Companion Datasets (Study 1 & Study 2): Driving Sustainable Choices—Warning Messages in Car Ads and Their Effects on Attention and Emotions

This record contains the two anonymized datasets and accompanying documentation for the article Driving Sustainable Choices: The Impact of Warning Messages in Car Advertisements on Consumer Attention and Emotional Responses. The research examines how message framing (gain vs. loss) and warning format (text vs. image) shape visual attention (eye-tracking) and emotional/evaluative responses (self-reports) in static car advertisements. We provide one dataset per study: Study 1 (between-subjects, lab eye-tracking) — participants were randomly assigned to a gain-framed or loss-framed ad. Primary outcomes are AOI-level fixation metrics (e.g., fixation duration, fixation count) for text and image warning elements; environmental concern was measured as a moderator. Study 2 (within-subjects, self-reports) — each participant evaluated both ads. Outcomes include valence, arousal, dominance (SAM), subjective attention (internal vs. external), and perceived effectiveness, alongside environmental concern and basic sociodemographics. Files in this record Study_1_final.sav — Eye-tracking dataset for Study 1 (lab). Core variables (high-level): participant ID (anonymized), condition assignment (gain vs. loss), AOI-level fixation metrics for warning-text and warning-image (e.g., total fixation duration [ms], fixation count), environmental concern (scale), data-quality indicators. Study_2_final.sav — Self-report dataset for Study 2 (survey/ratings). Core variables (high-level): participant ID (anonymized), ad identifier/order, SAM valence/arousal/dominance, subjective attention (internal–external), perceived effectiveness, environmental concern (scale), sociodemographics. Methods snapshot Stimuli & AOIs. Static car ads for a virtual brand to avoid trademark issues; standardized size and colors. Warnings included both text and image elements aligned to the assigned frame. AOIs for warning-text and warning-image were predefined and used consistently across participants. Eye-tracking (Study 1). Recorded with Tobii Pro hardware (60 Hz) and parsed in Tobii Pro Lab® using I-VT (≥ ~60 ms). Primary metrics are AOI-level fixation duration and count. Self-reports (Study 2). SAM (valence/arousal/dominance; 9-point), subjective attention (internal–external), perceived effectiveness (Likert), plus environmental concern and sociodemographics. Analytic orientation. Study 1 focused on multivariate comparisons of AOI metrics across framing conditions; Study 2 modeled within-person differences across ads, testing main effects and the role of environmental concern.本数据集包含两篇经匿名化处理的数据集及配套文档，对应论文《驾驶可持续选择：汽车广告中警示信息对消费者注意力与情绪反应的影响》。 本研究探讨了信息框架（获益型vs损失型）与警示形式（文本型vs图像型）如何影响静态汽车广告中的视觉注意力（眼动追踪）与情绪/评价反应（自我报告量表）。本研究配套两个数据集，分别对应两项实验： 实验1（被试间设计，实验室眼动追踪）：参与者被随机分配至获益框架组或损失框架组的广告条件。核心因变量为文本与图像警示元素的兴趣区（Area of Interest, AOI）水平注视指标（如注视时长、注视次数）；环境关注度作为调节变量被纳入测量。 实验2（被试内设计，自我报告）：每名参与者均评估两款广告。测量指标包括愉悦度、唤醒度、支配度（Self-Assessment Manikin, SAM）、主观注意力（内在vs外在）、感知有效性，同时纳入环境关注度与基础社会人口学变量。 本数据集包含的文件如下： Study_1_final.sav：实验1的眼动追踪数据集（实验室环境）。 核心变量（顶层分类）：匿名化参与者ID、实验条件分配（获益型vs损失型）、文本警示与图像警示的兴趣区注视指标（如总注视时长[毫秒]、注视次数）、环境关注度量表、数据质量指标。 Study_2_final.sav：实验2的自我报告数据集（问卷/评分）。 核心变量（顶层分类）：匿名化参与者ID、广告标识/呈现顺序、SAM愉悦度、唤醒度、支配度、主观注意力（内在-外在）、感知有效性、环境关注度量表、社会人口学变量。 方法概述 刺激材料与兴趣区：为规避商标侵权问题，实验采用虚拟品牌的静态汽车广告，尺寸与色彩均经过标准化处理。警示信息包含与实验框架匹配的文本与图像元素；文本警示与图像警示的兴趣区均已预先定义，且在所有参与者中保持一致。 眼动追踪（实验1）：采用Tobii Pro硬件设备（60Hz）进行数据采集，使用Tobii Pro Lab®软件通过I-VT算法（≥约60毫秒）完成数据解析。核心指标为兴趣区水平的注视时长与注视次数。 自我报告测量（实验2）：采用SAM量表（愉悦度、唤醒度、支配度，9点计分）、主观注意力（内在-外在维度）、感知有效性（李克特量表），同时纳入环境关注度与社会人口学变量。 分析思路：实验1聚焦于不同信息框架条件下兴趣区指标的多变量比较；实验2则针对广告间的被试内差异构建模型，检验主效应以及环境关注度的调节作用。