Seeing Without Asking: A Multi-Method Construct Validation of Workforce Insights through Welliba’s Excelerate Processing using Publicly Available Information.

This study examines whether Welliba’s passive‐data approach can match or surpass insights derived from established survey methods in capturing employee experience (EX). Drawing on publicly accessible information—from sources like review platforms and market intelligent platforms—Welliba’s model generates EX metrics. Firstly, these were evaluated against eNPS scores. Results indicate a robust, positive correlation (r = .74, p < .001), suggesting that organizations scoring high on eNPS also tend to excel on Welliba’s metrics. A subsequent comparison with Gallup’s widely recognized survey measures (e.g., intent to leave, anger, stress, life evaluation, and engagement) reveals that Welliba’s approach explains a substantial portion of variance in key outcomes. For instance, turnover risk was predicted at over 60% (R² = .65), highlighting how elements such as workload, communication patterns, and task variety align with employee retention. Other findings demonstrate the complexity of factors like autonomy, which can promote intrinsic motivation yet also heighten stress. Overall, these analyses support the construct validity of Welliba’s passive data framework, indicating that it mirrors or refines more traditional survey‐based strategies. Additionally, the ability to capture near real‐time data without extensive employee surveys underscores the method’s agility and potential to inform timely interventions. As organizations seek scalable, less intrusive approaches to assessing workforce sentiment, Welliba’s model offers a credible alternative that may reduce both cost and response burden, provided that transparency and ethical data use are maintained.