The business landscape is increasingly shifting toward digitalization, focusing on overcoming challenges and complexities while improving accessibility. This transformation integrates automated systems and technology to enhance operational processes and efficiency. Digital technologies enable businesses to manage data more effectively, streamline operations, and reduce human errors, resulting in faster task completion (Acioli et al., 2021).

Industry 4.0 (I4.0) technologies are pivotal in the logistics sector, driving a shift from manual processes to automation and digitalization and they enhance performance, operational efficiency, and the development of innovative business models, products, and services (Atumonye, 2022). In logistics, advanced technologies facilitate the creation of automated systems and remote access controls, improving efficiency in logistics operations (Schumacher et al., 2016). As technology rapidly evolves, companies strive to keep pace, recognizing the need for technological systems that align with organizational planning and strategies. This trend has led to a surge in information systems projects aimed at aligning Information Technology (IT) with company strategies.

These information systems projects, particularly in Business Intelligence (BI), are instrumental in improving decision-making quality and generating value. However, they require substantial investments in systems and resources. As investment levels rise, it becomes essential for organizations to understand the potential benefits and unique value that these systems can provide.

The economic, political, social, environmental, and technological transformations that have emerged and delineated the main characteristics of contemporary society have inevitably induced shifts in the paradigms of organizational management and work processes. Given the complexity and ambiguity of operational contexts, the strategic nature of decisions and responses has become an integral part of the daily reality of organizations, including the role of leaders. Thus, considering the systemic and contingent dimensions that define and characterize each organization, particularly educational institutions, the exercise of leadership and the requisite skills have undergone continuous redefinitions. This evolution ensures an active and decisive contribution to the ongoing enhancement of the effectiveness and efficiency of the organizational system as a whole.

In this context, transformational leadership has been highlighted and portrayed in the literature as a differentiating factor for success within organizations, showing positive correlations with various performance levels—individual, group, and organizational (Judge and Piccolo, 2004; Wang et al., 2011).

Additionally, associated with the numerous changes and upheavals in the environment, there are ethical and moral dimensions that influence the exercise of leadership. Therefore, leadership must be grounded in both the establishment of a vigorous and empowering emotional connection between the leader and followers and in moral and ethical principles that are socially and institutionally accepted and expressed through the existing organizational culture. This includes the values, beliefs, and ideals cultivated and expanded within organizations.

Given the importance of ethics and morals in organizations and the crucial factors underlying their development, Maier (2002) noted that leadership is among the most significant contributors to this objective. The author also emphasized the responsibility of leaders in charting the course forward and in fostering strategies that build trust, encouragement, and reinforcement of excellent performance within the organizational climate. This approach aims to ensure that organizations managed by these leaders become more humane, respectful, responsible, and ethical, ultimately serving as true examples of altruism—prioritizing the well-being of others. It is also important to see this in BI.

In today’s competitive business environment, data and information are crucial for guiding decision-making processes and enhancing corporate performance. Companies are compelled to improve their operations to maintain a competitive edge, using data to generate insights, manage risks, devise strategies, cut costs, and streamline processes (Sonda, 2024). Executives acknowledge the importance of the ongoing “information revolution” and are increasingly investing in IT, seeking assurances that these investments will provide returns (Rodrigues, 2017). IT has evolved from a supportive role to a critical source of knowledge and competitive advantage, impacting both operational and strategic levels.

BI systems exemplify this shift by offering powerful tools for data analysis and managerial decision-making. These systems scrutinize databases to help managers develop effective strategies through data processing and analysis (Olaoye and Potter, 2024). BI systems organize data into actionable insights, providing a comprehensive view of business performance through visualization techniques and detailed analysis (Angeloni and Reis, 2006). Industry analysis, such as Agiu et al. (2014), anticipate continued growth in BI system adoption due to their evident benefits in improving decision-making and operational efficiency.

The latest projections from Gartner, Inc. (Table 1) reveal that global spending on IT is expected to reach $5 trillion in 2024, representing a 6.8% increase from the previous year. 2024 is anticipated to be a pivotal year for the sector, with IT services expected to become the largest segment of IT spending for the first time. Total spending on IT services is forecasted to increase by 8.7%, reaching $1.5 trillion, driven by investments in projects aimed at enhancing organizational efficiency and optimization.

Despite substantial investments, project success is not guaranteed. It depends not just on the systems or technology but also on factors like implementation, adoption, and effective utilization. According to Standish Group Report (2014), 31.1% of information systems projects are terminated before completion, and nearly half of ongoing projects exceed their budgets, with final costs averaging twice the initial estimates.

