artificial Intelligence (AI) adoption plays a significant role in enhancing both green innovation capabilities (GIC) and overall organizational performance (OPE). More importantly, GIC emerges as a key mechanism through which AI applications are translated into measurable sustainability outcomes, underscoring its role as a strategic bridge between digital transformation and environmental performance.

Keywords

dynamic capabilities theory, responsible innovation, digital trans-formation, foreign direct investments, sustainable industrialization

Author Biography

K. M. Tousif Bin Parves

References

1. Abbas, A., Luo, X., Shahzad, F., & Wattoo, M. U. (2023). Optimizing organizational performance in manufacturing: The role of IT capability, green supply chain integration, and green innovation. Journal of Cleaner Production, 423, 138848.
   View in Google Scholar
2. Alexander, Z., Chau, D. H. P., & Saldaña, C. (2024). An interrogative survey of explainable AI in manufacturing. IEEE Transactions on Industrial Informatics, 20, 7069–7081.
   View in Google Scholar
3. Amaranti, R., Govindaraju, R., & Irianto, D. (2019). Green dynamic capability for enhancing green innovations performance in a manufacturing company: A conceptual framework. IOP Conference Series: Materials Science and Engineering, 703(1), 012023.
   View in Google Scholar
4. Appiah, L. O. (2024). Does proactive boundary‐spanning search drive green innovation? Exploring the significance of green dynamic capabilities and analytics capabilities. Corporate Social Responsibility and Environmental Management, 31(4), 2589–2599.
   View in Google Scholar
5. Arinez, J., Chang, Q., Gao, R. X., Xu, C., & Zhang, J. (2020). Artificial intelligence in advanced manufacturing: Current status and future outlook. Journal of Manufacturing Science and Engineering - Transactions of The ASME, 142(11), 1–53.
   View in Google Scholar
6. Augustin‐Behravesh, S., Gomez‐Trujillo, A. M., Perdomo‐Charry, G., Djunaedi, M. K. D., & Ong, A. K. S. (2025). Sustainability strategies and corporate legitimacy: Analyzing firm performance through green innovation and technological turbulence. Business Strategy and the Environment.
   View in Google Scholar
7. Balcıoğlu, Y. S., Çelik, A. A., & Altındağ, E. (2024). Artificial intelligence integration in Sustainable Business Practices: A text mining analysis of USA firms. Sustainability, 16(15), 6334.
   View in Google Scholar
8. Biggi, G., Iori, M., Mazzei, J., & Mina, A. (2025). Green intelligence: The AI content of green technologies. Eurasian Business Review, 5(2), 343–370.
   View in Google Scholar
9. Castañé, G., Dolgui, A., Kousi, N., Meyers, B., Thevenin, S., Vyhmeister, E., & Östberg, P. (2022). The ASSISTANT project: AI for high level decisions in manufacturing. International Journal of Production Research, 61(7), 2288–2306.
   View in Google Scholar
10. Cheng, J., Xu, N. R., Khan, N. U., & Singh, H. S. M. (2025). The impacts of artificial intelligence literacy, green absorptive capacity, and green information system on green innovation. Corporate Social Responsibility and Environmental Management, 32(2), 2375–2389.
    View in Google Scholar
11. Chotia, V., Cheng, Y., Agarwal, R., & Vishnoi, S. K. (2024). AI-enabled green business strategy: Path to carbon neutrality via environmental performance and green process innovation. Technological Forecasting and Social Change, 202, 123315.
    View in Google Scholar
12. Da Silva Nascimento, L., Da Rosa, J. R., Da Silva, A. R., & Reichert, F. M. (2023). Social, environmental, and economic dimensions of innovation capabilities: Theorizing from sustainable business. Business Strategy and the Environment, 33(2), 441–461.
    View in Google Scholar
13. Du, J., Cai, H., & Jin, X. (2024). Exploring the association between artificial intelligence management and green innovation: Expanding the research field for sustainable outcomes. Sustainability, 16(21), 9315.
    View in Google Scholar
14. Fowler, J. W., Kempf, K. G., & Mönch, L. (2023). Guest editorial special section on production-level artificial intelligence applications in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 36(4), 558–559.
    View in Google Scholar
15. Gazi, Md. A. I., Rahman, Md. K. H., Masud, A. A., Amin, M. B., Chaity, N. S., Senathirajah, A. R. bin S., & Abdullah, M. (2024). AI capability and sustainable performance: Unveiling the mediating effects of organizational creativity and green innovation with knowledge sharing culture as a moderator. Sustainability, 16(17), 7466.
