Digital solutions for workplace safety: An empirical study on their adoption in Italian metalworking SMEs

被引:0
作者
Cagno, Enrico [1 ]
Accordini, Davide [1 ]
Neri, Alessandra [1 ]
Negri, Elisa [1 ]
Macchi, Marco [1 ]
机构
[1] Dept Management Econ & Ind Engn, Via Lambruschini 4b, Milan, Italy
关键词
Occupational safety; Industry; 5.0; Digital solutions; Barriers; Drivers; SMEs; OSH INTERVENTIONS; WEARABLE HEALTH; OCCUPATIONAL-SAFETY; CONSTRUCTION SAFETY; AUGMENTED REALITY; OLDER-ADULTS; FIRM SIZE; SYSTEM; RISK; IDENTIFICATION;
D O I
10.1016/j.ssci.2024.106598
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Occupational safety is a critical aspect of the manufacturing sector, especially for small and medium-sized enterprises, which often face a safety divide compared to large companies due to significant differences in resources and awareness. Digital solutions can provide interesting support for dealing with specific hazardous situations and improving safety performance. However, there is a digital divide based on company size when it comes to the adoption of innovative digital solutions by small and medium-sized enterprises. This digital divide could widen the safety divide. To bridge these divides, the present research, through an extensive survey conducted among employers of Italian metalworking small and medium enterprises, explores various digital solutions and their potential to tackle hazardous situations in the workplace; it also addresses barriers and drivers influencing the adoption of the solutions and evaluates the results against different contextual factors characterizing the studied enterprises. Key barriers adopting digital solutions include the lack of perceived benefits, privacy concerns, implementation difficulties, and cost. On the other hand, the clarity and trustworthiness of the data collected and the ease of use of a digital solution can support the adoption. The study offers academic and managerial insights and contributes to the debate on the transition to Industry 5.0.
引用
收藏
页数:20
相关论文
共 161 条
  • [61] A stratified Bayesian decision-making model for occupational risk assessment of production facilities
    Gul, Muhammet
    Yucesan, Melih
    Karci, Coskun
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 133
  • [62] Guo B.H.W., 2017, AUSTR U BUILD ED ASS, P496, DOI [10.29007/zvfp, DOI 10.29007/ZVFP]
  • [63] Hall M, 2010, CRIM LAW REV, P31
  • [64] Han WJ, 2019, COMPUTING IN CIVIL ENGINEERING 2019: SMART CITIES, SUSTAINABILITY, AND RESILIENCE, P242
  • [65] Hayek A., 2018, Smart Wearable System for Safety-Related Industrial IoT Applications, P154, DOI [10.1007/ 978-3-319-93797-7_17, DOI 10.1007/978-3-319-93797-7_17]
  • [66] Mine Safety System Using Wireless Sensor Network
    Henriques, Valdo
    Malekian, Reza
    [J]. IEEE ACCESS, 2016, 4 : 3511 - 3521
  • [67] The digital divide in light of sustainable development: An approach through advanced machine learning techniques
    Hidalgo, Antonio
    Gabaly, Samuel
    Morales-Alonso, Gustavo
    Uruena, Alberto
    [J]. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2020, 150
  • [68] Wristband-type wearable health devices to measure construction workers' physical demands
    Hwang, Sungjoo
    Lee, SangHyun
    [J]. AUTOMATION IN CONSTRUCTION, 2017, 83 : 330 - 340
  • [69] INAIL, 2006, Infor.MO
  • [70] INAIL, 2022, PRE.VI.S: Il sistema di monitoraggio dei fattori di rischio lavorativo attraverso l'attivita di vigilanza