Innovation Discovery

Network Analysis of Research and Invention Activity for Technology Management

Tugrul Daim, Alan Pilkington

• Publisher: WSPC (EUROPE)
• Year: 2018
• eBook ISBN: 9781786344076
• Topic: Business
• Subtopic: Business General
• Index: Business

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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.

--> Contents:

• Part 1:
  • Bibliometrics: The Case of Comparing an Ecosystem Using System and Network Approaches (Marco Tregua, Anna D'Auria, Tiziana Russo Spena, and Francesco Bifulco)
  • Bibliometrics and Patents: Case of Forecasting of Biosensor Technologies for Emerging Point-of-Care and Medical IoT Applications (Nasir Jamil Sheikh, and Omar Sheikh)
  • Patents: The Case of Exploitation of the Patent System Among SMEs and Private Inventors in Finland (J Talvela, M Karvonen, and T Kässi)
  • Patents: Case of Analyzing Technological Knowledge Diffusion Among Technological Fields Using Patent Data: The Example of Microfluidics (Zheng Qiao, Lu-Cheng Huang, Fei-Fei Wu, Dan Wu, and Hui Zhang)
• Part 2:
  • Patents and Networks: Case of Discerning the Evolutionary Nature of Technological Change in the Complex Product Industry (Fei Yuan and Kumiko Miyazaki)
  • Patents and Networks: Case of Identification of Core Industry Actors for Electric Vehicle Battery by Application of Knowledge Flow (Yuan Yuan Shi and Tugrul Daim)
  • Patents and Networks: Case of Social Network Analysis for Innovation (Antonello Cammarano, Mauro Caputo, Emilia Lamberti, and Francesca Michelino)
  • Patents and Networks: Case of Cochlear Implant Technology Evolution Using Patent Classification Data (Srigowtham Arunagiri and Mary Mathew)
• Part 3:
  • Bibliometrics and Networks: Case of a Multinational Perspective on How Eco-Innovation has Evolved in Academic Literature (Blanca de-Miguel-Molina, María de-Miguel-Molina, María-del-Val Segarra-Oña, and Ángel Peiró-Signes)
  • Bibliometrics and Social Network Analysis Supporting the Research Development of Emerging Areas: Case Studies from Thailand (Nathasit Gerdsri and Alisa Kongthon)
  • Bibliometrics and Networks: Trends and Typology of Emerging Antenna Propagation Technologies (Yasutomo Takano, Yuya Kajikawa, and Makoto Ando)
  • Bibliometrics and Networks: Case of Project Management and the Emergence of a Knowledge-Based Discipline (Alan Pilkington, Kah-Hin Chai, and Le Yang)
• Part 4:
  • Emerging Networking Methods: Innovation Intermediaries in Technological Alliances (Calvin S Weng)
  • Emerging Networking Methods: Analysing Funding Patterns and Their Evolution in Two Medical Research Topics (Blanca de-Miguel-Molina, Scott W Cunningham, and Fernando Palop)
• Part 5:
  • Advanced Methods: Identifying the Technology Profiles of R&D Performing Firms — A Matching of R&D and Patent Data (Peter Neuhäusler, Rainer Frietsch, Carolin Mund, and Verena Eckl)
  • Advanced Methods: Identification of Promising High-Tech Solutions with Semantic Technologies: Energy, Pharma, and Other Industries (Irina V Efimenko, and Vladimir F Khoroshevsky)
  • Advanced Methods: Operationalizing Social Network Services Data — Deep Content Analysis to Comprehend Brand Presence (Arash Hajikhani and Jari Porras)
  • Advanced Methods: Technological Frontiers and Embeddings — A Visualization Approach (Scott W Cunningham, Jan H Kwakkel, and Sertaç Oruç)
  • Advanced Methods: Opportunities and Potential of the Internet of Things for Solving Social Issues (Yasutomo Takano and Yuya Kajikawa)
  • Advanced Methods: Exploring Technology Convergence as a Measure of Transition Toward Connected Lighting System (Nina Chaichi and Tugrul Daim)

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--> Readership: Graduate and post-graduate students and researches looking to expand their knowledge in bibliometrics, social networks, tecnology innovaiton and technology management. -->
Keywords:Bibliometrics;Technology Management;Patents;Innovation;Scientometric;Network Analysis;Network ToolsReview:0

