Business Analytics in the Automotive Industry: A Bibliometric Analysis from 2020 – 2025

Business Analytics in the Automotive Industry: A Bibliometric Analysis from 2020 – 2025

Mohamad Zulhelmi Othman¹, Erne Suzila Kassim², Emi Normalina Omar^3*, Iffan Maflahah⁴

¹Proton Centre of Excellence Westbound Expressway 47600 Subang Jaya Subang Jaya Selangor Malaysia

^2,3Universiti Teknologi MARA Kampus Puncak Alam Selangor, Malaysia

⁴Department of Agroindustrial Technology, University of Trunojoyo Madura Bangkalan, East Java   Indonesia *corresponding author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000332

Received: 04 September 2025; Accepted: 12 September 2025; Published: 10 October 2025

ABSTRACT

This study examines the shifting role of business analytics within the automotive sector, focusing on research from 2020 to 2025. Given the rapid rise of digitalization, artificial intelligence, and data-driven methods, the authors aim to chart prevailing research directions, highlight key thematic concerns, and point out overlooked areas. Drawing from nearly 300 Scopus-indexed articles and conference proceedings, the analysis leverages VOS viewers to visualize the research landscape through network, overlay, and density maps. The findings reveal five main thematic clusters, with recurring attention to decision making, machine learning, Industry 4.0, and the Internet of Things. Much of the literature centers on operational and strategic improvements within automotive organizations. That said, topics such as electric vehicles, smart cities, and customer analytics remain notably underrepresented. While the field is clearly moving toward sustainable and intelligent systems, there is a distinct lack of focus on consumer behavior and analytics specific to electric vehicles. In summary, business analytics appears deeply embedded in core automotive functions, yet there is a clear need for future research to address consumer engagement and regional challenges particularly in emerging markets. It is important to note that the study’s scope was limited to English-language publications within the business and management domain, potentially leaving out significant technical contributions.

Keywords—Business Analytics,  Automotive Industry, Bibliometric Analysis, Machine Learning, Digital Transformation

INTRODUCTION

The automotive sector is currently undergoing a profound transformation, largely propelled by the integration of digital technologies, extensive data utilization, and advanced analytics. These innovations are fundamentally reshaping operational processes, enabling more streamlined supply chains, heightened efficiency, and enhanced customer engagement. A notable example is predictive maintenance, which utilizes sophisticated data analysis to forecast potential vehicle malfunctions and facilitate proactive interventions [1]. This approach not only minimizes operational disruptions but also bolsters overall reliability for both manufacturers and end-users. As digitalization continues to accelerate within the industry, it is evident that business analytics will be instrumental in guiding future developments and maintaining competitive advantage. Moreover, digitalization has profoundly altered the automotive supply chain, enhancing climate-related competitiveness across the sector. Through the implementation of innovations such as closed-loop logistics which prioritize resource efficiency and waste reduction and advanced inventory management systems, organizations are able to optimize stock levels and minimize both transportation requirements and storage demands [2]. Collectively, these technological advancements have had a marked effect on the industry’s overall environmental footprint.

Simultaneously, the integration of artificial intelligence and deep learning is reshaping fundamental automotive processes. Notable applications include autonomous driving systems that utilize AI for perception and decision-making, AI-driven vehicle inspection for automated defect identification, and quality control mechanisms that ensure consistent product standards. The increasing prevalence of these technologies highlights the transformative influence of business analytics in the automotive industry, leading to improvements in operational efficiency, safety, and the development of innovative functionalities [3]. The rapid advancement of these technologies across multiple sectors has prompted a marked increase in academic interest. As a result, conducting a comprehensive review of existing literature has become essential for grasping the direction and development of research in this domain. Such a review enables scholars to chart how research questions, methodologies, and findings have evolved, offering meaningful insights into the current landscape and where future inquiry might head.

