Integrating PCA and Knowledge Management within a Resource-Based View for Tirta Moedal Water-Asset Resilience

Abstract

The reliability of urban water-supply services depends heavily on raw-water quality assurance, yet the public-asset-management literature has largely treated raw-water ecological risk and internal organisational capability as separate domains. This study bridges that gap by integrating a multivariate statistical diagnosis of raw-water quality into the Resource-Based View (RBV) and Knowledge Management (KM) frameworks for production-asset governance. It focuses on the Kaligarang River, the principal source for Perumda Air Minum Tirta Moedal of Semarang City. From monitoring archives spanning 2012–2022, the 2017–2022 window was the effectively analysed period; 1,120 valid observations from four strategic monitoring points were analysed using Principal Component Analysis (PCA) with Varimax rotation. After eliminating five low-communality parameters, 21 variables were reduced into six principal components explaining 70.37% of total variance (KMO = 0.820; Bartlett’s χ² = 15,117.004; df = 210; p < 0.001). The components represent soil mineralisation and salinity, physical-metallic corrosivity, nutrient eutrophication, thermal-physical balance, complex organic leachate load, and specific toxicity. These findings are synthesised into an asset risk register linking each component to physical asset-degradation mechanisms (incrustation, corrosion, impeller abrasion) and chemical-cost escalation, drawing on the IIMM and AWWA J100 resilience standards. The study proposes a SECI knowledge-conversion model enriched by local work culture and human-centric wellness programmes alongside a VRIO capability assessment. Its novelty lies in operationalising PCA as a trigger for the knowledge-creation cycle in public-service asset management—a bridge that explicitly frames ecological data as a direct determinant of operational reliability.

Keywords:  public asset management; raw-water quality; principal component analysis; knowledge management; resource-based view; IIMM