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Giyilebilir Teknolojilerle İş Sağlığı ve Güvenliğini Geliştirme: Üretim Sektöründe İnsan Kaynakları Stratejileri için Çok Kriterli Karar Verme Çerçevesi

Year 2025, Volume: 9 Issue: 3, 1043 - 1064, 19.09.2025

Abstract

Bu çalışma, üretim sektöründe İş Sağlığı ve Güvenliği (İSG) kapsamında giyilebilir teknolojilerin önceliklendirilmesine yönelik hem teknik performansı hem de iş gücü entegrasyonunu ele alan yenilikçi bir çerçeve geliştirmektedir. Çalışmanın amacı, İlk olarak, güvenlik etkisi, maliyet etkinliği, güvenilirlik, eğitim kolaylığı ve çalışanların benimsenmesi gibi giyilebilir teknolojilerin benimsenmesini ve etkinliğini etkileyen temel kriterleri belirlemek; ikinci olarak ise İnsan Kaynakları (İK) uygulayıcıları, paydaşlar ve İSG yöneticilerinin teknolojileri değerlendirmesini ve seçmesini destekleyen yapılandırılmış bir karar verme yaklaşımı oluşturmaktır. Fuzzy DEMATEL metodolojisi ile kriterler arasındaki nedensel ilişkileri analiz ederken, PROMETHEE metodolojisi alternatifleri sıralamada kullanılmıştır. Önceliklendirme süreci için Gaz Algılama Sensörleri, Yorgunluk İzleme Bantları, Akıllı Kasklar ve Dış İskeletler gibi giyilebilir teknoloji alternatifleri bir simülasyon olarak seçilmiş olup, bu alternatifler çeşitli kullanım alanları ve zorlukları yansıtmaktadır. Çalışma, maliyet etkinliği ve güvenlik etkisinin en etkili faktörler olduğunu ortaya koymaktadır. Bulgular, Gaz Algılama Sensörleri'nin, üstün güvenlik ve güvenilirlik performansı nedeniyle en üst sırada yer aldığını, ardından Yorgunluk İzleme Bantları ve Akıllı Kasklar'ın geldiğini, Dış İskeletler'in ise maliyet ve eğitim zorlukları nedeniyle en alt sırada yer aldığını göstermektedir. Çalışma, teknik çözümleri iş gücü hazırlığı ile uyumlu hale getirmeye vurgu yaparak, çalışanların teknolojiyi benimsemesi ve hedefe yönelik eğitim programlarının geliştirilmesi gibi karar vericilere yönelik uygulanabilir öneriler sunmaktadır. Teknoloji Kabul Modeli'ne (TAM) dayanan çalışma, benimseme davranışını açıklamak için insan odaklı kriterlerle teknik değerlendirmeyi birleştirmekte ve diğer sektörlerde iş yeri güvenliğini artırmayı hedefleyen ölçeklenebilir, pratik bir karar verme çerçevesi sunmaktadır.

