1 Introduction

The world is rapidly transitioning from the” Age of IT” to the” Age of Data”, following the explosive growth of information and massive revolution in science and technology (Su et al., 2022). The proliferation of the web, social media, mobile devices, and sensor networks, combined with falling storage and computing resource costs have resulted in an increasing digital records, leading to the surge of big data (Agarwal & Dhar, 2014; O.Müller et al., 2016). The term big data is defined as datasets that have become too large and difficult to work with using traditional database management systems and tools (Hashem et al., 2015). Following its enormous impact on society, big data analytics has lately been titled” the next frontier for innovation” (Manyika et al., 2011). Big data analytics (BDA) is known as the process and tools to examine big data to uncover insightful information such as hidden patterns and unknown correlations of data from within and outside organizations (H. Chen et al., 2012; Grover et al., 2018). BDA facilitates better decision-making across organizational processes across functions and businesses (D. Q. Chen et al., 2021), and offers tremendous opportunities for advancing sustainable development to improve economic, environmental, and societal outcomes (Barnes et al., 2022). To gain insights and competitive advantages, an increasing number of businesses have already invested in BDA (Song et al., 2022). Given its benefits, many large corporations have implemented BDA for various purposes, such as predicting new market trends, and evaluating consumer behavior and experiences to discover potential opportunities (Mandal, 2018). However, not only large organizations seize the opportunities of BDA, but also small and medium-sized enterprises (SMEs)Footnote 1 are realizing the benefits of BDA and are investing in BDA.

The extant literature emphasizes on BDA for large organizations (Gangwar, 2018; Lai et al., 2018; Verma & Chaurasia, 2019), and shows that they have up to 60% adoption rates of BDA (Dresner Advisory Services market study, 2019). Compared to SMEs, large organizations have dedicated departments for research and development, easy access to talent, and predetermined budgets for training. Therefore, large organizations can easily leverage and utilize the use of new technologies, including BDA. Unlike large organizations, SMEs have limited access to funds and resources, and hence they do not possess as much power to invest in new technologies. Therefore, it is expected that BDA adoption for SMEs is structurally different, and hence this paper uses a different lens to study the context of BDA adoption for SMEs.

An essential part of Vision 2030 in Saudi Arabia is to support its national economy through SMEs. The Saudi Vision 2030 realizes that SMEs are essential economic growth drivers in creating more jobs, encouraging innovation, and increasing the nation’s exports. In 2016, SMEs in Saudi Arabia contributed only 20% to the GDP, whereas they are sought to reach 70% in advanced economiesFootnote 2. The ambition of the Saudi Vision is to increase the GDP contribution of SMEs to 35% by 2030. In 2016, the SME General Authority (aka “Monsha’at”; translated “institutions”) was established to empower the SME sector in the country, supporting growth and competitiveness in accordance with the eighth goal of the sustainable development goals of the United Nations “Decent Work and Economic Growth” (abbreviated SDG8). As such, the government continues to support SMEs by providing more business-friendly regulations and accessibility to funding2. Since BDA aims to support SMEs in the next transformative phase, it will encourage SMEs to reap the benefits of adopting BDA to gain valuable insights, enhance decision-making, and improve business performance.

A comprehensive review of the extant literature on BDA shows that there is limited empirical evidence on BDA adoption, linked with performance for SMEs. Previous studies focused on organizations that have already adopted BDA in specific industries such as manufacturing (Lutfi, Alsyouf, et al., 2022; Maroufkhani et al., 2020) and supply chain (Alaskar et al., 2021; D. Q. Chen et al., 2021; Lai et al., 2018). Many of the results in these studies may not be generalized to a broader range of industries, and hence calling for broader contexts. Hence, this research aims to address the following questions:

  • RQ1. Do SMEs adopt BDA? If yes, then how does this adoption vary across different industries?

  • RQ2. What are the factors that influence BDA adoption?

  • RQ3. Does BDA adoption affect SMEs performance? 

The current study aims at addressing these research questions using a research model that utilizes Technology-Organization-Environment (TOE) framework and Resource-Based View (RBV) theory. We employed quantitative methods to empirically test the research model for 233 SMEs based in Saudi Arabia using structural equation modeling. The main result of the study highlights the importance of organizational factors compared with technological and environmental factors in relation to BDA adoption, and that BDA adoption has a strong influence on the performance of SMEs measured by financial, market, and business process factors. In addition, this study shows that BDA adoption for SMEs varies across industries, and that real estate, financial services, and technology sectors have relatively higher adoption rates than logistics, manufacturing, and wholesale/retailer sectors. The research model has a number of theoretical and empirical contributions by including important factors to TOE that influence BDA adoption, extending the performance measures of SMEs, and by broadening the context of BDA adoption across various industries.

The remainder of the paper is organized as follows: Section 2 reviews the existing literature to emphasize the areas where our study fills a gap. Section 3 focuses on developing a theoretical framework, and a set of hypotheses, while Section 4 describes the methods used in this investigation. Section 5 presents the results of the research model, and Section 6 discusses the findings of the research. We explore theoretical and managerial implications of this study in Sections 7 and 8. Finally, Section 9 summarizes the key results and highlights limitations of the study and future work.

2 Research Background

2.1 Big Data Analytics (BDA)

BDA has been one of the most essential fields for academics and practitioners over the past few years (H. Chen et al., 2012). A significant interest in BDA emerged from the opportunities generated from the data and analysis in various organizations (Gandomi & Haider, 2015). BDA aims at effectively managing large amounts of data to improve business insights and enhance business performance value (Côrte-Real et al., 2017; Popovič et al., 2018). Consequently, companies continuously attempt to extract valuable insights and improve their decision-making processes from the expanding volume, velocity, and variety of their business data (LaValle et al., 2010). Although there are several technical definitions of BDA in the literature (Ferraris et al., 2019; Grover et al., 2018; Gantz & Reinsel, 2011), a broader definition of BDA is used in this study, which states that BDA is a comprehensive process encompassing gathering, analyzing, using, and interpreting data for multiple functional divisions to generate actionable insights, produce business value, and build competitive advantage (Akter et al., 2019). Studies discuss various analytical methods necessary for organizations to analyze the high volume, velocity, and variety of data to make it more senseful (Gandomi & Haider, 2015). In this sense, Sivarajah et al. (2017) explain five analytical methods for discovering data insights, namely “descriptive analytics, inquisitive analytics, predictive analytics, prescriptive analytics, and pre-emptive analytics.”

The literature on BDA explores the relationship between BDA adoption and the impact generated from this adoption on organizations. However, the influence of BDA adoption on different types of firm performance is unclear (Huang et al., 2020). Prior research asserts that organizations benefit from BDA adoption as it enhances organizational performance. As such, Zhu et al. (2021) show how adopting BDA would help organizations in creating short-term value (such as improving operational efficiency) and long-term business value (such as business growth). A study by Danielsen et al. (2021) examines the role of big data in an obscure environment by investigating the relationship between BDA and competitive advantage through operational and dynamic capabilities. Similarly, Song et al. (2022) show that infrastructure and value attributes of business models play a crucial role in improving the relationship between BDA capabilities and competitive performance (e.g. financial and growth performance). Raguseo and Vitari (2018) report that business value for firms may be derived from BDA investments. They show that customer satisfaction and business value gained from investments of BDA improved financial performance for organizations. In addition, Behl (2022) conducted an empirical study that shows BDA aids start-ups in driving revenue and improving social and financial status. A study conducted by Maroufkhani et al. (2020) highlights the significant influence of BDA on financial and market performance for SMEs. In light of existing literature, this paper classifies BDA value into three categories of firm performance: financial, market, and business process performance, which are explained further in the next section.

