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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Attracting the best candidates online for job vacancies has become a challenging task for companies. One thing that could influence the attractiveness of organisations for employees is their reputation that is an essential component of marketing research and plays a crucial role in customer and employee acquisition and retention. Prior research has shown the importance for companies to improve their corporate reputation (CR) for its effect on attracting the best candidates for job vacancies. Company ratings and vacancy advertisements are nowadays a massive, rich valued, online data source for forming opinions regarding corporations. This study focuses on the effect of CR cues that are present in the description of online vacancies on vacancy attractiveness. Our findings show that departments that are responsible for writing vacancy descriptions are recommended to include the CR themes citizenship, leadership, innovation, and governance and to exclude performance. This will increase vacancies’ attractiveness which helps prevent labour shortage.
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Companies use crowdsourcing to solve specific problems or to search for innovation. By using open innovation platforms, where community members propose ideas, companies can better serve customer needs. So far, it remains unclear which factors influence idea implementation in crowd sourcing context. With the research idea that we present here, we aim to get a better understanding of the success and failure of ideas by examining relationships between characteristics of ideators, characteristics of ideas and the likelihood of implementation. In order to test the methodological approach that we propose in this paper in which we investigate for business relevant innovativeness as well as sentiment based on text analytics, data including unstructured text was mined from Dell IdeaStorm using webcrawling and scraping techniques. Some relevant hypotheses that we define in this paper were confirmed on the Dell IdeaStorm dataset but in order to generalize our findings we want to apply to the Leg o dataset in our current work in progress. Possible implications of our novel research idea can be used to fill theoretical gaps in marketing literature, help companies to better structure their search for innovation and for ideators to better understand factors contributing to successful idea generation.
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Ontwikkelen van een tool om trends en scenario’s in kaart te brengen waarmee beter richting gegeven kan worden aan voor de praktijk relevante onderwijsprogramma’s en onderzoeksprojecten. In eerste instantie doen wij dat voor het domein commerciële economie (marketing & customer experience). Doel Dit project heeft twee doelen: 1: Ontwikkeling praktijk relevante opleidingsprogramma’s en onderzoeks programma’s. Dit doen wij door trends na te gaan middels literatuurstudie, interviews met toonaangevende mensen in het vakgebied en een conferentie waarin wij scenarios bouwen met experts 2: Train the trainer programma zodat wij ons de skills om dit zelf te kunnen binnen de HU eigen maken Resultaten Het project levert een aantal scenario’s op waarop wij ons kunnen voorbereiden en waarvan we de ontwikkeling in de toekomst kunnen monitoren. Hierdoor blijven onze onderwijs en onderzoeksprogramma’s bij de tijd. Looptijd 01 september 2020 - 01 december 2020 Aanpak Dit programma wordt ontwikkeld samen met De Ruijter strategie die in Nederland toonaangevend is op dit gebied en het Nederlands Instituut voor Marketing. Interne HU partners zijn het Institute for Marketing & Commerce en het Lectoraat Marketing en Customer Experience. Fase 1 is literatuuronderzoek. Hiervoor wordt o.a. ook via webscraping en Natural Language Processing informatie gehaald uit job ads van toonaangevende bedrijven. Fase 2 zijn interviews met toonaangevende mensen in de praktijk en wetenschap. Fase 3 een werkconferentie met 25 experts om scenario’s te ontwikkelen waarna een eindrapport wordt gemaakt. Hierna vinden de train the trainer sessies plaats, worden de scenario’s voorbereid en wordt de organisatie ingericht om e.e.a. in de tijd te monitoren. Relevantie van het project Het in kaart brengen van trends is een specialisme. Dat geldt ook voor scenario denken. Wij willen ons dat eigen maken zodat wij steeds relevanter worden voor de praktijk. Dit is goed voor studenten, docenten, werkgevers en de maatschappij
