We examined the entrepreneurial orientation and sustainability orientation, a persistent and conflicting duality, of sustainable entrepreneurs and their evaluation of competing priorities in sustainability decision making. We conducted an exploratory, mixed-method study of 24 sustainable fashion firms and collected data through structured surveys and rich in-depth interviews. Through our inductive and deductive analysis, we derive three sustainability decision making profiles (singular, flexible and holistic) with distinct prioritization logic (nested, ordered and aligned, respectively). We find different configurations of entrepreneurial orientation correspond to the sustainability decision making profiles. We extend the literature by showing how the reflexivity of entrepreneurial orientation interacts with sustainability orientation.
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In this paper we present a method to detect the three dimensional position and orientation of a Wii Remote with one or more emissive spheres attached to it, providing an input device that has six degrees of freedom. Unlike other systems, our system can focus in different directions surrounding the user, with a high precision, and at a low cost. We describe the way object-, motion- and orientation tracking is done, as well as the applicability of the final product. We further describe how to improve the noisy data that is retrieved from the sensors of the Wii Remote, how to smooth detected positions, and how to extrapolate position and orientation.
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Recent marketing literature suggests that brand orientation is an alternative concept for the public health because it is believed to address the shortcomings of market orientation. Brand orientation is specifically of interest to the sector of healthcare, due to the complex nature of the sector. The purpose of the study described in this paper was to find out what the possibilities are of brand orientation for local healthcare providers. The study shows that brand orientation is relevant for physical therapy primary healthcare organizations (PTPHOs), but is not always adopted effectively. PTPHOs are strongly focused on the patient as the only stakeholder. A more powerful option would be to choose a brand positioning strategy for all relevant stakeholders. PTPHOs have to design integrated marketing activities to encourage consumers directly to use our products/services, and to encourage suppliers, distributors, and other key stakeholders to promote our products/services to consumers. In order to safeguard their role and position in the context of community care for the future, the PTPHO is challenged to become more active and more visible, and must collaborate more with other (healthcare) professionals in the community, broaden their services, and focus more on the (future) needs of the citizen.
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Drones have been verified as the camera of 2024 due to the enormous exponential growth in terms of the relevant technologies and applications such as smart agriculture, transportation, inspection, logistics, surveillance and interaction. Therefore, the commercial solutions to deploy drones in different working places have become a crucial demand for companies. Warehouses are one of the most promising industrial domains to utilize drones to automate different operations such as inventory scanning, goods transportation to the delivery lines, area monitoring on demand and so on. On the other hands, deploying drones (or even mobile robots) in such challenging environment needs to enable accurate state estimation in terms of position and orientation to allow autonomous navigation. This is because GPS signals are not available in warehouses due to the obstruction by the closed-sky areas and the signal deflection by structures. Vision-based positioning systems are the most promising techniques to achieve reliable position estimation in indoor environments. This is because of using low-cost sensors (cameras), the utilization of dense environmental features and the possibilities to operate in indoor/outdoor areas. Therefore, this proposal aims to address a crucial question for industrial applications with our industrial partners to explore limitations and develop solutions towards robust state estimation of drones in challenging environments such as warehouses and greenhouses. The results of this project will be used as the baseline to develop other navigation technologies towards full autonomous deployment of drones such as mapping, localization, docking and maneuvering to safely deploy drones in GPS-denied areas.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
