A decision-oriented framework for managing barriers to human–robot collaboration in construction: an ISM–fuzzy MICMAC approach

Abstract

The implementation of human–robot collaboration (HRC) in the construction industry has gained increasing attention due to its potential to enhance productivity, safety and efficiency. However, its adoption remains constrained by multiple interrelated barriers. This study systematically analyses barriers affecting HRC implementation from a managerial decision-making perspective. An integrated interpretive structural modelling (ISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) approach was employed to identify, structure and classify relationships among eight key barriers. The findings reveal that HRC implementation is governed by a highly interconnected hierarchical system of barriers. Regulatory and legal factors and safety considerations emerge as key driving barriers, influencing technological, financial, organizational and education/training-related barriers. ISM establishes hierarchical relationships among the barriers, while fuzzy MICMAC further classifies them according to their driving and dependence power, providing deeper insight into their systemic influence. From a managerial perspective, effective HRC implementation requires coordinated interventions that prioritize regulatory compliance and safety while strengthening technological capabilities, financial capacity, workforce training and organizational readiness. Addressing these interconnected barriers improves communication, human acceptance and overall HRC effectiveness. The study provides a structured decision-support framework to help construction managers and stakeholders understand barrier interdependencies, prioritize interventions and develop strategies for successful HRC implementation.