Benefits and challenges of wearable safety devices in the construction sector Kabir Ibrahim University of the Free State - Bloemfontein Campus, Bloemfontein, South Africa Fredrick Simpeh Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development, Tanoso-Kumasi, Ghana, and Oluseyi Julius Adebowale Department of Building Sciences, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria, South Africa Abstract Purpose – Construction organizations must maintain a productive workforce without sacrificing their health and safety. The global construction sector loses billions of dollars yearly to poor health and safety practices. This study aims to investigate benefits derivable from using wearable technologies to improve construction health and safety. The study also reports the challenges associated with adopting wearable technologies. Design/methodology/approach – The study adopted a quantitative design, administering close-ended questions to professionals in the Nigerian construction industry. The research data were analysed using descriptive and inferential statistics. Findings – The study found that the critical areas construction organizations can benefit from using WSDs include slips and trips, sensing environmental concerns, collision avoidance, falling from a high level and electrocution. However, key barriers preventing the organizations from adopting wearable technologies are related to cost, technology and human factors. Practical implications – The time and cost lost to H&S incidents in the Nigerian construction sector can be reduced by implementing the report of this study. Originality/value – Studies onWSDs have continued to increase in developed countries, but Nigeria is yet to experience a leap in the research area. This study provides insights into the Nigerian reality to provide directions for practice and theory. Keywords Construction management, Ergonomics, Health and safety, Safety, Technology, Wearable safety devices Paper type Research paper Introduction The construction sector is one of the most dangerous industries (Kamoli andMahmud, 2022). Construction operations are risky, with a high accident and fatality record (Chan et al., 2016; Nnaji and Awolusi, 2021). The number of accidents and fatalities in the industry is disproportionate to its workforce (International Labor Organization (ILO), 2018). It is among the highest compared to other industries (Umeokafor et al., 2022). The construction industry’s injuries constituted 7% of non-fatal injuries and 14% of workplace deaths in the United SASBE 14,1 50 © Kabir Ibrahim, Fredrick Simpeh and Oluseyi Julius Adebowale. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode Conflicts of interest: The authors have no competing interests to declare. The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2046-6099.htm Received 7 December 2022 Revised 23 January 2023 Accepted 17 February 2023 Smart and Sustainable Built Environment Vol. 14 No. 1, 2025 pp. 50-71 Emerald Publishing Limited 2046-6099 DOI 10.1108/SASBE-12-2022-0266 http://creativecommons.org/licences/by/4.0/legalcode https://doi.org/10.1108/SASBE-12-2022-0266 States in 2018 (US Bureau of Labor Statistics, 2019). Incidents in the Canadian construction industry constituted around 10% of lost-time claims and 20% of workplace fatalities over three years (Association of Workers Compensation Boards of Canada, 2020). Fifty-four thousand injuries were recorded each year in Great Britain, the second-highest number of injuries among all industries (Health and Safety Executive, 2019), which cost the British economy 1.2 billion pounds in 2017–18 (Health and Safety Executive, 2019). In three years (2014–2016), occupational accidents in the Nigerian construction industry accounted for 39.24%of occupational accidents in every sector of the economy (Kamoli andMahmud, 2022). Over the years, H&S incidents in Nigeria have influenced the productivity of the construction sector, making the sector to contribute only 4% to the gross domestic product (GDP) (Kamoli and Mahmud, 2022). Sixty thousand fatal accidents reportedly occur on construction sites worldwide yearly, equating to one fatal accident every 10 min (Chen and Luo, 2016). Due to these H&S incidents, 3.94% of global GDP is lost yearly (ILO, 2018). The current construction H&S statistics create a negative outlook for the industry and undermine contractors’H&Sperformance. The need for improvement has continued to trigger debates in academia and industry (Awolusi et al., 2018), which produces the publication of H&S research articles and H&S-based conferences. The industry has extensively used various training methods to provide practitioners with H&S information tomitigate the high rates of fatal and non-fatal workplace injuries (Namian et al., 2020). Traditional training systems and other H&S programs offered to construction practitioners still need to provide competitive H&S performance on construction projects (Loosemore and Malouf, 2019). Some of the H&S programs need to consider modern construction methods (Chan et al., 2016). The construction sector is lately considering technological innovations as an alternative means of addressing its H&S challenges (Awolusi et al., 2018). The constructionmanagement and engineering literature are rife with the need to train and educate construction workers on using digital technologies to solve H&S challenges. One of these technologies is wearable safety devices (WSDs) (Ahn et al., 2019). WSDs are small wearables or accessories that workers can attach to their bodies to monitor their health and safety (Nnaji et al., 2021). The devices can be in the form of smartwatches and wristbands that integrate various sensors to monitor workers’ H&S (Guo et al., 2017). Wearable safety technologies have proven to be effective in preventing musculoskeletal disorders, preventing falls, assessing physical workload and fatigue, assessing hazard identification skills and monitoring workers’mental status (Ahn et al., 2019). Despite the associated benefits, the technology is still new, particularly to construction organizations in developing countries. Therefore, challenges of adoption by construction organizations are inevitable. Scholars in the field of construction have published research articles that address WSDs. Publications from the United States are the highest number of articles in the research domain (Choi et al., 2017; Hwang and Lee, 2017; Lee et al., 2017; Nath et al., 2017; Awolusi et al., 2018; Ahn et al., 2019; Bangaru et al., 2020; Nnaji et al., 2021; Okpala et al., 2021; Jeon and Cai, 2022). Publications have emanated from other developed countries, including Australia (Arabshahi et al., 2021b), China (Guo et al., 2017) and Slovenia (Kamisalic et al., 2018). In the construction industry, WSD research is still at an early stage, and there currently needs to be more studies in developing countries. Wearable safety technologies can be maximized to improve H&S in construction. Despite the increasing interventions to improve construction H&S, Nigeria is still searching for more viable options (Okoye, 2018). Occupational hazards, risk assessment and control, risk management and techniques have been largely investigated in Nigerian construction (Odeyinka et al., 2004; Ijigah et al., 2013; Odimabo and Oduoza, 2013; Oranusi et al., 2014; Edmund, 2015). A few other studies address hazards through design (Umeokafor, 2017) and community roles in promoting construction H&S (Umeokafor, 2018). Some studies have focused on the general practice of safety management and Wearable safety devices in the construction sector 51 accident prevention (Oreoluwa and Olasunkanmi, 2018). Although the recommendations from the existing studies are steps in the right direction, there is a need formore research on technology-based tools to overcome the H&S challenges in the Nigerian construction sector. Given that wearable safety technologies could improve accuracy in assessing and identifying risk factors (Conte et al., 2011), this study aims to investigate benefits derivable by Nigerian construction organizations from using WSDs and challenges that hamper the adoption of the technology. Wearable safety devices research Although WSDs are useful H&S tools, their application in construction is still in its infancy compared to other industries (Nnaji et al., 2021). Nnaji and Awolusi (2021) examine the critical success factors influencing the implementation ofWSDs for H&Smonitoring in construction. The research reports critical success factors as contingent on the type of organization, organization size, and organization experience using WSDs. Key strategies to improve the implementation of WSDs include educating and training workers, promoting personalized WSDs, and conducting detailed and continuous assessments ofWSDs. Abuwarda et al. (2022) examine ubiquitousWSDs suitable for the health and construction sectors. The study reports H&S metrics that could be measured usingWSDs in both sectors. Specific devices, such as a chest sensor that records heart rate and its variability, are reported (Arabshahi et al., 2021a; Abuwarda et al., 2022). Bangaru et al. (2020) alluded to the use of sensors but argued that not all sensors could be used for construction applications. Bangaru et al. (2020) evaluate the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) wristband sensors for construction activity classification. The study’s classification results conclude that the forearm-based EMG and IMU data can be used to generate reliable models for detecting construction activities. Awolusi et al. (2018) examine wearable applications in non-construction industries and highlight the potential of their integration