Factor Influencing the Adoption of Big Data Analytics: A Systematic Literature and Experts Review

In the current market landscape, enterprises face tremendous pressure to remain competitive and innovative to extend their businesses globally. Thus, a need exists for a new data analysis technique and a tool known as Big Data Analytics (BDA), which refers to massive data sets in light of their volume, velocity, variety, and veracity. Small and Medium Enterprises (SMEs) face challenges in obtaining and utilizing the knowledge derived from big data to make informed decisions regarding market selection and adopting appropriate internationalization strategies. These enterprises encounter resource limitations that hinder their ability to effectively acquire and implement big data insights for strategic decision-making in these areas. This systematic literature review aims to investigate the state of research on adopting big data analytics in SMEs. The study focuses on identifying key factors that influence the adoption of BDA. The study extracted 13 significant factors that are the highest influencers for BDA in SMEs. Those factors are top management support, training, relative advantage, it infrastructure, security, compatibility, complexity, adaptability, government it policies, competency, collaboration, digital transformation tools, and decision quality. The findings of this review are useful for practitioners, researchers, and decision-makers in understanding the factors that influence the adoption of big data analytics and the potential benefits and challenges associated with its implementation. Findings will shed light on the interplay between data governance and other factors influencing adoption, providing valuable insights for organizations seeking to establish robust data governance frameworks that support successful BDA initiatives. Plain Language Summary Key Factors in Adopting Big Data Analytics: Insights from Literature and Expert Reviews Purpose: The purpose of our study is to investigate the adoption of Big Data Analytics (BDA) in Small and Medium Enterprises (SMEs) and identify the key factors influencing its implementation. Methods: To achieve our research objective, we conducted a systematic literature review to gather and critically evaluate relevant studies on BDA adoption in SMEs. This approach allowed us to synthesize and analyze a wide range of existing research to gain comprehensive insights. Conclusions: Our study revealed 13 significant factors that have a strong influence on BDA adoption in SMEs. These factors include top management support, training, relative advantage, IT infrastructure, security, compatibility, complexity, adaptability, government IT policies, competency, collaboration, digital transformation tools, and decision quality. Implications: The findings of our research have practical implications for SMEs, researchers, and decision-makers. Understanding these key factors can help SMEs develop effective strategies to successfully implement BDA and gain a competitive advantage in the global market. For researchers, our study contributes to the body of knowledge on BDA adoption in SMEs, paving the way for further investigations and academic discussions in this area. Limitations: Despite the valuable insights gained from the systematic literature review, our study has certain limitations. The focus on SMEs may restrict the generalizability of the findings to larger enterprises. Additionally, while we strived to encompass a wide range of studies, there might be some relevant research that was not included in our analysis. These limitations should be considered when interpreting the results and applying them to specific contexts.


