Evaluating User Satisfaction and Intent to Use with Low-Code/No-Code Backtesting Platform using the Technology Acceptance Model and the DeLone & Mclean Model

Abstract

This study evaluates user satisfaction and intent to use Backthought, a low-code/no-code backtesting platform developed via AppSheet. Designed for mobile accessibility, the platform enables non-technical users to construct and evaluate trading strategies without programming knowledge. It supports intraday backtesting using EMA, MACD, and RSI indicators across 25 companies continuously listed in the Thailand SET50 Index from 2016 to 2025, providing performance visualizations and trade logs for strategy assessment. Platform reliability was verified by cross-validating outputs against an Excel-based model using the same dataset and trading logic. To examine user responses, a quantitative survey of 79 undergraduate students in engineering and technology-related disciplines was conducted based on the Technology Acceptance Model (TAM) and the DeLone and McLean Information Systems Success Model. Structural equation modeling revealed that perceived usefulness significantly enhanced user satisfaction (β = 0.809, p < 0.001), which in turn strongly influenced intent to use (β = 0.913, p < 0.001). Additionally, perceived ease of use positively affected perceived usefulness (β = 0.527, p < 0.001). Overall, the findings indicate that low-code/no-code applications can reduce technical barriers and support wider adoption of financial strategy testing tools.