Examining the Relationship Between Simple Technology Implementation, Data Literacy, and Spectral Preprocessing Accuracy: A SEM-PLS Approach Using the Savitzky–Golay Method in Chemometric Analysis

Abstract

The advancement of digital technology has significantly influenced chemometric analysis, particularly in spectral data preprocessing. The Savitzky–Golay method is widely used to improve signal quality, while tools such as Orange Data Mining offer user-friendly analytical capabilities for beginner researchers. This study aims to analyze the effect of simple technology implementation on the accuracy of spectral preprocessing and to examine the moderating role of community data literacy. A quantitative approach was employed using SEM-PLS with data collected from 113 respondents through a Likert scale questionnaire. The results show that spectral preprocessing accuracy meets validity and reliability criteria, while simple technology implementation and data literacy constructs do not fully satisfy these requirements. Despite the absence of multicollinearity issues, measurement limitations affect the structural model evaluation. In conclusion, although preprocessing performance is consistent, improvements in measurement indicators are necessary to better assess the relationship between technology implementation and analytical outcomes.