Introduction
The importance of the big data movement in supporting SMEs in collecting data and transforming it into competitive advantages has been widely documented throughout the worldwide market (e.g., Coˆrte-Real et al., 2017;Sivarajah et al., 2017).Adopting Big Data Analytics (BDA) enables managers to gain insight into their firm, allowing them to track performance and improve decision-making processes (Lutfi et al., 2022;Maroufkhani et al., 2023;Pauleen & Wang, 2017).
Despite its relative novelty, big data is a vital source of immense economic and social value and competitive advantage aligned with the SME's capital assets and human talent.The Prompt Cloud report in 2016 indicated the growth of big data from a U.S.$6.8 billion industry to a U.S.$32 billion industry in 3 years.In addition, the prediction of IDC indicated the big data technology and services market to grow by 23.1% compound annual rate, achieving U.S.$48.6 billion by 2019.Organizational data volumes rapidly increase, ranging from terabytes to tens or even hundreds of petabytes.As a result, business and IT leaders are actively seeking ways to capitalize on this vast amount of data to gain and sustain a competitive advantage.Studies have shown that companies allocate .10% of their IT budget exclusively to data-related initiatives (Toh et al., 2017) and are revolutionizing business by leveraging BDA as a strategic asset to bring about their decision-making and to enhance the processes and outcomes of their businesses (Grover et al., 2018;Lutfi et al., 2022;Maroufkhani et al., 2023).
More specifically, BDA refers to applying statistical, processing, and analytics methods of big data for business development and advancements.BDA has become indispensable in addressing specific customer requirements crucial to competitive advantage development and sustainability (Amankwah-Amoah & Adomako, 2019).As a result, businesses are adopting analytics initiatives for predicting the inclination of customers to buy particular new products for multiple reasons, among which are; (1) to make accurate, personalized recommendations for future purchases or to offer discounts, (2) to identify the causes behind roadblocks, failures and defects in real-time, or predict/fix potential ones before their occurrence, (3) to comprehend the experience of customers with products/services by analyzing online consumer reviews or using call center data to improve quality and develop innovative products, (4) to be able to respond promptly during a crisis and to develop ways to detect anomalies, (5) to enhance internal processes and determine operational roadblocks within the business enterprise.Analytics of structured and unstructured data streams can provide insights that could answer questions that remain unanswered in business.In the past decade, no business trend has had a considerable impact on IT investments as BDA has in the current times (Amankwah-Amoah & Adomako, 2019).
Nevertheless, BDA hype may lead to unexpected pressure on firms toward its adoption.It should, however, be kept in mind that a big data project's success lies in realizing the value of a strategic business from which firms can obtain a competitive advantage (Al-Dmour et al., 2023;Lutfi et al., 2022;Moraes et al., 2022;Sedkaoui et al., 2021).
Research exploring the utilization of big data in the context of internationalization in businesses is limited, particularly when it comes to understanding its theoretical and practical implications throughout the internationalization process.Nevertheless, the volume of big data continues to grow significantly.At the same time, SMEs face challenges in effectively analyzing and interpreting this vast amount of data due to constraints in available methods, technologies, tools, and processes (Baki, 2022;Mukred et al., 2023;Sena et al., 2019).
More importantly, the big data era has led to the technologies, methods, developments, and applications for effectively using massive data amounts to bring about decision-making and identify valuable knowledge.Big data is not just a single product but rather a combination of technologies and approaches for collecting, storing, processing, and accessing data and analyzing and visualizing it.The multiple layers involved in these processes make it challenging for small and medium-sized enterprises (SMEs) to manage, given their limited resources and abilities (Khan & Vorley, 2017).Moreover, big data refers to strategic technology and a competitive advantage source for businesses, notwithstanding their industry.It empowers data analysts to clarify Exabyte's diverse data sets, which are transformed into proper knowledge and information (Khan & Vorley, 2017).Big data can benefit businesses through high-performance analytics that stem from various sources (structured and non-structured ones).
The value of big data can only be realized if it can be conveniently accessed by key stakeholders regularly.Transactional data is typically created and stored digitally.This enables them to collect precise and thorough data about various items in real or near-real time, ranging from product inventory to employee sick days.SEMs can use big data to create segmentations specific to their services and products.In this context, comprehensive big data analytics can help improve decision-making, mitigate risk, and uncover insights that might otherwise remain hidden.It enables the development of new products and services, the enhancement of existing ones, and the formulation of unique business models (Sedkaoui et al., 2021).
Many researchers paid attention to the BDA form different perspectives (Roth et al., 2020).As an example, Dahiya et al. (2022) acknowledge the immense business value and valuable insights that BDA can offer and propose a framework that focuses on the relationship between the firm's specificity of BDA knowledge and its competitive advantage.Additionally, Waqas and Tan (2023) examined the role of big data analytics (BDA) and green technological innovation capabilities in promoting sustainable performance within the manufacturing industry and suggested that leveraging BDA and green technological innovation capabilities can help enhance environmentally sustainable practices in production processes.In the same line of research, Pancic´et al. (2023) investigated the factors of big data analytics and block-chain adoption and their subsequent influence on firm performance, driven by the increasing popularity of business intelligence.This highlights the importance of BDA as a professional tool to be used by governments and organizations worldwide (Hawash et al., 2023;Mukred et al., 2022).
The organization of this writing paper is as follows: the second portion is devoted to the role of big data analytics in SMEs and is followed by the third section, which presents the methodology.The findings and discussions are shown in the fourth portion, and the fifth section is devoted to the implications, which the conclusion will then follow.

