“TRADE FACILITATION, MULTIMODAL CONNECTIVITY, AND DIGITAL VISIBILITY FOR AFCFTA-READY LOGISTICS IN UGANDA: THE
MEDIATING ROLE OF INTER-AGENCY COORDINATION
AND THE MODERATING ROLE OF
REGULATORY QUALITY.”
Background
Trade facilitation, multimodal connectivity and digital visibility are central to modern logistics and to a country’s ability to compete in regional and continental markets. Globally, the last two decades have seen a steady shift from tariff-centred trade policy to a stronger emphasis on reducing non-tariff barriers, improving customs and border processes, and investing in transport and information infrastructure that enables fast, predictable movement of goods. Institutions such as the World Trade Organization (WTO), the World Bank (through diagnostic tools such as the Logistics Performance Index), the International Trade Centre (ITC), and regional development banks now place trade facilitation, multimodal transport and digital trade-visibility tools (electronic single windows, e-customs, electronic freight tracking and port community systems) at the centre of strategies to increase trade flows, attract investment and reduce the costs of doing business. These global instruments and benchmarks shape donor programmes and national reforms aimed at lowering trade transaction costs and integrating producers into global and regional value chains.
Global trade in goods and services has grown massively over the past few decades, underpinned by multilateral institutions, regional trade agreements, and growing interconnectedness. While precise annual global trade values fluctuate, world merchandise trade has often been reported in the tens of trillions of U.S. dollars. For instance, as of the early 2010s, global trade volumes were estimated to be around US$30 trillion. International bodies such as the World Trade Organization (WTO) play a central role in regulating global trade, negotiating rules (including trade facilitation), and resolving disputes. Other key institutions include UNCTAD (United Nations Conference on Trade and Development), which monitors trade trends and structural issues, and regional development banks (e.g., African Development Bank) which fund infrastructure and trade-related projects.
The African Continental Free Trade Area (AfCFTA) represents the continent’s most ambitious attempt to deepen intra-African trade by creating a single market for goods and services across African Union member states. By design, AfCFTA seeks to expand market access and reduce tariffs while relying heavily on domestic trade facilitation, transport connectivity and digital systems to convert tariff preferences into real export gains for firms. By late 2024–2025 the agreement had progressed from signature to broad ratification and phased implementation, including guided-trade initiatives intended to allow members to test tariff liberalization while domestic systems are adapted. However, successful utilization of AfCFTA preferences depends on the capacity of national logistics systems, border agencies and private sector actors to meet rules of origin, sanitary and phytosanitary (SPS) standards, and documentary requirements areas directly affected by trade facilitation, multimodal connectivity and digital visibility.
At the global level, the WTO continues to host trade facilitation negotiations and provides a normative and technical assistance role; the World Bank, the International Monetary Fund and regional development banks publish diagnostics (for example, the Logistics Performance Index and trade costs estimates) that are used to benchmark performance and attract targeted investments. These instruments make clear that improvements in customs efficiency, inter-agency coordination and the digital exchange of trade data yield measurable reductions in time and cost to trade — but also that infrastructure gaps (poor roads, limited rail, inadequate warehousing and weak port interfaces) and institutional fragmentation frequently blunt the benefits of liberalized market access.
Uganda is an active participant in the global and regional trade architecture. It has been a member of the WTO since 1 January 1995 and is party to regional economic communities and trade arrangements including the East African Community (EAC) and the Common Market for Eastern and Southern Africa (COMESA). Uganda formally ratified the AfCFTA instruments and has taken steps to align national policy with AfCFTA implementation, including the launch of national AfCFTA implementation strategies and free-zone/export facilitation initiatives in 2024. These multiple memberships give Uganda preferential market access across overlapping trading blocs but also create regulatory complexity and convergence challenges for customs, rules of origin and standards compliance.
Problem statement
Over the past three decades, Uganda has made significant strides in liberalizing its trade regime, expanding export diversification, and improving regional connectivity. Despite these efforts, critical structural and operational challenges continue to undermine the country’s intra-African trade potential and limit its competitiveness, while intra-African trade is a major component of Uganda’s external linkages, formal intraregional trade remains relatively modest and imbalanced. In 2022, Uganda’s formal intra-regional trade stood at US$ 2.59 billion, of which 69% were exports and 31% were imports, within these flows, Kenya accounted for 52% of total intra-regional trade, South Sudan for 24%, and the DRC for 17%, Despite this, Uganda ran a trade deficit of US$ 322.2 million in the first half of FY 2024/25 with EAC partners, reversing a prior surplus.
