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.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc862880 |
| Date | 08 1900 |
| Creators | Gerhart, Natalie |
| Contributors | Windsor, John, Sidorova, Anna, Prybutok, Victor |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Gerhart, Natalie, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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