The main motivation to develop this work lies on the impact that BI systems influence the competitive advantage of a logistics company, which is scarce in literature.

In logistics and supply chain operations, companies are rapidly adopting essential I4.0 technologies, such as Big Data, Cloud Computing, and BI. These technologies are being utilized across various areas, including planning and scheduling, supply chain management, production, and delivery and returns. The adoption of these technologies is expected to improve logistics processes by enhancing efficiency, reducing lead times, and optimizing workflows (Roshid et al., 2024). Leveraging the interconnected nature of I4.0 allows for the implementation of innovative systems that use real-time data to intelligently support decision-making processes.

The use of data within enterprises is becoming increasingly crucial in the modern world. However, a significant amount of this data goes unused and therefore loses value. BI plays a vital role in the fourth industrial revolution (I4.0) by utilizing data and information to facilitate informed decision-making, and it will continue to do so in the future according to Faber’s research in Faber (2024).

This study examines the influence of these technologies on gaining a competitive advantage, specifically within a prominent Portuguese logistics company. The central research question guiding this study is: What impacts does BI have on competitive advantage in a logistics company?

This question explores the potential benefits of BI technologies for organizations, specifically in terms of enhancing strategic positioning, improving decision-making processes, increasing operational efficiency, and fostering innovation.

To fully address this topic, the study has set three main objectives. Firstly, to determine whether the logistics company being studied is aligned with its goals and objectives. Secondly, to understand the extend which the BI technologies integrate with overall strategy and contribute to competitive positioning, and to gain an understanding of current usage and challenges faced in order to enhance future utilization. The third objective is to identify key areas for improvement and development within the BI tool. This will optimize processes and improve decision-making, ultimately giving the company a competitive edge through BI technologies.

The data gathered from participants’ responses will provide valuable insights and actionable recommendations for the company to strategically position itself in the market using effective BI technology.

The document is structured into several sections. This structure ensures a comprehensive exploration of the research topic. Literature Review is described in section 2. This section reviews existing literature in the key topics in study. Methodology can be found in section 3. This section explains the research method used in the study and describes how data was collected. Section 4 presents the Case Study following by section 5 with a Discussion of the results. Finally, in section 6, conclusions are reported and summarizes the findings and limitations of the study and suggests areas for future research.

In 2011, the term “Industry 4.0” was introduced by a team from various backgrounds. This term refers to the digital revolution and has gained significant attention from companies in different industries (Tubis and Grzybowska, 2022).

The fourth industrial revolution, known as “Industry 4.0,” is marked by the integration of advanced technologies such as the Internet of Things (IoT), real-time data management and analysis, and automated machinery into industrial systems (Hasnan and Yusoff, 2018). According to Bag et al. (2021), I4.0 technology emphasizes digitalization to enhance company operations through the deployment of emerging technologies and automated systems. In the contemporary digital landscape, logistics companies leverage online services via web servers and applications, utilizing these digital technologies to effectively manage and access data (Cichosz et al., 2020).

Several countries, including the United States, China, and Japan, have adopted strategies to bolster their manufacturing sectors through technological advancements. Portugal is also advancing in this area with its I4.0 plan, which comprises 60 initiatives from both public and private sectors aimed at affecting over 50,000 businesses nationwide. The initial phase of this plan will focus on training and upskilling more than 20,000 individuals in digital competencies. Furthermore, a substantial investment of 4.5 billion euros is projected to be injected into the economy over the next 4 years as part of these efforts (Portugal.Gov.PT, 2017).

Esmaeilian et al. (2020) suggest that by utilizing I4.0 technologies, a smart production network can achieve efficient real-time monitoring, agile communication, autonomous functionality, and smooth material flow. Technological advancements have created new opportunities for businesses to develop unique value propositions, ranging from tailored customizations to novel service innovations.

The success of businesses is now heavily dependent on digital skills, as they have become crucial for both companies and workers. This is highlighted by Digital Economy and Society Index (2022) report, which has deemed these skills as “critical” rather than “optional.” In order to adapt to the rapidly changing digital landscape, a proposal known as the “digital decade” emphasizes the importance of businesses and public services undergoing digital transformations in various aspects.

The DESI 2022 report reveals that in the year 2021, 26% of citizens in the European Union (EU) have surpassed basic digital skills and have shown proficiency in all five categories of the Digital Skills Indicator (DSI). Possessing skills beyond the basic level is essential not only for increasing competitiveness in the job market, but also for promoting the use of digital solutions in the business industry.

