Employee acceptance of wearable technology in the workplace

Jacobs, Jan; Hettinger, Lawrence J.; Huang, Yueng Hsiang; Jeffries, Susan; Lesch, Mary F.; Simmons, Leigh Ann; Verma, Santosh K.; Willetts, Joanna

doi:10.1016/j.apergo.2019.03.003

Cited by 110 publications

(73 citation statements)

References 20 publications

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“…Some respondents felt that measuring employees in the workplace can lead to more effective monitoring of workers, which is not primarily aimed at improving occupational safety and health. Privacy and trust are essential elements when promoting the use of wearables at the construction site, as indicated by previous studies (Jacobs et al, 2019;Callejas-Sandoval & Kwon, 2019;Choi et al, 2017). The results of the survey also showed a negative association between concerns and willingness to share workrelated personal data.…”

Section: Discussionsupporting

confidence: 59%

“…Employees were more reluctant to share their data for supporting workflow. Jacobs et al (2019) suggested that the use of wearable data collection technologies in the work context should be aimed at improving workplace safety in order to achieve user acceptance and realize the potential benefits of wearable technologies. Our findings show that employees' willingness to share their personal data is linked with safety improvements.…”

Section: Discussionmentioning

confidence: 99%

“…Some of these earlier studies have presented empirical models affecting user acceptance of new technologies, focusing, for example, on mobile computing devices (Son et al, 2012), scanner technologies (Sepasgozar et al, 2017) and building information modeling (Acquah et al, 2018;Son et al, 2015). In addition, some previous studies have examined customers' intentions to adopt wearable technologies (Yang et al, 2016) and employee acceptance of wearables in workplaces (Jacobs et al 2019). Although there are many technology adoption and user acceptance related studies, research on employees' perceptions of emerging IoT technologies in the construction industry has remained limited.…”

Section: Introductionmentioning

confidence: 99%

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IoT-based safety monitoring from the perspective of construction site workers

Häikiö

Kallio

Mäkelä

et al. 2020

IJOOES

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Occupational safety and health is traditionally a challenging area in the labor-intensive construction industry as accidents at work and non-ergonomic work conditions lead to absences and premature retirement of construction workers. Recently, the rise of the Internet of Things (IoT) and its accompanying technologies (e.g. wearable technologies) has enhanced interest in the occupational safety and health of construction work. The level of technology acceptance among construction workers is a crucial element in the adoption of these technologies. The main objectives of this study are to enhance understanding about construction workers’ attitudes towards IoT-based data-intensive work safety and wellbeing solutions and to identify factors that can promote technology adoption. Data for the study was collected through an online survey of 4385 construction workers. Based on the survey data it seems that construction workers would accept the sharing and utilizing data collected from them in the worksite environment if it could help identify employee personal health risks or promote personal and colleagues' occupational safety. Respondents were most concerned about privacy and security regarding wearables in the workplace. It can be concluded that user acceptance and trust building are key components in the adoption of IoT-based occupational safety and health solutions. Future studies should investigate methods for actively involving construction workers in the design and development process of IoT-based work safety solutions and examine technological solutions that promote trust building among construction workers.

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Section: Discussionsupporting

confidence: 59%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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IoT-based safety monitoring from the perspective of construction site workers

Häikiö

Kallio

Mäkelä

et al. 2020

IJOOES

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“…Workers' acceptance to wear these sensors is the first step in their implementation. The authors of [50] evaluate workers' opinion through an online survey and found that the companies' safety climate and workers' expected performance are two major factors in adoption of wearable sensors. They suggest that organizations who are interested in using wearable technologies to: (1) improve general workplace safety; (2) improve the safety atmosphere on site; (3) investigate practical ways to prove the applicability of wearable technologies for safety improvement on-site; (4) maintain an interactive and communicative environment between employees and employer to better illustrate the expectations and outcomes to the workers.…”

Section: Wearable Technologymentioning

confidence: 99%

Leading Indicators—A Conceptual IoT-Based Framework to Produce Active Leading Indicators for Construction Safety

Costin

Wehle

Adibfar

2019

Safety

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Active leading indicators (ALIs) have the potential to identify safety hazards and prompt immediate actions to prevent incidents. Currently, there is a major gap in research that incorporates a fully automated ALI system because implementation has been hindered by a lack of established industry thresholds of measurable performance that would trigger an actionable response. Therefore, this paper addresses this gap by presenting a new method that utilizes the Internet of Things (IoT) to collect quantifiable data which can trigger an actionable response in real time based on established thresholds. This novel method integrates the Construction Industry Institute (CII) active leading indicator framework with a prototype IoT-based system. Significantly, the ALI provides the physical–virtual feedback loop, which is an essential aspect of the IoT system because it provides real-time feedback to both the users and systems. This paper also identifies potential inputs to the ALI framework from emerging IoT-enabled systems. A case study was presented to initially validate the IoT-based ALI framework. Bluetooth-enabled heart rate monitors were issued to workers in a hazardous and critical mining construction site. The ALIs that were recorded included heart rate and body temperature. Thresholds were established that alerted the monitoring safety staff when a worker exhibited potentially unsafe conditions. The results of the study demonstrated the feasibility of the system. Additionally, other results included worker resistance; non-disclosing of medical conditions, and limitations for IoT connectivity.

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“…In our case, users are fleet drivers, often on long overnight hauls, and the technology proposed is the least intrusive that would guarantee their security throughout the journey. According to a recent survey conducted in the USA, more than 65% of workers have positive willingness to use wearable devices if used to improve their safety [9]. Although in this paper we only cover the solution for subject identification, wearable devices could also be used to monitor driver drowsiness and stress levels with minimal load on the wireless network.…”

Section: Introductionmentioning

confidence: 99%

Towards Continuous Subject Identification Using Wearable Devices and Deep CNNs

Nadas

Jaber

Berghe

et al. 2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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Subject identification has several applications. In transportation companies, knowing who is driving their vehicles might prevent theft or other ill-intended actions. On the other hand, privacy concerns, and the respective legislation, hinder the applicability of several traditional recognition techniques based on invasive technologies, such as video cameras. Moreover, in order to keep the driver's distractions to a minimum, this technologies must be non-disruptive, that is, they must be able to identify the subject seamlessly without them taking any action. In this context, we propose using deep learning applied to smart watch data for recognizing the person driving a vehicle based on a training set. Our proposal relies on the possibility of using transfer learning to avoid long training sessions for new drivers and to deliver a solution which can be deployed in practice. In this paper, we describe the convolutional neural network used in the solution and present results according to a real data-set collected by us, achieving accuracies ranging from 75 to 94%.

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Employee acceptance of wearable technology in the workplace

Cited by 110 publications

References 20 publications

IoT-based safety monitoring from the perspective of construction site workers

IoT-based safety monitoring from the perspective of construction site workers

Leading Indicators—A Conceptual IoT-Based Framework to Produce Active Leading Indicators for Construction Safety

Towards Continuous Subject Identification Using Wearable Devices and Deep CNNs

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