Keynote Speakers

Robert B. Mellor

 Kingston University, London

Knowledge-sharing in the development of Science and Technology Parks

Tech Entrepreneurship is of immense value in regional development and the national economy. Tech Entrepreneurship is partly dependent on economically sheltered environments known as Science and Technology Parks (STPs), who actively seek innovators and also encourage innovation amongst the constituent firms, including by networking and knowledge spill-over between the inhabitants, Universities and sources of capital. The low success rate (~20%) of STPs led us to use the ideas of Stiglitz to investigate how STP architecture can best cope with a changing and challenging innovation environment, through start-up to early maturity and full maturity. Results from using econometric methods (SEM, Monte-Carlo etc) show that it is very beneficial to have a central Cluster Initiative (CI) controlling the decision-making process (“star, hierarchy”) in the early stages of STP development, where potential gains and losses are relatively modest. However in the early maturity stage with commitment to a high-growth trajectory, a high quality of decision–making is required amongst managers and decisions are best taken by the CI with the input of more knowledgeable on-cluster firms. The situation where CI is supported by good-quality knowledge-sharing decisions from on-cluster firms – an ambidextrous situation – is superior when good innovations abound and the STP has acquired some maturity. However, in environments with a surfeit of poor-fit innovations, this becomes a high-risk strategy with high potential losses and indeed in this situation, retaining a hierarchical (CI only) decision process is most helpful, even when the quality of decision–making amongst CI managers is as poor as coin-flipping.

To summarise, STP development from small is not linear but Y-shaped: A successful strategy involves the start-up STP attracting enough small innovative firms which – in turn – attract larger firms, whose detailed sector-relevant insight improves CI decision-making. The most valuable ratio includes the CI and only any two of the larger firms; including more decision-makers does not improve the quality of decisions much but does drive the transaction costs up exponentially. If the STP cannot attract larger firms with experienced management, then the STP is best served with the CI continuing alone but, especially under conditions of growth and expansion, the cost of poor decisions will increase until eventually market failure ensues.

 

Nadir Kolachi

 University of Fujairah, United Arab Emirates (UAE)

Different theories, emerging concepts and diversified applications in modern education (A case of evaluations & recommendations)

_Contents & Contexts
_Theories & Concepts
_Practical Corporate methods
_Technology integration in learning
_Interdisciplinary Industry linkages & curriculum collaboration
_Professional development with ethical sense of care & ownership

The speech will cover different theories being taught at Business schools, emerging concepts that are interlinked with other disciplines and diversified applications in modern educational system around the world.  The speech will also cover the digital integration in modern education. The speech will focus on the following parameters that are required by educators, policy makers and scholars around the world.

 

More details will be added in the speech by way of mini cases, charts and discussions.

 

Vinod Chopra

Goodwill Cryogenics Enterprises, India

A Scientist’s Views on how to improve Economy of Developing Countries

Detailed outline to be added soon.

 

John Edwards

Aston University, UK

Knowledge: the ultimate intangible?

The adjective intangible has three related meanings. One is central to this conference – non- physical assets. Another is “unable to be touched”. A third is “difficult or impossible to define or understand”. Knowledge and software both satisfy the first two of these definitions. This presentation is about the third one.
Philosophers and others have been considering the question “what is knowledge?” for thousands of years, yielding many useful insights, but not approaching complete agreement.
Despite the efforts of a few people such as Müller-Merbach and Tsoukas, most of these insights have been ignored by knowledge management researchers. One welcome exception is that knowledge management research has been heavily influenced by Polanyi’s ideas of
tacit knowing, especially when the concepts of tacit and explicit knowledge were taken up by Nonaka and Takeuchi as part of their SECI model. It is therefore accepted that, as humans, “we can know more than we can tell”. Thus human knowledge apparently satisfies the third meaning of intangible as well.
Software, by contrast, is possible to define, by listing all the associated code. How difficult this is to understand depends on how good you are at that type of coding. What that software does, however, is more problematic. The software that enables the construction of deep learning systems has now reached the point where the systems cannot “tell” (explain the
reasoning leading to a particular result) either, at least not in any way that a human can understand.
Many organizations make decisions about us that affect our lives. Offering/denying us credit, jobs, welfare benefits, medical treatment. All these decisions must be based on knowledge to some extent, unless they are taken completely at random. Technically, automated decision- making is becoming ever more feasible. However, the European Union’s General Data Protection Regulation (GDPR) Article 22 states “The data subject shall have the right not to be subject to a decision based solely on automated processing”, and the associated Recital 71 gives the data subject “the right…to obtain an explanation of the decision reached after such assessment and to challenge the decision”.
How is an organization going to do that with a decision based on a deep learning system?
What if the data subject does not understand the human explanation that is given?
Is there a similar right to an explanation of a decision made by a human?
Is there a difference between this and tacit human knowledge?
This presentation will consider these and other related questions.

 

Julee Hafner

Julee Hafner
The Chicago School of Professional Psychology, USA

What needs to happen in the new Knowledge Economy?

For more than a decade, dramatic changes have constantly been shaping the business environment. It used to be true that a worker could expect to have one career and develop one set of skills for their working lifetime. Job mobility was limited. Organizations updated systems could remain stable and competitive, with little attention paid to workers. Organizations often did not realize the impact of intangible, tacit assets within the knowledge worker.

As organizations became more globally based, they depended increasingly on explicit assets, and they were left behind. The new knowledge worker has become autonomous. The new asset in this economy is knowledge. Changes in the knowledge economy are seen in three areas: first, within the knowledge worker – intellectual capital, second, the process of remaining sustainable – updating competencies, and third, the interrelationship of the global marketplace.

The need to constantly update knowledge within organizations for strategic and competitive advantage requires a new mindset and adoption of unlearning models.

In this keynote presentation, the new leader will be expected to create an environment to support knowledge workers. It is the process of challenging old assumptions, updating the current mind-set, and collaborative exchange within teams, suppliers and customers that will allow for sustainability. We explore how we can make this mindset shift and other changes to remain competitive in today’s everchanging knowledge economy.