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Abstract

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.

Keywords

big data analytics systematic literature review technology adoption experts rank factors extraction small and medium-sized enterprises

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., Côrte-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, Pancić 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 data-centered 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 decision-makers 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 decision-making (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 decision-making 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, 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 decision-makers, 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.

Figure 1.

Methodology adapted from Mukred et al. (2016).

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.

Table 1.

Inclusion Criteria.

No	Criteria
1	The journals published from 2016 to 2022
2	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.

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

Table 2.

Exclusion Criteria.

No	Criteria
1	The journals published before 2016
2	Journal has not been written in English.
3	The content is not available.
4	The paper does not include information to answer research questions.
5	The articles from the selected databases.

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.

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:

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.

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.

Administer the questionnaire: Administer the questionnaire to the experts either in person, by mail, or online.

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.

Validate the results: Validate the results by consulting additional experts to ensure the robustness of the ranking.

Interpret the results: Interpret the results in light of the research question or topic and provide recommendations for further research or action.

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.

Pieces of Research Per Year.

Year	No of studies	Authors
2022	10	(Maroufkhani et al., 2023), (Youssef et al., 2022), (Iranmanesh et al., 2023; Moraes et al., 2022), (Lutfi et al., 2022), (Li et al., 2022), (Chong & Lim, 2022), (Binsawad et al., 2022), (Guomin Chen et al., 2022), (Chen et al., 2022)
2021	16	(Villarejo-Ramos et al., 2021), (Gong & Janssen, 2021), (Lei et al., 2021), (Shahbaz et al., 2021), (Raut et al., 2021), (Dubey et al., 2021), (Wook et al., 2021), (Alalawneh & Alkhatib, 2021), (Rahman et al., 2023), (Bakici et al., 2023), (Benzidia et al., 2021), (Keshavarz et al., 2021), (Ghaleb et al., 2021), (Park & Kim, 2021)
2020	11	(Saleem et al., 2020), (Shabbir & Gardezi, 2020), (Ghasemaghaei, 2020), (Yadegaridehkordi et al., 2020), (Cabrera-Sánchez & Villarejo-Ramos, 2020), (Mikalef & Krogstie, 2020), (Dubey et al., 2020), (Shahbaz et al., 2020),
2019	10	(Raut et al., 2019), (Moktadir et al., 2019), (Ghasemaghaei, 2019), (Nam et al., 2019), (Okcu et al., 2019), (Shahbaz et al., 2019), (Shamim et al., 2019), (Singh & El-Kassar, 2019), (Dubey et al., 2019), (Shorfuzzaman et al., 2019)
2018	8	(Yadegaridehkordi et al., 2018), (Verma et al., 2018), (Caesarius & Hohenthal, 2018), (Raguseo, 2018), (Lai et al., 2018), (Joshi & Biswas, 2018), (Dubey et al., 2018), (Mahesh et al., 2018)
2017	4	(Brock & Khan, 2017), (Gunasekaran et al., 2017), (Chavez et al., 2017), (Kim & Park, 2017)
2016	1	(Akter et al., 2016)

Figure 2.

Studies per year.

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.

Figure 3.

Studies per country.

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.

Figure 4.

Studies citations.

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.

Figure 5.

The most used theories in SLR.

Factors Extracted by SLR

To answer the study question, the study carried out the SLR to determine the top factors that affect BDA adoption among SMEs, upon which the factors that influence the adoption of BDA are identified. This is followed by factors extraction, categorized based on their frequent mention in literature, after which they are forwarded to experts for ranking and for additional recommended factors that are not in the list.

An effective SLR involves many steps presented and discussed in the following subsection.

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;

Included—if the proposed factor citations are ≥3, the factor is deemed important because it impacts the decision to adopt BDA.

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.

Table 4.

Ranking of the Extracted Factors From Systematic Literature Review.