In this context, bibliometric analysis stands out as a valuable methodological tool. It allows researchers to pinpoint influential publications, map citation patterns, and identify prevailing as well as emerging themes within the literature. Additionally, this approach highlights gaps in the scholarship that merit further exploration. As highlighted by [4], bibliometric analyses are particularly effective at revealing thematic groupings and delineating areas of expertise, thereby providing useful guidance for subsequent research and the allocation of resources within the field. Ultimately, this structured approach ensures that future studies build thoughtfully and productively upon the established body of knowledge.

This research conducts a detailed bibliometric analysis to chart the changing landscape of business analytics in the automotive industry, focusing on scholarly work published from 2020 to 2025. The goal is to identify the major trends, emerging research areas, and persistent gaps shaping how data-driven insights are being used across the sector. Utilizing sophisticated visualization tools like VOS viewer, the study systematically reviews a substantial collection of academic literature, making it possible to map out key themes and spotlight underexplored topics. The significance of this work lies in its potential to guide both researchers and practitioners. By outlining active areas of investigation and highlighting where more attention is needed, the study offers a strategic reference for anyone aiming to understand or contribute to the ongoing transformation driven by business analytics in the automotive field. Ultimately, the research aims to encourage continued innovation and foster broader adoption of data-centric approaches within the industry.

LITERATURE REVIEW

A bibliometric analysis of business analytics (BA) in the automotive sector from 2020 to 2025 reveals a domain undergoing rapid transformation, propelled by technological advancement, digitalization, and mounting sustainability pressures. The exponential growth in data and increased computational capabilities have enabled BA applications to proliferate across the automotive value chain ranging from supply chain optimization and predictive maintenance to personalized user experiences and autonomous vehicle innovation.

Recent systematic and bibliometric studies highlight several dominant themes: machine learning’s pivotal role in demand forecasting, big data analytics driving improvements in manufacturing efficiency, and the influence of BA on sustainable business practices. Emerging research is also delving into real-time decision-making powered by IoT integration, as well as the potential of blockchain to enable secure data sharing. There’s an increasing scholarly focus on ethical considerations, particularly data privacy and algorithmic bias—issues that can no longer be ignored as analytics become more deeply embedded in industry operations.

Notwithstanding this progress, notable research gaps persist. Methodological rigor is often lacking, and topics like cybersecurity readiness, especially as it relates to analytics, remain underexplored. Addressing these gaps is crucial, as the stakes for both industry practitioners and policymakers continue to rise alongside the growing complexity of the automotive landscape. In sum, this review provides a comprehensive snapshot of the current state and likely future directions for business analytics in the automotive sector, serving as a valuable resource for academics, industry professionals, and decision-makers seeking to navigate this evolving field.

A. Growth and Scope of Business Analytics in Automotive

First, confirm Business analytics has really surged in prominence lately, especially as organizations across all sorts of industries realize just how much it can do for them [5]. With more data floating around than ever before and analytics tech getting smarter by the minute, the appeal is obvious. In the automotive sector, in particular, business analytics is now central to improving everything from supply chains and manufacturing workflows to customer engagement and product innovation. Companies that can actually turn their mountains of data into real insights can pulling ahead cutting costs, boosting performance, and, honestly, getting a much clearer read on what their customers want. In this competitive landscape, data-driven decision-making is not only helpful but also much essential.

Business analytics represents a multidisciplinary field, integrating technical skills such as statistical analysis and programming with critical problem-solving abilities and a solid grasp of business fundamentals. This synergy allows professionals in the automotive sector to drive innovation and efficiency across a range of functions. For example, data analysis can streamline manufacturing workflows, enhance the reliability of supply chain logistics, and increase the effectiveness of customer relationship management. Furthermore, analytics supports the development of more precise marketing campaigns and enables organizations to base strategic decisions on empirical evidence [5,6]. Ultimately, the application of business analytics empowers automotive firms to improve overall performance through informed, data-driven insights.

