“Adoption of big data analytics in medium-large supply chain firms in Saudi Arabia”

Big Data Analytics (BDA) is one of the most digital innovations for supporting sup- ply chain firms’ activities. Empirically, multiple benefits of BDA in Supply Chain Management (SCM) have been demonstrated. The study aimed to investigate the re- lationship between technical, organizational, and environmental factors and supply chain firms’ performance using the Technology-Organization-Environment (TOE) framework and the Diffusion of Innovation (DOI) theory. This study was conducted at medium-large supply chain firms in Saudi Arabia, the sample size reached 700 firms recognized by Saudi Arabia’s Ministry of Commerce and Industry in different domains. In this study, a questionnaire was used to collect primary data. The collected data are analyzed using SPSS version 26.0. SPSS is used to describe respondents’ demographic profiles. The percentage of respondents to the questionnaire reached 57%. In addi- tion, to test hypotheses and accomplish research goals, PLS-SEM version 3.0 is used to examine the relationship between independent and dependent variables. From the PLS results, the study reported that complexity ( β = 0.097, t = 2.817), security ( β = 0.222, t = 3.486), IT expertise ( β = 0.108, t = 1.993), and external support ( β = 0.211, t = 3.468) were positively related to firm’s performance; in contrast, relative advantage ( β = –0.006, t = 0.200), compatibility ( β = –0.020, t = 0.314), top management support ( β = –0.046, t = 0.386), organizational resources ( β = –0.065, t = 1.179), competitive pressure ( β = –0.011, t = 0.199), and privacy ( β = –0.05, t = 0.872) were negatively related to firm’s performance.


INTRODUCTION
Saudi Arabia has established goals for 2020 and a strategic vision for 2030, focusing on logistics and supply chain management. One of the primary goals of these strategies is to diversify the country's economic streams away from oil. In Saudi Arabia, BDA technologies will be one of the primary facilitators for the success of Vision 2030, since data will power the Kingdom's blueprint for the future, creating new opportunities, expanding the economy, and transforming society, which are key objectives of the Saudi Vision 2030. Another goal is to increase investments and lead the digital economy through technology. The Saudi government is also looking for private-sector collaborations to develop information technology infrastructure and telecommunications (Thamir et al., 2020). Implementation of BDA may be problematic due to the high cost, liabilities, complexities, and applicability associated with new technological innovation. These factors might contribute to ambiguity in the choice to implement BDA in SCM operations.
Saudi Arabia, continuously shifting environmental patterns, mostly as a result of the Kingdom's Vision 2030 implementation and other un-expected situations like as the COVID-19 epidemic, may cause Saudi firms to re-evaluate their choices, potentially leading them to adopt BDA in order to sustain, or indeed obtain, a market leader (Thamir et al., 2020). Additionally, big data helps firms improve their supplier evaluations and govern procurement activities in any circumstance. Firms may also simulate their supply networks using big data. While numerous studies have exhibited the advantages of utilizing BDA in SCM, research in developing economies is limited. Most of the previous research focused on rich industrialized countries. In contrast, few prior published studies have evaluated Saudi Arabia's drive to adopt and apply big data analytics technology to SC firms (Thamir et al., 2020). Furthermore, Choi et al. (2018) indicate increasing uses of BD technologies and strategies in many areas of SCM, such as prediction, income management, and risk assessment. However, many CEOs still do not use big data analytics in their decision-making activities.
The expected sample size reaches near 700 firms recognized by the Ministry of Commerce and Industry in Saudi Arabia in different domains. Therefore, the findings of this study will provide generalization knowledge and determine the main factors facing firms to adopt big data through testing the adoption of a big data model in firms. Furthermore, this study is important as it contributes to expanding the literature on BDA and SCM by conducting in Saudi Arabia. Moreover, it might used as a reference for future academics seeking to expand their study focus on factors affecting the adoption of big data technologies in middle east countries.

LITERATURE REVIEW
A supply chain is a network of suppliers, manufacturers, transportation companies, warehouses, retailers, and customers. Supply chain management aims to control the movement of finances, information, and goods within a supply chain in order to maintain a high degree of product availability and service to the customer at the lowest feasible cost. Nowadays, there is a plethora of records generated as a result of transactions between suppliers and purchasers. The use of big data methods and tactics in a variety of supply chain management themes, including forecasting, revenue management, and risk analysis, using case studies from leading brands. However, despite the rising use of big data in supply chain management, some managers continue to avoid incorporating big data analytics into their decision-making processes.

