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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.
ARTICLE INFO
Article history:
Received 03 July 2025
Received in rev. form 12 Sept 2025
Accepted 05 October 2025
Keywords:
Artificial Intelligence, Road Haulage Companies, Operational Performance, Driver Training
JEL Classification:
L91, O33
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.
© 2025 by the authors. Licensee SSBFNET, Istanbul, Turkey. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Introduction
Artificial intelligence (AI) has revolutionized last-mile delivery, streamlined supply chain operations, improved customer experience, optimized routes, enabled effective inventory management, facilitated predictive maintenance, and empowered data-driven decision-making (Xu & Landicho, 2024). The majority of early adopters (63%) of artificial intelligence (AI) claimed increases in income, and approximately 44% indicated considerable cost savings. In Europe, artificial intelligence (AI) is transforming the transportation industry. Several transportation-related industries have already engaged their services to facilitate autonomous vehicles, such as cars, trains, ships, and airplanes, to improve traffic patterns (Zhang, Li, & Liu, 2022). AI also contributes to the safety, cleanliness, intelligence, and efficiency of all transportation modes. For example, autonomous transportation driven by artificial intelligence assists with lowering logistics costs (Hao, 2024).
Technology has a large effect on how businesses run these days, although adoption may vary slightly across industries. The use of artificial intelligence (AI) in the logistics and supply chain management industry has significantly improved organizations (Abusaq, 2023). Artificial intelligence is no longer a pipe dream in the field of supply chain administration. Numerous businesses are now aware of the benefits of using artificial intelligence (AI) to digitally mirror their operational processes (Ahmad, 2022). These include improvements in inventory management, high-quality assurance, warehouse and back-office logistics, and relationship management with suppliers (Dong et al., 2023). Artificial intelligence is turning into a fulcrum that holds all the management operations together, from the acquisition of raw materials to the oversight of dependable suppliers to mechanize various warehouse methods for maximizing delivery schedules and routes for shipping (Ziakkas & Plioutsias, 2024).
Each supply chain connection must function effectively to increase a company's profitability and guarantee competitive advantage. Global firms such as Amazon and the Mediterranean Shipping Company (MSC) have incorporated artificial intelligence, such as machine learning (ML) and expert systems (ES), to increase their productivity. Artificial intelligence (AI) assists firms by streamlining their shipping procedures, including order processing, logistics, and inventory control. AI has also been employed in the analysis of data concerning shipping routes, cargo volume, and delivery timetables to maximize the supply chain's efficiency and decrease delays while increasing efficiency (Xie & Fengquan, 2024). Artificial intelligence (AI) applications in Zimbabwe are hindered by a combination of human and technological issues, including human perception and obsolete (legacy) technologies (Kanyepe & Kasambuwa, 2023).
Zimbabwean firms that employ outdated systems without artificial intelligence capabilities encounter problems, including inefficient inventory management, high operating costs, poor forecasting, and expensive warehouse expenses. There has been persistent use of legal systems in Zimbabwe. A legacy system is antiquated hardware or software that is still in use but cannot communicate with more modern systems because of its outdated technology. Most Zimbabwean firms employ a frankensteining ideology on their digital systems to address the negative effects associated with the use of legacy systems (Kanyepe & Kasambuwa, 2023). Most African businesses attempt to cut expenses by integrating different technologies at minimal cost (Gupta, 2022).
According to the survey, nearly 60% of SMEs resort to "frankensteining" their systems to avoid incurring the astronomically high implementation costs involved in building a custom system that would have satisfied their particular requirements. Frankenstein is the practice of using several software packages simultaneously to perform a single task. Most firms considered it too expensive to implement the latest digital management systems that have artificial intelligence capabilities; hence, the adoption of the frankensteining ideology. An example of this would be to keep sales data in an ERP system while generating Excel reports. This is one of the many reasons for the disarray in business supply chains' operational performance. Operational performance has changed significantly in corporate activities that employ legacy systems in their networks and those with artificial intelligence at the core.
Most business owners in Zimbabwe, especially SMEs, fall under the laggard framework in regard to technology adoption. The use of AI to enhance operational performance in a range of industries, including logistics and transportation, has been studied extensively. Previous research has frequently offered broad findings that are applicable to a range of contexts. Although specialized AI technologies such as machine learning and expert systems have remained the focus of some studies, little is understood about their usefulness and practical application in the haulage sector, particularly in Zimbabwe. Little research has been conducted on how haulage companies might use AI to improve their operational performance in the real world, especially in underdeveloped nations such as Zimbabwe. There is a deficiency of studies that concentrate only on the haulage industry in Zimbabwe, considering its distinct obstacles, regulatory framework, infrastructure limitations, and market dynamics. The actual application of AI technology, such as expert systems and machine learning algorithms, within Zimbabwean haulage companies requires further investigation. This involves assessing the efficacy, scalability, and degree of flexibility in regional circumstances. An empirical investigation is needed to analyse how the implementation of artificial intelligence (AI) affects key execution indicators (KPIs) in Zimbabwean haulage companies, such as cost savings, increased productivity, customer happiness, and environmental sustainability. Investigating in-depth case studies of haulage companies in Zimbabwe that have adopted AI technology can yield important information about the best practices, obstacles faced, and lessons discovered.
