This article seeks to contribute to the literature on circular business model innovation in fashion retail. Our research question is which ‘model’—or combination of models—would be ideal as a business case crafting multiple value creation in small fashion retail. We focus on a qualitative, single in-depth case study—pop-up store KLEER—that we operated for a duration of three months in the Autumn of 2020. The shop served as a ‘testlab’ for action research to experiment with different business models around buying, swapping, and borrowing second-hand clothing. Adopting the Business Model Template (BMT) as a conceptual lens, we undertook a sensory ethnography which led to disclose three key strategies for circular business model innovation in fashion retail: Fashion-as-a-Service (F-a-a-S) instead of Product-as-a-Service (P-a-a-S) (1), Place-based value proposition (2) and Community as co-creator (3). Drawing on these findings, we reflect on ethnography in the context of a real pop-up store as methodological approach for business model experimentation. As a practical implication, we propose a tailor-made BMT for sustainable SME fashion retailers. Poldner K, Overdiek A, Evangelista A. Fashion-as-a-Service: Circular Business Model Innovation in Retail. Sustainability. 2022; 14(20):13273. https://doi.org/10.3390/su142013273
DOCUMENT
Retail industry consists of the establishment of selling consumer goods (i.e. technology, pharmaceuticals, food and beverages, apparels and accessories, home improvement etc.) and services (i.e. specialty and movies) to customers through multiple channels of distribution including both the traditional brickand-mortar and online retailing. Managing corporate reputation of retail companies is crucial as it has many advantages, for instance, it has been proven to impact generated revenues (Wang et al., 2016). But, in order to be able to manage corporate reputation, one has to be able to measure it, or, nowadays even better, listen to relevant social signals that are out there on the public web. One of the most extensive and widely used frameworks for measuring corporate reputation is through conducting elaborated surveys with respective stakeholders (Fombrun et al., 2015). This approach is valuable but deemed to be laborious and resource-heavy and will not allow to generate automatic alerts and quick and live insights that are extremely needed in this era of internet. For these purposes a social listening approach is needed that can be tailored to online data such as consumer reviews as the main data source. Online review datasets are a form of electronic Word-of-Mouth (WOM) that, when a data source is picked that is relevant to retail, commonly contain relevant information about customers’ perceptions regarding products (Pookulangara, 2011) and that are massively available. The algorithm that we have built in our application provides retailers with reputation scores for all variables that are deemed to be relevant to retail in the model of Fombrun et al. (2015). Examples of such variables for products and services are high quality, good value, stands behind, and meets customer needs. We propose a new set of subvariables with which these variables can be operationalized for retail in particular. Scores are being calculated using proportions of positive opinion pairs such as <fast, delivery> or <rude, staff> that have been designed per variable. With these important insights extracted, companies can act accordingly and proceed to improve their corporate reputation. It is important to emphasize that, once the design is complete and implemented, all processing can be performed completely automatic and unsupervised. The application makes use of a state of the art aspect-based sentiment analysis (ABSA) framework because of ABSA’s ability to generate sentiment scores for all relevant variables and aspects. Since most online data is in open form and we deliberately want to avoid labelling any data by human experts, the unsupervised aspectator algorithm has been picked. It employs a lexicon to calculate sentiment scores and uses syntactic dependency paths to discover candidate aspects (Bancken et al., 2014). We have applied our approach to a large number of online review datasets that we sampled from a list of 50 top global retailers according to National Retail Federation (2020), including both offline and online operation, and that we scraped from trustpilot, a public website that is well-known to retailers. The algorithm has carefully been evaluated by manually annotating a randomly sampled subset of the datasets for validation purposes by two independent annotators. The Kappa’s score on this subset was 80%.
