Creativiteit en coalitievorming

Creativiteit gebruiken we allemaal, de een wat meer dan de ander. Je hoeft je maar te vervelen, en je gaat al op zoek naar nieuwe activiteiten. Je bedenkt mogelijke activiteiten en kiest de beste; je bent dus creatief bezig. Onderwijl leer je allerlei nieuwe dingen. Dat doe je door bestaande kennis, die je dus soms eerst moet verwerven, te combineren. Dit wordt de psychologische P-creativiteit genoemd[1]. Of je kunt totaal nieuwe, innoverende dingen bedenken waar nog nooit iemand eerder op is gekomen, de historische H-creativiteit[1]

Coalition Formation in Networked Innovation: Directions for Future Research

During the last several decades, we see a tendency towards openly distributed knowledge. Whereas we experienced an open source movement in the 80\u27s, we now see that open learning and open innovation have become popular. Akin to open source code encouraging transformational creativity (Boden, 2004), open or networked innovation may lead to more effective organisational learning (Sloep, 2009a). This process of open knowledge exchange involves short time commitments, similar to those in Ad-Hoc Transient Communities (AHTC). We would like to pose a new view on the interpersonal ties in networked innovation, that is, the view of interpersonal ties as coalitions. Networked innovation occurs on three levels: the micro level, the meso level, and the macro level. Micro level coalitions are formed on the personal level. Meso level coalitions are formed by units of people within organisations. Organisations form coalitions or alliances with other parties on the macro level of networked innovation. Each of these levels has their own problems that hinder the process of networked innovation. Examples are self-interest (micro), negative ties between units (meso) and so-called logrolling. People need to be informed of the value of their candidate coalitions so as to develop intrinsic motivation for co-operation. We propose the view of interpersonal ties within networked innovation as coalitions. We compare characteristics of Granovetter\u27s characteristics of interpersonal ties (Granovetter, 1973)with the characteristics that were identified by Begley et al. (2008)to underscore this view. Afterwards, we propose an initial model of the antecedents of coalitions. These antecedents were described earlier by Brass et al. (2004)and we suggest an extension of this list of antecedents. Besides, we provide an initial model that visualises the relations between the antecedents and coalitions. As this is part of ongoing research, we provide a methodology for further investigation of the process of coalition formation within networked innovation. This includes an extensive literature review, model development, simulation and verification through an online experiment. This will hopefully answer our questions on how coalitions are formed within networked innovation, what the structure of these coalitions is, how they are sustained and how the payoff is divided

Coalition Formation in Networked Innovation: Directions for Future Research

During the last several decades, we see a tendency towards openly distributed knowledge. Whereas we experienced an open source movement in the 80\u27s, we now see that open learning and open innovation have become popular. Akin to open source code encouraging transformational creativity (Boden, 2004), open or networked innovation may lead to more effective organisational learning (Sloep, 2009a). This process of open knowledge exchange involves short time commitments, similar to those in Ad-Hoc Transient Communities (AHTC). We would like to pose a new view on the interpersonal ties in networked innovation, that is, the view of interpersonal ties as coalitions. Networked innovation occurs on three levels: the micro level, the meso level, and the macro level. Micro level coalitions are formed on the personal level. Meso level coalitions are formed by units of people within organisations. Organisations form coalitions or alliances with other parties on the macro level of networked innovation. Each of these levels has their own problems that hinder the process of networked innovation. Examples are self-interest (micro), negative ties between units (meso) and so-called logrolling. People need to be informed of the value of their candidate coalitions so as to develop intrinsic motivation for co-operation. We propose the view of interpersonal ties within networked innovation as coalitions. We compare characteristics of Granovetter\u27s characteristics of interpersonal ties (Granovetter, 1973)with the characteristics that were identified by Begley et al. (2008)to underscore this view. Afterwards, we propose an initial model of the antecedents of coalitions. These antecedents were described earlier by Brass et al. (2004)and we suggest an extension of this list of antecedents. Besides, we provide an initial model that visualises the relations between the antecedents and coalitions. As this is part of ongoing research, we provide a methodology for further investigation of the process of coalition formation within networked innovation. This includes an extensive literature review, model development, simulation and verification through an online experiment. This will hopefully answer our questions on how coalitions are formed within networked innovation, what the structure of these coalitions is, how they are sustained and how the payoff is divided

