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Operationalization and Detection Verification of a Narrative-Based UX Evaluation Framework: A Multiple-Case Analysis of a Public Digital Service Task

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2026, 31(10), pp.207~224
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 9, 2026
  • Accepted : October 8, 2026
  • Published : October 30, 2026

Sunyoung Jang 1,  Hokyoung Ryu 1

1한양대학교

Accredited

ABSTRACT

In public digital services, task completion alone cannot sufficiently explain the changes in users' states and perceived control (Dominance) that occur during the process of use. The purpose of this study is to verify the detection function of the operationalized analysis procedure of the narrative-based UX evaluation framework proposed in prior research by applying it to an actual public digital service task. Five participants performed the same task on a prototype that reproduced the resident registration card reissuance process of Gov24, and PAD self-reports anchored to 27 micro-events, behavioral data, and retrospective think-aloud (RTA) accounts were collected and compared. The stage assignments of two independent coders agreed on 26 events (Cohen's κ = .948), free narratives were classified into two cases of construction success, two of partial construction, and one of construction failure, and 16 convergent, 3 partially convergent, and 1 divergent case appeared across 20 analysis units. These results show that the objective task structure and the subjective experience structure constructed by users may not coincide, and that behavioral completion of a task does not guarantee the recovery of perceived situational control (Dominance). This study is meaningful in that it operationalized narrative into analyzable UX data on a shared micro-event timeline. These results constitute an initial verification of the framework's applicability in five cases, and its general validity will be examined with larger samples in a planned follow-up study.

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