Digital customs regulations and digital transformation as drivers of job proficiency in Peruvian customs agencies: The mediating role of professional competencies (PLS-SEM)

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Type of the article: Research Article

Abstract
Digital transformation is central to customs modernization, yet empirical evidence on how regulatory digitization translates into employees’ job proficiency through capability-building remains limited in Peru. This study aims to test the relationships among digital customs regulations, digital transformation, professional competencies, and job proficiency in Peruvian customs agencies, including the mediating role of professional competencies, using PLS-SEM. A quantitative explanatory–predictive, cross-sectional design was applied. Data were collected via a five-point online survey of 104 employees in Peruvian customs agencies (Lima–Callao) in October–November 2025, recruited through WhatsApp-based convenience/snowball sampling. The model was estimated in ADANCO 2.4.1 with a two-stage approach for the higher-order digital transformation construct and bootstrap inference. Digital customs regulations strongly predicted digital transformation (β = 0.872, p < 0.001) and generated a sizeable total indirect effect on job proficiency (β = 0.757, p < 0.001). Digital transformation increased professional competencies (β = 0.853, p < 0.001) and had a direct effect on job proficiency (β = 0.464, p = 0.026), while professional competencies also enhanced job proficiency (β = 0.473, p = 0.023). Mediation was supported for the pathway digital transformation → competencies → job proficiency (β_ind = 0.403, p = 0.034), yielding a total digital transformation effect of β = 0.867. Explanatory power was high (R²: DT = 0.761; competencies = 0.725; job proficiency = 0.814; SRMR = 0.041). The findings indicate that regulatory-driven digitization improves work proficiency primarily when accompanied by systematic competency development, highlighting training and institutional enablers as priorities in customs reform.

Acknowledgments
A la Dirección de Investigación de la Universidad Peruana de Ciencias Aplicadas por el apoyo brindado para la realización de este trabajo de investigación a través del incentivo UPC-EXPOST-2026-1. [We would like to thank the Research Directorate of the Universidad Peruana de Ciencias Aplicadas for their support in carrying out this research through the UPC-EXPOST-2026-1 grant.]

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    • Figure 1. Structural model result
    • Table 1. Demographic profile
    • Table 2. Factor loading, reliability, and convergent validity
    • Table 3. Discriminant validity
    • Table 4. VIF indicators
    • Table 5. Hypotheses testing results – Direct effects
    • Table 6. Hypothesis result – Indirect effects
    • Table 7. Effect sizes
    • Table 8. Overall model adjustment
    • Table A1. Questionnaire
    • Conceptualization
      Jorge Miguel Chavez Diaz, Percy Quispe Farfan, Yaritza Estrada Izquierdo
    • Formal Analysis
      Jorge Miguel Chavez Diaz, Percy Quispe Farfan
    • Investigation
      Jorge Miguel Chavez Diaz, Percy Quispe Farfan, Yaritza Estrada Izquierdo
    • Methodology
      Jorge Miguel Chavez Diaz, Percy Quispe Farfan, Somnuk Aujirapongpan
    • Project administration
      Jorge Miguel Chavez Diaz, Percy Quispe Farfan
    • Resources
      Jorge Miguel Chavez Diaz
    • Software
      Jorge Miguel Chavez Diaz, Percy Quispe Farfan
    • Validation
      Jorge Miguel Chavez Diaz, Somnuk Aujirapongpan
    • Writing – original draft
      Jorge Miguel Chavez Diaz, Percy Quispe Farfan, Yaritza Estrada Izquierdo, Somnuk Aujirapongpan
    • Writing – review & editing
      Jorge Miguel Chavez Diaz, Percy Quispe Farfan, Yaritza Estrada Izquierdo, Somnuk Aujirapongpan
    • Data curation
      Percy Quispe Farfan
    • Funding acquisition
      Percy Quispe Farfan
    • Visualization
      Percy Quispe Farfan
    • Supervision
      Somnuk Aujirapongpan