In considering the benefits and values of BDA adoption highlighted in previous studies, researchers have given much attention to comprehending antecedents of BDA adoption in organizations. Various studies focused on examining the determinants of BDA in large firms (Gangwar, 2018; Lai et al., 2018; Verma & Chaurasia, 2019); however, they call for further investigation of BDA adoption and value for SMEs (Maroufkhani et al., 2020, 2023; Mikalef et al., 2019a; Stentoft et al., 2021). Studies on BDA in large organizations may not be applicable to the context of SMEs due to the differences between large organizations and SMEs. Large organizations are characterized by having dedicated departments for research and development, easy access to talent, and predetermined budgets for training. Therefore, large organizations can leverage and utilize new technologies, i.e. BDA. Unlike large organizations, SMEs have limited access to funds and resources, and hence they do not possess much power to invest in new technologies. Another difference discussed by Ghobadian and Gallear (1996) is regarding the organizational structure where large organizations, on the one hand, have several hierarchical layers which limit the visibility of top management, and keep them far away from the delivery point. Therefore, it makes it necessary to study ways or mechanisms to enhance top management support in large organizations. On the other hand, SMEs have a flat structure which results in a more visibility of top management and close to the delivery point. In terms of response to change in organizations, large organizations tend to have a slow response to market changes with a high degree of resistance, whereas SMEs have less resistance to changes, with quick adaptation to market changes (Ghobadian & Gallear, 1996). A further difference lies in the decision-making process as large organizations usually have extended decision-making chains with fact-based decision-making. In contrast, SMEs have short decision-making chains, and their decisions are more likely to be based on 'gut feeling' rather than fact-based decisions (Ghobadian & Gallear, 1996). Therefore, it is expected that BDA adoption is structurally different, and hence this study uses a different lens to study the context of BDA adoption for SMEs.

Most studies published on BDA adoption in SMEs investigate factors that affect adoption of BDA in SMEs without a holistic view on performance measures of SMEs (Ajimoko, 2018; Lai et al., 2018; Lutfi, Alsyouf, et al., 2022; Song et al., 2022; Verma & Bhattacharyya, 2017). However, this study has a wider lens that considers the technology-organization-environment (TOE) framework as well as the resource-based view theory (RBV), providing a comprehensive conceptual model beyond examining determinants of BDA adoption, and identifying relationships between adoption and performance of SMEs.

3 Research Model and Hypotheses Development

3.1 Development of Research Model

The theoretical foundation of this study comprises two theories: technology-organization-environment (TOE) framework by Tornatzky et al. (1990) and resource-based view theory (RBV) by Barney (1991). From the several theories in the IS literature, we employ the TOE model as a foundational theory to investigate BDA adoption, and to categorize the key factors that impact BDA adoption for SMEs. The TOE model was developed by Tornatzky et al. (1990), and it is the most widely used theory for information technology adoption (Lai et al., 2018). According to Tornatzky et al. (1990), the decision of organizations to implement new IT is affected by three contextual factors: technology, organization, and environment. In the context of BDA, the technological aspect discusses the internal and external technology components necessary for BDA adoption (Maroufkhani et al., 2020). The organizational aspect refers to the properties and characteristics of organizations that influence BDA adoption (Lai et al., 2018). The environmental aspect relates to the elements organizations may encounter when confronted with their external borders in considering BDA adoption (W. Xu et al., 2017). In this study, the TOE model is relevant to BDA adoption because it has explanatory power in providing a complete and unified picture to understand the different aspects that help to examine the internal factors in businesses that play a role in BDA adoption. Also, it encompasses environmental factors to support a comprehensive understanding of the mechanism throughout the decision-making process associated with adopting BDA innovation (Lai et al., 2018). In addition, the unique characteristics of BDA does not only depend on the technologies and techniques for the adoption, but also it extends the influence by various factors within and outside the organization. Therefore, additional elements are needed to demonstrate the successful BDA adoption and usage over time. Due to these advantages provided by the TOE framework, many studies have used it to examine the organizational adoption of BDA in different contexts (Gangwar, 2018; Lai et al., 2018; Lutfi, Alsyouf, et al., 2022; Maroufkhani et al., 2020; Verma & Chaurasia, 2019).

IS scholars have widely employed the resource-based view theory (RBV) in technology adoption studies (Ghasemaghaei, 2019; Lutfi, Al-Khasawneh, et al., 2022; Maroufkhani et al., 2020; Mikalef & Krogstie, 2020). The resource-based view states that the competitive advantage of a business can be achieved through obtaining valuable resources and using them effectively to build unique values and capabilities (Barney, 1991). According to RBV theory, enterprises have two types of resources: tangible and intangible. However, only the resources considered” valuable, rare, inimitable, and non-substitutable (VRIN)” can create a competitive advantage (Barney, 1991). Grant (2010) provides further details on the previous resources dividing them into financial and physical resources (tangible resources), knowledge and skills of employees (human skills resources), and organizational learning and culture (intangible resources). Both academic and practitioner literature is giving significant attention to the way firms are benefiting from the use of BDA through RBV (H. Chen et al., 2012; Popovič et al., 2018). R. Sharma et al. (2014) argue that some evidence showed that investing in business analytics could generate value, but the claim that” business analytics results in value” requires deeper examination. In the BDA context, the analytics and insights obtained from big data are considered essential intangible resources (D. D. Q. Chen et al., 2015; Lutfi, Al-Khasawneh, et al., 2022). In the context of RBV, creating value from analytics and insights can help to obtain valuable, rare, inimitable, and non-substitutable resources (Lutfi, Al-Khasawneh, et al., 2022). Therefore, it is important to understand how enterprises can utilize these BDA values to help SMEs enhance their business performance and gain a competitive advantage (D. Q. Chen et al., 2015; Ghasemaghaei, 2019; Lutfi, Al-Khasawneh, et al., 2022). In light of the above discussion, this study examines the impact of analyzing big data analytics and extracting valuable insights as crucial resources to improve SME performance.

Drawing on the TOE framework and considering the value created from the BDA based on RBV, this research proposes a comprehensive framework that models BDA adoption as a function of technological, organizational, and environmental factors, considering that BDA adoption would impact the performance of SMEs. The research framework is shown in Fig. 1. The left side of the proposed research model depicts the determinants of BDA adoption. In considering these determinants, we propose nine potential factors that are critical for the BDA context, especially for SMEs, and classify them into the TOE factors: technology factors (complexity, compatibility, cost of adoption, and security risks), organization factors (top management support, data-driven culture, and organization readiness), and environment factors (competitive pressure, government support, and environmental uncertainty). The right side of the framework shows the effect of BDA adoption on SMEs’ performance (financial performance, market performance, and business process performance). In the following subsections, we attempt to explain each factor of the conceptual framework and develop the hypotheses of the research.

