Optimization Study of Multidimensional Big Data Matrix Model in Enterprise Performance Evaluation System

被引:7
作者
Fu, Honglin [1 ]
机构
[1] Hong Kong Baptist Univ, Kowloon Tong, Kowloon, Hong Kong, Peoples R China
关键词
PREDICTION;
D O I
10.1155/2021/4351944
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper uses a multidimensional big data matrix model to optimize the analysis and conduct a systematic construction of the enterprise performance evaluation system. The adoption of new research methods and perspectives to promote the study of the use of performance information is of great significance to achieve the effectiveness, science, and sustainability of corporate performance management. To solve the problem of objectivity and scientificity of performance information use, this part attempts to analyze performance information use from the perspective of the multidimensional big data matrix, focusing on the techniques and methods in the process of promoting performance information use from the multidimensional big data matrix and tries to construct a system model of enterprise performance information use from two dimensions: the use of performance information sources and the use of performance information results. Based on multiple theoretical hypotheses, a theoretical and empirical basis is provided for the division of demand dimensions of enterprise performance evaluation system. Through social capital theory, three dimensions of network social capital, cognitive social capital, and structural social capital are hypothesized, and the logistic regression method is applied for empirical study. The results show that these three dimensions have significant effects on the knowledge demand of enterprise performance evaluation systems. It is verified that the multidimensional big data matrix can enhance the quality of performance information sources and improve the objectivity of performance information. In the performance information source use dimension, the analysis verified that the collection and preprocessing technology of big data can realize the automation, real-time, and diversification of information collection and preprocessing, and enhance the objectivity of performance information. Big data helps to improve the quality and effectiveness of performance information results use. In the dimension of using performance information results, the distributed computing and analysis processing technology of big data can assist the decision support system, and the use of information can be shifted from micromanagement to decision support, to realize the scientific use of performance information and improve the quality of enterprise management decisions.
引用
收藏
页数:12
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共 26 条
[1]   Multimodal Data Guided Spatial Feature Fusion and Grouping Strategy for E-Commerce Commodity Demand Forecasting [J].
Cai, Weiwei ;
Song, Yaping ;
Wei, Zhanguo .
MOBILE INFORMATION SYSTEMS, 2021, 2021 (2021)
[2]   Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model [J].
Chehbi-Gamoura, Samia ;
Derrouiche, Ridha ;
Damand, David ;
Barth, Marc .
PRODUCTION PLANNING & CONTROL, 2020, 31 (05) :355-382
[3]   RETRACTED: AGTH-Net: Attention-Based Graph Convolution-Guided Third-Order Hourglass Network for Sports Video Classification (Retracted Article) [J].
Gao, Ming ;
Cai, Weiwei ;
Liu, Runmin .
JOURNAL OF HEALTHCARE ENGINEERING, 2021, 2021
[4]   A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context [J].
Gawankar, Shradha A. ;
Gunasekaran, Angappa ;
Kamble, Sachin .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2020, 58 (05) :1574-1593
[5]   Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach [J].
Hoepken, Wolfram ;
Eberle, Tobias ;
Fuchs, Matthias ;
Lexhagen, Maria .
JOURNAL OF TRAVEL RESEARCH, 2021, 60 (05) :998-1017
[6]   Computing Server Power Modeling in a Data Center: Survey, Taxonomy, and Performance Evaluation [J].
Ismail, Leila ;
Materwala, Huned .
ACM COMPUTING SURVEYS, 2020, 53 (03)
[7]   A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation [J].
Jiang, Yizhang ;
Gu, Xiaoqing ;
Wu, Dongrui ;
Hang, Wenlong ;
Xue, Jing ;
Qiu, Shi ;
Lin, Chin-Teng .
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2021, 18 (01) :40-52
[8]   Developing firms' growth approaches as a multidimensional decision to enhance key stakeholders' wellbeing [J].
Kumar, V. ;
Ramachandran, Divya .
INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING, 2021, 38 (02) :402-424
[9]   Clinical trial design: Past, present, and future in the context of big data and precision medicine [J].
Li, Allen ;
Bergan, Raymond C. .
CANCER, 2020, 126 (22) :4838-4846
[10]   Evaluation and Prediction of Blast Furnace Status Based on Big Data Platform of Ironmaking and Data Mining [J].
Li, Hongyang ;
Bu, Xiangping ;
Liu, Xiaojie ;
Li, Xin ;
Li, Hongwei ;
Liu, Fulong ;
Lyu, Qing .
ISIJ INTERNATIONAL, 2021, 61 (01) :108-118