The main purpose of this dissertation is to identify the factors that explain success and failure in SME business transfers. Three key concepts have been defined in the research framework: firm resources, capabilities (of predecessor and successor) and (successor’s) strategic renewal. Altogether these three key concepts serve as predictors for the transfer outcomes: exit choice, transfer duration, obtained price, satisfaction and the post-transfer firm performance. Testing reveals that both firm resources and owner capabilities are of importance for exit choice. Results indicate further that especially “acquisition experience” and “years of ownership” predict the exit choice in well performing firms. In poorly performing firms, firm resources prevail as the predictors for exit choice. Most consistently, owner capabilities like “familiarity with the successor” and “flexibility” and not firm resources predict success during a transfer. The firm resource “succession planning” predicts only the level of satisfaction with the transfer. Regarding owner capabilities, a distinction is made between generic and specific human capital. Results indicate the importance of specific human capital (owner competencies and experience) rather than generic human capital (level of education). All types of renewal (i.e. product/market innovation, organizational change or a combination of the two) after succession show better post-transfer firm performance compared to no changes in the first two years.
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Purpose: To gain a rich understanding of the experiences and opinions of patients, healthcare professionals, and policymakers regarding the design of OGR with structure, process, environment, and outcome components. Methods: Qualitative research based on the constructive grounded theory approach is performed. Semi-structured interviews were conducted with patients who received OGR (n=13), two focus groups with healthcare professionals (n=13), and one focus group with policymakers (n=4). The Post-acute Care Rehabilitation quality framework was used as a theoretical background in all research steps. Results: The data analysis of all perspectives resulted in seven themes: the outcome of OGR focuses on the patient’s independence and regaining control over their functioning at home. Essential process elements are a patient-oriented network, a well-coordinated dedicated team at home, and blended eHealth applications. Additionally, closer cooperation in integrated care and refinement regarding financial, time-management, and technological challenges is needed with implementation into a permanent structure. All steps should be influenced by the stimulating aspect of the physical and social rehabilitation environment. Conclusion: The three perspectives generally complement each other to regain patients’ quality of life and autonomy. This study demonstrates an overview of the building blocks that can be used in developing and designing an OGR trajectory.
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This literature review explores ways older workers might continue to make waves and impact their work organization. The topic of the paper is grounded in the problem of an ageing organizational population looming in the near future. The work presented here is a start to helping management in knowledge-intensive organizations to understand how to effectively utilize the capacities of older knowledge workers by stimulating intergenerational learning as a means to retain critical organizational knowledge, encourage innovation and promote organizational learning through knowledge building. First, the concept of intergenerational learning is developed followed by a discussion of the organizational factors important for it to take place. The last section presents ideas on how to design and implement intergenerational learning as an organizational development program.
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In the past decades, we have faced an increase in the digitization, digitalization, and digital transformation of our work and daily life. Breakthroughs of digital technologies in fields such as artificial intelligence, telecommunications, and data science bring solutions for large societal questions but also pose a new challenge: how to equip our (future)workforce with the necessary digital skills, knowledge, and mindset to respond to and drive digital transformation?Developing and supporting our human capital is paramount and failure to do so may leave us behind on individual (digital divide), organizational (economic disadvantages), and societal level (failure in addressing grand societal challenges). Digital transformation necessitates continuous learning approaches and scaffolding of interdisciplinary collaboration and innovation practices that match complex real-world problems. Research and industry have advocated for setting up learning communities as a space in which (future) professionals of different backgrounds can work, learn, and innovate together. However, insights into how and under which circumstances learning communities contribute to accelerated learning and innovation for digital transformation are lacking. In this project, we will study 13 existing and developing learning communities that work on challenges related to digital transformation to understand their working mechanisms. We will develop a wide variety of methods and tools to support learning communities and integrate these in a Learning Communities Incubator. These insights, methods and tools will result in more effective learning communities that will eventually (a) increase the potential of human capital to innovate and (b) accelerate the innovation for digital transformation
The demand for mobile agents in industrial environments to perform various tasks is growing tremendously in recent years. However, changing environments, security considerations and robustness against failure are major persistent challenges autonomous agents have to face when operating alongside other mobile agents. Currently, such problems remain largely unsolved. Collaborative multi-platform Cyber- Physical-Systems (CPSs) in which different agents flexibly contribute with their relative equipment and capabilities forming a symbiotic network solving multiple objectives simultaneously are highly desirable. Our proposed SMART-AGENTS platform will enable flexibility and modularity providing multi-objective solutions, demonstrated in two industrial domains: logistics (cycle-counting in warehouses) and agriculture (pest and disease identification in greenhouses). Aerial vehicles are limited in their computational power due to weight limitations but offer large mobility to provide access to otherwise unreachable places and an “eagle eye” to inform about terrain, obstacles by taking pictures and videos. Specialized autonomous agents carrying optical sensors will enable disease classification and product recognition improving green- and warehouse productivity. Newly developed micro-electromechanical systems (MEMS) sensor arrays will create 3D flow-based images of surroundings even in dark and hazy conditions contributing to the multi-sensor system, including cameras, wireless signatures and magnetic field information shared among the symbiotic fleet. Integration of mobile systems, such as smart phones, which are not explicitly controlled, will provide valuable information about human as well as equipment movement in the environment by generating data from relative positioning sensors, such as wireless and magnetic signatures. Newly developed algorithms will enable robust autonomous navigation and control of the fleet in dynamic environments incorporating the multi-sensor data generated by the variety of mobile actors. The proposed SMART-AGENTS platform will use real-time 5G communication and edge computing providing new organizational structures to cope with scalability and integration of multiple devices/agents. It will enable a symbiosis of the complementary CPSs using a combination of equipment yielding efficiency and versatility of operation.
