Doctor of Philosophy, Case Western Reserve University, 2016, Management

In an effort to deepen customer relationships (Relationship Marketing), marketers and online firms deliver personalized communications based on a consumers' digital footprint and other Big Data that they think will improve its effect; but the personalized messages are sometimes perceived to be “creepy” by the recipient. Marketers are admonished to not be creepy, but, there is not a unified definition of what creepy is or isn't, nor have the factors leading to perceived creepiness been clearly identified—there is a common feeling of discomfort, but no unified definition. The goal of this study is to address three research questions. First, what is creepy? Second, what factors lead to perceived creepiness? And third, can a scale to measure perceived creepiness be operationalized and used to validate those factors? I conducted a three-part; sequential, mixed methods study to define perceived creepiness and to identify the antecedents and consequences of perceived creepiness in personalized online messages. The study confirmed that transparency by the firm about their data collection, use and sharing practices and that enabling the consumer to exercise control over the collection, use and sharing of their personal information (including the ability to opt–out of personalized messages) are antecedents of perceived creepiness. Also, whether the message was “in context” or “out of context” had an effect on if the message was perceived to be creepy. It also suggests that trust in the sender has a direct effect on perceived creepiness; and perceived creepiness has a negative effect on customer satisfaction, which can harm brand reputation, sales, and revenue. This research makes a scholarly contribution by providing a theoretical framework for a Theory of Perceived Creepiness. It also makes a contribution to practice by providing marketers with an understanding of what leads to perceived creepiness, so that they can take action to avoid negative effects of personalized com (open full item for complete abstract)

Committee: Richard Boland Jr. (Committee Chair); Mary Culnan (Committee Member); Kalle Lyytinen (Committee Member); Casey Newmeyer (Committee Member) Subjects: Information Science; Management; Marketing; Mass Media

MS, Kent State University, 2019, College of Arts and Sciences / Department of Computer Science

There is much evidence exists that a considerable number of users are either unaware of the side-effects of sharing personal information online or lack adequate knowledge about their rights or means concerning protecting their privacy. This becomes even more disturbing when we realize the vulnerability of children's information in social media environments. Their information is often posted online by their parents or guardians, resulting in a compromise in privacy. In this thesis, the study is separated into two phases, the first of which is a survey to understand parents' perceptions of children's online privacy. The second phase is based on the points learned in the first phase, to use a game-based learning in an attempt to educate parents when sharing their children's information online. The initial findings demonstrate that although parents and guardians might have a certain level of awareness and knowledge about their children's online privacy, their actions in some cases still violate their children's privacy. In addition, parents are unaware of the long-term impact for creating their children's digital footprints. The results of the survey and the game participation show that parents need to be more aware of their actions when sharing children's personal information, photos and videos on any social media platform. In addition, more needs to be done to develop tools or educate parents on methods to protect children's online privacy as the advancement of technology and increasing privacy violation cases on vulnerable groups of people become more and more complex.

Committee: Kambiz Ghazinour (Advisor) Subjects: Computer Science

Doctor of Philosophy (PhD), Ohio University, 2023, Instructional Technology (Education)

Educational institutions increasingly rely on educational technology to deliver academic experiences, particularly since the COVID-19 pandemic. This study focused on graduate students' attitudes about online privacy specific to use of technology for educational purposes. Students who participated were all current students at a university in the midwestern United States in one of three graduate programs in educational technology. The study used a qualitative methodology and relied on survey and interview tools to collect data in spring 2023. Seven findings emerged from the data to explore to address the research questions. To narrow the focus on recommended actions that the research location could reasonably address, three findings are centered with near-term actions that the institution could take to address students' privacy needs. The first finding was that the COVID-19 pandemic increased technology adoption and influenced attitudes about educational technology use as part of teaching and learning activities. The near term action is that the university should determine which technologies are in use for teaching and learning. The second findings was that preserving users' online privacy is a shared responsibility. To address this finding in the near term, faculty and staff should receive regular training to understand and preserve student privacy. The third finding was that the university should proactively engage users about online privacy. To address this finding, the university should develop regular communication with students about privacy. Actions like these that support students' online privacy may increase student confidence in the institution and increase awareness of online privacy, overall.