According to Tubis and Grzybowska (2022), I4.0 has evolved from a focus solely on factory operations to a broader digital transformation impacting both industrial and consumer markets. This shift involves implementing intelligent manufacturing processes and digitizing entire value delivery channels, extending to areas like Logistics 4.0 (L4.0). Hofmann and Rüsch (2017) argue that I4.0 offers significant opportunities for enhancing efficiency in logistics management. They emphasize that the potential of technologies like Blockchain to enable decentralized control of value chains, leading to improvements in automated production and self-optimized delivery, which reduces the need for human intervention.

Initially, research concentrated on digitizing production processes, leading to the development of the “smart factory” or digital factory concept. I4.0 technologies are now being applied to production, supply chain operations, and Maintenance 4.0. These technologies are designed to improve economic efficiency, environmental sustainability, and social contributions within logistics. Companies adopting I4.0 will digitize processes from planning to delivery, thereby enhancing logistics through improved flow management, operational optimization, and reduced lead times (El Hamdi and Abouabdellah, 2022).

I4.0 technology automates and streamlines logistics operations, providing tools for problem-solving and data management. The logistics sector adopts digital technologies to tackle business challenges and enhance performance. Automation and technological advancements simplify operations and offer analytical capabilities. Over the past 4 years, I4.0 has driven significant digital transformation in logistics, improving productivity and efficiency for both consumers and firms (Feng and Ye, 2021; Efthymiou and Ponis, 2021).

Digital transformation greatly improves responsiveness to demand fluctuations and enhances flexibility in supply chain and logistics operations. Information systems use advanced computing tools to efficiently process and evaluate data, analyzing business processes. I4.0 technology fosters communication and IT infrastructure growth, moving manual operations toward digitalization and reducing communication gaps. Advanced technologies optimize logistic processes, enabling companies to develop effective systems for streamlined supply chain and inventory control activities (Enrique et al., 2022; Krishnan et al., 2024).

The rapid advancement of IT and I4.0 technologies leads to digital logistics and supply chain systems, enabling personalized products and services for consumers. I4.0 technology helps logistics companies create efficient communication systems, promoting smooth interactions with management and consumers, and providing real-time supply chain updates. It also allows for automated and remote systems to control access, facilitating high-quality data collection and analysis. These automated systems enhance operational performance by improving warehouse management and delivery tracking, minimizing communication gaps, and providing quick access to relevant information (Althabatah et al., 2023).

L4.0 marks a new era in logistics management by leveraging digital advancements, transforming traditional logistics practices into modern, innovative operations. This shift presents both challenges and opportunities for organizations (Szymańska et al., 2017). A data-centric approach is essential, as the growing volume of logistics data requires investment in BI systems and the development of skills to use them effectively (Karki, 2024). BI systems enhance visibility into logistics operations, helping identify inefficiencies and enabling data-driven decision-making to improve performance. To capitalize on L4.0, organizations must invest in new technologies and adopt data-centric methodologies (Cichosz et al., 2020).

L4.0 involves the coordination of material and information streams within enterprises, overseeing transportation, storage, and associated data to deliver products efficiently and cost-effectively. Key logistics components include procurement, manufacturing, distribution, and reverse logistics. With rising demands for sustainability, L4.0 integrates transportation systems and provides decentralized real-time data on logistics networks. It focuses on fulfilling personalized customer needs sustainably and cost-effectively using digital technologies. Publications on L4.0 began in 2015, following the introduction of I4.0 in 2011, highlighting its role in creating responsive, customer-centric supply chains (Strandhagen et al., 2017; Winkelhaus and Grosse, 2019). Figure 1 shows the number of articles citing L4.0 since 2015 (source: Scopus database). This clearly demonstrate the growing academic interest in the topic.

Strandhagen et al. (2017) highlight the importance of L4.0 as an extension of the I4.0 concept, focusing on improvements in operational aspects such as sustainability, efficiency, and customer responsiveness. According to the same author, L4.0 impacts five key business elements: data collection and processing, assistance systems, networking and integration, decentralization and service orientation, and self-organization and autonomy.