The Role of Big Data Analytics in Small and Medium Enterprises
Big data analysis entails extensive data analytic tools, without which big data is essentially data that has not been evaluated.The combination of big data and big data analysis tools has the potential to lead to innovation (Stubbs, 2014).This can be contrasted with an invention, which is unique and original, whereas innovation is new but not always authentic.Most innovations are just improvements to existing products, information, or processes.In this aspect, big data insights improve existing goods, processes, and knowledge.
The analysis of online reviews or feedback from customers, such as those at Amazon, Best Buy, Walmart, and other businesses, reveals customer-driven design ideas that have the potential to spur innovation.This is true for attributes of products that can be gleaned from online reviews.Using the data and insights gleaned from these audits, new models are created to better the products and services already on the market (Jin et al., 2016).This data is readily available on websites and social media.SMEs use big data differently, with larger companies using it to streamline processes, improve customer service, and maximize efficiency.Companies stay competitive by finding innovative ways to collect, store, and analyze data.For instance, start-ups often create unique ways to use sensors and collect data from their products and services to enhance their value (Gobble, 2013).Big data also allows the government to have a deeper understanding of its constituents and provide novel responses to national and international issues (Bibri, 2018).Lamba and Singh (2017) emphasized the value of big data in gaining important and fundamental insights into efficient operations and supply chain management.In addition, industries like transportation, inventory management, cost optimization, and process development can all profit from big data.The amount of data collected on the factory floor is continuously increasing, as is the amount of data generated by various sources.Nonetheless, datasets are complicated, so conventional data processing programs cannot process them.As a result, big data analytics has altered this process by reducing production difficulties and waste while increasing predictive manufacturing and maintenance.
Big data analytics produce information and insights that could lead to informed decisions because such insights pave the way for different probable scenarios.Product creation, feature prioritization, ad testing, brand strengths, marketing mix optimization, customer segmentation, text mining, and pricing are all decision-making areas where big data analytics is applied.Therefore, they also contribute to making informed decisions (Agrawal, 2014).Concerning this, the pioneering decision support systems incepted in the mid-1960s have since been enhanced in terms of their efficiency and effectiveness, with advanced predictive analytics allowing more datacentered research.BDA begins with data analysis and prediction making, and from this, new organizational competencies for making decisions and competitive advantage are generated (Akter et al., 2019).Moreover, insights generated by big data are invaluable in their facilitation of informed decision-making for areas of critical development, including economic productivity, healthcare, natural disaster prediction, and handling and energy (Jin et al., 2016).The literature shows and supports that firms can gain substantial value and competitive advantage by basing their decisions on precise information.As a result, businesses are attempting to leverage big data to reach business decisions.Overall, big data can contribute to business value, through which revenues can increase (Sivarajah et al., 2017).Regardless of the business size, most businesses are attempting to exploit big data power to enhance their decision-making and technologies to produce new methods for data usage and activities that could lead to knowledge discovery (Storey & Song, 2017).
It is undeniable that data-driven decisions have greatly aided conventional decision-making processes.BDA has provided new models for precise decision-making in a context where the human decision-making process is fraught with complexity, intuitions, and personal bias (Chunfang & Zhongliang, 2015).Business decisionmakers utilize big data insights, but most companies store data in isolated sources.Integrating these sources can be difficult, but big data analytics can bring them together in one place for improved data-driven decisionmaking (Faroukhi et al., 2020).
Personal instincts and accurate data mostly back successful decisions, so quality data and other facilitating factors must be used for decision-making.According to Shahid and Sheikh (2021), real-time data points are necessary for data-driven decision-making.And in this background, big data and big data analytics can provide decision support capabilities (Power, 2014) for business performance enhancement, accurate forecasting, and fewer inventory costs (Pauleen & Wang, 2017;Shahat Osman & Elragal, 2021).Moreover, valuable insights from big data quickly lead to high profitability and an intelligent decisionmaking process (Canizo et al., 2017;Qi et al., 2016).
Big data is characterized by its large size, diverse types of structured and unstructured data, fast pace, and importance.A dataset over one terabyte is considered big data due to its volume.The velocity of data refers to the speed at which it is created and analyzed, resulting in real-time insights.The variety of data encompasses different forms, including audio, text, video, images, messaging, email, websites, social media, industrial sensors, blogs, and wearable devices.Big data, in a nutshell, is massive in quantity, changing at a breakneck pace, and diverse.Technology advancements in ''big data'' have made it possible to gather internal and external data sets, which can then be analyzed to yield valuable insights (Hallikainen et al., 2020).Big data tools and techniques differ from traditional relational tools as they use multiple tools for collecting, storing, and analyzing data to uncover patterns, connections, and insights.Unlike standard business intelligence tools, big data analytics can provide deeper insights for gaining a competitive advantage through productivity, innovation, and better decision-making (Mikalef & Krogstie, 2020).
In the present era of digitalization, big data and analytics have become one of the top trends from companies all over the globe.With data generation in companies throughout different sources/information systems, data volume, variety, and velocity are increasing, which is where big data analytics (BDA) becomes invaluable.Big data essentially refers to the large volume of structured and unstructured data that business is overwhelmed with daily.However, big data significance is not limited to the amount of data an organization has but what it does with that data.Big data analytics offer SEMs with a way to analyze data produced from different sources to determine the answers to questions concerning cost reduction, time reduction, new product development, optimized offerings, and informed decision-making (Al-Dmour et al., 2023;Kushwaha et al., 2021;Ranjan & Foropon, 2021;Shahbaz et al., 2021).
The effect of big data on companies of every size and industry is well documented and has the potential to transform the business world and society.Furthermore, the literature contains empirical evidence revealing retail companies' potential to obtain competitive advantage through data analyzed through big data analytics technologies.Regardless, SMEs are still in their infancy regarding BDA adoption (Lutfi et al., 2022;Moraes et al., 2022).Thus, this study highlights the factors impacting technology adoption among such enterprises.
Viewed through a pragmatic lens, big data analytics can pave the way for new opportunities, including but not limited to increased operational efficiency, improved strategic directions, optimum customer service, new products, services, customers, and market identification and development (Lutfi et al., 2022(Lutfi et al., , 2023)).
Therefore, the research is expected to provide insight into big data adoption challenges among SMEs in Saudi Arabia.As such, the study and its findings affect authorities, SME professionals, major stakeholders, and professional institutions.The findings are also expected to contribute to the development of performance through BDA adoption by SMEs management and decisionmakers, and to literature concerning BDA adoption, with exceptional insight into such adoption's challenges.