Uganda’s export base has undergone a shift but remains vulnerable. In the year to June 2025, merchandise exports surged by 64.3%, rising from US$ 702.5 million in June 2024 to US$ 1.15 billion. However, this growth is heavily driven by gold, which in that period accounted for 39.3% of export earnings, pointing to a still-concentrated export pattern. Uganda’s gold exports, in particular, leapt more than tenfold in 2023 to US$ 2.3 billion, according to central bank data. Moreover, the traditional export of coffee now accounts for only about 20.9% of Uganda’s exports, down from its historical dominance.
The persistence of border inefficiencies and weak trade infrastructure is a severe drag on regional trade. A recent study by the Private Sector Foundation Uganda (PSFU) and AGRA found that border clearance for perishable goods in Uganda can stretch up to 14 days, far exceeding the East African Community target of 48 hours. The same report estimated that these bottlenecks cost Uganda up to US$ 3.5 billion annually in lost perishable exports.
A substantial portion of Uganda’s trade with its African neighbors remains informal, creating both economic leakage and under-realized tax revenue. According to UBOS, in 2023 Kenya accounted for 63.5% of Uganda’s informal imports, valued at US$ 78.7 million, while the DRC accounted for 21.4% (US$ 26.6 million) of those informal flows. The same report estimates that informal cross-border trade (exports and imports) exceeds US$ 500 million, pointing to significant under-documentation.
The cost of poor transport infrastructure and weak multimodal connectivity further erodes Uganda’s trade competitiveness. For instance, Uganda ranks near the bottom on the World Bank’s Logistics Performance Index (LPI) for trade-transport infrastructure. Only 17% of Uganda’s national roads are paved, and only about 25% of its existing railway network is operational. Taken together, these challenges indicate that despite bold reforms, Uganda struggles to convert trade policy gains into sustainable, efficient, and inclusive intra-African trade growth. The persistence of; trade imbalances, Export concentration, Border delays and clearance inefficiencies, High informal trade, and Poor transport infrastructure.
These inefficiencies especially undermine Uganda’s capacity to benefit from regional integration frameworks such as the African Continental Free Trade Area (AfCFTA), where speed, predictability, low cost, and regulatory coherence are critical. Without targeted interventions addressing coordination at border agencies, enforcement of regulations, and infrastructure bottlenecks, Uganda risks being left behind in the intra-African trade space failing to fully capitalize on its geographical position, resource base, and market potential.
Despite Uganda’s active membership in multiple African trade organizations including the East African Community (EAC), the Common Market for Eastern and Southern Africa (COMESA), and the newer African Continental Free Trade Area (AfCFTA) these entities have consistently failed to catalyze significant growth in intra-continental trade, as evidenced by persistent non-tariff barriers, cumbersome customs procedures, poor cross-border infrastructure, and a chronic lack of implementation and enforcement of agreed protocols, which collectively continue to stifle the free flow of goods, undermine competitiveness, and prevent Ugandan and other African businesses from accessing the envisioned integrated market. Despite ongoing government investments in road infrastructure, border post modernization, and customs reforms, Uganda’s logistics performance indicators such as clearance times, cargo dwell time, and transport costs remain significantly higher than global and regional benchmarks. These bottlenecks undermine the country’s readiness to fully exploit AfCFTA’s market integration agenda. It is against this background that this study intends to investigate into, trade facilitation, multimodal connectivity, and digital visibility for AFCFTA-ready logistics in Uganda: the mediating role of inter-agency coordination and the moderating role of regulatory quality.
Objectives of the study
- To assess the influence of Trade Facilitation Practices on Logistics Performance for Trade.
- To investigate the influence of Multimodal Connectivity on Logistics Performance for Trade.
- To examine the influence of digital visibility in areas of tracking and data sharing on Logistics Performance for Trade.
Conceptual frame work
Moderator
Independent variables
Dependent variable
Inter-Agency Coordination (Mediator)
CHAPTER THREE
RESEARCH METHODOLOGY
3.0 Introduction
This chapter presents the research paradigm, research design adopted for the study, population, sample and sampling strategies, data collection methods and tools, data quality control, ethics, gender consideration, the limitations and delimiters of the study.
