Organizing entrepreneurial collaboration in small, self-directed teams is gaining popularity. The underlying co-creation processes of developing a shared team vision were analyzed with a core focus on three underlying processes that originate from the shared mental models framework. These processes are: 1) the emergence of individual visions and vision integration, 2) conflict solving, and 3) redesigning the emerging knowledge structure. Key in the analysis is the impact of these three processes on two outcome variables: 1)the perceived strength of the co-creation process, 2) the final team vision. The influence of business expertise and the relationship between personality traits and intellectual synergy was also studied. The impact of the three quality shared mental model (SMM) variables proves to be significant and strong, but indirect. To be effective, individual visions need to be debated during a second conflict phase. Subsequently, redesigning the shared knowledge structure resulting from the conflict solving phase is a key process in a third elaboration phase. This sequence positively influences the experienced strength of the co-creation process, the latter directly enhancing the quality of the final team vision. The indirect effect reveals that in order to be effective, the three SMM processes need to be combined, and that the influence follows a specific path. Furthermore, higher averages as well as a diversity of business expertise enhance the quality of the final team vision. Significant relationships between personality and an intellectual synergy were found. The results offer applicable insights for team learning and group dynamics in developing an entrepreneurial team vision. LinkedIn: https://www.linkedin.com/in/rainer-hensel-phd-8ba44a43/ https://www.linkedin.com/in/ronald-visser-4591034/
Organizations are struggling to choose from or combine the different business process management paradigms offered in today's BPM landscape, such as workflow management, dynamic case management and straight through processing. The field of declarative processes seems to be able to address this challenge by offering a unified approach to business process modeling, providing variable amounts of flow at execution time and different levels of autonomy to the actors based on models using a single formalism. The notion of declarativity in business processes seems to be ill defined and is often treated as a black and white distinction. However, a number of quite different formalisms have been developed that are broadly agreed to be declarative. This paper proposes a number of qualitative characteristics to characterize the declarative nature of process modeling formalisms. The characteristics are evaluated by applying them to a number of relevant process modeling formalisms, both imperative and declarative, and we discuss how these characteristics can be utilized to create business processes that offer activity flows that are known up front where needed, and allow ad hoc approaches to offer experts freedom and to support impediment driven approaches in an STP context.
De markt voor Business Process Management (BPM) software groeit razend snel. Voor 2010 wordt er een marktomvang voorspeld van tussen de 1 tot 6 miljard dollar, dit betekend dat deze markt sinds 2005 meer dan verdubbeld is. BPM krijgt ook in toenemende mate publiciteit in de markt echter dan gaat het veelal om wat BPM nu precies wel en niet is en niet over hoe het toegepast kan worden. Hetzelfde geldt voor BPM software, beter bekend als Business Process Management Systemen (BPMS). Het onderzoek beschreven in dit proefschrift focust op BPMS, het ontstaan, waar het naartoe gaat en wat er allemaal komt kijken bij de invoering en het gebruik ervan. De hoofdonderzoeksvraag in dit proefschrift is: Welke factoren en competenties bepalen het succes van de implementatie van Business Process Management Systemen in een specifieke situatie? Centraal in dit proefschrift staan de volgende onderzoeksvragen: 1. Wat zijn de succes factoren bij de implementatie van Business Process Management Systemen? 2. Welke competenties hebben stakeholders in een Business Process Management Systeem implementatie project nodig? 3. Hoe ziet een Business Process Management Systeem implementatie methodiek eruit welke rekening houdt met de omgevingsfactoren van een organisatie?
MULTIFILE
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
