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E-grāmata: Innovation Discovery: Network Analysis Of Research And Invention Activity For Technology Management

Edited by (Portland State Univ, Usa & Higher Sch Of Economics, Russia & Chaoyang Univ Of Tech, Taiwan), Edited by (Univ Of Westminster, Uk)
  • Formāts: 672 pages
  • Sērija : Series on Technology Management 30
  • Izdošanas datums: 23-Jan-2018
  • Izdevniecība: World Scientific Europe Ltd
  • Valoda: eng
  • ISBN-13: 9781786344076
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Innovation Discovery: Network Analysis Of Research And Invention Activity For Technology ManagementPalielināt
  • Formāts: 672 pages
  • Sērija : Series on Technology Management 30
  • Izdošanas datums: 23-Jan-2018
  • Izdevniecība: World Scientific Europe Ltd
  • Valoda: eng
  • ISBN-13: 9781786344076
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The use of bibliometrics for the analysis of technology management is on the rise in our increasingly technological societies. Many are using these tools to document or record the rise of various technologies, making it necessary to take stock of the value and application of scientometric methods and their measures.Innovation Discovery shows the current state of play within the field of management of technology, and discusses how we can use networks to explore, understand and generate theory around the innovation process. It looks at the different streams of analysis used to understand bibliometric data, and presents alternative and novel ways of applying these techniques.Written as a comprehensive review of approaches by leading researchers in the field, this book is suitable for graduate and post-graduate students and researches looking to expand their knowledge and embark on further investigations in technology management.
About the Editors v
About the Authors ix
Introduction xxxix
Part 1 1(90)
1 Bibliometrics: The Case of Comparing an Ecosystem Using System and Network Approaches
3(22)
Marco Tregua
Anna D'Auria
Tiziana Russo Spena
Francesco Bifulco
1 Introduction
4(2)
2 Research Process
6(2)
2.1 Data collection and analysis
7(1)
3 Findings
8(10)
3.1 Overview of IE
8(2)
3.2 Comparing literature on IE and IS
10(3)
3.3 Comparing literature on IE and IN
13(5)
4 Conclusion and Implications
18(1)
5 Limitations and Further Research
19(1)
Acknowledgments
20(1)
References
20(5)
2 Bibliometrics and Patents: Case of Forecasting Biosensor Technologies for Emerging Point-of-Care and Medical IoT Applications
25(20)
Nasir Jamil Sheikh
Omar Sheikh
1 Introduction
25(1)
2 Literature Review
26(6)
2.1 Bibliometrics and patent analysis
26(3)
2.2 Biosensors
29(1)
2.3 POC diagnostics
30(1)
2.4 IoT
31(1)
3 Methodology and Results
32(8)
4 Conclusion
40(1)
Acknowledgments
41(1)
References
41(4)
3 Patents: The Case of Exploitation of the Patent System among SMEs and Private Inventors in Finland
45(24)
J. Talvela
M. Karvonen
T. Kassi
1 Introduction
46(1)
2 Role of Patenting to Protect Inventions
47(5)
2.1 The patent system and motives of patenting
47(2)
2.2 SME perspective toward protection of inventions
49(3)
2.3 Private inventors and patent protection
52(1)
3 Methods and Patent Statistics
52(5)
3.1 Patent statistics and SMEs
52(4)
3.2 Semi-structured interviews
56(1)
4 Results from Interviews
57(8)
4.1 The conception of IP as a business tool
57(1)
4.2 Processes for developing IP in companies
58(3)
4.3 Motives for patenting
61(2)
4.4 Sources of information and education in IPR
63(1)
4.5 Individual inventors and IP
64(1)
5 Conclusions
65(2)
Acknowledgments
67(1)
References
67(2)
