Container terminal efficiency under external shocks: A hybrid DEA-Regression analysis of Dar es Salaam port (2019–2023)

Purpose. This paper evaluates the technical Efficiency of Dar es Salaam Port’s container terminal operations over the period 2019–2023, explicitly accounting for the influence of macroeconomic conditions and the COVID-19 pandemic on performance metrics. The study addresses a critical gap in port efficiency research by disentangling internal operational capability from external contextual factors in a developing-economy maritime gateway serving landlocked East and Central African countries. Methodology. A two-stage hybrid analytical framework integrates input-oriented Data Envelopment Analysis (DEA) with Contextual Value Added (CVA) regression. DEA efficiency scores are computed using a three-year rolling window approach with inputs (quay length, gantry cranes, terminal area) and outputs (container throughput, vessel calls). Second-stage ordinary least squares regression isolates the effects of GDP, trade volume, and pandemic disruption on measured efficiency. Quantitative findings are triangulated with qualitative stakeholder surveys (n=45) and semi-structured interviews to capture operational perceptions and institutional constraints. Results. DEA analysis reveals temporal efficiency variation ranging from 0.838 (2019) to 0.966 (2021), with post-pandemic decline to 0.890 (2023). CVA regression identifies a statistically significant negative relationship between trade volume and efficiency (β = −1.76×10⁻⁵, p = 0.03), indicating binding infrastructure constraints. The COVID-19 dummy exhibits a paradoxical positive coefficient (β = +0.090, p = 0.02), reflecting efficiency gains under suppressed demand rather than genuine productivity enhancement. Theoretical contribution. This study advances port efficiency assessment by demonstrating that unadjusted frontier methods can mask capacity deficits when external demand fluctuates. The hybrid DEA-CVA framework enables evidence-based attribution of efficiency sources, enhancing policy relevance. Practical implications. Findings underscore the urgent need for infrastructure expansion and procedural digitalization to accommodate regional trade growth under the African Continental Free Trade Area. Sustainable Development Goals (SDGs): SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation, and Infrastructure

Authors

Institutions

Publication Details

Journal
Journal of Sustainable Development of Transport and Logistics
Published
2025-12-01
DOI
https://doi.org/10.14254/jsdtl.2025.10-2.7
Primary Topic
Maritime Ports and Logistics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Container terminal efficiency under external shocks: A hybrid DEA-Regression analysis of Dar es Salaam port (2019–2023)

Benjamin Mbeba Meli, Nasibueli Richard Moshi
Journal of Sustainable Development of Transport and Logistics
Maritime Ports and Logistics
article

Container terminal efficiency under external shocks: A hybrid DEA-Regression analysis of Dar es Salaam port (2019–2023)

Benjamin Mbeba Meli, Nasibueli Richard Moshi
article en

Abstract

Purpose. This paper evaluates the technical Efficiency of Dar es Salaam Port’s container terminal operations over the period 2019–2023, explicitly accounting for the influence of macroeconomic conditions and the COVID-19 pandemic on performance metrics. The study addresses a critical gap in port efficiency research by disentangling internal operational capability from external contextual factors in a developing-economy maritime gateway serving landlocked East and Central African countries. Methodology. A two-stage hybrid analytical framework integrates input-oriented Data Envelopment Analysis (DEA) with Contextual Value Added (CVA) regression. DEA efficiency scores are computed using a three-year rolling window approach with inputs (quay length, gantry cranes, terminal area) and outputs (container throughput, vessel calls). Second-stage ordinary least squares regression isolates the effects of GDP, trade volume, and pandemic disruption on measured efficiency. Quantitative findings are triangulated with qualitative stakeholder surveys (n=45) and semi-structured interviews to capture operational perceptions and institutional constraints. Results. DEA analysis reveals temporal efficiency variation ranging from 0.838 (2019) to 0.966 (2021), with post-pandemic decline to 0.890 (2023). CVA regression identifies a statistically significant negative relationship between trade volume and efficiency (β = −1.76×10⁻⁵, p = 0.03), indicating binding infrastructure constraints. The COVID-19 dummy exhibits a paradoxical positive coefficient (β = +0.090, p = 0.02), reflecting efficiency gains under suppressed demand rather than genuine productivity enhancement. Theoretical contribution. This study advances port efficiency assessment by demonstrating that unadjusted frontier methods can mask capacity deficits when external demand fluctuates. The hybrid DEA-CVA framework enables evidence-based attribution of efficiency sources, enhancing policy relevance. Practical implications. Findings underscore the urgent need for infrastructure expansion and procedural digitalization to accommodate regional trade growth under the African Continental Free Trade Area. Sustainable Development Goals (SDGs): SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation, and Infrastructure

Journal of Sustainable Development of Transport and LogisticsVol. 10(2)
University of Dar es Salaam (TZ), Institute of Finance Management (TZ)
Openalex Percentile: Top 50%
Maritime Ports and Logistics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.