Zoekresultaten

Fashion-as-a-Service

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

Op weg naar een ontdekkingsruimte als overlevingsstrategie

In de snel veranderende detailhandel zien we de opkomst van een nieuw soort winkel. Dit document gaat over winkels waarin zogenaamdeontdekkingsruimten kunnen worden ingericht met meerdere functies en met verkopers die als curator van hun eigen collectie en productaanbod fungeren.

Serendipity and retail

Verbinding als sleutel tot binnenstedelijke kwaliteit

Deze publicatie bevat de belangrijkste bevindingen van een verkenning van de kansen die de inzet van retail en cultuur bieden voor de ontwikkeling van kwaliteit in binnensteden. Een groot aantal gemeenten wordt geconfronteerd met vraagstukken over de herinrichting van zowel hun kernen als de omliggende wijken en gebieden. Tegelijkertijd geldt dat zowel retail als cultuur zich voor belangrijke uitdagingen gesteld zien in het postcorona tijdperk. In ‘Verbinding als sleutel tot stedelijke kwaliteit’ wordt de problematiek uiteengezet, de kansen en mogelijkheden van een ontwerpbenadering toegelicht en een tweetal cases behandeld: Hart van Zuid in Rotterdam en Osdorpplein in Amsterdam.

RFID in Retail: New approaches, new viewpoints

RFID technology is a promising technology currently finding its way into the field of customer interaction strategy, supply chain accuracy and inventory management. Primarily, RFID tags are seen as substitutes of traditional barcodes, yet they can add a lot of value and functionality. Where barcodes require a scanning device to be placed directly in front of the tag to be read, RFID tag readers are able to scan all tags that are in the proximity of the scanner . The next difference is that whereas barcodes usually are the same for all articles of the of the same type (i.e. a jar of peanut butter of brand x), RFID tags will be unique for each individual product occurrence. This opens up the possibility of tracking the entire history of a specific occurrence of a product. Moreover, due to the nature of the scanning technology, it suddenly becomes achievable for manufacturers to track individual products through all stages of production and base inventory management and front office planning on real-time data at item level from production facilities.

Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web

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%.