Research Networks’ Exogenous Evaluation
It is pertinent to remember that the evaluative preferences of members of the networks may be in accordance with external indicators agencies impose. As a Physics researcher interviewed told us, “the evaluation directs posture and not always in a good direction, because objective meet external, exogenous standards. And if the guys have belief in the good direction of what they are doing, they should continue, even if no more points come from an external evaluation. There has to be more freedom to deliver an original production.” Very often the external evaluation applies directly toward parameters that influence the choice of research themes, such as the research edits when they include preevaluation indicators.
External indicators, exogenous to the micro level, however, are important sources of information to be used in a critic manner in the discussion with the actors of collaboration network. Certainly, all evaluations must have consequences, practical results, and even with external requirements interference not always totally desirable, RNPE should be maintained.
Some of the most common indicators are those of context and impact, described later, which we call research networks exogenous indicators. In general, evaluative and accrediting agencies, S&T systems, or ministries of education and science and technology provide such indicators. They are external indicators, sources for national and international rankings, and for bibliometric databases such as Web of Knowledge, Science Citation Index, Scopus, Google Scholar, PubMed, Ricyt, Scielo, Redalyc, and Journal Citation Report, which should be intensively discussed with the members of the collaborative networks in the permanent sense of where we are, where we want to be or get, with what kind of ethical and epistemological baggage.
Research networks exogenous indicators are those related to the outsides of the network, and we consider them to be divided between two dimensions: research network external context and research network impact. At research network external context dimension, some categories can be identified: science globalization; science policy; science rewards; international and national rankings; and evaluation systems and national and international legislation (Adams, 2012; Defazio et al., 2009; Hessels and van Lente, 2008; Kitagawa and Lightowler, 2013; Postiglione, 2013). Some categories can be considered as belonging to research network impacts: visibility and impact of explicit knowledge production, that is, publications citations (Adler et al., 2009; Jacob and Meek, 2013); and knowledge networks; societal impacts, economics impacts, and technology transfer (Aksnes and Rip, 2009; Bornmann, 2012; De Filippo et al., 2012; Furtado et al., 2009; Guennif and Ramani, 2012; Kalucy et al., 2009; Salles et al., 2011).
It is important to understand the distinction between measuring efficiency (i.e., the ratio of output to input) and impact (i.e., the capacity to produce effects). At the network research level, the impact of research investments goes beyond knowledge production and can be expressed by educating new scientists, training skilled graduates, creating new networks, stimulating social interaction, creating new scientific instrumentation and methodologies, improving university-industry collaboration, and improving knowledge transfer and innovation (Agasisti et al., 2012; Bruneel et al., 2010; Meyer, 2002; Salter and Martin, 2001). Some studies focus on how incentives for collaboration shape collaborative behavior and research productivity in the context of EU-funded research networks (Defazio et al., 2009; Protogerou et al., 2010; Roediger-Schluga and Barber, 2008); other studies analyze the effects of affiliation (university research centers) on the productivity and collaboration patterns by facilitating cross-discipline, crosssector, and interinstitutional productivity and collaborations (Ponomariov and Boardman, 2010; Rossoni et al., 2008).