4 Patents: Case of Analyzing Technological Knowledge Diffusion among Technological Fields using Patent Data: The Example of Microfluidics
69(22)
Zheng Qiao
Lu-Cheng Huang
Fei-Fei Wu
Dan Wu
Hui Zhang
1 Introduction
70(1)
2 Related Works
71(2)
2.1 Technological knowledge diffusion
71(1)
2.2 Patent citation analysis
72(1)
3 Method
73(7)
3.1 Generating the patent citation network
73(1)
3.2 Establishing the technological knowledge diffusion matrix among technological fields
74(2)
3.3 Setting up indicators to measure the diffusion depth and breadth of technological fields
76(1)
3.3.1 Diffusion Depth of technological fields
76(1)
3.3.2 Diffusion Breadth of technological fields
77(1)
3.3.3 Diffusion types of technological fields
79(1)
4 Empirical Study: The Case of Microfluidics
80(6)
4.1 Data collection
80(1)
4.2 Results and technological implications
81(5)
5 Conclusion
86(1)
Acknowledgments
87(1)
References
87(4)
Part 2 91(112)
5 Patents and Networks: Case of Discerning the Evolutionary Nature of Technological Change in the Complex Product Industry
93(28)
Fei Yuan
Kumiko Miyazaki
1 Introduction
94(1)
2 Theoretical Fundamentals
95(3)
3 Overview of EV Technology
98(1)
4 Methodology of Dynamic Analysis Based on Identification of Technological Trajectory
99(6)
4.1 Selection of patent data set
99(1)
4.2 Dividing the stages of technological evolution
100(2)
4.3 Identification of main path
102(2)
4.4 Comparison of main paths at different stages
104(1)
5 Empirical Results: Dynamic Analysis of Technological Trajectory in EV Industry
105(9)
5.1 Stages of EV technology evolution
106(3)
5.2 Analysis of dynamic changing process of technological trajectories
109(1)
5.2.1 Network analysis
109(1)
5.2.2 Connectivity analysis
109(5)
6 Discussions and Implications
114(4)
Acknowledgments
118(1)
References
118(3)
6 Patents and Networks: Case of Identification of Core Industry Actors for Electric Vehicle Battery by Application of Knowledge Flow
121(26)
Yuan Yuan Shi
Tugrul Daim
1 Introduction
121(2)
2 Literature Review
123(3)
2.1 Development of the electric battery
123(1)
2.2 Patent analysis
124(2)
3 Methodology
126(3)
3.1 Construct a correlation matrix of technologies
126(1)
3.2 Construct a correlation matrix of industries
127(1)
3.3 Build an industrial correlation map
127(1)
3.4 Recognize and analyze main patentees
128(1)
4 Results and Analysis
129(12)
4.1 Lead-Acid battery
129(3)
4.2 Metal air battery
132(2)
4.3 Molten salt battery
134(2)
4.4 NiMH battery
136(2)
4.5 Li-ion battery
138(3)
5 Conclusion
141(2)
References
143(4)
7 Patents and Networks: Case of Social Network Analysis for Innovation
147(28)
Antonello Cammarano
Mauro Caputo
Emilia Lamberti
Francesca Michelino
1 Introduction
148(1)
2 Theoretical Background
149(5)
2.1 Network strategies
149(1)
2.2 Innovation strategies
150(1)
2.2.1 Technological strategies
151(1)
2.2.2 Open business models
152(1)
2.2.3 Intangibles portfolio
153(1)
3 Methodological Framework
154(10)
3.1 Network strategies
157(2)
3.2 Technological strategies
159(2)
3.3 Open business models
161(1)
3.4 Intangibles portfolio
162(1)
3.5 Innovation and financial outputs and context features
163(1)
4 Results
164(5)
5 Conclusion
169(2)
Acknowledgments
171(1)
References
171(4)
8 Patents and Networks: Case of Cochlear Implant Technology Evolution using Patent Classification Data
175(28)
Srigowtham Arunagiri
Mary Mathew
1 Introduction
176(1)
2 Literature Review
177(3)
2.1 Defining technology evolution
177(1)
2.2 Patent information: A tool for technology evolution
178(1)
2.3 Technology evolution: Current approaches
179(1)
3 Methodology
180(4)
3.1 Retrieving patents from USPTO
181(1)
3.2 Computing correlation matrix
182(1)
3.3 Constructing patent class network
