Social media offers a powerful platform for the independent digital content producer community to develop, disperse, and maintain their brands. In terms of information systems research, the broad majority of the work has not examined hedonic consumption on Social Media Sites (SMS). The focus has mostly been on the organizational perspectives and utilitarian gains from these services. Unlike through traditional commerce channels, including e-commerce retailers, consumption enhancing hedonic utility is experienced differently in the context of a social media site; consequently, the dynamic of the decision-making process shifts when it is made in a social context. Previous research assumed a limited influence of a small, immediate group of peers. But the rules change when the network of peers expands exponentially. The assertion is that, while there are individual differences in the level of susceptibility to influence coming from others, these are not the most important pieces of the analysis--unlike research centered completely on influence. Rather, the context of the consumption can play an important role in the way social influence factors affect consumer behavior on Social Media Sites. Over the course of three studies, this dissertation will examine factors that influence consumer decision-making and the brand personalities created and interpreted in these SMS. Study one examines the role of different types of peer influence on consumer decision-making on Facebook. Study two observes the impact of different types of producer message posts with the different types of influence on decision-making on Twitter. Study three will conclude this work with an exploratory empirical investigation of actual twitter postings of a set of musicians. These studies contribute to the body of IS literature by evaluating the specific behavioral changes related to consumption in the context of digital social media: (a) the power of social influencers in contrast to personal preferences on SMS, (b) the effect on consumers of producer message types and content on SMS at both the profile level and the individual message level.

A Pairwise Comparison Matrix (PCM) is used to compute for relative priorities of criteria or alternatives and are integral components of widely applied decision making tools: the Analytic Hierarchy Process (AHP) and its generalized form, the Analytic Network Process (ANP). However, a PCM suffers from several issues limiting its application to large-scale decision problems, specifically: (1) to the curse of dimensionality, that is, a large number of pairwise comparisons need to be elicited from a decision maker (DM), (2) inconsistent and (3) imprecise preferences maybe obtained due to the limited cognitive power of DMs. This dissertation proposes a PCM Framework for Large-Scale Decisions to address these limitations in three phases as follows. The first phase proposes a binary integer program (BIP) to intelligently decompose a PCM into several mutually exclusive subsets using interdependence scores. As a result, the number of pairwise comparisons is reduced and the consistency of the PCM is improved. Since the subsets are disjoint, the most independent pivot element is identified to connect all subsets. This is done to derive the global weights of the elements from the original PCM. The proposed BIP is applied to both AHP and ANP methodologies. However, it is noted that the optimal number of subsets is provided subjectively by the DM and hence is subject to biases and judgement errors. The second phase proposes a trade-off PCM decomposition methodology to decompose a PCM into a number of optimally identified subsets. A BIP is proposed to balance the: (1) time savings by reducing pairwise comparisons, the level of PCM inconsistency, and (2) the accuracy of the weights. The proposed methodology is applied to the AHP to demonstrate its advantages and is compared to established methodologies. In the third phase, a beta distribution is proposed to generalize a wide variety of imprecise pairwise comparison distributions via a method of moments methodology. A Non-Linear Programming model is then developed that calculates PCM element weights which maximizes the preferences of the DM as well as minimizes the inconsistency simultaneously. Comparison experiments are conducted using datasets collected from literature to validate the proposed methodology.

This study explores the impact of feedback and feedforward and personality on computer-mediated behavior change. The impact of the effects were studied using subjects who entered information relevant to their diet and exercise into an online tool. Subjects were divided into four experimental groups: those receiving only feedback, those receiving only feedforward, those receiving both, and those receiving none. Results were analyzed using regression analysis. Results indicate that both feedforward and feedback impact behavior change and that individuals with individuals ranking low in conscientiousness experienced behavior change equivalent to that of individuals with high conscientiousness in the presence of feedforward and/or feedback.

Ample evidence exists to support the conclusion that enterprise search is failing its users. This failure is costing corporate America billions of dollars every year. Most enterprise search engines are built using web search engines as their foundations. These search engines are optimized for web use and are inadequate when used inside the firewall. Without the ability to use popularity-based measures for ranking documents returned to the searcher, these search engines must rely on full-text search technologies. The Information Science literature explains why full-text search, by itself, fails to adequately discriminate relevant from irrelevant documents. This failure in discrimination results in far too many documents being returned to the searcher, which causes enterprise searchers to abandon their searches in favor of re-creating the documents or information they seek. This dissertation describes and evaluates a potential solution to the problem of failed enterprise search derived from the Information Science literature: subject-aided search. In subject-aided search, full-text search is augmented with a search of subject metadata coded into each document based upon a hierarchically structured subject index. Using the Design Science methodology, this dissertation develops and evaluates three IT artifacts in the search for a solution to the wicked problem of enterprise search failure.