This study intends to assess the potential of artificial intelligence in optimizing the performance of operations of haulage businesses in Zimbabwe while considering both human and technological aspects. Companies in the haulage industry should ideally be able to achieve their goals while spending the least amount of money. Every business must achieve operational excellence to succeed in today's competitive globalized market. Tahir et al. (2024) reported that companies that embrace artificial intelligence can improve their operational efficiency. Nonetheless, most haulage companies in Zimbabwe do not operate at their best and experience a situation of poor operational performance.
Ying and Jin (2024) identify human and technological variables as the primary causes of performance disarray. According to a Vanson Bourne survey, 68% of the results indicated that outdated or legacy systems were the major obstacle to effective operational performance, and 45% declared that these systems prohibited their companies from achieving their goals and providing the services they needed. Adding a laggard mentality to the fray, haulage firms are further spiralling down the abyss, which is called poor operational performance. Since artificial intelligence (AI) has permeated many industries in Zimbabwe, organizations in the haulage sector have incorporated artificial intelligence (AI), or at least some AI, to enhance their operating procedures and output (Mataruka et al., 2023). Currently, no information is available regarding whether artificial intelligence (AI) has helped haulage companies operate efficiently. The objective of this study is to examine the potential of AI to increase the operational performance of haulage enterprises in Zimbabwe given the existing knowledge gap. This study addressed the following research questions:
i. What is the effect of dynamic routing on operational performance in the road haulage industry?
ii. What is the effect of expert systems on organisational performance in the road haulage industry?
iii. What is the effect of predictive maintenance on organisational performance in the road haulage industry?
iv. What is the influence of machine learning on organisational performance in the road haulage industry?
v. How does driver training moderate the effect of artificial intelligence on organisational performance in the road haulage industry?
Literature Review
This section provides the major building blocks of this study.
Artificial Intelligence
Ying and Jin (2024) defined it as the mechanical imitation of human cognitive processes, specifically computer systems. Ongati and Omollo (2024) also defined artificial intelligence as the ability of a machine or an automated device controlled by a system to complete tasks typically completed by sentient entities. Song and Minku (2023) referred to this as the application's ability to mimic human intelligence. Artificial intelligence is a combination of different building blocks, including expert systems (ESs) and machine-learning (ML) technology. The operating efficiency of the haulage industry has been greatly improved by AI technology.
The implementation of artificial intelligence (AI) technology has played a crucial role in certain businesses because it is practically necessary. According to Hernel and Johneros (2024), AI is anticipated to be a crucial and significant industrial application of the twenty-first century. Artificial intelligence (AI) integration in an ever-evolving supply chain management (SCM) ecosystem has the potential to drastically alter operational paradigms. This transformation affects practices both upstream and downstream, such as client interaction, distributing tactics, and post purchase solutions, as well as manufacturing intricacies, supplier relations, and raw material acquisition. The practical implementation of many theoretical principles has only recently been made possible by the availability of sophisticated processors, fast networks, and large data storage capacities. Process digitization is now essential for efficient logistical operations owing to shifting customer needs (Hernel & Johneros 2024).
Computer cognitive ability or artificial intelligence-driven management of supply chains may fill in the blanks and expedite error-free logistics management by sourcing the initial supplies to provide the end result. Artificial intelligence (AI) is used in information management systems from surveillance footage and sensors, and data are then analysed via advanced-driver-assistance systems (ADASs). Integrated brakes, autonomous driving, and lane-keeping interventions are examples of ADAS capabilities. By warning drivers of potential hazards or even taking control of the vehicle in emergency situations, these systems aid in the prevention of accidents. The use of ADAS has resulted in a 22% decrease in accidents involving haulage vehicles and harnessing the full benefits of AI in the haulage industry (Khanafer, 2024).
Operational Performance
Chen and Tajdini (2024) indicated that multiple definitions characterize operational performance. Jing and Zhang (2024) claimed that operational performance gauges how successfully a business performs its primary business functions. According to Hejazi et al. (2022), operational efficiency is the ratio of an organization's output to its input for business operations. This is a measurement of resource allocation. Operational performance, as described by Jing and Zhang (2024), is the quantifiable portion of performance pertaining to the results of the operations department, including processes, quality, inventories, and dependability. According to Chen and Tajdini (2024), operational performance involves monitoring a project's progress and capacity to meet its financial objectives. According to Khanafer (2024), effective process planning and control yield good operational performance. Operational performance is the capacity of different company units to work together more productively and generate greater production (Chen & Tajdini, 2024).
Operational performance in the haulage industry is critical to ensuring efficiency, cost effectiveness, and customer satisfaction. Operational excellence is now essential to every company's success in today's globalized and cutthroat industry. Achieving the maximum degree of efficacy and efficiency in the management of systems, processes, and resources to supply goods and services provided to customers is known as operational excellence. It involves streamlining every facet of an organization's activities to reduce costs, increase productivity, and improve quality. Building a lean, agile, and flexible business that can react rapidly to shifting consumer needs, market dynamics, and industry trends is the goal of operational excellence. Rather than a one-time event, perfection is the goal on an ongoing path. Operational excellence within the operations of the supply chain refers to attaining a maximum degree of efficacy and efficiency in the management of systems, processes, and resources to provide goods and services to clients at the most affordable price while satisfying their needs for quality and delivery.