3.2 Hypotheses Development

3.2.1 Technological Factors

The set of technological factors discusses the internal and external technology components necessary for BDA adoption (Maroufkhani et al., 2020). The Technology component of conceptual frameworks aids in identifying if the firm’s technical readiness will drive or hinder the BDA adoption (Gangwar, 2018). The present paper uses four characteristics of technology (complexity, compatibility, cost of adoption, and security risks) as follows.

Fig. 1
figure 1

Proposed Research Model

Full size image

Complexity

Rogers (2003) finds that one of the reasons that lead to the failure of new technology or system adoption is its complexity. Complexity is defined by Lai et al. (2018) as the extent to which a technology is seen as challenging to use and difficult to understand for organizations, and it is considered a key element in the technological factors (W. Xu et al., 2017). A recent review of the literature on this topic finds that SMEs are less likely to adopt a technology if they believe this technology necessitates significant efforts (Al-Sharafi et al., 2023; Lutfi, Alsyouf, et al., 2022; Maroufkhani et al., 2023). In this sense, it is suggested that implementing BDA requires training for employees, time to implement, and a certain level of data security, in addition to technological and financing constraints, making BDA implementation a complex and risky process (Lai et al., 2018). For these reasons, complexity is regarded as one of the primary inhibitors of SMEs’ intentions to use BDA. Hence, this study hypothesizes that:

  • H1: Complexity of BDA negatively influences its adoption in SMEs.

Compatibility

Compatibility is defined as the extent to which the technology is considered to be consistent with the potential consumers’ current values, prior experiences, and requirements (Rogers, 2003). Compatibility is determined by corporate culture, business strategies, organization current values and preferences, prior knowledge, existing work practices, organizational goals, information system, and employees’ capabilities characteristics (Gangwar, 2018). Recent studies advocate that compatibility has a significant positive relationship with firms’ intention to adopt BDA (Gangwar, 2018; Verma & Chaurasia, 2019; W. Xu et al., 2017). Furthermore, Gangwar (2018) argue that organizations tend to have positive attitudes on technological innovation due to the good fit between technological innovation and people, processes, and practices; therefore, the following hypothesis is proposed in this study:

  • H2:Compatibility of BDA positively influences its adoption in SMEs.

Cost of Adoption

Another major technical aspect for SMEs that influences the behavioral intention to use and accept technological innovations is the cost associated with adopting technological innovation (Ramayah et al., 2016). The cost of adoption refers to the expenditures related to adopting BDA in organizations, including the time and effort required to restructure and re-engineer the organization process (Verma & Bhattacharyya, 2017). Moreover, cost of adoption involves various components, such as hardware and software investment and the expenses of hiring and training employees (Sun et al., 2018). Kapoor et al. (2015) explore vital role cost plays in exceeding the intention to adopt to the actual adoption. Therefore, we hypothesize:

  • H3:Cost negatively influences BDA adoption for SMEs.

Security Risks

One of the most frequently stated problems in adopting big data is the security and privacy risks associated with big data, which impact BDA adoption, which may damage corporate reputations (Baig et al., 2019). The challenges of data security andprivacy associated with emerging technology are considered inhibitors for successful adoption journeys acrossentities3. Firms collect and analyze vast volumes of sensitive information about their employees, customers, trade secrets, intellectual property, and financial data from various sources; as a result, they become vulnerable to targeted attacks (Tankard, 2012). Asiaei and Ab. Rahim (2019) show that security is a primary concern for business owners when they adopt data-related services. From this view, the term” security risks” also refers to the risks associated with third-party tools usage or assistance in providing a BDA solution (Maroufkhani et al., 2020). Accordingly, this study develops the following hypothesis:

  • H4: Security risks negatively influence BDA adoption for SMEs.

3.2.2 Organizational Factors

The set of organizational factors refers to the characteristics of organizations that influence IT adoption (Lai et al., 2018). This study considers top management support, data-driven culture, and organizational readiness as a proxy for organizational factors that affect BDA adoption for SMEs.

Top Management Support

Top management refers to top management’s attitude toward supporting relevant technology, and the funding available for IT implementation (Jang et al., 2019). The importance of management support in encouraging institutions to adopt new technology is indisputable (Hassan Reza et al., 2021). Previous studies emphasize that top management support is a crucial factor in successful innovation adoption (Alaskar et al., 2021; D. Q. Chen et al., 2021; Lutfi, Alsyouf, et al., 2022; Lutfi et al., 2023; Maroufkhani et al., 2020; Verma & Chaurasia, 2019). However, a lack of top-level support in SMEs can easily obstruct technology implementation regardless of how helpful or cost-effective the technology is (Ajimoko, 2018). Hence, the following hypothesis is postulated:

  • H5: Top management support positively influences BDA adoption in SMEs.

Data-Driven Culture

Organizational culture is defined by Liu et al. (2010) as a group of shared values, beliefs, and assumptions recognized in the organization’s practices and goals that help members understand how the organization operates. According to the report published by Digital Government Authority finds that an organization’s culture plays a significant role in determining the behavior and relationships of its employees, as well as its public imageFootnote 3. Karaboğa et al. (2019) suggest that the readiness of organizational culture to the initiatives related to big data is more important than big data applications alone. They point out that data-driven culture has been used to describe when organization decisions are based on insights from data rather than decision-makers’ intuitions. Therefore, it requires businesses to make BDA a part of corporate culture and enhance all employees’ positive attitude toward it (Grover et al., 2018; Müller & Jensen, 2017). Thus, the following hypothesis is proposed by this study:

  • H6: Data-driven culture positively influences BDA adoption in SMEs.

Organization Readiness

Gangwar (2018) describes organizational readiness as the ability and willingness to provide the required technological, financial, and human resources to adopt the new technology. Prior research shows that these required resources include financial capital and IT sophistication (Alaskar et al., 2021). IT sophistication, in particular, consists of the technical components of IT infrastructure and the organization’s IT human resources, and the availability of experts to do business analytics is a crucial indicator of organizational readiness in BDA adoption (D. Q. Chen et al., 2015). In this paper, we categorize the organization’s resources into three categories: financial resources, technological resources, and human resources. Financial resources refer to what an organization is required to invest in new IT innovation. Technological resources related to the IT infrastructure, such as technical platforms, databases, and tools, are needed to store, transform, and analyze big data. Human resources are related to the availability of IT professionals with the skills and capabilities to perform big data-related projects (D. Q. Chen et al., 2015; Gangwar, 2018). Prior studies provided evidence that organizational readiness has a substantial, positive impact on business to adopt BDA (Alaskar et al., 2021; Ganeshkumar et al., 2023; Lutfi et al., 2023; Maroufkhani et al., 2020). Thus, we hypothesize:

  • H7: Organizational readiness positively influences BDA adoption for SMEs.

3.2.3  Environmental Factors

The set of environmental factors refer to the elements that organizations may encounter when confronted with their external borders (W. Xu et al., 2017). The environmental dimension aids in giving a better understanding of the impact of various external pressures on organizational adoption (Gutierrez et al., 2015). As this study focuses on SMEs, we consider three factors in the environmental context: competitive pressure, government support, and environmental uncertainty.