Methodology
A number of essential variables should be considered when creating a model for implementing BDA in SMEs.However, it's vital to remember that the relative importance of these variables might change from one business to the next.Therefore, any applicable model must be adjusted accordingly.It is also essential to consider these factors' interaction and dependencies.Consequently, it is crucial to have a proper methodology for extracting and identifying the factors before developing any model.Thus, this methodology is used to determine the significant factors that might influence the adoption of BDA in SMEs.
In Figure 1, we can see the four steps that make up the methodology of this study: an SLR, factor extraction, expert consultation on the BDA's factors, and factor analysis.
Questionnaires with a semi-structured format were used to gather information from 10 different private sector experts.Questionnaires were sent via internet platforms to score the elements that were retrieved and provide comments in the areas that were made available.Each questionnaire started with the interviewer introducing themselves and providing some background information on the reason for conducting the study.The primary focus of this study was the concern that factors either helped enable or hindered the influence to promote behavioral intentions to adopt such a system.This methodology was adopted by Mukred et al. (2016).
Conducting SLR and expert ranking can effectively identify the factors that influence the adoption of technology (S.Aldossari & Mokhtar, 2020;Alzahrani et al., 2021;Mukred et al., 2019;Oumran et al., 2021) such as BDA in small and medium-sized enterprises (SMEs).
A systematic literature review involves searching for, selecting, and critically evaluating relevant research studies (M.Aldossari & Zin, 2019).This can provide a comprehensive overview of the current state of knowledge on the factors that influence the adoption of big data analytics in SMEs.After identifying the relevant studies, the findings can be synthesized to identify the key factors consistently reported in the literature (Antons et al., 2023).
Expert ranking, on the other hand, involves soliciting the opinions of experts views on the factors that influence the adoption of big data analytics in SMEs.This can be done through interviews, surveys, or focus groups.The experts can be asked to rank the factors in order of importance or to provide suggestions for factors not identified in the literature (Alzahrani et al., 2021).This study uses a questionnaire to survey experts on the ranking of the factors.
Combining the results of a systematic literature review and expert ranking, a comprehensive and validated list of factors influencing the adoption of BDA in SMEs can be obtained.This list can then be used as the foundation for developing a model for identifying and understanding these factors and designing strategies to support the adoption of BDA in SMEs.
The following sections are the detailed techniques of SLR and expert consultations.

