A new approach to collaborative creativity support of new product designers

Bitter-Rijpkema, M., Sloep, P. B., Sie, R., Van Rosmalen, P., Retalis, S., & Katsamani, M. (2011). A new approach to collaborative creativity support of new product designers. International Journal of Web Based Communities, 7(4), 478-492. DOI: 10.1504/IJWBC.2011.042992Effective collaborative creativity is crucial to contemporary professionals who have to continuously produce innovative products and services. The technological nature and complexity of the innovations require team work, among specialists from different disciplines. Often these teams work in a distributed fashion, across boundaries of time and place. Therefore they need electronic “spaces” that support (‘afford’) their creative collaboration. Co-creativity support is not only a matter of making appropriate groupware spaces available but also of providing concurrent support in all these dimensions. These considerations inspired the development of the idSpace platform. idSpace is a collaboration platform integrating a variety of creativity tools with pedagogy-based guidance. It aims to optimize both the use of creativity techniques themselves and of the supporting processes of team collaboration and knowledge creation. In this paper we zoom in on Knowledge-sharing Strategies for Collaborative Creativity (KS4CC). We show how collaborative creativity can be enhanced via integration of pattern-based pedagogical flow support, including suggestions of optimal use of creativity techniques. The KS4CC strategies consist of a merger of learning and collaboration flow patterns with support for the application of creative techniques

Applying the positioning phase of the digital transformation model in practice for SMEs: toward systematic development of digitalization

Digital transformation (DT) refers to the changes in ways of working and business offering caused by adoption of digital technologies in an organization. Small and medium-sized enterprises (SMEs) are struggling with this transformation because of their limited resources and know-how. Thus, SMEs need practical grassroots-level help for DT that allows the companies to analyze where they stand in digitalization, and how they should proceed. This article discusses how SMEs can be supported in their DT by utilizing the DT model consisting of four consecutive phases for supporting companies’ systematic development of digitalization. The article focuses on the first phase of the DT model, positioning, where company’s digitalization status is analyzed in detail, and development ideas are identified. The positioning phase was conducted for 19 SMEs in Northern Ostrobothnia, Finland. The results indicate that the used process and tools were suitable to support SMEs for analyzing their digitalization status and identifying areas for improvement. The DT model and piloted tools have been published as a free-of-charge ApuaDigiin.fi online service to facilitate their widespread use in the future. In this way, public regional business development authors or research organizations can utilize the online service while supporting the digitalization of SMEs

Practical Theory Extension in Event-B

Abstract. The Rodin tool for Event-B supports formal modelling and proof using a mathematical language that is based on predicate logic and set theory. Although Rodin has in-built support for a rich set of operators and proof rules, for some application areas there may be a need to extend the set of operators and proof rules supported by the tool. This paper outlines a new feature of the Rodin tool, the theory component, that allows users to extend the mathematical language supported by the tool. Using theories, Rodin users may define new data types and polymorphic operators in a systematic and practical way. Theories also allow users to extend the proof capabilities of Rodin by defining new proof rules that get incorporated into the proof mechanisms. Soundness of new definitions and rules is provided through validity proof obligations.

The Definition of Intelligent Computer Aided Software Engineering (I-CASE) Tools

The growing complexity of the software systems being developed and the use of different methodologies indicate the need for more computer support for automating software development process and evolution activity. Currently, Computer-Aided Software Engineering (CASE), which is a set of software systems aimed to support set of software process activities, does this automation. While CASE tools prove its importance to develop high quality software, unfortunately CASE tools doesn’t cover all software development activities. This is because some activities need intellectual human skills, which are not currently available as computer software. To solve this shortcoming, Artificial Intelligence (AI) approaches are the ones that can be used to develop software tools imitating these intellectual skills. This paper presents the definition of Intelligent Computer Aided Software Engineering (I-CASE). The definition encompasses two steps. The first step is a clear decomposition of each basic software development activity to sub activities, and classify each one of them whether it is an intellectual or procedural job. The second step is the addressing of each intellectual (un-automated) one to proper AI-based approach. These tools may be integrated into a package as an Integrated Development Environment (IDE) or could be used individually. The discussion and the next implementation step are reported. Keywords: Software Engineering, CASE tools, Artificial Intelligenc