    View in Google Scholar
16. Gürlek, M., & Tuna, M. (2017). Reinforcing competitive advantage through green organizational culture and green innovation. Service Industries Journal, 38(7–8), 467–491.
    View in Google Scholar
17. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2018). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.
    View in Google Scholar
18. Hao, J., & Zhang, S. (2024). The impact of artificial intelligence on ESG performance of manufacturing firms: The mediating role of ambidextrous green innovation. Systems, 12(11), 499.
    View in Google Scholar
19. Hussain, M., Yang, S., Maqsood, U. S., & Zahid, R. M. A. (2024). Tapping into the green potential: The power of artificial intelligence adoption in corporate green innovation drive. Business Strategy and the Environment, 33(5), 4375–4396.
    View in Google Scholar
20. Jahan, F., Chowdhury, S., & Hoque, M. (2024). The effect of AI adoption and green leadership on organizational environmental performance in SMEs. International Journal of Religion, 5(11), 2207–2222.
    View in Google Scholar
21. Jiang, L., Xuan, Y., & Zhang, K. (2024). Unlocking innovation potential: The impact of artificial intelligence transformation on enterprise innovation capacity. European Journal of Innovation Management.
    View in Google Scholar
22. Jing, Z., Zheng, Y., & Guo, H. (2023). A study of the impact of digital competence and organizational agility on green innovation performance of manufacturing firms—the moderating effect based on knowledge inertia. Administrative Sciences, 13(12), 250.
    View in Google Scholar
23. Khan, S., Mehmood, S., & Khan, S. U. (2024). Navigating innovation in the age of AI: How generative AI and innovation influence organizational performance in the manufacturing sector. Journal of Manufacturing Technology Management.
    View in Google Scholar
24. Kovalenko, I., Barton, K., Moyne, J., & Tilbury, D. M. (2023). Opportunities and challenges to integrate artificial intelligence into manufacturing systems: Thoughts from a panel discussion [Opinion]. IEEE Robotics & Automation Magazine, 30, 109–112.
    View in Google Scholar
25. Lawati, E. H. A., Ali, M. A. M., & Tahir, N. M. (2024). The importance of artificial intelligence in green innovation. In 2024 IEEE 14th international conference on control system, computing and engineering (ICCSCE) (pp. 327–332). IEEE.
    View in Google Scholar
26. Li, D., Xiao, J., & Yang, F. (2024). Artificial intelligence and enterprise green innovation: Intrinsic mechanisms and heterogeneous effects. Sustainability, 16(21), 9246.
    View in Google Scholar
27. Li, D., Xiao, J., & Yang, F. (2024). Artificial intelligence and enterprise green innovation: Intrinsic mechanisms and heterogeneous effects. Sustainability, 16(21), 9246.
    View in Google Scholar
28. Lin, J., Zeng, Y., Wu, S., & Liu, X. (2024). How does artificial intelligence affect the environmental performance of organizations? The role of green innovation and green culture. Information & Management, 61(2), 103924.
    View in Google Scholar
29. Madzík, P., Falát, L., Yadav, N., Lizarelli, F. L., & Čarnogurský, K. (2024). Exploring uncharted territories of sustainable manufacturing: A cutting-edge AI approach to uncover hidden research avenues in green innovations. Journal of Innovation & Knowledge, 9(3), 100498.
    View in Google Scholar
30. Malik, I., Mehraj, D., Nissa, V. U., & Wani, A. K. (2025). Unveiling the dynamics of sustainable development: A holistic examination of green organizational learning, innovation, and performance in Indian SMEs. Business Strategy and Development, 8(1), e70060.
    View in Google Scholar
31. Marco‐Lajara, B., Zaragoza‐Sáez, P., & Martínez‐Falcó, J. (2023). Green innovation. In Research anthology on business law, policy, and social responsibility (pp. 916–931). IGI Global.
    View in Google Scholar
32. Martínez‐Falcó, J., Sánchez‐García, E., Marco‐Lajara, B., & Millán‐Tudela, L. A. (2024). Green innovation. In Advances in logistics, operations, and management science (pp. 150–167). Hershey, PA: IGI Global.
    View in Google Scholar
33. Muller, K., & Cohen, J. (1989). Statistical power analysis for the behavioral sciences. Technometrics, 31(4), 499.
    View in Google Scholar