Systematic Literature Review (SLR)
In literature, BDA has yet to be extensively studied as studies dedicated to the topic are still few and far between, particularly in the field of adoption.BDA adoption in practice is also in the initial stage, and as such, an SLR lens is needed to present an overview of BDA studies to provide direction on how it is adopted among SMEs via the categorization of diverse existing top factors.Furthermore, assessing BDA implementation among enterprises is essential to determine the primary drivers and results and, in so doing, build the basis for its effective implementation that would enable organizations to exploit big data effectively and efficiently.Therefore, in this study, the author attempted to determine the highest potential number of studies that explored BDA business values at the level of SMEs.The search terms were thus chosen to include articles using and citing BDA and performance outcomes in the business fields.Terms limiting the drivers of BDA were not selected as the area is relatively novel, and only a few studies have made their explorations based on individual drivers.
The purpose of the SLR is to make several contributions, the first of which is to identify the number of studies dedicated to the topic in several well-reputed journals, particularly BDA implementation, and the second to determine the drivers and results identified in the papers when it comes to BDA implementation success.The SLR primarily provides a general review of BDA studies.In the next sub-sections, the SLR phases follow, and the analysis results are presented in the results and discussion section.
More importantly, technology acceptance is deemed to determine successful product/technology adoption, and its examination from the user's perspective can provide new information concerning the likes and dislikes of its various features, the product, and the user's attitude toward it (Hawash et al., 2023;Mukred et al., 2023).Therefore, a systematic literature review on the topic must present a complete picture of the acceptance level of big data technology.
Protocol Development.The first step in the SLR is the development of the protocol (Gruenhagen & Parker, 2020;Ji et al., 2023).This study followed the protocol established in the Cochrane Handbook for Systematic Reviews of Intervention (Cumpston et al., 2019).Such protocol addresses the study objectives: identify the factors influencing adopting the BDA and synthesize past findings on such adoption.The conditions for paper inclusion and exclusion, the strategy used to search the papers, assess their quality and categorize the results are all part of the SLR.
To begin with, the conditions required to identify papers categorization are as follows; First, the study developed a primary research question of ''What are the factors influencing BDA adoption?''to determine its answer; Second, the study conducted a thorough and robust research process to identify the papers published in reputable libraries online.The study used five main keywords that are BDA-related to categorize the published studies from reputable online libraries (Web of Science and Scopus).The keywords are: ''Big data analytics factors,'' ''Big data analytics adoption,'' ''Big data analytics models,'' ''Big data analytics in SMEs'' and ''Big data analytics for Decision making.''Following this step, the keywords were modified as varying sources differed in their inclusion criteria syntax.
The exclusion criteria are listed in Table 1.The information was collected from books, chapters, journal articles, conference proceedings, and other online sources.Appendix A contains the list of the final articles considered.
Third, articles were manually searched from books, conference proceedings, journal publications, and online sources through keywords.The Endnote reference manager software was used for managing the required information of references and bibliography, comprising of author's name, article title, conference/journal name, publishing year, and page numbering of the article.The process of searching, with keyword presentation, is illustrated below.The table contains the initial search, inclusion, exclusion, and filtering using the title, abstract, and content.The following Table 1 lists the criteria for the inclusion of articles.
Table 2 contains the criteria for the exclusion of articles Various phases were undergone to carry out the study selection process, with the first one entailing review of article titles based on the inclusion and exclusion conditions.The next step involved excluding irrelevant papers, after which the remaining articles' abstracts and findings were reviewed.The relevant papers were listed, and the top ones were included and assessed based on the conditions for inclusion and exclusion.
Finally, the study conducted a Quality assessment (QA) that has a key role in the established SLR protocol.Therefore, QA was conducted for the selected papers, where the authors reviewed the articles.They were evaluated in light of the defined research question.Another applied protocol is the quality criteria (QR), which needed to be defined for each research question, and they are the following; QR1: The paper describes information regarding the defined research questions.QR2: The paper falls on the specific study periods (dates), from 2016 to 2022, in BDA adoption.QR3: The paper presents BDA methods in detail.QR4: The paper contains a description of the benefits that BDA provides to benefits to SMEs.QR5: The paper is carried out in the BDA field.
The final list included papers reviewed and analyzed manually by the authors, and they have appropriated weights based on the review, QA, and QR.The consequences are as follows; for a wholly explained question (1), for an incomplete explanation (0.5), and lack of details regarding the defined question (0).
Quantifying the evaluation was further analyzed by assigning a total score to each paper based on their importance to this study, after which the assigned values of the two research questions are summed up for each article.English language as a medium of writing 3 The content is available.4 The paper includes information to answer research questions. 5 The articles in the selected databases.The content is not available.4 The paper does not include information to answer research questions. 5 The articles from the selected databases.

SAGE Open
Data Extraction.The selected papers were then reviewed to extract data based on the research questions, definitions, and review assessment.In the Findings and Discussion section, figures and tables display the extracted data, while Appendix A contains the selected papers, their titles, references, and publication years.
Data extraction was followed by drawing up a list of factors from the total sample works, numbering 60.Then, the research questions, keywords, search process, exclusion and inclusion conditions, and filtering process were obtained using the outlined keywords.The entire 507 titles in the libraries had their distinct folders, and their titles were checked manually and sufficiently.Next, the same works were determined through their titles and were dropped from the 507 articles.After manual filtering and initial selection, the remaining articles numbered 203.
After the scrutiny of the 203 articles' abstracts, 118 were selected.Following a manual filtering process of the contents, 60 articles were selected, after which the exclusion and inclusion process was carried out manually, and all 60 papers were exposed to the citations management process using Endnote Library.The study made references manually as citations were downloaded straight from the internet, and there was some information needed: for instance, information that may be lacking the name of the author, the title of the article, publishing place, page number, and the like.Finally, the selected papers were matched with the research questions in the protocol of the proposed SLR.
Furthermore, the BDA-selected studies were reviewed to highlight the general factors examined by the authors.The highlighted factors from which the top ones were selected are available in the next section.The following sub-section presents detailed explanations of the preparation of the factors with citations and frequencies.

