Author: Tausif A. Bordoloi
Organization: University of Manchester
Source: A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy (Ph.D.) in the Faculty of Humanities, Alliance Manchester Business School
For FULL-TEXT https://www.research.manchester.ac.uk/portal/files/234004561/FULL_TEXT.PDF
The notion of cyber-physical convergence, which indicates the pervasive integration of digital
technologies in manufacturing, has rapidly gained prominence around the world because of its
potential to accelerate economic productivity gains. A question of significance but relatively
little empirical scrutiny is how cyber-physical convergence is characterised and realised in
innovation generation, innovation adoption and innovation policy. These issues are addressed
in this doctoral thesis, resulting in three journal-format papers.
The first paper characterises and operationalises the notion of cyber-physical convergence, and
then measures the growth and trajectories of scholarly research associated with the notion by
employing text-mining and bibliometric approaches. The second paper is an inductive case
study on the relative influences of geographical and non-geographical proximity factors in the
adoption of digital technologies by small- and medium-sized automotive and aerospace firms
(SMEs) co-localised in North West England. The third paper is an integrative literature review
of three industrial policy initiatives – Germany’s Industrie 4.0, Smart Manufacturing in the
U.S. and the High Value Manufacturing in the U.K., to analyse the framing and execution of
policy vision to support specific types of technologies underpinning convergence.
There are three main contributions of this thesis: first, it systematically delineates the cyberphysical convergence research domain into five data-centric capabilities – namely, Monitoring, Analytics, Modelling and Simulation, Transmission and Security, and sheds light on national research performance indicators for the period 2010–2019; second, it provides micro-level evidence indicating that geographical proximity among automotive and aerospace SMEs and also other types of economic actors is not the leading factor in technology adoption, rather
institutional and cognitive (knowledge) factors play a more prominent role; and third, it offers
insights pertaining to policy design and implementation, with Industrie 4.0 being more explicit
and comprehensive than Smart Manufacturing and High Value Manufacturing in its framing,
usage and implementation of a vision to support specific types of technologies. These contributions allow deriving policy and managerial implications regarding: the significance of data in manufacturing and funding data-centric capabilities, including questions of trade-offs between funding specific capabilities; the importance of interactions with non-
localised actors for the purpose of technology adoption; and the consideration and systematic
execution of policy vision to support and accelerate the realisation of cyber-physical
Technology foresight allows an organization to systematically keep track of emerging technologies to be adopted in its technology development plan. In Thailand, the National Institute for Emergency Medicine (NIEM) develops the nation-wide emergency service system. While foresight methods such as scenario planning and technology roadmapping are already deployed at NIEM, the experts are unable to stay up to date on emerging technologies around the globe and face challenges to incorporate them into the
emergency service in a timely fashion. This research tackles the issue by developing a framework that integrates Emerging Technology Identification (ETI) into Scenario-based Technology Roadmapping (SB-TRM) for Technology Foresight. To ensure its robustness, Action Research methodology together with methods of systematic literature review, workshop, and semi-structured interview to deploy the proposed framework at NIEM, evaluate outcome, and specify learning with degrees of validity and confidence that the novel framework would be applicable in similar settings. The integrated framework with the integration of ETI into SB-TRM was successfully implemented by using an accompanied manual prepared in accordance with the European Commission’s Good Foresight Standard and validated by Delphi panel experts prior to action taking. The practical findings from the four workshops indicated the transferability for NIEM to adopt the novel framework for future foresight activities. The research provided an evidence that the implementation of ETI improved the SB-TRM process by providing experts insights on emerging technologies and allowing them to anticipate future outcomes in forms of descripted scenarios and technology roadmap which reduced the complexity compared to technology roadmapping or scenario planning alone. The research had two practical implications: NIEM and other technologybased organizations can readily apply the validated manual and foresight-related
collaboration among the public, private, and academic institutions was improved. The research also had major social implication as Thailand’s ambulance service and its development policy were effectively updated on the technology state-of-the-art. The action research elucidated the integrated framework and the guidelines as the new knowledge in the theory of foresight for practitioners to adopt for foresight practice in general. The research had a limitation due to a single case in the national level was studied. Future researches could benefit from exploring additional cases in the private sector.
Doctoral Candidate: Nonthapat Pulsiri
University: The Institute for Knowledge and Innovation South-East Asia (IKI-SEA), Graduate School of Bangkok University
Degree Program: Doctor of Philosophy in Knowledge Management and Innovation Management
Interdisciplinary research centers are typically presented as a means for exploiting opportunities in science where the complexity of the research problem calls for sustained interaction among multiple disciplines. This study analyzed the effects of an interdisciplinary research center (NIMBioS) on the publication and collaboration behaviors of faculty affiliated with the center. The study also sought to determine what factors contributed to these effects for participants whose publication and collaboration behaviors were changed the most after affiliation.
The study employed a mixed-method case study approach, using quantitative bibliometric data along with qualitative data collected from interviews. Publication data for each participant in the study was collected from Web of Science (WOS) and analyzed by year against several demographic control variables to understand what effect affiliation with NIMBioS had on publication behaviors of participants. In addition to bibliometrics, a selection of study participants who demonstrated the most change in publication and collaboration behaviors since their affiliation with NIMBioS were interviewed to determine (a) what benefits (if any) participants felt they achieved as a result of participating in their working group, and (b) what factors (if any) participants felt may have contributed to the impact of NIMBioS affiliation on their publication and collaboration behavior.