References

  • Abdullah, F. M., Al-Ahmari, A. M., & Anwar, S. (2023). An integrated fuzzy DEMATEL and fuzzy TOPSIS method for analyzing smart manufacturing technologies. Processes, 11(3), 906.
  • Aksüt, G., Tamer, E. R. E. N., & ALAKAŞ, H. M. (2024). Using wearable technological devices to improve workplace health and safety: An assessment on a sector base with multi-criteria decision-making methods. Ain Shams Engineering Journal, 15(2), 102423.
  • Awolusi, I., Marks, E., & Hallowell, M. (2018). Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices. Automation in construction, 85, 96-106.
  • Badida, P., Janakiraman, S., & Jayaprakash, J. (2023). Occupational health and safety risk assessment using a fuzzy multi-criteria approach in a hospital in Chennai, India. International journal of occupational safety and ergonomics, 29(3), 1047-1056.
  • Balamurugan, K., Latchoumi, T. P., & Ezhilarasi, T. P. (2022). Wearables to improve efficiency, productivity, and safety of operations. In Smart manufacturing technologies for industry 4.0(pp. 75-90). CRC Press.
  • Brans, J. P., & Vincke, P. (1985). A preference ranking organization method: The PROMETHEE method for multiple criteria decision-making. Management Science, 31(6), 647–656.
  • Cimbaljević, M., Demirović Bajrami, D., Kovačić, S., Pavluković, V., Stankov, U., & Vujičić, M. (2024). Employees' technology adoption in the context of smart tourism development: the role of technological acceptance and technological readiness. European Journal of Innovation Management, 27(8), 2457-2482.
  • Dabbagh, R., & Yousefi, S. (2019). A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis. Journal of safety research, 71, 111-123.
  • Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205, 219.
  • De Fazio, R., Al-Hinnawi, A. R., De Vittorio, M., & Visconti, P. (2022). An energy-autonomous smart shirt employing wearable sensors for users’ safety and protection in hazardous workplaces. Applied Sciences, 12(6), 2926.
  • Dehghani, M., Kennedy, R. W., Mashatan, A., Rese, A., & Karavidas, D. (2022). High interest, low adoption. A mixed-method investigation into the factors influencing organisational adoption of blockchain technology. Journal of Business Research, 149, 393-411.
  • Ghasemi, P., Mehdiabadi, A., Spulbar, C., & Birau, R. (2021). Ranking of sustainable medical tourism destinations in Iran: an integrated approach using fuzzy SWARA-PROMETHEE. Sustainability, 13(2), 683.
  • Gul, M. (2018). A review of occupational health and safety risk assessment approaches based on multi-criteria decision-making methods and their fuzzy versions. Human and ecological risk assessment: an international journal, 24(7), 1723-1760.
  • Hashemi-Petroodi, S. E., Dolgui, A., Kovalev, S., Kovalyov, M. Y., & Thevenin, S. (2021). Workforce reconfiguration strategies in manufacturing systems: a state of the art. International Journal of Production Research, 59(22), 6721-6744.
  • Hosseini, S. M., Paydar, M. M., & Hajiaghaei-Keshteli, M. (2021). Recovery solutions for ecotourism centers during the Covid-19 pandemic: Utilizing Fuzzy DEMATEL and Fuzzy VIKOR methods. Expert Systems with Applications, 185, 115594.
  • Ibrahim, K., Simpeh, F., & Adebowale, O. J. (2025). Benefits and challenges of wearable safety devices in the construction sector. Smart and Sustainable Built Environment, 14(1), 50-71.
  • Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.
  • Kumar Bhardwaj, A., Garg, A., & Gajpal, Y. (2021). Determinants of blockchain technology adoption in supply chains by small and medium enterprises (SMEs) in India. Mathematical Problems in Engineering, 2021(1), 5537395.
  • La Fata, C. M., Giallanza, A., Micale, R., & La Scalia, G. (2021). Ranking of occupational health and safety risks by a multi-criteria perspective: Inclusion of human factors and application of VIKOR. Safety science, 138, 105234.
  • Mejia, C., Ciarlante, K., & Chheda, K. (2021). A wearable technology solution and research agenda for housekeeper safety and health. International Journal of Contemporary Hospitality Management, 33(10), 3223-3255.
  • Mukhuty, S., Upadhyay, A., & Rothwell, H. (2022). Strategic sustainable development of Industry 4.0 through the lens of social responsibility: The role of human resource practices. Business Strategy and the Environment, 31(5), 2068-2081.
  • Nguyen, H. T., & Chu, T. C. (2023). Ranking Startups Using DEMATEL-ANP-Based Fuzzy PROMETHEE II. Axioms, 12(6), 528.
  • Nnaji, C., Awolusi, I., Park, J., & Albert, A. (2021). Wearable sensing devices: towards the development of a personalized system for construction safety and health risk mitigation. Sensors, 21(3), 682.
  • Patel, V., Chesmore, A., Legner, C. M., & Pandey, S. (2022). Trends in workplace wearable technologies and connected‐worker solutions for next‐generation occupational safety, health, and productivity. Advanced Intelligent Systems, 4(1), 2100099.
  • Piwowar-Sulej, K. (2022). Environmental strategies and human resource development consistency: Research in the manufacturing industry. Journal of Cleaner Production, 330, 129538.
  • Rajendran, S., Giridhar, S., Chaudhari, S., & Gupta, P. K. (2021). Technological advancements in occupational health and safety. Measurement: Sensors, 15, 100045.
  • Schall Jr, M. C., Sesek, R. F., & Cavuoto, L. A. (2018). Barriers to the adoption of wearable sensors in the workplace: A survey of occupational safety and health professionals. Human factors, 60(3), 351-362.
  • Silva, P. (2015). Davis' technology acceptance model (TAM)(1989). Information seeking behavior and technology adoption: Theories and trends, 205-219.
  • Svertoka, E., Saafi, S., Rusu-Casandra, A., Burget, R., Marghescu, I., Hosek, J., & Ometov, A. (2021). Wearables for industrial work safety: A survey. Sensors, 21(11), 3844.
  • Wang, W., Chen, L., Xiong, M., & Wang, Y. (2023). Accelerating AI adoption with responsible AI signals and employee engagement mechanisms in health care. Information Systems Frontiers, 25(6), 2239-2256.
  • Wong, T. K. M., Man, S. S., & Chan, A. H. S. (2021). Exploring the acceptance of PPE by construction workers: An extension of the technology acceptance model with safety management practices and safety consciousness. Safety science, 139, 105239.
  • Yang, Y., Chan, A. P., Shan, M., Gao, R., Bao, F., Lyu, S., ... & Guan, J. (2021). Opportunities and challenges for construction health and safety technologies under the COVID-19 pandemic in Chinese construction projects. International journal of environmental research and public health, 18(24), 13038.