What\u27s in it for me? Recommendation of peers in networked innovation

Several studies have shown that connecting to people in other networks foster creativity and innovation. However, it is often difficult to tell what the prospective value of such alliances is. Cooperative game theory offers an a priori estimation of the value of future collaborations. We present an agent-based social simulation approach to recommending valuable peers in networked innovation. Results indicate that power as such does not lead to a winning coalition in networked innovation. The recommendation proved to be successful for low-strength agents, which connected to high-strength agents in their network. Future work includes tests in real-life and other recommendation strategies

If We Work Together, I Will Have Greater Power: Coalitions in Networked Innovation

The present article uses agent-based social simulation to study rational behaviour in networked innovation. A simulation model that includes network characteristics and network participant\u27s characteristics is run using parameter sweeping, yielding 1450 simulation cases. The notion of coalitions was used to denote partnerships in networked innovation. Coalitions compete against each other and several variables were observed for winning coalitions. Close analysis of the variations and their influence on the average power per winning coalition was analysed using stepwise multiple regression analysis. The analysis brought forward two main conclusions. First, as average betweenness centrality per winning coalition increases, the average power per winning coalition decreases. This implies that having high betweenness centrality as a network participant makes it easier to build a successful coalition, as a coalition needs lower average power to succeed. Second, as the number of network participants increases, the average power per winning coalition decreases. This implies that in a larger network, it may be easier to form a successful coalition. The results form the basis for the development of a utility-based recommendation system that helps people choose optimal partners in an innovation network

A simulation for content-based and utility-based recommendation of candidate coalitions in virtual creativity teams

Psychological literature shows that people do not always make rational choices with respect to whom to collaborate with. Providing the value of candidate connections may help them choosing the right people to connect with in a network. This paper presents a model about coalitions in creativity that will be used to generate content-based and knowledge-based recommendations of candidate coalitions

Artificial Intelligence to Enhance Learning Design in UOW Online, a Unified Approach to Fully Online Learning

The current article presents a framework for the design and support of UOW Online, an entirely new unified university strategy for fully online learning. To aid teachers in the learning design process, we aim to create more awareness for teachers by determining the underlying learning design of their subject. To ensure the approach can be scaled up to cater for potentially hundreds of subjects, the manual labeling serves as input for an Artificial Intelligence (AI) algorithm that will train a model to label intended learning activities automatically. In addition to student demographics and behavior, the learning design and subject content will be used to augment an AI model that predicts future student outcomes. Future work focuses on the collection of necessary learning activities and manual encoding of these learning activities

Goals, motivation for, and outcomes of personal learning through networks: results of a tweetstorm

Recent developments in the use of social media for learning have posed serious challenges for learners. The information overload that these online social tools create has changed the way learners learn and from whom they learn. An investigation of learners, goals, motivations and expected outcomes when using a personal learning network is essential since there have been few empirical studies in the domain. Previous research focused on the factors that influence learning in virtual environments, but these studies were mainly conducted in an era in which online social media were not yet used for personal learning networks. The current paper reports findings of a study that examined factors impacting professional learning through networks

Supporting co-creation with software, the idSpace platform

Innovation, in general, requires teamwork among specialist of different disciplines. The idSpace project developed ideas on how teams of collaborating innovators could best be supported. These ideas were embodied in a platform that the project developed. This idSpace platform allows its users to choose between various creativity techniques, pedagogical approaches and context-aware uses of stored information on projects, people and techniques. The platform follows a general process metaphor with specialised modules for specific parts, i.e. it starts with defining the problem to be addressed and through a sequence of steps concludes with a proposed solution. The platform was designed and developed by a multi-disciplinary team. It was evaluated through a realistic usage scenario which focused on the integral platform, from both an end-user and expert user perspective embodying a combination of qualitative and quantitative measurements on usability, general functionality and creativity aspects. This combination, as will be explained, proved to be a powerful way to prioritise and steer the further development of the platform