No	Factor	Frequency	Included/Excluded
1	Compatibility	10	Yes
2	Complexity	9	Yes
3	Top management support	9	Yes
4	Analytics capability	8	Yes
5	Competency	8	Yes
6	Cost of Adoption	8	Yes
7	Decision quality	8	Yes
8	Environmental characteristics	8	Yes
9	Financial factors	6	Yes
10	Organizational performance	6	Yes
11	Data quality	5	Yes
12	Government IT policies	5	Yes
13	Perceived Ease of Use	5	Yes
14	Perceived Usefulness	5	Yes
15	connectivity	4	Yes
16	Relative Advantage	4	Yes
17	Technology factors	4	Yes
18	organizational readiness	4	Yes
19	Assimilation	3	Yes
20	collaboration	3	Yes
21	Facilitating Conditions	3	Yes
22	Perceived benefits	3	Yes
23	Performance expectancy	3	Yes
24	Technical Skills	3	Yes
25	Training	3	Yes
26	Uncertainty	3	Yes
27	Accuracy	2	No
28	Business Knowledge	2	No
29	capabilities	2	No
30	Change efficacy	2	No
31	Control orientation	2	No
32	Coordination	2	No
33	Data-driven Culture	2	No
34	Effort Expectancy	2	No
35	government support	2	No
36	Resistance to Use	2	No
37	Self-efficacy	2	No
38	Social Influence	2	No
39	Technology Management	2	No
40	Trialability	2	No
41	Organizational Size	2	No
42	Planning	2	No

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).

Table 5.

List of Factors Recommended by Experts.

No	Factor
1	Top Management Support
2	Training
3	Relative Advantage
4	IT Infrastructure
5	Security
6	Compatibility
7	Complexity
8	Adaptability
9	Government IT policies
10	Competency
11	Collaboration
12	Digital Transformation Tools
13	Decision Quality

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.

Footnotes

Appendix A

Table A1.

List of Literature Included in the Study.

