Journal of Business and Management Sciences
ISSN (Print): 2333-4495 ISSN (Online): 2333-4533 Website: Editor-in-chief: Heap-Yih Chong
Open Access
Journal Browser
Journal of Business and Management Sciences. 2021, 9(1), 50-57
DOI: 10.12691/jbms-9-1-6
Open AccessArticle

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

Tsung-Chun Chen1 and Fu-Hsiang Kuo2,

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

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

Pub. Date: February 28, 2021

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


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.

data envelopment analysis artificial intelligence operating efficiency data mining methodology

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit


Figure of 3


[1]  Poole, D., Mackworth, A., and Goebel, R, Computational Intelligence: A Logical Approach, Oxford University Press, New York, 1998.
[2]  McCarthy, J, What is Artificial Intelligence, [online]. Stanford University. Available, 2007. at:
[3]  Shein, E., Winning the war for AI talent, 2018.
[4]  Schendel, D.E., and Patton, G.R, “Corporate Stagnation and Turnaround,” Journal of Economics and Business, 28(3). 236-241. 1976.
[5]  Krueger, D.A., and Willard, G.E, “Turnarounds: A Process, not an Event. In Academy of Management Proceedings,” 1991(1). 26-30. Briarcliff Manor, NY 10510: Academy of Management, 1991.
[6]  Pandit, N. R, “Some Recommendations for Improved Research on Corporate Turnaround,” M@n@gement, 3(2). 31-56. 2000.
[7]  Zeng K.L., Liu Y., and Wang, X, “Uncertainty in the Global Economy: Great Concerns Should Be Given,” Finance and Economics, 2005(1). 164-169. 2005.
[8]  Anon, “Change is changing,” Harvard Business Review, 79(4). 125-135. 2001b.
[9]  Wetlaufer, S, “The Business Case Against Revolution,” Harvard Business Review, 49(2), 113-125. 2001.
[10]  Gallagher, M., Austin, S. and Caffyn, S, Continuous Improvement in Action: The Journey of Eight Companies, Kogan Page, London. 1997.
[11]  Caffyn, S, “Development of a Continuous Improvement Self- Assessment Tools”, International Journal of Operations & Production Management, 19(11). 1138-53. 1999.
[12]  Hamel, G, “Innovations new Math,” Fortune, 44(1). 130-133. 2001.
[13]  Heggde, G.S., and Panikar, S, “Causes of Sickness and Turnaround Strategies in Public and Private Sector Organizations,” Vilakshan: The XIMB Journal of Management, 7(3). 53-70. 2011.
[14]  Okwir, S., Ulfvengren, P., Angelis, J., Ruiz, F., and Guerrero, Y.M.N, “Managing turnaround performance through Collaborative Decision Making,” Journal of Air Transport Management, 58, 183-196. 2017.
[15]  Urbach, N., Drews, P., and Ross, J., “Digital Business Transformation and the Changing Role of the IT Function, Comments on the special issue,” MIS Q Exec, 16(2). 2017.
[16]  Cavanillas, J.M., Curry, E., and Wahlster, W., New Horizons for a Data-Driven Economy: a Roadmap for Usage and Exploitation of Big Data in Europe. Springer. 2016.
[17]  Zeyu, J., Shuiping, Y., Mingduan, Z., Yongqiang, C., and Yi, L., “Model study for Intelligent Transportation System with Big Data,” Procedia Computer Science, 107. 418-426. 2017.
[18]  Stacey, B., Michelle D., Cooch, J., and Paul, R., “Data Mining,” University of Iowa, 26-43. 2002.
[19]  Liu, H.H., and Kuo, F.H., “Determinants of School Efficiencies from Innovative Teaching through Digital Mobile E-Learning for High Schools: Application of Bootstrap Truncated Regression Model,” Asian Journal of Economic Modelling, 5(47). 431-449. 2017.
[20]  Nold Hughes, P.A., and Edwards, M.E., “Leviathan vs. Lilliputian: a Data Envelopment Analysis of Government Efficiency,” Journal of Regional Science, 40(4). 649-669. 2000.
[21]  Charnes, A., Cooper, W.W., and Rhodes, E., “Measuring the Efficiency of Decision-Making Units.” European Journal of Operational Research. 2(6), 429-444. 1978.
[22]  Banker, R.D., Charnes, A., and Cooper, W.W., “Some Models for Estimating Technical and Ccale Inefficiencies in Data Envelopment Analysis,” Management Science, 30(9). 1078-1092. 1984.
[23]  Berry, M. J., and Linoff, G., Data Mining Techniques: for Marketing, Sales, and Customer Support, John Wiley & Sons, Inc. 1997.
[24]  Roll, Y., Golany, B., and Seroussy, D., “Measuring the Efficiency of Maintenance Units in the Israeli Air Force,” European Journal of Operational Research, 43(2). 136-142. 1989.