Latent Segmentation of Technology Adoption Behaviour Using FIMIX-PLS

Digital transformation in Micro, Small, and Medium Enterprises (MSMEs) requires analytical approaches capable of capturing heterogeneous technology adoption behaviour, as homogeneous models often overlook hidden structural differences. This study investigates latent heterogeneity in MSME digital adoption using the Finite Mixture Partial Least Squares (FIMIX-PLS) approach within the Technology Acceptance Model framework based on survey data from 210 MSMEs in West Java, Indonesia. The optimal segmentation solution was determined through a combination of statistical criteria, including AIC, BIC, CAIC, and entropy values, together with practical considerations of segment size proportions to ensure parameter estimation stability and maintain generalisability given the moderate sample size. The results identify three statistically distinct latent segments with entropy values above 0.7, indicating reliable class separation. The three-segment solution produces a more balanced distribution than four-segment and five-segment. The results indicate substantial behavioral heterogeneity across user segments. Segments 1 and 2 exhibit strong positive relationships across all structural paths, with Perceived Ease of Use emerging as the dominant factor shaping attitude, which subsequently drives behavioral intention and actual use, showing stronger effects than the global model. In contrast, Segment 3 demonstrates an opposing pattern with mostly negative coefficients, suggesting low technology acceptance and potential resistance toward the system. These findings confirm that technology adoption mechanisms are not homogeneous and vary significantly across user groups