RETRACTED ARTICLE: Application of artificial intelligence in enterprise knowledge management performance evaluation

Huang, Zilong; He, Jun; Ren, Xiaocong

doi:10.1080/14778238.2020.1850187

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…According to the RBV and DCT, businesses should have the corresponding dynamic capabilities to deal with digital development trends. As a scarce resource for businesses, AI offers strong technical support for innovative behavior [ 123 ]. Businesses should also conform to the prevailing trajectory of DT to promote the amalgamation of AI and corporate innovation alongside environmentally friendly and sustainable growth [ 124 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence and green product innovation: Moderating effect of organizational capital

Ying,

Jin

2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence and green product innovation: Moderating effect of organizational capital

Ying,

Jin

2024

Heliyon

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“…Campus development has evolved into a multifaceted endeavor, encompassing influences on education, society, culture, and the economy [1][2]. The integration of artificial intelligence technology in campus spatial planning, design, and management has become increasingly prevalent [3]. Utilizing AI can facilitate the creation of environments tailored to educational requirements, augment student experiences and well-being, and bolster institutional brand recognition [4][5].…”

Section: Research Background and Significancementioning

confidence: 99%

Research on the Optimal Design of Campus Space Based on Artificial Intelligence and its Impact on Education Quality

Yang,

Xie

2023

Proceedings of the 4th International Conference on Education, Knowledge and Information Management, ICEKIM 2023, May 26–28, 202

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This study, the application of artificial intelligence optimization in education is investigated, with a particular focus on campus planning, teaching management, and student management. By comparing AI-designed campuses to their traditional counterparts, the research concludes that AI optimization fosters a more efficient and intelligent learning environment. Additionally, the current status and future prospects of AI optimization within the educational sphere are examined, along with recommendations for further research.

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“…Liang and Li [16] believed that compared with the traditional performance evaluation system, 360-degree evaluation has its advantages and is more comprehensive and accurate. Huang et al [17] research believes that when the main purpose of the 360-degree evaluation is to serve the development of employees and provide help for employees' careers, the evaluation of evaluators will be more objective and fair. Zhang et al [18] believe that 360-degree evaluation uses multiple perspectives to evaluate employee performance, especially in providing feedback and guidance, allocating bonuses and opportunities, and avoiding evaluation errors.…”

Section: Related Workmentioning

confidence: 99%

BP Neural Network-Based Evaluation Method for Enterprise Comprehensive Performance

Wenjing

2022

Mathematical Problems in Engineering

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Comprehensive performance evaluation is an important basis for improving the training effect of enterprise employees and the effective allocation of enterprise resources. Based on AHP and BP neural network theory, this paper constructs a comprehensive performance evaluation method for enterprises, AHP is used to calculate the weight of the index, and then the importance index is screened. The model proposes a conceptual model of comprehensive performance of manufacturing enterprises from the support layer, core layer, and promotion layer and constructs a manufacturing system from horizontal and vertical. The influencing factors of comprehensive performance solve the quantification problem of enterprise comprehensive performance evaluation and have obvious guiding value for the research on the integration mode and path of industrialization and industrialization of regional manufacturing enterprises. In the simulation process, the weight of each index in the evaluation system is first determined by the analytic hierarchy process; then the evaluation index membership score table is established, and fuzzy mathematics is used to calculate the expert’s score, so as to solve the problem caused by the intermediate value. The uncertainty caused by the jump is finally established by the analytic hierarchy process, and the neural network is used to simulate the sample. The experimental results show that by using AHP to collect training samples for neural network evaluation, the comprehensive performance evaluation system has good fitness and achieves the best comprehensive consideration of accuracy and training time when there are 17 hidden layer neurons. The maximum relative error is 1.64%, which is much lower than the general accuracy requirement of 5%, which effectively improves the performance and calculation accuracy of the network.

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RETRACTED ARTICLE: Application of artificial intelligence in enterprise knowledge management performance evaluation

Cited by 12 publications

References 15 publications

Artificial intelligence and green product innovation: Moderating effect of organizational capital

Artificial intelligence and green product innovation: Moderating effect of organizational capital

Research on the Optimal Design of Campus Space Based on Artificial Intelligence and its Impact on Education Quality

BP Neural Network-Based Evaluation Method for Enterprise Comprehensive Performance

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