According to Kinyua, Arani, & Egessa (2024), supply chain efficiency is the process of maximizing all tasks and operations along the value chain to provide customers with business value. Supply chain operations must be optimized in today's fast-paced global market if companies want to increase efficiency and obtain competitive edge organizations that can achieve seamless coordination throughout the supply chain by optimizing procedures, integrating state-of-the-art technology, and utilizing data-driven decision-making. According to Khanafer (2024), the haulage industry's operational performance is significantly influenced by the use of sophisticated technologies. Telematics, GPS tracking, and fleet management software, for example, have revolutionized the way haulage operations are managed (Dey, 2021).
Dynamic Routing
Dynamic routing is a networking technique that enables routers to automatically adjust their paths on the basis of current network conditions (Khadraoui, & Zemmouri, 2021). Unlike static routing, where routes are manually configured and remain fixed, dynamic routing protocols allow routers to share information about network topology changes. This adaptability helps maintain optimal data flow and enhances fault tolerance. Common dynamic routing protocols include the routing information protocol (RIP), open shortest path first (OSPF), and border gateway protocol (BGP). These protocols use algorithms to determine the best paths for data packets, taking into account factors such as bandwidth, network congestion, and link failure. As a result, dynamic routing can efficiently manage large and complex networks, ensuring that data packets reach their destinations quickly and reliably. This capability is essential for modern internet infrastructure and enterprise networks, where uptime and performance are critical for the user experience and operational efficiency (Sokolova, & Kozynets, 2024).
Expert Systems
Expert systems are advanced computer programs designed to emulate the decision-making ability of a human expert in a specific domain (Chen, & Tajdini, 2024). They utilize a knowledge base, which contains domain-specific facts and rules, and an inference engine that processes this information to solve complex problems. Expert systems in the road haulage industry are designed to enhance decision-making and operational efficiency by mimicking the expertise of human professionals. These systems utilize a comprehensive knowledge base that encompasses logistics, route planning, vehicle maintenance, and regulatory compliance. By integrating data from various sources, such as traffic patterns, weather conditions, and vehicle performance, expert systems can provide actionable insights and recommendations (Chen, Esperança, & Wang, 2022). For example, they can optimize delivery routes to minimize fuel consumption and reduce transit times while also suggesting preventive maintenance schedules to avoid breakdowns. Additionally, expert systems can assist in compliance with legal requirements, ensuring that drivers and vehicles meet industry standards. This technology not only improves efficiency but also enhances safety by reducing the risk of human error in decision-making. Expert systems play crucial roles in streamlining operations, reducing costs, and increasing competitiveness in the road haulage industry (Jing, & Zhang, 2024).
Predictive Maintenance
Predictive maintenance in the road haulage industry involves the use of data analysis and monitoring tools to anticipate vehicle maintenance needs before failures occur. By leveraging technologies such as IoT sensors, telematics, and machine learning algorithms, companies can collect real-time data on vehicle performance and conditions (Kinyua, Arani, & Egessa, 2024). These data help identify patterns and predict potential issues, allowing timely interventions. For example, if a sensor detects unusual vibrations in an engine, maintenance can be scheduled before a breakdown happens, minimizing downtime and reducing repair costs. Predictive maintenance enhances fleet efficiency by ensuring that vehicles are in optimal condition, improving safety, and extending asset lifespan (Fatima, et al, 2020). Additionally, it allows logistics companies to optimize their operations, reducing unexpected delays and enhancing service reliability. Predictive maintenance transforms traditional reactive maintenance approaches into proactive strategies, leading to significant cost savings and improved operational performance in the highly competitive road haulage sector (Kong, & Xu, 2024).
Machine Learning
Machine learning in the road haulage industry leverages advanced algorithms and data analytics to optimize various aspects of logistics and transportation (Ali, et al 2021). By analysing vast amounts of data from sources such as GPS tracking, traffic patterns, and vehicle performance, machine learning models can identify trends and make predictions that enhance operational efficiency. For example, these models can optimize route planning by predicting traffic congestion, thereby reducing delivery times and fuel consumption. Additionally, machine learning can improve fleet management by forecasting maintenance needs, helping to prevent breakdowns and extending vehicle lifespans. It can also enhance demand forecasting, allowing companies to adjust their logistics strategies in response to changing market conditions. Furthermore, machine learning applications can support driver behavior analysis, promote safer driving practices and reduce accident rates. The integration of machine learning in the road haulage industry leads to increased efficiency, cost savings, and improved service quality, positioning companies for greater competitiveness (Kong, & Xu, 2024).
Theoretical Framework
Theory underpinning the study
The primary theoretical frameworks for this study are diffusion of innovations theory, resource-based theory and institutional theory. Diffusion of innovations theory introduces a diffusion concept that provides a structure for understanding how new ideas and technology proliferate within a community or organization (Lundblad, 2003). When applying this theory to artificial intelligence (AI), it is necessary to examine the elements that influence the adoption of AI technologies as well as the stages at which they spread throughout various industries, including the haulage sector. The theory alludes that communication channels can increase awareness of AI technologies among potential adopters. They gain knowledge of the benefits and possible uses of AI in their particular setting. Haulage businesses may acquire knowledge regarding autonomous vehicles, predictive maintenance, and AI-driven route optimization (Lundblad, 2003).