Experts Ranking Technique
The expert ranking technique extracts factors relevant to a particular topic or research question.It is a form of expert judgment where a group of experts in the field is asked to rank a set of items or factors based on their importance or relevance to the topic (Wang et al., 2016).The items or factors can be anything from variables in a study to potential solutions to a problem, product or service characteristics.
The methodology for consulting experts to rank factors typically involves several steps: 1. Identify the experts: Identify a group with knowledge and expertise about the research question or topic.They should be selected based on their relevant qualifications, experience, and publications.It is important to note that the sample size of experts should be large enough to reflect a consensus view and avoid bias or outliers.Also, the questionnaire should be straightforward to understand to avoid confusion and misinterpretation.In this study, the ranking process is typically conducted through a survey or questionnaire, where the experts are asked to assign a score or ranking to each item or factor.Once the data is collected, the results are analyzed, and the factors with the highest scores or rankings are considered the most important or relevant.This method is often used in IT, IS, marketing, management, and engineering, where experts have specialized knowledge and expertise that can provide valuable insights (Mukred, 2017;Mukred et al., 2019).The experts' experience and integrity are significant in selecting technology adoption factors (Mosweu et al., 2016).In addition, the evaluation method used by the experts has had positive outcomes in the past when used in the identification of the factors for IS adoption by various organizations in developed and developing nations (Gruenhagen & Parker, 2020;Hawash et al., 2020;Mukred et al., 2019;Schneider & Sunyaev, 2016).
Hence, after the above step, the factors that have the potential to influence BDA were finally identified and narrowed down, after which they were forwarded through email to 10 experts with IT, IS, and technology adoption backgrounds and expertise, and they were requested to add or remove any factor at their discretion.
Each factor is described in the questionnaire to be easily understood and to obtain an accurate response.The respondents' evaluation was carried out through an online survey.They were requested to appraise each factor's importance in light of BDA adoption on a 5-point Likert scale, with 1 reflecting the factor's very low priority and five reflecting its very high importance.The respondents were experts with sufficient IT, SME, and technology adoption knowledge and at least 5 years of experience in the mentioned fields.They were also PhD holders who had published works in WoS or Scopus.

Findings and Discussion
The research conducted in this study aimed to investigate the factors influencing the adoption of big data analytics.We gathered a wealth of data and information on the topic through a combination of SLR and expert rank.This section will present our research findings, highlighting the most important and relevant information uncovered.The results are discussed and analyzed in detail, and we provide a comprehensive overview of our findings.

Descriptive of SLR Findings
The systematic literature review conducted in this study aimed to uncover critical findings related to big data analytics adoption.Through an extensive search and analysis of relevant literature, we identified several key themes and trends that emerged to address the study's research question.In this section, we will present the findings of our literature review, highlighting the most significant and relevant information uncovered.These findings provide valuable insights into the current state of knowledge of big data adoption and will serve as a foundation for further research and analysis.
Years, Citations, and Publication Outlet.The publications were mainly carried out in 2021, with 16 studies, followed in descending order by several studies in 2021, 2022, 2020, 2019, 2018, and 2017-after which there was only one study conducted in 2016 (refer to Table 3).The studies' diversity for every year is depicted in Figure 2.
Table 3 lists the included works.It is clear from the table that an increasing number of studies have been dedicated to the concept through the years, which underlines the BDA's significance and applications in the field.Figure 2 shows the range of years and the number of years within them.
In Figure 3, the studies conducted in 20 countries are presented, with nine studies from China, eight from India, six from Malaysia, five from the U.S, four from Pakistan and Iran, three each from France, Korea, UK, and Saudi Arabia, two from Spain and Jordan, and one each from Turkey, Norway, Bangladesh, Netherlands, Scandinavia, Tanzania, Brazil, and Sri Lanka.
SLR-included studies also have higher citations varying from each other, as presented in Figure 4. Most of the studies were cited over 10 times.
Theory Focus.The SLR analysis shows that TOE was the top dominating theory used by BDA adoption studies, with TAM, UTAUT, Resource Based Theory, and Diffusion and Innovation Theory following suit.Also, several studies investigated the adoption of BDA without using theories, as shown in Figure 5.
The results of the collected data from the SLR are prepared in detail under this sub-section and the winnowing of the factors.The factors' number of citations was counted and used to show the value of their importance.This process was carried out based on the following testing value; 1. Included-if the proposed factor citations are ø 3, the factor is deemed important because it impacts the decision to adopt BDA. 2. Excluded-if the proposed factor citations are \3, the factor is deemed less important as it does not impact the decision to adopt BDA.
Frequency refers to the number of citations of the factors in past studies, although this does not represent the ordinariness and common characteristics of the factor (Finney & Corbett, 2007;Law & Ngai, 2007;Mukred et al., 2019).The factors with the most citations noted in the SLR are tabulated in Table 4.
The rest of the factors following the filtering process were only mentioned a few times in the literature and were thus excluded.Thus, a list of 21 factors was sent to experts to rank.

