Mining Patent Big Data to Forecast Enterprise Performance

2018 ◽  
pp. 687-698
Author(s):  
Yu-Jing Chiu
Keyword(s):  
Big Data ◽  
Enterprise Performance

The Impact of Big Data Capability on Enterprise Performance—theMediating Effect of Supply Chain Flexibility

2021 ◽  
Vol 7 (6) ◽  
pp. 5641-5655
Author(s):  
Ji Feng ◽  
Cheng Guiqing ◽  
Jia Xuedi ◽  
Luo Qubo ◽  
Wu Fan
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Positive Impact ◽  
Mediating Effect ◽  
Enterprise Performance ◽  
Chain Flexibility ◽  
Supply Chain Flexibility ◽  
The Impact ◽  
Positive Effect ◽  
Development Capability

Based on 234 survey data of 35 pilot demonstration enterprises in intelligent manufacturing, this paper tested the mediating role of supply chain flexibility in the process of big data capability affecting enterprise performance. The empirical results show that the foundation capability, application capability, and development capability all have a significant positive impact on enterprises performance. Big data foundation capability has a significant positive effect on the supply chain flexibility in terms of product flexibility, logistics flexibility and production flexibility, and it has no significant effect on purchasing flexibility and information flexibility. Both big data application capability and big data development capability have a positive effect on supply chain flexibility. In addition to purchasing flexibility, the other dimensions of supply chain flexibility and supply chain flexibility comprehensive factors all have a mediating effect on the relationship between big data capabilities and firm performance. The conclusions of this study have a positive enlightenment role for enterprises to develop big data capabilities and create a flexible supply chain to meet the needs of the market and customers.

Descriptive and Predictive Analytical Methods for Big Data

Web Services ◽  
2019 ◽  
pp. 314-331 ◽  
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim
Keyword(s):  
Big Data ◽  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Knowledge Discovery ◽  
Data Visualization ◽  
Analytical Methods ◽  
Data Analytics ◽  
Enterprise Performance ◽  
Analytical Tools

Data analytics and modeling are powerful analytical tools for knowledge discovery through examining and capturing the complex and hidden relationships and patterns among the quantitative variables in the existing massive structured Big Data in efforts to predict future enterprise performance. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools for analyzing structured Big Data. The chapter covers descriptive and predictive analytical methods. Descriptive analytical tools such as mean, median, mode, variance, standard deviation, and data visualization methods (e.g., histograms, line charts) are covered. Predictive analytical tools for analyzing Big Data such as correlation, simple- and multiple- linear regression are also covered in the chapter.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Honglin Fu
Keyword(s):  
Social Capital ◽  
Big Data ◽  
Performance Evaluation ◽  
Evaluation System ◽  
Information Use ◽  
Three Dimensions ◽  
Data Matrix ◽  
Enterprise Performance ◽  
Performance Information ◽  
Performance Evaluation System

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.

Descriptive and Predictive Analytical Methods for Big Data

2016 ◽  
pp. 66-83
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim
Keyword(s):  
Big Data ◽  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Knowledge Discovery ◽  
Data Visualization ◽  
Analytical Methods ◽  
Data Analytics ◽  
Enterprise Performance ◽  
Analytical Tools

Data analytics and modeling are powerful analytical tools for knowledge discovery through examining and capturing the complex and hidden relationships and patterns among the quantitative variables in the existing massive structured Big Data in efforts to predict future enterprise performance. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools for analyzing structured Big Data. The chapter covers descriptive and predictive analytical methods. Descriptive analytical tools such as mean, median, mode, variance, standard deviation, and data visualization methods (e.g., histograms, line charts) are covered. Predictive analytical tools for analyzing Big Data such as correlation, simple- and multiple- linear regression are also covered in the chapter.

scholarly journals Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhengna Qin ◽  
Haojie Liao ◽  
Ling Chen ◽  
Lei Zhang
Keyword(s):  
Big Data ◽  
Performance Management ◽  
Information Fusion ◽  
High Efficiency ◽  
Enterprise Management ◽  
Enterprise Performance ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Maximum Acceleration ◽  
Index Weight

With the development of the Internet, big data collection, analysis, and processing are flourishing. The study aims to explore the performance management of power enterprises based on multisource information fusion and big data. First, the application of big data to enterprise management is analyzed. Second, the multisource information fusion method is introduced, and the multisource information fusion model is implemented. Finally, the fuzzy language algorithm is used to evaluate the performance management of power enterprises. The results show that the proposed multisource information fusion algorithm has high efficiency in evaluating enterprise performance management. The evaluation result is closer to the actual value than other algorithms, and the maximum acceleration ratio can reach 7, indicating that the algorithm is suitable for processing big data. The performance evaluation shows that enterprises pay most attention to the quality of their products; the weight reached 0.414; and the index weight difference is large. This study promotes the reform of the performance management mode and improves the management efficiency of enterprises through the proposed enterprise performance management strategy. It provides a great reference for the application of big data and information fusion technology.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

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