Part 1

Chapter 1

Bibliometrics: The Case of Comparing an Ecosystem Using System and Network Approaches

Marco Tregua^∗, Anna D’Auria^†,¶, Tiziana Russo Spena^‡,
and Francesco Bifulco^§

Department of Economics, Management, Institutions
University of Naples Federico II
Naples, Italy
^∗[email protected]
^†[email protected]
^‡[email protected]
^§[email protected]

Abstract

In recent years, there has been a growing debate on the contributions to innovation contexts. This research aims to analyze the differences emerging from the conceptualization of contexts in the “innovation ecosystem” (IE) and its linkages to the innovation system (IS) and network concepts and depict the current research trends in innovation and their expected evolution.

Our methodological choice is a bibliometric analysis as it allows us to review and focus on the theoretical proposals by the scholars in detail. The research process has been conducted through a co-word analysis on the authors’ keywords as this approach is considered to be the most suitable to compare the dynamics of different research trends on a specific topic. The analysis was performed in two steps: the first based on all the research papers collected and the second based on a sub-dataset extrapolated from the main datasets on IS and innovation network (IN) literatures.

The results demonstrate that both IS and IN research streams are, in some ways, connected with the IE perspective. The definitions and concepts used in the IS and IN literatures have been identified as replicated in the ecosystem approach even if specific insights have been generated.

Keywords: Innovation; technology; system; network; ecosystem; bibliometric analysis.

1.Introduction

Innovation has been found to occur more in wider industry-based contexts than in single firms. The best-known context by which scholars of economics have framed innovation is within systems, in reference to the studies on the “innovation system” (IS) which are well established in the existing literature, in particular contributions from Freeman (1988), Niosi et al. (1993), and Cooke et al. (1997). In addition, scholars are interested in the process underlying the national (Freeman, 1987, 1988) and regional (Cooke et al., 1997) dynamics of innovation, as well as sectoral and industrial transformations (Breschi and Malerba, 1997). These studies understood innovation as the result of a complex set of relationships including different actors in the system, whose activities and interactions initiate, import, modify, and diffuse the system (Freeman, 1995; Cooke et al., 1997). Within the systems literature, research is ongoing (Lundvall, 2007) to define the advancements in advantages, relationships, and the features shaping the systems of innovation.

Despite these studies, scholars within economics and other disciplines are increasingly labeling innovation as taking place within different contexts. Within the economic and business literature, the “innovation network” (IN) (Dhanaraj and Parkhe, 2006) and “innovation ecosystem” (IE) (Adner, 2006; Gawer and Cusumano, 2014) have been proposed as labels useful for depicting how different actors in a systems context interplay to achieve innovation-driven goals.

IN (Dhanaraj and Parkhe, 2006; Möller and Rajala, 2007) has been used to define an evolving system of mutually dependent actors based on resource relationships (Ahuja, 2000; Westerlund and Rajala, 2010). Within the IN, resources are created, exchanged, transformed, and combined through formal and informal relationships, and linkages between organizations are required to develop complex technologies and bring them into the market.

The IE perspective (Adner, 2006; Gawer and Cusumano, 2014) has been characterized mainly by an interdependent approach to innovation and the way relationships are shaped by actors, since one of the main features of an IE is openness; moreover, scholars focusing on IE highlighted the significance of technology in supporting the ties among the actors useful in favoring openness.

As has been observed in both the academic and business literature, IE is broadly used to depict the complexity of innovation at present (Gawer and Cusumano, 2014). In many cases, a great overlap or even interchange between scholars studying IE has existed among those who approach innovation in the IN and IE literature (D’Auria et al., 2017; Russo-Spena et al., 2017). All three fields of study are concerned with technology and innovation, even though the focus within IS is more on the macro level of institutional structures required to take advantage of innovation for economic growth (Niosi et al., 1993), while the IE and IN literature focus on the industry level or technological innovation of a specific kind to sustain network or business growth (Adner, 2006; Dhanaraj and Parkhe, 2006).

Moreover, both IN and IE have been defined as an evolving system of mutual dependency based on resource relationships in which a systemic character is the outcome of interactions, processes, procedures, and institutionalization.

This chapter aims to clarify the differences emerging from conceptualization of innovation contexts as the “innovation ecosystem”, and its linkages to IS and IN concepts so as to depict the current research trends in innovation and their expected evolution.