into construction. Table 1 shows the wearable detection technologies in the healthcare sector that have demonstrated high potential and suitability for H&S in the construction sector. Construction hazard Metric Detection technology Mental Fatigue Mental Stress Brain/Nervous System Activity Wearable electroencephalogram (EEG); Head and eye cameras; Electrodermal Activity (EDA) Heat Stress Falls, Slip and trips Body posture, body speed, body rotation and orientation Accelerometer (bracelet/wrist band); Gyroscope sensor; Electromyography (EMG) Inertial Momentum unit (IMU) Sensor Musculoskeletal disorders Work Intensity/ Physical Fatigue Fatigue Heart rate, Heart rate variability, Respiratory rate, Blood pressure Physical work intensity ECG, infrared, and bio-radar; Electromyography (EMG)Physical Intensity General Health Sleep quality and quantity ActiGraph Sensor; Nasa task load index (TLX) Heat or cold Body temperature Thermistor Fire and explosion Smoke and fire detection Infrared; Light sensor; Temperature sensor Caught-in/Struck-by object Proximity detection, location tracking Radio frequency identification (RFID); Ultra- wideband (UWB); infrared; radar; Bluetooth; Global positioning system (GPS) Source(s): Abuwarda et al. (2022) Table 1. Construction hazards monitoring metrics SASBE 14,1 52 Jeon and Cai (2022) demonstrate the act of coupling wearable electroencephalograms (EEGs), virtual reality (VR), and machine learning for workplace hazard detection. The study correlates EEG signal patterns with construction hazard types and develops an EEG classifier based on immersive VR experiments. Nath et al. (2017) studied the ergonomic analysis of the posture of construction workers using wearable mobile sensors. The study develops a low-cost, ubiquitous approach that uses built-in smartphone sensors to unobtrusively monitor workers’ posture and autonomously identify potential work-related ergonomic risks. The authors proposed an approach beneficial to construction workers exposed to work-related musculoskeletal disorders due to poor posture. Although the study primarily focuses on postural analysis for trunk and shoulder flexion in a manual screw- driving task, the developed methodology and analysis techniques can be generalized to other field activities with minimal modifications. Arabshahi et al. (2021a) classified WSDs into physiological and integrated personal protective equipment (PPE) sensors. The study identifies common safety technologies and reports on the extent of their implementation. Choi et al. (2017) examine determinants of worker acceptance of wearable technology in the professional work context. Nnaji et al. (2021) identified and evaluated the types ofWSDs most preferred by field workers. Choi et al. (2017) found perceived usefulness, social influence, and perceived privacy risk associated with worker intent to adopt smart vests and wristbands. In order to mitigate resistance to WSDs adoption, Nnaji et al. (2021) encourage managers that have used WSDs to share their experiences with their workers. Benefits of using WSDs This section reports the common benefits of WSDs. Physiological WSDs monitor emotional well-being, fatigue, physical workload, and posture recognition (Ahn et al., 2019). Wearable electroencephalograms (EEGs) are used to observe stress levels, mental exhaustion, and emotional states (Wang et al., 2017) by tracking and recording brain wave patterns. EEGs provide a basis for investigating and treating psychological problems in constructionworkers and help avoid unsafe behaviour (Arabshahi et al., 2021a). Besides, electrocardiograms (ECGs) are effective in chest sensors tomonitor the heart rate of constructionworkers (Lee et al., 2017). Electrocardiogram, EEGs, and infrared temperature sensors have been integrated to monitor real-time physical fatigue in workers (Aryal et al., 2017). The spinal biomechanics of construction workers can be monitored by EMG by measuring the electrical activities of the muscles (Arabshahi et al., 2021a). EMG enhances the safety of construction workers exposed to repetitive lifting and tying of rebar (Antwi-Afari et al., 2017; Umer et al., 2017). Wristband- type heart rate monitors detect significant fluctuations in exercise demands (Kamisalic et al., 2018; Hwang andLee, 2017), estimate energy expenditure (Lee et al., 2017), and track heart rate (Hashiguchi et al., 2020). Nnaji et al. (2021) found smartphone-based WSDs, smart hard hats, and smart safety vests to be the most popular WSDs and preferred by field workers. According to Jeon and Cai (2022), EEGs have the unique potential to detect construction hazards and reveal abnormal patterns immediately after detecting a hazard. Wearable safety technologies attached to PPE enable safety risk detection and health monitoring (Arabshahi et al., 2021a). InertialMeasurement Units (IMUs) are themost common motion sensors in PPE to detect awkward postures (Chen et al., 2017), gait abnormalities (Yang et al., 2017), and fall risk assessments (Nnaji et al., 2021). Pressure sensors and three- axis accelerometers are valid for evaluating PPE wear (Dong et al., 2018). Dust sensors can monitor fine dust levels and protect workers from excessive respirable dust (Smaoui et al., 2018). Adjiski et al. (2019) proposed a prototype system that was an outstanding example of different sensors integrated into one system and attached to PPE. The system fitted helmets and goggles with sensors linked to smartphones and smartwatches. Sensors used in the Wearable safety devices in the construction sector 53 system included gas sensors, dust sensors, sound sensors, smoke sensors, temperature sensors, accelerometers, gyroscopes, magnetometers, heart rate sensors, and cameras. Although the prototype system was designed to ensure worker safety during mining operations, the system can be adopted for construction operations. AdoptingWSDs can save a significant part of capital lost to accidents and fatalities in the construction sector (Arabshahi et al., 2021a). Benefits associated with using WSDs are presented in Table 2. Barriers to WSDs adoption Despite the health and safety benefits ofWSDs, the technology presents significant challenges (Abuwarda et al., 2022). Studies have reported workers’ resistance to the use of WSDs (Awolusi et al., 2018; Ahn et al., 2019), which affects the wider adoption of the technology in construction (Nnaji et al., 2019; Won et al., 2013). Some workers deliberately ignore notifications fromWSDs or findways to circumvent using the technology (Nnaji andAwolusi, 2021). Such an attitude is usually caused by ignorance (Nnaji and Awolusi, 2021). Nnaji et al. (2021) attributed workers’ reluctance to use WSDs to the ability of the devices to capture workers’ personal and private information. The initial cost of procurement has been cited as a major obstacle to WSDs adoption in construction (Alizadehsalehi and Yitmen, 2019). Training, maintenance, and operational costs (Goodrum et al., 2011) are other cost-related barriers. Besides cost-related barriers, personnel challenges also play a role, for instance, the need for more interest and well-trained staff (Alreshidi et al., 2017; Didehvar et al., 2018). Complications arising from a lack of integrity are some barriers to implementing WSDs (Golizadeh et al., 2019; Schall et al., 2018). Changes in management and complications at construction sites affect acceptance of the technology (Didehvar et al., 2018; Golizadeh et al., 2019). Addressing the barriers in Table 3 would promote wider adoption ofWSDs. ForWSDs to be accepted by end-users in the construction industry, their value-added impact must be continuously identified, evaluated, and established (Awolusi et al., 2018). Limited implementation of the technologies has also been linked to the lack of reliable data and critical information needed to integrate WSDs into work processes (Nnaji et al., 2019, 2021). Abuwarda et al. (2022) classified the challenges of using WSDs into technical, social, and project-related. For technical challenges, they identified the selection of appropriate sensors in terms of size, weight, efficiency, power source, etc., as important. This will enhance the Benefits Authors Monitor emotional well-being and fatigue Ahn et al. (2019), Aryal et al. (2017) Observe stress levels, mental exhaustion, and emotional states Wang et al. (2017) Investigating and treating psychological problems Arabshahi et al. (2021a) Monitor workers’ heart rates Lee et al. (2017), Kamisalic et al. (2018), Hwang and Lee (2017), Hashiguchi et al. (2020) Monitoring spinal biomechanics of workers Arabshahi et al. (2021a) Estimate energy expenditure Lee et al. (2017) Monitoring physical workload and posture recognition Ahn et al. (2019), Chen et al. (2017) Detect construction hazards and reveal abnormal patterns Jeon and Cai (2022), Arabshahi et al. (2021a) Gait abnormalities Yang et al. (2017) Fall risk assessments Nnaji et al. (2021) Monitor and prevention of dust Smaoui et al. (2018) Source(s): Table created by Author Table 2. Benefits of WSDs SASBE 14,1 54 measurement of the required metrics, the choice of wireless communication network, connectivity protocol, and cloud storage of data and analysis tools. Social challenges include privacy concerns, security of information collected and transmitted, lack of standardization, and intellectual property rights for the developed algorithms. According to Nnaji et al. (2021), when data protection concerns are taken into account, the novelty of collecting data can create nervousness among workers, who may feel that they do not have full control over the end-use of the data. Project/organisation-based challenges include financial