Abstract
Automated real-time data collection is becoming more prevalent in construction, with workers’ location data being a pivotal component in detecting poor logistics and inefficient construction flows. However, the collection of location data for productivity monitoring raises significant concerns about privacy and wellbeing implications for workers. Implementing such technological solutions requires an understanding of how humans may respond to sensor-based automated data collection, making this a socio-technical issue. This study identifies the drivers of construction workers’ acceptance of radio-based location tracking technology for productivity measurement using a modified Technology Acceptance Model (TAM) and offers a sociotechnical understanding of technology acceptance with implications for managing how new technologies are introduced on construction projects. Using a large residential project in Lima, Peru as a case study, construction workers were monitored using Bluetooth Low Energy (BLE) technology, and data were gathered using mixed methods. A k-means clustering analysis showed two forms of acceptance among workers: supporters (37%) and acceptance with reservations (63%). Partial least squares Structural Equation Modelling (PLS-SEM) results showed that perceived usefulness and perceived stress underpinned workers’ attitudes and intention to accept the technology. Perceived privacy risk, however, emerged as the sole most significant predictor at the end of the monitoring process. Findings further suggest that workers’ acceptance of the technology is influenced by the perception that it is also beneficial for safety management. Building on the preceding, the paper highlights the need for employee orientation focused on addressing perceived privacy concerns by leveraging positive perceptions about using monitoring technologies for improving onsite safety, logistics and productivity. This requires management of construction firms to develop narratives that reflect their goals for rolling out technologies in ways that ensure workers’ buy-in, and a re-focus on problem framing and collective approaches to identifying functional and less intrusive forms of monitoring technologies.
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