Competitive Pressure

In Saudi Arabia, one of the strategic objectives of the ninth goal “Industry, Innovation, and Infrastructure” of the sustainable development (SDG9) is to encourage adoption and utilization of technologies and digitalization in the organizationsFootnote 4. This objective motivates enterprises, especially SMEs, to adopt new technology like BDA to analyze their data and extract valuable insights. In turn, this will increase its competitiveness in the market and place more pressure on their competitors to adopt BDA. In broad terms, competitive pressure can be defined as the pressure a company experiences from its competitors (W. Xu et al., 2017). The previous study finds that businesses can compete in three ways: by adjusting the rules of competition, modifying the industry structure, or by utilizing a new IT innovation to gain a competitive edge. In the context of competing through new IT innovation utilization, Alaskar et al. (2021) clarify that when there are more competitors in the market, BDA will be more likely to be used, and firms will be required to adopt BDA to attain their competitive position. Therefore, adopting BDA can give SMEs a competitive advantage and encourage competitive differentiation compared to non-adopters. Accordingly, this study hypothesizes:

  • H8: Competitive pressure positively influences BDA adoption in SMEs.

Government Support

Government support involves regulatory support in facilitating firms to adopt BDA (J.-H. Park & Kim, 2021). Lutfi, Alsyouf, et al. (2022) find that SME CEOs acknowledge that government support and incentives play a crucial role in facilitating the adoption of IT innovations by businesses and would prompt their adoption. In SMEs, the Saudi government supports SMEs with emerging technologies to raise their competitiveness, efficiency and productivityFootnote 5. Monsha’at established Thakaa Center (translated “Intelligence Center'') as one of the innovation centers, where it is the first specialized center to serve entrepreneurs and SMEs in Saudi Arabia in the fields of Data Analytics, Cyber Security, Internet of Things (IoTs) and Artificial Intelligence5. The objectives of Thakaa Center are to provide consultation, workshops, and camps by providing lectures and course series on usage and applications of emerging technologies. Moreover, the center supports SMEs in implementing technical solutions, finding innovative solutions, and growing their businesses. While there exists a variety of definitions for government support, this study utilizes the definition proposed by Maroufkhani et al. (2020) which refers to the government support through regulations and incentives to adopt emerging technologies, including BDA. Therefore, this study suggests the following hypothesis:

  • H9: Government support positively affects BDA adoption for SMEs.

Environmental Uncertainty

In this study, the term environmental uncertainty is defined as changes in technology, market, and competition landscape that intensify pressure on corporate management to enhance company performance (Aprisma & Sudaryati, 2020). Companies that operate in a state of high environmental uncertainty will always look for new opportunities and invest in innovations compared to others (S. Sharma, 2000). Moreover, uncertainty in environments push companies to adopt technological innovations in their organizations to maintain their competitiveness (Bolloju & Turban, 2007). Prior studies noted that environmental uncertainty places pressure on businesses to use organizational knowledge to drive their actions, specifically key decision-makers to analyze quickly and act effectively (Chatterjee et al., 2023; D. Q. Chen et al., 2015). As a result, when faced with such environmental uncertainty, the necessity for BDA becomes crucial for corporate decision-makers (D. Q. Chen et al., 2015). Thus, this study provides the following hypothesis:

  • H10: Environmental uncertainty has a positive effect on BDA adoption for SMEs.

3.2.4 BDA Adoption and SME Performance

BDA is seen to enable “improved business efficiency and effectiveness because of its high operational and strategic potential performance” (Wamba et al., 2017). The business value generated from BDA is a critical factor that enables organizations to enhance their performance (Lutfi, Al-Khasawneh, et al., 2022). To illustrate, leveraging BDA through analyzing the high velocity, variety, and volume to extract high-quality insights improves decision-making processes in organizations, which create value for the organization and ultimately affect its overall performance. Therefore, we suggest that adopting BDA for SMEs and utilizing data that can be recognized as a valuable resource may significantly influence SMEs’ performance. The value of BDA to SMEs can be either tangible (such as revenue growth and cost reduction), or intangible (such as competitive advantage, product or service innovation, and brand reputation). Even though firm performance can be perceived as one construct, extant literature in the context of BDA uses different measures of firm performance, such as financial (Raguseo & Vitari, 2018; Lutfi, Al-Khasawneh, et al. 2022;Thanabalan et al., 2024), market (Maroufkhani et al., 2020; Wamba et al., 2017; Mikalef et al., 2019b), and business process performance (Aydiner et al., 2019; Ramakrishnan Ramanathan et al., 2017). In this study, we utilize these three measures of firm performance (financial, market, and business process performance) to measure the impact of BDA adoption on varying SME performances. As this study focuses on SMEs, the variation in their performance is expected to be large. Hence, using one measure of firm performance might not provide a full picture of the impact generated by BDA adoption on SMEs. Therefore, the use of multiple measures of firm performance helps in capturing this variation in perspectives of BDA value to SMEs. The following sections provide a description of each type of the performance measure, followed by a set of hypotheses for the relationship between BDA adoption and SME performance measured by financial, market, and business process.

Financial Performance

Prior studies indicate that the created value from BDA investments positively impacts a firm’s financial performance (Gupta et al., 2020; Müller et al., 2016; Raguseo & Vitari, 2018; Wamba et al., 2017). The term financial performance in this study is defined as the ability to improve revenue growth and business profitability (Raguseo & Vitari, 2018). Akter et al. (2016) went further by examining the role of aligning the analytics capability and business strategy to achieve a return on investment and sales growth. Similarly, Maroufkhani et al. (2020) investigate how adopting BDA can help SMEs to improve their financial and market performance. In the same way, a recent study by Lutfi, Al-Khasawneh, et al. (2022) discover that adopting BDA and leveraging analytical tools help SMEs interpret data for decision-making, create value, and enhance SMEs’ financial performance. Raguseo and Vitari (2018) found the insights offered by BDA help better understand customers' preferences and characteristics. This understanding allows businesses to personalize their offers to better suit their customers' needs. As a result, customer satisfaction is increased, leading to the establishment of customer loyalty, increasing purchase frequency, boosting sales, and reducing the cost of customer acquisition (CAC), which consequently enhances financial performance (Raguseo & Vitari, 2018). Thanabalan et al. (2024) highlights that financial performance is a critical indicator of an organization’s excellent performance, which can be achieved through valuable insights and accurate forecasts generated by BDA. The advanced analytics of BDA can improve the accuracy of the organization forecasts with minimum waste of money, time, and resources that can reduce the organization cost, and lead to achieving higher financial gains (Thanabalan et al., 2024).

Market Performance

Leveraging advanced solutions provided by BDA can help organizations identify market opportunities and threats, and then improve the organization’s marketing position (Maroufkhani et al., 2020; Wamba et al., 2017; Z. Xu et al., 2016). For instance, Côrte-Real et al. (2017) indicate that the insights generated from the BDA application can allow businesses to manage internal and external knowledge which leads to creating organizational agility (i.e. identifying potential opportunities and threats, responding more quickly to market changes, and adopting new technologies to effectively and efficiently produce new products). Gupta et al. (2020) examine the importance of managerial decision-making skills in facilitating technological capabilities (i.e. big data predictive analysis) as functional mechanisms for impacting the firm’s market performance. Similarly, Raguseo and Vitari (2018) find that BDA can enable firms to enter new markets, innovate new products, and eventually improve their market performance and customer satisfaction. Mikalef et al. (2019b) argue that adopting BDA can help businesses uncover hidden insights in their market and modify their innovation capabilities. The results of the study conducted by Thanabalan et al. (2024) confirm the benefits of BDA capabilities in helping organizations create accurate market requirements predictions which enable them to align their business strategies and processes to fully meet the market demand and changes. From the various perspectives of market performance, we find that Raguseo & Vitari’s (2018) definition of market performance is more relevant to the context of this study; in which market performance is defined as the ability of an organization to enhance its position against rivals by enhancing its ability to enter new markets quickly, release new products and services frequently, and increase its market share.

