2021 Vol.4 Jun.No.2

• Title: Design of enterprise human capital value measurement model based on binary tree
• Name: Loknath Sai Ambati
• Company: Dakota State University
• Abstract:

  Due to relatively limited factors considered, such as time and influencing factors, the measurement accuracy of enterprise human capital value measurement model is consideredinsufficient. To alleviate this issue,we design a measurement model of enterprise human capital value based on binary tree. Firstly, set the assumptions of the measurement model, and then determine the construction parameters of the human capital measurement model, that is, analyze the factors affecting the value of enterprise human capital, solve the problem of less factors considered in the traditional measurement model, and finally realize the construction of the enterprise human capital value measurement model based on the two-step binary tree form. The results show that: 1) under the application of this measurement model, the measured value of enterprise human capital can promote its own proportion multiple of 3-8 times of material capital, which fully shows the importance of managers to enterprise development; 2) The root mean square error between the enterprise human capital value measured by this measurement model and the actual value is less than the results measured by random reward evaluation method, complete value measurement method and reverse evaluation method, which proves the effectiveness of this measurement model.

   

• Keyword: binary tree; Enterprise human capital value; measurement model
• DOI: 10.12250/jpciams2021090224
• Citation form: Loknath Sai Ambati.Design of enterprise human capital value measurement model based on binary tree[J]. Computer Informatization and Mechanical System,2021,Vol.4,pp.29-36

Reference

1. Yao Zengfu, Tang Huajun, LIU Xin. 2017. Research on threshold effect on the relationship between factor accumulation,human capital and agricultural environment efficiency[J]. journal of chongqinguniversity(social science edition), 86(8):843-846.

2. Carla White, Jeannine M. Conway, Paula K. Davis, et al. 2017. AACP Special Taskforce White Paper on Diversifying Our Investment in Human Capital[J]. American journal of pharmaceutical education, 81(8):S13.

3. Olena Leonchuk, Denis O. Gray.2019.Scientific and technological (human) social capital formation and Industry–University Cooperative Research Centers: a quasi-experimental evaluation of graduate student outcomes[J]. The Journal of Technology Transfer, 44(5):1638-1664.

4. Mamei, Marco, Pancotto, Francesca, De Nadai, Marco, et al. 2018. Is social capital associated with synchronization in human communication? An analysis of Italian call records and measures of civic engagement[J]. Epj Data Science, 7(1):25.

5. Fernández-de-Ua Laura, Aranda Ismael, Rossi Sergio, et al. 2018. Divergent phenological and leaf gas exchange strategies of two competing tree species drive contrasting responses to drought at their altitudinal boundary[J]. Tree Physiology, 38(8):1152-1165.

6. Liu Haixing, Wang Yuntao, Zhang Chi, et al. 2018. Assessing real options in urban surface water flood risk management under climate change[J]. Natural Hazards, 94(1):1-18.

7. Young Ryu, Young-Oh Kim, Seung BeomSeo, et al. 2018. Application of real option analysis for planning under climate change uncertainty: a case study for evaluation of flood mitigation plans in Korea[J]. Mitigation and Adaptation Strategies for Global Change, 23(1–2):92-97.

8. Cheng Yiying, Steven P. Clark. 2019. Real options with endogenous convenience yield[J]. Journal of Corporate Accounting & Finance, 30(3):30-43.

9. J. Ma, J. Wang, X.-P. Zhou, et al. 2018. Research on fire risk assessment of high-rise buildings based on fuzzy mathematics and set-value statistics theory[J]. Journal of Computers (Taiwan), 29(2):145-160.

10. Liu Xiang, Zhang Chen. 2017. Corporate governance, social responsibility information disclosure, and enterprise value in China[J]. Journal of Cleaner Production, 142(2):1075-1084.

11. A. van der Linden, G. W. J. van de Ven, S. J. Oosting, et al. 2018. LiGAPS-Beef, a mechanistic model to explore potential and feed-limited beef production 3: Model evaluation[J]. Animal, 13(4):1-11.

12. Li Bo, Zeng Yi Fan, Zhang Bei-Bei, et al. 2018. A risk evaluation model for karst groundwater pollution based on geographic information system and artificial neural network applications[J]. Environmental Earth Sciences, 77(9):344.

13. Bo Wang, You Li, Shuming Wang, et al. 2018. A Multi-Objective Portfolio Selection Model With Fuzzy Value-at-Risk Ratio[J]. IEEE Transactions on Fuzzy Systems, 26(6): 3673 - 3687.

14. Erik Stenberg, Yang Cao, Eva Szabo, et al. 2018. Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery[J]. Obesity Surgery, 28(26):1869-1875.

15. Jeremy Ziring, SprihaGogia, Remle Newton-Dame,et al. 2018. An All-Payer Risk Model for Super-Utilization in a Large Safety Net System[J]. Journal of General Internal Medicine, 33(8):596-598.