Assessing the Operational Performance of the Transformation AI Industry in Taiwan - Critical Factors for the Transition
| Autor: | Fu-Hsiang Kuo, Tsung-Chun Chen |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Operational performance
business.industry Critical factors Big data 030204 cardiovascular system & hematology Environmental economics Profit (economics) 03 medical and health sciences 0302 clinical medicine Transformation (function) 030220 oncology & carcinogenesis Sustainability Data envelopment analysis Operational efficiency business |
| Zdroj: | Journal of Business and Management Sciences. 9:50-57 |
| ISSN: | 2333-4495 |
| DOI: | 10.12691/jbms-9-1-6 |
| Popis: | This research that by estimating the companies of the technical efficiency (TE) and the results of the data mining methodology (DMM), explaining find company efficiency and the companies characteristics. First, we will apply a Data Envelopment Analysis (DEA) analysis model to assess Taiwan companies' operational efficiency. Then, we will use a big data model to identify critical factors for a sustainability transition. (1) In this study, we found that a total of four companies—Hon hai, Ares, Yulon, and Micro-stra—successfully transformed steps (TE = 1). (2) According to the results of the above DMM model. Thus, were the companies able to make good on the promise of AI. We demonstrated the need for more AI talent to transform their steps and increase RD spending successfully. Due to reduced labor costs, the EFA was reduced, and NBR and EPS increased significantly after the transition. So, these critical factors will help the enterprise to transfer its AI industry operation type successfully. Further, we discover that AI can be applicable to save employment and increase its short-term profit. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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