Work Performance Measurement of Data Entry Employees in E-Commerce Industry Based on Mental Workload Value
DOI: 10.21512/comtech.v10i2.5688
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Summary
This study investigates the mental workload of data entry employees in the e-commerce industry, aiming to identify factors influencing performance and determine appropriate system improvements. The research is motivated by the high error rates and cognitive stress associated with prolonged data entry tasks, which are critical for e-commerce operations but often lack ergonomic or cognitive support. The primary objective was to measure mental workload values and assess how specific environmental and task variables affect work performance. The experimental design involved four undergraduate students with prior e-commerce data entry experience, aged 20–23, who performed data entry tasks in a laboratory setting designed to mimic real-world conditions, including ambient noise levels. The study utilized a multi-modal measurement approach to capture subjective, psychophysiological, and performance data. Mental workload was assessed using Electroencephalogram (EEG) sensors (Emotiv) to record focus, engagement, and stress levels every 30 minutes, alongside heart rate measurements before and after work. Subjective workload was measured via the NASA-TLX questionnaire, and performance was quantified by reaction time and error counts. The experiment tested two types of work instructions (placed near the employee vs. on the wall) and varied task demands (5 vs. 10 articles) and difficulties (complete vs. incomplete information). Noise levels were controlled at low (82–87 dB) and high (102–107 dB) intensities. Results indicated that in the initial condition, mental workload was high, with EEG data showing average focus at 46%, engagement at 70%, and stress at 51%. Statistical analysis revealed that mental effort and mental fatigue were the significant determinants of mental workload, correlating significantly with heart rate. Regarding work performance, task demand was the only factor found to have a significant impact; specifically, higher task demands negatively affected performance. In contrast, noise level and task difficulty did not significantly influence work performance, nor did their interactions. A paired t-test confirmed that the placement of work instructions significantly affected performance, with instructions placed near the employee yielding better results than those on the wall. The study rejected hypotheses suggesting that noise level or task difficulty independently drive performance outcomes in this context. The findings suggest that e-commerce companies should prioritize adjusting task demands to match individual employee capacities and place work instructions in close proximity to the worker to reduce cognitive load and error rates. The study concludes that while mental workload is a critical driver of performance, it is not significantly mediated by ambient noise or task complexity in this specific data entry context. Future research should incorporate ergonomic factors and machine learning algorithms to predict mental workload in real-time, addressing the limitations of the current study’s short working duration and lack of ergonomic variables.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 4 | 2026-08-23 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.8-27b-gittensor | summ-v5 | 3 | 2026-08-23 |
| tag | success | vector_similarity | — | — | 11 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-09 |
Summary generated by qwen3.8-27b-gittensor on 2026-08-23; verification: pending re-verification.
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- Empirical Findings: self report data, physiological data