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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The overarching aim of this paper is to define, develop and present a processing pipeline that has practical application for companies, meaning, being extendable, representative from marketing perspective, and reusable with high reliability for any new, unseen data that generates insights for evaluation of the reputation construct based on collected reviews for any (e.g. retail) organisation that is willing to analyse or improve its performance. First, determinant attributes have to be defined in order to generate insights for evaluation with respect to corporate reputation. Second, in order to generate insights data has to be collected and therefore a method has to be developed in order to extract online stakeholder data from reviews. Furthermore, a suitable algorithm has to be created to assess the extracted information based on the determinant attributes in order to analyse the data. Preliminary results indicate that application of our processing pipeline to online employee review data that are publicly available on the web is valid.
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Citizens regularly search the Web to make informed decisions on daily life questions, like online purchases, but how they reason with the results is unknown. This reasoning involves engaging with data in ways that require statistical literacy, which is crucial for navigating contemporary data. However, many adults struggle to critically evaluate and interpret such data and make data-informed decisions. Existing literature provides limited insight into how citizens engage with web-sourced information. We investigated: How do adults reason statistically with web-search results to answer daily life questions? In this case study, we observed and interviewed three vocationally educated adults searching for products or mortgages. Unlike data producers, consumers handle pre-existing, often ambiguous data with unclear populations and no single dataset. Participants encountered unstructured (web links) and structured data (prices). We analysed their reasoning and the process of preparing data, which is part of data-ing. Key data-ing actions included judging relevance and trustworthiness of the data and using proxy variables when relevant data were missing (e.g., price for product quality). Participants’ statistical reasoning was mainly informal. For example, they reasoned about association but did not calculate a measure of it, nor assess underlying distributions. This study theoretically contributes to understanding data-ing and why contemporary data may necessitate updating the investigative cycle. As current education focuses mainly on producers’ tasks, we advocate including consumers’ tasks by using authentic contexts (e.g., music, environment, deferred payment) to promote data exploration, informal statistical reasoning, and critical web-search skills—including selecting and filtering information, identifying bias, and evaluating sources.
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Copyright enforcement by private third parties – does it work uniformly across the EU? Since the inception of Napster, home copying of digital files has taken a flight. The first providers of software or infrastructure for the illegal exchange of files were held contributory or vicariously liable for copyright infringement. In response, they quickly diluted the chain of liability to such an extent that neither the software producers, nor the service providers could be held liable. Moving further down the communication chain, the rights holders are now requiring Internet Service Providers (ISPs) that provide access to end customers to help them with the enforcement of their rights. This article discusses case-law regarding the enforcement of copyright by Internet Access Providers throughout Europe. At first glance, copyright enforcement has been harmonised by means of a number of directives, and article 8(3) of the Copyright Directive (2001/29/EC) regulates that EU Member States must ensure the position of rights holders with regard to injunctions against ISPs. Problem solved? Case law from Denmark, Ireland, Belgium, Norway, England, The Netherlands, Austria and the Court of Justice of the EU was studied. In addition, the legal practice in Germany was examined. The period of time covered by case law is from 2003 to 2013, the case law gives insight into the differences that still exist after the implementation of the directive.
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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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On average 125 murders take place in the Netherlands on an annual basis. However, not all such incidents can be solved. Currently there are more than 1700 unsolved homicide cases on the shelf at the National Police that classify as a andapos;cold caseandapos;. Investigation into these types of capital offenses takes a lot of time, money, and capacity. Applications of the current working method and available techniques are very labor-intensive and time-consuming. In addition, the pressure on the executive Police officers is high-from the Police organization, the Public Prosecution Service, the media, the next of kin, as well as society in general. From an investigative point of view, it is relevant to provide direction in the criminal investigation and formulate and evaluate various case scenarios, while reducing a risk of andapos;tunnel visionandapos;. From a scientific point of view, more research into homicide cases in the Netherlands is of eminent importance. Remarkably little has been written in scientific literature about this type of crime. The project andapos;Cold Case: Solved andamp; Unsolvedandapos; focused on the use of open, publicly available information sources to collect the data and gain more insight into homicide cases in The Netherlands. Applicability of various modern techniques, such as web-scraping, API software and Artificial Intelligence (AI) was explored to facilitate and automate data collection and processing tasks. A first concept of a andapos;smartandapos; database was proposed, combining a web-based database platform with AI modules to filter and (pre-)process the data. With further development and training of AI modules, such a database might eventually support data-driven generation and/or prioritization of investigative scenarios. The data collected in the process was used in three scientific studies aimed at uncovering the relationships and patterns in the homicide data for The Netherlands.
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Corporate reputation is an intangible resource that is closely tied to an organization’s success but measuring it and to derive actions that can improve the reputations can be a long and expensive journey for an organization. In the available literature, corporate reputation is primarily measured through surveys, which can be time and cost intensive. This paper uses online reviews on the web as the source for a machine-learning driven aspect-based sentiment analysis that can enable organizations to evaluate their corporate reputation on a fine-grained level. The analysis is done unsupervised without organizations needing to manually label datasets. Using the insights generated through the analysis, on one hand, organizations can save costs and time to measure corporate reputation, and, on the other hand, it provides an in-depth analysis that splits the overall reputation into multiple aspects, with which organizations can identify weaknesses and in turn improve their corporate reputa tion. Therefore, this research is relevant for organizations aiming to understand and improve their corporate reputation to achieve success, for example, in form of financial performance, or for organizations that help and consult other organizations on their journeys to increased success. Our approach is validated, evaluated and illustrated with Trustpilot review data.
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In my previous post on AI engineering I defined the concepts involved in this new discipline and explained that with the current state of the practice, AI engineers could also be named machine learning (ML) engineers. In this post I would like to 1) define our view on the profession of applied AI engineer and 2) present the toolbox of an AI engineer with tools, methods and techniques to defy the challenges AI engineers typically face. I end this post with a short overview of related work and future directions. Attached to it is an extensive list of references and additional reading material.
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The aim of this QSR 2022 on tourism is to make an attempt to assess available information about the tourism industry from three countries and various sources and present it in a comprehensive manner. We, thereby, describe common features of regional tourism structures, as well as differences, and we present some of the identified data incompatibilities (sections 2.2 and 2.3). The recommendations in section 3 present avenues along which data collection and monitoring can be improved, inspired by a set of key forces driving change intourism that stakeholders should be prepared for (section 2.4).
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Purpose: Small and medium-sized entities (SMEs) operating in the alternative financing sector are typically heterogenous in nature making them differ greatly from traditional banks. Where traditional banks must comply with strict banking regulations, developing uniform regulations for the alternative financing sector remains a challenge. This paper examines the current challenges and solutions from a sociological and institutional perspective in developing standards for SMEs operating in the alternative financing sector in the Netherlands. Adopting minimum quality standards should lead to increased transparency and public trust in the non-banking sector.
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