Advanced digital technologies like Artificial Intelligence (AI), Blockchain, Cloud Computing, and BI are transforming logistics by enabling semi-autonomous operations and enhancing cross-organizational information exchange (Sigov et al., 2022). This shift affects crucial logistics functions—such as transportation, inventory management, material handling, and supply chain configuration—leading to better sustainability, traceability, efficiency, and customer responsiveness by integrating physical supply chains with digital data processes (Strandhagen et al., 2017).

L4.0, a relatively new concept, is still being explored for its full potential and benefits (Choi, 2021). Current production and logistics challenges include global market expansion, demographic changes, shorter product lifecycles, and increasing customer demands for personalized and sustainable products (Dallasega et al., 2022). Despite being new, L4.0 is gaining traction, with increasing scholarly attention since 2017 (Tubis and Grzybowska, 2022). This concept represents a major shift in supply chain management through advanced technologies and data-driven decision-making, utilizing emerging technologies like cyber-physical systems to enhance traditional logistics operations. The success of L4.0 largely depends on technological investments and capabilities (Bag et al., 2020). As logistics become more complex and data-driven, Big Data Analytics (BDA) plays a critical role in improving efficiency, flexibility, and responsiveness in logistics processes (Roshid et al., 2024).

The distinctions between big data, BDA, and BI can be confusing. Typically, BI involves a process where raw data is collected, converted into valuable information, and used to support decision-making and drive business outcomes (Larson and Chang, 2016). With advancements in Information and communications Technology (ICT) and data storage, “raw data” has transformed into “big data.” Jin and Kim (2018) argue that BI and big data/BDA now function together as an integrated decision support system, encompassing all steps from data collection to decision-making. BDA focuses on analyzing large and varied data sets to uncover patterns, correlations, market trends, and customer preferences (Ikegwu et al., 2022). In the context of L4.0, BDA is crucial for managing the vast amounts of data generated by interconnected logistics systems and devices.

Tiwari (2020) highlights that in the era of I4.0, logistics plays a crucial role in integrating digital technologies due to its dual focus on transporting physical goods and relying on accurate, real-time data. For organizations to effectively leverage integrated cyber-physical systems, they must align their internal technology with the pace of external stakeholders like customers and suppliers, necessitating proactive investments in emerging technologies (Gupta et al., 2019; Frank et al., 2019).

Santos and Marques (2022) point out that BDA enhances managerial decision-making, especially in supply chain risk management, by enabling real-time customer needs and demand assessment. Research has shown that BDA brings several benefits to the supply chain, including better accessibility, adaptability, compatibility, connectivity, control, and coordination. Additionally, Dubey et al. (2019) found that BDA not only improves visibility in the supply chain but also builds trust among supply chain partners.

Supply chain decision-makers are focused on strategies to effectively leverage Big Data to maximize its value (Hrouga and Sbihi, 2023). Globalization, driven by just-in-time manufacturing and small batch production, accelerates industrial digitization (Sindhwani et al., 2022). Although implementing L4.0 presents challenges, the benefits—including cost savings, efficiency, and enhanced customer experiences—are significant. Zhang et al. (2021) note that few companies have successfully completed the transition to L4.0. To achieve this, decision-makers need to understand the benefits of digital technologies and their impact on logistics processes (Yang et al., 2021). BDA and BI are both crucial for contemporary data-driven decision-making.

In today’s competitive business landscape, companies must be adaptable to address various challenges, which underscores the need for advanced decision support tools. The vast amount of available information calls for sophisticated systems to manage and utilize this data effectively.

According to Olszak and Ziemba (2007), BI systems are IT solutions designed to convert data into actionable insights and knowledge, thereby enhancing decision-making, forecasting, strategic planning, and organizational actions. Williams and Williams (2007) describe BI as a combination of methodologies and technologies that organize and analyze relevant information across business cycles to support decision-making and improve performance, including increased sales, cost reduction, and better outcomes. Agiu et al. (2014) view BI as the use of software and business applications or external data analysis to aid decision-making within a company.

BI involves two main activities: collecting information from source systems, often in its raw state, and processing it into valuable insights to support decision-making (Watson and Wixom, 2007). It includes exploring, integrating, aggregating, and performing multidimensional analysis on data from various sources (Olszak and Ziemba, 2007).

The research initiative begins with gathering data and information on foundational concepts related to I4.0, L4.0, and BI to understand previous work and studies in these fields. The literature review focuses on understanding these concepts in depth and identifying essential technologies for digital transformation that provide a competitive edge. This exploration helps clarify the criteria companies use to invest in L4.0 technologies and shapes the research approach and identifies areas for further investigation.