Real-time decision support in online training environments

Multi-agency crisis management represents one of the most complex real-world situations, requiring rapid negotiation and decision-making under extreme pressure. However, the training provided to Gold Commanders (strategic planners), typically lacks the stress of a real crisis and research tells us that behaviour and decision-making are significantly affected by stress. It is therefore vital that training puts trainees under the pressure of a real crisis situation as far as is possible. The Pandora+ system has been developed to provide a realistic, immersive, augmented reality training environment in which the stress of each individual trainee can be managed by the trainer, during a training event, with the support of system intelligence. The system uses AI planning techniques to manage an unfolding crisis scenario, modelled as an event network which can be dynamically updated by the trainer during a training event. This modelling includes points of decision for trainees managed by automated rules from a knowledge base, behavioural modelling of the trainees, and ambient management of the environment to provide affective inputs to control and manage trainee stress. In this context, the system controls and reacts to trainee performance in relation to the events and decision points and can dynamically remodel and reconfigure the event network to respond appropriately to trainee decisions. The environment can also represent any missing trainees within the scenario and has the potential to provide training in any domain where an unfolding scenario of events are required for training

Foundations for knowledge advancement and relevance to practice

Madureira, L., Popovic, A., & Castelli, M. (2023). Competitive Intelligence Empirical Validation and Application: Foundations for Knowledge Advancement and Relevance to Practice. Journal of Information Science. https://doi.org/10.1177/01655515231191221--- The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - IDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMSThe competitive intelligence (CI) construct must be scientifically defined, characterised, empirically validated and accurately measured to grow in science and business. This study aims at elevating the accuracy of the empirical validation of the CI construct suggested and confirmed by Madureira, Popovic and Castelli to serve as the scientific foundation for CI praxis. This construct is selected due to its unmatched recency, thoroughness, universality identified limitations of its empirical validation. We relied on a multistrand design of fully sequential with equivalent status qualitative and quantitative mix-methods followed by the triangulation of the findings and the development of the meta-inferences. Validity, reliability and applicability were tested using computer-aided text analysis and artificial intelligence methods based on 61 in-depth interviews with CI subject matter experts. Contributions to knowledge advancement and relevance to practice derive from the scientific-grade empirical construct validation, providing undisputed levels of accuracy, consistency, applicability, and triangulation of results. This study highlights three critical implications. First, the delimitation of the body of knowledge and recognition of the CI domain serve as the baseline for theory development. Second, the validated construct guarantees reproducibility, replicability and generalisability, laying the foundations for establishing CI science, practice and education. Third, creating a common language and shared understanding will drive the much-claimed definitional consensus. This study thus stands as a foundational pillar in supporting CI praxis in improving decision-making quality and the performance of organisations.publishersversionepub_ahead_of_prin

Development of Artificial Intelligence Systems in terms of People-Process-Data-Technology (2PDT)

Background: Artificial Intelligence (AI) has become a ubiquitous phenomenon in recent times, with most organizations today attempting to maneuver their way around developing AI systems with the aim of improving the products and services they provide. However, what complicates developing AI systems is the paucity in frameworks to support organizations with AI System Development (SD). As a result, many organizations are using existing approaches which have been previously applied in earlier emerging Information Technology endeavors. This study explored how a framework can promote effective organizational AI SD. To achieve this a holistic framework for AI SD was conceptualized and examined from an organizational perspective. Method: This study has examined the conceptualized framework People-Process-Data-Technology (2PDT) in AI, through a case study research design. The empirical data was analyzed based on 12 case studies within Australia including 39 interviews with AI experts. We have applied thematic analysis to investigate requirements of organizational AI SD. Results: The results demonstrate that organizations are challenged by key factors, which inhibits their ability to effectively develop AI systems. For example, organizations are not achieving successful delivery of AI systems due to a lack of required skills. Additionally, a plethora of AI technology which is constantly evolving, poor data quality, and the paucity of AI SD frameworks are all contributing to unpredictable delivery outcomes. Conclusion: This paper investigated requirements for effective organizational AI SD by examining the 2PDT framework. The results contribute to AI phenomenon by developing the requirements for AI SD, in terms of people, process, data and technology. It contributes to theory by evaluating and developing AI requirements for effective AI systems. The examination of the framework and case study approach added valuable knowledge to the AI domain. In addition, we contributed to practice by identifying requirements that organizations should consider in achieving better AI SD outcomes