Many exciting approaches are blossoming to tackle facets of tech emergence and related concepts. To give the flavor, two conferences held in Leiden, The Netherlands, in September 2018, touched on many pertinent aspects, including:
- Global Tech Mining (GTM) Conference – theoretical roots of emergence (Burmaoglu et al.), visualizing cross-topic keyword aggregation (Li), detecting hotspots (Li et al.), technological convergence as antecedent of speciation (Caferoglu and Moehrle), science-technology interactions (Winnink; Qi et al.), determining technology fronts (Garechana et al.), network analyses re: technological evolution (Boelman et al.), crowdfunding text mining of consumer oriented innovations (Boye et al.), and evaluating tech emergence (Burmaoglu and Saritas)
- Science & Technology Indicators (STI) Conference – measuring scientific novelty, as tagged by initial journal co-citations (Mairesse and Pezzoni; Carayol et al.); applying stochastic citation time series analyses to measure topical emergence (Förster et al.); and emerging technology forecasting (Garechana et al.)
The scope of measuring tech emergence touches on choices concerning:
- what scale?
- what data?
- what analytical approaches?
- what outputs, toward what ends?
An essential scale choice presents between “macro” – i.e., which research domains are ascending? — and “micro” — within a domain, what topics are accelerating? Data possibilities range widely – e.g., R&D publication abstracts, patent full texts, altmetrics, combining with auxiliary sources (Wikipedia), etc. Approaches include various types of text mining, bibliographic coupling (of cited topics, journals, fields, authors), social network analyses, etc. Choice of approach affects what parts of the data one treats (various topical text fields, keywords, authors/affiliations, journals, citations). End targets diverge too — one could imagine measuring emergence to study scientific evolution; offer individual science or technology or innovation indicators; contribute to composite indicators (e.g., dashboards) for science policy or technology management; provide competitive technical intelligence by identifying key players in particular frontier topics; and so on. These measures share a common thread of empirical analyses, but also offer potential value in tapping experts’ knowledge.
We see rich synergies among analyses treating these different facets of tech emergence. BUT, we also confront practicalities in arranging a fun, learning experience without undue burden on participants or contest administrators. We thus adopt this 2-part approach:
- A tightly delimited “contest” (some might call it a game)
- A “Measuring Tech Emergence” “conference” track at the 9th Global TechMining Conference consisting of “contest” and “non-contest” (i.e. approaches outside contest constraints) submissions.