2026/07/07 by Gerko Schaap, Nick Goossen, Sebastian M. Dennerlein +2 · 1 voice
Psychology · Decision Sciences · #Flow Experience in Various Fields #Mental Health Research Topics #Personal Information Management and User Behavior
paper · doi:10.1108/jwl-12-2025-0438
openalex publication_date 2026/07/07 · openalex created_date 2026/07/08 · openalex updated_date 2026/07/26
Purpose This study aims to explore how an experience sampling method (ESM) can be applied to understand workplace learning (WPL) dynamics. The primary objectives were to evaluate compliance and data quality indicators in time-based and event-based sampling approaches and to explore insights into dynamic WPL processes using these approaches. Design/methodology/approach Two ESM studies were conducted in a student WPL context using time-based (five weeks; 22 participants, 238 observations) and event-based (six weeks; 33 participants, 326 observations) sampling approaches. ESM items were closed-ended questions capturing WPL activities and perceived goal achievement and open-ended questions to capture reflections on learning outcomes. Compliance and data quality indicators (reflection quality, word counts and response duration) were analysed descriptively. Learning trajectories were constructed via within-participant sequences, showcasing four illustrative cases. Findings Although a time-based sampling approach resulted in higher compliance, data quality was generally lower compared to the event-based sampling approach. Reporting WPL experiences outside workday hours and closer to deadlines was associated with low-quality data. Within-case analyses show variations in learning activity sequences, timing and fluctuations in goal achievement. Research limitations/implications ESM is suitable for studying WPL, yet requires careful design choices regarding sampling approaches, prompt timing and data quality checks. The findings support further research on temporal learning trajectories as it happens in day-to-day practice. Originality/value This study is the first in the field of WPL to provide insight into ESM with (open-ended) data quality assessments and case-trajectory analysis of two sampling approaches, thereby providing insights into future ESM research in WPL.