1. Introduction

In today’s fast-paced and tech-driven world, digital transformation has become a vital strategy for organisations that want to stay ahead of the competition. It’s not just about adopting new technologies; it’s about weaving digital capabilities into the very fabric of organisational processes, decision-making, business models, and how value is created. Recent research shows that embracing digital transformation can significantly boost organisational performance by enhancing IT capabilities, fostering a digital mindset, driving innovation, and increasing adaptability (Barba-Sánchez et al., 2024; Froehlich et al., 2024). As a result, many see digital transformation as a key organisational capability that can lead to improved productivity, operational efficiency, innovation, and ultimately, business growth.

This need for digital transformation is especially critical in the logistics and supply chain sector. Logistics companies navigate complex and fiercely competitive landscapes, where timely deliveries, shifting customer expectations, transportation hurdles, rising operational costs, and the demand for precise, real-time information are the norm. To thrive, these firms must constantly refine their ability to coordinate resources, oversee operations, manage inventory, track shipments, and swiftly adapt to evolving customer and market needs. Digital technologies like business intelligence, automation, artificial intelligence, cloud computing, and real-time tracking systems offer logistics companies the chance to rethink their processes and boost operational performance. Recent studies back this up, showing that digital transformation can enhance logistics performance by improving process integration, information accessibility, automation, and operational coordination (Lam et al., 2024; Mvubu & Naude, 2024).

One key aspect of digital transformation is business intelligence. This refers to how organisations gather, analyse, interpret, and use data to make informed decisions. In the realm of logistics and supply chain management, business intelligence plays a crucial role in areas like demand forecasting, route optimisation, inventory management, fleet monitoring, customer analysis, resource allocation, and performance evaluation. By turning vast amounts of operational data into actionable insights, business intelligence helps managers spot inefficiencies, anticipate changes, allocate resources wisely, and boost overall productivity. Recent studies highlight that having strong data-driven capabilities is becoming increasingly vital for organisations aiming to enhance their ability to detect environmental shifts and make timely strategic choices (Sagala & Őri, 2024). However, there’s still a lack of empirical evidence on how much business intelligence actually boosts productivity in logistics companies within developing economies.

Another major facet of digital transformation is automation. Logistics operations often involve a lot of repetitive tasks, such as order processing, documentation, inventory tracking, shipment monitoring, data entry, and warehouse management. Relying on manual processes can lead to longer processing times, higher operating costs, and a greater chance of human error. Automation allows companies to revamp these processes using smart software, robotics, robotic process automation, and integrated digital systems. These technologies can enhance processing speed, accuracy, resource utilisation, and overall operational efficiency. Lam et al. (2024) show that digital transformation and robotic process automation can streamline logistics processes by cutting down on repetitive tasks and boosting the effectiveness of operational activities. However, it remains to be seen whether automation truly makes a significant difference in operational speed and efficiency for logistics and supply chain companies in Lagos State requires further empirical investigation.

While technology is a crucial piece of the puzzle, it alone doesn’t guarantee a successful digital transformation. The backing of leadership is just as vital, as digital transformation often calls for shifts in organisational structures, employee roles, business processes, resource distribution, and even the culture of the organisation. Leaders play a key role in ensuring resources are available, fostering employee commitment, promoting the adoption of new technologies, and encouraging a culture of experimentation, learning, and innovation. When leaders actively support digital transformation, they can effectively communicate its goals, motivate employees to embrace new tools, and cultivate an environment that nurtures innovation. Recent findings suggest that strong leadership can significantly enhance an organisation’s ability to turn digital transformation capabilities into better strategic and market results (Joshi et al., 2025). Thus, grasping the role of leadership support is crucial for understanding how digital transformation can drive innovation and business growth.

The Dynamic Capabilities View (DCV) offers a solid theoretical framework for unpacking these connections. This theory posits that companies gain a competitive edge by building capabilities that allow them to identify opportunities and threats, seize those opportunities, and reconfigure resources in response to a shifting landscape. In the realm of digital transformation, business intelligence can boost an organisation’s ability to sense changes by delivering timely information and insights; automation can empower the firm to seize opportunities more swiftly and efficiently; and leadership support can aid in organisational reconfiguration by fostering innovation, learning, and technological advancement. Recent research has increasingly utilised the dynamic capabilities perspective to shed light on how organisations cultivate and leverage digital capabilities to enhance their performance (Al-Moaid & Almarhdi, 2024; Froehlich et al., 2024).