The resource-based view (RBV) of the firm was proposed by Wernerfelt (1984), and in this study, an organization is considered a collection of resources and capabilities that can be properly coordinated in a manner that may enhance corporate competitiveness. Organizations may enhance their competitiveness by properly coordinating available corporate resources and making use of modern technology and technical knowhow to equip the organization to take advantage of market conditions (Barney, 1991; Grant, 1991). Proponents of the RBV argue that organisations that have minimal resources may only break even, whereas the possession of strategic resources enhances an organization's chances of standing above the rest in the industry.
Institutional theory examines how organizations and their behaviors are influenced by norms, rules and institutional structures. This theory was used to examine how institutional dynamics in the road haulage sector influence the adoption of AI technologies and how these institutional dynamics interact with AI technologies to influence road haulage sector operations. This theory was used to investigate the effect of institutional dynamics on the development of AI technologies in road haulage companies. It also provides insights into how AI technologies can shape a safety culture through communication, training and monitoring mechanisms.
Empirical Review and Hypothesis Development
Research by Chingwaro et al. (2024) and Richey et al. (2023) attests that companies that embrace artificial intelligence are able to improve the efficiency of their operations. Nonetheless, most haulage companies in Zimbabwe are not operating at their best and are experiencing poor operational performance. The literature cited by Maisiri (2024) identifies human and technological variables as the primary causes of performance disarray. This hypothesis posits that the overall application of artificial intelligence (AI) technologies has a major effect on hauling businesses' operating performance in Zimbabwe. Operational performance in this context includes various metrics, such as delivery efficiency, cost reduction, improved decision-making, customer satisfaction, and overall productivity. Companies within the road haulage industry should ideally be able to achieve their goals while spending the least amount of money possible. On the basis of the above arguments, the following hypothesis is posited:
H1 Establishing the effect of dynamic routing on operational performance
Route optimization is one of the main uses of expert systems in haulage (Shehadeh et al., 2024). To identify the most effective routes, these algorithms examine delivery timetables, weather forecasts, traffic patterns, and road conditions. According to previous studies, optimized routing can save much fuel and shorten delivery times, which can save money and improve customer satisfaction (Shehadeh et al., 2024). Expert systems offer insights into driver behaviour, vehicle performance, and maintenance requirements and are applied in real-time fleet management. Expert systems aid in the scheduling of preventative maintenance, which lowers downtime and increases fleet longevity. Expert systems used in proactive fleet management have been shown to reduce operating costs by as much as 20%. A vehicle's ideal load is ensured by effective load planning, which balances the weight distribution and maximizes the cargo space (Shehadeh et al., 2024). Significant amounts of information can be processed by expert systems to suggest the optimal loading patterns, lowering the chance of overloading and increasing fuel economy. In accordance with research by Shehadeh et al. (2024), the use of expert systems in load planning could result in a 15% increase in load efficiency.
H2 Evaluate the effect of predictive maintenance on organisational performance
Businesses can anticipate mechanical faults before they occur and reduce downtime and maintenance costs by employing ML strategies to handle information from sensors placed in cars (Bhatti, et al. 2024). Predictive maintenance models can reduce vehicle downtime by 20-25% and maintenance expenses by 10%-15% (Theissler, et al., 2021). Furthermore, ML improves load optimization. To maximize space utilization and preserve vehicle stability, algorithms can forecast the best cargo distribution and loading patterns. Theissler et al. (2021) reported that up to 15% greater loading efficiency might be achieved via ML-based load optimization. For example, route optimization algorithms find the most effective routes for delivery trucks on the basis of historical trends and real-time traffic data. Arena et al. (2021) reported that delivery times can be shortened by 20% and that fuel consumption can be reduced by up to 15% with AI-driven route optimization (Arena et al., 2021). Furthermore, AI-powered predictive maintenance has revolutionized the way in which vehicle downtime decreases (Bibri et al., 2024). AI can anticipate mechanical issues before they occur, enabling prompt repair and minimizing unplanned breakdowns by evaluating data from sensors installed in trucks (Kim, et al., 2020). Additionally, data collection on a variety of metrics, including fuel consumption, driving behavior, and engine performance, is facilitated by Internet of Things (IoT) integration devices in automobiles (Sumbal et al., 2024). Predictive maintenance and route optimization depend on these data to increase operational efficiency. Businesses that use fleet management systems enabled by the Internet of Things report 10-15% faster delivery times and 20% less fuel used (Arena et al, 2021).