Business Process Performance

Business process performance focuses on the extent to which businesses perceive BDA solutions to provide internal benefits for the organization’s efficiency. In that perspective, Paul et al. (2000) state that “a process-oriented assessment of IT business value [is] based on the argument that the first-order impacts of IT investment occur at the process level.” Hence, the value of BDA is perceived to improve business processes in a similar fashion. According to Melville et al. (2004), business process performance is a “range of measures associated with operational efficiency enhancement within specific business processes.” They illustrate that business process performance includes on-time shipping, customer satisfaction, and inventory turnover. Ramakrishnan Ramanathan et al. (2017) confirm that aligning BDA adoption with firm objectives and business processes results in performance gains. In a similar context, Aydiner et al. (2019) posits that enhancements in decision-making positively influences business process performance, and that adopting business analytics aids business process performance at the enterprise level. They utilize aspects such as process efficiency, low cost, increased productivity, rapid response to customers, and efficient communication as factors in the construct of business process performance. Müller et al. (2018) find that exploiting organization assets such as BDA can help improve data-driven decision-making process, which consequently increases business productivity. Dubey et al. (2019) find that incorporating big data with predictive analytics into operational decisions can help enterprises to enhance their cost and operation performance. Hence, building on prior research, we consider business process performance a multidimensional construct that includes: supply chain management, decision-making process, business operations, business costs, and customers’ relationships and satisfaction.

Based on the previous arguments, the following hypotheses are proposed:

  • H11: BDA adoption positively influences SMEs financial performance.

  • H12: BDA adoption positively influences SMEs market performance.

  • H13: BDA adoption positively influences SMEs business process performance.

4 4. Research Methodology

4.1 Data Collection

This study was carried out with a quantitative approach by developing an online questionnaire, and it was conducted in Saudi Arabia from November to December 2021. The target respondents were owners, CEOs, and IT managers of SMEs in Saudi Arabia, as they are the decision-makers and have sufficient information and future directions for their enterprises regarding adoption of IT technologies. The authors identified the inclusion criteria as defined by Monsha’at: the enterprise’s full-time employee number is fewer than 250, and the volume of generated revenue is less than 200 million Saudi RiyalsFootnote 6. The authors used different ways to collect a list of SMEs in Saudi Arabia. First, the authors used the LinkedIn platform as a source to identify companies according to the criteria mentioned previously. Second, the authors attended national events for SMEs in Riyadh, where they gathered business cards from these enterprises, and contacted them through their emails. Third, the authors searched for the SMEs’ names on the Monsha’at website and its social media channels. After that, the questionnaire was distributed through their LinkedIn pages, Twitter accounts, and emails with a cover letter that included the purpose of this study and the meaning of BDA. The questionnaire contained questions about company size and annual income to differentiate SMEs from large enterprises and ensure the study only included SMEs. Additionally, a follow-up procedure was conducted with reminder messages to the non-respondents after 1 to 2 weeks to increase the response rate. In total, more than 440 SMEs were surveyed, and 273 responses were received, resulting in about 60% response rate. After eliminating invalid responses and responses belonging to large enterprises (the number of employees is more significant than 250 and generates more than SR 200 million in revenue)6, the remaining becomes 233 completed responses. This reflects an acceptable minimum sample size requirement for this study, conforming to the requirements of sample size as described by Hair et al. (2017).

4.2 Measurement of the Variables

The research constructs are measured by multiple items adapted from existing literature with appropriate adjustments to collect the data (details on constructs are shown in Appendix Table 6). The scales of the questionnaire are based on a five-point Likert scale with options ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). However, complexity and cost are measured as follows: the scale for items in complexity construct ranges from 1 (Very Easy) to 5 (Very Difficult), and the scale of items in cost construct ranges from 1 (Very Low) to 5 (Very High). The construct pertaining to BDA is measured using levels of adoption, ranging from 1 (Not considering) to 5 (Have fully adopted services, infrastructure, or platforms of BDA). The questionnaire is divided into four sections, and each contains a set of closed-ended questions, with the first section being focused on basic information of SMEs (i.e., industry, size, and income). The second section aims to explore the factors influencing the adoption of BDA. The last two sections consist of the organization’s level of BDA adoption, and BDA impact on SMEs performance.

4.3 Data Description

In examining the sample of the SMEs in our dataset, only one in every five SMEs are fully adopting BDA services, infrastructure, or platforms. About one third of the surveyed SMEs are currently experimenting with different BDA solutions, whereas more than quarter are aware of BDA, and considering it in the next five years. However, about 10% are not considering adopting the technology at all. In examining the adoption level by industry (see Fig. 2), SMEs in the Building/Real estate and Financial/service sectors have the highest adoption rates of BDA (about 38 to 47%). However, SMEs in Manufacturing, Logistics, and Retail/Wholesale sectors have the lowest adoption rate of BDA, and they represent the highest proportion of SMEs that are not considering BDA in the future (22 to 33%).

Fig. 2
figure 2

Levels of BDA adoption across industries

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Out of the total number of respondents, 32.6% are in the Technology sector. The majority of the organization’s size is divided into two groups: 1–5 employees (36.5%) and 6–50 employees (33.9%). In addition, more than half of the SMEs estimated their annual revenue below 3 million Saudi Riyals (52.8%). A complete set of descriptive statistics for the SME sample dataset is shown in Table 1.

4.4 Common Method Bias and Non-Response Bias

In this study, we conduct several statistical tests to assess common method variance (CMV) using two common techniques: Harman’s one-factor and collinearity diagnosis. First, the Harmon one-factor test is performed for all items in the exploratory factor analysis. The results indicate that the first-factor cumulative variance is 28.3%, which is less than the threshold value of 50% of the total variance (Podsakoff & Organ, 1986). Second, we use collinearity diagnosis on all items, and the results presented in Appendix Table 7 show that all variance inflation factor (VIF) values are less than the cut-off point (VIF < 5) (Hair et al., 2006). All previous analyses revealed that CMV is not an issue for the sample of this study.

We also tested non-response bias using a t-test by comparing the early and late responses at a 95% confidence interval. The t-test result showed that the p values were p > 0.05, and there are no significant differences between the two groups of responses as shown in Appendix Table 8. Therefore, we conclude that the data sample in this study has no non-response bias.

