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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In a recent official statement, Google highlighted the negative effects of fake reviews on review websites and specifically requested companies not to buy and users not to accept payments to provide fake reviews (Google, 2019). Also, governmental authorities started acting against organisations that show to have a high number of fake reviews on their apps (DigitalTrends, 2018; Gov UK, 2020; ACM, 2017). However, while the phenomenon of fake reviews is well-known in industries as online journalism and business and travel portals, it remains a difficult challenge in software engineering (Martens & Maalej, 2019). Fake reviews threaten the reputation of an organisation and lead to a disvalued source to determine the public opinion about brands. Negative fake reviews can lead to confusion for customers and a loss of sales. Positive fake reviews might also lead to wrong insights about real users’ needs and requirements. Although fake reviews have been studied for a while now, there are only a limited number of spam detection models available for companies to protect their corporate reputation. Especially in times with the coronavirus, organisations need to put extra focus on online presence and limit the amount of negative input that affects their competitive position which can even lead to business loss. Given state-of-the-art derived features that can be engineered from review texts, a spam detector based on supervised machine learning is derived in an experiment that performs quite well on the well-known Amazon Mechanical Turk dataset.
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Although systematic reviews are considered as central components in evidence-based practice, they currently face an important challenge to keep up with the exponential publication rate of clinical trials. After initial publication, only a minority of the systematic reviews are updated, and it often takes multiple years before these results become accessible. Consequently, many systematic reviews are not up to date, thereby increasing the time-gap between research findings and clinical practice. A potential solution is offered by a living systematic reviews approach. These types of studies are characterized by a workflow of continuous updates which decreases the time it takes to disseminate new findings. Although living systematic reviews are specifically designed to continuously synthesize new evidence in rapidly emerging topics, they have also considerable potential in slower developing domains, such as rehabilitation science. In this commentary, we outline the rationale and required steps to transition a regular systematic review into a living systematic review. We also propose a workflow that is designed for rehabilitation science.
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MOOC’s (Massive Open Online Courses) zijn een opkomend fenomeen. In deze whitepaper wordt gekeken naar de mogelijkheid voor inpassing van MOOC’s in het huidig onderwijs. Wat zijn de voor- en nadelen van de online cursussen? Welke kansen biedt het?
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Internet technology offers a lot of new opportunities for the dissemination of information, sharing of support and consultation of professionals. Innovating professionals from multiple disciplines have begun to exploit the new opportunities for parenting support. The studies presented in this book are meant to deepen our insights in the subject of online parenting support and investigate the feasibility to use single session email consultation to empower parents. This publication includes: - A systematic review of 75 studies on online parenting support. - A meta-analytic review of 12 studies on online tools to improve parenting. - A content analysis of 129 parenting questions and responses in single session email consultation. - An analysis and validation study of the newly developed Guiding the Empowerment Process model. - An evaluation study of the effects of single session email consultation on parental empowerment. The results of this research indicate that the Internet is not only a source of information, but it can also be an instrument for support and training, aiming to improve parental competencies.
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Background. A number of parenting programs, aimed at improving parenting competencies,have recently been adapted or designed with the use of online technologies. Although webbased services have been claimed to hold promise for parent support, a meta-analytic review of online parenting interventions is lacking. Method. A systematic review was undertaken of studies (n= 19), published between 2000 and 2010, that describe parenting programs of which the primary components were delivered online. Seven programs were adaptations of traditional, mostly evidence-based, parenting interventions, using the unique opportunities of internet technology. Twelve studies (with in total 54 outcomes, Ntot parents = 1,615 and Ntot children = 740) were included in a meta-analysis. Results. The meta-analysis showed a statistically significant medium effect across parents outcomes (ES = 0.67; se = 0.25) and child outcomes (ES = 0.42; se = 0.15). Conclusions. The results of this review show that webbased parenting programs with new technologies offer opportunities for sharing social support, consulting professionals and training parental competencies. The meta-analytic results show that guided and self-guided online interventions can make a significant positive contribution for parents and children. The relation with other meta-analyses in the domains of parent education and web-based interventions is discussed.
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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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In March 2020 schools in The Netherlands closed to contain the spread of Covid-19 virus. Shortly after, schools took to online education. The condensed setting of the Covid-19 situation provided a background to study which learning activities and tools teachers choose in online education and how they use them to promote interaction. Interaction is quintessential to learning but in online education it is not easy to provide room for interaction. Our central research question therefore is how interaction within online education activities change over time. An online longitudinal survey amongst teachers was conducted. The first four rounds took place in the early stages of the lockdowns and shortly after. In total 179 different secondary school teachers participated of whom 16 responded three rounds or more. Most teachers use tools in online education that can facilitate more interaction than necessary for the Instructional Design. This means that improving interaction in online education is more a pedagogical challenge than a technical one. It was also found that teachers who deploy Instructional Designs that require more interaction use more and different tools. However, only few of these tools seem to facilitate the interactive quality the teachers pursued. Over time we saw the interactive quality of Instructional Design and tools converge. We are in awe of the artful way in which some teachers manage to combine the possibilities of different tools to establish high interactive quality in the online learning processes they conduct.
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Background: Due to demographic transitions and budget restraints, it is now necessary to search for comprehensive new strategies, in order to constitute a sustainable healthcare system. Recently, various online care platforms for community-dwelling older adults were introduced in several European countries. These platforms have aimed at solidifying social cohesion in the community, so as to support the older adults in coordinating or managing their care and to enhance the self-reliance of these older adults. Consequently, these platforms might contribute to a more sustainable healthcare system. The main research question of this study was twofold: Which online care platforms for older adults are available in the Netherlands and what are their characteristics? Methods: The researchers have performed a scoping review of the online care platforms in the Netherlands, according to the six steps of Arksey & O’Malley (2005), which were as follows: (1) Identifying the research question; (2) Identifying any relevant studies; (3) Selecting the studies; (4) Charting the data; (5) Collating, summarising and reporting on the results; together with (6) consultations with the relevant stakeholders. The study searched for evidence in online scientific databases (Phase 1) and on the Internet (Phase 2). The relevant studies that were published between February 2012 and October 2017 were included. Results: The review resulted in an overview of 21 care platforms, for which 3 types were identified: (1) Community Care Platforms; (2) Care Network Platforms; and (3) System Integrator Platforms. Conclusion: This typology of platforms can guide users – for instance, older adults, care professionals, informal caregivers and municipalities, in choosing a suitable care platform, i.e. the typology gives users insight into the functionalities, goals and target groups which allows them to choose a platform that matches their needs. As far as the authors know, no studies have previously reported on the effects of the online care platforms for older adults in the Netherlands, so further research is required on their impacts and on their benefits.
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This study utilises a quantitative observation study on student journalists (n=47), as well as reconstruction interviews with experienced editors and reporters in newsrooms (n=12), to understand how Dutch journalists search, select, and verify sources online. Through the recording of screen activity, we show that search strategies are heavily influenced by how the search engine sorts and ranks potential sources. Eventual selection of sources remains relatively traditional, focused on legacy media and their websites. Moreover, online news production clearly challenges the verification process. Results suggest that journalists use no explicit but only so-called hybrid methods of verifications, such as background checks of websites and social media accounts, and cross-checking of sources.
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