| Author: | Miao, Shucheng |
| Title: | Two studies on the artificial intelligence investments of firms, efficiency enhancement and spillover effect in supply chain |
| Advisors: | Yeung, C. L. Andy (LMS) |
| Degree: | M.Phil. |
| Year: | 2023 |
| Subject: | Artificial intelligence --Economic aspects Manufacturing industries -- Management Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Logistics and Maritime Studies |
| Pages: | x, 101 pages : color illustrations |
| Language: | English |
| Abstract: | The past decade has seen the rapid development of artificial intelligence (AI) along with related technologies such as machine learning, natural language processing, deep learning, and computer vision. AI becomes more powerful to directly impact the creation and development of a wide range of products and services, thereby transforming the entire economy and society. Particularly, AI has revolutionized the development of new manufacturing technologies and products that can be applied in a wide range of fields. AI Investments have led the manufacturing industry toward intelligent manufacturing, which is constantly developing. Undoubtedly, the breakthroughs brought about by AI technology can significantly change the production methods of firms, thereby affecting their operational management. A recent report suggests that with the rapid growth of AI Investments, more and more firms have commercially implemented AI (from 20% in 2017 to 50% in 2022) (McKinsey, 2017, 2022). However, the actual return on AI Investments still needs to be determined. Only 10% of more than 3,000 firms in the survey reported that their AI investments have yielded significant benefits and mitigated relevant operational risks (Jeans, 2020). Therefore, it is crucial and topical to examine the true impact of AI on firms’ operational efficiency. In Study 1, I leverage a unique proprietary data set provided by Burning Glass Technologies (BGT) and use machine-generated keywords to identify AI specialist jobs. By matching AI talent recruitment information with Compustat’s data and using the fixed effects panel models (FEM) and dynamic panel data (DPD) models, I can longitudinally estimate the impact of AI investments on firms. Specifically, I apply stochastic frontier analysis (SFA) and generalized method of moments (GMM) techniques to examine the impact of AI investments on the operational efficiency of firms and relevant moderators. Study 2 is based on the findings of Study 1, and further examines the impact of focal firms’ AI investments on suppliers’ operational efficiency and relevant moderators. By introducing FactSet Revere Supply Chain Relationship data, I illustrate the spillover effect of AI investments from the perspective of social network theory and provide valuable insights for improving the buyer-supplier relationship. This research enables us to understand the real business value of AI and create new opportunities to explore the impact of AI at the firm level. It also provides valuable insights into the contextual factors that make firms’ AI capability a more crucial asset in the era of the intelligent machine era. |
| Rights: | All rights reserved |
| Access: | open access |
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