Be Like Amazon

Gaining competitive advantage requires a detailed benchmark of performance against other market players, and profound gap analysis in comparison to the best practice applies to the audit of the digital presence (online footprint analysis), which, based on scripting codes, is done easily on its premises. SEO software and social medial listening tools allow for a comprehensive understanding of the digital performance of the company. Unfortunately, some businesses underestimate the potential dormant in different types of live analytics and performance trackers and being unaware of the analysis capacity to be performed on competitors and market leaders completely off cost. The case presents a study of a digital footprint template analysis performed for Amazon in the United Arab Emirates and shows the comparison with local and global market players like Noon.com, eBay, or Alibaba using software like SEMrush, SimilarWeb, and Brand24 to showcase how to leverage on gathered insights and gain competitive advantage.

Digital Gifts and Tourism Mementos: A Sustainable Approach

Touristic destinations all around the world are struggling to digitally transform the touristic experience and the touristic products they offer and to capitalize a good experience with new tourists and returning ones. There is a lot of research on digital solutions assisting tourism, but it does not provide a follow-up digital product, therefore depending only on physical gifts, postcards and mementos. In this work, we propose a novel platform that can provide a personalized digital memento or digital gift for every route or tourist destination that can become the digital point of reference of visitors’ experience giving new dimensions for commercialization to the existing physical mementos at the gift shops. The purpose of this study is to analyze what comprises a memorable touristic experience and to design and, finally, present a total solution that builds and offers a personal e-souvenir of a touristic experience to the tourist for him to hold, sport and share just using his mobile phone. We propose a digital memento-building platform that includes the personalized experience in visits taking place while in vacations. The visitors are usually taking pictures along routes they follow that later need further organization and processing and that in no way substitutes the physical mementos. In our approach, we propose a solution that generates a unique and personalized e-souvenir through a collage of the selfies and photos creating the digital equivalent of the touristic postcards but in our case personalized with the visitor photos with minimum amount of effort and produced real-time. Our approach is also providing a platform to the photographers and designers of touristic destinations to build and graphically generated memento artifacts—templates specific for one or more destinations or routes. In this way, the approach serves the tourism industry vertically, covering all aspects, i.e., the tourist-visitor, the tourism professional players such as photographers, designers of physical mementos and, of course, the touristic destination providing a digital footprint to server marketing of the destination through sharing on social media and word-of-mouth, of course. To support our approach, we have built and run a fully working prototype in the touristic center of Athens, Greece, with real users and designers for several weeks during summer vacations. The results have been greatly encouraging from end-users and professionals. The study shows that it is possible for various lines of business to come together and work along one another for an improve touristic experience using mobile technologies in a personalized, targeted approach. The touristic destination, graphic designers, photographers, tourist agent specialists, software developers and visitors can all now have a digital personalized memorable gift from the visit.

Karen Ever After: The Career & Legal Consequences of being the Racist White Lady in an Internet Meme

With the coupled crises of the COVID-19 pandemic and racial protest movements in 2020, the “Karen” meme gained national attention as more incidents were posted online. These incidents have shown to be destructive to all parties involved as victims often experience negative health effects while “Karens” experience doxing. This study investigated the defaming career and legal consequences following the release of video content on social media in which a white woman dubbed as a “Karen” displays racist behaviors. Utilizing a directed qualitative content analysis, the sourcing of incident content came from a study by Been Verified that evaluated 150 incidents. A 2-part data-collection was implemented utilizing Advanced Google Search Engine and Chan McNamarah’s research on racialized police communication. Incidents were subsequently processing using a selection criterion to be considered for consequence assessment. Out of 56 qualified incidents, 27% had discovered career consequences and 27% had discovered legal consequences. This research contributes to a largely undiscussed field in academia by providing an in-depth assessment on defaming Internet trends. It has implications for HR specialists and policymakers in making appropriate decisions to ensure accountability is taken in online matters. It also holds significance for Internet-users in understanding how one’s digital footprint can be perceived online.

Digital footprint in predicting school graduates’ educational strategy

This research article aims at evaluating the results of the school graduates’ educational strategy transformation with the help of digital footprint data. The analysis of official and unofficial Internet communities of universities in the social network «VKontakte» shows that their active users are school graduates, who thus receive the necessary information about the university. The method presented can become a promising tool to forecast the demand for higher education. The study covers the period from 2019 to 2021, a total of 502 thousand user profiles having been identified, 246 thousand accounts included in the final sample. The results show that during the analyzed period the number of user subscriptions to university communities has decreased in all Russian regions. The orientation towards universities located in other regions has also changed: these were more popular before the pandemic, being subscribed to by the majority of students, whereas after the spread of COVID-19, most of the graduates began to choose universities located in their native region. A significant limitation of this method is that a number of profiles are closed, which logically does not allow to use them in further analysis. The results of the study show, however, high validity and wide opportunities for the use of the digital footprint method when predicting young people’s educational trajectory and planning the enrollment campaign, both on the scale of the university and the whole country.

Computational analysis of the digital footprint using machine learning and artificial intelligence

The article discusses the procedure of step-by-step formalization of the software model of the system for accounting and forecasting the effectiveness of employees of an IT enterprise The issue of control of social interaction is considered. The relational model from game theory based on a specific data model template is proposed. Generalization in form of highlighting the categorical apparatus of metrics that directly or indirectly affect the procedure for assessing work efficiency, downtime or incorrect use of a working device (computer) is proposed. An architectural model of the application is proposed and substantiated, a model of a system for working with metrics is determined, the implementation of the necessary analysis tools in form of a combination of various machine learning algorithms used in systems with a binary classification based on decision making by the operator (the object of analysis) is described. The article summarizes the economic effect of the implementation of this approach in employee control systems.

Predictive Credit Risk Analytics Using Borrowers' Digital Footprint and Methods of Statistical Machine Learning

The article considers the problem of reducing the banks credit risks associated with the insolvency of borrowers — individuals using financial, socio-economic factors and additional data about borrowers digital footprint. A critical analysis of existing approaches, methods and models in this area has been carried out and a number of significant shortcomings identified that limit their application. There is no comprehensive approach to identifying a borrowers creditworthiness based on information, including data from social networks and search engines. The new methodological approach for assessing the borrowers risk profile based on the phased processing of quantitative and qualitative data and modeling using methods of statistical analysis and machine learning is proposed. Machine learning methods are supposed to solve clustering and classification problems. They allow to automatically determine the data structure and make decisions through flexible and local training on the data. The method of hierarchical clustering and the k-means method are used to identify similar social, anthropometric and financial indicators, as well as indicators characterizing the digital footprint of borrowers, and to determine the borrowers risk profile over group. The obtained homogeneous groups of borrowers with a unique risk profile are further used for detailed data analysis in the predictive classification model. The classification model is based on the stochastic gradient boosting method to predict the risk profile of a potencial borrower. The suggested approach for individuals creditworthiness assessing will reduce the banks credit risks, increase its stability and profitability. The implementation results are of practical importance. Comparative analysis of the effectiveness of the existing and the proposed methodology for assessing credit risk showed that the new methodology provides predictive ana­lytics of heterogeneous information about a potential borrower and the accuracy of analytics is higher. The proposed techniques are the core for the decision support system for justification of individuals credit conditions, minimizing the aggregate credit risks.