Dataset of Effect of Payment Anxiety and Trust in Shaping Behavioral Intention toward Digital Payment

This dataset contains primary data from a questionnaire survey measuring the adoption level and user acceptance of two main digital payment methods: QRIS (Quick Response Code Indonesian Standard) and NFC (Near Field Communication). This data was collected to analyze the factors influencing users' Behavioral Intention in adopting contactless payment technologies based on technology acceptance models. This dataset is highly relevant for researchers in information systems, consumer behavior, and financial technology (fintech). Methodology and Data Collection Method: Online questionnaire survey (Google Forms). Sample Size: 146 respondents (N=146). Measurement Scale: Likert scale used to measure respondents' perceptions and attitudes toward the research instruments. File Format: Excel (.xlsx), consisting of 56 columns (including Timestamp). Data Structure and Variables (Constructs) The data includes basic demographic information (age) as well as the familiarity level and usage frequency of each technology. Each technology (QRIS and NFC) is measured using 5 (five) main variables, each with 5 question indicators: PE (Performance Expectancy): The extent to which users believe that using the technology (QRIS/NFC) will help them attain gains in transaction speed and efficiency. (Indicators: PE1 - PE5) PEOU (Perceived Ease of Use): The degree to which users believe that using the technology will be free of effort, measuring how easy it is to learn and operate. (Indicators: PEOU1 - PEOU5) TR (Trust): The users' level of trust in system security, third-party reliability, and personal data protection. (Indicators: TR1 - TR5) PA (Perceived Anxiety / Risk): The level of anxiety or concern regarding security risks, system errors, financial loss, and fraud. (Indicators: PA1 - PA5) BI (Behavioral Intention): The users' intention or plan to continue using, preferring, and recommending the technology in the future. (Indicators: BI1 - BI5) Reuse Potential This data is highly suitable for statistical testing using SEM-PLS (e.g., SmartPLS), AMOS, or linear regression via SPSS/Python/R to validate hypotheses regarding digital payment technology adoption models.