3.1 Research paradigm
The influence of Trade facilitation, multimodal connectivity, and digital visibility for AFCFTA-ready logistics in Uganda: the mediating role of inter-agency coordination and the moderating role of regulatory quality, that can be explained using different theoretical frames. However, for purpose of answering the research question, a pragmatic philosophical stance is adopted. This is concerned with what works and provides solutions to an identified problem (Creswell, 2013; Patton, 2002. Pragmatism allows the researcher to emphasize the research problem and use all approaches available to address the problem. It is an approach that uses mixed methods. How “Trade facilitation, multimodal connectivity, and digital visibility for AFCFTA-ready logistics in uganda: the Mediating role of inter-agency coordination And the moderating role of Regulatory quality” and why there has been a slow readiness to adopt and uptake of railway freight transport” as constructed by respondents and its implications are explored. Thus pragmatism will give the researcher the freedom of choice of methods, techniques, and procedures of research that best meets the needs and purpose of the study (Creswell, 2013b.).
3.2 Research Design
Different research designs relate to philosophical assumptions and the research design is associated with a pragmatic paradigm (Creswell, 2013; Kothari, 2004). The research design associated with the pragmatic paradigm involves mixed methods (Creswell, 2012). The current study will adopt a concurrent parallel design combining the survey design applied within a case study. The mixing of the two designs will provide a better understanding of the research problem since it utilizes and it builds upon the strengths of both quantitative and qualitative data (Creswell, 2008; Saunders, et al., 2012).
3.2.1 Case Study Research Design
The case study is a method of study that focuses on in-depth rather than breadth. This research will use a case study design involving Uganda railways. The case study is a design in qualitative research an objective as well as a product of inquiry (Creswell, 2013). It is where the researcher explores real-life multiple bounded systems (cases) over time through detailed in-depth data collection involving multiple sources of information in which inferences can be drawn (Creswell, 2013). The case study design will be a basis to explore role of railway transport on carbon emission reduction(Ritchie, et al., 2013).
3.2.2 Survey Research Design
Creswell (2012) defines survey research design as procedures in quantitative research in which investigators administer a survey to a sample or to the entire population of people to describe the attitudes, opinions, behaviors, or characteristics of the population. The current study will adopt a cross-sectional survey design since the researcher shall collect data at one point in time and measure the practices, awareness, and readiness then (Creswell, 2012). Furthermore, survey research typically collects data using two basic forms: questionnaire and interview which will be applied in the current study. The questionnaire and interview will be administered to stakeholders such as Uganda Railways Corporation (URC) staff, Ministry of Works and Transport officials, NEMA officers, freight and logistics companies, include climate experts, policy makers and technical personnel
3.3 Area of Study
The study area will include; Uganda Railways Corporation (URC) , Ministry of Works and Transport, NEMA, freight and logistics companies. Although each of the selected cases has its history, they are considered to be centers of excellence in their respective line of research within national, regional, and international collaborations
3.4 Population
The population is the entire set of respondents from whom the study sample with common observable characteristics (sample) will be drawn (Taherdoost, 2018). The population of the study is 469 respondents, this will include respondents from Uganda Railways Corporation (URC), Ministry of Works and Transport and Freight and logistics companies.
3.5 Sample Size and Sampling Strategies
A sample is any part of a fully defined population (Banerjee and Chaudhury, 2010). Here below the sample size and sampling strategies of the study are explained.
3.5.1 Sample Size
The ideal sample size for researchers as respondents will be calculated using the Cochran formula at the desired level of precision, confidence level, and the estimated proportion of the attribute present in the population.
The Cochran formula is:
Where:
- e is the desired level of precision (i.e. the margin of error),
- p is the (estimated) proportion of the population that has the attribute in question,
- q is 1 – p.
- the z-value is found in a Z-table.
This give a sample size of 212 Respondents. However, to allow a representative sample from each study site, Stratified sampling will be used to generate an appropriate sample depending on the population of respondents available in each study site. Consequently, Cochran’s formula is modified to calculate a sample for small (Hypergeometric) populations, applied as shown below:
n=
Where:
n= Sample size
N= Population size
Z=z-score
e= Margin of error
P= Sample proportion (If unknown we use 0.5)
N=469, Z-score at 95% confidence level=1.96, e=5%, P=0.5
Substituting into the formula
n=
Sample size (n) = 212
For each stratum, a proportionate stratification shall be given by the formula below.
Where: =Strata sample size,
=Strata population size,
=strata
However, to attain a balance in response from each of the study sites, the sample for the respondent for the questionnaires shall be as here below derived from the calculation:
Sample for ministry of works () = = 25
Sample for NEMA () = = 16
Sample for URC () = = 171
The sample size for key informants includes: climate experts, policy makers and technical personnel, from each of the selected organizations will be one respondent per category per institute which makes the total of 9 Respondents. Thus the total sample size will be 221 Respondents for both questionnaire and interview.