Results of the study indicate that affiliation with a NIMBioS working group has a significant positive effect on participant collaboration activities (i.e. number of co-authors, number of international co-authors, number of cross-institutional co-authors), and a moderate effect on publication activities (i.e. publishing in new fields). Qualitative analysis of interdisciplinarity showed a shift in publication WOS subject categories (SCs) toward mathematical fields. Factors contributing to success cited by interviewees included organized leadership, a positive atmosphere, breaking into sub-groups, and the ability to collaborate with researchers with whom they would not have interacted outside of the group.
Doctoral candidate: Pamela Rene Bishop
University: University of Tennessee, Knoxville
Committee Members: Schuyler W. Huck, Jennifer K. Richards, Bonnie H. Ownley
Degree program: Doctor of Philosophy – Educational Psychology and Research
Because the existing applications of Technology Opportunity Analysis (TOA) text data mining framework developed by Alan Porter and other researchers used small datasets, previous research never pushed the limits of the methodology and failed to identify areas for future research associated with using larger datasets. This research developed extensions to the TOA framework to improve its performance and scalability and proved that the Technology Opportunity Analysis text data mining framework could be successfully scaled to analyze large datasets. The work included the development of a comprehensive set of new or significantly improved data extraction filters and data cleaning thesauruses, a data model and architecture based on relational database and online analytical processing technologies that provides an open platform provides easy, standards-compliant access to browsing, reporting, and data mining software that support either SQL or MDX queries, and a report distribution framework that does not require the end-users of the output of Technology Opportunity Analysis to use any specialized or prohibitively expensive client applications beyond the standard Microsoft Office applications and Adobe Acrobat Reader. In addition, it demonstrated that the time necessary to complete the data acquisition, cleaning, and transformation tasks can be reduced by at least 75% by creating libraries of import filters for commonly used data sources, eliminating unnecessary steps, using 64-bit native databases and extraction filters, improving the data model and architecture, and using significantly better data cleaning thesauruses. This work is significant because it enables a variety of research paths applying alternative statistical or data mining algorithms that previously would have been impossible to undertake. Thesauruses and fuzzy logic routines to clean and group the data are presented and their accuracy is tested on gene expression, energy storage, photovoltaics, smart materials, bioinformatics, quantum computing, wind turbine, nanotube, global warming, and data fusion data sets and benchmarked against existing thesauruses and fuzzy logic routines. A database on photovoltaic solar cell research that integrates data from 116,240 records from thirteen bibliographic, patent, and funding abstract databases was used to illustrate the concepts developed and tested in this dissertation.
Doctoral candidate: Richard Peyton George
University: Capella University
Degree program: Doctor of Philosophy – Due Diligence / Data Mining
Water scarcity in the World today is growing faster than expected and it is
among the main problems of 21st century to be faced by the World.
Central Asia is considered like a region with enough water resources, however,
an ineffective use of water, it’s allocation, rapidly growing population, and lack
of knowledge in sharing common basin among riparian countries could lead to
serious consequences. In this work we wanted to analyse literature about water
issues with the aim to understand when and how water disputes were
occurring. For this purpose, we have used bibliometric analysis to define and
look for better and reliable dates for the Thesis. All the dates and articles which
were used for this work were taken from the Web of Science. Furthermore,
thanks to VantagePoint and Social Networks Analysis we have got specific
articles which were sorted and divided according to your preference, calculating
measures of centrality to determine the importance of each keyword.
In following chapters we have also presented small research on the Central
Asian example, and the role of Kyrgyzstan in water sharing is illustrated.
Masters candidate: Meerim Avazbekova
Thesis Supervisor: Professor Blanca de Miguel
University: Polytechnic University of Valencia
Degree program: Master Degree in Business Management, Products and Services
Full-text available here https://riunet.upv.es/bitstream/handle/10251/44346/TFM.pdf?sequence=2
In this thesis, a framework based on text mining techniques is proposed to discover useful intelligence implicit in large bodies of electronic text sources. This intelligence is a prime requirement for successful R&D management. This research extends the approach called “Technology Opportunities Analysis” (developed by the Technology Policy and Assessment Center, Georgia Institute of Technology, in conjunction with Search Technology, Inc.) to create the proposed framework. The commercialized software, called VantagePoint, is mainly used to perform basic analyses. In addition to utilizing functions in VantagePoint, this thesis also implements a novel text association rule mining algorithm for gathering related concepts among text data. Two algorithms based on text association rule mining are also implemented. The first algorithm called “tree-structured networks” is used to capture important aspects of both parent-child (hierarchical structure) and sibling relations (non-hierarchical structure) among related terms. The second algorithm called “concept-grouping” is used to construct term thesauri for data preprocessing. Finally, the framework is applied to Thai xvi S&T publication abstracts toward the objective of improving R&D management. The results of the study can help support strategic decision-making on the direction of S&T programs in Thailand.
Doctoral candidate: Alisa Kongthon
Committee: Alan L. Porter, Xiaoming Huo, Donghua Zhu, Jye-Chyi Lu, and Susan E. Cozzens
University: Georgia Tech
Degree program: Doctor of Philosophy in Industrial Engineering
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