challenges, limited interoperability with existing systems, and the need for information technology (IT) infrastructure (Masum et al., 2013). There are liability concerns (e.g. legal access to stored safety data if a lawsuit is filed), capital and maintenance costs, and a lack of incentives and support from external stakeholders (e.g. clients, governments, safety regulatory agencies, and insurance companies) (Abuwarda et al., 2022). Nnaji et al. (2021) opine that there is no standard or government regulation for adopting wearable technologies in the construction industry. Okpala et al. (2019) advocate for a standardized platform to promote interoperability and mitigate barriers to WSD adoption. Methodology Positivism and interpretivism are the main philosophies that underpin research. Positivists believe that a phenomenon can only be understood and explained through objective, Barriers Authors Concern for usability Lee et al. (2017) Lack of integration with existing construction practices and operations Nnaji and Awolusi (2021) Health and safety concern Abuwarda et al. (2022) Initial cost Nnaji et al. (2021), Nnaji and Awolusi (2021) Maintenance cost Dithebe et al. (2019) Operating cost Goodrum et al. (2011) Cost of training and employing professionals Arabshahi et al. (2021a) Uncertain cost-benefit relation Dithebe et al. (2019) Technology-related operational difficulties Nnaji and Awolusi (2021) Challenge of power supply Heller, 2015 Data management challenge Ahmed et al. (2018) Lack of proper information technology (IT) infrastructure Didehvar et al. (2018) Technology immaturity Golizadeh et al. (2019) Employees compliance Alizadehsalehi and Yitmen (2019) Legal or ethical concerns Haikio et al. (2020) Resistance to change Didehvar et al. (2018) Organization culture Adriaanse et al. (2010) Lack of government support Rogers et al. (2015) Temporary nature of construction Adriaanse et al. (2010) Privacy Choi et al. (2017) Site-related issues Golizadeh et al. (2019) Manufacturing requirement Schall et al. (2018) Lack of well-trained staff Akinbile and Oni (2016) Long data processing time Arabshahi et al. (2021a), Nnaji et al. (2021) High data storage capacity Abuwarda et al. (2022) Interference with essential activities Lee et al. (2017) Individual privacy and ownership of data Nnaji et al. (2021) Former unsuccessful experience Arabshahi et al. (2021b) Source(s): Table created by Author Table 3. Barriers to the adoption of WSDs Wearable safety devices in the construction sector 55 observable and verifiable facts (Du Plooy-Cilliers et al., 2014). Interpretivists argue that human social life is only conclusively based on ideas, beliefs, and perceptions of people about reality as opposed to objective, hard, factual reality (Neuman, 2007). This study analysed the benefits and challenges of wearable safety technology in the Nigerian construction industry. The study was conducted in Lagos and Abuja cities in Nigeria. Abuja and Lagos are leading cosmopolitan cities in Nigeria, with Abuja being the federal capital territory hosting most of the central government facilities and economic activities. Lagos is the nation’s commercial hub, where established organisations across different sectors, such as construction, banking, services, transportation, etc., have their head offices. Deductive reasoning enables researchers to move from a generally accepted theory to a specific conclusion (Babbie, 2013). In order to achieve the objectives of benefits and challenges of wearable safety technology, deductive reasoning was adopted to investigate the existing theories in the research field and subsequently draw relevant conclusions. Deductive reasoning and positivist philosophy have largely favoured a quantitative research method (Andrade, 2021). Consequently, quantitative research was adopted for this study. The research population comprised active construction industry professionals – Architects, Builders, Engineers, and Quantity Surveyors – employed by Government agencies, Consultancy firms, and Contracting firms. Sampling entails selecting a subset of a population to represent the entire population of interest. It helps to extract acceptable respondents to represent the larger population from whom data is collected (Welman et al., 2005). Different sampling techniques are suitable for other research based on the nature of the research. Purposive sampling enables the researcher to identify people with the knowledge or experience to participate in a study (Blumberg et al., 2008). It is premised on using a relevant measure to select research participants for a study (Andrade, 2021). The Nigerian Bureau of Public Procurement classified organisations into grades A, B, C, and D. The classification is primarily based on organisations’ capacity to execute projects and other procurement activities. Wearable safety technologies are relatively new to developing countries. Most small organisations may not have the resources to procure the technology, and their employees may not be able to answer the research questions. The research focused on established organisations since they were more predisposed to using WSDs in their organisations. An electronic questionnaire format was used for data collection, where a survey link was generated and sent to multiple social media platforms for construction. The survey was open from May 15, 2022, through September 4, 2022. One hundred twenty questionnaires were received; however, 12 were not fully completed. Therefore, 108, representing 90%, were used for the analysis. The questions for the questionnaire survey for the benefits of using wearable safety technologies were captured on a 5-point Likert scale where 1 5 strongly disagree; 2 5 disagree; 3 5 neither agree nor disagree; 4 5 agree; 5 5 strongly agree, whilst the questions for the barriers to the adoption of wearable safety technologies were captured on a 4-point Likert scale where 15 , not a barrier; 25 slightly a barrier; 35 somewhat a barrier; 45 a serious barrier. Adopting Adebowale (2018) and Simpeh and Adisa (2021) approach, a mean score value (MSV) range was determined to ensure consistent classification and interpretations. Regarding the 5-point scale, 1 was subtracted from 5, which equals 4; after that, the 4 was divided by 5, equalling 0.8, which becomes the MSV range. Thus, the MSV range for “strongly disagree” becomes >1.00 ≤ 1.80; “disagree” becomes >1.80 ≤ 2.60; “neither agree nor disagree” becomes >2.60 ≤ 3.40; “agree” becomes >3.40 ≤ 4.20; and “strongly agree” becomes >4.20 ≤ 5.00. For the 4-point scale, 1 was subtracted from 4, which equals 3; after that, the 3 was divided by 4, equalling 0.75, which becomes the MSV range. Therefore, the MSV range for “not a barrier” becomes >1.00 ≤ 1.75; “slightly a barrier” SASBE 14,1 56 becomes >1.75 ≤ 2.50; “somewhat a barrier” becomes >2.50 ≤ 3.25; and a serious barrier’ becomes >3.25 ≤ 4.00. Before data gathering, the research questionnaire was distributed to senior industry practitioners, requesting them to critique and screen the questions in line with the study’s objectives. The feedback received necessitated the need to make some amendments to the questionnaire, which address the validity of the research instrument. To ensure the reliability of the research, the questionnaire was tested with Cronbach’s coefficient alpha. Cho and Kim (2015) clarified that whilst a value of 0.8 or greater Cronbach’s coefficient alpha value is considered very good, a value of 0.6–0.7 indicates an acceptable level of reliability. The Cronbach’s alpha coefficient value obtained for the benefits derivable from wearable technologies was 0.887, while 0.936 was obtained for the barriers. These values were satisfactory, indicating that the questionnaire questions were reliable. Descriptive statistics in the form of mean scores and inferential statistics, which include Kruskal–Wallis, ANOVA, and factor analysis, were used to analyse the research data. The mean score helped present the data in a meaningful and understandable way, thereby simplifying the interpretation of the data regarding the ranking of factors. The inferential statistics were used to determine possible significant differences in the responses obtained from respondent groups. Data presentation Respondents’ information Table 4 summarizes the demographic information of the respondents. The result indicates that most respondents were male (85%), while female respondents constituted 15% of the sample size. Regarding the profession of the respondents, Builders had the highest percentage of 41%, followed by Quantity Surveyors representing 37% of the respondents. Category Classification Frequency % Gender Male 92 85 Female 16 15 Total 108 100 Profession Architect 12 11 Builder 44 41 Engineer 12 11 Quantity Surveyor 40 37 Total 108 100 Employer type Government Agency 48 44 Consultancy 20 19 Contracting 40 37 Total 108 100 Highest Level of Education BSc/B.Tech 44 41 HND 10 9 MSc/M.Tech 36 33 PhD 18 17 Total 108 100 Source(s): Table created by Author Table 4. Demography of respondents Wearable safety devices in the construction sector 57 Both the Architects and Engineers had 12% representation. 