scholarly journals Analysis of the Relationship between the Operation Characteristics of Global Energy Internet and the Influencing Factors under the Background of Big Data and Big Energy

2019 ◽  
Author(s):  
Yan Yang ◽  
Fengkai Qiu ◽  
Jiaxin Zhao ◽  
Ming Zeng ◽  
Yongli Wang ◽  
...  
Keyword(s):  
Big Data ◽  
Influencing Factors ◽  
Global Energy ◽  
Energy Internet ◽  
The Relationship ◽  
Operation Characteristics

THE MODEL FOR MANAGING INVESTMENTS IN THE MOSCOW ENVIRONMENT BASED ON A POWER REGRESSION EQUATION

2020 ◽  
Vol 2 (7) ◽  
pp. 91-99
Author(s):  
E. V. KOSTYRIN ◽  
◽  
M. S. SINODSKAYA ◽  
Keyword(s):  
Solid Waste ◽  
Influencing Factors ◽  
Pair Correlation ◽  
Approximation Error ◽  
Regression Equation ◽  
Regression Equations ◽  
The Impact ◽  
The Relationship ◽  
Average Approximation ◽  
Average Approximation Error

The article analyzes the impact of certain factors on the volume of investments in the environment. Regression equations describing the relationship between the volume of investment in the environment and each of the influencing factors are constructed, the coefficients of the Pearson pair correlation between the dependent variable and the influencing factors, as well as pairwise between the influencing factors, are calculated. The average approximation error for each regression equation is determined. A correlation matrix is constructed and a conclusion is made. The developed econometric model is implemented in the program of separate collection of municipal solid waste (MSW) in Moscow. The efficiency of the model of investment management in the environment is evaluated on the example of the growth of planned investments in the activities of companies specializing in the export and processing of solid waste.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

scholarly journals Using PLS-SEM Model to Explore the Influencing Factors of Learning Satisfaction in Blended Learning

2021 ◽  
Vol 11 (5) ◽  
pp. 249
Author(s):  
Chun-Hsiung Huang
Keyword(s):  
Blended Learning ◽  
Influencing Factors ◽  
Structural Equation ◽  
Positive Impact ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Learning Motivation ◽  
Perceived Ease Of Use ◽  
Learning Satisfaction ◽  
The Relationship

This research explores the influencing factors of learning satisfaction in blended learning. Three dimensions are proposed: perceived usefulness, perceived ease of use, and learning motivation. It studied how these variables affect students’ learning satisfaction. The research hypotheses are: (1) Perceived ease of use positively affects perceived usefulness; (2) Perceived usefulness and perceived ease of use will have a positive effect on learning motivation; (3) Learning motivation positively affects learning satisfaction; (4) Perceived usefulness has a positive intermediary effect on the relationship between perceived ease of use and learning motivation. Participants included 173 freshmen who took the first-year interactive game design course at Ling Tung University in Taichung, Taiwan. The questionnaire survey method is applied in this research to analyze the relationship between the variables and verify the hypothesis based on the collected 173 valid questionnaires. The partial least square method structural equation model (PLS-SEM) is used to carry out structural equation modeling to study the relationship between latent variables. It explains that the perceived ease of use affects the perceived usefulness. Perceived usefulness and perceived ease of use have a positive impact on learning motivation. Learning motivation has a positive impact on learning satisfaction. Perceived usefulness as an intermediary factor of perceived ease of use has an indirect impact on learning motivation. The contribution of this research is to provide empirical evidence and explain what factors may affect learning satisfaction. Some other related factors that may affect learning satisfaction should be taken as the factors that teachers should pay attention to when implementing blended learning.

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