MULTIFILE
Design educators and industry partners are critical knowledge managers and co-drivers of change, and design graduate and post-graduate students can act as catalysts for new ideas, energy, and perspectives. In this article, we will explore how design advances industry development through the lens of a longitudinal inquiry into activities carried out as part of a Dutch design faculty-industry collaboration. We analyze seventy-five (75) Master of Science (MSc) thesis outcomes and seven (7) Doctorate (PhD) thesis outcomes (five in progress) to identify ways that design activities have influenced advances in the Dutch aviation industry over time. Based on these findings, we then introduce an Industry Design Framework, which organizes the industry/design relationship as a three-layered system. This novel approach to engaging industry in design research and design education has immediate practical value and theoretical significance, both in the present and for future research. https://doi.org/10.1016/j.sheji.2019.07.003 LinkedIn: https://www.linkedin.com/in/christine-de-lille-8039372/
MULTIFILE
We produceren en consumeren meer mode dan we nodig hebben, met te veel impact op mens en milieu. Mode aankopen zijn vaak impulsief en worden ter plekke, in de winkel besloten. Daar ligt dus een kans, maar wanneer gaan we als consument vaker voor duurzame mode kiezen, en hoe kunnen duurzame mode retailers ons daartoe verleiden?
Cities, the living place of 75% of European population, are crucial for sustainable transition in a just society. Therefore, the EU has launched a Mission for 100 Climate-Neutral Smart Cities (100CNSC). Construction is a key industry in making cities more sustainable. Currently, construction consumes 50% resources, uses 40% energy, and emits 36% greenhouse gasses. The sector is not cost-efficient, not human-friendly, and not healthy – it is negatively known for “3D: dirty, dangerous, demanding”. As such, the construction sector is not attractive for educated and skilled young professionals that are needed for the sustainable transition and for resolving the housing crisis. In contrast with the non-circular designs, materials and techniques that are still common in the construction industry, some other industries and fields have cultivated higher standards for sustainable products, especially in clean and efficient assembly and disassembly. Examples can be found in the maritime and off-shore industry, smart manufacturing, small electronics, and retail. The Hague University of Applied Sciences (THUAS) aims to become the leader of a strong European consortium for preliminary research to develop knowledge that is needed for the upcoming Horizon Europe proposal (within Cluster 4, Destination 1 - Re-manufacturing and De-manufacturing technologies) in relation with the EU Mission 100CNSC. The goals of this preliminary research are: (a) to articulate new concepts that will become an input for a new research proposal and (b) to organize a high-quality European consortium with high-quality partners for a lasting collaboration. This preliminary research project focuses on the question: How can the construction sector adopt and adapt the best practices in assembly and disassembly from other industries –including maritime, manufacturing and retails– in order to enhance circular urban construction and renovation with an active involvement of educated and skilled young professionals?
The hospitality industry in the Netherlands has been slow to adopt artificial intelligence (AI), despite its potential to improve service efficiency and address workforce challenges. While some industries have embraced AI agents—automated systems interacting with users—for customer service, hospitality adoption remains limited. Many hotels struggle to integrate AI in ways that enhance guest experiences while ensuring workforce sustainability, a paradox. Workforce sustainability means keeping employees skilled and adaptable. This research addresses this paradox observed in professional practice, focusing on three key gaps in AI integration: • Hotel employees lack the skills and knowledge to adapt to AI-enhanced workplaces. • Hotel managers lack clear AI strategies that maintain service quality and employee well-being, ensuring AI complements rather than replaces human service. • AI developers often lack a clear understanding of the hospitality industry’s specific needs, hindering the development of effective solutions. This leads to the central question: How can AI agents be co-developed by hotel professionals and technical experts to enhance service efficiency while supporting a sustainable hospitality workforce? A one-year KIEM project provides the ideal framework for an agile, practice-based investigation in real hospitality environments. The project will unfold in four phases: (1) co-developing conversational AI chatbots with hotel businesses and technology providers, (2) testing the chatbot integration in hotels, (3) evaluating the impact on service efficiency and workforce sustainability, and (4) initiating a community of AI agent practice in service industry. Conducted in collaboration with industry partners, the research ensures findings are directly applicable to real-world hospitality challenges. By bridging academic research and industry needs, this project will generate insights into AI-driven service innovations that benefit hotel operations, employees, and AI developers. Beyond hospitality, its findings will offer scalable strategies for responsible AI adoption in sectors like healthcare, banking, and retail, fostering a more sustainable future of work.
