Complex Cause-Effect Relationships of Social Capital, Leader-Member Exchange, and Safety Behavior of Workers in Small-Medium Construction Firms and the Moderating Role of Age

Abstract

Due to workers’ vulnerability in construction sites, workplace safety has become of particular interest, and the current literature offers myriad approaches to dealing with it. From a social and organizational lens, this study explores an empirical model that integrates the dimensions of social capital theory (SCT) and leader-member exchange (LMX) in modelling the safety behavior of construction workers, particularly relevant in small-medium construction firms. The data were collected from 232 construction workers in the central Philippines. The responses were analyzed using partial least squares—structural equation modeling to investigate five hypothesized paths, including the influence of SCT dimensions (e.g., structural, relational, and cognitive) on LMX and LMX on safety behaviors (i.e., compliance and participation). We also tested whether the relationship of LMX to safety behaviors is moderated by age. The results indicate that the three dimensions of SCT have a significant and direct influence on LMX. In addition, LMX directly affects safety participation but does not significantly affect safety compliance. Particularly in small and medium construction firms with relatively flat organizational structures and supervisors displaying diverse roles, these findings suggest that the social relationships of workers tend to promote their trust and professional respect for supervisors who can leverage their position to encourage them participate in safety initiatives. On the other hand, age negatively influences the relationship of LMX to safety participation, indicating that younger workers tend to better translate high quality LMX into initiatives that promote overall workplace safety. Our findings offer the first evidence of the positive relationship between SCT and LMX in advancing the safety participation of construction workers. From these insights, practical inputs to the design of relevant measures and future research works are outlined.

Construction Safety Risk Model with Construction Accident Network: A Graph Convolutional Network Approach

“…However, adopting optimization methods or sampling techniques, such as taking an unbiased random walk inside the graph space, would reduce the computational cost of the GCN algorithm. (6) The developed construction safety model could be enhanced to predict construction risks based on project type, along with other input attributes, such as the age and experience of the worker, the activity performed, and workplace and human factors. This would allow construction professionals to evaluate different accident scenarios and flag high-risk accident scenarios while considering dependency information based on the construction project type.…”

“…Despite some improvements in the sector and years of effort put into risk minimization, the situation regarding occupational safety and health (OSH)-related injuries and fatalities remains alarming [1] and is a constant concern [2], displaying high incident rates [3][4][5]. Research has focused on workplace safety, due to the vulnerable position of construction workers [6].…”

Safety Performance Assessment of Construction Sites under the Influence of Psychological Factors: An Analysis Based on the Extension Cloud Model

“…A clear organizational structure with well-defined rules and regulations is one of the effective means to reduce the leader–member exchange ambivalence [ 29 ], but this ambivalence is difficult to erase completely. The leader–member exchange ambivalence is an implicit factor that cannot be precisely described using a single indicator.…”

Supervisor Leadership and Construction Worker Safety Behavior: A Systematic Literature Review

“…Su et al [37] showed that LMX has significant effects on different types of safety violations through attitudinal ambivalence, group safety norm, and perceived behavioral control. However, [38] indicated that LMX directly influences safety participation but does not significantly impact safety compliance.…”