Optimizing road haulage firms' operational performance in Zimbabwe through artificial intelligence: Moderating effect of driver training
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| Pubblicato in: | International Journal of Research in Business and Social Science vol. 14, no. 7 (2025), p. 78-93 |
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Society for the Study of Business and Finance
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| Accesso online: | Citation/Abstract Full Text Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Chibaro, Munyaradzi |u Department of Management, University of Botswana, Botswana | |
| 245 | 1 | |a Optimizing road haulage firms' operational performance in Zimbabwe through artificial intelligence: Moderating effect of driver training | |
| 260 | |b Society for the Study of Business and Finance |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a Zimbabwe | |
| 653 | |a Predictive maintenance | ||
| 653 | |a Driver education | ||
| 653 | |a Regions | ||
| 653 | |a Politics | ||
| 653 | |a Drivers | ||
| 653 | |a Inventory control | ||
| 653 | |a Logistics | ||
| 653 | |a Organizational effectiveness | ||
| 653 | |a Efficiency | ||
| 653 | |a Usefulness | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Data | ||
| 653 | |a Decision making | ||
| 653 | |a Case studies | ||
| 653 | |a Cost control | ||
| 653 | |a Inventory management | ||
| 653 | |a Alternative approaches | ||
| 653 | |a Inventory | ||
| 653 | |a Operating costs | ||
| 653 | |a Software | ||
| 653 | |a Training | ||
| 653 | |a Productivity | ||
| 653 | |a Happiness | ||
| 653 | |a Best practice | ||
| 653 | |a Roads & highways | ||
| 653 | |a Suppliers | ||
| 653 | |a Machine learning | ||
| 653 | |a Technology adoption | ||
| 653 | |a Sustainable development | ||
| 653 | |a Supply chains | ||
| 653 | |a Legacy systems | ||
| 700 | 1 | |a Chisungo, Chisungo |u Department of Supply Chain Management, Chinhoyi University of Technology, Zimbabwe | |
| 700 | 1 | |a Manyanga, Wilbert |u Workwell Research Unit, Department of Management Sciences, North-West University, South Africa | |
| 700 | 1 | |a Kanyepe, James |u Department of Management, University of Botswana, Botswana | |
| 700 | 1 | |a Chikwere, David |u Department of Supply Chain Management, Leeds Beckett University, United Kingdom | |
| 700 | 1 | |a Bhebhe, Thomas | |
| 773 | 0 | |t International Journal of Research in Business and Social Science |g vol. 14, no. 7 (2025), p. 78-93 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3285779482/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3285779482/fulltext/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3285779482/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |