Local online retail platforms (LORPs) are gaining popularity as digital channels that can increase physical retail agglomerations’ attractiveness and viability by stimulating online sales and consumer footfall. However, insights are needed to enrich academic understanding and guide practitioners in their decision-making process regarding use and optimization of these platforms for boosting retail agglomeration vitality. Drawing on uses and gratifications theory, an online survey of 442 Dutch consumers revealed that positive attitudes toward browsing LORPs induced both online purchase and offline visit intentions. Interestingly, despite LORPs' local focus, non-place-specific motives more substantially impacted positive browsing-related attitudes toward LORPs than place-specific ones.
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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%.
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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
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