Dataset for: An Empirical Study on Smart Home User Usage Intentions Based on the Technology Acceptance Model and User Experience Theory

This repository contains the de-identified analytic dataset, questionnaire materials, codebook, and analysis-related files for the manuscript entitled “An Empirical Study on Smart Home User Usage Intentions Based on the Technology Acceptance Model and User Experience Theory.”

The study employed a cross-sectional online questionnaire design to examine whether an extended Technology Acceptance Model (TAM) integrating user experience constructs could explain intention to use smart home technology. The target population consisted of smart home users and potential users in Anhui Province, China. Data were collected from May 2024 to March 2025 through Wenjuanxing, with recruitment primarily conducted via WeChat and Moments in several cities, including Hefei, Wuhu, Lu’an, and Bozhou. After screening, 583 valid questionnaires were retained for the final analysis.

The final de-identified analytic dataset includes demographic variables and item-level responses for nine latent constructs: subjective norm (SN), system quality (SQ), perceived usefulness (PU), perceived ease of use (PEOU), perceived value (PV), perceived hedonic quality (PHQ), perceived attractiveness (PA), attitude (A), and intention to use (ITU). Each construct was measured with four observed items, resulting in 36 scale items in total. All scale items were measured on a seven-point Likert scale.

This repository includes:(1) the de-identified final analytic dataset;(2) a README file describing file structure and reuse notes;(3) a variable codebook/data dictionary;(4) the questionnaire instrument;(5) analysis-related materials supporting replication of the reported results.

All identifying information has been removed from the shared dataset. Statistical analyses reported in the manuscript were conducted using SPSS 26.0 and AMOS 30.0. The study received ethical approval from the Academic Committee of Anhui Wenda University of Information Engineering (approval no. 202301).