Predicting User Trust, Adoption Intention, and Business Communication Effectiveness in LLM-Based Translation Using Machine Learning

Tri Kurnia; Achmad Fitro

doi:10.55227/ijhess.v6i2.2502

Authors

Tri Kurnia State University of Surabaya
Achmad Fitro Universitas Negeri Surabaya

DOI:

https://doi.org/10.55227/ijhess.v6i2.2502

Keywords:

Large Language Model, Business Translation, User Trust, Adoption Intention, Machine Learning

Abstract

The use of Large Language Models (LLMs) for cross-border business translation is growing rapidly, yet quantitative research integrating accuracy, trust, adoption, and communication effectiveness remains limited. This study explains and predicts these relationships using a cross-sectional survey combining explanatory and predictive approaches. Data were collected from 207 respondents through a 24-item questionnaire covering five dimensions: Usage, Translation Accuracy, User Trust, Adoption Intention, and Business Communication Effectiveness. All dimensions were reliable (Cronbach's alpha 0.91-0.94) and all items were valid. Analysis combined multiple linear regression, classical assumption tests, and a Random Forest algorithm on the same framework. Usage and Translation Accuracy significantly predicted User Trust (R² = 0.90); User Trust was the most dominant predictor of Adoption Intention (feature importance 72.6%); and User Trust together with Adoption Intention explained 91% of the variance in Business Communication Effectiveness. High inter-dimensional correlations (0.93-0.96) indicated potential common method bias, tested via Harman's test, ridge regression, and HC3, all consistent with the main models. The novelty of this study lies in combining explanatory and predictive approaches within a single framework accompanied by robustness checks; strengthening user trust emerges as the key strategic pathway toward sustainable adoption and effective LLM-based business communication.

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