مقاله انگلیسی رایگان در مورد خوشه بندی استخراج متن

مقاله سال 2017

Effective management and planning of research and development activities require strategic allocation of available resources (Arroyabe et al., 2015; Berloznik and Van Langenhove, 1998). This issue manifests itself at different scales and plays a role in private companies and public authorities as well as academia. For example, individual scientists and research departments have a keen interest in spending their time and money in areas with potential for high impact (Kajikawa et al., 2008; Kostoff, 2008; Kostoff and Schaller, 2001; Leydesdorff et al., 1994; Ogawa and Kajikawa, 2015). Likewise, (inter)national governmental institutions seek to establish policy instruments (e.g. legislation and funding schemes) that give priority to development and application of innovative solutions with the highest positive contribution for society (Coccia, 2009; Kidwell, 2013).

Identification of such high-potential research and development areas is a challenging task. Making well-informed decisions requires detailed knowledge of past findings and current trends, and a deep understanding of emerging technology pathways (Leydesdorff et al., 1994; Yoon and Park, 2005). At the same time, it asks for a broad perspective to oversee future needs while identifying the opportunities that arise in neighboring research domains. The context in which such decisions are made is becoming increasingly complex because traditional science and engineering domains are getting more and more interconnected (Morillo et al., 2003; Porter and Rafols, 2009). In addition, the information that is documented in patents, reports and research papers continues to grow in size at an exponential rate (Kajikawa et al., 2008; Kostoff and Schaller, 2001; Larsen and Von Ins, 2010; Bengisu and Nekhili, 2006). The availability of input for research and technology planning can therefore be perceived as overwhelming, especially for decision-makers who are new to the field. The inability to properly analyze and comprehend all this information may lead to wrong recommendations and suboptimal priorities in research and development agendas.

Science foresight refers to the collection of analysis and prediction methods that can assist the development of a science vision in order to prepare for future challenges or needs in science (Martin, 1995; Martin, 2010). It has successfully been implemented in different fields, such as economy (Nassirtoussi et al., 2014), environmental science (Dubarić et al., 2011; Iniyan and Sumathy, 2003), foresight (Saritas and Burmaoglu, 2015; Su and Lee, 2010), health science (Pereira and Escuder, 1999; Abbott et al., 2014), politics (Coates, 1985), nano science and technology (Huang et al., 2011; de Miranda Santo et al., 2006; Robinson et al., 2007), and social science (Baloglu and Assante, 1999; Singh et al., 2007). The literature on science foresight covers a wide variety of qualitative and quantitative means for monitoring clues and indicators of evolving trends and developments (Coates, 1985). To facilitate successful science foresight analyses, it is clear that the methodology needs to be matched with e.g., the purpose of the study, the size and quality of the database, and the type of output that is expected. However, the available information about the relative effectiveness of different science foresight methods is very limited, and it is therefore difficult to support such decisions. In addition, most methods perform well at some, but typically not all aspects of science foresight. The potential of combining the positive sides of different science foresight methods into one overall framework has so far remained relatively unexplored.