182(1)
3.4 Analyzing network parameters
183(1)
3.5 Analysis
183(1)
4 Results
184(14)
4.1 Patent classes network for time period 1 (1977-1994)
184(2)
4.2 Patent classes network for time period 2 (1995-2003)
186(5)
4.3 Patent classes network for time period 3 (2004-2010)
191(2)
4.4 Emerging and disappearing patent classes across time periods
193(5)
5 Conclusions
198(1)
Acknowledgments
199(1)
References
200(3)
Part 3 203(130)
9 Bibliometrics and Networks: Case of a Multinational Perspective on How Eco-Innovation has Evolved in Academic Literature
205(48)
Blanca de-Miguel-Molina
Maria de-Miguel-Molina
Maria-del-Val Segarra-Ona
Angel Peiro-Signes
1 Introduction
206(3)
2 Method
209(5)
2.1 Data collection
209(4)
2.2 Steps of data analysis
213(1)
3 Results
214(34)
3.1 Conceptualizations about innovation and the environment
214(1)
3.2 Analysis by periods
214(10)
3.3 Analysis by countries
224(24)
4 Conclusions
248(1)
Acknowledgments
249(1)
References
249(4)
10 Bibliometrics and Social Network Analysis Supporting the Research Development of Emerging Areas: Case Studies from Thailand
253(26)
Nathasit Gerdsri
Alisa Kongthon
1 Introduction
254(1)
2 Literature Review
255(3)
2.1 Technology intelligence
255(1)
2.2 Bibliometric analysis and text mining
256(1)
2.3 Applications of bibliometric analysis and text mining to generate technology intelligence
257(1)
2.4 Development of social communities and networks
257(1)
3 Analysis Approach
258(1)
4 A Case Analysis on the Emerging Research Field of Biomedical Engineering in Thailand
259(8)
4.1 Research approach: Methodology and data analysis
260(2)
4.2 Analysis results
262(1)
4.2.1 Identification of BME research areas that mainly focus in Thailand
262(1)
4.2.2 Identification of BME communities in both medical and engineering fields
263(1)
4.2.3 Identification of the BME existing and hidden social networks in Thailand
263(1)
4.3 Managerial discussion on the analysis results
264(1)
4.3.1 Identification of the active areas of BME research in Thailand
266(1)
4.3.2 Development of the BME expert communities in Thailand
266(1)
4.3.3 Discovery of the existing and hidden networks of BME engineering experts in Thailand
267(1)
5 A Case Analysis on the Emerging Research Field of Data Science in Thailand
267(7)
5.1 Research approach: Methodology and data analysis
268(1)
5.2 Analysis results
269(1)
5.2.1 Identify the research areas/topics of data science topical emphases in Thailand
269(1)
5.2.2 Identify key experts in the field of data science in Thailand
270(1)
5.2.3 Identify the existing and hidden social networks of experts in the field of data science in Thailand
272(1)
5.3 Managerial discussion on the analysis results
273(1)
6 Conclusion
274(1)
Acknowledgments
274(1)
References
274(5)
11 Bibliometrics and Networks: Trends and Typology of Emerging Antenna Propagation Technologies
279(26)
Yasutomo Takano
Yuya Kajikawa
Makoto Ando
1 Introduction
280(2)
2 Methodology
282(8)
2.1 Data collection and creating citation networks and identifying base-clusters
282(1)
2.2 Creating sub-clusters from the base-cluster
283(1)
2.3 Filtering of sub-clusters for improved reliability
283(1)
2.4 Plotting of the sub-clusters into the RCS and evaluation
284(1)
2.4.1 Anderson's technological cycle and key performance parameters
285(1)
2.4.2 The relationship between technological cycle and RCS
287(1)
2.4.3 The limitations of the conventional approach and the advantages of the RCS
288(2)
3 Results
290(6)
3.1 Evaluation of (M-1)
291(1)
3.2 Evaluation of (I-1)
292(2)
3.3 Evaluation of (B-1)
294(1)
3.4 Evaluation of (C-1)
295(1)
4 Discussion
296(5)
4.1 From an interview with an expert
296(1)
