Modeling Values of Internet of Things (IoT) Consumers’ for Privacy Decision-Making

The Internet of Things (IoT) has been gaining popularity; however, consumer privacy remains a challenge. Particularly, the lack of knowledge about IoT consumer’s privacy concerns coupled with uncertainty about the protection mechanisms provided by IoT manufactures inhibits one’s ability to make self-interested privacy decisions. Informed by multi-attribute utility theory, this study presents a decision model referred to as Privacy Expectation-Perception (EP) Utility Model. The model explicates consumers privacy concerns represented as privacy protection objectives and calculates the expected utility of achieving the objectives to the best level. The model is applied to assess the achievement of privacy objectives for a set of alternatives (i.e. IoT devices) and calculates the perceived utility of the IoT devices. This analysis provides privacy expectation-perception utility gap, an indicator of how well an IoT device achieves one’s privacy expectations. This study is expected to contribute to privacy concern and privacy decision making literature. The utility model will be a useful decision aid for both IoT consumers and manufacturers to strategize about privacy protection

DYNAMICS OF IDENTITY THREATS IN ONLINE SOCIAL NETWORKS: MODELLING INDIVIDUAL AND ORGANIZATIONAL PERSPECTIVES

This dissertation examines the identity threats perceived by individuals and organizations in Online Social Networks (OSNs). The research constitutes two major studies. Using the concepts of Value Focused Thinking and the related methodology of Multiple Objectives Decision Analysis, the first research study develops the qualitative and quantitative value models to explain the social identity threats perceived by individuals in Online Social Networks. The qualitative value model defines value hierarchy i.e. the fundamental objectives to prevent social identity threats and taxonomy of user responses, referred to as Social Identity Protection Responses (SIPR), to avert the social identity threats. The quantitative value model describes the utility of the current social networking sites and SIPR to achieve the fundamental objectives for averting social identity threats in OSNs. The second research study examines the threats to the external identity of organizations i.e. Information Security Reputation (ISR) in the aftermath of a data breach. The threat analysis is undertaken by examining the discourses related to the data breach at Home Depot and JPMorgan Chase in the popular microblogging website, Twitter, to identify: 1) the dimensions of information security discussed in the Twitter postings; 2) the attribution of data breach responsibility and the related sentiments expressed in the Twitter postings; and 3) the subsequent diffusion of the tweets that threaten organizational reputation

Use of IT and Wellbeing: A Literature Review

This paper undertakes a systematic review of the Information Systems literature on wellbeing. Therefore, we conduct a full-text analysis of relevant outlets (journals and conferences) to figure out the use of information technology for wellbeing. We show that literature can be synthesized into three groups: motivation, technostress, and the young generation. These three groups share seven types of IT and nine types of wellbeing. This investigation of the use of information technology for wellbeing is almost the first in the IS field. Therefore, this investigation could contribute to academic researchers and practitioners when they need to refer and get some insight for future research and development on wellbeing

Dynamics of Data Breaches in Online Social Networks: Understanding Threats to Organizational Information Security Reputation

The consequences of data breaches can be severe for the Information Security Reputation (ISR) of organizations. Using social media analytical techniques, this study examines Twitter postings to identify (1) ISR dimensions attributed as being responsible for data breaches and (2) social media sentiments in the aftermath of data breaches. By analyzing tweets related to the data breaches at Home Depot and JPMorgan Chase in 2014, the results suggest that five dimensions of organizational ISR are put into question: Risk and Resilience Structure; Security Ethics and Practices; Structures of Governance and Responsibility; Response Readiness; Social and Moral Benevolence. The attributions and sentiments vary for the five ISR dimensions. Moreover, tweets that attribute data breach responsibility carry more negative sentiments. This study makes an important theoretical contribution by identifying threats to ISR of organizations in social networks. The findings could benefit organizational strategies for social media reputation management and post-data breach intervention

Social Movement Sustainability on Social Media: An Analysis of the Women’s March Movement on Twitter

