Inefficiencies associated with online information search are becoming increasingly prevalent in digital environments due to a surge in Consumer Generated Content (CGC). Despite growing scholarly interest in investigating users' information search behavior in CGC environments, there is a paucity of studies that explores the phenomenon from a theory-guided angle. Drawing on Information Foraging Theory (IFT), we re-conceptualize online information search as a form of adaptive user behavior in response to system design constraints. Through this theoretical lens, we advance separate taxonomies for online information search tactics and strategies, both of which constitute essential building blocks of the search process. Furthermore, we construct a research framework that bridges the gap between online information search tactics and strategies by articulating how technology-enabled search tactics contribute to the fulfillment of strategic search goals. We validate our research framework via an online experiment by recruiting participants from Amazon Mechanical Turk (AMT). Participants were tasked to perform searches on custom-developed online review websites, which were modeled after a popular online review website and populated with real restaurant review data. Empirical findings reveal that the provision of different search features indeed engenders distinct search tactics, thereby allowing users varying levels of search determination control and search manipulation control. In turn, both types of search controls affects users' result anticipation and search costs, which when combined, determine the efficiency of goal-oriented search strategy and the utility of exploratory search strategy. This study provides valuable insights that can guide future research and practice.
| Identifer | oai:union.ndltd.org:hkbu.edu.hk/oai:repository.hkbu.edu.hk:etd_oa-1326 |
| Date | 01 September 2016 |
| Creators | Liu, Fei |
| Publisher | HKBU Institutional Repository |
| Source Sets | Hong Kong Baptist University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Open Access Theses and Dissertations |
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