Decision support system for science communication: Reflective practitioners through a science communication laboratory

Science communication professionals deal with non-linear ill-defined wicked problems on a daily basis. In order to cope with these problems professional decision support for science communication professionals needs to be developed. Support systems that lead to overview on the one hand and focus at the other. In an ideal world these support systems perfectly resemble to realty. However, difficulties in parameterization of all kind of aspects, such as societal and ethical values, lead to support systems that must rely on the reflective interaction between the science communication professional and a supportive tool. In this paper we focus on two examples of professional support development in which taken decisions on responsible innovation and on consumer acceptance of smart technology plays a role. The first supports interactive business-to-business decision making and the second interactive business-to-consumer decision making. From these examples it becomes clear that even the development of support tools (social tooling) as a technological platform leads to a more reflective science communication professional and 13th International Public Communication of Science and Technology Conference 5-8 May 2014, Salvador, Brazil community of science communication professionals. We conclude this paper saying that a „Science Communication Laboratory‟ could be a future development in social learning amongst science communication professionals. Introduction Science communication is a developing discipline in which next to straight forwarded science education also dialogue and citizen science plays an important role. The latter often channelled by dialogue or other two-way communication channels. As a result the science communication (SC) professional has to deal with all kinds of professional roles, such as knowledge broker, intermediate, spokesman (Wehrmann & Henze, 2014; Mulder et al, 2008; Meyer, 2010) on a daily basis. And also corporate science communication of researchers and their institutions gets more and more important in the light of university‟s positioning in an international cooperation for research and higher education (Van der Sanden & Osseweijer, 2012;Verouden et al, 2013). These development lead to complex science communication practices that entail many decisions on various communication processes, channels and means, at various moments with all kinds of target audiences. But how to make decisions in the first place? Decision making contains next to rational components irrational reasoning (Cook et al, 2007; Ariely, 2010). Therefore, SC-professionals deal with an inevitable feeling of uncertainty. The aim of this paper is to explain how at the one hand parameterization of support tools for SC-professional is rather difficult, but at the other hand leads to a more professional and reflective SC-practice. Even the development of such support systems entail accountable SC-professionals. Uncertainty & scenarios There are various kinds of uncertainty a SC-professional has to deal with. A distinction is made between uncertainty due to a lack of knowledge and uncertainty due to variability inherent to the system under consideration (Walker et al, 2003). A decision support instrument needs to elaborate on these aspects of uncertainty. The big question then is how to make such uncertainty explicit. How to support bounded rationality 13th International Public Communication of Science and Technology Conference 5-8 May 2014, Salvador, Brazil (Kahneman, 2003), which all professionals have to deal with? As Jones (1999) writes, bounded rationality asserts that decision makers intended to be rational; that is, they are goal oriented and adaptive, yet because of human cognitive and emotional architecture, they occasionally perform worse on being goal oriented and adaptive in important decisions. Limits on rational adaptation are of two types. First procedural limits, which limit how we go about making decisions. And second, substantive limits, which affect particular choices directly. Hollnagel (2007, p.4) state that: “decision making is an activity or a phenomenon rather than a process, thereby replacing the idealistic assumptions about a rational decision maker with a more realistic set of assumptions about decision making as a facet of work. ” Such assumptions like: 1) decision making is not a discrete and identifiable event; 2) decision making is not primarily a choice amongst alternatives; 3) decision making is not usually a distinct event that take place at a specific point in time that be isolated from what goes on in the environment. Daily scenario thinking by SC-professionals, “if.then. but. ” is based on these assumptions and feelings of uncertainty. Scenarios, as Schoemaker (1995) writes, are amongst the many tools a manager can use for strategic planning. Scenarios stand out for their ability to capture a whole range of possibilities in rich detail. By identifying basic trends and uncertainties a manager can construct a series of scenarios that will help to compensate for the usual errors in decision making: overconfidence and tunnel vision. Decision support for SC-professionals is meant to support scenario thinking and therefore professional reflections on complex dynamic science communication opportunities. Decision-support system & modelling As a means to cope with this uncertainty and play with various SC-scenarios, decision support systems can be used. Such systems could help make assumptions explicit can provide an environment to test different scenarios and their expected outcomes. An ideal support system describes the decision process of SC-management in which theory and data (e.g. concerning consumers), SC-management information, SCmanagement goals, professional‟s experience, creativity, intuition and feedback are combined. SC-management is defined in this paper as strategic questions a SC-manager might have concerning the design of a SC-processes towards various target audiences 13th International Public Communication of Science and Technology Conference 5-8 May 2014, Salvador, Brazil resulting in a SC-strategy that might change the behavioural intent of target audiences, their view or behavior concerning science and technology development, and/or possibly their willingness for collaboration and cooperation in science and technology development. Modelling and simulation are key words in our approaches towards a decision support system, eventually leading to a possible visual SC-management „dashboard‟ for SC-professionals. Simulated representations of target audiences have been developed by various researchers on e.g. target audience‟s need (Jager and Janssen, 2012; Van der Sanden et al, 2013). The insights obtained from these studies show the dynamic psychological and sociological aspects of target audiences and the many choices/scenarios a SC-professional can make or choose from. Therefore, the two examples of SC-support described in this paper on consumer behaviour concerning the introduction of smart energy meters and the SC-support for research & development management, bridge between theory and an uncertain practice and make possible decisions more insightful and tangible. The main challenge in developing decision support systems is to keep outcomes understandable, manageable and meaningful for the SC-professional and to provide a useful interactive tool with which to test possible effects of science communication efforts. The user of the tool, guided by the results from simulations and visualisations of available data and theories, can then make a better-informed decision. The decision support systems should not be considered as a decision-making entity, but the intelligence and creativity is in the use of the tool by professionals (Pommeranz, 2012). This makes their practice more reflective and more accountable since they are deeper involved in the possibilities at stake. Methodology & results In the two cases described in this paper an agent-based model was developed based on survey data. We made use of structural equation model using Amos 16.0 software, in which the various measured variables and latent variables are put together in an assessed model based on confirmatory factor analysis. This results in assessed theoretical models which describe: a) consumer behaviour regarding acceptance of smart 13th International Public Communication of Science and Technology Conference 5-8 May 2014, Salvador, Brazil energy meters (Van der Sanden et al, 2013) (see fig. 1) b) the assessed interrelations between key performance indicators (KPI) of innovation management (Flipse et al, 2013) (see fig. 2).