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Abstract: Foresight of early-stage technology trends via bibliometrics is often criticized for its perceived limitations in articulating practical relevance or predicting application success. To address that gap, foresight researchers and practitioners usually rely on expert interviews to qualitatively validate their quantitative findings. This study introduces a novel, data-driven approach to validate the relevance of early-stage technology trends for businesses and to detect early implementation efforts by technology leaders. By combining a bibliometric analysis of scientific publications with trend insights from online job postings as an innovative foresight data source, we use the presumably most early-phased data sources of both perspectives - science and practice - for our assessment of future technology innovation fields. The presented research is part of a larger project which strives to deepen our understanding of the links between scientific advancements and business innovation efficiency, thereby providing a more comprehensive perspective on the commercial viability of emerging technologies.
Keywords: Emerging technologies; innovation fields; technology trend topics; technology foresight; technology trend analysis; data-driven foresight; bibliometrics; job postings data; innovation efficiency.
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1 Introduction
Bibliometric analysis of scientific publications is a well-established method for data-driven foresight of emerging technology trends in various industries (Huang and Chang 2014; Stelzer et al. 2015; Woon et al. 2011). However, it is subject to the limitation that scientific success of a technology or research field does not necessarily lead to market success or innovation breakthroughs (Stelzer et al. 2015). To overcome this deficiency and to include contextual factors into the trend analyses, qualitative foresight methods such as expert interviews or scenario techniques are often combined with bibliometrics (Hanisch and Wald 2012; Niu 2014). In general, the literature strongly recommends combining two or more methods for effective foresight (Haegeman et al. 2013; Lüdeke 2013; Malanowski and Zweck 2007). Very popular in this regard, is the combination of quantitative methods such as bibliometrics or system dynamics with the qualitative scenario technique or roadmaps (Geum et al. 2014; Hirsch et al. 2013; Zhang et al. 2013). In our research, we take an alternative approach by combining two quantitative methods, respectively the quantitative analysis of two different textual data sources. We start off with a tool-based bibliometric analysis of 15,447 innovation-related research papers, that results in 37...