5 Research Findings

5.1  Assessment of Measurement Model

This study performs various sets of analysis to examine internal consistency, convergent validity, and discriminant validity. The measurement instrument reliability is evaluated using Cronbach’s alpha to assess the internal consistency of each factor within the total items used in each construct (Table 2). The results indicate that all the constructs have highly reliable measures. Cronbach’s alpha values range from as high as 0.909 (for financial and business process performance) to as low as 0.717 (for competitive pressure). Since all of the constructs have Cronbach’s alpha values higher than 0.7, then they represent acceptable values of Cronbach’s alpha (F. Hair Jr et al., 2014). The standardized factor loadings of each construct, average variance extracted (AVE), and composite reliability (CR) are used to measure convergent validity. F. Hair Jr et al. (2014) suggest that factor loadings and AVE values should be above 0.5, and CR should be above 0.7. As shown in Table 2, all of the constructs exceed the thresholds, and they show acceptable convergent validity. The assessment of the construct discriminant validity is conducted using the Fornell-Larcker criterion (Fornell & Larcker, 1981) and Heterotrait-Monotrait (HTMT) criteria (Henseler et al., 2015). According to Fornell and Larcker (1981), the constructs have good discriminant validity when the correlations between each construct and other constructs are less than the square root of the AVE. For all the constructs, the results (in Table 3) suggest that all the constructs fulfill the threshold requirements and demonstrate sufficient discriminant validity. According to Henseler et al. (2015), the result of HTMT values should be less than the standard value of 0.85 or 0.9, and all the constructs show sufficient discriminant validity, as shown in Table 4.

Table 1 Descriptive statistics of the data sample from SMEs
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Table 2 Reliability and Convergent Validity
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Table 3 Discriminant validity (Fornell and Larcker)
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Table 4 Discriminant validity (HTMT)
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5.2  Assessment of Structural Model

This study uses Statistical Package for the Social Sciences (SPSS) software and AMOS to analyze the data sample. Partial least squares-structural equation modeling (PLS-SEM) is used to test the hypotheses, which examine the relative effect of the independent factors on the dependent variables. The proportion of variance explained is used to show predictive accuracy (R2) of the research model. The results of the analysis show that the proposed research model is able to explain 46% of the variance in BDA adoption and 61% of the variance in SME performance. More detailed results of the antecedents of BDA and its consequences on performance of SMEs is shown in Table 5. The results of the assessment indicate that Complexity (β = -0.312; p < 0.001) has a negative impact on BDA adoption. Furthermore, the results show positive effects of Compatibility (β = 0.439; p < 0.001), Top Management Support (β = 0.367; p < 0.001), Data-driven Culture (β = 0.501; p < 0.001), Organizational Readiness (β = 0.464; p < 0.001), and Environmental Uncertainty (β = 0.403; p < 0.001) on BDA adoption. The results show no impact of Cost of Adoption (β = -0.080; p > 0.05), Security Risks (β = 0.111; p > 0.05), Competitive Pressure (β = -0.039; p > 0.05), or Government Support (β = 0.127; p > 0.05) on BDA adoption. Other findings regarding SME performance demonstrate that Financial Performance (β = 0.412; p < 0.001), Market Performance (β = 0.421; p < 0.001), and Business Process Performance (β = 0.343; p < 0.001) are positively affected by BDA adoption. As a result, all of the hypotheses in the research model are supported except for H3, H4, H8, and H9 (see Table 5).

Table 5 Structural model path analysis for SMEs performances
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6 Discussion

The study sought to answer the research questions by demonstrating the factors influencing BDA adoption on performance of SMEs by proposing a research model using TOE framework and RBV theory and provide empirical investigations. The results of analysis of the technological aspects presented in this study show that compatibility is the strongest technological factor, followed by complexity, as shown by the path coefficient and significance level. Surprisingly, the results show that there is no effect for adoption cost and security risks on BDA adoption for SMEs.

In detailing the findings of the technological aspect, the significant impact of complexity on BDA adoption is consistent with the findings on SMEs (Al-Sharafi et al., 2023; Lutfi, Alsyouf, et al., 2022; Maroufkhani et al., 2020), while it is inconsistent with (Alaskar et al., 2021; Lai et al., 2018) who investigated large firms. According to Maroufkhani et al. (2020), the association of the complexity factor and BDA adoption because of the lack of internal experience, means that SMEs may find it difficult to adopt new technologies. As a result, managers need more confidence in their firm’s ability to implement BDA successfully (Maroufkhani et al., 2020). Another possible explanation for BDA complexity is that it may require a high level of understanding of SMEs’ current technical status and the required adjustments in SME infrastructures, procedures, processes, and tools before and during the integration stages. Also, complexities of the technology will bring some technical issues that may appear during the adoption stage, which could increase the implementation barriers and adoption workflow seamlessly, and that may require hiring specialized experts to solve them. Therefore, BDA implementation may take a longer time and require more efforts, affecting SMEs’ service level and competitive performance in highly dynamic and competitive markets.

Technological compatibility has a significant effect on BDA adoption, and it is consistent with the findings of prior research (Al-Sharafi et al., 2023; Gangwar, 2018; Maroufkhani et al., 2023; W. Xu et al., 2017). However, this finding is contrary to some studies such as those of D. Q. Chen et al., 2015, Maroufkhani et al., 2020, and Verma & Bhattacharyya, 2017, in which they suggest that compatibility in adopting BDA has less impact on decisions for SMEs managers. Gangwar (2018) claims that companies are willing to adopt BDA when the organization’s existing procedures, practices, employees, and technical architecture are compatible with this new technology. Also, the decision of an SME manager to adopt BDA is influenced by the changes made by BDA if they are consistent with the procedures, practices, and values for their organizations. E. Park et al. (2019) point out that compatibility and complexity affect employees’ perceived ease of use. Maroufkhani et al. (2023) explain the previous statement that employees are willing to adopt and use BDA if they believe the technology is compatible and consistent with the current practices used in their businesses. Further, the authors suggest that the complexity in BDA usage and its learning curve may inhibit adoption for SMEs. Moreover, this result provides evidence that the compatibility of BDA will encourage SMEs to adopt BDA, enabling technology scalability, and leading to acquiring benefits of integrating additional technology without disruption.

Contrary to previous findings pertaining cost of BDA adoption (Kapoor et al., 2015; M. Sharma et al., 2023; Verma & Bhattacharyya, 2017), the empirical results in this study show no evidence related to the effect of the cost on BDA adoption for SMEs. Several possible explanations exist for the insignificant effect of this relationship. First, SMEs may use infrastructure-as-a-service (IAAS) for their IT infrastructure needs, which does not require much investment for BDA. Alternatively, SMEs may use ready-made packages for their analytics needs, IT appliances, software-as-a-service, or data-as-a-service, which all have relatively low cost compared to large-scale, in-house infrastructure and licensing costs. Second, the nature of analytics teams in SMEs is small, which does not require much investment in training and development. Third, the support from the government for technology startup companies provides an aid for high cost emerging and new technologies. The new establishment of the small and medium enterprise bank (SME Bank) in 2021 seeks to increase SMEs’ financial volume to 20% by providing innovative digital funding solutionsFootnote 7. The bank aims to help strengthen SMEs’ roles in the Saudi economy as it provided SR 221 billion as total financial facilities to SMEs with 47 funding partners in the second quarter of 20227. Second, the National Technology Development Program (NTDP) launched a new initiative called the Technology Development Financing initiative, which is dedicated to technology startups, and it provides SMEs a financing guarantee of up to 15 million Saudi Riyals to achieve growth and developmentFootnote 8. Thus, the above arguments, along with the government support to SMEs may explain the insignificant effect of the cost of adoption of BDA for SMEs.