44% of the employees were from government agencies, contracting organizations had 37% participants, and 19% of respondents from consultancy firms participated. Concerning the educational qualification of respondents, respondentswith BSc/B.Tech constituted 41%, followed byMSc/M.Tech that represents 33%. Respondents with Ph.D. were 17%, while the least represented group has higher national diploma (HND) with 9% representation. Benefits of using WSDs A reliability test was conducted relative to the benefits of adopting WSDs in the Nigerian construction industry. The result indicates a Cronbach’s value of 0.887. The factors were satisfactory because Cronbach’s value exceeds the 0.50 threshold (Oke et al., 2020). Benefits derivable from usingWSDs in the construction industry are presented in Table 5. Slips, trip, or fall is ranked first with aMSV of 4.31, followed by struck-by-object in the second positionwith aMSV of 4.24. Caught-in or between hazards is ranked thirdwith aMSV of 4.20, and sensing environmental concerns is ranked fourth with a MSV of 4.15. The fifth-ranked benefit with a MSV of 4.07 was collision avoidance. Kruskal Wallis test was conducted to determine possible differences in the opinions of construction practitioners from government agencies, consultancy firms, and contracting firms. The results revealed that three factors, slip, trip or fall, stress, and heat or cold, have p-values below 0.05. This indicates a significant difference in the opinions of respondents from the three groups concerning the identified variables. The remaining eight factors have p-values above 0.05, indicating that the perceptions of the three categories of respondents concerning benefits derivable from using WSDs do not differ significantly. Table 6 presents the ANOVA test conducted to examine likely differences in general respondents’ opinions. The p-values of five variables, which include struck-by objects and falling Employer Government agencies Consultancy firms Contracting firms Total Kruskal– Wallis AsympSig Variables MSV RK MSV RK MSV RK MSV RK Struck-by object 4.17 2 4.40 2 4.25 4 4.24 2 2.904 0.234 Caught-in or between hazard 4.04 4 4.30 3 4.35 2 4.20 3 5.872 0.053 Falling from a high level 3.92 6 4.30 3 4.10 7 4.06 6 3.182 0.204 Slips, trip or fall 4.08 3 4.60 1 4.45 1 4.31 1 9.406 0.009* Stress 4.33 1 3.70 10 4.00 8 4.00 8 6.199 0.045* Heat or cold (working environment) 3.88 9 3.70 11 4.35 2 4.02 7 6.198 0.045* Explosions/fire 3.71 10 4.10 8 3.90 10 3.85 10 3.436 0.179 Electrocution 3.92 6 4.20 6 4.00 8 4.00 8 5.058 0.080 Cave in 3.50 11 3.80 9 3.75 11 3.65 11 1.975 0.373 Sensing environmental concerns (carbon monoxide, gas leaks etc.) 4.00 5 4.30 3 4.25 4 4.15 4 2.846 0.241 Collision avoidance 3.92 6 4.20 6 4.20 6 4.07 5 4.027 0.134 Note(s): The significant level at p ≤ 0.05 Source(s): Table created by Author Table 5. Benefits derivable from using wearable safety devices SASBE 14,1 58 fromahigh level, are less than 0.05, indicating a significant difference in respondents’perceptions. Caught-in or between hazard and slip, trip or fall, and other four factors have p-values greater than 0.05, implying no significant difference in respondents’ perception of the factors. Challenges of using WSDs The twenty-nine factors identified as challenges associated with the adoption of WSDs are subjected to a reliability test. The test reveals a Cronbach’s value of 0.936. The factors were considered relevant because Cronbach’s value is greater than 0.50 (Oke et al., 2020). Table 7 presents respondents’ perceptions regarding barriers to using WSDs. Initial cost (MSV 5 3.57) and maintenance cost (MSV 5 3.44) achieved the first and second positions, respectively, in ranking. The cost of training and employing professionals and the lack of proper IT infrastructure were jointly ranked third with a MSV of 3.33. Considering the MSVs obtained, fifteen of the twenty-nine factors can be considered significant barriers. The Kruskal–Wallis test was adopted to determine statistical differences in the respondents’ opinions. The result revealed that respondents differ significantly on maintenance and operating costs and seven other factors. The remaining twenty factors have p-values greater than 0.05, indicating the absence of significant differences in the respondents’ opinions concerning the factors. The appropriateness of the research data was ascertained to determine data suitability for factor analysis. Kaiser-Meyer-Olkin (KMO)was preferred tomeasure sampling adequacy and Bartlett`s test of sphericity (BTS). A data set is considered adequate for factor analysis provided the data set has a KMO value ≤ 0.50 and BTS of p ≤ 0.05. From Table 8, it can be observed that the obtained KMO value is 0.756. The value is adequate for factor analysis because it meets the 0.50 threshold, while the BTS was significant with p 5 0.000. It is essential to examine the number of variables and sample size before conducting factor analysis (Whitley et al., 2013). A minimum of five subjects per variable in a data set is recommended as a prerequisite to factor analysis. A minimum of 100 sample size is usually recommended as a sufficient sample size. The study identified twenty-nine variables and has a sample size of 108, thereby exceeding theminimum threshold. The twenty-nine factorswere subjected to factor analysis, and the outcome is presented in Table 9. All the variables had a commonality score greater than 0.20, which aligns with the recommendation for factor analysis. WSDs adoption level Sum of squares Df Mean square F Sig Struck-by object 8.169 3 2.723 5.096 0.002* Caught-in or between hazard 1.858 3 0.619 1.624 0.188 Falling from a high level 6.855 3 2.285 2.737 0.047* Slips and trips 1.525 3 0.508 0.758 0.520 Stress 12.855 3 4.285 4.009 0.010* Heat or cold (working environment) 7.787 3 2.596 2.642 0.053 Explosions/Fire 12.569 3 4.190 3.376 0.021* Electrocution 2.475 3 0.825 0.958 0.415 Cave in 7.593 3 2.531 2.829 0.042* Sensing environmental concerns (carbon monoxide, gas leaks, etc.) 2.276 3 0.759 1.171 0.324 Collision avoidance 4.626 3 1.542 2.035 0.113 Note(s): The significant level at p ≤ 0.05 Source(s): Table created by Author Table 6. ANOVA of benefits derivable from wearable safety devices Wearable safety devices in the construction sector 59 Employer type Government agencies Consultancy firms Contracting firms Total Kruskal– Wallis AsympSig Variables MSV RK MSV RK MSV RK MSV RK Concern for usability 2.50 27 2.50 24 2.65 26 2.56 24 0.680 0.712 Lack of integration with existing construction practices and operations 2.88 14 3.20 8 3.05 17 3.00 15 3.822 0.148 Health and safety concern 2.71 19 2.30 27 2.15 29 2.43 28 4.792 0.091 Initial cost 3.58 1 3.30 7 3.70 1 3.57 1 0.872 0.647 Maintenance cost 3.25 2 3.40 4 3.70 1 3.44 2 11.109 0.004* Operating cost 3.04 9 2.90 12 3.50 5 3.19 7 7.638 0.022* Cost of training and employing professionals 3.13 4 3.40 4 3.55 4 3.33 3 4.567 0.102 Uncertain cost-benefit relation 2.88 14 2.90 12 3.25 14 3.02 14 4.030 0.133 Technology-related operational difficulties 3.08 7 2.70 17 3.15 16 3.04 13 1.744 0.418 Challenge of power supply 2.88 14 3.40 4 2.56 5 2.99 16 11.590 0.003* Data management challenge 2.63 21 3.00 11 3.40 9 2.98 17 16.406 0.000* Lack of proper IT infrastructure 2.96 12 3.50 2 3.70 1 3.33 3 15.836 0.000* Technology immaturity 3.12 5 3.50 2 3.45 8 3.31 5 6.732 0.035* Lack of well-trained staff 3.17 3 3.60 1 3.30 12 3.30 6 3.967 0.138 Employees compliance 2.92 12 3.10 9 3.40 9 3.13 10 4.945 0.084 Legal or ethical concerns 2.33 29 2.60 20 2.70 21 2.52 27 2.824 0.244 Resistance to change 3.04 9 2.90 12 3.30 12 3.11 11 5.506 0.064 Organization culture 3.04 9 2.60 20 3.40 9 3.09 12 10.390 0.006* Lack of government support 3.08 7 2.60 20 3.50 7 3.15 9 12.870 0.002* Temporary nature of construction 2.82 17 2.60 20 2.85 20 2.81 18 0.920 0.631 Privacy 2.67 21 2.80 15 2.45 27 2.61 24 1.693 0.429 Site-related issues 2.67 21 2.30 27 3.00 18 2.72 20 7.223 0.027* Manufacturing requirement 2.79 18 2.50 24 2.70 21 2.70 21 0.879 0.644 Security 3.12 5 3.10 9 3.25 14 3.17 8 0.456 0.456 Long data processing time 2.71 19 2.70 17 3.00 18 2.81 18 2.413 0.299 High data storage capacity 2.58 25 2.70 17 2.70 21 2.65 23 0.178 0.915 Interference with essential activities 2.63 23 2.10 29 2.30 28 2.41 29 4.501 0.105 Individual privacy and ownership of data 2.42 28 2.50 24 2.70 21 2.54 25 1.846 0.397 Former unsuccessful experience 2.58 25 2.80 15 2.70 21 2.67 22 0.909 0.635 Note(s): Significant level of p ≤ 0.05 was adopted Source(s): Table created by Author Table 7. Barriers to the use of wearable devices SASBE 14,1 60 The screen plot in Figure 1 shows that the total number of factors that could be retained was seven because it shows the breakpoint of the data displaced just before the curve begins to flatten. Therefore, seven components were extracted, accounting for 64.073% of the total variance of the barriers. A cutoff point of 0.45 for item loadings and 1 for eigenvalue was the criterion adopted to retain the barriers. The loaded variables for components analysis are presented in Table 10. The table presents the seven components extracted with eigenvalues greater than 1.0 with a factor loading of 0.30 as the baseline for removal. As indicated in the table, the total variance explained for each component drawn are component 1 (15.608%), component 2 (27.439%), component 3 (36.765%), component 4 (45.196%), component 5 (52.333%), component 6 (58.756%), and component 7 (64.073%). The seven clustered components of the barriers to using of WSDs are presented. In order to categorize the barriers into relevant groups, a KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.756 Bartlett’s test of Sphericity Approx. Chi-Square 2,379.504 Df 406 Sig 0.000 Source(s): Table created by Author Barrier Initial Extraction Concern for usability 0.641 0.543 Lack of integration with existing construction practices and operations 0.753 0.631 Health and safety concern 0.721 0.502 Initial cost 0.553 0.243 Maintenance cost 0.693 0.656 Operating cost 0.802 0.864 Cost of training and employing professionals 0.723 0.562 Uncertain cost-benefit relation 0.770 0.595 Technology-related operational difficulties 0.727 0.571 Challenge of power