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
This study proposes a framework to measure touristification of consumption spaces, consisting of concentration of retail capital, business displacement and standardization of the consumption landscape. This framework is tested using business registration data and rent price estimates for consumption spaces in Amsterdam between 2005 and 2020. Touristification emerges from concentrations of retail capital and standardization, but occurs without causing significant business displacement. A cluster analysis identifies different variations of touristification. Besides the more typical cases these include nightlife areas, gentrifying consumption spaces and specialized retail areas. This suggests that local contingencies cause consumption spaces to respond differently to increasing tourism.
Het aantal winkelbezoekers loopt in Europa al jaren terug, vooral in economisch zwakkere regio’s. Dit geldt in het bijzonder voor ouderen, waarvan de verwachting is dat ze in de toekomst fysieke winkels nog meer de rug zullen toekeren. Om de winkelervaring te verbeteren, investeren winkeliers steeds meer in opkomende digitale technologieën zoals apps, interactieve en digitale schermen, sociale robots en zelfscankassa’s. Deze instore technologieën slaan vooral bij jongere klanten aan, oudere klanten blijken door hun beperkingen (o.a. zien, horen, mobiliteit, informatieverwerking en digitale vaardigheden) nog steeds veel barrières te ervaren bij het bezoek aan winkels en het gebruik van instore technologieën. Dit is niet alleen nadelig voor winkeliers omdat ouderen een substantieel, stijgend, en koopkrachtig deel van de bevolking vertegenwoordigen dat relatief trouw is aan regionale winkelgebieden, maar het zet ook de inclusie van ouderen in Europa onder druk omdat winkelbezoek bijdraagt aan hun sociale welbevinden. Met dit onderzoeksproject onderzoekt het nieuwe consortium van twee hogescholen en drie buitenlandse universiteiten hoe instore technologieën ouderen in Europa kunnen helpen bij het wegnemen van barrières om tot een goede winkelervaring te komen. Het project brengt de onderzoeksprogramma’s van het lectorenplatform Retail Innovation Platform (Hogeschool van Amsterdam, Hogeschool Saxion), de Retail en Marketingtechnologie groep (University of Bristol), de human-computer interaction groep (University of Calabria), en de engaging co-design research group (Aalto University) samen. Het project sluit aan bij nationale en Europese initiatieven zoals de Kennis- en Innovatieagenda Sleuteltechnologieën 2024-2027, The DIGITAL Europe Programme en de Strategy for the rights of persons with disabilities 2021-2030. Door de relaties tussen ouderen, opkomende digitale technologie, en winkelgedrag over verschillende Europese regio’s te onderzoeken, sluit het project tevens aan bij Interreg Europa en het Europees Fonds voor Regionale Ontwikkeling.
