Big data analytics (BDA) marks a pivotal milestone in many sectors for several purposes. While there has been an apparent surge on data analytics research recently, comprehensive systematic reviews remain limited. BDA is an advanced analytical technique of data management which helps to create meaningful insights that aid complex decision-making. ^{1, 2} Power et al. ³ claimed that business analytics and data analytics are specific subtypes of analytics where diagnostic, predictive and prescriptive subcategories rooted within the types. Policy makers and managers in contemporary business environments that are changing rapidly prefer to make decisions which are based on real-time data rather than relying on their internal insights. The implementation of BDA has emerged as a pivotal force influencing various sectors on a global scale, fundamentally altering organizational operations and decision-making processes. As of 2023, nearly 92% of universal digital leaders stated that their companies had adopted cloud technology to a certain extent. ⁴ Big data analytics was the second most popular adopted technology with around 61% adoption rate, and by 2027, global digital transformation spending is forecast to reach USD 3.9 trillion. ⁴ As firms increasingly recognize the importance of insights derived from data, the integration of BDA has become essential for maintaining competitive advantages and enhancing operational efficiency.

This trend is particularly evident in sectors such as healthcare, finance, retail, and education, where the ability to analyze large volumes of data can lead to improved outcomes and strategic advantages. ^{5, 6} Despite its potential, the global adoption of BDA has not been uniform. Most businesses responding to a 2023 survey conducted by Statista Research Department stated that investment in data and analytics was a top priority. However, only 37% mentioned that their efforts to improve data quality had been successful, highlighting an ongoing challenge faced by organizations across industry sectors. ⁴ Elgendy et al. ⁷ argued that there is a sheer need for data-driven cultures where data is treated as a significant asset at any organization. Various factors influence the rate and success of BDA implementation, including technological readiness, organizational culture, and external pressures. ^{8– 10} For instance, organizations with a robust technological infrastructure and a culture that embraces innovation are more likely to adopt BDA effectively. Conversely, those facing challenges such as data silos, lack of skilled personnel, or resistance to change may struggle to implement these technologies successfully. ^{11, 12} In the healthcare sector, BDA enhances patient care, streamlines operations, and improves research outcomes. By analyzing patient data, healthcare providers can identify trends, predict outcomes, and personalize treatment plans. ⁶ Similarly, in finance, BDA enables organizations to detect fraud, assess risks, and optimize investment strategies through real-time data analysis. ¹³ Retail establishments utilize BDA to gain insights into consumer behavior, optimize supply chain logistics, and enhance customer engagement. ¹⁴ By analyzing purchasing behaviors and preferences, businesses can tailor their products and marketing strategies to align more closely with consumer demands. Big data is a vital aspect of innovation, which has recently gained attention from academics and practitioners in the higher education sector. ¹⁵ In the realm of the education sector, using data analytics has augmented the capacity of institutions to monitor, evaluate and enhance student learning outcomes. By harnessing extensive datasets, universities are making informed decisions concerning student support, curriculum innovation and institutional management. This transformation has become evident within online education, particularly after the COVID-19 pandemic. As institutions transitioned to remote learning, data analytics emerged as a critical component in preserving academic continuity, providing novel methodologies to assess and evaluate students’ engagement and performance, the process defined as “learning analytics”. ¹⁶

In line with the practical side, studies on data analytics adoption have been growing in the past decade, coming from different fields or focuses. One of the first comprehensive bibliometric analyses on big data analytics adoption was published in 2021, spanning studies from 2014 to 2018. ¹⁷ The study employs 516 published papers to explore the trends, tools and techniques used in BDA adoption, particularly within the supply chain industry. In addition, this study conducted a Systematic Literature Review (SLR) of 79 papers summarizing the problem addressed in each paper and the proposed solution. This study provided a broad overview of big data analytics adoption, including applications, benefits and challenges across many sectors. The authors categorize the reviewed papers in this study into key areas, revealing that manufacturing and service industries are the most studied. Furthermore, the study highlights that the motivation for the research stemmed from the benefits of adopting BDA combined with a lack of sufficient research in the area.