4.2 Mechanism of obtaining specific sub-cluster and new hub-paper
297(1)
4.3 Role of RCS, its limitations, and future works
298(3)
5 Conclusion
301(1)
Acknowledgments
301(1)
References
301(4)
12 Bibliometrics and Networks: Case of Project Management and the Emergence of a Knowledge-based Discipline
305(28)
Alan Pilkington
Kah-Hin Chai
Le Yang
1 Introduction
306(1)
2 What is PM9
307(2)
3 Methodology
309(3)
3.1 Input data selection
310(1)
3.2 Data purification
311(1)
4 Results and Discussion
312(12)
4.1 The location of PM research: Most-cited journals
312(7)
4.2 Co-citation network analysis on core literature
319(1)
4.2.1 Early period
320(1)
4.2.2 Middle period
323(1)
4.2.3 Recent period
323(1)
5 Discussion
324(1)
6 Conclusion and Recommendation
325(1)
References
326(7)
Part 4 333(72)
13 Emerging Networking Methods: Innovation Intermediaries in Technological Alliances
335(22)
Calvin S. Weng
1 Introduction
336(1)
2 Innovation Intermediary and Alliance Network
337(2)
3 Methodology and Data
339(4)
3.1 Data
339(1)
3.2 Methodology
340(1)
3.2.1 Two-mode network
340(1)
3.2.2 Bipartite graph
341(1)
3.2.3 Measurement of network
342(1)
3.2.4 Identification of brokerage roles
342(1)
4 Analysis and Results
343(8)
4.1 Data selected
343(1)
4.2 Two-mode network structure
344(1)
4.3 Measures of centrality
344(3)
4.4 Identification of brokerage roles
347(4)
5 Conclusion
351(1)
Acknowledgments
352(1)
References
353(4)
14 Emerging Networking Methods: Analyzing Funding Patterns and Their Evolution in Two Medical Research Topics
357(48)
Blanca de-Miguel-Molina
Scott W. Cunningham
Fernando Palop
1 Introduction
358(1)
2 Literature Review
359(3)
3 Methodology
362(2)
4 Data
364(2)
5 Results
366(23)
5.1 Co-funded research into ovarian cancer
368(3)
5.2 Co-funded research into breast cancer
371(7)
5.3 Evolution in the funding of MESH
378(11)
6 Conclusions
389(1)
Acknowledgments
390(1)
References
390(3)
Annex 1: Cliques in 2013 for ovarian cancer
393(3)
Annex 2: Cliques in 2013 for breast cancer
396(9)
Part 5 405(178)
15 Advanced Methods: Identifying the Technology Profiles of R&D Performing Firms - A Matching of R&D and Patent Data
407(24)
Peter Neuhausler
Rainer Frietsch
Carolin Mund
Verena Eckl
1 Introduction
408(2)
2 The Data
410(3)
2.1 Business R&D expenditures
410(2)
2.2 Patent filings
412(1)
3 The Matching Algorithm
413(5)
3.1 Step one - text cleaning
413(1)
3.2 Calculation of the similarity scores
414(1)
3.3 Selection of the matches
415(2)
3.4 Coverage of the matched dataset
417(1)
4 Concordance Between Technology Fields and Economic Sectors
418(2)
5 Results
420(5)
5.1 R&D expenditures by technology fields
420(4)
5.2 Conclusions
424(1)
Acknowledgments
425(1)
References
426(1)
Annex
427(4)
16 Advanced Methods: Identification of Promising High-Tech Solutions with Semantic Technologies: Energy, Pharma, and Other Industries
431(40)
Irma V. Efimenko
Vladimir F. Khoroshevsky
1 Introduction
432(1)
2 Related Work and Main Challenges
433(4)
2.1 Technology life cycle models: Hype Cycles
433(1)
2.2 Bibliometric analysis and technology indicators
434(1)
2.3 Knowledge engineering and natural language processing
435(2)
3 Methodology and Algorithms
437(10)
3.1 Technology trends: Black Box
437(1)
3.1.1 Principle
437(1)
3.2 Technology life cycle and document genres
438(1)
3.3 Technology trend indicators: Ontological engineering
439(2)
3.4 Classification of indicators
441(2)
3.5 General model and maturity levels
443(3)
3.6 Linguistic scales and sentiment analysis
446(1)
4 Data Processing Pipeline
447(4)
4.1 Pipeline scheme
447(1)
4.2 NLP algorithms: Indicators
447(2)
4.3 NLP algorithms: Technological concepts
449(2)
5 Validation
451(11)