Experts' Consultations
The list of the factors was analyzed on selected criteria and their influence on adopting BDA.The experts were unanimous in selecting the 12 top factors influencing behavioral intention toward BDA adoption and one factor to be as affected by the actual use (refer to Table 5).
In summary, security, compatibility, complexity, and adaptability factors are essential when adopting big data analytics.Therefore, organizations must ensure that they have the necessary security measures in place, that their systems and tools are compatible with their existing    infrastructure, that they have the resources and expertise to handle the complexity of big data, and that they can adapt to new technologies as they become available (Lutfi et al., 2022;Maroufkhani et al., 2023).
Security is a significant concern for organizations that adopt big data analytics.As large amounts of sensitive information are collected and stored, there is an increased risk of unauthorized access or data breaches.To mitigate this risk, organizations should implement robust security measures such as encryption, firewalls, and regular security audits (Lutfi et al., 2022).
Compatibility is another essential factor to consider when adopting big data analytics.Organizations must ensure that the systems and tools they use for big data analytics are compatible with their existing infrastructure and can integrate with other systems (Lutfi et al., 2022;Maroufkhani et al., 2023).
Complexity is also a significant challenge when it comes to big data analytics.The sheer volume and variety of data can make it challenging to manage and analyze, and organizations must have the necessary expertise and resources to handle the complexity (Lutfi et al., 2022;Maroufkhani et al., 2023).
Adaptability is crucial in big data analytics as technology constantly evolves.Therefore, organizations must adapt to new tools and techniques as they become available to stay competitive and make the most of the insights provided by big data analytics (Ngo et al., 2020).
In addition, organizational factors, top management support, relative advantage, IT infrastructure, and training are all critical factors to consider when adopting big data analytics.Organizations must secure the support and commitment of top management, clearly demonstrate the relative advantage of the technology, ensure that they have the appropriate IT infrastructure, and provide their employees with the necessary training and education to effectively use the technology.
Top management support is crucial for successfully adopting big data analytics within an organization.Without the permission and commitment of top management, it can be challenging to secure the necessary resources and funding for the project and gain buy-in from other stakeholders.Additionally, top management can help set the strategic direction for the project and ensure that it aligns with the organization's overall goals (El-Haddadeh et al., 2021).
Relative advantage is another critical factor to consider when adopting big data analytics.Organizations must demonstrate the benefits and value that the technology can provide, such as improved decision-making, increased efficiency, and cost savings.Without a clear understanding of the relative advantage, it cannot be easy to gain support and funding for the project (Lutfi et al., 2023).
IT infrastructure is also a significant factor in adopting big data analytics.Organizations must have the necessary hardware, software, and network infrastructure to support storing, processing, and analyzing large amounts of data.Without the appropriate infrastructure, it can be not easy to effectively utilize the insights provided by big data analytics (Al-Dmour et al., 2023).
Training is essential in adopting big data analytics, enabling employees to use the technology effectively and efficiently.Therefore, organizations must provide their employees with the necessary training and education to understand the technology and how to use it to gain insights.Without proper training, employees may be unable to use the technology effectively and may not fully realize its potential benefits (Lutfi et al., 2023).
On the other hand, the environmental factors that were extracted include Government IT policies, competency, collaboration, and digital transformation tools, which are all essential factors to consider when adopting big data analytics.Organizations must understand and comply with government IT policies, ensure they have the necessary skills and expertise, collaborate with other organizations and government agencies, and utilize the appropriate digital transformation tools to collect, store, process, and analyze large amounts of data and gain insights from it.
Government IT policies play a significant role in adopting big data analytics.Government policies can influence the legal and regulatory framework for the use and handling of data, which in turn can affect the ability of organizations to collect, store, and analyze data.Therefore, organizations must understand and comply with government IT policies to effectively adopt big data analytics (Ghaleb et al., 2021).
Competency is also a crucial factor in the adoption of big data analytics.Organizations must have the necessary skills and expertise to use the technology and gain insights from the data effectively.Without the appropriate competency, organizations may struggle to make sense of the data and may not be able to fully realize the benefits of big data analytics (Ngo et al., 2020).
Collaboration is essential in adopting big data analytics, as it allows organizations to share resources and expertise and gain insights from the collective data.
Collaboration can take many forms, such as partnerships between organizations or sharing of data and insights between government agencies (Park & Kim, 2021).
The study found that digital transformation tools are necessary for organizations to adopt big data analytics.These tools can include data visualization tools, data management tools, cloud computing platforms, and machine learning and artificial intelligence platforms.These tools enable organizations to collect, store, process, and analyze large amounts of data and gain insights from it.

