Author(s)

Changjiang Yu ¹, Xiao He ¹, Jianjun Lin ¹, Yuan Zhu ¹, Changhao Zhou ¹

Affiliation(s)

¹ Law and Business School, Shangrao Normal University, Shangrao, Jiangxi, China

Corresponding Author

Xiao He

ABSTRACT

Employee recruitment is one of the important commonalities of all enterprises. Traditional employee recruitment relies on manual communication, which has problems such as complex processes, long processing times, and low matching degree. This article aims to explore the common automation management system of intelligent enterprises, with employee recruitment as the research focus. Firstly, Microsoft Power BI is used to process and analyze employee data to identify the required positions in the company. The Survey Monkey tool is used to obtain the direction of enterprise planning through a questionnaire survey; Next, natural language processing is used to analyze the candidate information of submitted resumes, and collaborative filtering algorithms are used to find candidates who meet the company's job requirements and recommend positions to them; This article uses the Calendar tool to coordinate interview time, and finally uses support vector machines to analyze the interview results to determine whether the candidate meets the requirements. After experimental comparison, the precision rate of the system for job recommendation reached 91%, the F1 value of the system model reached 0.9, and the processing time for one piece of information was only 0.106 seconds. The quality of candidates using this system has significantly improved. This indicates that the system has the advantages of short processing time and high matching degree in employee recruitment, which is also helpful for improving employee quality. It provides ideas for the research of building an intelligent enterprise common automation management system.

KEYWORDS

Intelligent Management System, Intelligent Employee Recruitment, Job Recommendation, SVM Algorithm

CITE THIS PAPER

Changjiang Yu, Xiao He, Jianjun Lin, Yuan Zhu, Changhao Zhou, Construction of Common Automation Management System for Intelligent Enterprises. Accounting and Corporate Management (2023) Vol. 5: 40-49. DOI: http://dx.doi.org/10.23977/acccm.2023.051207.

REFERENCES

[1] Jackson D, Riebe L, Macau F. Determining factors in graduate recruitment and preparing students for success. Education+ Training, 2022, 64(5): 681-699.
[2] Kucherov D, Tsybova V. The contribution of e-recruitment practices to e-recruitment outcomes in Russian companies. Measuring Business Excellence, 2022, 26(3): 366-377.
[3] Köchling A, Wehner M C. Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research, 2020, 13(3): 795-848.
[4] Gilch P M, Sieweke J. Recruiting digital talent: The strategic role of recruitment in organisations' digital transformation. German Journal of Human Resource Management, 2021, 35(1): 53-82.
[5] Muduli A, Trivedi J J. Recruitment methods, recruitment outcomes and information credibility and sufficiency. Benchmarking: An International Journal, 2020, 27(4): 1615-1631.
[6] Lu Jinyi, Xu Jianhua, Wang Hanqing, Wu Qiao. Research on Juvenile Service Positions in the United States Public Library--Content Analysis based on the ALA recruitment website. Book intelligence work, 2021, 65(2): 137-146.
[7] Bintoro B P K, Lutfiani N, Julianingsih D. Analysis of the effect of service quality on company reputation on purchase decisions for professional recruitment services. APTISI Transactions on Management (ATM), 2023, 7(1): 35-41.
[8] Rodrigues D, Martinez L F. The influence of digital marketing on recruitment effectiveness: a qualitative study. European Journal of Management Studies, 2020, 25(1): 23-44.
[9] Kot S, Hussain H I, Bilan S,Haseeb M, Mihardjo L W W. The role of artificial intelligence recruitment and quality to explain the phenomenon of employer reputation. Journal of Business Economics and Management, 2021, 22(4): 867-883.
[10] Ore O, Sposato M. Opportunities and risks of artificial intelligence in recruitment and selection. International Journal of Organizational Analysis, 2022, 30(6): 1771-1782.
[11] Koechling A, Wehner M C, Warkocz J. Can I show my skills? Affective responses to artificial intelligence in the recruitment process. Review of Managerial Science, 2023, 17(6): 2109-2138.
[12] FraiJ J D, László V. A literature review: artificial intelligence impact on the recruitment process. International Journal of Engineering and Management Sciences, 2021, 6(1): 108-119.
[13] Niehueser W, Boak G. Introducing artificial intelligence into a human resources function. Industrial and commercial training, 2020, 52(2): 121-130.
[14] Kshetri N. Evolving uses of artificial intelligence in human resource management in emerging economies in the global South: some preliminary evidence. Management Research Review, 2021, 44(7): 970-990.

Sponsors, Associates, and Links

---

• Information Systems and Economics
  
  
• Accounting, Auditing and Finance
  
  
• Industrial Engineering and Innovation Management
  
  
• Tourism Management and Technology Economy
  
  
• Journal of Computational and Financial Econometrics
  
  
• Financial Engineering and Risk Management
  
  
• Social Security and Administration Management
  
  
• Population, Resources & Environmental Economics
  
  
• Statistics & Quantitative Economics
  
  
• Agricultural & Forestry Economics and Management
  
  
• Social Medicine and Health Management
  
  
• Land Resource Management
  
  
• Information, Library and Archival Science
  
  
• Journal of Human Resource Development
  
  
• Manufacturing and Service Operations Management
  
  
• Operational Research and Cybernetics