The automotive industry is currently undergoing a significant transformation, characterized by the integration of business analytics and big data analytics with advanced digital technologies. This evolution is largely propelled by the sector’s imperative to navigate rapid technological advancements, increased sustainability expectations, and unpredictable events such as the COVID-19 pandemic [6]. In practical terms, organizations are deploying analytics and digital tools to streamline supply chain operations, enhance product development processes, and anticipate customer preferences with greater accuracy. Empirical evidence supports the notion that the adoption of big data analytics and customer relationship management systems contributes positively to total quality management, leading to increased operational efficiency, cost reduction, and improved reliability of automotive products.

Moreover, the ongoing digital transformation is reshaping the corporate culture within automotive firms, fostering stronger and more dynamic relationships among customers, suppliers, and partners. This shift not only enables innovative marketing strategies and targeted campaigns but also supports the development of effective customer loyalty programs and data-driven approaches to market expansion [6]. Collectively, these advancements are positioning the automotive sector to be more agile, competitive, and responsive to the evolving needs of the market.

B. Trends in the automotive :

Business Models for Electric Vehicles (EVs)

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Recent years have witnessed a marked intensification in scholarly inquiry into business model frameworks designed specifically for electric vehicles (EVs). This heightened academic attention mirrors the broader automotive sector’s accelerating transition toward sustainable practices and the electrification of transport. A comprehensive review of over one hundred research articles uncovers concentrated scholarly engagement with several core dimensions: the advancement of charging infrastructure, the refinement of driver-centric services, the development of advanced electricity management strategies, the structuring of commercial contracts within the EV ecosystem, and the operational design of EV manufacturing facilities [7].

Within this body of literature, two principal and evolving research trajectories are evident. The first centers upon technological innovation and resource efficiency, including cutting-edge developments in battery systems, the integration of lightweight materials, and the optimization of drivetrain architectures—all directed toward maximizing vehicle performance while minimizing environmental impact [7]. The second theme emphasizes the management of electricity and the holistic oversight of product life cycles. This encompasses efforts to optimize energy use, harmonize EVs with smart grid technologies, develop robust frameworks for battery recycling and reuse, and ensure environmentally responsible disposal at end-of-life.

In terms of research leadership, China, the United States, and Germany emerge as dominant contributors, reflecting both their strategic industrial priorities and their commitment to progressive automotive policy [7]. The collective insights derived from this research provide critical guidance for policymakers and industry leaders alike. By assimilating these focal areas and thematic developments, stakeholders are better positioned to restructure business models in pursuit of ambitious sustainability objectives, accelerate the transition to electric mobility, and capture the economic opportunities inherent in the evolving EV marketplace.

Extended Reality (XR) Technologies in Automotive

The automotive industry has increasingly embraced extended reality (XR) technologies encompassing virtual, augmented, and mixed reality in recent years [8]. A review of over 1,500 academic publications between 2012 and 2022 indicates a marked acceleration in XR-related research within this field, with the most significant growth occurring over the past four years. Notably, Germany, the United States, and China have emerged as the principal contributors to this research surge [8].

XR applications now permeate several key stages of the automotive lifecycle. These include virtual prototyping, which enables engineers to assess and refine vehicle designs in simulated environments; collaborative digital design processes; and advanced manufacturing solutions, where production workflows and workforce training programs are optimized through immersive technologies. Additionally, XR is transforming customer engagement, offering virtual showrooms and interactive sales experiences, while also enhancing technician and driver training via realistic simulations. Ergonomic studies benefit as well, as researchers analyze driver and pedestrian interactions to inform improvements in safety and comfort [8].

Recent trends highlight a compelling convergence of Extended Reality (XR) with Artificial Intelligence (AI), particularly evident in the rapidly evolving realm of autonomous vehicles. This integration suggests a future where XR, AI, and automated driving technologies will synergistically redefine the automotive landscape. This holistic approach promises to foster significant innovation across various facets of the industry, spanning design processes, optimized production methodologies, and ultimately, a significantly enhanced and personalized user experience. The combination of XR for visualization and simulation, coupled with AI for decision-making and control, is poised to unlock new possibilities in vehicle development, validation, and ultimately, the way we interact with and utilize transportation [8].