3.5.2 Sampling Techniques
Three sampling techniques will be used to select the respondents as here below explained
3.5.2.1 Stratified Sampling
Stratified sampling is where the population is divided into strata (or subgroups) and a random sample is taken from each subgroup. A subgroup is a natural set of items. Subgroups might be based on company size, gender, or occupation (to name but a few). Stratified sampling is often used where there is a great deal of variation within a population. Its purpose is to ensure that every stratum is adequately represented. A sample for each of the study sites as a stratum will be calculated.
3.5.2.2 Random Sampling
At each institution, the respondents for questionnaires will be selected using random sampling. The respondents’ list shall be requested from the Human Resources Office in each institute. The entire population of the respondents at each institute will be given a number code and the numbers will be put together in a bag and randomly select one by one with replacement until all the required sample at each site is selected. All those selected will then be contacted with a request to participate in the study by answering a questionnaire.
3.5.2.3 Purposive Sampling
Purposive sampling known as judgmental sampling is defined as selecting a relatively small number of respondents who can provide valuable information related to the research questions under examination (Teddlie and Tashakkori, 2009). The rationale for purposive sampling is its ability to enable the selection of informed persons who possess vital information, comprehensive enough to gain a better insight into the problem under study. Purposive sampling will be used to select key informants. These include; climate experts, policy makers and technical personnel.
The office-bearers in the identified categories above are assumed to be information-rich on issues related to climate change practices within institutes by virtual of their roles, training, and skills. A list of office holders in those categories will be identified with the help of the Human Resources Office at each institute. Where there is more than one person in each category, the most senior will be selected. The selected respondents will then be contacted requested for an interview.
3.6 Data collection methods and tools
Data will be collected using multiple data collection tools. The tools will include: questionnaires, interviews, and document reviews
3.6.1 Questionnaire
Questionnaires will be used to collect both quantitative and qualitative data for analyzing the ,
Uganda Railways Corporation (URC), Ministry of Works and Transport and Freight and logistics companies. Questionnaires will be used to collect data from respondents who are currently knowledgeable on the study topic. These include; climate experts, policy makers and technical personnel. Researchers as respondents in this study are important since they play a critical role in the research data lifecycle. Questionnaires with options for selecting and measuring based on the Likert scale are considered appropriate for the study.The questionnaire will be concurrently distributed to all study institutes. Thereafter all physically completed questionnaires shall be collected and input into the Google forms by the researcher and input into SPSS for analysis.
3.6.2 Interview
The interview is a data collection method in qualitative methods. It is where the respondents reply to questions asked by the interviewer verbal communicating and spoken narratives of insights constructing their social world (Ritchie, et al., 2013). This method will be used to collect verbal responses from respondents. Interviews will be conducted with climate experts, policy makers and technical personnel. Interviewing this category of respondents will reveal in-depth personal accounts and explore issues in detail about research data, its management, readiness, challenges experienced, and thereafter interviewees may make suggestions proposing a measure that could enhance the study. Interviews shall be held at the respondents’ place of work or by telephone whatever is convenient to the interviewee. Interviews will be recorded on request and thereafter transcribed into textual data for analysis. The text will be assumed to be a replica of the verbal responses of the respondents.
3.6.3 Document Review
Written documents are a pervasive socially constructed representation of reality in institutions (Patton, 2002). However, documents pose challenges among which are access to official documents, understanding how and why they were produced, and difficulty to determine their accuracy. Nonetheless, as a data collection method, documents to be reviewed will include: legal and policy documents; strategic and annual plans; statistical and research-related reports of institutes understudy and collaborators, and oversight institutions’ reports. Despite the challenges, documents are an important source of data related to the institutional context that could be useful in understanding the study.
3.7 Data Collection Tools
Data will be collected using: Questionnaire, Interview guides and a Document review guide.
3.8 Data Quality Control
Data quality control are measures put in place to ensure data integrity and authenticity and to safeguard the quality of the research output.
3.8.1 Validity
Validity is how well an instrument measures what it is supposed to measure (Taherdoost, 2018). The goal of validity is to ensure accurate, objective, and neutral representation of the topic under study (Marwill and Rossman, 2011). A pre-test will be carried out to assess the face validity of the data collection tools to gauge the meaning and attributes of the questions both in interview and questionnaire (Ng ’ Eno, 2018). The pre-test shall also assess whether the data collection tools capture the information required for the study. It will also help to give confidence to the researcher in data collection and eliminate barriers such as resistance to recording and mistrust of the researcher’s agenda which shall consequently strengthen the study (Marwill and Rossman, 2011).
