In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed. Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are two text data computer algorithms that have received much attention individually in the text data literature for topic extraction studies but not for document classification nor for comparison studies. Since classification is considered an important human function and has been studied in the areas of cognitive science and information science, in this dissertation a research study was performed to compare LDA, LSA and humans as document classifiers. The research questions posed in this study are: R1: How accurate is LDA and LSA in classifying documents in a corpus of textual data over a known set of topics? R2: How accurate are humans in performing the same classification task? R3: How does LDA classification performance compare to LSA classification performance? To address these questions, a classification study involving human subjects was designed where humans were asked to generate and classify documents …

This research study involves three essays and examines CRQ-driven decision making from the points of view of the common firm, social-commerce platform provider, and social-commerce echo-system. It addresses CRQ's progression from traditional business-to-consumer (B2C) initiatives to social platform-specific antecedents and to environment-driven factors lying outside the direct control of the platform provider, yet influencing social commerce business decisions, such as user-generated content from peers (e.g. family, friends) and expert authority (e.g. specialists, experts, professional organizations). The research method used statistical, data mining and computer science techniques. The results suggest that social platform providers should take a proactive approach to CRQ, fully leverage their online platform to improve CRQ while paying special attention to security as a potential barrier, and consider the analysis of elements of the echo-system such as the electronic word of mouth (eWOM) to further drive CRQ and determine the level of alignment between customers and experts, suppliers and products featured, that may lead to value-added managerial insights such as the prioritization, promotion and optimization of such relationships.

Behavioral operations management (BOM) has gained popularity in the last two decades. The main theme in this new stream of research is to include the human behavior in Operations Management (OM) models to increase the effectiveness of such models. BOM is classified into 4 areas: cognitive psychology, social psychology, group dynamics and system dynamics (Bendoly et al. 2010). This dissertation will focus on the first class, namely cognitive psychology. Cognitive psychology is further classified into heuristics and biases. Tversky and Kahneman (1974) discussed 3 heuristics and 13 cognitive biases that usually face decision makers. This dissertation is going to study 6 cognitive biases under the representativeness heuristic. The model in this dissertation states that cognitive reflection of the individual (Frederick 2005) and training about cognitive biases in the form of warning (Kaufmann and Michel 2009) will help decisions’ makers make less biased decisions. The 6 cognitive biases investigated in this dissertation are insensitivity to prior probability, insensitivity to sample size, misconception of chance, insensitivity to predictability, the illusion of validity and misconception of regression. 6 scenarios in OM contexts have been used in this study. Each scenario corresponds to one cognitive bias. Experimental design has been used as the research …

This work embraces three essays on information privacy: 1) Measures of Personal Information Privacy in Social Networks: Information Control and Situation Awareness, 2) Care to Share your Personal Information? and 3) Privacy Breaches: How Resilient Are You? Every transaction made either online or offline, and every social interaction that is transferred or stored electronically in some way, are generally consumed as big data and ultimately drives the analytics from which consumers benefit. However, this raises some concerns about privacy and ethics. For example, should companies that consumers interact with be allowed to sell their personal information? Consumers derive certain benefits such as personalized content when they choose to offer their data to many websites. However, consumers providing personal data to websites subject themselves to possible privacy invasion when third parties purchase their data. In this case, since the consumer willfully gave away their personal information, is it genuinely personal, and should they retain some, if any, control over it? Theories such as privacy calculus and protection motivation theory (PMT) are a couple of prominent examples that focus on the privacy risks and benefits that drive consumer behavior. However, there is still a lack of research on the instantiation of privacy …