The Dilemma of User Engagement in Privacy Notices

Digital communication strategies used by private companies, entrepreneurs, and public entities to attract long-stay tourists: a review

Globalization and the changes in the technological and social industries have facilitated international mobility in such a way that the distinctions between tourism and other forms of mobility closer to migration have practically become blurred. Improvements in digital communication have promoted people’s search for a better quality of life in terms of health, work, leisure, and other aspects. New forms of communication have started to gradually displace the more conventional media. In this context, private companies, entrepreneurs, and public entities have grown aware of the importance of digital communication to achieve a greater scope in their population attraction policies. In this context, the present study aims to identify which digital communication strategies are used to generate interest and attraction to the destination. In our Systematic Literature Review (SLR), we examine the relationship between the terms “lifestyle migration” and “digital communication”, obtaining 294 articles found in Web of Science (WOS) and Scopus. After the analysis, we found 16 potential articles that were linked to the research objectives. The results of the methodology are classified according to the different digital communication actions identified, namely (i) Social Network Relationship; (ii) Digital Marketing, and (iii) Virtual Reality and Augmented Reality. Therefore, this this study contributes to link lifestyle migration and digital communication to destination management offices, companies and entrepreneurs. The paper finishes with a discussion of theoretical and practical implications of research on digital communication strategies in the field of long-stay tourism.

Investigation the role of contrast on habituation and sensitisation effects in peripheral areas of graphical user interfaces

Graphical user interfaces are designed so that the most important elements are usually located in the central part of the screen, where they catch the user’s attention. However, there are situations where it is necessary to attract the user’s attention to make him/her notice, e.g., a critical alert, which is customarily displayed in the peripheral area so as not to interact with the main content. Therefore, our focus is to deliver an increased visibility of content in the peripheral area of the display in a non-intrusive way. Thus, the main purpose of this work is to analyze the visibility of the stimulus (in the form of colored discs), displayed in the peripheral area of a screen, which distracts users from the central part of the interface. The habituation and sensitization effects were considered to study which parameters catch and hold the user’s attention, despite the length of their interaction with the system. The experiments performed indicated how the parameters should be set to reduce the habituation effect without the need to use content with the highest visual intensity. The results showed that a high visual intensity is not necessarily needed for the best impact. A medium contrast level, a horizontal or vertical display localization, and a flashing frequency of 2 Hz are sufficient to obtain the best visibility in the peripheral area. In the case of critical alerts and the need for short-term intensive stimuli, it is worth highlighting these with high contrast. This configuration should be the most effective if it is not a continuous operation. However, they can cause unnecessary irritation or even cognitive load for more extended usage.

The addiction behavior of short-form video app TikTok: The information quality and system quality perspective

TikTok has one of the most advanced algorithm systems and is the most addictive as compared to other social media platforms. While research on social media addiction is abundant, we know much less about how the TikTok information system environment affects users' internal states of enjoyment, concentration, and time distortion (which scholars define as the flow experience), which in turn influences their addiction behavior. To fill this gap, this study collects responses from 659 adolescents in China aged between 10 and 19 years old, and the data is then analyzed using Partial Least Square (PLS). We find that the system quality has a stronger influence than information quality in determining adolescents' experience with TikTok and that the flow experience has significant direct and indirect effects on TikTok addiction behavior. Notably, this study finds that TikTok addiction is determined by users' mental concentration on the medium and its content. Several theoretical insights from the stimulus-organism-response (S-O-R) model and the flow theory are used to explain the findings.

What Users Want From Cloud Deletion and the Information They Need: A Participatory Action Study

Current cloud deletion mechanisms fall short in meeting users’ various deletion needs. They assume all data is deleted the same way—data is temporally removed (or hidden) from users’ cloud accounts before being completely deleted. This assumption neglects users’ desire to have data completely deleted instantly or prefer to have it recoverable for a more extended period. To this date, these preferences have not been explored. To address this gap, we conducted a participatory study with four groups of active cloud users (five subjects per group). We examined their deletion preferences and the information they require to aid deletion. In particular, we explored how users want to delete cloud data and identify what information about cloud deletion they consider essential, the time it should be made available to them, and the communication channel that should be used. We show that cloud deletion preferences are complex and multi-dimensional, varying between subjects and groups. Information about deletion should be within reach when needed, for instance, be part of deletion controls. Based on these findings, we discuss the implications of our study in improving the current deletion mechanism to accommodate these preferences.

Privacy Policies of IoT Devices: Collection and Analysis

Currently, personal data collection and processing are widely used while providing digital services within mobile sensing networks for their operation, personalization, and improvement. Personal data are any data that identifiably describe a person. Legislative and regulatory documents adopted in recent years define the key requirements for the processing of personal data. They are based on the principles of lawfulness, fairness, and transparency of personal data processing. Privacy policies are the only legitimate way to provide information on how the personal data of service and device users is collected, processed, and stored. Therefore, the problem of making privacy policies clear and transparent is extremely important as its solution would allow end users to comprehend the risks associated with personal data processing. Currently, a number of approaches for analyzing privacy policies written in natural language have been proposed. Most of them require a large training dataset of privacy policies. In the paper, we examine the existing corpora of privacy policies available for training, discuss their features and conclude on the need for a new dataset of privacy policies for devices and services of the Internet of Things as a part of mobile sensing networks. The authors develop a new technique for collecting and cleaning such privacy policies. The proposed technique differs from existing ones by the usage of e-commerce platforms as a starting point for document search and enables more targeted collection of the URLs to the IoT device manufacturers' privacy policies. The software tool implementing this technique was used to collect a new corpus of documents in English containing 592 unique privacy policies. The collected corpus contains mainly privacy policies that are developed for the Internet of Things and reflect the latest legislative requirements. The paper also presents the results of the statistical and semantic analysis of the collected privacy policies. These results could be further used by the researchers when elaborating techniques for analysis of the privacy policies written in natural language targeted to enhance their transparency for the end user.