Facilitating Creativity in Collaborative Work with Computational Intelligence Software

The use of computational intelligence for leveraging social creativity is a relatively new approach that allows organizations to find creative solutions to complex problems in which the interaction between stakeholders is crucial. The creative solutions that come from joint thinking-from the combined knowledge and abilities of people with diverse perspectives-contrast with traditional views of creativity that focus primarily on the individual as the main contributor of creativity. In an effort to support social creativity in organizations, in this paper we present computational intelligence software tools for that aim and an architecture for creating software mashups based on the concept of affinity space. The affinity space defines a digital setting to facilitate specific scenarios in collaborative business environments. The solution presented includes a set of free and open source software tools ranging from newly developed brainstorming applications to an expertise recommender for enhancing social creativity in the enterprise. The current paper addresses software design issues and presents reflections on the research work undertaken in the COLLAGE project between 2012 and 2015

Defining dimensions in expertise recommender systems for enhancing open collaborative innovation

In open innovation a firm’s R&D crosses not only internal boundaries but disciplines. It is an interactive process of knowledge generation and transfer between internal and external firms. However, the search for an external partner can be time consuming and costly. Open innovation marketplaces broker relationships between seekers and solvers of challenges. Seekers have a problem which they need to solve and solvers are a community of people with the right skills to discover innovative ideas to address them. Despite the assistance of open innovation marketplaces, the process of matching seekers and solvers remains a challenge. It will be argued in this article that expertise recommender systems in an open innovation marketplace can facilitate finding the “right partner” leading to benefits not only for the seeker and the solver but also for the marketplace. With this aim, a list of appropriated dimensions to be considered for the expertise recommender system are defined. An illustrative example is also provided

Identification and search for suitable Open Innovation partners

The main goal of this thesis is to find innovation-partner search approaches. The found approaches will be assessed regarding their suitability in Open Innovation, and then adapted to their implementation into SOI. This will respond to the demands from industry for a methodical support in finding partners for OI. The main questions stated for this research are: How can a firm identify suitable partners for OI collaboration within all SH/people and firms involved in the innovation process? Are there specific approaches for specific type of partner? What are the requirements for an “OI-partner search approach”? How can search approaches be looked for in a methodical system? How can these approaches be assessed regarding OI in order to classify them? How can these OI partner-search approaches be implemented by firms