The study emphasizes examining the benefits and challenges of BI implementation. While methods such as focus groups, brainstorming sessions, interviews, and surveys were considered, the Delphi method was chosen for its effectiveness in analyzing the strategic impact of BI technologies from a future-oriented perspective. The Delphi method is valuable for gaining insights into what will be relevant regarding BI technologies in this context.

To ground the research, a case study was conducted with a leading logistics operator in Portugal. Case studies are versatile and commonly used to develop, challenge, or explain theories, elucidate situations, establish solutions, and explore phenomena (Meirinhos and Osório, 2010).

The Delphi method is designed to address complex issues by organizing a structured communication framework that gathers and refines expert opinions. According to Linstone and Turoff (1975), it involves systematically collecting and synthesizing informed judgments from experts to reach a consensus over time (Williams and Webb, 1994).

The method works by distilling and combining the collective knowledge and experiences of a panel of experts through a series of questionnaires, with controlled feedback provided between rounds (Gupta and Clarke, 1996). The goal is to achieve a reliable consensus on the issue rather than just classifying or evaluating information (Dickson et al., 1984).

In summary, the Delphi method involves multiple rounds of questioning, with each round building on the previous responses to refine and converge toward a consensus. This approach allows for the aggregation of expert insights to make more accurate predictions, provides a structured way to understand and integrate expert opinions, and enables respondents to adjust their answers based on feedback from earlier rounds.

The Delphi method utilizes a structured, iterative approach to gather and refine insights from experts while promoting learning among participants. It effectively collects knowledge without the challenges of face-to-face interactions, such as group conflicts or individual dominance, embodying the principle that “the whole is greater than the sum of its parts” (Gupta and Clarke, 1996).

The process involves querying a panel of experts on a specific topic through multiple rounds of questionnaires. Experts remain anonymous, and their responses are aggregated and summarized after each round. This summary is then redistributed for further feedback, with each iteration aimed at achieving a higher level of consensus (Linstone and Turoff, 1975; Williams and Webb, 1994).

Meyrick (2003) notes that the Delphi method leverages the expertise of specialists, allowing them to review and reflect on each other’s viewpoints through the iterative questionnaire process, thereby enhancing the depth and reliability of the collected insights. As for Munaretto et al. (2013), its primary characteristics, advantages, and benefits are:

Anonymity: Enhances spontaneity and openness by preventing influence from dominant or persuasive individuals, and reduces reluctance to express unpopular opinions. This helps mitigate personal biases and distortions from participant interactions, although it might lead to incomplete recall or missed viewpoints (Grisham, 2009).

Feedback: Crucial for maintaining focus on the study’s objectives, setting goals, and enabling participants to review and refine their opinions based on collective feedback. While it helps in building consensus and reducing disagreements, excessive reliance on feedback might exclude dissenting views from the analysis (Marques and Freitas, 2018; Yousuf, 2007). Feedback is a key differentiator of the Delphi method from traditional opinion polling (Linstone and Turoff, 2002).

Flexibility: Allows participants to consider peers’ opinions and arguments, which can prompt reconsideration of their own views. However, this flexibility might lead to forced consensus where participants might passively adopt others’ opinions (potentially compromising the authenticity of the consensus).

Utilization of experts: Ensures that reliable concepts and judgments are formed based on expert knowledge, though it can lead to rapid consensus formation that might not always reflect nuanced opinions.

Consensus: Aims to align expert opinions and identify reasons for disagreement. However, there is a risk of creating an artificial consensus if not managed carefully.

Interactivity: Facilitates non-hierarchical exchanges and enhances response adequacy through reciprocal learning among respondents. However, online interactions might lead to less detailed responses despite being quicker and more cost-effective, potentially limiting the depth of feedback (Grisham, 2009).

The case study focuses on a leading logistics operator in Portugal, a major player in the national market with over 2,500 employees and operations across the Iberian Peninsula. This large organization recently embarked on a BI project using Power BI.

Power BI, developed by Microsoft, is a robust business analytics service that offers interactive visualizations and BI capabilities. It provides a user-friendly interface for creating reports and dashboards, aiding organizations in making data-driven decisions. Key features of Power BI include data connectivity, interactive dashboards, and a range of visualization options such as charts, graphs, maps, and gauges. These tools enable the company to analyze data effectively and respond swiftly to market changes, thereby staying competitive amidst price, supplier, and customer volatility. Due to confidentiality, further details about the company’s specific operations and BI implementation cannot be disclosed.

