The COVID-19 pandemic social distancing forced a shift from Face-to-Face (F2F) to virtual work sessions, applying innovative digital tools. These tools have previously been neglected, mainly due to a conservative approach or prioritization. Consequently, their effectiveness was never tested in depth. While applying these innovative digital tools during the COVID-19 pandemic was clearly preferable to shutting down organizational activity, managers and workers recognized the advantages of these alternatives and tended to apply them in the post-COVID-19 period. However, in this post-pandemic period, which is free from social distancing limitations, a relatively full space of choices was introduced again, which raised the question whether these alternatives should be kept. Therefore, this study examines whether digital communication tools can adequately substitute F2F sessions in project management. We conducted an experiment with participants $\left(n=269\right),\text{asking}\text{them}$ to perform project-oriented tasks on four platforms: as individuals, in an F2F group meeting, on Zoom, or using WhatsApp. The results indicate that while an F2F meeting is more effective than individual work, Zoom and WhatsApp are not. These findings appear surprising and may contradict the concept of group empowerment. The use of digital sharing platforms did not affect the tasks’ performances nor create synergy. This raises the issue of whether these digital means are here to stay, should be discarded, or must be upgraded.

The digital era introduces significant challenges for privacy protection, which grow constantly as technology advances. Privacy is a personal trait, and individuals may desire a different level of privacy, which is known as their “privacy concern”. To achieve privacy, the individual has to act in the digital world, taking steps that define their “privacy behavior”. It has been found that there is a gap between people’s privacy concern and their privacy behavior, a phenomenon that is called the “privacy paradox”. In this research, we investigated if the privacy paradox is domain-specific; in other words, does it vary for an individual when that person moves between different domains, for example, when using e-Health services vs. online social networks? A unique metric was developed to estimate the paradox in a way that enables comparisons, and an empirical study in which $(n=437)$ validated participants acted in eight domains. It was found that the domain does indeed affect the magnitude of the privacy paradox. This finding has a profound significance both for understanding the privacy paradox phenomenon and for the process of developing effective means to protect privacy.

The way users manage access to their information and computers has a tremendous effect on the overall security and privacy of individuals and organizations. Usually, access management is conducted using a choice architecture , a behavioral economics concept that describes the way decisions are framed to users. Studies have consistently shown that the design of choice architectures, mainly the selection of default options, has a strong effect on the final decisions users make by nudging them toward certain behaviors. In this article, we propose a method for optimizing access control choice architectures in online social networks. We empirically evaluate the methodology on Facebook, the world's largest online social network, by measuring how well the default options cover the existing user choices and preferences and toward which outcome the choice architecture nudges users. The evaluation includes two parts: (a) collecting access control decisions made by 266 users of Facebook for a period of 3 months; and (b) surveying 533 participants who were asked to express their preferences regarding default options. We demonstrate how optimal defaults can be algorithmically identified from users’ decisions and preferences, and we measure how existing defaults address users’ preferences compared with the optimal ones. We analyze how access control defaults can better serve existing users, and we discuss how our method can be used to establish a common measuring tool when examining the effects of default options.

COVID-19 has long become a worldwide pandemic. It is responsible for the death of over two million people and posed an economic recession. This paper studies the spread pattern of COVID-19, aiming to establish a prediction model for this event. We harness Data Mining and Machine Learning methodologies to train regression models to predict the number of confirmed cases in a spatial-temporal space. We introduce an innovative concept ‒ the Center of Infection Mass (CoIM) ‒ adapted from the field of physics. We empirically evaluated our model on western European countries, based on the CoIM index and other features, and showed that a relatively high accurate prediction of the spread can be obtained. Our contribution is twofold: first, we introduced a prediction methodology and proved empirically that a prediction can be made even to the range of over a month; second, we showed promise in adopting the CoIM index to prediction models, when models that adopt the CoIM yield significantly better results than those that discard it. By applying our model, and better controlling the inherent tradeoff between life-saving and economy, we believe that decision-makers can take close to optimal measures. Thus, this methodology may contribute to public welfare.

In the contemporary digital era, users may take actions which may affect their privacy and security without being aware of the consequences. Knowing the consequences, may be perceived as a higher level of usability, above the basic operational skills. This phenomenon is mainly a direct outcome of the gap between users’ knowledge and the complexity of the digital systems. The ability to master those systems is known as technological literacy, which thus is a critical factor to protect privacy and security. However, the process of acquiring technological literacy is costly and not trivial. In this research, we address the problem with a qualitative approach, and introduce a methodology named OTLA (Optimising Technological Literacy Acquirement) to achieve optimality between the cost of the teaching process and its benefits. We conducted an empirical study with n = 320 participants on five technological literacy issues, and showed that optimal solutions to this problem can be found. Thus, OTLA has a significant contribution to protecting users’ privacy and security in the digital world.