3.8.2 Reliability
Reliability will be ensured by the data collection tools being subjected to respondents who were identified for the purpose. All researchers shall be subjected to a questionnaire across the study sites and interviews shall be conducted for pre-identified key informants in each institute. Finally, pre-identified documents shall be reviewed across the three institutes of the study.
3.9 Data presentation and analysis
Data shall be collected from each study site and analyzed. Thereafter, cross-case conclusions shall be made and the report written. However, all collected data shall be under the custody of the researcher. Qualitative data will be presented using the themes derived from the interpretations analyzed by NVIVO software to give deeper research insights. The software will be used to create mind maps to quicken the process of analyzing qualitative data (Godau, 2014). Qualitative data will be presented in form of direct quotations from respondents explaining in detail their experiences and practices.
Quantitative data will be analyzed using Statistical Package for Social Scientists (SPSS) and presented using interactive statistical analysis.
3.10 Ethical consideration
This study will strictly adhere to the ethical standards outlined in the Makerere University Research Ethics Guidelines, which emphasize respect for persons, beneficence, justice, and responsible conduct of research. Prior to data collection, the researcher will obtain ethical clearance from the Makerere University Research Ethics Committee (REC) and seek formal authorization from the Ministry of Works and Transport, the National Environment Management Authority (NEMA), and the Uganda Railways Corporation to ensure institutional compliance. Participation in the study will be entirely voluntary, and all selected officials will be provided with detailed information about the purpose of the study, the procedures involved, the potential risks and benefits, and their rights as participants. Written informed consent will be obtained before any interview or data collection activity is conducted.
To uphold confidentiality, all data will be handled in accordance with Makerere University’s data protection and privacy guidelines. Participants’ identities will be protected through the use of codes, pseudonyms, and secure data storage systems, and no personal identifiers will appear in transcripts or the final report unless explicit permission is granted. Data will be stored securely in password-protected digital files and locked cabinets accessible only to the researcher. The researcher will ensure that no participant suffers any form of psychological, social, or professional harm, and questions will be framed sensitively to avoid discomfort or undue pressure. Participants will also be informed of their right to withdraw from the study at any stage without any negative consequences.
In line with the principle of beneficence, the study will ensure that collected data is used strictly for academic purposes and will not compromise the integrity, reputation, or operational activities of the participating institutions. Conflict of interest concerns will be openly declared, and the researcher will maintain objectivity throughout the research process. Respect for institutional and governmental policies will be observed, and all procedures will comply with national research ethics requirements, including environmental and transport-related regulations. Upon completion, the study findings may be shared with participating institutions as part of knowledge dissemination, in accordance with Makerere University guidelines.
3.11 Gender consideration
The researcher gave equal opportunity for both male and female respondents to participate in the study in the course of collecting data.
3.12 Limitations of the study
The study will adhere to strict ethical standards to protect participants and ensure the integrity of the research. Informed consent will be obtained from all officials of the Uganda Railways Corporation, NEMA, and the Ministry of Works and Transport after clearly explaining the purpose, procedures, benefits, and voluntary nature of their participation. Participants will be assured of their right to withdraw at any stage without consequence, and confidentiality will be maintained by anonymizing identities and securely storing all data. The study will avoid any form of psychological, social, or professional harm by ensuring respectful engagement and asking only relevant, non-threatening questions. Permission will be sought from the respective institutions, and all data collected will be used solely for academic purposes. Ethical approval will be obtained from the appropriate institutional review board to ensure full compliance with national and institutional research guidelines.
3.13 Delimiters of the study
This study is delimited to examining the role of railway freight transport in reducing carbon emissions and enhancing environmental sustainability within Uganda. Specifically, the study will focus on three key institutions: the Uganda Railways Corporation (URC), the National Environment Management Authority (NEMA), and the Ministry of Works and Transport. Only officials directly involved in railway operations, environmental regulation, and transport policy will be included. The study is further limited to railway freight transport and does not cover passenger rail services or other modes of transport such as road, air, or water. Geographically, the study is restricted to Kampala and selected operational points where these institutions operate. The study will also rely primarily on self-reported data from interviews and questionnaires, which may limit the depth of operational insights. Time constraints and resource availability additionally restrict the scope to data that can be collected within the study period.
