---
res:
  bibo_abstract:
  - "Background: Artificial intelligence–based skin cancer screening apps (AISCSAs)
    offer diagnostic potential but face limited adoption. App store cues, such as
    ratings, may influence acceptance; yet, little is known about how users cognitively
    process app store information in high-stakes health contexts. To address this
    gap, eye-tracking was used to measure visual attention while participants evaluated
    a mock AISCSA app store listing.\r\n\r\nObjective: This study aimed to test whether
    a single negative rating captures visual attention and whether an extended technology
    acceptance model (TAM) can predict behavioral intention to use (BI) AISCSAs.\r\n\r\nMethods:
    Participants (N=76) evaluated a mock app store listing for an AISCSA under positive
    (n=42) or negative (n=34) rating conditions while their eye movements were recorded.
    Analyses combined fixation durations in defined areas of interest (AOIs) with
    self-reported measures of perceived usefulness (PU), perceived ease of use (PEOU),
    trust, BI, willingness to pay, and the self-rated importance of app attributes.\r\n\r\nResults:
    Normalized fixation durations (seconds per square pixel) revealed the highest
    attention to the description (0.166 s/px2), followed by the reviews (0.11 s/px2)
    and the ratings (0.04 s/px2), while the price and the data protection received
    the least attention. Of the 5 self-rated app attributes, only reviews correlated
    positively with fixation durations on the reviews-AOI (r=0.28; P=.01). Rating
    valence had no significant effect on gaze patterns, PU, PEOU, trust, BI, or willingness
    to pay (all Ps>.05). However, PEOU (P=.001), PU (P<.001), and trust (P<.001) were
    significantly correlated with BI.\r\n\r\nConclusions: Although the expected attentional
    capture effect of the negative rating was not observed, the weak or nonexistent
    associations between fixation durations on the AOIs and the self-rated importance
    of app attributes suggest that eye-tracking captures aspects of information processing
    that are not directly reflected in self-reported evaluations. These findings indicate
    that eye-tracking provides a more direct approximation of actual user behavior
    by revealing implicit attentional processes beyond what is captured by questionnaires.
    While the technology acceptance model constructs and trust predicted BI, rating
    valence alone did not affect acceptance or gaze behavior. In high-stakes health
    contexts, textual information may outweigh rating valence in driving adoption.
    Future research should explore conditions under which rating valence matters,
    including more extreme rating contrasts, variations in accompanying review texts,
    and the influence of individual differences such as preexisting attitudes toward
    artificial intelligence and levels of artificial intelligence literacy.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Inga
      foaf_name: Jagemann, Inga
      foaf_surname: Jagemann
      foaf_workInfoHomepage: http://www.librecat.org/personId=252878
    orcid: 0000-0002-3468-3423
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-3468-3423/work/217580444
  - foaf_Person:
      foaf_givenName: Sabrina
      foaf_name: Hegner, Sabrina
      foaf_surname: Hegner
  - foaf_Person:
      foaf_givenName: Gerrit
      foaf_name: Hirschfeld, Gerrit
      foaf_surname: Hirschfeld
      foaf_workInfoHomepage: http://www.librecat.org/personId=234690
    orcid: 0000-0003-2143-4564
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0003-2143-4564/work/217580445
  bibo_doi: 10.2196/93489
  bibo_volume: 13
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2292-9495
  dct_language: eng
  dct_publisher: JMIR Publications Inc.@
  dct_title: 'The Role of Rating Valence in AI Skin Cancer App Acceptance: Eye-Tracking
    and Questionnaire Study@'
...