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
Year: 2022

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
convergence.

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.

http://dspace.bu.ac.th/bitstream/123456789/4635/1/nonthapat_puls.pdf

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
Year: 2020

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
Year: 2006

http://search.proquest.com/docview/304911340

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
Year: 2014

Full-text available here https://riunet.upv.es/bitstream/handle/10251/44346/TFM.pdf?sequence=2