The Influence of Field Supervision on Construction Work Quality: a PLS-SEM Model of the Batupanga Community Health Center Project

Construction quality depends on effective field supervision, compliance with technical specifications, and timely corrective actions. However, empirical evidence regarding these relationships in healthcare facility construction projects remains limited. This study examined the relationship between field supervision and construction work quality in the Batupanga Community Health Center construction project in Luyo District, Polewali Mandar Regency, Indonesia. A quantitative research approach was employed, involving 15 respondents purposively selected based on their direct involvement in project implementation. Data were collected through questionnaires comprising five indicators for each construct, supplemented by field observations and project documentation. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). Measurement model evaluation indicated acceptable convergent validity and internal consistency reliability, with average variance extracted (AVE) values of 0.582 and 0.512 and composite reliability values of 0.873 and 0.838 for the respective constructs. The structural model revealed a positive and statistically significant association between field supervision and perceived construction work quality (B = 0.512; t = 2.418; p = 0.017). Field supervision explained 26.2% of the variance in perceived construction work quality (R² = 0.262), with an effect size of 0.355 (f²). Inspector accuracy and inspection intensity emerged as prominent indicators of field supervision. These findings underscored the importance of systematic inspections, effective coordination, and documented corrective actions in supporting construction quality. However, the limited explanatory power of the model suggested that additional factors also contributed to variations in perceived work quality. The study concluded that field supervision was positively associated with construction work quality, although the small, purposively selected sample and single-project setting limited the generalizability of the findings. Future research should include larger samples across multiple construction projects and examine additional predictors, including workforce competence, material quality, design clarity, and environmental conditions.