Workforce Competencies for Sensing Technologies in the Construction Industry

Abstract Data sensing technologies, such as laser scanners and global positioning systems, offer significant potential to enhance safety, productivity, and quality in construction. However, their adoption is often hindered by a lack of clearly defined workforce competencies tailored to these technologies. This study addresses this gap by developing domain-specific competencies grounded in empirical research and industry practice. A comprehensive literature review identified preliminary knowledge, skills, and abilities, which were refined through a Delphi study with experienced construction professionals. To assess the perceived importance and degree of consensus surrounding these competencies, statistical methods such as the median absolute deviation and relative importance index were employed. Nineteen knowledge areas reached consensus, with high priority given to “types of sensing technologies,” “applications of sensing technologies,” and “safety management.” Among the eight skills evaluated, “technical skills,” “collaborative skills,” and “mapping and visualization skills” ranked highest. For abilities, “problem-solving,” “decision-making,” and “critical thinking” were top-rated. A focus group further contextualized these findings, validating the relevance of all identified competencies and proposing revisions to their prioritization. Participants ranked “cost–benefit analysis,” “data security,” and “client-centric knowledge” significantly higher, citing real-world decision-making and client engagement challenges. Similarly, “communication skills” and “data analytics” were elevated among skills, and “analytical thinking” and “safety awareness” gained prominence among abilities. These insights highlight the distinction between theoretical importance and practical impact, emphasizing the need for curricula and training that reflect field-based priorities. The combined Delphi and focus-group results produced a validated set of competencies to guide sensing technology implementation in construction. These outcomes provide guidance for aligning construction education and training programs with the evolving demands of digital construction, as well as offering strategic insights for curriculum design, workforce training, and policy development aimed at closing the technology–competency gap.