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Accepted for/Published in: JMIR Research Protocols

Date Submitted: Mar 13, 2025
Date Accepted: Aug 26, 2025

The final, peer-reviewed published version of this preprint can be found here:

Exploring Subjective Well-Being in Human-Machine Interaction: Protocol for a Mixed Methods, Cross-Sectional Analysis in Manufacturing 5.0

Bassi G, Orso V, Salcuni S, Gamberini L

Exploring Subjective Well-Being in Human-Machine Interaction: Protocol for a Mixed Methods, Cross-Sectional Analysis in Manufacturing 5.0

JMIR Res Protoc 2025;14:e73896

DOI: 10.2196/73896

PMID: 41236791

PMCID: 12663708

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Exploring operators’ subjective well-being in interaction with advanced production technologies: A protocol study for mixed-method cross-sectional analysis in manufacturing 5.0

  • Giulia Bassi; 
  • Valeria Orso; 
  • Silvia Salcuni; 
  • Luciano Gamberini

ABSTRACT

Background:

Human-robot interaction (HRI) and collaboration (HRC) have 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 aims to fill that gap by examining operators’ subjective well-being, considering both cognitive (work-related quality of life and satisfaction with life) and affective (anxiety, depression, and stress) components of those who work with advanced production technologies in manufacturing companies.

Methods:

We adopt a mixed-method approach, incorporating both quantitative and qualitative data collection techniques. Quantitative data will be gathered via a digital survey containing self-report questionnaires assessing operators’ subjective well-being (cognitive and affective), fluency in HRI, and negative attitudes towards advanced production technologies. A path analysis will be performed to explore the multiple mediating roles of fluency in HRI and negative attitudes towards such technologies between cognitive and affective well-being. We further qualitatively investigate the operators’ direct experiences in HRI and HRC using semi-structured audio-recorded interviews. A thematic analysis relying on text-mining techniques will then be conducted to explore operators’ textual data.

Results:

We expect that fluency in HRI 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. Additionally, 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 HRI.

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 HRI and HRC


 Citation

Please cite as:

Bassi G, Orso V, Salcuni S, Gamberini L

Exploring Subjective Well-Being in Human-Machine Interaction: Protocol for a Mixed Methods, Cross-Sectional Analysis in Manufacturing 5.0

JMIR Res Protoc 2025;14:e73896

DOI: 10.2196/73896

PMID: 41236791

PMCID: 12663708

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