Social media has emerged as a powerful medium to organize and mobilize social movements. In particular, the connective action of social media builds associations and allows for the continuity of social movements. Yet there is a lack of research on how connective action emergent from social media messages sustains long-term social movements. Accordingly, in this study, we concentrate on Twitter messages related to Women’s March protests held in 2017, 2018, and 2019. Using an interpretive analysis followed by the topic modeling approach, we analyzed the tweets to identify the different types of messages associated with the movement. These messages were classified using a set of categories and subcategories. Furthermore, we conducted a temporal analysis of the message (sub)categories to understand how distinct messages allow movement continuity beyond a specific protest march. Results suggest that while most of the messages are used to motivate and mobilize individuals, the connective action tactics employed through messages sent before, during, and after the marches allowed Women’s March to become a broader and more persistent movement. We advance theoretical propositions to explain the sustainability of a long-term social movement on social media, exemplified through large-scale connective action that persists over time. In doing so, this study contributes to connective action research by providing message categorization that synthesizes the meaning of message content. The findings could help social movement organizers learn different ways to frame messages that resonate with broader social media users. Moreover, our approach to analyzing a large set of tweets might interest other qualitative researchers

SOCIAL MEDIA AND SOCIAL MOVEMENTS: A CASE OF WOMEN’S MARCH

Social media is a powerful medium to organize and mobilize social movements. In this study, we concentrate on the content of messages sent through social media during a particular social movement. Specifically, we focus on Twitter messages related to the Women’s March. Accordingly, we conduct a qualitative analysis of the content of the tweets posted during the marches held in January 2017 and January 2018. Through our analysis, we identify the different types of messages associated with the movement. These messages are classified through a set of categories and sub-categories. We found that most of the messages are used to motivate and mobilize individuals. Our research contributes to social movement and social media literature and enhances the understanding of how social media is used to motivate and mobilize people. The findings could also help social movement organizers in learning different ways in which social media can help them to frame their messages beyond motivation and mobilization

COVID-19 and Health Misinformation: A Topology and Classification Model

Misinformation on social media related to the ongoing Covid-19 pandemic presents a significant threat to the public and society. This study examines how misinformation influences individuals’ health behavior. Building on the Health Belief Model, we analyze over 5K fact-checked articles shared on social media platforms to identify different categories or topics of misinformation. We also analyze the veracity of the misinformation topics. Overall, thirteen topics emerged from our analysis, with most of the misinformation questioning the benefits of preventive actions such as masking or social distancing and undermining the severity of the pandemic. We also found a significant amount of misinformation related to sources such as the government, health agencies, and institutes that communicate about the pandemic. Further, we utilized the thirteen misinformation topics for training and building a classification model. The aim of this multiclass classifier is to classify the misinformation topics and predict topic labels for any new data. The evaluation results suggest that the Multiclass Support Vector Machine (MSVM)-based classifier achieved high performance for accuracy (88%), precision (85%), recall (83%), and F-measure (82%). The findings have implications for social media research. Public health experts and policymakers might find insights helpful in designing better communication and intervention strategies to counter the false narrative about the pandemic. The study lays the ground to examine further individuals’ health attitudes and behavior upon exposure to misinformation

Investigation of Health Misinformation During the Covid-19 Pandemic

This study examines how misinformation related to Covid-19 on social media exacerbates individuals’ perceptions of health threats. Informed by the Health Belief Model, we analyze over 5K fact-checked articles to identify different categories or topics of misinformation. We also analyze the veracity of the misinformation topics. Overall, thirteen topics emerged from our analysis, with most of the misinformation questioning the benefits of preventive actions and undermining the severity of the pandemic. We also found significant misinformation related to official sources such as health agencies and research institutes communicating about the pandemic. The findings have implications for social media and health research. Public health experts and policymakers might find insights helpful in designing better communication and intervention strategies to counter the false narrative about the pandemic. The study lays the ground to examine further individuals’ health attitudes and behavior upon exposure to misinformation

Disinformation and Social Movements: An Empirical Investigation

Our research in progress investigates digital disinformation and its impact on farmers’ movement in India. False and defaming information can lead to unintended outcomes and has potential to cause public harm, especially through social media propagation. We conducted a preliminary analysis of articles to understand the prevalence of disinformation in the farmers’ movement. Future work will include social media analysis on collected Twitter data to validate the emerged categories of disinformation in the social movement