The contributions of the present study to literature are three-fold. The study utilizes a differed approach in reviewing the literature in determining the factors of branches of data analytics adoption and adds to the novelty by linking the factors with the respective theoretical framework, thus enabling future researchers to develop new conceptual models that integrate two or more of the theories used in the past. The findings of this paper are expected to add to the literature of data analytics adoption and systematic review by offering an explicit topic related to data analytics adoption. However, this can also be extended to other forms of innovation and technology adoption, as the world is now evolving, and many organizations in different sectors give emerging technologies prominent priority. Another uniqueness of this paper is that it provides a holistic view of the antecedents of individual-level data analytics adoption. Employees play a pivotal role in the success of any organization. Therefore, employees need to adapt to novel technology and change for the organization to benefit.

The study seeks to address four main research objectives: 1.

To explore publication trends, key authors and methodological approaches used in existing studies.

This paper is organized as follows. Section 2 discusses similar research conducted in the area. Section 3 presents the Materials and Methods including the systematic literature review execution. Section 4 interprets the findings, followed by the discussion of results in Section 5. Finally, the conclusions in Section 6 summarizes the findings, considers the limitations, implications arising from the study and directions for future research.

Studies on data analytics adoption have been growing over the past focusing on different domains across different sectors. However, limited literature reviews have been conducted, and the findings are often confined to a particular sector. One research closely related to the current study is a systematic review of the literature conducted by Ref. 18 to investigate the technological, organizational and environmental factors that affect the adoption of business analytics. The authors utilized the PRISMA technique to conduct an in-depth analysis of relevant research papers published between 2012 and 2022 ultimately selecting 29 articles for thorough examination. The researchers adopted the Technological-Organizational-Environmental (TOE) framework as an overarching theoretical lens to evaluate technology adoption at an organizational level. However, this study focuses solely on organizational adoption using the TOE framework. The authors highlight that future research could explore other theoretical frameworks and include a broader range of sources. Another systematic review conducted by Adrian et al. ¹⁹ using a relatively small sample of 18 relevant papers published between 2010 and 2017, identified ten key factors that influence the success of BDA implementation within organizations namely organization capability, human capability, analytics capability, analytics culture, environment, data management, data and information quality, system quality and perceived benefits. Aldossari et al. ²⁰ also conducted a systematic literature review to identify key factors that influence big data analytics adoption in Small and Medium Enterprises (SME’s). The study aims to understand these factors to help SME’s effectively implement BDA and gain a competitive advantage, focusing on articles published between 2016 and 2022. After extracting 60 factors from the literature, a filtering process based on the frequency of citations narrowed it down to 21 factors, which were then sent to 10 private sector experts for ranking. The authors then identified 13 significant factors that are the highest influencers for BDA adoption in SMEs based on SLR expert ranking. However, the research has only been limited to SMEs and adoption at organizational level which emphasizes the need to explore other sectors. In another study, Al-Azzam et al. ²¹ highlights that although the knowledge in the area will expand due to the continuous enhancements in the development of big data application, academics are struggling to establish the key theories.

The existing literature, summarized in Table 1 has largely focused on determinants of BDA adoption, drawing from models such as the Technology Acceptance Model (TAM), Task-Technology Fit (TTF), and the Technology-Organization-Environment (TOE) framework. However, there is a noticeable gap in understanding how these adoption factors translate into firm-level value realization and strategic outcomes. Additionally, individual-level enablers, such as trust, user acceptance, and management support, are underexplored in their connection to organizational performance. The current research landscape also reveals theoretical and contextual fragmentation. For example, while Resource-Based View (RBV) and Dynamic Capabilities theories provide a strategic lens for understanding value creation, they are seldom integrated with adoption models. Moreover, specific application contexts such as logistics, public institutions, and learning analytics remain peripheral in empirical investigations.

Due to these limitations, there is a rising need for a more structured and comprehensive review of data analytics adoption models and theories that can guide rigorous research in various settings, including diverse industries and economies, thus emphasizing the urgency in implementing data analytics to cater to the benefits it offers.

Given the rapid evolution of BDA research from 2020 to 2024, there is a timely need to systematically synthesize existing knowledge, map out emerging trends, and identify critical gaps to address this need. This paper undertakes a Systematic Literature Review (SLR) by analyzing peer-reviewed studies using bibliometric mapping and qualitative synthesis techniques.