Even though there’s a growing interest among scholars in digital transformation, there are still some notable gaps in the existing literature. For starters, many previous studies have looked at digital transformation as a broad technological concept, often overlooking the specific organisational mechanisms that actually drive better business outcomes. While there’s evidence that digitalisation can boost a company’s performance, productivity, agility, and innovation, the unique roles of business intelligence, automation, and leadership support haven’t been fully integrated into a cohesive empirical framework. Additionally, a lot of the current research comes from developed countries and tech-savvy business environments, which might not translate well to developing economies. In places like Nigeria, factors such as technological infrastructure, digital skills, investment capacity, organisational readiness, and institutional conditions can significantly influence the results of digital transformation.

This contextual gap is especially important when we consider Nigeria and Lagos State. Lagos stands out as one of Nigeria’s key commercial and economic hubs, bustling with logistics, transportation, manufacturing, warehousing, distribution, e-commerce, and supply chain activities. The growing complexity of these operations puts a lot of pressure on logistics companies to enhance productivity, speed, efficiency, and innovation. However, logistics firms in Lagos face challenges like traffic congestion, infrastructure limitations, rising operating costs, information management issues, and ever-evolving customer expectations. This is where digital transformation can really shine, offering logistics companies a vital chance to boost their competitive edge and achieve sustainable growth.

While embracing digital technologies is a step in the right direction, it doesn’t always guarantee better outcomes for organisations. Logistics companies might pour resources into digital systems but still face hurdles like productivity issues, inefficient processes, delays, a lack of innovation, and sluggish growth. This often happens because simply adopting technology isn’t enough; it needs to be paired with the right organisational capabilities and strong leadership support. This raises a crucial question: how do different aspects of digital transformation actually impact organisational success in Lagos State’s logistics sector? There’s a noticeable lack of empirical evidence on whether business intelligence boosts productivity, if automation speeds up operations and enhances efficiency, and whether leadership backing fosters innovation. Filling these gaps is vital for both theoretical understanding and practical application. From a theoretical standpoint, this study builds on the Dynamic Capabilities View by exploring how specific technological and organisational capabilities lead to improvements in productivity, operational efficiency, and innovation in the logistics field. On the empirical side, it provides insights from a developing African economy that hasn’t received much attention in digital transformation studies. In practice, the results can help logistics managers pinpoint the digital capabilities and leadership conditions necessary to maximise the value of their digital transformation investments.

Therefore, this study examines Digital Transformation and Business Growth in Logistics Companies: An Empirical Assessment, with a focus on business intelligence, automation, and leadership support. It looks at how business intelligence can enhance productivity, evaluates the role of automation in boosting operational speed and efficiency, and assesses how leadership support can drive innovation in selected logistics and supply chain companies in Lagos State. By weaving these elements together within the Dynamic Capabilities framework, the study aims to provide a comprehensive understanding of the landscape of how digital transformation can contribute to improved organisational outcomes and business growth in Nigeria’s logistics and supply chain sector.

1.1. Research Hypotheses

• H1: Business intelligence does not significantly impact business growth (specifically in terms of productivity improvement, operational speed and efficiency, and innovation) within selected logistics and supply chain companies in Lagos State.

• H2: Automation does not have a significant influence on business growth (including productivity improvement, operational speed, efficiency, and innovation) in selected logistics and supply chain companies in Lagos State.

• H3: Leadership support does not significantly affect business growth (particularly regarding productivity improvement, operational speed, efficiency, and innovation) in selected logistics and supply chain companies in Lagos State.

2. Literature Review

2.1. Digital Transformation

Digital transformation has become a key strategic capability for companies looking to revamp their processes, harness digital technologies, and boost overall performance. Research shows that this transformation can significantly enhance a firm’s performance by fostering a digital mindset and maximising IT capabilities (Barba-Sánchez et al., 2024). In a similar vein, Rana and Daultani (2025) discovered that digital transformation in supply chains leads to better performance by bolstering organisational capabilities and responsiveness. More recent findings suggest that digital transformation can drive business growth by redefining how value is created, streamlining operational processes, and spurring innovation (Xing et al., 2025). However, the success of digital transformation hinges on specific technological and organisational capabilities, not just the mere adoption of technology. This study breaks down digital transformation into three key areas: business intelligence, automation, and leadership support, to highlight their unique roles in fostering growth within logistics companies.