In this case study, the Welphi online platform was utilized to distribute the questionnaire to respondents (Welphi, 2024). Online surveys offer significant advantages, including their non-intrusive nature, cost-effectiveness, and time savings due to the elimination of manual data entry and paper-related expenses (Wright, 2005).

However, one challenge of online surveys is the potential for low response rates. To address this, follow-up reminders can be employed, as demonstrated by Kaplowitz et al. (2004), who found that reminder emails can enhance response rates. The Welphi platform supports this approach by enabling the scheduling of reminder emails to encourage higher participation.

Before distributing the questionnaire, participants were informed through internal announcements and a comprehensive Word document detailing the study’s topic, methodology, and platform.

The Welphi platform allows for the division of questionnaires into various sections and sub-sections. The study’s dimensions scheme for the questionary are detailed in Figure 2.

The platform uses a round system to anonymously compare participants’ opinions with their peers, fostering group consensus without face-to-face interaction. Welphi supports asynchronous, online, participatory, and interactive comparisons of ideas. Anonymity encourages participants to adjust their initial positions based on group feedback, promoting consensus. Participants can respond without creating a profile or logging in.

The tool can be accessed at the user’s convenience via a link provided in the initial email, minimizing scheduling conflicts. This platform enables the creation of questionnaires to gather opinions from multiple participants who have busy schedules or are geographically dispersed, as was the case with this company. Given that the company has offices spread across Portugal, it would be challenging to bring all participants together in one location on the same day.

Welphi’s distinguishing features include the ability to create questionnaires and rounds, invite participants, set approval and rejection rules, develop response scales, share round results, and generate statistical graphs. One significant advantage is the automatic generation of statistics for result analysis. Welphi also allows the establishment of approval and rejection rules to manage consensus and decide on elements for subsequent rounds. Data can be exported to Excel for further analysis.

The next few steps that will be described will be applied in the case study. The implementation of the Delphi method involves several steps, which, according to the consulted literature (Yousuf, 2007), can be outlined as follows: Selection of the expert panel; Development of the first questionnaire; Initial contact with experts and invitation to join the research; Distribution of the first questionnaire; Collection of responses to the first questionnaire; Qualitative analysis of responses; Preparation and distribution of the second questionnaire with feedback; Collection and analysis of responses to the second questionnaire; Comparative analysis of responses from both rounds.

The study aimed to assess how BI technologies are integrated into the company’s strategy and their impact on competitive advantage. The results revealed that employees view BI as essential for enhancing the company’s competitive edge through improved decision-making and strategic planning, aligned with its goals and objectives, based on accurate data. Future strategies will need to focus on developing BI technologies to continue creating business value.

Participants emphasized the importance of aligning BI tools with company objectives by centralizing and unifying data, which is critical for gaining insights that support strategic decisions. This alignment suggests that top management should prioritize improvements in project management and IT consolidation.

A key challenge identified was the lack of employee awareness regarding the critical success factors of the BI project. Feedback from the second round of the study highlighted insufficient internal communication about the BI tool, indicating an area for management to address. The study also underscored the necessity of BI indicators and the need for a continuous improvement process to keep up with technological advances and fully utilize BI capabilities.

A potential statement for the questionnaire could be: “Company provide incentives, such as courses or training, for my development in BI” This is in light of the observation that the company’s communication regarding BI technology was found to be inadequate.

In summary, the study highlights the significant benefits of BI technologies in strategic implementation and development. It stresses the transformative potential of BI in the logistics industry and emphasizes that successful implementation requires meticulous planning, ongoing collaboration, and a commitment to technological advancement.

This study has several limitations that should be acknowledged. As this is a case study, caution should be exercised when generalizing the findings and conclusions of this study, and the confidentiality of sociodemographic data makes it difficult to characterize the company and its employees. Firstly, given more time, it would have been beneficial to conduct the questionnaires in bilanguage (English and Portuguese) since this study is all written in English. Secondly, the online data collection method sometimes resulted in a low response rate, requiring follow-up emails to prevent high absenteeism.

Future research could include a more diverse group of experts, especially from the company’s offices in Spain. Comparative studies across different organizations in the transport and logistics sector could provide a broader understanding of the implementation and impact of BI technology. Longitudinal studies tracking BI technology adoption over time would capture technological advancements and market changes, offering insights into the long-term benefits and challenges. Employing mixed methods, such as combining quantitative surveys with qualitative interviews, would provide a holistic view of BI technology’s impact and user experiences.