Optimizing road haulage firms’ operational performance in Zimbabwe through artificial intelligence: Moderating effect of driver training

Munyaradzi Chibaro, Chisungo Chisungo, Wilbert Manyanga, James Kanyepe, David Chikwere and Thomas Bhebhe
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Munyaradzi Chibaro: University of Botswana
Chisungo Chisungo: Chinhoyi University of Technology
Wilbert Manyanga: North-West University
James Kanyepe: University of Botswana
David Chikwere: Leeds Beckett University
Thomas Bhebhe: Chinhoyi University of Technology

International Journal of Research in Business and Social Science (2147-4478), 2025, vol. 14, issue 7, 78-92

Abstract: This study investigates the use of artificial intelligence (AI) to improve operational performance in Zimbabwean road haulage enterprises, with a focus on driver training as a moderator. As the logistics industry faces new difficulties, AI technologies have great promise for increasing efficiency and decision making. However, the usefulness of these technologies is determined by the skill levels of the drivers using them. This study demonstrated how extensive driver training improves the capacity to comprehend AI-generated insights, resulting in better route management, lower operating costs, and increased safety. This study examines how AI affects key performance variables such as cost savings, productivity, customer happiness, and environmental sustainability, using real data from road haulage companies. Key findings demonstrate how AI is transforming decision-making, improving operational effectiveness, and optimizing routes. The research highlights several noteworthy obstacles in addition to their obvious advantages, such as budgetary limitations, difficulty in obtaining pertinent data, and the need for more regionalized AI solutions. The findings, which are based on case studies and performance data from diverse enterprises, indicate that (i) organizations that invest in both AI and driver training benefit from a synergistic impact, resulting in higher operational outcomes, (ii) there is need to combine technical improvements with human experience to achieve maximum performance in Zimbabwe's competitive road-haulage market and finally (iii) this study offers helpful recommendations for successfully integrating artificial intelligence (AI) into haulage processes, along with insights into best practices and alternative approaches to overcome current obstacles. This study emphasizes the importance of context-specific solutions in emerging regions, enhancing the expanding corpus of knowledge on AI applications, particularly in logistics. Key Words: Artificial intelligence; road haulage companies; operational performance; driver training

Date: 2025
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