Post-Crisis Servicescapes: Anonymised Dataset and Replication Package

This record contains the anonymised dataset, codebook, and reproducible R scripts for the project “Post-Crisis Servicescapes”.

The package includes data from two complementary empirical studies conducted in the canton of Fribourg, Switzerland.

Study 1 uses structural equation modelling and bootstrapped mediation analysis to examine whether residual infection-related fear predicts avoidance of public service environments through differentiated forms of health anxiety.

Study 2 uses a 2 × 2 experimental design to test how store density (high vs. low) and visible sanitary signals (present vs. absent) affect perceived crowding and shopping satisfaction.

The shared dataset contains the full anonymised sample (N = 476). The analytic sample used in the paper (N = 425) is obtained through a documented exclusion criterion (responses completed in ≤ 300 seconds), implemented in the R scripts.

Files included:- anonymised CSV dataset- variable codebook- reproducible R scripts- renv lockfile- repository README and licence

The broader theoretical contribution concerns vulnerability-sensitive appraisal in shared service environments, with the COVID-19 aftermath serving as a specific empirical case.

Minor update: README updated with final DOI and citation.

本数据集记录包含“后危机服务场景”（Post-Crisis Servicescapes）项目的匿名化数据集、变量编码手册（codebook）与可复现R脚本（R scripts）。
本数据包涵盖在瑞士弗里堡州（canton of Fribourg）开展的两项互补实证研究所采集的全部数据。
研究1采用结构方程模型（structural equation modelling）与Bootstrap中介分析（bootstrapped mediation analysis），旨在检验残留感染相关恐惧是否会通过差异化的健康焦虑形式，预测公众对公共服务场景的规避行为。
研究2采用2×2实验设计（2 × 2 experimental design），探究店铺密度（高/低）与可见卫生信号（存在/缺失）如何影响感知拥挤程度与购物满意度。
本次共享的数据集包含全部匿名化样本（N=476）。论文中使用的分析样本（N=425）则通过已文档化的排除标准（作答时长≤300秒）筛选得到，该筛选规则已在R脚本中实现。
包含文件如下：
- 匿名化CSV数据集
- 变量编码手册（codebook）
- 可复现R脚本（R scripts）
- renv锁定文件（renv lockfile）
- 仓库自述文件（README）与许可证（licence）
本研究的核心理论贡献聚焦于共享服务场景中的脆弱性敏感评估，以新冠疫情后时代作为具体实证案例。
小幅更新：已更新自述文件（README），补充最终数字对象标识符（DOI）与引用信息。