There is considerable and growing interest in the emergence of novel technologies, especially from the policy-making perspective. Yet as an area of study, emerging technologies lacks key foundational elements, namely a consensus on what classifies a technology as 'emergent' and strong research designs that operationalize central theoretical concepts. The present paper aims to fill this gap by developing a definition of 'emerging technologies' and linking this conceptual effort with the development of a framework for the operationalisation of technological emergence. The definition is developed by combining a basic understanding of the term and in particular the concept of 'emergence' with a review of key innovation studies dealing with definitional issues of technological emergence. The resulting definition identifies five attributes that feature in the emergence of novel technologies. These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. The framework for operationalising emerging technologies is then elaborated on the basis of the proposed attributes. To do so, we identify and review major empirical approaches (mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies (these include indicators and trend analysis, citation analysis, co-word analysis, overlay mapping, and combinations thereof) and elaborate on how these can be used to operationalise the different attributes of emergence.

There is considerable and growing interest in the emergence of novel technologies, especially from the policy-making perspective. Yet as an area of study, emerging technologies lacks key foundational elements, namely a consensus on what classifies a technology as 'emergent' and strong research designs that operationalize central theoretical concepts. The present paper aims to fill this gap by developing a definition of 'emerging technologies' and linking this conceptual effort with the development of a framework for the operationalisation of technological emergence. The definition is developed by combining a basic understanding of the term and in particular the concept of 'emergence' with a review of key innovation studies dealing with definitional issues of technological emergence. The resulting definition identifies five attributes that feature in the emergence of novel technologies. These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. The framework for operationalising emerging technologies is then elaborated on the basis of the proposed attributes. To do so, we identify and review major empirical approaches (mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies (these include indicators and trend analysis, citation analysis, co-word analysis, overlay mapping, and combinations thereof) and elaborate on how these can be used to operationalise the different attributes of emergence.

In the present paper we investigate whether and to what extent green innovations significantly differ from non-green ones, in terms of i) inter- and intra-organizational relationships leading to their development and ii) technological characteristics, as complexity and novelty. Then, we study the impact of these organizational factors and technological features on the value of green innovations. In particular, we focus on a specific type of green innovations, as green technological innovations, and consider green patents as proxy for them. Analyzing green and non-green patents developed by a sample of companies included in the Dow Jones Sustainability World Index, we find that green innovations have important peculiarities compared to conventional ones. Specifically, developing green innovations requires establishing collaborations with external actors as well as among internal actors to a greater extent, while the technologies underling green innovations seem to be characterized by a higher degree of complexity and novelty. With regard to the value of green innovations, results show that the most valuable ones are those that more highly rely on collaborations among internal actors, whereas higher levels of novelty seem to be detrimental, at least in the short-medium term. ©2011 eContent Management

This article contributes to the development of methods for analysing research funding systems by exploring the robustness and comparability of emerging approaches to generate funding landscapes useful for policy making. We use a novel data set of manually extracted and coded data on the funding acknowledgements of 7,510 publications representing UK cancer research in the year 2011 and compare these “reference data” with funding data provided by Web of Science (WoS) and MEDLINE/PubMed. Findings show high recall (around 93%) of WoS funding data. By contrast, MEDLINE/PubMed data retrieved less than half of the UK cancer publications acknowledging at least one funder. Conversely, both databases have high precision (+90%): That is, few cases of publications with no acknowledgment to funders are identified as having funding data. Nonetheless, funders acknowledged in UK cancer publications were not correctly listed by MEDLINE/PubMed and WoS in around 75% and 32% of the cases, respectively. Reference data on the UK cancer research funding system are used as a case study to demonstrate the utility of funding data for strategic intelligence applications (e.g., mapping of funding landscape and co‐funding activity, comparison of funders' research portfolios).