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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%.

Digital Tailspin

Privacy, copyright, classified documents and state secrets, but also spontaneous network phenomena like flash mobs and hashtag revolutions, reveal one thing – we lost control over the digital world. We experience a digital tailspin, or as Michael Seemann calls it in this essay: a loss of control or Kontrollverlust. Data we never knew existed is finding paths that were not intended and reveals information that we would never have thought of on our own. Traditional institutions and concepts of freedom are threatened by this digital tailspin. But that doesn’t mean we are lost. A new game emerges, where a different set of rules applies. To take part, we need to embrace a new way of thinking and a radical new ethics – we need to search for freedom in completely different places. While the Old Game depended upon top-down hierarchies and a trust in the protective power of state justice systems, the New Game asks you to let go of all these certainties. Strategies to play the game of digital tailspin rely on flexibility, openness, transparency and what is dubbed ‘antifragility’. In Digital Tailspin: Ten Rules for the Internet After Snowden Michael Seemann examines which strategies are most appropriate in the New Game and why.

Measuring safety in aviation

No More Surprises: Stand Assignment Algorithm with Likelihood of Turnaround Time Deviation

Airport management is frequently faced with a problem of assigning flights to available stands and parking positions in the most economical way that would comply with airline policies and suffer minimum changes due to any operational disruptions. This work presents a novel approach to the most common airport problem – efficient stand assignment. The described algorithm combines benefits of data-mining and metaheuristic approaches and generates qualitative solutions, aware of delay trends and airport performance perturbations. The presented work provides promising solutions from the starting moments of computation, in addition, it delivers to the airport stakeholders delay-aware stand assignment, and facilitates the estimation of risk and consequences of any operational disruptions on the slot adherence.

EIP-AGRI Focus Group Bee health and sustainable beekeeping

The EIP-AGRI Focus Group on Bee health and sustainable beekeeping was established in spring 2019 to identify, structure and develop main replies to the main question: How can we ensure the sustainability of beekeeping in the face of challenges linked to pests and diseases, intensification of agriculture and climate change?

Global China Insights

Journal: doing business with China