Co-creation as a concept and process has been prominent in both marketing and design research over the past ten years. Referring respectively to the active collaboration of firms with their stakeholders in value creation, or to the participation of design users in the design research process, there has arguably been little common discourse between these academic disciplines. This article seeks to redress this deficiency by connecting marketing and design research together—and particularly the concepts of co-creation and co-design—to advance theory and broaden the scope of applied research into the topic. It does this by elaborating the notion of the pop-up store as temporary place of consumer/user engagement, to build common ground for theory and experimentation in terms of allowing marketers insight into what is meaningful to consumers and in terms of facilitating co-design. The article describes two case studies, which outline how this can occur and concludes by proposing principles and an agenda for future marketing/design pop-up research. This is the peer reviewed version of the following article: Overdiek A. & Warnaby G. (2020), "Co-creation and co-design in pop-up stores: the intersection of marketing and design research?", Creativity & Innovation Management, Vol. 29, Issue S1, pp. 63-74, which has been published in final form at https://doi.org/10.1111/caim.12373. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. LinkedIn: https://nl.linkedin.com/in/overdiek12345
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This project builds upon a collaboration which has been established since 15 years in the field of social work between teachers and lecturers of Zuyd University, HU University and Elte University. Another network joining this project was CARe Europe, an NGO aimed at improving community care throughout Europe. Before the start of the project already HU University, Tallinn Mental Health Centre and Kwintes were participating in this network. In the course of several international meetings (e.g. CARe Europe conference in Prague in 2005, ENSACT conferences in Dubrovnik in 2009, and Brussels in April 2011, ESN conference in Brussels in March 2011), and many local meetings, it became clear that professionals in the social sector have difficulties to change current practices. There is a great need to develop new methods, which professionals can use to create community care.
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This paper analyses co-creation in urban living labs through a multi-level network perspective on system innovation. We draw on the case House of Skills, a large, multi-stakeholder living lab aimed at developing a ‘skills-based’ approach towards labour market innovation within the Amsterdam Metropolitan Region. Ouranalysis helps understand stakeholder dynamics towards system innovation, drawing on an innovative living lab example and taking into consideration the multi-layered structures that comprise the collaboration. Our conceptual framework provides an important theoretical contribution to innovation studies and offers a practical repertoire that can help practitioners improve co-creation of shared value in living labs, towards orchestrating flexible structures that strengthen the impact of their initiatives.
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Active participation of stakeholders in health research practice is important to generate societal impact of outcomes, as innovations will more likely be implemented and disseminated in clinical practice. To foster a co-creative process, numerous frameworks and tools are available. As they originate from different professions, it is not evident that health researchers are aware of these tools, or able to select and use them in a meaningful way. This article describes the bottom-up development process of a compass and presents the final outcome. This Co-creation Impact Compass combines a well-known business model with tools from design thinking that promote active participation by all relevant stakeholders. It aims to support healthcare researchers to select helpful and valid co-creation tools for the right purpose and at the right moment. Using the Co-creation Impact Compass might increase the researchers’ understanding of the value of co-creation, and it provides help to engage stakeholders in all phases of a research project.
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The current COVID-19 pandemic confines people to their homes, disrupting the fragile social fabric of deprived neighbourhoods and citizen’s participation options. In deprived neighbourhoods, community engagement is central in building community resilience, an important resource for health and a prerequisite for effective health promotion programmes. It provides access to vulnerable groups and helps understand experiences, assets, needs and problems of citizens. Most importantly, community activities, including social support, primary care or improving urban space, enhance health through empowerment, strengthened social networks, mutual respect and providing a sense of purpose and meaning. In the context of inequalities associated with COVID-19, these aspects are crucial for citizens of deprived neighbourhoods who often feel their needs and priorities are ignored. In this perspectives paper, illustrated by a varied overview of community actions in the UK and The Netherlands, we demonstrate how citizens, communities and organizations may build resilience and community power. Based on in-depth discussion among the authors we distilled six features of community actions: increase in mutual aid and neighbourhood ties, the central role of community-based organizations (CBOs), changing patterns of volunteering, use of digital media and health promotion opportunities. We argue that in order to enable and sustain resilient and confident, ‘disaster-proof’, communities, areas which merit investment include supporting active citizens, new (digital) ways of community engagement, transforming formal organizations, alignment with the (local) context and applying knowledge in the field of health promotion in new ways, focussing on learning and co-creation with citizen initiatives.
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Ageing-in-place is the preferred way of living for older individuals in an ageing society. It can be facilitated through architectural and technological solutions in the home environment. Dementia poses additional challenges when designing, constructing, or retrofitting housing facilities that support ageing-in-place. Older adults with dementia and their partners ask for living environments that support independence, compensate for declining and vitality, and lower the burden of family care. This study reports the design process of a demonstration home for people with dementia through performing a literature review and focus group sessions. This design incorporates modifications in terms of architecture, interior design, the indoor environment, and technological solutions. Current design guidelines are frequently based on small-scale studies, and, therefore, more systematic field research should be performed to provide further evidence for the efficacy of solutions. The dwellings of people with dementia are used to investigate the many aspects of supportive living environments for older adults with dementia and as educational and training settings for professionals from the fields of nursing, construction, and building services engineering.
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In the Netherlands, client and family participation in care for people with intellectual disabilities has been in vogue for a long time, and increasingly receives attention (KPMG and Vilans 2017). However, the perspective and experiential knowledge of service users and relatives is often still insuBiciently used for the co-creation of care. The professional perspective is often still dominant. In addition, professionals mainly focus on clients and less on relatives, even though relatives often play an important role in the client’s (quality of) life (Wiersma 2017). The project ‘Inclusive Collaboration in Disability Care’[1] (ICDC) focusses on enhancing equal communication between people with intellectual disabilities, their relatives, and professional caregivers, and hence contributes to redressing power imbalances in longterm care. It investigates the question: “How can the triangle of client, relative and professional caregiver together co-create better care and support?”.
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Immersive technologies are redefining and revolutionizing the staging of experiences and co-creation of value, implicating the management of customer experiences. However, limited studies have looked at the role of immersive technologies as part of the customer experience management (CXM) process. Incorporating the concepts of experience economy and value co-creation, this study proposes a dynamic CXM framework that highlights the emerging field of immersive technologies like augmented and virtual reality as part of business and marketing research. The framework acts as a guide for researchers and industry practitioners to initiate immersive technology ventures that are rooted in the co-creation and management of customer experiences
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Population ageing has become a domain of international discussions and research throughout the spectrum of disciplines including housing, urban planning, and real estate. Older people are encouraged to continue living in their homes in their familiar environment, and this is referred to as “ageing-in-place”. Enabling one to age-in-place requires new housing arrangements that facilitate and enable older adults to live comfortably into old age, preferably with others. Innovative examples are provided from a Dutch social housing association, illustrating a new approach to environmental design that focuses more on building new communities in conjunction with the building itself, as opposed to the occupational therapeutic approaches and environmental support. Transformation projects, referred to as “Second Youth Experiments”, are conducted using the Røring method, which is based on the principles of co-creation. De Benring in Voorst, The Netherlands, is provided as a case study of an innovative transformation project. This project shows how social and technological innovations can be integrated in the retrofitting of existing real estate for older people. It leads to a flexible use of the real estate, which makes the building system- and customer preference proof. Original article at: https://doi.org/10.3390/buildings8070089 © 2018 by the authors. Licensee MDPI.
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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