Industry 4.0 Mobile Workflow Quality Assessment Using SmartPLS: Data Readiness, AI Accessibility, and Satisfaction Drivers

Abstract

Low-code platforms such as Google AppSheet enable industrial workflow digitization, where responsiveness and AI assistance shape operational reliability. Sustained adoption depends on measurable task performance and accessible AI features across heterogeneous mobile and web clients. Prior low-code acceptance research relied on self-reports and did not unify objective performance, data readiness, and AI accessibility into a single model. This work aims to quantify the data-to-performance-to-continuance mechanism for AppSheet deployments using PLS-SEM. Task scripts captured open, sync, and submit times, plus error events, and reduced them into a five-level objective performance index. Survey responses from 260 users were modeled in SmartPLS with 5,000 bootstrap resamples. Reliability and validity are supported (Cronbach’s alpha ranged 0.885–0.916; composite reliability ranged 0.925–0.947; AVE ranged 0.755–0.856). The model explains satisfaction and continued use intention (R² = 0.299; R² = 0.264). Objective performance drives perceived performance (β = 0.488), and AI accessibility drives ease of use (β = 0.349), while satisfaction, trust, and usefulness predict intention (β = 0.246, 0.232, 0.199). These results support deployment controls that couple data readiness validation, performance monitoring, and accessible AI interaction design for Industry 4.0 workflows. Future technology extends this framework with longitudinal telemetry, multi-indicator performance constructs, and role-stratified multi-group estimation.  

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