The literature has adopted diverse methodologies in conducting research on data analytics adoption. One such approach is a Systematic literature review (SLR). SLR represent a methodological approach to synthesizing scientific evidence aimed at addressing a specific research question in a manner that is reproducible, while ensuring that all published evidence pertaining to the subject matter has been included. ¹⁸ SLR is considered the appropriate methodological tool for the present study due to its ability to thoroughly summarize the influential determinants of data analytics adoption at both individual and organizational levels across different sectors. SLR follows a stepwise approach that first defines the aim of the review, formulates research questions, selects suitable evidence, evaluates the quality of evidence, collects data and analyze the results. ²⁰

In this study, the systematic review adhered to the new PRISMA guidelines proposed by Page et al. ²² to ensure transparency and accuracy in reporting the findings. The SLR was conducted in three distinct stages as applied by Horani et al. ¹⁸ These stages include: (1) planning stage; (2) execution stage; and (3) summarizing stage as discussed in detail below.

This section reports the main findings from the systematic literature review with the aim of addressing the research objectives listed in Section 1. This section is structured into three subsections. While Section 4.1 conducts a descriptive analysis of the selected studies, Section 4.2 identifies key theoretical frameworks and determinants, and Section 4.3 outlines results from the keyword, content and co-authorship analysis.

This section aims to discuss the main findings, highlight research gaps and provide future research agenda and implications of theory and practices to achieve the research objectives in Section 1.

Analysis of publication trends indicates growing interest in the topic, particularly post-2018, reflecting the increasing organizational emphasis on data-driven decision making. However, the research remains unevenly distributed across industries and geographies, with a concentration in sectors like manufacturing, healthcare, finance and IT and in developed economies. ^{10, 21, 31, 36, 40, 41} There is limited research density surrounding keywords associated with specific sectors, like logistics and small and medium-sized enterprises (SMEs). This indicates a gap in sector-focused studies that could shed light on unique adoption challenges and solutions. Furthermore, the absence of substantial studies within the public sector, education, and developing countries suggests overlooked barriers and facilitators of technological adoption in these critical contexts.

A key insight from this review is that these five dimensions are deeply interconnected. Technological readiness, such as data infrastructure and analytics capabilities, often depends on organizational culture and support. Relative advantage, sometimes referred to as Perceived strategic value ^{23, 42, 43} positively influenced BD adoption. Firms which recognized the value of BDA were more inclined to adopt it than others. Similarly, individual-level factors like data literacy and user attitude are shaped by organizational training programs and leadership commitment. Environmental factors, including competitive pressure and regulatory environments, act as external motivators that influence organizational behavior. Moreover, data-related factors such as quality, availability and volume serve as foundational enablers across all other categories. The evidence suggests that determinants related to the individual are pivotal in BDA adoption. The study classifies individual factors as individual capabilities, personality traits, individual beliefs and individual behavioral factors as summarized in Table 8. Empirical investigations have indicated that adoption of emergent technologies may necessitate the incorporation of “soft skills” alongside behavioral intentions, technical proficiencies and domain-specific knowledge. ²¹ The study further adds that it is imperative to consider social influences, belief systems and contextual factors when advocating for the adoption of novel technologies. Human expertise and skills, which produced similar outcomes across all findings, was the most employed determinant and a significant factor for adoption across education, finance, manufacturing and supply chain sectors at both organizational and individual levels. ^{11, 27, 40, 44, 45}

Despite the breadth of studies, several gaps persist, as evident from the content analysis conducted by Vos viewer. There is insufficient theoretical cohesion among clusters like the Technology Acceptance Model (TAM) and Resource-Based View. The division indicates a need for unified conceptual models that merge behavioral, organizational, and resource-based perspectives for a multi-level adoption study. There exists a notable gap in understanding how individual user behavior influences firm-level success. Most of the research focuses on initial adoption and its determinants, with post-adoption behaviors such as usage maturity, sustained benefits, and organizational learning being notably underrepresented. The long-term impacts on performance are also scarcely discussed, highlighting an area ripe for exploration. Understanding how organizations leverage technology after its adoption to achieve sustained benefits remains largely unexplored. While themes such as trust and organizational capabilities are present, their representation is scarce. This indicates a gap in the understanding of human-centric factors, such as Digital literacy, Resistance to change, Organizational culture, Leadership roles in technology adoption. More emphasis on these dimensions is essential for comprehensively understanding adoption dynamics and user engagement. Keywords associated with newer technologies like AI, blockchain, and edge computing are notably missing or underrepresented, reflecting a temporal lag in research adaptation to evolving technological trends. There is limited integration of how learning analytics contributes to organizational performance. This highlights the need to explore how data driven learning systems contribute to firm performance and innovation. There is limited research on how top management support influences employee level technology acceptance. Investigation of the moderating role of top management support on the technology adoption intention is vital. There is also a lack of cross-industry and interdisciplinary approaches that could explore the intersection of big data analytics (BDA) with themes like sustainability, governance, or ethics, indicating a need for more interconnected research paradigms.

