Ubiquitous computing results in access to vast amounts of data, which is changing the way humans interact with each other, with computers, and with their environments. Information is literally at our fingertips with touchscreen technology, but it is not valuable until it is understood. As a result, selecting which information to use in a decision process is a challenge in the current information environment (Lu & Yuan, 2011). The purpose of this dissertation was to investigate how individual decision makers, in different decision contexts, determine when to stop collecting information given the availability of virtually unlimited information. Decision makers must make an ultimate decision, but also must make a decision that he or she has enough information to make the final decision (Browne, Pitts, & Wetherbe, 2007). In determining how much information to collect, researchers found that people engage in ‘satisficing' in order to make decisions, particularly when there is more information than it is possible to manage (Simon, 1957). A more recent elucidation of information use relies on the idea of stopping rules, identifying five common stopping rules information seekers use: mental list, representational stability, difference threshold, magnitude threshold, and single criterion (Browne et al., 2007). Prior research indicates a lack of understanding in the areas of information use (Prabha, Connaway, Olszewski, & Jenkins, 2007) and information overload (Eppler & Mengis, 2004) in Information Systems literature. Moreover, research indicates a lack of clarity in what information should be used in different decision contexts (Kowalczyk & Buxmann, 2014). The increase in the availability of information further complicates and necessitates research in this area. This dissertation seeks to fill these gaps in the literature by determining how information use changes across decision contexts and the relationships between stopping rules. Two unique methodologies were used to test the hypotheses in the conceptual model, which both contribute to research on information stopping rules. One tracks the participant during an online search, the second asks follow-up survey questions on a Likert scale. One of four search tasks (professional or personal context and a big data analytics understanding or restaurant location search) was randomly assigned to each participant. Results show different stopping rules are more useful for different decision contexts. Specifically, professional tasks are more likely to use stopping rules with an a priori decision on how much information to collect, while personal tasks encourage users to determine how much information to collect during the search process. The analysis also shows that different stopping rules have different emphases on quality and quantity of information. Specifically, representational stability requires both a high quality and quantity of information, while other stopping rules indicate a preference for one of the two. Finally, information quality and quantity ultimately have a positive relationship with decision confidence, satisfaction, and efficiency. The findings of this research are useful to practitioners and academics tackling issues with the availability of more information. As systems are designed for information search, understanding information stopping rules become increasingly important.

Dual process theories of mind have been developed over the last decades to posit that humans use heuristics or mental shortcuts (automatic) and analytical (reflective) reasoning while consuming information. Can such theories be used to support users' information consumption in the presence of problematic content in online spaces? To answer, I merge these theories with the idea of affordances from HCI to into the concept of dual process cognitive affordances, consisting of automatic affordance and reflective affordance. Using this concept, I built and tested a set of systems to address two categories of online problematic content: misinformation and filter bubbles. In the first system, NudgeCred, I use cognitive heuristics from the MAIN model to design automatic affordances for better credibility assessment of news tweets from mainstream and misinformative sources. In TransparencyCue, I show the promise of value-centered automatic affordance design inside news articles differentiating content quality. To encourage information consumption outside their ideological filter bubble, in NewsComp, I use comparative annotation to design reflective affordances that enable active engagement with stories from opposing-leaning sources. In OtherTube, I use parasocial interaction, that is, experiencing information feed through the eyes of someone else, to design a reflective affordance that enables recognition of filter bubbles in their YouTube recommendation feeds. Each system shows various degrees of success and outlines considerations in cognitive affordances design. Overall, this thesis showcases the utility of design strategies centered on dual process information cognition model of human mind to combat problematic information space. / Doctor of Philosophy / Over the last several decades, billions of users have moved to the internet for everyday information gathering, allowing information flow around the globe at a massive scale. This flow is managed by algorithms personalized to each users' need, creating a complicated trio of producer-algorithm-consumer. This has resulted in some unforeseen challenges. Bad information producers takes the advantage of system to promote problematic content, such as, false information, termed as misinformation. Personalized algorithms have created filters of what people see oftentimes isolating them from diverse perspectives of information, creating a distorted perception of reality. Augmenting the online technology infrastructure to combat these challenges has become crucial and the overall goal of this thesis. Cognitive psychologists theorize that two cognitive processes are at play when people consume information, also known as dual process theories. Can we design new tools to combat these challenges by tapping into each of these processes? In this thesis, I answer this question through a series of studies. In each of these studies, I combine this theory from psychology with design guides from Human-Computer Interaction to design socio-technical design. I evaluated each of these systems through controlled experimentation. The result of these studies informs ways we can capitalize on users' information processing mechanism to combat various types of problematic information online.

Master of Science / Department of Agricultural Economics / Glynn Tonsor / Recent predictions estimate that the global population will reach more than 9 billion by the year 2050 (Kochhar, 2014). Coupled with this challenge, environmental issues and climate change influence agricultural production over the globe (Jacobsen et al., 2013). Changes in the food chain have been in response to consumers becoming interested in how their food is produced as it relates to food safety. Some of these changes have come in the form of labeling of production methods and the increasing volume of organic products in the marketplace. In the livestock sector, production methods include administration of antibiotics and hormones to prevent disease, increase gains and increase the health of animals (Allen et al., 2013; Thornton, 2010). A potential solution of decreasing the amount of antibiotics and hormones in the future is the use of ribonucleic acid interference (RNAi). RNA interference is a method of silencing a targeted gene and suppressing expression (Bradford et al., 2016). The focus of this research is to explore the determinants of acceptance and willingness to pay for beef products utilizing RNAi technology in the food system. Through the means of a national survey, consumers were asked their demographic, food purchasing habits, and food safety concerns to identify potential acceptors of the technology. Respondents received information treatments and external articles regarding RNAi technology as well as information about governmental labeling regulations of the beef steaks. Choice experiment questions, and a dichotomous choice sequence were utilized to determine willingness to pay estimates of beef steak attributes by consumers. Results showed that respondents likely require a discount for beef steaks produced with RNAi technology. In some instances, some consumers would be willing to pay a premium for beef steaks with RNAi in certain label settings. These results of this study could be used in the realm of animal science to help with the introduction of this technology in the food system. The survey results could assist with future promotion and framing of the technology to a wide variety of consumers.

This article investigates the impacts of asymmetric information within a Lucas (1978) asset pricing economy. Asymmetry enters via the assumption that one group of agents is equipped with superior information about the dividend process. The agents maximize their lifetime utility of the underlying consumption process obtained from the agents' budget constraints, where the agents have the opportunity to invest in a risk asset to transfer income from the current to future periods. Since a closed form solution for the market price cannot be derived analytically, projection methods are applied, as described in Judd (1998), to approximate the expectation integrals in the agents' Euler equation. We derive the result that the informed trader only clearly improves his situation as compared to the non-trade situation if the uninformed trader only observes his own endowment but not the endowment of the informed trader. In the case where agents observe each others' endowment trade never results in a Pareto improvement. (auhtor's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"