3,470 research outputs found

    OfS consultation on harassment and sexual misconduct in higher education

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    Ubiquitous Social Networking:Concept and Evaluation

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    Reliable online social network data collection

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    Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users’ behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users’ behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.Postprin

    E-Commerce Digital Information Transparency and Satisfaction. Can We Have Too Much of a Good Thing?

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    Despite core product and service quality improvements and advances in shopping processes and technology, customers often report being unsatisfied with their online purchases. One plausible reason for lower customer satisfaction rates is too much or too little information that is shared with the customers about their orders. We show that when forming their perceptions about the purchases, customers form digital information satisfaction (DIS) levels as they evaluate supplementary informational services in addition to the core product being purchased. We believe that DIS is one of the dimensions of overall customer satisfaction. We also show that supplementary informational services are essential in meeting the increased informational needs of online shopping and, thus, can explain the decreased overall customer satisfaction level through the decreases in DIS. We develop and test the Digital Information Transparency and Satisfaction (DITS) model that shows how supplemental informational services influence digital information satisfaction (DIS_ in e-commerce. By doing so, this dissertation introduces a new dimension of satisfaction in the era of online shopping. This helps close the knowledge gap in the current research on overall customer satisfaction by showing that too much information transparency can harm the overall experience of the customers, thus leading to decreases in DIS. The study results provide a platform for future research on the influence of informational services provided during online shopping. Explaining the role of information shared with the customers in their perceptions of transparency and, consequently, DIS may help provide crucial practical business insights. Thus, by proposing the DITS model, this dissertation brings contributions to both theory and praxis by enhancing the understanding of DIS, which can serve as a robust foundation for future research on decreasing levels of overall customer satisfaction in a digital setting, as well as help companies improve their customer relationships

    Disclosure of Personal Information under Risk of Privacy Shocks

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    Companies are under an increasing pressure by policy makers to publicize data breaches. Such notification obligations require announcing the loss of personal data collected from customers, because of hacker attacks or other incidents. While notification is likely to impact on firms’ reputation, we know little about the impact such notifications have on consumers with respect to disclosure of their personal data. We present the problem as a dynamic lottery with personal data under the risk of privacy shocks and experimentally study how the privacy breach notification changes an individual’s behavior regarding data disclosure. Our results suggest that the notification induces individuals – disregarding the sensitivity of their data – to disclose more
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