Download Anticipating Future Innovation Pathways Through Large Data by Tugrul U. Daim, Denise Chiavetta, Alan L. Porter, Ozcan PDF

By Tugrul U. Daim, Denise Chiavetta, Alan L. Porter, Ozcan Saritas

This e-book goals to spot promising destiny developmental possibilities and functions for Tech Mining. in particular, the enclosed contributions will pursue 3 converging themes:

  • The expanding availability of digital textual content information assets when it comes to technological know-how, know-how and Innovation (ST&I).
  • The a number of tools which are in a position to deal with this knowledge successfully and contain capability to faucet into human services and interests.
  • Translating these analyses to supply invaluable intelligence on most likely destiny advancements of specific rising S&T pursuits.

Tech Mining might be outlined as textual content analyses of ST&I info assets to generate aggressive Technical Intelligence (CTI). It combines bibliometrics and complex textual content analytic, drawing on really expert wisdom referring to ST&I. Tech Mining can also be considered as a unique type of “Big info” analytics since it searches on a objective rising expertise (or key association) of curiosity in international databases. One then downloads, generally, millions of field-structured textual content documents (usually abstracts), and analyses these for valuable CTI. Forecasting Innovation Pathways (FIP) is a technique drawing on Tech Mining plus extra steps to elicit stakeholder and professional wisdom to hyperlink contemporary ST&I task to most likely destiny improvement.

A decade in the past, we demeaned administration of expertise (MOT) as a bit of self-satisfied and ignorant. such a lot know-how managers relied overwhelmingly on informal human judgment, principally oblivious of the possibility of empirical analyses to notify R&D administration and technology coverage. CTI, Tech Mining, and FIP are altering that. the buildup of Tech Mining study over the last decade bargains a wealthy source of potential to get at rising expertise advancements and organizational networks to this point. Efforts to bridge from these fresh histories of improvement to undertaking most probably FIP, despite the fact that, turn out significantly more durable. One concentration of this quantity is to increase the repertoire of data assets; that might increase FIP.

Featuring circumstances of novel ways and functions of Tech Mining and FIP, this quantity will current frontier advances in ST&I textual content analytics that might be of curiosity to scholars, researchers, practitioners, students and coverage makers within the fields of R&D making plans, know-how administration, technology coverage and innovation strategy.

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Nazrul and Kumiko (2010) analyzed the strengths and weaknesses of different countries in nanotechnology research based on tech mining techniques. Porter and Newman (2011) proposed a five-stage framework of tech mining to answer typical questions in technology management. Park et al. (2013a, b) adopted TRIZ evolution trends as criteria for evaluating technologies in patents. Zhang et al. (2014) provided six “term clumping” steps that clean and consolidate topical content in such text sources. Becker and Sanders (2006) illustrated how tech mining could profit from innovations in meta-analysis and social impact assessment.

Soddy (1922), Bertalanffy (1960), Ackoff (1974), Checkland (1981), Maxwell (1984) and Loveridge (2009), among others, have all pointed, in different ways, to the need to accept that the STEEPV set is dynamic and that ‘solutions to problems’ become non-viable quickly and often before a study of them is complete. FTA then needs to: • Adopt the notion of situations and their dynamism • Adopt the principle of amelioration • Reject the notion of problem-solving that yields solutions to well-specified problems that are not typical of the real world • Encourage people to become, on the basis of learning how to learn, how to think and to appreciate numeracy in appropriate ways • Create appreciative capabilities in breadth and depth to cope with the crisis in which FTA is already embroiled brought about by the algorithm-big data duo • Adopt due diligence as a learning-based investigation that eschews structured checklists and similar questionnaires that might constrain what approximates a forensic investigation that requires learning.

The fourth dictum relates to reasoning from thinking, learning and numeracy, and its outcome as appreciation of a situation (Fig. 9). In FTA using due diligence, reasoning will be an essential step to coping with ignorance as set out in Fig. 2 and policy dilemmas (Fig. 3) and the three previous dicta when facing the ‘algorithm-big data’ duo. These are steps that fixed process methods have difficulty with. Changes in how FTA is practiced are needed for the implementation of due diligence in FTA. These will need to enable practitioners to: Fig.

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