Customer Relationship Management and Big Data Analytics

The integration of Customer Relationship Management (CRM) systems with Big Data Analytics (BDA) has become a pivotal focus within the automotive industry’s marketing strategies, particularly in the aftermath of the COVID-19 pandemic [6]. The crisis acted as a catalyst for rapid digital transformation, compelling automotive firms to adopt data-centric approaches to sustain their market position. Case studies of leading automotive companies demonstrate that effective CRM implementation supports the adoption of industry best practices, which in turn drives improvements in business outcomes and Total Quality Management (TQM) [6]. Notably, these advancements are evident in areas such as personalized customer engagement, more effective targeted marketing, and greater efficiency within sales operations.

Nevertheless, the deployment of these advanced technologies is not without significant obstacles. Persistent issues including fragmented data environments, shortages of skilled personnel, complex system integrations, and ongoing concerns about data privacy frequently impede successful adoption. These challenges underscore the necessity for continued academic inquiry aimed at developing robust frameworks for overcoming such barriers. Future research should prioritize areas such as evaluating the influence of various CRM models on customer retention, creating predictive analytics tools for sales forecasting, and examining the ethical implications of leveraging customer data for personalized marketing within the automotive sector. Addressing these topics will be critical for optimizing digital marketing strategies and maximizing returns on CRM and BDA investments throughout the automotive value chain.

Interdisciplinary and Comprehensive Applications

Business analytics within the automotive industry extends far beyond isolated departments; it permeates virtually every aspect of operations, forming a comprehensive framework for decision-making. For instance, predictive modeling now underpins supply chain management, facilitating more accurate demand forecasts, sharper inventory control, and greater logistical efficiency. On the manufacturing side, analytics serve to pinpoint production bottlenecks, elevate quality control standards, and streamline overall workflow.

In addition, business analytics has become central to customer engagement strategies, supporting the personalization of marketing efforts, the deeper analysis of consumer preferences, and the ongoing improvement of customer service operations. Finally, analytics contribute to sustainability initiatives by enabling firms to monitor environmental impacts, optimize resource consumption, and implement eco-conscious practices [5,6]. Altogether, analytics serves as a crucial, integrative force within the modern automotive sector.

The interdisciplinary approach to solving complex problems requires combining machine learning techniques with domain-specific knowledge. This synergy allows for the development of more robust, accurate, and insightful solutions that leverage the strengths of both fields. By integrating machine learning’s ability to identify patterns and make predictions with the nuanced understanding of a particular domain, such as healthcare, finance, or engineering, we can support more informed and effective decision-making processes. This collaborative method can lead to novel breakthroughs and overcome limitations that might arise from relying solely on either machine learning algorithms or traditional domain expertise [5].

METHODOLOGY

This section explained the method that has been used in this study.

A. Search Strategy

Define The keywords used in the bibliometric analysis include:

“business analytic” OR “business analytics” OR “big data” AND “automotive” OR “automobile” OR “vehicle” OR “car”

A comprehensive literature search was carried out via the Scopus database, targeting journal articles and conference proceedings published between January 1, 2020, and December 31, 2025. To avoid any issues with translation errors creeping in, only works published in English made the cut. When it came to figures, some clear guidelines were enforced: captions are placed directly beneath each figure and written in sentence case (the first word and proper nouns get capitalized). This study focus on close attention to the overall look and clarity of the charts such as colors and shades were chosen purposefully to boost readability and accessibility.