The findings of this study also indicate that there is no effect of security risks on BDA adoption for SMEs. This result is contrary to findings of previous studies showing that security risks have a negative impact on BDA adoption (Baig et al., 2019; Lutfi, Alsyouf, et al., 2022; Maroufkhani et al., 2020). One potential explanation for this insignificant relationship is the size and value of SMEs. As organizations grow, they become more concerned about their resources and data, which may create higher concerns to security issues. Another explanation is the trustworthiness of service providers of BDA through their reputation from their client’s experience (Bush et al., 2008). Another possible explanation is the high regulations of data and information security by the government on public, commercial, and personal data, and exchange through establishment of at least two regulatory bodies. The first is the National Cybersecurity Authority (NCA) founded in 2017 with two leading roles related to cybersecurity: regulatory and operational functionsFootnote 9. The second is the National Data Management Office (NDMO) which is concerned with the transfer and processing of data outside the geographical borders of the Kingdom, as well as the protection of personal dataFootnote 10. As SMEs comply with these regulatory bodies, it may become less of a concern to consider security as risks associated with BDA adoption. Therefore, all of the mentioned reasons may explain the absence of a relationship between security risks and the adoption of BDA.

The findings of the study examine the three organizational factors (top management support, data-driven culture, and organizational readiness), and it turns out that data-driven culture is among the most critical factors for BDA adoption, followed by organization readiness, and then top management support.

Data-driven culture, an important organizational factor for SMEs, significantly affects BDA adoption as confirmed in the results of the study. SMEs require an organizational-wide attitude to succeed in their big data initiatives, which may be attained through data-driven culture (Karaboğa et al., 2019). This relationship is supported by the findings of a McKinsey report published in 2011 on big data, pointing out that a company’s” data-driven mindset” is one of the critical elements of big data’s value in organizations (Manyika et al., 2011). A data-driven culture is considered the heart of the data decision-making process, which requires all the management and employees to understand the value of data to drive better business insights for better impact. As a result, the most prominent finding from the analysis is this vital influence of data-driven cultural approach on business. A lack of data-driven culture in SMEs (e.g. storing data without leveraging business in making decisions) may prevent SMEs from reaping BDA’s potential benefits, and eventually leading to challenges in enhancing overall performance. Chatterjee et al. (2021) point out that establishing a data-driven culture is one of the business leaders’ responsibilities to emphasize its usefulness to achieve business success. Therefore, SMEs leaders need to create a data-driven culture by promoting the essential aspects of a data-driven organization (Anderson, 2015).

The results of this study confirm that organizational readiness is essential for BDA adoption in SMEs, consistent with findings of previous research (D. Q. Chen et al., 2015; Ganeshkumar et al., 2023; Lutfi et al., 2023; Maroufkhani et al., 2020). One of the initiatives that may support the organizational readiness in Saudi Arabia is the Tech Crew initiativeFootnote 11. The initiative seeks to attach and sustain the Saudi tech talents in SMEs by providing salary subsidies and financial incentives7. This initiative helps SMEs acquire experts to incorporate and apply domain specific knowledge for smooth BDA adoption. As the lack of adequate technological, financial, and skilled human resources leads to SMEs’ problematic adoption of BDA (Maroufkhani et al., 2020). If a business lacks the necessary resources and capabilities, it is unlikely to adopt BDA (Lutfi et al., 2023). As a result, sufficient financial and technology resources, analytics capabilities, and skilled resources are critical in the implementation stage.

The results indicate that there is an influence of top management support on BDA adoption, which is consistent with results of previous studies (Alaskar et al., 2021; D. Q. Chen et al., 2015; Lutfi et al., 2023; Maroufkhani et al., 2023; Verma & Chaurasia, 2019). Top management in SMEs are responsible for setting visions, determining future directions, and shaping business strategies. Adopting new technologies has long-term implications for SMEs. The primary decision-makers in SMEs are the owners and managers, and their decisions impact the level of support to adopt BDA (Maroufkhani et al., 2020). Therefore, top management should think strategically about selecting the technology that will align with the overall SME strategy to have a successful adoption. Besides, top management support in adopting new technologies is necessary as they have a comprehensive understanding of the market potential opportunities, industry, and technology landscape, and it influences their decision to select the most appropriate technology that aligns with the SMEs’ needs. In addition, implementing BDA requires top management to provide adequate financial and technical support, the hiring of skilled staff, and the providing of the necessary training for current workers, search for qualified BDA vendors, and the allocation of sufficient resources (Lutfi, Alsyouf, et al., 2022). Therefore, top management support can facilitate learning and deploying technology throughout the organization.

Among the three environmental characteristics (competitive pressure, government support, and environment uncertainty), it is found that only environmental uncertainty plays a role in BDA adoption among SMEs. Environmental uncertainty shows its effect on BDA adoption, meaning that SMEs respond to BDA adoption when faced with environments with high market and technology change rates. The results agree with that of Iranmanesh et al. (2023), which find that environmental uncertainty is a significant driver of BDA intention. Furthermore, D. Q. Chen et al. (2015) claim that in turbulent markets, dynamic capabilities rely on rapidly generating new situation-specific knowledge rather than depending on current knowledge. Therefore, BDA can help SMEs understand the current situation for the business and the reasons behind those market turbulences. Also, BDA may help predict future conditions, determine actions and strategies, hunt for opportunities, and prevent potential risks. Furthermore, Iranmanesh et al. (2023) explain that BDA’s predictive ability helps SMEs translate real-time data into valuable information, such as forecasting client preferences and generating alternatives based on collective knowledge. In addition, the previous authors revealed that using BDA’s prescriptive analytic methodologies, SMEs might stimulate the outcomes of alternative options and make strategic decisions that increase firm performance.

Pertaining to the competitive pressure factor, the results show that adoption rate is as low as 22% for SMEs. This may indicate that BDA is at its early stage for SMEs, which may not create high pressure on the market, or may not represent a strong trend as a new technology. Another explanation stems from the low overall adoption rate of BDA is that SMEs may still not see BDA as high value for their businesses, and hence their decision to adopt BDA is not affected by competitors. The findings of the study also show no significant relationship between government support and BDA adoption, which is inconsistent with the findings of a number of studies (Ghobakhloo et al., 2011; Lai et al., 2018; Lutfi, AlKhasawneh, et al., 2022). However, this may be due to the perception that government support in Saudi Arabia is given towards the establishment of SMEs rather than adoption of certain technologies. Another possible reason is relevant to the lack of effect of cost on BDA adoption, as such managers and CEOs of SMEs may not be influenced by government support due to their perception of the low or insignificant cost of BDA.

