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Text mining is a research method that uses interdisciplinary techniques to identify patterns and trends in unstructured data and can be used to detect research trends and developments in various fields. Thus, this study attempts to identify research trends in the issue of privacy concern (PC), focusing on the differences between the first two decades of the 21st century. This is done by applying an automated textual analysis tool (Voyant Tools) on a vast corpora of academic papers conducted on this field. We found that while the first examined decade of PC research focused on commerce and marketing issues, there was much more research emphasis on the social dimension of privacy in the second decade. This difference was explained by the technological developments that were prominent in the respective decades. The research provided support for the use of textual analysis of academic literature as a tool to identify research trends in various fields, including technologically‐oriented fields. [ABSTRACT FROM AUTHOR]

In this study we analysed the citation trajectories of 200 tenured Israeli professors, focusing on the differences between the most impactful and the least impactful scholars from various disciplines. Seven typical citation growth patterns have been identified and quantified based on the Web of Science citation trajectories of the scholars. Major slope changes in the scholar's citation trajectories and their relative timing were utilized as indicators of a pattern. Significant differences in citation growth patterns were found between high‐impact and low‐impact professors. This research provides a methodological basis for predicting scholar's career evolution based on their citation growth patterns. [ABSTRACT FROM AUTHOR]

This research investigates the main factors which motivate users to provide falsified details upon website registration, and identifies the types of personal details that are most prone for falsification. In addition, we predict the tendency for identity falsification by examining various factors, such as: privacy concern and socio‐demographic factors. We also relate those issues to the contemporaneous COVID‐19 pandemic and examine its influence on privacy concerns and the willingness to expose personal details. To this end, a user study was carried out among 245 students of the Israeli academia, via a quantitative method using online closed‐ended questionnaires. We found that privacy‐related factors are the most prevalent for identity falsification. In addition, the regression showed the higher the privacy concerns rates, the higher chance for identity falsification. It seems that the COVID‐19 pandemic increased privacy concerns among online users, which may even increase the tendency of the examined behavior. [ABSTRACT FROM AUTHOR]