B. Exclusion Criteria

In conducting the analysis, concentrated were exclusively on empirical studies which those presenting original data or analysis. Any publication lacking new research findings was excluded from consideration. This meant removing items such as lecture notes, symposium papers, trade magazines, book reviews, letters to the editor, full books, and standalone book chapters. This study also excluded works focused purely on bibliometric analyses or literature reviews; while these are valuable, they do not contribute fresh empirical evidence. For clarity and consistency, only English-language articles were included. Finally, the review was limited to works categorized within the Business and Management field, ensuring direct relevance to our research goals.

TABLE 1 DATA SET INFORMATION

Fig.1 Publication by Year

Fig. 1 illustrates the annual publication trend from 2020 to 2025 for studies using the keywords “business analytic” OR “business analytics” OR “big data” AND “automotive” OR “automobile” OR “vehicle” OR “car”. Over the past five years, research activity in this field has shown considerable variability, almost unpredictable at times. In 2020, there were 49 published articles, which set a relatively strong baseline for engagement. The following year, 2021, saw an increase to 58 publications, marking a notable growth of about 18 percent. That upward trend, unfortunately, didn’t hold.

By 2022, the number of articles had dropped to 39, and 2023 saw a further, though smaller, decrease to 38. There was a modest rebound in 2024, with 44 publications, suggesting a possible recovery. The    most recent data point, 2025, presents a sharp decline to just 7 publications. It’s important to view this figure with caution, as it is likely to only reflect the first quarter of the year. The numbers may rise as the year progresses, and more research is published. These fluctuations invite further scrutiny. Possible contributing factors include changes in funding, shifts in research priorities, and the overall state of the academic community in this area. A more detailed analysis would be necessary to pinpoint the underlying causes and to anticipate whether publication activity will stabilize or recover as the year continues.

ANALYSIS AND RESULTS

The bibliometric analysis was conducted using VOS viewer to explore keyword relationships, themes, and developments in business analytics research in the automotive industry. The analysis was based on the keyword search:

“business analytic” OR “business analytics” OR “big data” AND “automotive” OR “automobile” OR “vehicle” OR “car”

Three types of visualization were produced: network maps to show relationships between entities, density maps to highlight areas of concentration, and overlay maps to compare different datasets spatially.

A. Network Visualization

The network visualization effectively illustrates the relationships between keywords found across the article set. By mapping out how frequently certain keywords appear together, the visualization automatically organizes them into distinct clusters, each marked by a different color for clarity. This makes it straightforward to identify related themes within the dataset.

Additionally, the size of each node representing an individual keyword reflects how often that keyword appears in the collection. Larger nodes indicate greater frequency, signaling the most prominent concepts in the articles. Altogether, this visualization serves as a practical tool for uncovering central themes and the connections among them, offering valuable insight into the structure and focus of the text corpus.

Fig. 2 Network Visualization of Business Analytics in Automotive Industry

1. The red cluster unmistakably emphasizes core concepts such as decision making, supply chains, competition, and sustainability. This concentration strongly signals the critical relationship between business analytics and strategic planning within the automotive sector. Organizations that leverage data-driven insights are better equipped to refine supply chain operations, enhance their competitive positioning, and implement more effective sustainability initiatives. In today’s rapidly changing market environment, these analytical approaches are not just beneficial, they are essential for firms aiming to boost performance and resilience.
2. The green cluster centers on concepts like data technologies, decision support systems, and information systems essentially, the technical framework underpinning analytics. These systems serve as the infrastructure for collecting, storing, processing, and analyzing data. Without such foundational elements, effective analytics simply wouldn’t happen. They enable organizations to generate meaningful insights and support informed decision-making, making them indispensable to the entire analytics process.
3. Within the yellow cluster, terms like “learning systems” and “intelligent system” clearly underscore the expanding influence of artificial intelligence in the domain of data analysis. This vocabulary points to a notable shift: analytical processes are increasingly automated and adaptive, with machines now capable of independently learning from data, recognizing patterns, and generating predictions often with minimal human oversight. Such developments indicate the transformative potential of AI in this field, facilitating quicker insights, more precise forecasting, and the unveiling of intricate relationships within complex datasets.
4. The blue cluster represents a significant concentration of emerging research areas, notably including machine learning, electric vehicles, smart cities, and data mining. These focal points indicate a marked increase in both scholarly and industrial interest in the advancement of intelligent transportation systems and innovative mobility solutions. The adoption and integration of such technologies possess the potential to fundamentally transform urban transportation, offering prospects for enhanced efficiency, sustainability, and improved connectivity within cities.
5. The purple cluster essentially encompasses foundational technologies, namely, the Internet of Things (IoT), cloud computing, and blockchain. These are not peripheral; they provide the essential infrastructure for contemporary analytics systems. IoT, for instance, serves as a continuous source of data streams, channeling vast amounts of information into analytical frameworks. Cloud computing delivers the necessary computational resources and storage capacity, both of which are critical for processing large-scale datasets. Blockchain, meanwhile, is positioned as a mechanism for ensuring the security and transparency of data management, especially in contexts where trust and auditability are paramount. Collectively, these technologies constitute the core architecture that enables advanced analytics and data-driven decision-making.

