Simple Technology Is Not Enough: A SEM-PLS Analysis of Data Literacy and Spectral Signal Analysis Quality in Beginner Researcher Communities

Abstract

The development of simple technology such as Orange Data Mining has improved accessibility in chemometric data analysis, particularly for beginner researchers. However, the quality of spectral signal analysis remains dependent on users’ analytical capabilities. This study aims to examine the effect of Simple Technology Implementation on the effectiveness of spectral analysis, with community data literacy as a moderating variable. A quantitative approach was employed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with data collected from 113 respondents through a 5-point Likert scale questionnaire. The results indicate that several indicators do not meet validity criteria (outer loading < 0.70; AVE < 0.50), although some constructs demonstrate acceptable reliability. The data literacy variable also shows weak measurement performance. These findings suggest that simple technology alone is insufficient to improve analytical quality without adequate user understanding. Therefore, strengthening data literacy is essential to optimize the use of analytical tools in spectral data analysis.