Time-access regulations and vehicle restrictions are increasingly used, especially in western Europe, to improve social sustainability in urban areas. These regulations considerably affect the distribution process of retail chain organizations as well as the environmental burden. This paper studies the impact of governmental time windows, vehicle restrictions, and different retailers' logistical concepts on the financial and environmental performance of retailers. We use a case study with two cases that differ in their drop sizes as input for an experiment. The retailers provided all organizational, flow, and cost data of the distribution process between their distribution centers and their stores. We use these data to calculate the impacts of different scenarios on the retailers' financial and environmental performances based on a fractional factorial design in which urban policies and the retailers' logistical concepts are varied, using vehicle routing software. We test the propositions with a third case. We show that the cost impact of time windows is the largest for retailers who combine many deliveries in one vehicle round-trip. The cost increase due to vehicle restrictions is the largest for retailers whose round-trip lengths are restricted by vehicle capacity. Vehicle restrictions and time windows together do not increase a retailer's cost more than individually. Variations in delivery volume and store dispersion hardly influence the impact of urban policy and the retailer's logistical concept decisions. © 2009 INFORMS.
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Background and aim Self-management support is an integral part of current chronic care guidelines. The success of self-management interventions varies between individual patients, suggesting a need for tailored self-management support. Understanding the role of patient factors in the current decision making of health professionals can support future tailoring of self-management interventions. The aim of this study is to identify the relative importance of patient factors in health professionals’ decision making regarding self-management support. Method A factorial survey was presented to primary care physicians and nurses. The survey consisted of clinical vignettes (case descriptions), in which 11 patient factors were systematically varied. Each care provider received a set of 12 vignettes. For each vignette, they decided whether they would give this patient self-management support and whether they expected this support to be successful. The associations between respondent decisions and patient factors were explored using ordered logit regression. Results The survey was completed by 60 general practitioners and 80 nurses. Self-management support was unlikely to be provided in a third of the vignettes. The most important patient factor in the decision to provide self-management support as well as in the expectation that self-management support would be successful was motivation, followed by patient-provider relationship and illness perception. Other factors, such as depression or anxiety, education level, self-efficacy and social support, had a small impact on decisions. Disease, disease severity, knowledge of disease, and age were relatively unimportant factors. Conclusion This is the first study to explore the relative importance of patient factors in decision making and the expectations regarding the provision of self-management support to chronic disease patients. By far, the most important factor considered was patient’s motivation; unmotivated patients were less likely to receive self-management support. Future tailored interventions should incorporate strategies to enhance motivation in unmotivated patients. Furthermore, care providers should be better equipped to promote motivational change in their patients.
To benefit from the social capabilities of a robot math tutor, instead of being distracted by them, a novel approach is needed where the math task and the robot's social behaviors are better intertwined. We present concrete design specifications of how children can practice math via a personal conversation with a social robot and how the robot can scaffold instructions. We evaluated the designs with a three-session experimental user study (n = 130, 8-11 y.o.). Participants got better at math over time when the robot scaffolded instructions. Furthermore, the robot felt more as a friend when it personalized the conversation.
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