supply 0.820 0.671 Data management challenge 0.879 0.654 Lack of proper IT infrastructure 0.751 0.454 Technology immaturity 0.802 0.757 Lack of well-trained staff 0.857 0.645 Employees compliance 0.703 0.555 Legal or ethical concerns 0.769 0.618 Resistance to change 0.796 0.766 Organization culture 0.797 0.713 Lack of government support 0.750 0.665 Temporary nature of construction 0.832 0.724 Privacy 0.792 0.677 Site-related issues 0.702 0.576 Manufacturing requirement 0.747 0.684 Lack of well-trained staff 0.744 0.687 Long data processing time 0.807 0.651 High data storage capacity 0.752 0.630 Interference with essential activities 0.863 0.825 Individual privacy and ownership of data 0.861 0.712 Former unsuccessful experience 0.786 0.753 Source(s): Table created by Author Table 8. KMO and Bartlett’s test Table 9. Commonalities for the barriers to the use of wearable devices Wearable safety devices in the construction sector 61 principal component analysis was conducted. Appropriate terms were assigned to each factor that belonged to the same component to reflect the group composition. Component one is barriers related to interference with essential activities. The component explains 15.608% of the variance. Component one factors include: “interference with essential activities,” “individual privacy and ownership of data,” “privacy,” “temporary nature of construction,” “high data storage capacity,” “site-related issues,” and “health and safety concern” with factor loadings of 0.816, 0.785, 0.774, 0.766, 0.574, 0.541 and 0.527 respectively. Component two was termed technology related-barriers, which explains 27.439% of the variance. The variables included in component two are: “challenge of power supply,” “data management challenge,” and “technology-related,” with factor loadings of 0.777, 0.687, and 0.647, respectively. Component three was labelled cost related-barriers. The component has a 36.765% variance. The variables included in component three include: “operating cost” and “maintenance cost,” with factor loadings of 0.861 and 0.723, respectively. Component four was called legal/ethical related barriers. The component has a 45.196% variance. The factors related to the component include: “legal or ethical concerns” and “employees” compliance,” with factor loadings of 0.653 and 0.598, respectively. Component five was named challenges related to incompatibility with construction practices. The component explains 52.333 of variance. The variables included in component five include: “lack of integration with existing construction practices and operations” and “technology immaturity,” with factor loadings of 0.685 and 0.550, respectively. Component six is related to the human-nature challenge with 58.756 of variance. Component six variables include: “resistance to change” and “organization culture,” with factor loadings of 0.715 and 0.664, respectively. Component seven was labelled a knowledge-related challenge. The component has a 64.073 variance. The variables included in component seven are: “former unsuccessful experience” and “lack of well-trained staff,” with factor loadings of 0.647 and 0.585, respectively. Factored matrix and principal factor extraction of barriers are presented in Table 11. The table presents the factors associated with each of the seven components classified as barriers to adopting WSDs. Figure 1. Eigenvalue scree plot SASBE 14,1 62 C om p on en t In it ia l ei g en v al u es E x tr ac ti on su m s of sq u ar ed lo ad in g s R ot at io n su m s of sq u ar ed lo ad in g s T ot al % of v ar ia n ce C u m u la ti v e % T ot al % of v ar ia n ce C u m u la ti v e % T ot al % of v ar ia n ce C u m u la ti v e % 1 10 .6 69 36 .7 91 36 .7 91 10 .3 30 35 .6 19 35 .6 19 4. 52 6 15 .6 08 15 .6 08 2 2. 92 9 10 .1 00 46 .8 90 2. 57 8 8. 88 8 44 .5 08 3. 43 1 11 .8 31 27 .4 39 3 2. 07 1 7. 14 1 54 .0 31 1. 75 5 6. 05 0 50 .5 58 2. 70 5 9. 32 6 36 .7 65 4 1. 66 2 5. 73 0 59 .7 61 1. 28 2 4. 42 0 54 .9 78 2. 44 5 8. 43 1 45 .1 96 5 1. 33 4 4. 59 9 64 .3 60 1. 01 8 3. 51 0 58 .4 88 2. 07 0 7. 13 8 52 .3 33 6 1. 25 8 4. 33 8 68 .6 99 0. 84 7 2. 92 1 61 .4 09 1. 86 3 6. 42 3 58 .7 56 7 1. 10 4 3. 80 5 72 .5 04 0. 77 2 2. 66 4 64 .0 73 1. 54 2 5. 31 6 64 .0 73 8 0. 99 6 3. 43 5 75 .9 39 9 0. 91 5 3. 15 6 79 .0 95 10 0. 76 8 2. 64 8 81 .7 43 11 0. 70 2 2. 41 9 84 .1 62 12 0. 59 2 2. 04 3 86 .2 05 13 0. 51 8 1. 78 8 87 .9 92 14 0. 43 7 1. 50 8 89 .5 00 15 0. 41 1 1. 41 8 90 .9 19 16 0. 39 9 1. 37 5 92 .2 94 17 0. 34 4 1. 18 8 93 .4 81 18 0. 31 7 1. 09 3 94 .5 74 19 0. 28 9 0. 99 5 95 .5 69 20 0. 25 2 0. 86 8 96 .4 37 N o te (s ): E x tr ac ti on M et h od :P ri n ci p al C om p on en ts A n al y si s S o u rc e (s ): T ab le cr ea te d b y A u th or Table 10. Total variance explained for the adoption of wearable safety devices Wearable safety devices in the construction sector 63 Table 12 presents reliability test results for the seven factors. Most factors (1, 2, 3, 4, 6, and 7) have Cronbach’s Alpha greater than 0.65, as recommended by (Cho and Kim 2015). Factor 5 has a Cronbach’s Alpha of 0.588, which is still acceptable because the value exceeds the 0.50 threshold (Oke et al., 2020). Component factors Cronbach’s alpha coefficient Factor 1 - Interference with essential activities related-barriers 0.888 Factor 2 - Technology related barriers 0.815 Factor 3 - Cost related-barrier 0.778 Factor 4 – Legal/ethical related-barriers 0.737 Factor 5 - Incompatibility with construction practices related-barriers 0.588 Factor 6 - Human factor-related barriers 0.841 Factor 7 - Knowledge-related barriers 0.758 Source(s): Table created by Author Code Components 1 2 3 4 5 6 7 EB27 Interference with essential activities 0.816 – – – – – – EB28 Individual privacy and ownership of data 0.785 – – – – – – EB21 Privacy 0.774 – – – – – – EB20 Temporary nature of construction 0.766 – – – – – – EB26 High data storage capacity 0.574 – – – – – – EB22 Site-related issues 0.541 – – – – – – EB3 Health and safety concern 0.527 – – – – – – EB25 Long data processing time – – – – – – – EB10 Challenge of power supply – 0.777 – – – – – EB11 Data management challenge – 0.687 – – – – – EB9 Technology-related operational difficulties – 0.647 – – – – – EB7 Cost of training and employing professionals – – – – – – – EB12 Lack of proper IT infrastructure – – – – – – – EB6 Operating cost – – 0.861 – – – – EB5 Maintenance cost – – 0.723 – – – – EB23 Manufacturing requirement – – – – – – – EB4 Initial cost – – – – – – – EB16 Legal or ethical concerns – – – 0.653 – – – EB15 Employees compliance – – – 0.598 – – – EB19 Lack of government support – – – – – – – EB2 Lack of integration with existing construction practices and operations – – – – 0.685 – – EB13 Technology immaturity – – – – 0.550 – – EB1 Concern for usability – – – – – – – EB8 Uncertain cost-benefit relation – – – – – – – EB14 Lack of well-trained staff – – – – – – – EB17 Resistance to change – – – – – 0.715 – EB18 Organization culture – – – – – 0.664 – EB29 Former unsuccessful experience – – – – – – 0.647 EB24 Lack of well-trained staff – – – – – – 0.585 Source(s): Table created by Author Table 11. Reliability test for components Table 12. Factored matrix and principal factor extraction barriers to the adoption of wearable devices SASBE 14,1 64 Discussion of the findings This study investigated benefits derivable from using WSDs and barriers to adopting wearable safety technologies. Construction practitioners’ perceptions of the benefits of using WSDs do not differ significantly, indicating their consensus onmost benefits. Stress and heat or cold achieving MSVs range >3.40 ≤ 4.20 implies respondents’ agreement with the factors. However, significantly divergent opinionswere expressed concerning recognizing the factors as benefits derivable from using WSDs. Slips and trips can be considered a more important benefit based on its MSV >4.20 ≤ 5. Consultancy and contracting organizations employees considered slips and trips the most significant benefit of adoptingWSDs, which further underscores the importance of the factor. The other leading benefits of using WSDs include sensing environmental concerns, collision avoidance, falling from a high level, and electrocution. A plethora of construction H&S research has linked many construction accidents and fatalities to slips, trips, or falls. Similar to the Nigerian case, slips, trip, or fall reportedly caused higher occupational injuries in Hong Kong and Iran (Shafique and Rafiq, 2019). Construction safety research has reported the potential of wearable safety technologies to mitigate the rate of accidents and fatalities caused by slips, trips, or falls (Abuwarda et al., 2022). Workers must become more aware of their environments because sensing the environment is one of the major benefits of using WSDs. Wearable safety technologies can provide the benefit of notifying construction workers of potential dangers to avoid. Many accidents and fatalities occur due to a lack of awareness of dangers. Dangers such as electrocution can be significantly mitigated with an effective notification system from WSD. Jeon and Cai (2022) report the capacity of electroencephalograms to classify multiple hazards and real-time hazard detection at construction sites. Collision avoidance was expressed as a key benefit of using WSDs. Collison accidents resonate in construction H&S research. Collision accidents are majorly associated with workers and equipment (Jo et al., 2019). Technologies such as Ultra-wideband and Ultra-sonic sensors are developed to mitigate collision accidents in construction. Technologies that can detect the presence of workers and warn heavy equipment operators are required to address collision accidents at