Data Analytics Utilization and Credit Collection Management in Selected Retail Banks in Muntinlupa: Inputs to Performance Optimization

This study responds to a set of editorial revisions requiring a clarified unit of analysis, full re-analysis from raw respondent-level data, a defensible theoretical framework, and transparent reporting of sampling, reliability, and common-method-bias safeguards. It re-examines data analytics capability and credit collection management as they are perceived by bank employees — not as bank-level analytics capability and not as objective collection performance, which were not measured in this dataset and are flagged as a direction for future research rather than inferred. Fifty employees (27 Collection Officers, 13 Branch Managers, and 10 Data Analysts) of retail banks operating in Muntinlupa City, Philippines, completed a 127-item structured questionnaire between February 23 and March 24, 2026. Bank affiliation was identifiable for 24 respondents across ten named banks; the remaining 26 (52.0%) did not record a bank affiliation, a data-completeness gap that is reported openly rather than masked. All descriptive statistics, composite means, ANOVA results, post hoc comparisons, and correlations were recalculated directly from the 50-respondent raw dataset. The twelve capability and practice dimensions demonstrated strong internal consistency (α = .880 to .936; corrected item-total correlations .51 to .86) and were rated Highly Evident overall, ranging from Real-Time Reporting and Monitoring (M = 4.13, SD = 0.61) to Data Security and Confidentiality (M = 4.35, SD = 0.50). One-way ANOVA indicated role-based differences in ten of twelve dimensions at the conventional α = .05, but after Holm-Bonferroni correction for multiple comparisons only Data-Driven Decision Making (F = 6.87, p = .002, holm-p = .027, η² = .23) and Payment Reminder Systems (F = 7.16, p = .002, holm-p = .023, η² = .23) remained significant, with Tukey post hoc tests locating the difference between Collection Officers and Data Analysts. All 36 correlations between analytics-capability and collection-management dimensions were positive and remained significant after Holm correction (r = .439 to .823), and the overall composite correlation was very high (r = .909, 95% CI [.844, .947]). Harman’s single-factor test extracted 43.7% of variance on one unrotated factor, and a dimension-level principal-components analysis found a single dominant component (68.4% of variance, loadings .70 to .88) rather than two separable constructs — evidence that favors an integrated data-driven collection-capability framework over the Technology Acceptance Model, which was not retained because its core constructs (perceived usefulness and perceived ease of use) were not measured. An exploratory intraclass correlation computed on the bank-identified subsample (ICC(1) ≈ 0, k̄ = 2.08 across 10 banks) could not detect reliable bank-level clustering, a result attributed to the incomplete bank identifiers and small, unbalanced cluster sizes rather than to an absence of institutional effects. Respondents reported moderate-to-high challenges in both analytics utilization (M = 3.72) and collection management (M = 4.02), and rated proposed performance-optimization inputs as highly acceptable (M = 4.10). The study concludes that, at the level of employee perception, data analytics practices and collection-management practices are strongly and positively associated, while cautioning that this evidence is perceptual, single-method, and drawn from an incompletely documented sampling frame, and recommending that future iterations capture objective collection indicators, complete bank identifiers, and a larger, cluster-balanced sample suitable for multilevel analysis.

data analytics capability, credit collection management, employee perceptions, retail banking, common method bias

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