S_ID	Author	Journal	Title “	Data collection/Respondents	Year	Country	Citations
1	(Raut et al., 2019)	Journal of cleaner production	Linking big data analytics and operational sustainability practices for sustainable business management	316 respondents of Indian professional experts	2019	India	106
2	(Moktadir et al., 2019)	Computers & Industrial Engineering	Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh	Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies	2019	Bangladesh	79
3	(Villarejo-Ramos et al., 2021)	Frontiers in Psychology	Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model	Questionnaire distributed online by e-mail among 199 responsible for different functional areas in Spanish companies.	2021	Spain	0
4	(Gong & Janssen, 2021)	Journal of theoretical and applied electronic commerce research	Roles and Capabilities of Enterprise Architecture in Big Data Analytics Technology Adoption and Implementation	qualitative case study at the Dutch Tax and Customs Administration	2021	Netherlands	8
5	(Yadegaridehkordi et al., 2018)	Technological forecasting and social change	Influence of big data adoption on manufacturing companies’ performance: An integrated DEMATEL-ANFIS approach	Data was collected from 234 industrial managers who were involved in the decision-making process regarding IT procurement in Malaysian manufacturing companies	2018	Malaysia	70
6	(Verma et al., 2018)	Information Processing & Management	An Extension of the Technology Acceptance Model in the Big Data Analytics System Implementation Environment	A survey of 150 big data analytics users	2018	India	93
7	(Caesarius & Hohenthal, 2018)	Scandinavian Journal of Management	Searching for big data: How incumbents explore a possible adoption of big data technologies.	Drawing on a 4-year qualitative field study of four large Scandinavian firms,	2018	Scandinavia	35
8	(Raguseo, 2018)	International Journal of Information Management	Big data technologies: An empirical investigation on their adoption, benefits and risks for companies	A questionnaire was distributed to medium and large-sized French companies	2018	France	231
9	(Lai et al., 2018)	The International Journal of Logistics Management	Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation	survey data was collected from 210 organizations.	2018	China	96
10		Journal of Science and Technology Policy Management	Big data analytics adoption model for small and medium enterprises	A survey of 112 manufacturing SMEs in Iran	2020	Iran	10
11	(Akter et al., 2016)	International Journal of Production Economics	How to improve firm performance using big data analytics capability and business strategy alignment	Two Delphi studies and 152 online surveys of business analysts in the U.S.	2016	USA	641
12	(Lei et al., 2021)	Technology in Society	Modelling and analysis of big data platform group adoption behaviour based on social network analysis	real case survey of 54 big data platforms (BDPs), four types of networks	2021	China	1
13	(Ghasemaghaei, 2020)	International Journal of Information Management	The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage	The survey was sent to 571 North American IT managers and data analysts	2020	USA	42
14	(Ghasemaghaei, 2019)	Decision Support Systems	Does data analytics use improve firm decision making quality? The role of knowledge sharing and data analytics competency	Survey data collected from top and middle-level managers from 133 U.S.-based firms	2019	USA	47
15		International Journal of Information Management	Big data analytics adoption: Determinants and performances among small to medium-sized enterprises	Data analysis of 171 Iranian small and medium manufacturing	2020	Iran	29
16	(Yadegaridehkordi et al., 2020)	Electronic Commerce Research and Applications	The Impact of Big Data on Firm Performance in Hotel Industry	418 online survey questionnaire to collect the data from top managers and/or owners of Malaysian SMEs hotels	2020	Malaysia	35
17	(Joshi & Biswas, 2018)	International Conference on Smart System, Innovations and Computing	An Empirical Investigation of Impact of Organizational Factors on Big Data Adoption.	109 suitable responses are received	2018	India	5
18	(Nam et al., 2019)	International Journal of Information Management	Business analytics adoption process: An innovation diffusion perspective	170 Korean firmsThe questionnaire was distributed to 900 firms for 2 months. In total, 190 responses were returned with a response	2019	Korea	30
19	(Cabrera-Sánchez & Villarejo-Ramos, 2020)	Journal of Retailing and Consumer Services	Acceptance and use of big data techniques in services companies	199 Spanish services companies	2020	Spain	20
20	(Okcu et al., 2019)	Industrial Engineering in the Big Data Era	Factors Affecting Intention to Use Big Data Tools: An Extended Technology Acceptance Model	A total of 252 questionnaires were collected from Turkish airline company employees	2019	Turkey	12
21	(Brock & Khan, 2017)	Journal of Big Data	Big data analytics: does organizational factor matters impact technology acceptance?	e-mails are sent to 1,035 students, only 359 members responded back with filled questionnaires	2017	United Kingdom	64