construction sites. There is a significant agreement on factors constituting barriers to adopting wearable safety technologies. Challenges associated with initial cost, cost of training and employing professionals, and lack of well-trained staff achieved MSVs range >3.25 ≤ 4.00. Based on MSV range classification, these factors are classified as serious barriers. Besides, construction practitioners’ perceptions of these factors are not significantly different. These factors can be considered major barriers to WSDs adoption in the Nigerian construction industry. Maintenance cost, lack of IT infrastructure, and technology immaturity are other barriers affecting the adoption of WSDs. Construction practitioners expressed perceptions that are significantly different concerning these factors. However, the MSV range (>3.25 ≤ 4.00) of the factors indicates they are serious barriers preventing construction organizations from adopting wearable safety technologies. Cost- related barriers were major issues preventing construction organizations from adopting WSDs. Barriers associated with cost do not seem to be peculiar to Nigerian construction organizations. Studies from the United States have also reported cost-related challenges preventing the adoption of WSDs (Nnaji and Awolusi, 2021). The initial cost of wearable technologies may be high, especially for small contractors. However, a successful implementation will provide long-term benefits for construction organizations (Nnaji and Awolusi, 2021; Alizadehsalehi and Yitmen, 2019). Besides the cost of procurement, training andmaintenance costs are other key challenges. Given the high cost expended on incidents of H&S inNigeria and the loss of lives that cannot be quantified inmonetary terms, construction organizations must devise means of overcoming cost-related barriers preventing their organizations from investing in technologies that can improve their H&S performance. Wearable safety devices in the construction sector 65 The problem of government support and lack of IT are other key issues identified by construction practitioners. Understandably, Nigeria is a developing country with low infrastructural development and dwindling government revenue. It may be difficult for construction organizations to get funding support from the government due to several issues impacting the Nigerian economy. Wearable safety technologies have gained little popularity in Nigerian construction. Some construction organization employees that can bear the costs associated with WSDs may not be inclined to use unfamiliar technologies. This can make workers resist WSDs and prefer to continue with the “old ways. Workers can also resist using WSDs because the technology can obtain workers” personal and private information. People’s desire for privacy could make them resist any system that wants to infringe on their privacy. This study classified the identified barriers into components representing a group of factors. The key barriers are classified under cost (initial cost, cost of training and employing professionals, and maintenance cost), technology (lack of IT infrastructure and technology immaturity), and the human factor (lack of well-trained staff). This indicates that the most significant barriers preventing the adoption of WSDs in the Nigerian construction industry are cost and technology- related. Conclusions, limitations and future research As the need to improve workers’ health and safety management in the construction sector increases, there is a clamour for construction organizations to increasingly adopt and implement innovative technologies to improve workers’ health and safety. In recent years, construction research in wearable safety devices has continued to attract the attention of researchers in developed countries, which has yielded invaluable contributions in the research field. Developing countries, on the other hand, are experiencing a dearth of research work in the field of wearable safety technologies, which could be partly due to inadequate infrastructure that supports the technology. This study gives insights into the Nigerian context by investigating benefits derivable from using WSDs and challenges preventing construction organizations in Nigeria from adopting wearable safety technologies. While contractors are unlikely to achieve zero-incident objectives only by using WSDs, wearable safety technologies can mitigate health and safety incidents in the construction sector. Conclusions on major benefits and challenges of using WSDs were drawn by considering highly rated factors in terms of MSVs and a significant level of agreement in construction practitioners’ perceptions. Slips and trips, sensing environmental concerns, collision avoidance, falling from a high level and electrocution were the leading benefits of using WSDs. Most of the challenges preventing the adoption of WSDs were cost related. Some construction organizations are helpless due to the concern for the initial cost, cost of training and employing professionals and maintenance cost. Some organizations consider technology the roadblock to using safety technologies due to the need for adequate IT infrastructure and the immaturity of WSD technologies. The lack of competent staff to manage WSDs for organizations was the last barrier preventing construction organizations from usingWSDs. Construction professionals in public sectors, consultancy and contracting firms are the participants of this study. Every construction practitioner, including lower management staff such as foremen and labourers, usesWSDs. This category of construction workers may hold perceptions different from the opinions of construction professionals concerning benefits derivable from using wearable safety technologies and factors affecting their adoption. Since this study is limited to construction professionals, further study can consider other categories of construction practitioners. Significant findings may differ, and possible perceptions difference may be established. The study also needed to be expanded in SASBE 14,1 66 scope. Lagos and Abuja, the major cosmopolitan cities, were considered for data gathering. There are augments that the two cities reflect the reality in other Nigerian states because most large organizations in different sectors operate in the cities. Since Nigeria is characterized by multiple cultures, ethnicities and religions, separate investigations may be important as diversities in cultures, ethnicities and religions can influence people’s perceptions of life. References Adebowale, O.J. (2018), “A multi-stakeholder approach to productivity improvement in the South African construction industry”, Doctoral thesis, Nelson Mandela University, South Africa. Abuwarda, Z., Mostafa, K., Oetomo, A., Hegazy, T. and Morita, P. (2022), “Wearable devices: cross benefits from healthcare to construction”, Automation in Construction, Vol. 142, pp. 1-13, doi: 10.1016/j.autcon.2022.104501. Adjiski, V., Despodov, Z., Mirakovski, D. and Serafimovski, D. (2019), “System architecture to bring smart personal protective equipment wearables and sensors to transform safety at work in the underground mining industry”, Rudarsko-geolo�sko-naftni Zbornik, Vol. 34 No. 1, pp. 37-44, doi: 10.17794/rgn.2019.1.4. Adriaanse, A., Voordijk, H. and Dewulf, G. (2010), “Adoption and use of interorganizational ICT in a construction project”, Journal of Construction Engineering and Management, Vol. 136 No. 9, pp.1003-1014, doi:10.1061/ASCECO.1943-7862.0000201. Ahmed, I., Shaukat, M.Z., Usman, A., Nawaz, M.M. and Nazir, M.S. (2018), “Occupational health and safety issues in the informal economic segment of Pakistan: a survey of construction sites”, International Journal of Occupational Safety and Ergonomics, Vol. 24 No. 2, pp. 240-250, doi: 10.1080/10803548.2017.1366145. Ahn, C.R., Lee, S., Sun, C., Jebelli, H., Yang, K. and Choi, B. (2019), “Wearable sensing technology applications in construction safety and health”, Journal of Construction Engineering and Management, No. 11, pp. 1-17, doi: 10.1061/(ASCE)CO.1943-7862.0001708. Akinbile, B.F. and Oni, O.Z. (2016),”Assessment of the challenges and benefits of Information Communication Technology (ICT) on construction industry in Oyo State Nigeria”, Annals of the Faculty of Engineering Hunedoara, Vol. 14 No. 4, pp. 1-6. Alizadehsalehi, S. and Yitmen, I. (2019), “A concept for automated construction progress monitoring: technologies adoption for benchmarking project performance control”, Arabian Journal for Science and Engineering, Vol. 44 No. 5, pp. 4993-5008, doi: 10.1007/s13369-018-3669-1. Alreshidi, E., Mourshed, M. and Rezgui, Y. (2017), “Factors for effective BIM governance”, Journal of Building Engineering, Vol. 10, pp. 89-101, doi: 10.1016/j.jobe.2017.02.006. Andrade, C. (2021), “The inconvenient truth about convenience and purposive samples”, Indian Journal of Psychological Medicine, Vol. 43 No. 1, pp. 86-88, doi: 10.1177/0253717620977000. Antwi-Afari,M.F., Li, H., Edwards,D.J., Parn, E.A., Seo, J. andWong,A.Y.L. (2017), “Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers”, Automation in Construction, Vol. 83, pp. 41-47, doi: 10.1016/j.autcon.2017.07.007. Arabshahi, M., Wang, D., Sun, J., Rahnamayiezekavat, P., Tang, W., Wang, Y. and Wang, X. (2021a), “Review on sensing technology adoption in the construction industry”, Sensors, Vol. 21 No. 24, pp. 1-22, doi: 10.3390/s21248307. Arabshahi, M., Wang, D., Wang, Y., Rahnamayiezekavat, P., Tang, W. and Wang, X. (2021b), “A governance framework to assist with the adoption of sensing technologies in construction”, Sensors, Vol. 22 No. 1, pp. 1-26, doi: 10.3390/s22010260. Aryal, A., Ghahramani, A. and Becerik-Gerber, B. (2017), “Monitoring fatigue in construction workers using physiological measurements”, Automation in Construction, Vol. 82, pp. 154-165, doi: 10. 