Furthermore, upon examining the visualization, certain central themes are unmistakable: decision making, machine learning, and Industry 4.0. These are not isolated phenomena but act as vital connectors within the broader research landscape. Decision making represents the primary objective for many analytics initiatives, guiding the interpretation and application of data insights. Machine learning provides the methodologies and techniques essential for extracting value from complex datasets. Industry 4.0, with its emphasis on automation and interconnected systems, exemplifies a significant domain where these technologies converge and find practical application. Altogether, these three themes function as pivotal nodes, weaving together the various clusters and underscoring the interconnectedness of contemporary research in this field.

B. Overlay Visualization

This overlay visualization acts like a timeline, charting how the popularity of specific keywords in the dataset has shifted over time. It shows the average publication year tied to each term, so you can see which concepts were big back in the day and which ones are popping up more in recent research.

Down at the bottom, there is a color bar, it’s basically your key to decoding the timeline. Dark blue points to keywords that were mostly used in earlier works (earlier years ~2021), while yellow signals terms that are showing up in more recent publications (recent years ~ 2022). Just match the color to the keyword, and you will get a quick read on whether it’s an emerging trend or a fading topic. It is suitable for anyone trying to track shifts in the research landscape.

Fig.3 Overlay Visualization of Business Analytics in Automotive Industry

In the early days of incorporating business analytics within the automotive industry think of this as the “dark blue” phase scholarly attention was largely devoted to foundational concerns. Researchers focused on decision-making, supply chain optimization, and the broad implications of Industry 4.0. These initial efforts provided essential insights into operational challenges and created a foundation for future data-driven approaches in the sector.

As technology progressed and data became more abundant, the focus of research shifted noticeably (the “green” phase). Artificial intelligence, machine learning, advanced data management, and intelligent systems became central themes. This period marked a move toward more sophisticated analytical techniques, allowing for predictive modeling and deeper insights into complex automotive processes.

Most recently the “yellow” phase researchers have turned their attention to topics such as the Internet of Things, new data technologies, and sustainability. This current emphasis reflects a strategic pivot: the field is now grappling with how to develop intelligent transportation systems, enhance digital infrastructure for connected vehicles, and pursue sustainable solutions that address both environmental and resource efficiency challenges. The trajectory of this research underscores a growing recognition of the automotive industry’s role in broader societal and environmental contexts, pushing innovation toward a more intelligent, interconnected, and sustainable future for mobility.

C. Density Visualization

The density visualization operates much like a heat map, providing a clear, visual indication of keyword prevalence within the dataset. Brighter yellow areas correspond to terms that appear with high frequency, marking them as central themes or established areas of research. In contrast, regions shaded in green or blue point to keywords that are less common these might represent emerging topics, specialized subfields, or peripheral subjects. This approach allows researchers to quickly identify which concepts dominate the discourse and which are less explored, making it easier to recognize both core themes and possible directions for further investigation. Overall, visualization offers an efficient means of grasping the distribution and relative importance of ideas across the data.