H3 Establishing the influence of machine learning on organisational performance
A branch of artificial intelligence called machine learning (ML) uses algorithms and models of statistics to analyse and interpret complex data patterns. This hypothesis suggests that the deployment of ML technology within haulage companies can significantly enhance their operational performance. The narrative behind this hypothesis is that by leveraging these capabilities, ML technology can drive substantial improvements in the operational metrics of haulage companies, making operations more efficient and cost-effective. Machine learning (ML), a branch of AI, is revolutionizing various industries, including the haulage sector (Khazaelpour et al., 2024). Several facets of the haulage sector have been optimized through the use of machine learning. To find the most efficient routes for trucks, machine learning algorithms examine enormous volumes of data, such as traffic patterns, weather, and past delivery times. ML-driven route optimization has the potential to drastically reduce fuel usage and speed up deliveries by up to 20%. Developing and putting into practice mechanisms that support these decisions and projections is the fundamental task of machine learning. Other common applications of machine learning include filtering out spam, identifying fraudulent activity, identifying virus threats, optimizing business workflows, and predicting maintenance. Businesses can anticipate mechanical faults before they occur and reduce downtime and maintenance costs by employing ML strategies to handle information from sensors placed in cars. Predictive maintenance models can reduce vehicle downtime by 20-25% and maintenance expenses by 10%-15%. Furthermore, ML improves load optimization. To maximize space utilization and preserve vehicle stability, algorithms can forecast the best cargo distribution and loading patterns (Kim, Lee, & Hwang, 2020).
H4 Determine the effect of expert systems on organisational performance
Saadallah et al. (2024) defined a system of experts as a software application that simulates human or organizational behaviour and decision-making with in-depth competency and understanding in a particular field through the application of artificial intelligence (AI) solutions. An expert system, according to Alshahrani et al. (2024), is a computer program designed to make decisions and handle complicated problems, similar to a human expert. Expert systems are a branch of AI that employs computers to simulate how people make decisions in a specific subject. (Khazaelpour et al., 2024). Expert systems, another branch of AI, are designed to imitate the capacity for making choices of human specialists. This hypothesis suggests that the application of expert systems can favourably influence the operational performance of haulage companies. The narrative behind this hypothesis is that by integrating expert systems, haulage companies can benefit from enhanced decision-making processes, reduced operational errors, and improved overall efficiency. By retrieving the information from its knowledge base, it resolves the trickiest problems, such as an expert (Halfon et al., 2024). The system uses heuristics and facts to make decisions similar to those of a human expert in difficult issue solving. It is thus named because it is capable of solving any complex problem inside a given subject and possesses expert knowledge in that particular topic (Halfon et al., 2024). The knowledge base of an expert system stores the expert's knowledge, which determines the system's performance (Alshahrani et al., 2024). The system performs better when more knowledge is retained in the knowledge base. The introduction of a computerized automated system is needed because of the increasing importance and necessity of quick, timely, and continuous logistical support for business units (Sikka et al., 2024). This system will improve work efficiency and save managers and decision-makers' time across every level of the organization in the strategic and technical departments. In the global supply chain, the haulage industry, which includes the road transportation of goods, is essential.
H5 To test the moderating effect of driver training on the effect of artificial intelligence on organisational performance
Driver training enhanced by artificial intelligence (AI) significantly impacts organizational performance in the road haulage industry (Murtaza, et al, 2024). AI-driven training programs utilize advanced simulations and real-time data analysis to provide personalized learning experiences for drivers. These systems assess individual driving behaviours and identify strengths and areas for improvement, which leads to more effective and targeted training modules (Kheiri, 2024). By incorporating AI, organizations can reduce training time while increasing the retention of critical safety and operational protocols. Additionally, AI can analyse data from vehicle telematics to provide feedback on fuel efficiency, route optimization, and safe driving practices, fostering better decision-making among drivers (Murtaza, et al, 2024). As a result, improved driver performance leads to reduced accidents, lower maintenance costs, and increased customer satisfaction through timely deliveries. AI-driven driver training not only elevates safety standards but also contributes to greater operational efficiency and profitability, positioning companies for success in a competitive industry landscape. In summary, the following hypothesis is proposed:
H1b Driver training moderates the effect of dynamic routing on operational performance
H2b Driver training moderates the effect of predictive maintenance on organisational performance.
H3b Driver training moderates the effect of machine learning on organisational performance.
H4b Driver training moderates the effect of expert systems on organisational performance.
The current study explores the impact of artificial intelligence on the operational performance of the road haulage industry in Zimbabwe. Artificial intelligence (AI), which can be further delineated into key subvariables, namely, dynamic routing (DR), expert systems (ES), predictive maintenance (PM) and machine maintenance (MM). The dependent variable that serves as the focal point of analysis is operational performance. Finally, driver training (DT) moderates the indirect relationship between artificial intelligence (AI) and operating performance. (OC). To explore this relationship comprehensively, the researcher has formulated five distinct research hypotheses that will be thoroughly tested and evaluated. On the basis of these constructs, the study hypothesized relationships and formulated a conceptual framework, as shown in Figure 1.
Research Methodology
This study adopted a cross-sectional research design. A cross-sectional survey was adopted to narrow down the very broad field of this research within the road haulage sector. A cross-sectional survey was also necessary, as it enabled the researcher to gain a deep understanding of the context of the research. The study population was confined to ten prominent and registered road haulage firms in Zimbabwe. A database of all employees from the selected road haulage firms was obtained from the Human Resources Department of the listed firms. According to internal reports of human resources, a total of 113 samples were used as the basis for sample size determination. Since the population was known, Yamane's (1967) simplified formula for calculating sample size was used. A sample size of 88 respondents was used. A total of 88 questionnaires were administered. The questionnaire had closed-and open-ended questions. To ensure that the information acquired was kept confidential, the respondents were informed that the survey would be anonymous, and their agreement was requested prior to participation. We selected respondents via a basic random sampling procedure. To choose responders from each of the selected road haulage businesses, simple random sampling was utilized to increase unpredictability and eliminate bias.
