Insights into the performance of green supply chain in the Chinese semiconductor industry

Fakhar Shahzad, Younes Ben Zaied, Muhammad Asim Shahzad and Faisal Mahmood

International Journal of Production Economics, 2024, vol. 273, issue C

Abstract: To achieve the global carbon neutrality goal by 2050, businesses are urged to take the lead in adopting sustainable practices. Recently, there has been a growing interest among both academics and practitioners in utilizing artificial intelligence (AI) for digital transformation. However, measuring the impact of digital transformation on achieving carbon neutrality goals is still in its infancy, particularly in the context of the semiconductor industry. Therefore, this study aims to explore the nexus between AI capabilities, digital transformation, and carbon neutrality in enhancing green supply chain performance. A partial least squares structural equation modeling, bootstrapping, and importance-performance map analysis were employed to test the proposed research model. The data was obtained through a structured questionnaire from 426 respondents from semiconductor firms in China. The results revealed that AI capabilities positively impact the digital transformation of Chinese semiconductor firms. Furthermore, the findings demonstrated that digitally transformed firms are better equipped to achieve carbon neutrality objectives. Lastly, the study found a positive correlation between carbon neutrality and the overall performance of green supply chains in semiconductor manufacturing firms. These results serve as a valuable resource for logistics and supply chain managers, providing insights into how AI capabilities can be harnessed to enhance the performance of green supply chains.

Keywords: Artificial intelligence capabilities; Digital transformation; Carbon neutrality; Supply chain; Performance; Semiconductor firms (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:273:y:2024:i:c:s0925527324001439

DOI: 10.1016/j.ijpe.2024.109286

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