34. Nie, Y., & Su, J. (2022). Evaluating the green innovation ability of engineering teams in a hesitation fuzzy environment. Advances in Decision Sciences, 26(5), 53–76.
    View in Google Scholar
35. Nti, I. K., Adekoya, A. F., Weyori, B. A., & Nyarko-Boateng, O. (2021). Applications of artificial intelligence in engineering and manufacturing: A systematic review. Journal of Intelligent Manufacturing, 33(6), 1581–1601.
    View in Google Scholar
36. Pandithasekara, D. (2022). Green innovation practices and its impact on organizational performance: Evidence from apparel industry of Sri Lanka. International Journal of Research Publication and Reviews, 3(9), 743–759.
    View in Google Scholar
37. Papadopoulos, T., Sivarajah, U., Spanaki, K., Despoudi, S., & Gunasekaran, A. (2022). Editorial: Artificial intelligence (AI) and data sharing in manufacturing, production and operations management research. International Journal of Production Research, 60(14), 4361–4364.
    View in Google Scholar
38. Rahmani, A., Naeini, A. B., Mashayekh, J., Aboojafari, R., Daim, T., & Yalcin, H. (2024). Green innovation for a greener future: A meta-analysis of the impact on environmental performance. Journal of Cleaner Production, 460, 142547.
    View in Google Scholar
39. Raman, R., Gunasekar, S., Ray, S., Behera, D., Nedungadi, P., & Dénes, D. L. (2025). A holistic approach to Sustainable Development Goal 8: Integrating economic growth, employment, and sustainability. Equilibrium Quarterly Journal of Economics and Economic Policy, 20(1), 147–202.
    View in Google Scholar
40. Saiyed, S., Hasan, M., Chowdhury, R., Hossain, M. A., Musa, S., & Kumar, V. (2025a). Green human resource management practices on the sustainable performance of India’s sports sector. Retos, 67, 946–961.
    View in Google Scholar
41. Saiyed, S., Kumar, V., Islam, M. S., Tedjakusuma, A. P., & Verma, P. (2025b). Advancing sustainability in academic institutions: A GHRM Framework using fuzzy Delphi and DEMATEL method. Green Technologies and Sustainability, 100294.
    View in Google Scholar
42. Shui, X., Zhang, M., Wang, Y., & Smart, P. (2024). Do climate change regulatory pressures increase corporate environmental sustainability performance? The moderating roles of foreign market exposure and industry carbon intensity. British Journal of Management, 36(1), 223–239.
    View in Google Scholar
43. Thomas, A., Palladino, R., Nespoli, C., D’Agostino, M. T., & Russo, G. (2022). Determinants and outcomes of green innovations. In H. Elgaz, Z. Mahmood & N. Alrajeh (Eds.). Handbook of research on building greener economics and adopting digital tools in the era of climate change (Advances in environmental engineering and green technologies. (pp. 43–63). Hershey, PA: IGI Global.
    View in Google Scholar
44. Ullah, S., Kukreti, M., Sami, A., & Shaukat, M. R. (2024). Leveraging technological readiness and green dynamic capability to enhance sustainability performance in manufacturing firms. Journal of Manufacturing Technology Management.
    View in Google Scholar
45. Waltersmann, L., Kiemel, S., Stuhlsatz, J., Sauer, A., & Miehe, R. (2021). Artificial intelligence applications for increasing resource efficiency in manufacturing companies — A comprehensive review. Sustainability, 13(12), 6689.
    View in Google Scholar
46. Wang, J., Wang, X., Sun, F., & Li, X. (2024). The functional mechanisms through which artificial intelligence influences the innovation of green processes of enterprises. Systems, (9), 378.
    View in Google Scholar
47. Waqas, U., Umair, S., Mrugalska, B., & Sulaiman, M. a. B. A. (2025). Twin transformation: The role of digitalization with artificial intelligence, big data and circular supply chain in circular economy performance for sustainability. Equilibrium Quarterly Journal of Economics and Economic Policy, 20(2), 613–645.
    View in Google Scholar
48. Yang, Q., Sun, T., & Li, R. (2023). Does artificial intelligence promote green innovation? An assessment based on direct, indirect, spillover, and heterogeneity effects. Energy & Environment, 36(2), 1005–1037.
    View in Google Scholar
49. Zhao, K., Wu, C., & Liu, J. (2024). Can artificial intelligence effectively improve China’s environmental quality? A study based on the perspective of energy conservation, carbon reduction, and emission reduction. Sustainability, 16(17), 7574.
    View in Google Scholar