Exploring Subjective Well-Being in Human-Machine Interaction: Protocol for a Mixed Methods, Cross-Sectional Analysis in Manufacturing 5.0
Bibliographic record
Abstract
BACKGROUND: Human-machine interaction (HMI) has gained significant attention in the context of advanced production technologies, especially concerning trust and acceptance. However, the investigation of the subjective well-being of operators working with these technologies in manufacturing companies has been largely overlooked. Moreover, previous research mostly relied on a single data-collection method, either quantitative or qualitative, thereby failing to capture a rich picture of their cognitive and affective states. OBJECTIVE: This cross-sectional study protocol aimed to fill that gap by examining operators' subjective well-being and workplace dynamics, including fluency in HMI, negative attitudes toward technologies, and social relationships among coworkers in manufacturing companies. METHODS: We adopt a mixed methods approach, incorporating both quantitative and qualitative data collection techniques. Quantitative data will be gathered via a digital survey containing self-report questionnaires. A path analysis will be performed to explore the multiple mediating roles of fluency in HMI and negative attitudes toward such technologies between cognitive and affective well-being. We further qualitatively investigate the operators' lived experience in HMI using semistructured audio-recorded interviews. A thematic analysis relying on text-mining techniques will then be conducted to explore operators' textual data. RESULTS: We quantitatively expect that fluency in HMI may act as a protective factor for operators' affective well-being, while negative attitudes toward advanced production technologies may contribute to the development or worsening of operators' psychological distress. From a qualitative perspective, we intend to seamlessly merge quantitative insights to create a more comprehensive and well-grounded analysis. Moreover, the integrated interpretation of both the quantitative and qualitative data collected will generate a consensus report, which will aim to serve as a practical framework for guiding workplace policies and training programs meant to foster subjective well-being and effective HMI. At the time of publication, we have collected data from 12 participants and scheduled a further data collection session. CONCLUSIONS: Embracing one of the fundamental pillars of Industry 5.0, human-centricity, by detecting potential psychological issues early, organizations can create a workplace that prioritizes the well-being of operators. Early recognition and prevention are crucial to promoting operators' mental well-being involved in HMI. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/73896.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.078 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".