2.2. Business Intelligence

Business intelligence equips firms with the tools to turn organisational data into actionable insights that aid in planning, decision-making, and enhancing processes. Recent studies have shown that business intelligence can significantly boost internal process efficiency, leading to improved firm performance, especially in times of competitive uncertainty (Han et al., 2024). In the logistics sector, business intelligence plays a crucial role in areas like demand forecasting, resource allocation, route planning, inventory tracking, and performance evaluation, all of which contribute to increased productivity. Having access to timely and accurate information helps reduce delays in decision-making and allows managers to pinpoint operational inefficiencies. While existing research provides evidence of positive performance implications, limited empirical attention has been devoted to the productivity effects of business intelligence within Nigerian logistics firms. This contextual limitation motivates the present investigation. Hence, the study hypothesises that:

H1: Business intelligence plays a crucial role in driving growth for select logistics and supply chain companies in Lagos State.

2.3. Automation

In today’s fast-paced world, automation has emerged as a key player in enhancing the speed, accuracy, and efficiency of logistics operations. Recent studies on AI-driven warehouse order picking have shown that automated systems can significantly cut down on travel distances and processing times, all while boosting order-picking efficiency (Rad et al., 2025). Likewise, research into the digital transformation of logistics highlights that automation helps to eliminate repetitive tasks and improve overall operational effectiveness (Lam et al., 2024; Adewoye, Salau, & Adesokan, 2023). This means that logistics companies can process orders more quickly, reduce errors, make better use of their resources, and respond more effectively to customer needs. However, the impact of automation can differ based on factors like technological readiness, how well processes are integrated, and the capabilities of the organisation. Additionally, there’s still a lack of evidence from the Nigerian logistics scene. Therefore, this study aims to explore whether automation has a significant impact on operational speed and efficiency in logistics and supply chain companies in Lagos State, which leads us to H2.

H2: Automation has a significant impact on business growth for logistics and supply chain companies in Lagos State.

2.4. Leadership Support

The role of leadership support is increasingly acknowledged as a vital factor in driving digital transformation and fostering innovation within organisations. Leaders play a pivotal role in resource allocation, boosting employee commitment, promoting organisational learning, encouraging experimentation, and facilitating the acceptance of technological changes. Recent empirical research has found that support from leaders has a strong direct effect on open innovation, with factors like organisational culture, intellectual property, and collaboration further enhancing this connection (Wiroonrath et al., 2024). In a similar vein, Guo and Zhan (2025) highlight how crucial digital transformation and human–robot interaction are for boosting efficiency and growth in supply chain management. However, there’s still a lack of empirical evidence that ties together productivity, operational efficiency, and innovation as key factors of business growth, especially within the Nigerian logistics sector. This study aims to explore whether elements like business intelligence, automation, and leadership support play a role in enhancing these aspects of business growth.

3. Theoretical Review

The Dynamic Capabilities View stands out as the primary theoretical framework, while the Resource-Based View and Technology Acceptance Model act as supportive theories. This structured approach helps clarify how digital capabilities and the adoption of technology can lead to improvements in productivity, operational speed and efficiency, innovation, and ultimately, business growth in logistics firms.

3.1. Dynamic Capabilities View (DCV)

The Dynamic Capabilities View sheds light on how companies can develop, integrate, and reconfigure their resources and capabilities to adapt to fast-changing environments (Teece, 2007; Nadkarni & Prügl, 2020). This theory focuses on three key capabilities: sensing, seizing, and reconfiguring. In the context of this study, business intelligence embodies the sensing capability, as it allows logistics companies to pinpoint market and operational opportunities through data-driven insights. Automation represents the seizing capability, enabling firms to transform those opportunities into quicker and more efficient processes. Meanwhile, leadership support plays a vital role in reconfiguration by fostering organisational change, learning, resource mobilisation, and innovation. Recent research underscores the importance of dynamic capabilities in relation to digital transformation and organisational performance (Al-Moaid & Almarhdi, 2024; Froehlich et al., 2024). Thus, DCV serves as the core theoretical basis for understanding how digital transformation can drive business growth.