Advancing Occupational Health and Safety with Wearable Technologies: An MCDM Framework for HR Strategies in the Manufacturing Sector

Year 2025, Volume: 9 Issue: 3, 1043 - 1064, 19.09.2025

Abstract

This study develops a novel framework for prioritizing wearable technologies in Occupational Health and Safety (OHS) within the manufacturing sector, addressing both technical performance and workforce integration. The purpose of the framework is twofold: first, to identify the key criteria influencing the adoption and effectiveness of wearable technologies, such as safety impact, cost-effectiveness, reliability, ease of training, and employee adoption; and second, to create a structured decision-making approach that supports HR practitioners, stakeholders, and OHS managers in evaluating and selecting technologies. By integrating Fuzzy DEMATEL to analyze causal relationships among criteria and PROMETHEE to rank alternatives, the study reveals that cost-effectiveness and safety impact are the most influential drivers. The wearable technology alternatives, including Gas Detection Sensors, Fatigue-Monitoring Bands, Smart Helmets, and Exoskeletons, were selected as a simulation for the prioritization process, reflecting a diverse set of use cases and challenges. The findings highlight Gas Detection Sensors as the top-ranked technology due to their superior safety and reliability performance, followed by Fatigue-Monitoring Bands and Smart Helmets, while Exoskeletons rank lowest due to cost and training challenges. This framework emphasizes the alignment of technical solutions with workforce readiness, providing actionable insights for decision-makers, including strategies for enhancing employee adoption and targeted training programs. Grounded in the Technology Acceptance Model (TAM) to explain adoption behavior, the study bridges technical evaluation with human-centric criteria, offering a scalable, practical decision-making framework applicable to other industries aiming to enhance workplace safety through wearable technologies.