Before questionnaires were distributed, each organization was asked for permission to gather data. The respondents were informed about the goal of the study and invited to participate. If the responder consented to participate, they were given the questionnaire, an envelope to keep it secure, and a letter asking them to complete the questionnaire within a week. A structured questionnaire with Likert-type items ranging from 1 (strongly disagree) to 5 (strongly agree) was developed and implemented. The structured questionnaire items were adapted from similar research to meet the current requirements. The structured questionnaire items and references are shown in Table 1. Among the 88 respondents who consented to participate in the survey, 85 (97%) were returned and useable. The majority of respondents (45.4%) were between the ages of 30 and 49, with men dominating the study (56.8%). As a result, the respondents' profile indicates a varied pool (by gender and age) of participants who supplied balanced opinions for the study.
To collect data, a structured questionnaire with Likert scale items ranging from 1 (strongly disagree) to 5 (strongly agree) was developed and implemented to assess the potential of AI in optimizing the efficiency of operations of road haulage firms in Zimbabwe. The structured questionnaire items were adapted from similar research to meet the current requirements. Prior to sending the survey to the intended audience, the researcher conducted a pilot test with a small sample to identify any ambiguities, errors, or issues with it. Table 2 shows the structure of the questionnaire items and their sources. The researchers used structured questionnaires with closed-ended questions. Participants with prior expertise with artificial intelligence (AI), industry hauling and a significant position inside the companies. The respondents provided a balance between depth and manageability, allowing for a thorough exploration of artificial intelligence's potential in optimizing operational performance within the specific context of Zimbabwean road haulage firms. The study population included employees within the transport sector in Harare, Zimbabwe. Harare was chosen because of its centrality and hosting of many road transport firms in Zimbabwe. The structured questionnaire was distributed electronically and physically to 100 randomly selected employees between August and October 2024. The response rate was 90%. Thus, 88 questionnaires were returned and could be used. The study's sample profile is shown in Table 2 below:
Data analysis and techniques
To ensure the accuracy of the study responses, the gathered data were coded before being entered into the Statistical Package for Social Sciences (SPSS) version 23. Cronbach's alpha (α) was used to determine reliability. Before the hypotheses were tested, the data were validated via exploratory factor analysis (EFA), convergent validity, and discriminant validity. The hypotheses were examined via structural equation modelling, with good validity and normality tests, and the data were analysed via SPSS® version 23 and AMOS® version 23.
Results
Measurement model
The scale validation technique included exploratory factor analysis (EFA), convergent validity, and discriminant validity. This was conducted via SPSS V23 and AMOS V23. Prior to exploratory factor analysis, sample adequacy was assessed via the Kaiser?Meyer- Olkin (KMO) test and Bartlett's test of sphericity. Table 3 displays the results of the Kaiser?Meyer?Olkin (KMO) measure and Bartlett's test of sphericity.
Table 3 shows that the sample satisfied the minimum requirements and was acceptable (Heale and Twycross, 2015). Consequently, exploratory factor analysis was needed. Varimax rotation was used to perform factor analysis. Thus, the total variance explained by the data were 69.988%, and the solution yielded six components (DYR, EXS, PRM, DRT, and OP), with no items being removed due to low or multiple loadings. The study employed convergent and discriminant validity to assess data validity, as recommended by Field (2009). The observed model fit indices are shown in Table 4 below.
The structural model was evaluated on the basis of five criteria: the chi-square (χ2) likelihood ratio statistic, goodness-of-fit index (GFI), normed fit index (NFI), comparative fit index (CFI), and root mean square error of estimate (RMSEA). The model's chi-square test was not statistically significant (χ2 (33, N = 113) = 31.09, p =.147), indicating that it was suitable for the data. Except for one of the indices, the model produced good fit indices. The other fit measures helped to determine the suggested target values. The GFI had a value of .94, indicating a satisfactory match. The NFI was .78, which is lower than the target level of .95. The RMSEA was 0.043, and the CFI was .93. Overall, the fit indices indicate a good match between the model and the data. On the basis of these observations, it was decided to maintain the null hypothesis that the theoretical and observed covariance matrices are equal. Table 5 shows the observed model fit indices.
Table 5 shows that the standardized factor loadings for all the items exceeded the lowest requirement of 0.6. (Bagozzi and Yi, 1988). The critical ratios were also significant (p < 0.001). Furthermore, all AVEs for all buildings provide findings that exceed the minimal requirement of 0.5 established by Fornell and Larcker (1981). As a result, the minimum conditions for convergent validity were fulfilled.
Discriminant Validity
Discriminant validity is defined as the degree to which items within a construct are significantly correlated with other measures of the same variable while being unrelated to other items in another construct (Edward, 2013). The extracted average variance (AVE) was used to test discriminant validity by comparing it to squared inter-construct correlations (SICCs). If the average variance extracted values exceed the squared inter-construct correlations, discriminant validity is acceptable (Henseler et al., 2014). The results in Table 4 reveal that the requirements for discriminant validity were satisfied because they were all greater than their matching squared inter-constructs (Fornell and Larcker, 1981; Segars, 1997).
