Professionals' willingness to change is a necessity for successful implementation of changes in the organisation. This study focused on the influence of a transformational leadership style on professionals' willingness to change. This multiple case study was performed in three project management organisations that had recently implemented a new business information system. The research data were obtained through both qualitative and quantitative data collection. The qualitative investigation revealed that through leading by good example a manager has a positive influence on their employees' willingness to change. However, the quantitative investigation showed that there is no relationship between transformational leadership and the motivational factors of willingness to change. Finally, the study showed that the most important factors of employees' willingness to change are timing, involvement, emotions, necessity, and added value.
Professionals' willingness to change is a necessity for successful implementation of changes in the organisation. This study focused on the influence of a transformational leadership style on professionals' willingness to change. This multiple case study was performed in three project management organisations that had recently implemented a new business information system. The research data were obtained through both qualitative and quantitative data collection. The qualitative investigation revealed that through leading by good example a manager has a positive influence on their employees' willingness to change. However, the quantitative investigation showed that there is no relationship between transformational leadership and the motivational factors of willingness to change. Finally, the study showed that the most important factors of employees' willingness to change are timing, involvement, emotions, necessity, and added value
The Centre of Research in Knowledge Organisations and Knowledge Management of Zuyd University has developed a knowledge management scan. The scan initiates from two models. The first model is based on the Value Based Knowledge Management approach (Tissen, Andriessen & Lekanne Deprez, 1998) and includes 6 basic abilities of a knowledge-intensive organisation that will enable the organisation to operate successfully in a knowledge based economy (.The second model, developed by Wierdsma and Swieringa (2002), categorises organisations according to their level of learning that is to say, how it develops a specific learning ability. Both models are briefly reviewed within this paper. This knowledge management scan is a tool that enables an organisation to assess the development of its six basic abilities. Once the organisation has a clear insight into its own abilities, it will be able to strengthen its overall learning ability and improve the organisations’ competitive position. Additionally we take a close look at our research approach for developing and implementing the knowledge management scan. The scan encompasses 15 statements per ability (90 statements in total). The complete scan will be assessed on a five-point scale by a representative group of selected employees and managers of an organization, supervised by a researcher/consultant. During the analysis of the results and the presentation of recommendations, specific attention is paid to those statements that achieve high and low scores respectively (invitation to implement improvement actions) and statements that have a relatively high spread across a broad range (differences of opinion or the statement is open to different interpretations). In particular we have examined how the knowledge management scan was put into practice in one of the departments of Zuyd University. After a short summary of the organisation's initial situation, we discuss subsequent steps taken during the assessment, analysis and the advisory process. This paragraph is followed by a concise summary of the results generated by the scan. Finally we offer the recommendations and subsequent steps to be taken to implement these advices in the near future.
The Northern Netherlands (NN) finds itself at the junction of all the big transitions. Digitalisation is essential to follow through with these. Considering this, our region has the potential to make sizeable progress if it can successfully roll out widespread digitalisation. As a hardcore transition economy, the NN may even join the European frontrunners and act as an example for other regions. It is from this challenge that the NN will start with the European Digital Innovation Hub (EDIH NN). We have chosen to specialise in the area of Autonomous Systems, which includes multiple digital technologies that are relevant for the four transitions in the NN: (1) Smart Agro, (2) Smart Manufacturing, (3) Life Science and Health and (4) Utilities, Built Environment and Mobility. In the first three-year EDIH NN wants to support more than 750 companies and lay the foundation for long-term support of all companies. The following building blocks for EDIH NN are: • A Brokerage network that will identify issues regarding digitalisation and relay these to Solution Providers (high TRL) and knowledge providers (low TRL). • A Test Before Invest network (test and demo facilities) comprising 20+ organisations that will invest in Autonomous Systems within their domain, and collaborate towards becoming a European testing ground. • A Smart Factory Accelerator to strengthen the digital maturity of companies. • An Empowerment programme to strengthen companies in the areas of DEP Technologies: Cyber Security and Artificial Intelligence. • An approach based on High Performance Computing to make digitalisation more accessible. • The Smart Makers Academy: A programme aimed at matching supply and demand around digital skills, based on individual learning outcomes. • A Funding Readiness programme to help companies that need to invest for their digitalisation strategy, in finding funding opportunities. • A network to stimulate supply and demand around Autonomous Systems
Organisations are increasingly embedding Artificial Intelligence (AI) techniques and tools in their processes. Typical examples are generative AI for images, videos, text, and classification tasks commonly used, for example, in medical applications and industry. One danger of the proliferation of AI systems is the focus on the performance of AI models, neglecting important aspects such as fairness and sustainability. For example, an organisation might be tempted to use a model with better global performance, even if it works poorly for specific vulnerable groups. The same logic can be applied to high-performance models that require a significant amount of energy for training and usage. At the same time, many organisations recognise the need for responsible AI development that balances performance with fairness and sustainability. This KIEM project proposal aims to develop a tool that can be employed by organizations that develop and implement AI systems and aim to do so more responsibly. Through visual aiding and data visualisation, the tool facilitates making these trade-offs. By showing what these values mean in practice, which choices could be made and highlighting the relationship with performance, we aspire to educate users on how the use of different metrics impacts the decisions made by the model and its wider consequences, such as energy consumption or fairness-related harms. This tool is meant to facilitate conversation between developers, product owners and project leaders to assist them in making their choices more explicit and responsible.