3.2. The Resource-Based View (RBV)

The Resource-Based View (RBV) suggests that companies can gain a competitive edge by having and effectively using resources and capabilities that are valuable, rare, hard to replicate, and irreplaceable (Barney, 1991; Kraus et al., 2021). When it comes to digital transformation, things like business intelligence systems, automation technologies, digital know-how, and strong leadership can serve as key organisational resources that boost productivity, efficiency, and innovation. This theory supports the idea that variations in a company’s digital resources and capabilities can lead to different business outcomes. However, RBV tends to be somewhat static and doesn’t fully explain how companies continuously adapt their resources in response to technological changes. As a result, while RBV offers valuable insights, it serves more as a supporting framework rather than the main theoretical foundation for this study.

3.3. The Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM) focuses on how individuals accept and use technology, primarily through the lenses of perceived usefulness and perceived ease of use (Davis, 1989). This model is particularly relevant to our study because the successful rollout of business intelligence and automation hinges on employees’ readiness to embrace and utilise digital technologies. TAM can shed light on the behavioural aspects of adopting digital technology within logistics organisations. However, it mainly zeroes in on individual acceptance of technology rather than broader organisational outcomes like productivity, operational efficiency, innovation, and business growth. Therefore, while TAM offers a useful perspective on technology adoption, it’s not as well-suited as the Dynamic Capabilities View (DCV) for exploring the wider organisational relationships that this study aims to investigate.

4. Methodology

This study utilised a descriptive survey research design to explore how digital transformation impacts business growth at Transport Services Limited (TSL), a logistics and supply chain company based in Lagos State, Nigeria. This approach was chosen because it allowed for the organised collection of quantitative data from employees about their views and experiences with digital transformation and its effects on the company’s growth. The study focused on full-time and contract employees who are directly involved in TSL’s daily operations. These individuals were ideal respondents due to their firsthand experience with the organization’s digital processes and business activities. According to official staff records from TSL’s Human Resources Department, the study population consisted of 357 employees. By applying the Yamane (1967) sample size formula with a 5% margin of error, a sample of 189 respondents was calculated, and 146 completed questionnaires were returned, resulting in a response rate of 77.2%.

Data collection was carried out using a structured questionnaire that was crafted based on established measures found in relevant literature. To ensure both content and face validity, the instrument was reviewed by experts in business administration and logistics management, who evaluated the clarity, relevance, adequacy, and consistency of the measurement items in relation to the study’s objectives. A pilot test was then conducted with respondents who shared similar characteristics with the study population. Reliability was assessed using Cronbach’s alpha, with a benchmark of 0.70 set as the standard for acceptable internal consistency. Throughout the study, ethical principles were rigorously upheld. Participants were informed about the study’s purpose and took part voluntarily. Their confidentiality and anonymity were guaranteed, and they were assured that the information provided would be used solely for academic purposes. No one was forced to participate, and informed consent was obtained from all respondents before questionnaire administration.

5. Presentation of Data Analysis

5.1. Test of Research Hypotheses

Table 1. Multiple Regression Model Summary

Predictors: Business Intelligence, Automation, Leadership Support
Dependent Variable: Business Growth
$N=146$
Source: Researcher’s Field Survey, 2026

The model summary reveals a strong positive connection between the three predictors and business growth, highlighted by an $R$ value of 0.784. With an $R^2$ of 0.615, it shows that business intelligence, automation, and leadership support together account for about 61.5% of the variations in business growth among the chosen logistics and supply chain companies. The remaining 38.5% is attributed to other factors not captured in the model. The adjusted $R^2$ of 0.607 further confirms that the model maintains significant explanatory power even after considering the number of predictors and the sample size.

Table 2. ANOVA for Multiple Regression

Dependent Variable: Business Growth
Source: Researcher’s Field Survey, 2026

The ANOVA results indicate that the overall regression model is statistically significant, with $F(3,142)=75.56$ and . This suggests that when business intelligence, automation, and leadership support are analysed together, they significantly predict business growth. Thus, the findings provide strong evidence that these three predictors play a crucial role in explaining the variations in business growth among the selected logistics and supply chain companies in Lagos State.