Structural equation modelling was conducted to test H1-H4, and H1b-H4b. A good fit was also exhibited for the structural model (CMIN/DF=2.814; GFI=.899; AGFI=.901; NFI=.942; TLI=.915; CFI=.921; RMSEA=0.040) (Hair et al., 2010; Hooper et al., 2008).
Hypotheses tests, H1-H2, H3 and H4 were conducted, and the results are shown in Tables 7 and 8 below:
Table 7 shows that H1, H2, H3 and H4 were supported. This implies that dynamic routing (DYR), expert systems (EXS), predictive maintenance (PRM) and machine learning (MCL)→ have a direct influence on organisational performance.
H1b-no: Moderated regression analysis was used to test H1b-H4b. The results are summarized in Table 8.
Moderated regression analysis was used to test H1b-H4b. The results are summarized in Table 8.
Table 8 displays a single sample, demonstrating that driver training is critical in increasing the association between AI and organizational performance, as shown by the values (t=2.211; p=.000). A higher t value and p<0.05 imply a favourable impact on the competitiveness of road haulage transport businesses. The respondents stated that road haulage transport businesses provide extensive driver training.
Conclusions
The results in Table 8 show that the coefficients for the interaction terms (driver training × organisational performance) were insignificant (p>0.000). This suggests that driver training moderates the effect of artificial intelligence on organisational performance. Therefore, H1b-H4b were supported. A sample t test was considered relevant to test the research hypotheses that were put forward at the study inception at a significance level of 95% and a confidence level of 0.05. The results of the moderating analysis indicate that driver training significantly moderates the relationship between driver training and organisational performance. It can be concluded that driver training plays a significant role in mitigating the negative impact of AI on organisational performance.
Five proposed research questions resulting from the study's overall purpose were addressed, and the research project was finished in an effort to confirm or disprove the so-called underlying notions. The primary goal of this study was to examine how artificial intelligence influences the operational effectiveness of enterprises in Zimbabwe's road transport industry. The study's objectives were satisfied after a careful evaluation of the material from eighty-eight (88) participants, resulting in a 100% response rate.
This study aimed to determine the influence of dynamic routing on the operational performance of road haulage firms. The findings of this study indicate that dynamic routing has emerged as an important tool for improving the operational performance of road transport companies in Harare, Zimbabwe. These companies may optimize their delivery routes by using real-time data on traffic conditions, weather, and road closures, resulting in dramatically shorter travel times and lower fuel use. These findings substantiated Kanyepe, & Kasambuwa (2023), who posited that the impact of dynamic routing is a key performance factor for yielding significant gains in an entity. The findings from this study revealed that organizations that implemented this technology reported a 20% drop-in delivery time on average, resulting in higher customer satisfaction and service dependability. Additionally, fuel economy increased by approximately 15%, resulting in significant cost savings over time. This finding aligns with that of Chibaro et al. (2024), who posited that flexibility in changing routes reduces operational disruptions while also allowing businesses to respond quickly to unanticipated issues such as traffic jams or accidents. Furthermore, integrating dynamic routing systems improves resource allocation, allowing businesses to maximize fleet usage. In line with our observed results, prior research, such as Ying, & Jin (2024), has indicated that the efficacy of these AI systems is determined by the existing infrastructure and businesses' openness to adopt technological improvements. Our key finding on this objective revealed that the use of dynamic routing in Harare's road haulage industry is critical for enhancing efficiency, lowering costs, and retaining competitiveness in an increasingly difficult market.
The findings of this study revealed the essence of expert systems on road haulage enterprises' organizational performance by improving decision-making and simplifying operations. These AI-powered systems use a store of information to provide answers to complicated logistical difficulties, allowing managers to make educated decisions faster. The cog of these findings was in tandem with Khanafer et al. (2024), who postulated that expert systems, if effectively utilized, may monitor traffic situations, weather trends, and delivery needs to identify the best routes, dramatically increasing efficiency and lowering fuel costs. They also help with resource allocation by forecasting demand patterns, ensuring that the proper trucks and workers are deployed at the right times. This increases operational agility and improves customer service since businesses can quickly react to changing situations. Furthermore, expert systems enable knowledge exchange and training, allowing less experienced individuals to function at a greater level. According to our findings, past studies, such as Dong, Peng, & Zhang (2023), claimed that the integration of expert systems boosts efficiency while also encouraging a culture of continual improvement, equipping road haulage companies for long-term success in a competitive setting. The findings also claim that predictive maintenance improves organizational performance in road haulage companies by reducing downtime and maximizing asset use. The results of the findings are in accordance with the literature where Ongati, & Omollo (2024) states that firms may use powerful data analytics and machine learning algorithms to monitor vehicle health in real time and detect probable issues before they happen. According to the findings of this study, this proactive strategy enables prompt interventions, minimizing the chance of unanticipated malfunctions, which can disrupt logistics and cause costly delays. Chibaro et al. (2024) also argued that scheduled maintenance, for example, might be planned during off-peak hours, minimizing the impact on operations. Furthermore, predictive maintenance extends the life of vehicles by treating problems before they worsen, decreasing long-term maintenance expenditures. As a result of timely deliveries, the dependability, efficiency, and customer satisfaction of the fleet improve. Furthermore, by cultivating a culture of data-driven decision-making, road haulage companies can constantly improve their maintenance plans, promoting operational excellence and competitiveness in an increasingly demanding market. The argument from Ongati, & Omollo, (2024) supported the claim; therefore, it can be concluded that the study rejected the null hypothesis and accepted the claim that predictive maintenance contributes positively to the enhancement of road haulage firm performance.
