DEVELOPMENT OF A SURVEY QUESTIONNAIRE ON INDUSTRY 4.0 SAFETY CONTROLS IN MALAYSIAN MANUFACTURING INDUSTRIES
DOI:
https://doi.org/10.35631/IJIREV.826014Keywords:
Accident Risk Control, Industry 4.0, Manufacturing Safety, Occupational Safety and Health, Questionnaire Development, Rasch Measurement ModelAbstract
The increasing use of digital technologies in manufacturing is transforming occupational safety and health (OSH) practices. However, structured instruments for assessing how individual Industry 4.0 (IR4.0) technologies are applied as preventive safety controls remain limited. This study developed and conducted a preliminary assessment of a multi-section survey questionnaire for examining IR4.0-enabled safety controls in Malaysian manufacturing industries. A domain-driven approach was used to map nine IR4.0 technology domains to seven grouped occupational-risk areas: slips, trips and falls; machinery accidents, cuts and lacerations; work-related musculoskeletal disorders; heat-related illness; electrical shocks and fire accidents; noise exposure and eye injuries; and chemical or biological exposure. Expert content validity was assessed through an independent review by ten subject matter experts. The final questionnaire comprised organisational background questions, awareness and implementation questions, 64 dichotomously coded technology-specific application items, 18 technology-specific effectiveness and wider-application statements, and five general statements concerning implementation effects and challenges. Following ethical approval, a pilot study involving 30 respondents was conducted, and the 64 core application items were analysed using the dichotomous Rasch Measurement Model. Person reliability was 0.86, while item reliability was 0.75. Person measures averaged −1.70 logits and ranged from −3.50 to 0.45 logits, whereas item measures averaged 0.00 logits and ranged from −2.33 to 2.18 logits. These findings provide preliminary support for the wider administration of the instrument. The questionnaire offers a structured foundation for large-scale assessment of IR4.0-enabled OSH applications, although further analysis using the main-study dataset is required to examine dimensionality, item targeting, domain-specific functioning, differential item functioning, and measurement stability.
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