Fig. 4 Density Visualization of Business Analytics in Automotive Industry

The most dominant keywords in the visualization include decision making, machine learning, industry 4.0, and internet of things, all of which are closely linked to the use of business analytics in the automotive sector. Keywords such as supply chains, sustainable development, artificial intelligence, and data handling appear with moderate brightness, indicating their ongoing importance in supporting data-driven practices. Meanwhile, newer topics like smart cities, electric vehicle, and urban transport are present but less intense, suggesting that these areas are still developing and gaining research attention.

DISCUSSSION

The bibliometric analysis spanning 2020 to 2025 provides a nuanced examination of business analytics research within the automotive sector. Employing advanced methodologies such as network analysis, overlay mapping, and density visualizations the study delineates the complex and shifting contours of this research landscape. Notably, the findings reveal emergent trends, including the adoption of novel analytical techniques, the development of influential research clusters, and the evolving priorities of academic inquiry in this domain.

Significantly, the analysis uncovers existing gaps in current scholarship, highlighting areas where further investigation is both needed and promising. Such insights are instrumental for guiding future research directions and fostering innovation in the application of business analytics to automotive contexts. For scholars, practitioners, and policymakers alike, this comprehensive overview serves as an essential resource for engaging with and advancing this dynamic and increasingly pivotal field.

A. Authors and Affiliations Interpretation of Trends

The network visualization highlights five distinct clusters, each representing a specific area of research focus. Notably, decision making, machine learning, and Industry 4.0 stand out as central themes that are deeply interconnected across these clusters. This pattern suggests a strong scholarly interest in leveraging analytics to enhance strategic operations and drive improvements in production processes. For instance, Groupe Renault’s collaboration with Google Cloud exemplifies this trend, as they integrate AI and machine learning expertise with automotive industry insights to increase productivity, enhance production quality, and reduce carbon emissions [9].

The overlay visualization illustrates a clear evolution in research interests over time. In the early period (2020–2021), scholars primarily examined foundational subjects decision making, supply chains, and the broader framework of Industry 4.0. As the timeline progresses toward 2022–2025, there is a marked shift: contemporary studies increasingly engage with advanced topics such as the Internet of Things (IoT), sustainability, and digital infrastructure.

The density visualization further underscores this trend by highlighting the concepts most central to current academic discourse. Terms like machine learning, decision making, and IoT emerge as dominant themes within the literature. Meanwhile, areas including electric vehicles, urban transport, and smart cities are present, albeit less extensively explored, suggesting potential avenues for future research.

B. Identification of Research Gaps

Headings, Upon closer scrutiny, despite the visible growth in this field, there are still several significant gaps that limit both the depth and applicability of existing research. These omissions are not trivial; they fundamentally constrain our understanding and the practical deployment of analytics in key areas.

To begin with, there is a clear shortfall when it comes to customer-focused analytics. Most of the academic work tends to fixate on operational and system-level analysis, which, while necessary, overshadows the pressing need for more rigorous examination of the consumer perspective. For instance, research on consumer behavior remains superficial; we still lack comprehensive studies exploring how individuals interact with products or services, what influences their decision-making, and what factors underpin sustained engagement or disengagement.

Furthermore, measures of customer satisfaction are often insufficiently detailed. Standard metrics rarely probe the underlying causes of satisfaction or dissatisfaction, and there is a distinct absence of studies deploying advanced analytical or qualitative methods to unpack these drivers. Similarly, the literature does not adequately address the evolution of customer preferences, nor does it provide robust frameworks for organizations to leverage data in anticipating and responding to these changes. The approach to customer segmentation remains rudimentary, overly reliant on basic demographics, with limited engagement in more nuanced approaches such as behavioral or psychographic segmentation.