Table 3. Multiple Regression Coefficients

Dependent Variable: Business Growth
Source: Researcher’s Field Survey, 2026

Looking at the coefficient results, business intelligence shows a positive and statistically significant impact on business growth ($\beta =0.301$, $t=4.028$, ), supporting H01. This implies that a greater emphasis on business intelligence helps logistics and supply chain companies make better-informed decisions, spot opportunities, and ultimately boost their growth.

Automation also demonstrates a positive and statistically significant effect on business growth ($\beta =0.257$, $t=3.544$, $p=0.001$), which supports H02. This finding indicates that increased automation aids business growth by enhancing process efficiency, minimising operational delays, and improving service delivery.

Likewise, leadership support positively and significantly influences business growth ($\beta =0.216$, $t=3.046$, $p=0.003$), thus supporting H03. This finding suggests that management commitment and leadership support for technological and organisational initiatives play an important role in facilitating business growth.

To wrap things up, when we look at the three predictors, it’s clear that business intelligence stands out with the strongest impact on business growth ($\beta =0.301$). Following closely are automation ($\beta =0.257$) and leadership support ($\beta =0.216$). The results from the multiple regression analysis provide solid evidence that business intelligence, automation, and leadership support are all significant positive factors driving business growth in selected logistics and supply chain companies in Lagos State. Impressively, the model accounts for 61.5% of the variation in business growth, with each predictor making a statistically significant contribution. This means that H01, H02, and H03 are all validated.

5.2. Discussion of Findings

5.2.1. Business Intelligence and Business Growth

The study found that business intelligence plays a crucial and statistically significant role in fostering business growth among the selected logistics and supply chain companies in Lagos State ($\beta =0.301$, $t=4.028$, ). This suggests that organisations that are adept at gathering, analysing, and leveraging business information are in a better position to spot market opportunities, enhance strategic decision-making, meet customer needs, and ultimately drive growth. This insight is especially pertinent for logistics and supply chain firms, where vast amounts of operational, customer, inventory, transportation, and market data need to be transformed into actionable insights.

This finding aligns with the Dynamic Capabilities Theory, which posits that companies can achieve sustained performance by honing their abilities to identify opportunities and threats, capitalise on them, and adapt their resources in response to changing conditions. Business intelligence plays a key role in this process by turning organisational data into timely strategic insights. Recent empirical studies also support the idea that strong business intelligence capabilities can boost organisational performance through improved internal process efficiency. Han et al. (2024) discovered that business intelligence capabilities play a significant role in boosting firm performance, especially by enhancing internal processes.

This finding is in line with recent studies showing that digital capabilities can greatly improve business performance. For instance, Freitas Junior et al. (2024) highlighted that digital capabilities significantly impact business success, with organisational responsiveness being a key factor in how these capabilities generate value. Thus, this current finding adds to the existing literature by illustrating that business intelligence is more than just a tool for managing information; it’s a strategic asset that can drive business growth, particularly in Nigeria’s logistics and supply chain sector.

5.2.2. Automation and Business Growth

The results indicated that automation has a positive and statistically significant effect on business growth ($\beta =0.257$, $t=3.544$, $p=0.001$). This implies that embracing automated technologies can foster business growth by enhancing operational consistency, minimising processing delays, optimising resource use, and enabling quicker service delivery. In the realm of logistics and supply chain operations, automation can streamline the coordination of transportation, inventory management, order processing, tracking, and other routine tasks.

This finding aligns with the Dynamic Capabilities Theory, as automation allows companies to adapt their existing operational resources and processes in response to evolving market and technological landscapes. Instead of seeing technology as a standalone resource, the dynamic capabilities perspective posits that competitive advantages emerge when companies effectively integrate and continuously redeploy their technological resources within their operations. Recent studies back this view; for example, Chen et al. (2023) and Salau, Tella, Osawe, & Oladele (2025) found that digital transformation positively impacts firms’ operational efficiency, showcasing how technological advancements can lead to significant improvements. Similarly, recent logistics research indicates that digital transformation and logistics capabilities can contribute substantially to business performance, particularly when firms develop the organisational capabilities required to exploit digital technologies.

