Title: Big data modelling the knowledge economy

Authors: Robert B. Mellor

Addresses: Computing and Mathematics, Kingston University, London, UK

Abstract: A computer-generated 3D model illustrates the advantages of virtual in silico techniques. Derived from data for SMEs in service industries it enables a business owner (or consultant) to identify where any organisation is on a three-dimensional landscape and draw quantitative conclusions about fruitful future directions of travel plus how high the resulting benefits will be and what costs are due along the journey. This 'ready-to-go' landscape map is of immense value for academics and practitioners alike, and is easily-applicable. Anyone can create the three-dimensional fold and discuss the implications of growth and development with specific clients. Markov Chain Monte Carlo modelling is presented which, put simply, is throwing virtual balls down the basic fold to show how to predict outcomes of Knowledge Engineering projects. Results are shown for; adding multiskilled innovators, adding network input from external environments, costing management control effectively and explaining how IPR adds extraneous value.

Keywords: business analysis; computer model; entrepreneurship; innovation; KBV; profitability; SME.

DOI: 10.1504/IJKBD.2018.094896

International Journal of Knowledge-Based Development, 2018 Vol.9 No.3, pp.206 - 220

Received: 18 Jan 2017
Accepted: 20 Nov 2017
Published online: 26 Sep 2018 *

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