The glossary of terms used in both my PhD and in futures studies. I also outline the various theories that support both
Theory and Glossary
Glossary of terms
Entrepreneurship and innovation
Innovation and entrepreneurship are closely linked. Where an entrepreneur “undertakes the new combinations”, the innovator “finds the new combinations”. Mitra has shown that an innovator generates new products, services and process whereas an entrepreneur identifies and exploits the opportunity presented by the innovation.
An insight from the endogenous growth model described by Romer is that as the stock of knowledge increases, it will flow, or spillover, between the model’s agents. This flow, or distribution of knowledge, is one component of the knowledge economy’s production, distribution and application tripartite.
Productivity is a measure of the capacity of a business, government or economy to convert its resources into a valued output. Multifactor, or total factor, productivity is concerned with the ratio of the output to the suite of various input factors.
Strategic Foresight is the term used to replace “strategic thinking” when considering the future in a disciplined fashion, helps to expose and examine assumptions upon which any thinking about the future is based
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Computerisation of employment
Keyne’s predication of “technological unemployment” motivated Frey and Osborne’s research on the impact of technology such as robotics, big data and automation on work. They answered their primary question, “how susceptible are current jobs to computer technology”, by analysing the specific tasks of the O*Net standardised list of 702 jobs and found 47% of all current jobs are at risk.
While the above 47% figure relates to the USA workforce a paper in CEDA’s 2015 “Australia Future Workforce” report by Durrant-Whyte et al applied Frey and Osborne’s approach to the Australian workforce. They found that up to 40% of jobs are “susceptible to computerisation and automation in the next 10 to 15 years”.
Frey and Osborne’s work is built in part on the work of Autor & Price. The key insight of Autor & Price was that the jobs that people are paid to do can framed with respect to the types of tasks that are performed. They classified these tasks as routine/non-routine manual and routine/non-routine cognitive. Apart from non-routine cognitive, which they further delineated into analytical and interpersonal tasks, they found that the demand for all other tasks had declined since 1960.
One of the tenets of economic development is that for every local job created, additional jobs are also created. This employment multiplier is dependent upon the industry and whether or not the job is in the tradable or non-tradable sector. Moretti found that for every local manufacturing job created, another 1.6 jobs are created in the local non-tradable sector. A Lee & Rodriguez-Pose 2016 report on research that found 4.9 additional jobs in the non-tradable sector for every 1 job created in high-technology industries .
According to Kaplanis there are three factors that drive the formation of these additional jobs: increasing the density of high income workers drives an increase in the demand in the local non-tradable sector, rising production complementarities as the density of local skilled workforce rises, and improvements in firm productivity that benefit others.
The second-wave of knowledge economy studies, centred on the 1996 OECD report, rose out of the work of David, Foray, Lundvall as well as a team of Canadian OECD delegates. This body of work builds on the first wave in two ways: through a deeper understanding of knowledge in terms of economic growth, and secondly by describing the discrete elements of knowledge production, distribution and application as being connected in an “innovation system”.
The basis of the work undertaken leading up to the 1996 OECD report was the new understanding of economic growth. Where the three-factor neo-classical economic growth model, of labour, capital and resources was being expanded to including a fourth factor: knowledge. This four-factor “New Growth Theory” frames technical change as change that flows from the application of an expanding knowledge base, as a separate driver of economic growth to capital accumulation.
Lundvall in a similar manner to Machlup, describes knowledge using four categories (know-what, know-why, know-how, & know-who), and their related properties of tacitness and codification. He posits that the distinct process components of expanding the knowledge base, that is the production of knowledge, its distribution and its application, can apply to each of the four kinds of knowledge individually or as a group .
David and Foray use a “’systems-theoretic’ approach in examining the relationship between a society’s knowledge-base and its capacity to generate and utilise economically beneficial innovations”. In this “innovation system”, they distinguish between the stock of knowledge, the amount of, for example, scientific and technical knowledge that is available to a society’s economic actors, and the flow of knowledge. Which is how well that knowledge is distributed between the actors.
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