Committee: Jesse Strycker (Advisor); Greg Kessler (Committee Member); Min Lun Wu (Committee Member); Krisanna Machtmes (Committee Member); Laura Harrison (Committee Member) Subjects: Educational Technology; Instructional Design

Honors Theses, Ohio Dominican University, 2023, Honors Theses

Algorithms are used by social media platforms to gather information on users to better suggest updated content to maximize the time spent on their online platforms. The information gathered from the users (browsing history, search history, engaged posts, engaged channels, etc.) is analyzed by these algorithms and used to predict what content and channels to best offer the users for further engagement. Information such as liking patterns, following tendencies, content engagement behavior and more are fed to these algorithms to personalize the user's experience. The goal of this paper is to compare the effectiveness of the algorithms' ability to profile and induce users, in this case employed by Instagram, Twitter, and YouTube by tracking several variables, including interaction time and interaction rates with posts, channels, likes, and follows. The results of this study show the performance of each platform algorithm over weeks of observation and how they stack in terms of engagement effectiveness.

Committee: Alae Loukili (Advisor); Kristall Day (Other); Kenneth Fah (Other) Subjects: Computer Engineering; Computer Science

PHD, Kent State University, 2018, College of Arts and Sciences / Department of Computer Science

Social Routing in Online Social Networks (OSNs) is very challenging, as it must handle privacy and performance. This study proposes a Social Online Routing (SOR) protocol for OSNs that satisfies Stratified Privacy Model (SPM) core requirements and minimizes end-to-end routing delays corresponding to the social routing information elements exchanged under the SPM. SOR uses five messages (I-need Message, I-have Message, I-thank Message, I-like/dislike message, and the I-Ack Message) for carrying routing information. Forwarding models (I-need Module, I-have Module, I-thank Module, and I-ack Module) and routing algorithms (Topology aware Shortest-Path-Based routing algorithm, Social-Priority-Based routing algorithm, and Queue-aware Social-Priority-Based routing algorithm) are introduced. Four anonymization techniques are also utilized for stratified privacy. To evaluate the study's proposed protocol, an Online Social Networks Simulator is designed and implemented. Using real datasets from Google Plus, the simulator is used to evaluate end-to-end routing delays corresponding to the social routing information elements exchanged under the SPM.

Committee: Javed Khan Prof. (Advisor) Subjects: Computer Science

Doctor of Philosophy (PhD), Wright State University, 2017, Computer Science and Engineering PhD

As the Internet has grown increasingly popular as a communication and information sharing platform, it has given rise to two major types of Internet security threats related to two primary entities: end-users and network services. First, information leakages from networks can reveal sensitive information about end-users. Second, end-users systems can be compromised through attacks on network services, such as scanning-and-exploit attacks, spamming, drive-by downloads, and fake anti-virus software. Designing threat assessments to detect these threats is, therefore, of great importance, and a number of the detection systems have been proposed. However, these existing threat assessment systems face significant challenges in terms of i) behavioral diversity, ii) data heterogeneity, and iii) large data volume. To address the challenges of the two major threat types, this dissertation offers three unique contributions. First, we built a new system to identify network users via Domain Name System (DNS) traffic, which is one of the most important behavior-based tracking methods for addressing privacy threats. The goal of our system is to boost the effectiveness of existing user identification systems by designing effective fingerprint patterns based on semantically limited DNS queries that are missed by existing tracking efforts. Second, we built a novel system to detect fake anti-virus (AV) attacks, which represent an active trend in the distribution of Internet-based malware. Our system aims to boost the effectiveness of existing fake AV attack detection by detecting fake AV attacks in three challenging scenarios: i) fake AV webpages that require user interaction to install malware, instead of using malicious content to run automatic exploitation without users consent (e.g., shellcode); ii) fake AV webpages designed to impersonate real webpages using a few representative elements, such as the names and icons of anti-virus products from authentic anti-virus webpages (open full item for complete abstract)

Committee: Junjie Zhang Ph.D. (Advisor); Adam Robert Bryant Ph.D. (Committee Member); Bin Wang Ph.D. (Committee Member); Xuetao Wei Ph.D. (Committee Member) Subjects: Computer Science