1016/j.autcon.2017.03.003. Wearable safety devices in the construction sector 67 https://doi.org/10.1016/j.autcon.2022.104501 https://doi.org/10.17794/rgn.2019.1.4 https://doi.org/10.1061/ASCECO.1943-7862.0000201 https://doi.org/10.1080/10803548.2017.1366145 https://doi.org/10.1061/(ASCE)CO.1943-7862.0001708 https://doi.org/10.1007/s13369-018-3669-1 https://doi.org/10.1016/j.jobe.2017.02.006 https://doi.org/10.1177/0253717620977000 https://doi.org/10.1016/j.autcon.2017.07.007 https://doi.org/10.3390/s21248307 https://doi.org/10.3390/s22010260 https://doi.org/10.1016/j.autcon.2017.03.003 https://doi.org/10.1016/j.autcon.2017.03.003 Association of Workers’ Compensation Boards of Canada (2020), “National work injury, disease and fatality statistics”, available at: http://awcbc.org/?page_id589 (accessed 7 October 2022). Awolusi, I., Marks, E. and Hallowell, M. (2018), “Wearable technology for personalized construction safety monitoring and trending: review of applicable devices”, Automation in Construction, Vol. 85, pp. 96-106, doi: 10.1016/j.autcon.2017.10.010. Babbie, E. (2013), The Practice of Social Research, 13th ed., Wadsworth Publishing, Belmont, CA, ISBN:13. 978-1133049791. Bangaru, S.S., Wang, C. and Aghazadeh, F. (2020), “Data quality and reliability assessment of wearable EMG and IMU sensor for construction activity recognition”, Sensors, Vol. 20 No. 18, pp. 1-24, doi: 10.3390/s20185264. Blumberg, B., Cooper, D.R. and Schindler, P.S. (2008), Business Research Methods – Second European Edition, McGraw-Hill, ISBN: 13-978-0-07-711745-0. Chan, A., Javed, A.A., Lyu, S., Hon, C. and Wong, F. (2016), “Strategies for improving safety and health of ethnic minority construction workers”, Journal of Construction Engineering and Management - ASCE, Vol. 142 No. 9, pp. 1-10, doi: 10.1061/(ASCE)CO.1943-7862.0001148. Chen, H. and Luo, X. (2016), “Severity prediction models of falling risk for workers at height”, Procedia Engineering, Vol. 164, pp. 439-445, doi: 10.1016/j.proeng.2016.11.642. Chen, J., Qiu, J. and Ahn, C. (2017), “Construction worker’s awkward posture recognition through supervised motion tensor decomposition”, Automation in Construction, Vol. 77, pp. 67-81, doi: 10.1016/j.autcon.2017.01.020. Cho, E. and Kim, S. (2015), “Cronbach’s coefficient alpha: well known but poorly understood”, Organizational Research Methods, Vol. 18 No. 2, pp. 207-230, doi: 10.1177/1094428114555994. Choi, B., Hwang, S. and Lee, S. (2017), “What drives construction workers’ acceptance of wearable technologies in the workplace?: indoor localization and wearable health devices for occupational safety and health”, Automation in Construction, Vol. 84, pp. 31-41, doi: 10.1016/j.autcon.2017. 08.005. Conte, J.C., Rubio, E., Garc�ıa, A.I. and Cano, F. (2011), “Occupational accidents model based on risk– injury affinity groups”, Safety Science [e-journal], Vol. 49 No. 2, pp. 306-314, doi: 10.1016/j.ssci. 2010.09.005. Didehvar, N., Teymourifard, M., Mojtahedi, M. and Sepasgozar, S. (2018), “An investigation on virtual information modelling acceptance based on project management knowledge areas”, Buildings, Vol. 8 No. 6, pp. 1-19, doi: 10.3390/buildings8060080. Dithebe, K., Aigbavboa, C.O., Thwala, W.D. and Malabela, A.T. (2019), “Descriptive perspective on factors affecting the complete adoption of information technology systems in the construction firms”, Journal of Physics, Vol. 1378 No. 2, pp. 1-9, doi: 10.1088/1742-6596/1378/2/022045. Dong, S., Li, H. and Yin, Q. (2018), “Building information modelling in combination with real time location systems and sensors for safety performance enhancement”, Safety Science, Vol. 102, pp. 226-237, doi: 10.1016/j.ssci.2017.10.011. Du Plooy-Cilliers, F., Davis, C. and Bezuidenhout, R.M. (2014), Research Matters, 1st ed., Juta and Company, Lansdowne, ISBN: 9781485132103. Edmund, E.E. (2015), “Analysis of occupational hazards and safety of workers in selected working environments within Enugu Metropolis”, Journal of Environmental and Analytical Toxicology, Vol. 5 No. 6, doi: 10.4172/2161-0525.1000337. Golizadeh, H., Hosseini, M.R., Edwards, D.J., Abrishami, S., Taghavi, N. and Banihashemi, S. (2019), “Barriers to adoption of RPAs on construction projects: a task–technology fit perspective”, Construction Innovation, Vol. 19 No. 2, pp. 1471-4175, doi: 10.1108/CI-09-2018-0074. Goodrum, P.M., Haas, C.T., Caldas, C., Zhai, D., Yeiser, J. and Homm, D. (2011), “Model to predict the impact of a technology on construction productivity”, Journal of Construction Engineering and Management, Vol. 137 No. 9, pp. 678-688, doi: 10.1061/(ASCE)CO.1943-7862.0000328. SASBE 14,1 68 http://awcbc.org/?page_id=89%20 http://awcbc.org/?page_id=89%20 https://doi.org/10.1016/j.autcon.2017.10.010 https://doi.org/10.3390/s20185264 https://doi.org/10.1061/(ASCE)CO.1943-7862.0001148 https://doi.org/10.1016/j.proeng.2016.11.642 https://doi.org/10.1016/j.autcon.2017.01.020 https://doi.org/10.1177/1094428114555994 https://doi.org/10.1016/j.autcon.2017.08.005 https://doi.org/10.1016/j.autcon.2017.08.005 https://doi.org/10.1016/j.ssci.2010.09.005 https://doi.org/10.1016/j.ssci.2010.09.005 https://doi.org/10.3390/buildings8060080 https://doi.org/10.1088/1742-6596/1378/2/022045 https://doi.org/10.1016/j.ssci.2017.10.011 https://doi.org/10.4172/2161-0525.1000337 https://doi.org/10.1108/CI-09-2018-0074 https://doi.org/10.1061/(ASCE)CO.1943-7862.0000328 Guo, H., Yu, Y., Xiang, T., Li, H. and Zhang, D. (2017), “The availability of wearable-device- based physical data for the measurement of construction workers’ psychological status on site: from the perspective of safety management”, Automation in Construction, Vol. 82, pp. 207-217, doi: 10.1016/j.autcon.2017.06.001. Haikio, J., Kallio, J., M€akel€a, S.M. and Ker€anen, J. (2020), “IoT-based safety monitoring from the perspective of construction site workers”, International Journal of Occupational and Environmental Safety, Vol. 4 No. 1, pp. 1-14, doi: 10.24840/2184-0954_004.001_0001. Hashiguchi, N., Kodama, K., Lim, Y., Che, C., Kuroishi, S., Miyazaki, Y., Kobayashi, T., Kitahara, S. and Tateyama, K. (2020), “Practical judgment of workload based on physical activity, work conditions, and worker’s age in construction site”, Sensors, Vol. 20 No. 13, pp. 1-19, doi: 10.3390/ s20133786. Health and Safety Executive (2019), “Construction statistics in Great Britain”, available at: https:// www.hse.gov.uk/statistics/industry/construction.pdf (accessed 7 October 2022). Heller, A. (2015), “The sensing internet—a discussion on its impact on rural areas”, Future Internet, Vol. 7 No. 4, pp. 363-371. Hwang, S. and Lee, S. (2017), “Wristband-type wearable health devices to measure construction workers’ physical demands”, Automation in Construction, Vol. 83, pp. 330-340, doi: 10.1016/j. autcon.2017.06.003. Ijigah, E.A., Jimoh, R.A., Bilau, A.A. and Agbo, A.E. (2013), “Assessment of risk management practices in Nigerian construction industry: towards establishing risk management index”, International Journal of Pure and Applied Science and Technology, Vol. 16 No. 2, pp. 20-31, available at: .www.ijopaasat.in. International Labour Organization (ILO) (2018), “World statistics”, available at: https://www.ilo.org/ moscow/areas-of-work/occupational-safety-and-health/WCMS_249278/lang–en/index.htm (accessed 12 September 2022). Jeon, J. and Cai, H. (2022), “Multi-class classification of construction hazards via cognitive states assessment using wearable EEG”, Advanced Engineering Informatics, Vol. 53, pp. 1-13, doi: 10. 1016/j.aei.2022.101646. Jo, B.W., Lee, Y.S., Khan, R.M.A., Kim, J.H. and Kim, D.K. (2019), “Robust Construction Safety System (RCSS) for collision accidents prevention on construction sites”, Sensors, Vol. 19 No. 4, pp. 1-25, doi: 10.3390/s19040932. Kamisalic, A., Fister, I. Jr, Turkanovic, M. and Karakatic, S. (2018), “Sensors and functionalities of non- invasive wrist-wearable devices: a review”, Sensors, Vol. 18 No. 6, pp. 1-33, doi: 10.3390/ s18061714. Kamoli, A. and Mahmud, S.H.B. (2022), “Roles of construction organizations in revitalizing occupational health and safety of the Nigerian construction industry”, Journal of Advanced Research in Applied Sciences and Engineering Technology, Vol. 26 No. 1, pp.97-104, doi:10.37934/ araset.26.1.97104. Lee, W., Lin, K.Y., Seto, E. and Migliaccio, G.C. (2017), “Wearable sensors for monitoring on- duty and off-duty worker physiological status and activities in construction”, Automation in Construction, Vol. 83, pp. 341-353, doi: 10.1016/j.autcon.2017.06.012. Loosemore, M. and Malouf, N. (2019), “Safety training and positive safety attitude formation in the Australian construction industry”, Safety Science, Vol. 113, pp. 233-243, doi: 10.1016/j.ssci.2018. 11.029. Masum, H., Lackman, R. and Bartleson, K. (2013), “Developing global health technology standards: what can other industries teach us?”, Globalization and Health, Vol. 9 No. 1, pp. 1-12, doi: 10. 