5.1 Selection of a field (R&D domain) for validation .
451(2)
5.2 Data and results
453(1)
5.3 Evaluation of knowledge extraction results
454(2)
5.4 Evaluation of relevance
456(6)
6 Identification of Market Trends
462(4)
7 Conclusions
466(1)
Acknowledgments
466(1)
References
467(4)
17 Advanced Methods: Operationalizing Social Network Services Data - Deep Content Analysis to Comprehend Brand Presence
471(32)
Arash Hajikhani
Jari Porras
1 Introduction
472(2)
2 Social Media Data: Literature Review
474(5)
3 Research Design, Data Collection and Analysis
479(9)
3.1 Social media (Twitter) data retrieval
479(2)
3.2 Types of twitter profiles
481(1)
3.3 Sentiment analysis
482(5)
3.4 Crowd intelligence content evaluation
487(1)
4 Findings
488(4)
5 Discussions and Conclusions
492(3)
5.1 Lessons learned and implications for practice
494(1)
Acknowledgments
495(1)
References
496(2)
Appendix A
498(1)
Appendix B
499(1)
Appendix C
500(1)
Appendix D
501(1)
Appendix E
502(1)
18 Advanced Methods: Technological Frontiers and Embeddings - A Visualization Approach
503(28)
Scott W. Cunningham
Jan H. Kwakkel
Sertac Oruc
1 Introduction
504(2)
2 Methodological Review
506(3)
3 Theory and Framework
509(2)
4 Method
511(5)
5 Data, Analysis, and Results
516(9)
6 Reflection and Conclusions
525(3)
Acknowledgments
528(1)
References
528(3)
19 Advanced Methods: Opportunities and Potential of the Internet of Things for Solving Social Issues
531(28)
Yasutomo Takano
Yuya Kajikawa
1 Introduction
531(3)
1.1 Internet of Things
531(1)
1.2 Emerging technology and social issues
532(2)
2 Methodology
534(6)
2.1 Data collection
534(1)
2.2 Creating a citation network
535(2)
2.3 Linkage analysis
537(3)
3 Results
540(11)
3.1 Extraction of semantic linkages
540(2)
3.2 Division of the semantic linkages
542(1)
3.2.1 Division by degree of recognition
542(1)
3.2.2 Division by technology readiness levels
542(1)
3.2.3 Integrated divisions
543(1)
3.3 Demonstration of linkages
543(1)
3.3.1 Water issue and technology solving the issue: Water4 & SN4-4-1
545(1)
3.3.2 Energy issue and technology solving the issue: Energy2 & NFC11
546(1)
3.3.3 Healthcare issue and technology solving the issue: Healthcare6 & SN2-3-3
547(1)
3.3.4 Agricultural issue and technology solving the issue: Agriculture6 & SN4-4-1
548(1)
3.3.5 Biodiversity issue and technology solving the issue: Biodiversity5 & SN4-4-7
550(1)
4 Discussion
551(3)
4.1 Counterfeit new application
551(3)
5 Limitation
554(1)
6 Conclusion
554(1)
Acknowledgments
555(1)
References
555(4)
20 Advanced Methods: Exploring Technology Convergence as a Measure of Transition toward Connected Lighting System
559(26)
Nina Chaichi
Tugrul Daim
1 Introduction
560(2)
2 Methodology
562(3)
3 Case Analyses
565(14)
3.1 Case 1: Convergence analysis of lighting control techniques and strategies
565(10)
3.2 Case 2: Convergence analysis of energy efficiency technologies in street lighting
575(1)
3.3 Case 3: Convergence analysis of lighting devices or systems for working space
576(3)
4 Contributions and Conclusions
579(1)
Acknowledgments
579(1)
References
580(1)
Appendix
581(2)
Appendix I. Expert Identification using Social Network Analysis 583(2)
Appendix II. Bibexcel - Quick Start Guide to Bibliometrics and Citation Analysis 585(16)
Alan Pilkington
Appendix III. Supplementary Material 601(2)
Index 603
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