The current findings indicate that automation plays a crucial role in driving business growth, not just because companies are adopting new technologies, but because these automated processes allow logistics firms to rethink their operations, enhance their responsiveness, and make better use of their resources. This lends strong support to the idea that technological change truly becomes beneficial when it’s integrated into the organization’s capabilities.

5.2.3. Leadership Support and Business Growth

The study revealed a positive and statistically significant effect ($\beta =0.216$, $t=3.046$, $p=0.003$). While leadership support had the smallest standardised coefficient among the three predictors, its significant role highlights that simply investing in technology isn’t enough to sustain digital transformation. Factors like management commitment, resource allocation, strategic direction, employee motivation, and backing for tech initiatives are essential for turning digital capabilities into tangible business results.

This aligns well with Dynamic Capabilities Theory, especially regarding the processes of seizing opportunities and transforming organisational resources. Leadership is key in determining whether an organisation sees the strategic importance of digital technologies, allocates resources for their implementation, and reorganises processes accordingly. In this way, leadership support acts as a vital enabler that helps convert technological capabilities into real business value.

Recent studies back up this perspective. Research on digital transformation shows that organisational capital and governance conditions significantly affect how much value digital transformation can generate. Specifically, organisational capital and the quality of information have been found to influence the link between digital transformation and corporate value. Additionally, studies involving supply chain managers indicate that digital transformation is interconnected and that digital sensing, seizing, and transforming capabilities are particularly important in developing digital maturity.

The finding therefore reinforces the view that leadership support is an important complementary capability. Managers in logistics and supply chain organisations must provide strategic direction and organisational support if digital investments are to translate into sustainable business growth.

6. Conclusion and Recommendations

6.1. Conclusion

The study wraps up by highlighting how digital transformation capabilities are crucial for boosting business growth in selected logistics and supply chain companies in Lagos State. The multiple regression analysis revealed that factors like business intelligence, automation, and leadership support together account for a significant share of the variations in business growth. Among these, business intelligence stood out as the top predictor, showing that effectively leveraging data-driven insights can lead to better decision-making, spotting new opportunities, and being responsive to market changes. Automation also played a key role, indicating that tech-driven processes can enhance operational efficiency and foster organisational growth. Additionally, leadership support proved to be vital, underscoring the need for strong managerial commitment to drive digital transformation efforts. In essence, the findings suggest that simply investing in technology isn’t enough for sustainable business growth; it’s the combination of organisational capabilities and supportive leadership that truly makes a difference. Therefore, logistics and supply chain companies can achieve better growth results by weaving business intelligence, automation, and leadership support into their strategic and operational frameworks.

6.2. Recommendations

1. Business Intelligence: Logistics and supply chain companies should bolster their business intelligence capabilities by investing in the right data analytics tools and enhancing their employees’ analytical skills. This will empower managers to gain timely insights for strategic decision-making, market evaluations, and spotting growth opportunities.

2. Automation: Companies should look to broaden the automation of routine operational and administrative tasks. Special focus should be placed on processes like inventory management, order processing, transportation coordination, tracking, and customer service. This approach can help minimise inefficiencies, improve service delivery, streamline operations and support business growth.

3. Leadership Support: Management must show a stronger commitment to digital transformation. This means allocating sufficient financial resources, offering training opportunities, building the right technological infrastructure, and providing organisational support. Leaders should also clearly communicate the goals of digital transformation and actively encourage employees to get involved in technology-related initiatives.

6.3. Contributions to Knowledge

This study adds to our understanding by presenting empirical evidence on how business intelligence, automation, and leadership support work together to drive business growth in the logistics and supply chain sector in Lagos State. It builds on existing research in digital transformation by combining technological capabilities with leadership support into one comprehensive model. The findings highlight the significance of these three factors, revealing that business intelligence has the most substantial impact on business growth, followed by automation and leadership support. This study offers a tailored framework for logistics and supply chain organisations to effectively leverage digital capabilities for better business outcomes. Additionally, it provides valuable insights for managers and policymakers aiming to enhance digital transformation and competitiveness in Nigeria’s logistics and supply chain industry.