22	(Shahbaz et al., 2021)	Plos one	Impact of big data analytics on sales performance in pharmaceutical organizations: The role of customer relationship management capabilities	416 valid responses collected from pharmaceutical companies	2021	Pakistan,	0
23	(Shahbaz et al., 2019)	Journal of Big Data	Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change	Using a survey questionnaire, we analysed 224 valid responses in AMOS v21	2019	Pakistan	50
24	(Gunasekaran et al., 2017)	Journal of Business Research	Big data and predictive analytics for supply chain and organizational performance.	45 supply chain consultants and managers who are members of American Production and Inventory Control Society (APICS)	2017	USA	545
25		Technological Forecasting and Social Change	Role of big data analytics capability in developing integrated hospital supply chains and operational flexibility: An organizational information processing theory perspective	survey data from a sample of 105 senior executives from the Chinese hospitals	2021	China	13
26		International Journal of Production Economics	Integrating big data analytics into supply chain finance: The roles of information processing and data-driven culture	sample of 307 manufacturing firms in China.	2021	China	5
27	(Shamim et al., 2019)	Information & Management	Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view	primary datafrom 108 Chinese firms	2019	China	84
28	(Raut et al., 2021)	Transportation Research Part E: Logistics and Transportation Review	Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains	A sample of 297 responses from thirty-seven Indian manufacturing firms was collected.	2021	India	14
29	(Chavez et al., 2017)	Production Planning & Control	Data-driven supply chains, manufacturing capability and customer satisfaction	Manufacturing firms (n = 337)	2017	China	60
30	(Dubey et al., 2018)	Journal of Cleaner Production	Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour	Manufacturer of auto components (n = 190)	2018	India	58
31	(Dubey et al., 2021)	International Journal of Production Research	Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience	Manufacturing organisations, senior level Supply Chain managers (n = 213)	2021	India	168
32	(Saleem et al., 2020)	Asia Pacific Business Review	An empirical investigation on how big data analytics influence China SMEs performance: do product and process innovation matter?	12 Chinese SMEs’ officials using survey methods	2020	China	8
33	(Shabbir & Gardezi, 2020)	Journal of Big Data	Application of big data analytics and organizational performance: the mediating role of knowledge management practices	A total of 210 questionnaires filled and returned	2020	Pakistan,	11
34	(Wook et al., 2021)	Journal of Big Data	Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling	A total of 108 complete questionnaire responses were received	2021	Malaysia	1
35	(Mohamed & Weber, 2020)	In 2020 IEEE International Conference on Engineering, Technology and Innovation	Trends of digitalization and adoption of big data & analytics among UK SMEs: Analysis and lessons drawn from a case study of 53 SMEs	a case study of 53 UK SMEs,	2020	UK	2
36	(Alalawneh & Alkhatib, 2021)	The Electronic Journal of Information Systems in Developing Countries	The barriers to big data adoption in developing economies	A total of 23 experts completed the questionnaire.	2021	Jordan,	5
37	(Rahman et al., 2023)	IEEE Transactions on Engineering Management	Exploring the Factors Influencing Big Data Technology Acceptance	Out of 14 big data user groups (available on the Internet) consisting of 33,000 subscribers, two Hadoop user groups were sent survey questions.	2021	USA	2
38	(Bakici et al., 2023)	IEEE Transactions on Engineering Management	Big Data Adoption in Project Management: Insights From French Organizations	Qualitative thematic content analysis to summarize and categorize the empirical data into themes using NVivo	2021	France	0
39	(Singh & El-Kassar, 2019)	Journal of cleaner production	Role of big data analytics in developing sustainable capabilities	A total of 522 potential respondents were asked via email to participate in the survey questionnaire	2019	Arabic Countries	129
40	(Benzidia et al., 2021)	Technological Forecasting and Social Change	The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance	168 French hospitals	2021	France	17
41	(Keshavarz et al., 2021)	Sustainability	The Value of Big Data Analytics Pillars in Telecommunication Industry	qualitative approach and case study method and technique of data collection include semi-structure interview and document analysis	2021	Malaysia	1
42	(Dubey et al., 2019)	technological Forecasting and Social Change	Can big data and predictive analytics improve social and environmental sustainability?	205 questionnaires were returned completed and usable for data analysis	2019	India	211
43	(Mikalef & Krogstie, 2020)	European Journal of Information Systems	Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities	Survey data from 202 chief information officers and IT managers working in Norwegian firms	2020	Norway	43
44	(Ghaleb et al., 2021)	Sustainability	The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees	Survey on 302 Malaysian healthcare employees	2021	Malaysia	1
