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El. knyga: Knowledge Engineering: The Process Paradigm [Taylor & Francis e-book]

  • Formatas: 126 pages, 30 Tables, black and white; 6 Line drawings, black and white; 12 Halftones, black and white; 18 Illustrations, black and white
  • Išleidimo metai: 10-Nov-2020
  • Leidėjas: CRC Press
  • ISBN-13: 9781003055006
Kitos knygos pagal šią temą:
  • Taylor & Francis e-book
  • Kaina: 170,80 €*
  • * this price gives unlimited concurrent access for unlimited time
  • Standartinė kaina: 244,00 €
  • Sutaupote 30%
Knowledge Engineering: The Process ParadigmDidesnis paveikslėlis
  • Formatas: 126 pages, 30 Tables, black and white; 6 Line drawings, black and white; 12 Halftones, black and white; 18 Illustrations, black and white
  • Išleidimo metai: 10-Nov-2020
  • Leidėjas: CRC Press
  • ISBN-13: 9781003055006
Kitos knygos pagal šią temą:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Partnerių interneto svetainė: https://www.taylorfrancis.com/books/9781003055006
"This book shows how to develop process-oriented methodologies, and how knowledge management can dramatically reduce costs and increase speed of response, presents a wide range of quantitative methods applied to various knowledge engineering problems, and offers several graphical presentations of models and processes"--

Knowledge management is far-reaching. It can dramatically reduce costs such as costs of office work repetition, human resource retirement, information reuse, etc. Rather than "reinventing the wheel" and having it be a costly and inefficient activity, systematic reuse of knowledge can show substantial cost benefits immediately.

This book shows how to develop process-oriented methodologies, covers both interorganizational and enterprises models, discusses how knowledge management can dramatically reduce costs and increase speed of response, presents a wide range of quantitative methods applied to various knowledge engineering problems, and offers several graphical presentations of models and processes.

Academicians and practitioners in the area of knowledge management and engineering, especially managers in industries will fine this book useful. The material might also be useful in knowledge management graduate studies.

Preface ix
Introduction xi
Chapter 1 Knowledge and Global Learning
1(6)
Main Body
1(1)
Subsidiaries' Knowledge Stock
1(1)
Knowledge Transfer Mechanisms
1(1)
Data and Methods
2(2)
Dependent Variables
2(1)
Independent Variables
2(1)
Control Variables
3(1)
Methods
3(1)
Assessment of Measures
4(1)
Concluding Remarks
4(1)
References
5(2)
Chapter 2 Knowledge Processes, Intensity And Innovation
7(12)
Introduction
7(1)
Literature Review
7(1)
Main Body
8(7)
Hypotheses
8(1)
Analysis
9(3)
Measurement
12(3)
Concluding Remarks
15(2)
References
17(2)
Chapter 3 Knowledge With Innovation Performance
19(14)
Introduction
19(1)
Main Body
19(3)
Properties of Knowledge
20(1)
Absorptive Capacity
21(1)
The Research Framework of the Study
22(1)
Methods and Measurement
22(2)
Data Collection and the Sample
22(1)
Definitions and Measurements of the Constructs
23(1)
Dependent Variables
23(1)
Independent Variables
23(1)
Moderate Variables
23(1)
Results
24(3)
Model and Analysis
24(1)
Regression Analysis of Innovation Performance on Properties of Knowledge
25(1)
The Moderate Effects of a Firm's Absorptive Capacity
25(2)
Concluding Remarks
27(4)
References
31(2)
Chapter 4 Knowledge And Innovation In Networked Environments
33(12)
Literature Review
33(2)
From Dynamic Capabilities to Knowledge-Based Dynamic Capabilities
33(1)
The Construct of Knowledge-Based Dynamic Capabilities
33(2)
Network Embeddedness and Innovation
35(1)
Hypotheses Development
35(2)
The Link between Knowledge-Based Dynamic Capabilities and Innovation
35(1)
The Antecedents of Dynamic Capabilities: Network Embeddedness
35(2)
Methods and Materials
37(3)
Dependent Variable: Innovation Performance
37(1)
Knowledge-Based Dynamic Capabilities (KDC)
37(1)
Independent Variables: Network Embeddedness
38(2)
Evaluations
40(1)
Construct Validity and Reliability
40(1)
Hypothesis Testing
40(1)
Robustness of the Results
41(1)
Concluding Remarks
41(1)
References
42(3)
Chapter 5 Knowledge And Organizational Business Loss
45(10)
Research Scope
45(1)
Literature Review
45(2)
Key Success Factors Described in Literature
47(1)
Literature Critique
47(1)
Lessons Learned
47(1)
Proposed Framework
47(1)
Research Method and Results
48(4)
Concluding Remarks
52(1)
References and Further Reading
52(3)
Chapter 6 Knowledge Sharing Using Semantic Web
55(10)
Introduction
55(1)
Main Body
56(6)
Articulation
58(1)
Knowledge-Sharing Platform
58(4)
Concluding Remarks
62(1)
References
63(2)
Chapter 7 Knowledge Sharing And Tax Payment
65(18)
Introduction and Background
65(3)
Proposed Problem and Model
68(4)
Risk Modeling Structure
70(1)
Interval Programming
71(1)
Case Study
72(7)
Computing Mean Values (ti)
73(2)
Computing Loss Function (Li)
75(1)
Computing f(xi) for the Factors
76(2)
Computing Risk Function (R(xi)
78(1)
Concluding Remarks
79(1)
References
80(3)
Chapter 8 Knowledge Sharing For Enterprise Resources
83(10)
Introduction and Background
83(2)
Proposed Framework
85(4)
Network Range
87(1)
Tie Strength
88(1)
Network Efficiency
89(1)
Measuring CNIAM
89(1)
Case Study
89(3)
Concluding Remarks
92(1)
References
92(1)
Chapter 9 Knowledge Sharing And Organizational Culture
93(20)
Introduction
93(1)
Literature Review
94(6)
Research Importance and Necessity
100(1)
Research Hypotheses
100(1)
Research Method
101(8)
Data Analysis
102(6)
Variable Rankings Based on Friedman Test
108(1)
Concluding Remarks
109(2)
References
111(2)
Chapter 10 Knowledge Sharing And Learning Capability
113(12)
Introduction
113(1)
Organizational Learning Capability
114(3)
Organizational Learning Capability Dimensions
114(1)
Risk-Taking
114(1)
Interaction with the External Environment
115(1)
Dialogue
115(1)
Participative Decision Making
115(1)
Managerial Commitment
115(1)
Systems Perspective
116(1)
Openness and Experimentation
116(1)
Knowledge Transfer and Integration
116(1)
Teamwork
117(1)
Demonstration of Mission and Goals
117(1)
Problem Definition and Modeling
117(4)
Computational Results
121(1)
Concluding Remarks
121(1)
References
122(3)
Index 125
Hamed Fazlollahtabar earned a BSc and an MSc in Industrial Engineering from Mazandaran University of Science and Technology, Iran, in 2008 and 2010, respectively. He received his PhD in Industrial and Systems Engineering from Iran University of Science and Technology in 2015 and completed a postdoctoral research fellowship at Sharif University of Technology, Iran, in the area of reliability engineering for complex systems in 2017. He currently works in the Department of Industrial Engineering at Damghan University, Iran, and is on the editorial boards of several journals and on the technical committees of several conferences. His research interests are business intelligence and analytics, advanced robotic production systems, reliability engineering, and supply chain planning. He has published more than 280 research papers and eight books.
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