Integrating Risk Acceptance Criteria and Collection Process to Reduce Uncollectible Receivables and Its Impact on Revenue Performance: A PLS-SEM Approach in Non-Bank B2B Trade Credit Indonesia

Aulia  Deswara; Yana  Hendayana

doi:10.47747/ijbme.v7i3.3687

Authors

Aulia Deswara Universitas Widyatama, Bandung, Indonesia
Yana Hendayana Universitas Widyatama, Bandung, Indonesia

DOI:

https://doi.org/10.47747/ijbme.v7i3.3687

Keywords:

Risk Acceptance Criteria, Collection Process, Uncollectible Receivables, Revenue Performance

Abstract

Uncollectible receivables in Indonesia's business-to-business (B2B) trade credit industry have stagnated around 8% of total invoices, equivalent to the average profit margin of the B2B distribution sector, while 61.26% of businesses struggle to access formal bank financing, making trade credit the only available operational funding source despite its high risk. This study examines the influence of Risk Acceptance Criteria and Collection Process on Uncollectible Receivables, and their impact on Revenue Performance through a mediating mechanism, at a single non-bank B2B trade credit provider in Indonesia that underwent an extreme credit risk performance transformation during the observation period. Longitudinal data spanning 36 months (2023-2025) were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with a formative measurement model, complemented by out-of-sample predictive validation through PLSpredict and the Cross-Validated Predictive Ability Test (CVPAT). Results show Risk Acceptance Criteria (beta=-0.409; p=0.0075) and Collection Process (beta=-0.529; p=0.0008) significantly and negatively influence Uncollectible Receivables, with Collection Process consistently more dominant. Uncollectible Receivables exerts a highly significant negative influence on Revenue Performance (beta=-0.697; p<0.0001; f2=0.945) and mediates the influence of both exogenous constructs (H4 beta=0.285, p=0.013; H5 beta=0.369, p=0.0049). All five hypotheses are supported, and the validated model significantly outperforms the Linear Model and Indicator Average benchmarks in CVPAT testing. These findings address a literature gap in which both risk mechanisms have typically been examined separately, offering an Integrated B2B Credit Risk Management framework for non-bank trade credit providers in emerging markets.

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