Neglecting these customer-centric aspects is a significant oversight, as long-term success in any sector is contingent on a thorough understanding of end-user needs and motivations.

Additionally, the state of research on electric vehicle (EV) analytics is still in an embryonic phase. While EVs are sometimes included in broader datasets or visualizations, the field lacks dedicated, in-depth analysis of issues uniquely relevant to EV technology. Crucial topics such as the optimization of charging infrastructure, the performance characteristics of batteries, strategies for mitigating range anxiety, and the role of government incentives are addressed only sporadically, if at all.

Analyses also tend to lack granularity, rarely differentiating between specific EV models, charging solutions, or real-world driving scenarios. Environmental impact assessments are often limited in scope, neglecting the full lifecycle of EVs: from manufacturing to disposal, and from sourcing electricity to the broader implications for sustainability. Moreover, the potential of EVs to function as distributed energy resources within smart grids is under-explored, despite its relevance to energy optimization and grid stability.

Collectively, these research gaps both in customer analytics and EV-specific studies represent substantial missed opportunities. Addressing them is imperative for advancing the field, improving decision-making, and unlocking the full potential of analytics-driven innovation.

C. Implications

For researchers, this analysis acts as a strategic guide, highlighting areas where further investigation is genuinely needed. There is a clear call for more in-depth studies on consumer analytics, applications of EV-specific data, and diverse regional perspectives especially in developing economies where automotive markets are rapidly expanding.

Overall, the discipline is undergoing a transition from conventional operational analytics toward systems that are more integrated, intelligent, and sustainable. As digital technologies become more deeply embedded in the automotive sector, business analytics continues to serve as a driving force behind industry growth and innovation.

The bibliometric findings underscore a significant pivot toward real-time analytics, electric vehicles, and the integration of artificial intelligence. Yet, research focused on end-user data analytics particularly studies examining consumer behavior remains strikingly limited. While operational optimization is well-trodden ground, the consumer-facing dimension of analytics still presents a substantial research gap.

Building on the work of [4], it’s clear that VOS viewer serves as a valuable tool for pinpointing essential thematic clusters. Their analysis of education-related keywords aligns well with our findings. The clarity provided by network and overlay visualizations significantly enhances our study, offering a more structured and comprehensible view of the data.

CONCLUSION

This bibliometric review delves into the application and scholarly examination of business analytics within the automotive industry from 2020 to 2025. The findings indicate sustained academic interest, particularly in themes such as operational efficiency, predictive maintenance, and the adoption of digital technologies, including artificial intelligence and the Internet of Things. Key research clusters emerged, with frequent references to decision-making, machine learning, and Industry 4.0. Such patterns reveal that analytics is predominantly harnessed to optimize internal processes and inform operational decisions within automotive firms. While topics like electric vehicles and sustainability are gaining traction, they remain on the periphery and have not yet attained centrality in literature. A notable contribution to this report lies in its structured overview of current research trends, enabled by bibliometric tools. The implementation of network, density, and overlay visualizations has facilitated the identification of thematic patterns and research gaps in a systematic manner.

Nevertheless, certain limitations are apparent. The analysis is restricted to publications indexed in Scopus, focused exclusively on English-language journal articles and conference proceedings within the business and management domains. This scope may exclude pertinent research, particularly rooted in technical or engineering contexts. Future research should consider consumer-oriented analytics applications, examining how data can enhance customer experience and brand loyalty. There is also significant potential for further investigation in the electric vehicle sector, especially regarding charging infrastructure, battery management, and usage behavior. Furthermore, contributions from scholars in developing regions are currently limited and warrant encouragement to achieve a more comprehensive, global perspective. In summary, while business analytics plays a pivotal role in the automotive sector, its broader potential especially in the realms of customer engagement and sustainable mobility remains underexplored and presents promising opportunities for future research.

ACKNOWLEDGMENT

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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