Assessing the Effect of Human Resource Competence on Cargo Clearance Performance at Dar es Salaam Seaport, Tanzania

Alistidia Sadoth; Benitha Myamba

doi:10.18535/sshj.v9i10.2021

Abstract

This study assesses whether Human Resource Competence (HRC) improves Cargo Clearance Performance (CCP) at Dar es Salaam Seaport. Drawing on the Resource-Based View and technology-adoption theory, the study surveyed clearing and forwarding agents, customs officials, and port administrators and estimated relationships using PLS-SEM. The reflective measurement model met reliability and validity thresholds (loadings ≥ .71; α, CR ≥ .86; AVE ≥ .60). In the structural model, HRC had a positive, significant effect on CCP (β = 0.34, t = 5.98, p < .001), and the model explained 51% of CCP variance (R² = .51) with predictive relevance (Q² = .31) and acceptable fit (SRMR = .064). Findings indicate that competence training quality, system proficiency, problem-solving, and coordination translates into faster, more accurate, and more efficient clearance. Practical implications include role-specific training, competency assessments embedded in HR cycles, targeted coaching for exception handling and data quality, and periodic Time Release Studies to track gains. The study positions HRC as a strategic complement to digital and process reforms in achieving sustained port performance.

Keywords

Human Resource CompetenceCargo Clearance PerformanceDar es Salaam PortTrade Facilitation

References

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[refR-2] Ahn, Y. (2008). Competencies for port and logistics personnel. International Journal of Vocational Education and Training, 16(1), 7–26.Google Scholar ↗

[refR-3] Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.Google Scholar ↗

[refR-4] Barney, J. B., Ketchen, D. J., Jr., & Wright, M. (2021). Resource‐based theory and the value creation framework. Journal of Management, 47(7), 1936–1959.Google Scholar ↗

[refR-5] Bichou, K., & Gray, R. (2004). A logistics and supply chain management approach to port performance measurement. Maritime Policy & Management, 31(1), 47–67.Google Scholar ↗

[refR-6] Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.Google Scholar ↗

[refR-7] Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method (4th ed.). Wiley.Google Scholar ↗

[refR-8] Dwivedi, Y. K., Rana, N. P., Tamilmani, K., Lal, B., Sharma, S. K., & Raman, R. (2019). A meta-analysis of the unified theory of acceptance and use of technology (UTAUT). Information Systems Frontiers, 21(4), 719–739.Google Scholar ↗

[refR-9] Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.Google Scholar ↗

[refR-10] Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2019). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage.Google Scholar ↗

[refR-11] Heilig, L., Lalla-Ruiz, E., & Voß, S. (2017). Digital transformation in maritime ports: Analysis and a game theoretic framework. Transportation Research Part A: Policy and Practice, 110, 251–266.Google Scholar ↗

[refR-12] Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20.Google Scholar ↗

[refR-13] Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based SEM. Journal of the Academy of Marketing Science, 43(1), 115–135.Google Scholar ↗

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[refR-15] Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10.Google Scholar ↗

[refR-16] Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261.Google Scholar ↗

[refR-17] Munim, Z. H., & Schramm, H.-J. (2018). The impacts of port infrastructure and logistics performance on economic growth: The mediating role of seaborne trade. Maritime Economics & Logistics, 20(4), 1–21.Google Scholar ↗

[refR-18] Notteboom, T., & Rodrigue, J.-P. (2005). Port regionalization: Towards a new phase in port development. Maritime Policy & Management, 32(3), 297–313.Google Scholar ↗

[refR-19] Notteboom, T., Pallis, A. A., & Rodrigue, J.-P. (2022). Port Economics, Management and Policy (updated online ed.). Routledge/PortEconomics.Google Scholar ↗

[refR-20] Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.Google Scholar ↗

[refR-21] Organisation for Economic Co-operation and Development. (2018). Trade facilitation and the global economy. OECD Publishing.Google Scholar ↗

[refR-22] Organisation for Economic Co-operation and Development. (2023). Trade Facilitation Indicators 2023: 10 years of monitoring trade facilitation reforms. OECD Publishing.Google Scholar ↗

[refR-23] Pallis, A. A., & Rodrigue, J.-P. (2020). Port Economics, Management and Policy. Routledge.Google Scholar ↗

[refR-24] Raza, S. A., Kuo, Y.-H., & Shah, N. (2023). Digitalization in ports and logistics: Capabilities, performance, and sustainability. Journal of Maritime Affairs, 1–20. Advance online publication.Google Scholar ↗

[refR-25] Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research (pp. 1–40). Springer.Google Scholar ↗

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[refR-32] Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.Google Scholar ↗

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