The review offers a holistic approach that can guide both practical implementation and future research. For practitioners, findings emphasize the need for an integrated approach to analytics adoption that addresses technological capabilities, organizational support structures, employee engagement, external pressures and data maturity. For researchers, the results highlight the importance of expanding theoretical perspectives and applying more diverse methodologies to capture the complexity of adoption in real-world settings.

This study employed the SLR technique to explore the evolution of data analytics research and the determinants of data analytics adoption intention and implementation as available in scholarly literature. The Web of Science and Scopus databases were mainly selected for data retrieval, and several inclusion criteria were applied to select the final documents to be reviewed. Particularly, this study highlights that technology adoption depends not only on the organizational characteristics but also on the user’s individual beliefs, individual behavioral factors, personality traits and human capabilities. This study emphasizes the interplay between technological, organizational, environmental, individual and data-related factors which need to be considered when planning and implementing data analytics initiatives. The factors in the literature are derived from various theoretical backgrounds such as TOE, TAM, UTAUT, Task Technology fit and Institutional Theory. From these theories, it was found that TOE was employed the most for organizational level adoption. Researchers mainly utilized UTAUT to identify determinants affecting individual level adoption. Furthermore, studies have also integrated Institutional Theory, consisting of variables such as Coercive Pressure, Normative Pressure and Mimetic Pressure, the Initial Trust model, and the Theory of Perceived Risk with UTAUT to enhance the performance of the model in determining the intention to adopt data analytics at the individual level. Individual capabilities such as employee readiness, human expertise and skills, statistical background and critical thinking skills played important roles in data analytics adoption. Personality traits such as personal innovation and individual beliefs such as initial trust and perceived privacy and security have proven to significantly impact data analytics adoption. The study revealed that TOE was often coupled with Diffusion of Innovation and Critical Success Factors to examine antecedents of data analytics adoption at the organizational level. Technological and data related factors such as compatibility, complexity, relative advantage, infrastructure capabilities, system quality, information quality are crucial enablers for effective data analytics adoption. Organizational factors are identified as critical determinants of data analytics adoption. Significant organizational factors include facilitating conditions, organizational readiness, information sharing, internal training, absorptive capability and financial resources. In addition, factors related to the environmental dimension such as competitive pressure, mimetic pressure, government support, vendor and trading partner support, environmental uncertainty, and strategic orientation are found to be pivotal in influencing data analytics adoption.

This SLR contributes to the literature of SLR and data analytics adoption alike. First, this is among the first studies which presents the duality consisting of established theories and individual constructs of data analytics adoption and differentiates among individual and organizational level adoption. This evidence shows that studies on data analytics adoption may continue to develop in the future. Second, this study extends the method by providing a comprehensive and structured framework, highlighting trends in theoretical approaches, and uncovering gaps such as limited cross-industry research and underexplored individual factors through the content analysis. This study could be further utilized for other technological adoption as well. The insights from this review can guide organizations and individual users in making informed decisions and developing strategies to leverage data analytics for competitive advantage.

However, this study has some limitations. In terms of adoption, the study does not differentiate the actual usage and intention to use. However, based on the Theory of planned behavior, intention to use leads to actual usage but this may vary according to the context. Thus, future research may segregate the two levels of adoption exposure and relate it to the respective industry to project a clear understanding of the determinants at each level and sector. Secondly, the review is confined to empirical studies published in journal articles. The exclusion of other sources, such as conference proceedings and books, may result in the loss of potentially valuable insights. Thirdly, the study only considers empirical studies utilizing cross sectional data. Since data analytics adoption is an adoption process, future reviews can consider longitudinal studies to evaluate the effectiveness of data analytics adoption over time and identify the pivotal determinants which remain significant over time. Further this study generalizes data analytics adoption only. But, data analytics itself involves multiple branches in different domains, including big data analytics, business analytics, learning analytics, predictive analytics, prescriptive analytics, and descriptive analytics. Determinants may vary depending on the context of the type of analytics. Future research could distinguish the types of analytics to obtain more precise analysis results.