References

  • Abdullah, F. M., Al-Ahmari, A. M., & Anwar, S. (2023). An integrated fuzzy DEMATEL and fuzzy TOPSIS method for analyzing smart manufacturing technologies. Processes, 11(3), 906.
  • Aksüt, G., Tamer, E. R. E. N., & ALAKAŞ, H. M. (2024). Using wearable technological devices to improve workplace health and safety: An assessment on a sector base with multi-criteria decision-making methods. Ain Shams Engineering Journal, 15(2), 102423.
  • Awolusi, I., Marks, E., & Hallowell, M. (2018). Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices. Automation in construction, 85, 96-106.
  • Badida, P., Janakiraman, S., & Jayaprakash, J. (2023). Occupational health and safety risk assessment using a fuzzy multi-criteria approach in a hospital in Chennai, India. International journal of occupational safety and ergonomics, 29(3), 1047-1056.
  • Balamurugan, K., Latchoumi, T. P., & Ezhilarasi, T. P. (2022). Wearables to improve efficiency, productivity, and safety of operations. In Smart manufacturing technologies for industry 4.0(pp. 75-90). CRC Press.
  • Brans, J. P., & Vincke, P. (1985). A preference ranking organization method: The PROMETHEE method for multiple criteria decision-making. Management Science, 31(6), 647–656.
  • Cimbaljević, M., Demirović Bajrami, D., Kovačić, S., Pavluković, V., Stankov, U., & Vujičić, M. (2024). Employees' technology adoption in the context of smart tourism development: the role of technological acceptance and technological readiness. European Journal of Innovation Management, 27(8), 2457-2482.
  • Dabbagh, R., & Yousefi, S. (2019). A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis. Journal of safety research, 71, 111-123.
  • Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205, 219.
  • De Fazio, R., Al-Hinnawi, A. R., De Vittorio, M., & Visconti, P. (2022). An energy-autonomous smart shirt employing wearable sensors for users’ safety and protection in hazardous workplaces. Applied Sciences, 12(6), 2926.
  • Dehghani, M., Kennedy, R. W., Mashatan, A., Rese, A., & Karavidas, D. (2022). High interest, low adoption. A mixed-method investigation into the factors influencing organisational adoption of blockchain technology. Journal of Business Research, 149, 393-411.
  • Ghasemi, P., Mehdiabadi, A., Spulbar, C., & Birau, R. (2021). Ranking of sustainable medical tourism destinations in Iran: an integrated approach using fuzzy SWARA-PROMETHEE. Sustainability, 13(2), 683.
  • Gul, M. (2018). A review of occupational health and safety risk assessment approaches based on multi-criteria decision-making methods and their fuzzy versions. Human and ecological risk assessment: an international journal, 24(7), 1723-1760.
  • Hashemi-Petroodi, S. E., Dolgui, A., Kovalev, S., Kovalyov, M. Y., & Thevenin, S. (2021). Workforce reconfiguration strategies in manufacturing systems: a state of the art. International Journal of Production Research, 59(22), 6721-6744.
  • Hosseini, S. M., Paydar, M. M., & Hajiaghaei-Keshteli, M. (2021). Recovery solutions for ecotourism centers during the Covid-19 pandemic: Utilizing Fuzzy DEMATEL and Fuzzy VIKOR methods. Expert Systems with Applications, 185, 115594.
  • Ibrahim, K., Simpeh, F., & Adebowale, O. J. (2025). Benefits and challenges of wearable safety devices in the construction sector. Smart and Sustainable Built Environment, 14(1), 50-71.
  • Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.
  • Kumar Bhardwaj, A., Garg, A., & Gajpal, Y. (2021). Determinants of blockchain technology adoption in supply chains by small and medium enterprises (SMEs) in India. Mathematical Problems in Engineering, 2021(1), 5537395.
  • La Fata, C. M., Giallanza, A., Micale, R., & La Scalia, G. (2021). Ranking of occupational health and safety risks by a multi-criteria perspective: Inclusion of human factors and application of VIKOR. Safety science, 138, 105234.
  • Mejia, C., Ciarlante, K., & Chheda, K. (2021). A wearable technology solution and research agenda for housekeeper safety and health. International Journal of Contemporary Hospitality Management, 33(10), 3223-3255.
  • Mukhuty, S., Upadhyay, A., & Rothwell, H. (2022). Strategic sustainable development of Industry 4.0 through the lens of social responsibility: The role of human resource practices. Business Strategy and the Environment, 31(5), 2068-2081.
  • Nguyen, H. T., & Chu, T. C. (2023). Ranking Startups Using DEMATEL-ANP-Based Fuzzy PROMETHEE II. Axioms, 12(6), 528.
  • Nnaji, C., Awolusi, I., Park, J., & Albert, A. (2021). Wearable sensing devices: towards the development of a personalized system for construction safety and health risk mitigation. Sensors, 21(3), 682.
  • Patel, V., Chesmore, A., Legner, C. M., & Pandey, S. (2022). Trends in workplace wearable technologies and connected‐worker solutions for next‐generation occupational safety, health, and productivity. Advanced Intelligent Systems, 4(1), 2100099.
  • Piwowar-Sulej, K. (2022). Environmental strategies and human resource development consistency: Research in the manufacturing industry. Journal of Cleaner Production, 330, 129538.
  • Rajendran, S., Giridhar, S., Chaudhari, S., & Gupta, P. K. (2021). Technological advancements in occupational health and safety. Measurement: Sensors, 15, 100045.
  • Schall Jr, M. C., Sesek, R. F., & Cavuoto, L. A. (2018). Barriers to the adoption of wearable sensors in the workplace: A survey of occupational safety and health professionals. Human factors, 60(3), 351-362.
  • Silva, P. (2015). Davis' technology acceptance model (TAM)(1989). Information seeking behavior and technology adoption: Theories and trends, 205-219.
  • Svertoka, E., Saafi, S., Rusu-Casandra, A., Burget, R., Marghescu, I., Hosek, J., & Ometov, A. (2021). Wearables for industrial work safety: A survey. Sensors, 21(11), 3844.
  • Wang, W., Chen, L., Xiong, M., & Wang, Y. (2023). Accelerating AI adoption with responsible AI signals and employee engagement mechanisms in health care. Information Systems Frontiers, 25(6), 2239-2256.
  • Wong, T. K. M., Man, S. S., & Chan, A. H. S. (2021). Exploring the acceptance of PPE by construction workers: An extension of the technology acceptance model with safety management practices and safety consciousness. Safety science, 139, 105239.
  • Yang, Y., Chan, A. P., Shan, M., Gao, R., Bao, F., Lyu, S., ... & Guan, J. (2021). Opportunities and challenges for construction health and safety technologies under the COVID-19 pandemic in Chinese construction projects. International journal of environmental research and public health, 18(24), 13038.
There are 32 citations in total.

Details

Primary Language English
Subjects Fuzzy Computation, Business Administration
Journal Section Makaleler
Authors

Umut Elbir 0000-0003-1416-9731

Early Pub Date September 13, 2025
Publication Date September 19, 2025
Submission Date February 26, 2025
Acceptance Date April 24, 2025
Published in Issue Year 2025 Volume: 9 Issue: 3

Cite

APA Elbir, U. (2025). Advancing Occupational Health and Safety with Wearable Technologies: An MCDM Framework for HR Strategies in the Manufacturing Sector. Politik Ekonomik Kuram, 9(3), 1043-1064.

This work is licensed under a Creative Commons Attribution 4.0 International License.