Three Essays on Trust Mining in Online Social Networks

This dissertation research consists of three essays on studying trust in online social networks. Trust plays a critical role in online social relationships, because of the high levels of risk and uncertainty involved. Guided by relevant social science and computational graph theories, I develop conceptual and predictive models to gain insights into trusting behaviors in online social relationships. In the first essay, I propose a conceptual model of trust formation in online social networks. This is the first study that integrates the existing graph-based view of trust formation in social networks with socio-psychological theories of trust to provide a richer understanding of trusting behaviors in online social networks. I introduce new behavioral antecedents of trusting behaviors and redefine and integrate existing graph-based concepts to develop the proposed conceptual model. The empirical findings indicate that both socio-psychological and graph-based trust-related factors should be considered in studying trust formation in online social networks. In the second essay, I propose a theory-based predictive model to predict trust and distrust links in online social networks. Previous trust prediction models used limited network structural data to predict future trust/distrust relationships, ignoring the underlying behavioral trust-inducing factors. I identify a comprehensive set of behavioral and structural predictors of trust/distrust links based on related theories, and then build multiple supervised classification models to predict trust/distrust links in online social networks. The empirical results confirm the superior fit and predictive performance of the proposed model over the baselines. In the third essay, I propose a lexicon-based text mining model to mine trust related user-generated content (UGC). This is the first theory-based text mining model to examine important factors in online trusting decisions from UGC. I build domain-specific trustworthiness lexicons for online social networks based on related behavioral foundations and text mining techniques. Next, I propose a lexicon-based text mining model that automatically extracts and classifies trustworthiness characteristics from trust reviews. The empirical evaluations show the superior performance of the proposed text mining system over the baselines

Aligning Cybersecurity in Higher Education with Industry Needs

Cybersecurity is among the highest in-demand skills for Information Systems graduates and therefore is critical for the Information Systems curriculum. There is a substantial lack of skilled cybersecurity graduates. It is estimated that there is a global shortage of almost three and a half million cybersecurity professionals in 2022. Organizations are facing difficulties filling security positions. Thus, the Information Systems curriculum must be redesigned to meet business and industry needs and better prepare Information Systems graduates for cybersecurity careers. This study provides a model for designing a cybersecurity course that will align with industry needs to respond to the shortage of cybersecurity professionals. The proposed model is based on backward course design, aligned with the guidelines from the National Institute of Standards and Technology Cybersecurity Framework and The National Initiative for Cybersecurity Education Strategic Plan, and insights from interviews with industry professionals. We applied the model at a higher education institute in the USA, as higher education graduates fill most cybersecurity positions. The designed course was met with high levels of student satisfaction, positive industry feedback, and high levels of student success. Our proposed model can be applied to any educational institute and customized to desired needs of the institute, students, and the industry with minimal cost and time consideration

Aligning Information Systems Security in Higher Educaiton With Industry Needs

Organizations have become especially vulnerable to security threats to their most important asset, information. As a result, information security (ISec) has become one of the most demanded skills of Information System (IS) graduates and therefore is of critical importance for ISec curriculum. However, there is still a big shortage in skilled ISec graduates that meet industry needs. Organizations are facing difficulties filling security analyst positions, and it is predicted there will be a global shortage of two million cyber security professionals by 2019. Previous research stresses that IS curriculum need to be redesigned to meet the business and industry needs and better prepare IS graduates for future careers (Lee and Han 2008; Tan et al 2018). This study provides a framework for how to use backwards course design to develop an Information Systems Security course that will align with industry needs. The proposed framework uses the three main stages of backwards course design including, identifying desired results, determining acceptable evidence, and planning learning experiences and instruction. In stage one, course educational outcomes and learning goals are redesigned to align with the industry needs. In stage two, all the course evaluations criteria and assessment methods are designed to support the updated learning objectives, and in stage three, instructional methods and learning activities are redesigned. We use the theoretical framework to redesign an IS security course at a medium sized business school in the southeastern United States to align with industry needs by incorporating the current and future security needs of US companies. To address the security industry needs, we research current security trends and needs, future security plans and needs, and required and preferred qualifications of job candidates by US security companies. Multiple interviews are done to survey IS security experts to examine the current and future industry needs. We will compile our research findings to create the outcomes and learning goals. Then we will develop our assessments and course actives to support the learning goals and outcomes. The final redesigned course along practical implications are presented

The Effect of Website Quality on Repurchase Intention: The Moderating Role of Espoused Cultural Differences

Most previous research studied the effect of e-service and website quality on customer (re)purchase intention. Multiple studies found that customers’ espoused cultural values influenced their perception of e-service and website quality. However, the role of such differences on customers (re)purchase intention with respect to website quality has not been studied until now. In this study, we examined the moderating role of customer’s espoused cultural values on the effect of website quality and ultimately on repurchase intention using Hofstede’s cultural dimensions and Loiacono’s WebQual. Our empirical findings suggest that ignoring the espoused cultural differences of customers when examining the effect of website quality on (re)purchase intention leads to misleading results. Thus, this study extends previous research by adding an important construct to the previous general model (the direct effect of website quality on repurchase intention) and makes it easier to better understand online consumers’ repurchase intention

Predicting Opinion Leaders in Word-of-Mouth Communities

Opinion leaders play a major role in shaping potential customers’ minds in word-of-mouth communities. Prior research on opinion leaders in word-of-mouth communities has focused mostly on structural characteristics of social networks of the leaders, ignoring the predictive potential of the actual text that they write. We argue that textual characteristics of reviews, along with reviewer characteristics, could be used to predict opinion leadership. In this paper, we propose a predictive model to identify opinion leaders in online word-of-mouth communities using both review and reviewer characteristics. The results from our study indicate that the predictive performance of the classification models built using the proposed predictors is much better than that of a baseline bag-of-words model built using the actual review text