This study reveals the importance of BDA adoption to increase performance for SMEs. The findings of this study indicate that BDA adoption plays a crucial role in determining the effects on SMEs’ financial, market, and business process performance. A considerable amount of published literature finds that BDA creates business value and raises capabilities (Maroufkhani et al., 2020; Mikalef et al., 2019b; Popovič et al., 2018; Raguseo & Vitari, 2018). According to Raguseo and Vitari (2018), BDA boosts profitability of enterprises, transactional value, and consumer acquisition and retention. Popovič et al. (2018) also explore the association between leveraging information insights through BDA utilization in manufacturing decisions and high-value business performance. Maroufkhani et al. (2020) highlight various values of BDA adoption and its effect on firm performance and confirm the significant advantages of adopting BDA on SMEs’ financial and market performance. As a result, the present study conveys no doubts about the performance benefits gained from BDA adoption for SMEs. This study shows that the impact generated by BDA adoption on SMEs can be either tangible or intangible. The essential tangible values of BDA are improvement of supply chain management, increased market share, reduction of business costs, and increase in overall profitability. Intangible values are related to enhancing the internal decision-making process, improving customer relationships and satisfaction, and efficiently managing business operations. Therefore, BDA is seen as valuable investments for SMEs that can accelerate their competitiveness and economic growth, which in turn contributes to the achievement of the objectives of sustainable development goals to promote economic growth for Saudi Arabia through this important sector of the economy.

7 Theoretical Implications

From a theoretical perspective, this study emphasizes the contextual discrepancy between SME and large organizations in terms of the factors that drive BDA adoption. The research model incorporates important factors into TOE framework that are relevant to SMEs, such as data-driven culture (in organizational factors) and environment uncertainty (in environmental factors). The results of the study assert their importance and high relevance to BDA adoption as they shape the response to market and technology changes, affecting the decision-making process for SMEs. Further, the results of the study pinpoint the importance of organizational factors, highlighting that SMEs are more flexible and dynamic compared to large organizations.

Another theoretical contribution is the inclusion of business process performance of SMEs. Unlike previous research, which focuses on financials and market performance measures, this study provides a new lens to demonstrate the importance and impact of business process performance (i.e. improved decision making, cost reduction, and enhanced customer relationship) in comprehending the benefits of utilizing BDA for SMEs.

From an empirical perspective, the majority of prior research on BDA focuses on industries such as manufacturing (Lutfi, Alsyouf, et al., 2022; Maroufkhani et al., 2020) and supply chain (Alaskar et al., 2021; D. Q. Chen et al., 2021; Lai et al., 2018), examining antecedents and consequences of BDA adopters. However, this study broadens the scope of understanding the factors that trigger adoption in SMEs and influence their performance. Additionally, the BDA adoption factor is used as a scale to distinguish adoption levels (from “full adoption” to “no adoption”), and consequently provides better understanding of the effect of adoption on performance.

8 Managerial and Policy Implications

The findings of this study have several implications for management and policymakers. For management, the result of the study already establishes a strong relationship between BDA adoption and SME performance from three perspectives: financial, market, and business process. However, out of the three aspects of the TOE framework, it turns out that the organizational factors have the most influence on BDA adoption, and less influence from technological or environmental. To increase BDA adoption rate for SMEs, managers should pay more attention to the importance of organizational factors, i.e. data-driven culture, organizational readiness, and top management support. In fact, unlike Technological and Environmental factors, organizational factors are variables that can be altered from within the organization, as they can be self-choices by top management of SMEs and seen as levers of change in organizations. The results of this study corroborate the findings of previous work (Karaboğa et al., 2019) in indicating that data-driven culture is a crucial part of the BDA adoption and implementation phase. Top management of SMEs can shape the corporate culture and enhance a positive attitude among all employees towards the use of BDA (Maroufkhani et al., 2020). Therefore, SMEs are advised to focus on organizational factors more than other factors to ensure increased adoption of BDA.

For technological factors, the results pinpoint that only complexity and compatibility are of influence on BDA adoption, which can be managed through technological choices that increase adoption. For example, the use of off-the-shelf technologies and cloud computing products are seen as less complex and can be easily implemented. For environmental factors, it turns out that only environmental uncertainty is influential to BDA adoption, which requires SMEs to be aware of the market and watch for technology trends.

The results of the study in Fig. 2 indicate varying levels of adoption rates of BDA for different industries. Even though the overall adoption rate of BDA for SMEs in Saudi Arabia is as low as 22% compared to the global average rate (between 32–43% according to Dresner Advisory Services market study (2019)), specific industries such real estate, financial, and information technology have relatively higher adoption rates. However, the low overall adoption rate of BDA in KSA is driven by industries such as manufacturing, logistics, and retail/wholesale, for which there is an opportunity to increase BDA adoption rate. In fact, these industries are seen as interdependent, and may be considered integral parts of the industrial aims of the country. One of the objectives of the National Industrial Strategy (NIS) for Saudi Arabia is to build world-class supply chains, a competitive and reliable industrial ecosystem through high productivity, and advanced manufacturingFootnote 12. Utilizing BDA in SMEs, then, becomes an integral part of the ecosystem, aiding for high productivity and performance. Hence, policy makers may find it beneficial to provide incentives for SMEs, whether monetary or non-monetary, to increase adoption of BDA. Policy makers can also assist SMEs in adopting BDA by sharing success stories through national and international events. In doing so, SMEs may use a common platform to consult with experts and vendors at these events to promote solutions, facilitate knowledge sharing, and encourage partnership opportunities among stakeholders. Further, the role of SMEs is essential to the national economy in advancing the development wheel and broadening the base and productivity, as indicated in the NIS. As a result, increasing the adoption of BDA may encourage sustainable development in the economic dimension by implementing decent work and economic growth as demonstrated in SDG8 goals in SMEs.

9  Conclusion, Limitations, and Future Scope

The present study enriches our knowledge of utilizing BDA in the data age where it can turn data into valuable insights, which eventually can lead to an increase in organizations effectiveness, efficiency, and overall performance. BDA accelerates growth and empowers SMEs, as discussed in the paper. Our understanding of how adoption of BDA and its determinants may influence the performance of SMEs is notably underdeveloped. The proposed research model in this study is developed by utilizing technological, organizational, environmental, and performance dimensions derived from the TOE framework and RBV theory. The results of the study find support from previous research on the elements that determine BDA adoption, i.e. complexity, compatibility, top management support, organizational readiness, data-driven culture, and environmental uncertainty. Analysis of BDA adoption consequences reveal that BDA boosts financial, market, and business process performances for SMEs. However, this study reports on factors that may not be relevant to BDA adoption for SME, namely: adoption cost, security risks, market competitive pressure, and government support, which are discussed thoroughly in the paper.

Several limitations need to be noted regarding the present study. First, the context of study and its data collection process limits the generalizability for the findings to local and similar contexts. Second, this study did not consider other types of firm performance such as productivity, operational and customer-focus performance, and growth, which may indicate the value of BDA to SMEs. Last, the responses collected from managers and owners of SMEs may be subjective and susceptible to recall bias. However, several statistical tests were run to validate the methods in data collection and sample validity.

In light of the above research limitations, further studies may examine the effects of BDA adoption by combining quantitative and qualitative approaches through conducting interviews and utilizing secondary data from governmental, business websites, or reports. Considerably, more work is needed to expand the current study and the conceptual framework by including potential elements from multiple contexts, such as IT infrastructure, vendor support, and data quality. Furthermore, it would be a fruitful area for future research to investigate the effect of the data-driven culture, environmental uncertainty, and other elements as moderators between the factors and the BDA adoption. Future research is needed to analyze and examine the reasons behind disparities in adoption levels among industries.