Assessing the Operational Performance of the Transformation AI Industry in Taiwan - Critical Factors for the Transition

Tsung-Chun Chen¹ and Fu-Hsiang Kuo^2,

¹Department of Business, Putian University, Fujian 351100, China

²Department of Finance, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan, R. O. C.

Cite this paper:
Tsung-Chun Chen and Fu-Hsiang Kuo. Assessing the Operational Performance of the Transformation AI Industry in Taiwan - Critical Factors for the Transition. Journal of Business and Management Sciences. 2021; 9(1):50-57. doi: 10.12691/jbms-9-1-6

Abstract

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.

Keywords:
data envelopment analysis artificial intelligence operating efficiency data mining methodology

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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