45	(Dubey et al., 2020)	International Journal of Production Economics	Big Data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations	Survey of 256 responses gathered using a pre-tested questionnaire from manufacturing firms in India	2020	India	82
46	(Shahbaz et al., 2020)	Complexity	Investigating the Impact of Big Data Analytics on Perceived Sales Performance: The Mediating Role of Customer Relationship Management Capabilities	A total of 416 valid responses were selected for final analyses after discarding biased responses	2020	Pakistan	6
47	(Kim & Park, 2017)	Information Development	Identifying and prioritizing critical factors for promoting the implementation and usage of big data in healthcare	questionnaires through face-to face interviews or an email survey in Korea	2017	Korea	36
48	(Park & Kim, 2021)	Journal of Computer Information Systems	Factors Activating Big Data Adoption by Korean Firms	data collected from 50 experts and 226 companies,	2021	Korea	9
49	(Mahesh et al., 2018)	In 2018 Moratuwa Engineering Research Conference (MERCon)	Factors Affecting the Intention to Adopt Big Data Technology	survey was carried out to collect data from 30 licensed finance companies in Sri Lanka	2018	Sri Lanka	5
50	(Shorfuzzaman et al., 2019)	Computers in Human behavior	Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment	Amongst 140 lecturers in Saudi Arabian universities are surveyed to conduct the study	2019	Saudi Arabia	56
51	(Maroufkhani et al., 2023)	Industrial Management & Data Systems	Determinants of big data analytics adoption in small and medium sized enterprises (SMEs)	Data were collected from 171 SME manufacturing firms and analysed using the partial least squares technique	2022	Iran	3
52	(Youssef et al., 2022)	Journal of Retailing and Consumer Services	Cross-national differences in big data analytics adoption in the retail industry	A survey questionnaire was used to collect data from managers and decision-makers in the retail industry. Data of 2,278 respondents were analysed through structural equation modelling	2022	UK than in UAE and Egypt	3
53	(Iranmanesh et al., 2023)	Management Decision	Determinants of intention to adopt big data and outsourcing among SMEs: organisational and technological factors as moderators	The partial least squares approach was employed to analyse data collected from 187 SMEs	2022	Iran	0
54	(Moraes et al., 2022)	The Bottom Line	Antecedents of big data analytics adoption: an analysis with future managers in a developing country	The sample comprised 364 business students from a public university in Brazil. The methodology had a quantitative approach, with the use of structural equation modelling	2022	Brazil	0
55	(Lutfi et al., 2022)	Sustainability	Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs	A total of 500 questionnaire surveys were distributed but only 123 were retrieved, from which seven were deemed incomplete or had not met the inclusion conditions and, as such, there were 116 useable questionnaires in total, indicating a response rate of 23.2%	2022	Jordan	13
56	(Li et al., 2022)	Technological Forecasting and Social Change	Evaluating the impact of big data analytics usage on the decision-making quality of organizations	We collected data from 240 agricultural firms in China	2022	China	3
57	(Chong & Lim, 2022)	Sustainability	Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance	The study analysed data from 169 firms on the basis of the positivist paradigm and employed the partial least square to run the reflective formative two-stage analysis	2022	Malaysia	0
58	(Binsawad et al., 2022)	PeerJ Computer Science	People’s expectations and experiences of big data collection in the Saudi context	Data were collected from Saudi citizens in King Abdulaziz University, Jeddah, Saudi Arab. The survey was conducted in English, A total of 249 available data were processed for statistical analysis out of 260 responses	2022	Saudi Arabia	1
59	(Guomin Chen et al., 2022)	Mobile Information Systems	An Empirical Study on the Factors Influencing Users’ Continuance Intention of Using Online Learning Platforms for Secondary School Students by Big Data Analytics	Secondary school student users as the research object, constructed a model of factors influencing users’ intention to	2022	China	0
60	(Chen et al., 2022)	Cogent Business & Management	Data, attitudinal and organizational determinants of big data analytics systems use	Surveyed 236 actual users of BDA systems in different industries and used the PLS-SEM method to analyze the collected data	2022	Taiwan.	1″

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grant code: GUP-2022-061.

ORCID iD

Showimy Aldossari

Data Availability

The data associated with this article is available upon request.

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

Abstract

Plain Language Summary

Keywords

Introduction

The Role of Big Data Analytics in Small and Medium Enterprises

Methodology

Systematic Literature Review (SLR)

Protocol Development

Data Extraction

Experts Ranking Technique

Findings and Discussion

Descriptive of SLR Findings

Years, Citations, and Publication Outlet

Theory Focus

Factors Extracted by SLR

Experts’ Consultations

Implicatins

Conclusion

Footnotes

Appendix A

Declaration of Conflicting Interests

Funding

ORCID iD

Data Availability

References