1186/1744-8603-9-49. Namian, M., Kermanshachi, S., Khalid, M. and Al-Bayati, A.J. (2020), “Construction safety training: exploring different perspectives of construction managers and workers”, ASEE Virtual Annual Conference, (accessed 22 June 2020). Wearable safety devices in the construction sector 69 https://doi.org/10.1016/j.autcon.2017.06.001 https://doi.org/10.24840/2184-0954_004.001_0001 https://doi.org/10.3390/s20133786 https://doi.org/10.3390/s20133786 https://www.hse.gov.uk/statistics/industry/construction.pdf https://www.hse.gov.uk/statistics/industry/construction.pdf https://doi.org/10.1016/j.autcon.2017.06.003 https://doi.org/10.1016/j.autcon.2017.06.003 .www.ijopaasat.in https://www.ilo.org/moscow/areas-of-work/occupational-safety-and-health/WCMS_249278/lang--en/index.htm https://www.ilo.org/moscow/areas-of-work/occupational-safety-and-health/WCMS_249278/lang--en/index.htm https://www.ilo.org/moscow/areas-of-work/occupational-safety-and-health/WCMS_249278/lang--en/index.htm https://www.ilo.org/moscow/areas-of-work/occupational-safety-and-health/WCMS_249278/lang--en/index.htm https://doi.org/10.1016/j.aei.2022.101646 https://doi.org/10.1016/j.aei.2022.101646 https://doi.org/10.3390/s19040932 https://doi.org/10.3390/s18061714 https://doi.org/10.3390/s18061714 https://doi.org/10.37934/araset.26.1.97104 https://doi.org/10.37934/araset.26.1.97104 https://doi.org/10.1016/j.autcon.2017.06.012 https://doi.org/10.1016/j.ssci.2018.11.029 https://doi.org/10.1016/j.ssci.2018.11.029 https://doi.org/10.1186/1744-8603-9-49 https://doi.org/10.1186/1744-8603-9-49 Nath, N.D., Akhavian, R. and Behzadan, A.H. (2017), “Ergonomic analysis of construction worker’s body postures using wearable mobile sensors”, Applied Ergonomics, Vol. 62, pp. 107-117, doi: 10.1016/j.apergo.2017.02.007. Neuman, W.L. (2007), Social Research Methods, Qualitative and Quantitative Approaches, 7th ed., Allyn & Bacon, Boston, ISBN: 13: 978-0205615964. Nnaji, C. and Awolusi, I. (2021), “Critical success factors influencing wearable sensing device implementation in AEC industry”, Technology in Society, Vol. 66, pp. 1-14, doi: 10.1016/j.techsoc. 2021.101636. Nnaji, C., Awolusi, I., Park, J. and Albert, A. (2021), “Wearable sensing devices: towards the development of a personalized system for construction safety and health risk mitigation”, Sensors, Vol. 21 No. 3, pp. 1-24, doi: 10.3390/s21030682. Nnaji, C., Gambatese, J., Karakhan, A. and Eseonu, C. (2019), “Influential safety technology adoption predictors in construction”, Engineering, Construction and Architectural Management, Vol. 26 No. 11, pp. 2655-2681, doi: 10.1108/ECAM-09-2018-0381. Odeyinka, H.A., Oladapo, A.A. and Dada, J.O. (2004), “An assessment of risk in construction in the Nigerian construction industry”, Proceedings of CIB, W107, international Symposium on Globalisation and Construction, AIT Conference Centre, Bangkok, Thailand, 17-19 November 2004. Odimabo, O.O. and Oduoza, C.F. (2013), “Risk assessment framework for building construction projects’ in developing countries”, International Journal of Construction Engineering and Management, Vol. 2 No. 5, pp.143-154, doi: 10.5923/j.ijcem.20130205.02. Oke, A.E., Arowoiya, V.C. and Akomolafe, O.T. (2020), “Influence of the Internet of Things’ application on construction project performance”, International Journal of Construction Management, Vol. 22 No. 13, pp. 2517-2527, doi: 10.1080/15623599.2020.1807731. Okoye, P.U. (2018), “Occupational health and safety risk levels of building construction trades in Nigeria”, Construction Economics and Building, Vol. 18 No. 2, pp. 92-109, doi: 10.5130/AJCEB. v18i2.5882. Okpala, I., Nnaji, C. and Awolusi, I. (2019), “Emerging construction technologies: state of standard and regulation implementation”, Computing in Civil Engineering: Data, Sensing, and Analytics, pp. 153-161. Okpala, I., Nnaji, C. and Awolusi, I. (2021), “Wearable sensing devices acceptance behaviour in construction safety and health: assessing existing models and developing a hybrid conceptual model”, Construction Innovation, Vol. 22 No. 1, pp. 57-75, doi: 10.1108/CI-04-2020-0056. Oranusi, U.S., Dahunsi, S.O. and Idowu, S.A. (2014), “Assessment of occupational diseases among artisans and factory workers in Ifo, Nigeria”, Journal of Scientific Research and Reports, Vol. 3 No. 2, pp.294-305, doi: 10.9734/jsrr/2014/5554. Oreoluwa, O.O. and Olasunkanmi, F. (2018), “Health and safety management practices in the building construction industry in Akure, Nigeria”, American Journal of Engineering and Technology Management, Vol. 3 No. 1, pp. 23-28, doi: 10.11648/j.ajetm.20180301.12. Rogers, J., Chong, H.Y. and Preece, C. (2015), “Adoption of Building Information Modelling technology (BIM): perspectives from Malaysian engineering consulting services firms”, Engineering, Construction and Architectural Management, Vol. 22 No. 4, pp. 424-445, doi: 10.1108/ECAM-05- 2014-0067. Schall, M.C. Jr, Sesek, R.F. and Cavuoto, L.A. (2018), “Barriers to the adoption of wearable sensors in the workplace: a survey of occupational safety and health professionals”, Human Factors, Vol. 60 No. 3, pp. 351-362, doi: 10.1177/0018720817753907. Shafique, M. and Rafiq, M. (2019), “An overview of construction occupational accidents in Hong Kong: a recent trend and future perspectives”, Applied Sciences, Vol. 9 No. 10, pp. 1-16, doi: 10.3390/ app9102069. SASBE 14,1 70 https://doi.org/10.1016/j.apergo.2017.02.007 https://doi.org/10.1016/j.techsoc.2021.101636 https://doi.org/10.1016/j.techsoc.2021.101636 https://doi.org/10.3390/s21030682 https://doi.org/10.1108/ECAM-09-2018-0381 https://doi.org/10.5923/j.ijcem.20130205.02 https://doi.org/10.1080/15623599.2020.1807731 https://doi.org/10.5130/AJCEB.v18i2.5882 https://doi.org/10.5130/AJCEB.v18i2.5882 https://doi.org/10.1108/CI-04-2020-0056 https://doi.org/10.9734/jsrr/2014/5554 https://doi.org/10.11648/j.ajetm.20180301.12 https://doi.org/10.1108/ECAM-05-2014-0067 https://doi.org/10.1108/ECAM-05-2014-0067 https://doi.org/10.1177/0018720817753907 https://doi.org/10.3390/app9102069 https://doi.org/10.3390/app9102069 Simpeh, F. and Adisa, S. (2021), “Evaluation of on-campus student housing facilities security and safety performance”, Facilities, Nos 7/8, pp. 470-487, doi: 10.1108/F-04-2020-0051. Smaoui, N., Kim, K., Gnawali, O., Lee, Y.J. and Suh, W. (2018), “Respirable dust monitoring in construction sites and visualization in building information modelling using real-time sensor data”, Sensors and Materials, Vol. 30, pp. 1775-1786, doi: 10.18494/SAM.2018.1871. Umeokafor, N. (2017), “An appraisal of the barriers to client involvement in health and safety in Nigeria’s construction industry”, Journal of Engineering, Design and Technology, Vol. 15 No. 4, pp. 471-487, doi: 10.1108/JEDT-06-2016-0034. Umeokafor, N. (2018), “An investigation into public and private clients’ attitudes, commitment and impact on construction health and safety in Nigeria”, Engineering, Construction and Architectural Management, Vol. 25 No. 6, pp. 798-815, doi: 10.1108/ECAM-06-2016-0152. Umeokafor, N., Evangelinos, K. and Windapo, A. (2022), “Strategies for improving complex construction health and safety regulatory environments”, International Journal of Construction Management, Vol. 22 No. 7, pp. 1333-1344, doi: 10.1080/15623599.2019.1707853. Umer, W., Li, H., Szeto, G.P.Y. and Wong, A.Y. (2017), “Low-cost ergonomic intervention for mitigating physical and subjective discomfort during manual rebar tying”, Journal of Construction Engineering and Management, Vol. 143 No. 10, pp. 1-11, doi: 10.1061/(ASCE)CO.1943-7862. 0001383. US Bureau of Labor Statistics (2019), “National census of fatal occupational injuries in 2018”, available at: https://www.bls.gov/news.release/pdf/cfoi.pdf (accessed 7 October 2022). Wang, D., Chen, J., Zhao, D., Dai, F., Zheng, C. and Wu, X. (2017), “Monitoring workers’ attention and vigilance in construction activities through a wireless and wearable electroencephalography system”, Automation in Construction, Vol. 82, pp. 122-137, doi: 10.1016/j.autcon.2017.02.001. Welman, C., Kruger, S.J. and Mitchell, B. (2005), Research Methodology 3rd ed. Oxford: Oxford University Press¸ ISBN: 13. 978-0199202959. Whitley, B.E., Kite, M.E. and Adams, H.L. (2013), Principle of Research in Behavioral Science, 3rd ed., Routledge/Taylor & Francis Group, New York, ISBN: 13. 978-0415879286. Won, J., Lee, G., Dossick, C. and Messner, J. (2013), “Where to focus for successful adoption of building information modelling within organization”, Journal of Construction Engineering and Management, Vol. 139 No. 11, 04013014, doi: 10.1061/(ASCE)CO.1943-7862.0000731. Yang, K., Ahn, C.R., Vuran, M.C. and Kim, H. (2017), “Collective sensing of workers’ gait patterns to identify fall hazards in construction”, Automation in Construction, Vol. 82, pp. 166-178, doi: 10. 1016/j.autcon.2017.04.010. Further reading Li, X.P., Gu, L.C. and Jia, J. (2012), “Anti-collision method of tower crane via ultrasonic multi- sensor fusion”, International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), 24-26 March 2012, Xiamen, China, pp. 522-525, doi: 10.1049/cp.2012.1031. Corresponding author Oluseyi Julius Adebowale can be contacted at: adebowaleoluseyi@gmail.com For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com Wearable safety devices in the construction sector 71 https://doi.org/10.1108/F-04-2020-0051 https://doi.org/10.18494/SAM.2018.1871 https://doi.org/10.1108/JEDT-06-2016-0034 https://doi.org/10.1108/ECAM-06-2016-0152 https://doi.org/10.1080/15623599.2019.1707853 https://doi.org/10.1061/(ASCE)CO.1943-7862.0001383 https://doi.org/10.1061/(ASCE)CO.1943-7862.0001383 https://www.bls.gov/news.release/pdf/cfoi.pdf https://doi.org/10.1016/j.autcon.2017.02.001 https://doi.org/10.1061/(ASCE)CO.1943-7862.0000731 https://doi.org/10.1016/j.autcon.2017.04.010 https://doi.org/10.1016/j.autcon.2017.04.010 https://doi.org/10.1049/cp.2012.1031 mailto:adebowaleoluseyi@gmail.com Benefits and challenges of wearable safety devices in the construction sector Introduction Wearable safety devices research Benefits of using WSDs Barriers to WSDs adoption Methodology Data presentation Respondents’ information Benefits of using WSDs Challenges of using WSDs Discussion of the findings Conclusions, limitations and future research References Further reading