Zoekresultaten

Website blocking: evolution or revolution?

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.

Criminal clickbait: a panel data analysis on the attractiveness of online advertisements offering stolen data

Introduction: Few studies have examined the sales of stolen account credentials on darkweb markets. In this study, we tested how advertisement characteristics affect the popularity of illicit online advertisements offering account credentials. Unlike previous criminological research, we take a novel approach by assessing the applicability of knowledge on regular consumer behaviours instead of theories explaining offender behaviour.Methods: We scraped 1,565 unique advertisements offering credentials on a darkweb market. We used this panel data set to predict the simultaneous effects of the asking price, endorsement cues and title elements on advertisement popularity by estimating several hybrid panel data models.Results: Most of our findings disconfirm our hypotheses. Asking price did not affect advertisement popularity. Endorsement cues, including vendor reputation and cumulative sales and views, had mixed and negative relationships, respectively, with advertisement popularity.Discussion: Our results might suggest that account credentials are not simply regular products, but high-risk commodities that, paradoxically, become less attractive as they gain popularity. This study highlights the necessity of a deeper understanding of illicit online market dynamics to improve theories on illicit consumer behaviours and assist cybersecurity experts in disrupting criminal business models more effectively. We propose several avenues for future experimental research to gain further insights into these illicit processes.

Presence of Corporate Reputation Cues in Company Vacancy Texts Boosts Vacancy Attractiveness as Perceived by Employees

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.

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%.

A company’s corporate reputation through the eyes of employees measured with sentiment analysis of online reviews

Empowered by Innovation: Unravelling Determinants of Idea Implementation in Open Innovation Platforms

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.