NEW ARTIFACTS FOR THE KNOWLEDGE DISCOVERY VIA DATA ANALYTICS (KDDA) PROCESS

Recently, the interest in the business application of analytics and data science has increased significantly. The popularity of data analytics and data science comes from the clear articulation of business problem solving as an end goal. To address limitations in existing literature, this dissertation provides four novel design artifacts for Knowledge Discovery via Data Analytics (KDDA). The first artifact is a Snail Shell KDDA process model that extends existing knowledge discovery process models, but addresses many existing limitations. At the top level, the KDDA Process model highlights the iterative nature of KDDA projects and adds two new phases, namely Problem Formulation and Maintenance. At the second level, generic tasks of the KDDA process model are presented in a comparative manner, highlighting the differences between the new KDDA process model and the traditional knowledge discovery process models. Two case studies are used to demonstrate how to use KDDA process model to guide real world KDDA projects. The second artifact, a methodology for theory building based on quantitative data is a novel application of KDDA process model. The methodology is evaluated using a theory building case from the public health domain. It is not only an instantiation of the Snail Shell KDDA process model, but also makes theoretical contributions to theory building. It demonstrates how analytical techniques can be used as quantitative gauges to assess important construct relationships during the formative phase of theory building. The third artifact is a data mining ontology, the DM3 ontology, to bridge the semantic gap between business users and KDDA expert and facilitate analytical model maintenance and reuse. The DM3 ontology is evaluated using both criteria-based approach and task-based approach. The fourth artifact is a decision support framework for MCDA software selection. The framework enables users choose relevant MCDA software based on a specific decision making situation (DMS). A DMS modeling framework is developed to structure the DMS based on the decision problem and the users\u27 decision preferences and. The framework is implemented into a decision support system and evaluated using application examples from the real-estate domain

Understanding Sticky News: Analyzing the Effect of Content Appeal and Social Engagement for Sharing Political News Online

This dissertation investigates the concept of news stickiness and why certain news stories are shared more than others in an online environment. Building on theories of framing, uses and gratifications, and social psychology, the study is guided by the perspective that sharing behavior is considered a joint product of informational and personal factors. Previous research in the investigation of sharing motivations were usually one-sided, focusing on one particular attribute that contributes to the behavior; however, this dissertation argues the two key factors that drive news sharing each play a role in moving the audiences from content “internalizing” to content “externalizing.” Additionally, the dissertation also considers that the act of news sharing is carried out by humans and therefore, driven by the innate human needs that extend beyond content captivation. To bridge the gap in existing research, this dissertation adopts a mixed methods approach consisting of the following: 1) Framing analysis of the “most shared articles of the day” on the New York Times website, examining shared content characteristics; and 2) online experiment testing whether the content features concluded from the framing analysis would make news stories more likely to be shared, with a post-experiment questionnaire evaluating the audience’s psychological motivations for sharing. Findings revealed that news personalization, particularly the use of emotional testimony, localized identification, and partisan provocation, constitutes the key content appeal shared by all articles sampled. Moreover, social engagement appeal is made up of five elements that help explain sharing behavior: reciprocal value, individual interest, information utility, persuasion potential, and the bandwagon effect. This dissertation is a step forward toward better understanding of how to make news sticky, in a sense that the news will not only be read but will also be shared extensively. It provided recommendations for news organizations seeking to analyze web traffic data and produce content that deeply resonates with their audiences. This study further contributed to the theoretical frameworks in audience engagement by associating human psychology with news sharing and ultimately confronted concerns such as an attraction to ‘fake news’ or a lack of interest in critical news on key issues