Implicatins
The findings of a systematic literature review and expert ranking of the factors that influence the adoption of big data analytics in SMEs can have several important research implications.
Firstly, the findings can be used to identify gaps in the current body of knowledge on BDA adoption.For example, suppose certain factors are consistently recognized in the literature but not by experts, or vice versa.In that case, this may indicate a need for further research in those areas, which will open the door for future researchers to investigate the issues in this regard.
Secondly, the findings can be used to inform the development of models and frameworks for understanding and predicting the adoption of BDA in SMEs.This can be useful for practitioners and researchers alike in understanding the underlying drivers of adoption and how to design strategies to support it.
Thirdly, the findings can be used to inform the design of interventions and strategies to support the adoption of BDA in SMEs.For example, if the results indicate that a lack of leadership support is a major barrier to adoption, interventions to increase leadership buy-in may be required.The results also highlighted the most significant factors that will support adopting BDA and help set up the strategies and policies.
Finally, the findings can be used to inform the development of educational and training programs to support the development of the skills and expertise required for the effective use of BDA in SMEs.

Conclusion
This paper presents a systematic literature review and expert ranking of the factors influencing big data analytics adoption in small and medium-sized enterprises (SMEs).The systematic literature review was conducted by searching for and critically evaluating relevant research studies.The expert ranking was achieved by soliciting experts' opinions in the field through interviews, surveys, or focus groups.The systematic literature review results revealed that several key factors influence the adoption of big data analytics in SMEs, including organizational readiness, technical factors, economic factors, environmental factors, and social factors.The expert ranking results were consistent with the literature review findings and provided additional insights into the relative importance of these factors.The results of this study provide a comprehensive and validated list of factors that influence the adoption of big data analytics in SMEs.The study concludes by highlighting the importance of considering these factors when developing a model for identifying and understanding the factors that influence the adoption of big data analytics in SMEs and designing strategies to support the adoption.In summary, the paper provides a systematic approach to identifying the factors that influence the adoption of big data analytics in SMEs by conducting a literature review and expert ranking.The factors identified include organizational readiness, and technical, economic, environmental, and social factors, which are essential to consider when developing a model or strategies to support the adoption of big data analytics in SMEs.Future research in this regard is required to conduct more in-depth case studies or qualitative research to understand better how these identified factors manifest in different SME contexts.Such studies could shed light on SMEs' specific challenges and opportunities in adopting BDA and provide valuable insights for tailoring adoption strategies to meet individual, organizational needs.Given the dynamic nature of technology and business environments, conducting longitudinal studies to track the evolving adoption trends of big data analytics in SMEs over time would be beneficial.This longitudinal perspective can help researchers and practitioners observe changes in the importance of different factors and identify emerging challenges and best practices in the field.

2.
Develop the questionnaire: Develop a questionnaire or survey with a list of factors or items related to the research question or topic.The experts should be asked to rank each item or factor based on their importance or relevance to the research question or issue.3. Administer the questionnaire: Administer the questionnaire to the experts either in person, by mail, or online.4. Analyze the data: Analyze the data collected from the questionnaire to identify the factors or items with the highest rankings.These are the most important or relevant research questions or topic factors.5. Validate the results: Validate the results by consulting additional experts to ensure the robustness of the ranking.6. Interpret the results: Interpret the results in light of the research question or topic and provide recommendations for further research or action.

Figure 5 .
Figure 5.The most used theories in SLR.

Table 3 .
Pieces of Research Per Year.

Table 4 .
Ranking of the Extracted Factors From Systematic Literature Review.

Table 5 .
List of Factors Recommended by Experts.

Table A1 .
List of Literature Included in the Study.