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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Although it appears increasingly important yet potentially challenging to attract consumers to physical stores, location‐based messaging has been said to enable such attraction. Still, existing studies offer very limited insight into which particular location‐based persuasion approach retailers should use. This study aimed to establish and compare the potential of two discrepant persuasion strategies to influence consumers’ experiences and thereby stimulate them to visit the retailer's physical store. Drawing on persuasion theory and construal level theory, and using a vignette‐based online survey method, we determined that scarcity is a more effective persuasion strategy in the studied context than social proof; scarcity‐focused messages are experienced as more informative, more entertaining and less irritating, are therefore valued more, and are thus more likely to induce store visits. We discuss these findings and their implications for theory as well as for practice.
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Door de transitie naar een biobased en circulaire economie neemt de behoefte aan biomassa als bron van grondstoffen en chemicaliën toe. De teelt van vezelhennep staat daarom opnieuw in de belangstelling vanwege de veelzijdigheid van het gewas. De vezels uit de stengel worden bijvoorbeeld gebruikt voor textieltoepassingen en plantinhoudsstoffen uit de bladeren en bloemen (o.a. Cannabidiol (CBD)) worden gebruikt als voedingssupplement vanwege de gezondheidsbevorderende eigenschappen. Echter, vezelhennep bevat, naast het bekende CBD, nog een veel breder scala aan plantinhoudsstoffen waaraan gezondheidsbevorderende effecten worden toegeschreven. Afhankelijk van het productie/extractieproces en de gebruikte cultivars komen de andere plantinhoudsstoffen in meer of mindere mate in de producten terecht. Vanuit de producenten van vezelhennep extracten is er vraag naar betere karakterisatie van hun extracten en er is behoefte aan meer kennis over de gezondheidseffecten van de extracten zodat de toepasbaarheid vergroot kan worden. Het doel van dit project is dan ook om een relatie te leggen tussen samenstelling aan secundaire plantinhoudsstoffen van verschillende vezelhennepextracten en de gezondheidseffecten van deze extracten. Om dit doel te bereiken zal er onderzoek gedaan worden naar de invloed van verschillende extractiemethodes, cultivars en bewaarmethodes op de samenstelling aan plantinhoudsstoffen en zal een nieuwe methode voor het verkrijgen van plantinhoudsstoffen doorontwikkeld worden. Deze extracten worden vervolgens getest op hun effecten op de humane gezondheid middels een unieke combinatie aan modelsystemen om de relatie te kunnen leggen met specifieke samenstelling. Veroudering is hier als overkoepeld thema gekozen, omdat het in de vergrijzende samenleving steeds relevanter wordt om gezonder oud te worden. Als subthema’s is gekozen voor afweerfunctie, neuro-inflammatie en spierfunctie. De resultaten zullen worden toegepast om nieuwe, beter gekarakteriseerde extracten op de markt te kunnen brengen. Tevens is dit project, door het multidisciplinaire karakter, uitermate geschikt om een hybride leeromgeving te ontwikkelen waarin studenten worden geleerd om multidisciplinair te werken.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
