Digital Searching: A Grounded Theory Study on the Modern Search Experience

<p>This Grounded theory study explores US adults' modern information search process as they pursue information through digital search user interfaces and tools. To study the current search process, a systematic grounded theory methodology and two data collection methods, a think-aloud protocol and semi-structured interviews, are used to develop the theory. The emerging theory addressed two tightly connected research questions that asked, “What is the process by which humans search and discover information?” and “What is the process by which search and discovery interfaces and tools support the modern search process?”</p> <p>The study collects participant data from US adults who have recently completed internet-based searches or were employed as reference librarians or information specialists that regularly support internet-based searches. The 40 data collection events, 20 interviews, and 20 think-aloud search activities are constant comparatively analyzed. This analysis shows the importance of validating information during the search process as 1) establishing accurate information needs and 2) verifying information results. This theory and study findings can be applied to modern search interface designs or used to update modern information literacy curriculums.</p>

Name thesis:degree_name

M.A.

Level thesis:degree_level

Masters Thesis

Year

2022

Author dc:creator

• Parés, Nicolas Armando

Contributors dc:contributor

• Peter Organisciak
• Nick Cutforth
• Erin Elzi
• Maria Riva

• Grounded theory
• Information behavior
• Information seeking process
• Search experience
• Search process
• Computer Sciences
• Education
• Educational Assessment, Evaluation, and Research
• Information Literacy
• Library and Information Science

Statement dc:rights

• <p>Copyright is held by the author. User is responsible for all copyright compliance.</p>

Language dc:language

en

Repository record dc:identifier

https://digitalcommons.du.edu/etd/2150

OAI identifier oai:identifier

oai:digitalcommons.du.edu:etd-3141