To achieve this aim, our methodological choice is a bibliometric analysis (van Leeuwen et al., 2001) because it allows us to review and focus on the theoretical proposals by scholars and select the contributions we considered as significantly in line with the innovation topic that we had chosen. Based on this analysis, the contribution of this chapter rests on the opportunity to learn from specific insights generated in different literature and allows for a more comprehensive approach toward innovation in wider interconnected contexts, i.e., IE.

We organize the remainder of this chapter as follows: Section 2 illustrates the research process for reviewing the literature, and Section 3 presents the results obtained from an analysis of the comparison between IE and IS and IE and IN literatures. Section 4 provides the discussion and future implications.

2.Research Process

Our research aims led us to choose an approach useful for investigating the previous literature in order to achieve a better understanding of the contents related to IE research literature.

We selected the bibliometric analysis approach because of its demonstrated usefulness in obtaining detailed insights into the extant literature (Callon et al., 1986) and its versatility (van Leeuwen et al., 2001) in allowing different focus points based on similar data. In addition, a featured advantage of the bibliometric approach to research is the extensive coverage of scholars’ contributions provided by the comprehensive online databases (Baker, 1991). Also, the objectivity of the observation of the results is likewise relevant (Nerur et al., 2008) even if the authors’ role is crucial in adding in-depth commentaries to the findings that emerged.

We chose co-word analysis because this approach is suggested to be the most suitable to compare the dynamics of different research trends on a specific topic (Cobo et al., 2014), favoring the emergence of both similarities and differences among the different approaches highlighted in Section 1, namely ecosystem, system, and network, as contexts hosting innovation activities through the contributions of different actors. Furthermore, the focus on authors’ keywords has been proposed as a means of providing more detailed insights as compared to other keywords, e.g., that of the editors (Rip, 1998); similarly, we have already demonstrated in a previous analysis how paying attention to authors’ keywords leads to better-defined findings in comparing different research streams (D’Auria et al., 2017).

In addition, we planned to perform our analysis in two steps. First, a co-word analysis will be performed on the whole databases describing the three research contexts for innovation in order to depict whether and how IS and IN have inspired IE, which is a more recent trend. Then we will extrapolate a sub-dataset from the main datasets on the IS and IN, aiming at highlighting the contributions offered by some scholars, namely those citing the pillars of each research trend. In more detail, we built a sub-dataset composed of the scholars’ contributions, citing at least one of the three top-cited papers in IS and IN literatures. The two sub-datasets achieved in this way will be compared to the dataset embedding all the contributions on the IE to again perform a co-word analysis. The results will be useful in highlighting the potential contribution of scholars of IS and IN to IE. Finally, the results of this second step of our analysis will be compared to those of the first step to determine whether and how these additional results can provide more insights into the development of the research field through bibliometric analysis (van Leeuwen et al., 2001).

2.1.Data collection and analysis

We collected data from the “Web of Science^TM Core Collection” because it is considered to be the most reliable database to perform literature analyses (Bremholm, 2004). We built the first dataset with usage of the string “innovation system*”, and then a second one through the query “innovation network*”, and a third with “innovation ecosystem*”. We selected papers and book chapters published between 1986 and December 2016 and belonging to the following fields of science: management, business, economics, and business finance, due to their linkages to the three research trends in focus.

To perform the second step of our research, we extrapolated a sub-dataset from the first two datasets, namely those on IS and IN. The criterion to select these sub-datasets was the usage of at least one of the three top-cited publications of each dataset. The authors double-checked the sub-datasets to verify whether the criterion had been applied in a proper way.

The analyses of both the whole datasets and the sub-datasets have been performed through the usage of BibExcel (Persson, 2008) because this software is known for its stability in results, ease in comparing different findings, and a clear way of proposing a graphic analysis that can be investigated through additional software. An analysis of the keywords was performed in line with the methodological suggestions of Uschold and Gr...

Table of contents

• Cover
• Title
• Copyright
• About the Editors
• About the Authors
• Contents
• Introduction
• Part 1
• Part 2
• Part 3
• Part 4
• Part 5
• Appendix I. Expert Identification using Social Network Analysis
• Appendix II. Bibexcel — Quick Start Guide to Bibliometrics and Citation Analysis Alan Pilkington
• Appendix III. Supplementary Material
• Index