The study findings revealed a positive relationship between machine learning (ML) and organisational performance. Machine learning (ML) transforms organizational performance in road haulage companies by increasing efficiency and improving decision-making processes. This finding concurs with the results of Khanafer (2024), who postulated that machine learning algorithms can spot trends and optimize operations by evaluating massive volumes of data from numerous sources, such as traffic patterns, fuel usage, and vehicle performance. For example, these technologies help businesses more precisely forecast delivery timeframes, resulting in improved scheduling and resource utilization. In addition, machine learning aids in route optimization, lowering fuel costs and increasing delivery speed, all of which have a direct influence on customer happiness. Furthermore, predictive analytics may anticipate maintenance requirements, reduce vehicle downtime and increase asset longevity. When road haulage companies use machine learning, they can adapt more quickly to market needs and operational challenges, resulting in enhanced competitiveness. Finally, the findings of this study posit that ML integration not only improves operational efficiency but also develops a culture of continuous development, preparing businesses for long-term success in a changing industrial landscape.
The study findings claim that driver training is an important moderating element in Harare's road haulage enterprises, increasing the influence of artificial intelligence (AI) on organizational performance. While AI may enhance routing and improve fleet management, its efficiency is much greater when drivers are educated to use these technologies effectively. This finding is consistent with the empirical findings of Chibaro et al. (2024), who further asserted that well-trained drivers can comprehend AI insights, make real-time judgments, and overcome obstacles more effectively. This combination reduces operational costs, improves delivery times, and increases road safety. Thus, investing in driver training alongside and conclusively, AI installation is critical for optimum performance in the competitive haulage market.
The study used information from a few chosen road haulage companies, which might not be representative of the whole industry in Zimbabwe. Particularly when it comes to AI adoption and driver training results, some companies might have inconsistent or lacking records. The results are unique to the road haulage business in Zimbabwe and could not be readily transferable to other nations or transportation industries with dissimilar technological, economic, or regulatory frameworks. The variety of AI technologies and their degrees of complexity were not taken into consideration in the study. Businesses may employ various AI techniques (such as telematics, predictive maintenance, and route optimisation), which may have varying effects on operational results. The efficacy of driver training was evaluated using business records and self-reported data, both of which could be biassed or inaccurate. Training sessions were not directly observed in the study, and skill increases were not independently confirmed. Short-term operational performance measures were the focus of the study. Adoption of AI and ongoing driver training's long-term effects were not thoroughly investigated. Although they were not considered, variables including the volatility of fuel prices, the condition of the road infrastructure, and changes in regulations can have a big impact on operational performance.
The findings of this study were limited to only the road transport haulage industry in Harare, Zimbabwe. As such, these findings may not be applicable directly to other organisations in other sectors of the economy. It is therefore recommended that future studies be extended to other areas within wider business sectors, such as manufacturing, the construction industry, the chemical sector, the banking sector, and many other sectors, to determine whether the findings are in line with this study. To evaluate the long-term impacts of AI adoption and ongoing driver training on operational performance, future research should take a longitudinal approach. A focus on research contrasting haulage companies in Zimbabwe with those in other nations or areas may shed further light on the contextual elements affecting AI efficacy. To obtain deeper insights into the difficulties and best practices in using AI and training, future research can also use qualitative techniques like focus groups or interviews with managers, drivers, and legislators. Future research might also examine how infrastructure development, incentives, and government policy facilitate the adoption of AI and efficient driver training in the transportation industry.
The implications of this study are far reaching, particularly for the road transport haulage industry. The findings of this investigation offer a thorough grasp of how dynamic routing (DYR), expert systems (EXS), predictive maintenance (PRM) and machine learning (MCL) have a direct influence on haulage companies' organisational performance in Zimbabwe. The industry's significant ramifications underscore the necessity of deliberate investments, customized implementation strategies, personnel enhancement, and policy assistance. By addressing these areas, Haulage companies can improve their productivity, competitiveness, and operational efficiency, which will open the door for long-term growth and technological advancement. Researchers and policymakers alike are essential in enabling this transition by means of future studies that offer more profound understanding of the successful integration of these technologies and the legislation that enables them. These ramifications identify opportunities for development, funding, and additional research in the operational, strategic, and policy-making domains.
Acknowledgement
All authors have read and agreed to the published version of the manuscript.
Author Contributions: Conceptualization, M.C., C.C. and D.C.; methodology, M.C., D.C. validation, T.B.; formal analysis, M.C., J.K. and T.B.; investigation, D.C.; resources, T.B.; writing-original draft preparation, W.M..; writing-review and editing, J.K., W.M. and M.C.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data are not publicly available due to restrictions.
Conflicts of Interest: The authors declare no conflict of interest.
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