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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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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About this publication: Computer mediated interpersonal interactions are defining our daily lives as we know it. Studying this phenomenon with various methodologies, across different cultures and traditions is a crucial component in understanding social ties. This book brings together articles that approach online dating from a range of cultural and critical perspectives.The research decodes the level of engagement and manner of approaching online dating in various countries such as France, India, China, Turkey, Cuba, USA and Portugal. Mapping the history of dating and courtship shows the evolution of these practices even before the introduction of the online medium and traces parallels and differences between old and new traditions.
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Mode heeft een cruciale functie in de samenleving: zij maakt diversiteit en inclusiviteit mogelijk en is een middel voor individuen om zich uit te drukken. Desalniettemin is mode ook een raadsel op het gebied van duurzaamheid, zowel aan de sociale als aan de milieukant. Er bestaan echter alternatieven voor de huidige praktijken in de mode. Dit project heeft tot doel de ontwikkeling van een van die initiatieven te ondersteunen. In samenwerking met twee Nederlandse MKB bedrijven in de mode-industrie, willen we een of meer business modellen co-designen voor het vermarkten van circulair ontworpen laser geprinte T-shirts. Door lasertechnologie te introduceren in plaats van traditionele inktopties, kunnen de T- shirts hun CO2 voetafdruk verder verkleinen en een verstandig alternatief zijn voor individuen, die op zoek zijn naar duurzame modekeuzes. Maar hoewel de technologische haalbaarheid vaststaat, vereist het vermarkten sterke, schaalbare, bedrijfsmodellen. Via een haalbaarheidsstudie willen we dergelijke businessmodellen ontwikkelen en de commercialisering van deze producten ondersteunen. Wij zijn van plan de reacties van de consument op een dergelijke innovatie te bestuderen, evenals de belemmeringen en stimulansen vanuit het oogpunt van de consument, en de inkoop-, toeleveringsketen- en financiële kwesties die kunnen voortvloeien uit de schaalbaarheid van een potentieel bedrijfsmodel. Om praktische relevantie voor de bredere industrie te verzekeren, streven we ernaar om de resultaten te presenteren op evenementen georganiseerd door een van de consortiumpartners (in 2023), als ook om een teaching case en een wetenschappelijk artikel te ontwikkelen op basis van de resultaten van het project.