Big Data analytics adoption
The term "big data" refers to a massive dataset that cannot be controlled or analyzed using standard databases (Lin et al., 2019; Mishra et al., 2017). In comparison, other experts saw big data as a business strategy that enables organizations to examine merely a massive volume of data Malaka & Brown, 2015). In addition, BDA is a holistic strategy for managing, processing and analyzing data of various sizes of (volume, variety, velocity, veracity, and value) required to provide actionable information for sustained delivery, performance assessment, and competitive advantage (Emani et al, 2015).
Generally, big data adoption has an impact on SCM performance in many aspects, and it is shown in many studies such as Thamir et al. (2019) observed that recent research asserted that a sizable proportion of firms are incapable of leveraging value from the prospects that BDA may provide for their businesses. Some academics have even rejected those businesses may improve their performance through BDA. As a result, there may be a lack of knowledge and conflicting opinions regarding how firms might profit from BDA funds (Wamba et al., 2017). Additionally, the promises of BDA are not thoroughly investigated by businesses such as SCM companies (Mikalef et  With the support of an information system, business analytics has a strong association with supply chain performance. Nevertheless, the emergence of big data applications in supply chain management does open up new possibilities. Supply chain analytics is a generic phrase that is a term that refers to advanced big data analytics used in managing the supply chain. These analyses might be classified as predictive, descriptive, or prescriptive (Wang et al., 2016). Numerous benefits of BDA in SCM are supported by empirical research, including cost reductions, enhanced SC flexibility, and satisfied customers (Ramanathan et al., 2017). As a result, there is growing interest in defining a distinct skill set for SCM data scientists (Waller & Fawcett, 2013). Big data analytics is rapidly gaining traction among academics and is becoming a priority for businesses to deploy. Daily, incomprehensible amounts of data are transported and collected as a result of the widespread use and growth of big data-enabling tools such as social networks, mobile devices and identification technologies that give consent to the 'Internet of Things.' Given that increased data equates to increased knowledge, firms are increasingly leveraging these technologies to establish and preserve competitive advantage (Wamba et al., 2017). They play an important role in considering the aspects that affect any technology acceptance decision by generating a blueprint (Oliveira & Martins, 2011); theoretical models must be considered to solve big data acceptance and execution issues at the organizational or individual level. Several factors influence big data adoption, including IT infrastructure, usefulness, and complexity.

Underpinning theory
In conclusion, a survey of the literature, adopting big data analytics is the process through which an innovation changes the architecture of an organization. Big data adoption includes improved in- formation processing methods and technological advancements that support decision-making. It gives organizations new opportunities to use information and gain a competitive advantage. The use of big data enhances risk prediction, boosts production, and efficiently satisfies customers. In addition, big data usage helps businesses and sectors to outperform their rivals. Therefore, big data use could be time-consuming and expensive, but the long-term advantages might lead to success.
While big data have been frequently employed in predictive research, there are relatively few studies measuring prediction error in large data. More exactly, beyond the raw data's quality, the accuracy of big data analysis is strongly influenced by the model used to analyses the data. We still have a long way to go in terms of generating metrics that can be used to assess the accuracy of a method for analyzing large data. The majority of current research on big data applications in supply chain management is theoretical and conceptual, with a distinct dearth of research on analytical models. Additionally, present analytical models focus primarily on the use of big data in modelling sustainability. As a result, there is still a gap in the use of big data to supply chain optimization. Even though empirical research has examined factors influencing BD adoption in a variety of areas, the prior literature demonstrates a dearth of empirical evidence for big data adaptation in Saudi Arabia. As a result, this study will focus on medium-large supply chain enterprises in Saudi Arabia in order to address this absence from the literature

AIMS AND HYPOTHESES
This study aims to examine the relationship between technical, organizational, and environmental factors, and SCM performance at medium-large supply chain firms in Saudi Arabia Ten (10) hypotheses were formulated as follows: H1: Relative advantage is positively related to supply chain management performance at Saudi firms.
H2: Complexity is positively related to supply chain management performance at Saudi firms.
H3: Compatibility is positively related to supply chain management performance at Saudi firms.
H4: Security is positively related to supply chain management performance at Saudi firms.
H5: Top Management Support is positively related to supply chain management performance at Saudi firms.
H6: IT expertise is positively related to supply chain management performance at Saudi firms.
H7: Organizational resources are positively related to supply chain management performance at Saudi firms.
H8: Competitive pressure is positively related to supply chain management performance at Saudi firms.
H9: External support is positively related to supply chain management performance at Saudi firms.
H10: Privacy is positively related to supply chain management performance at Saudi firms. To achieve the study's objectives, a quantitative methodology was employed. In this study, a questionnaire was used to collect primary data, the respondents were asked to answer the items presented in a questionnaire. The researchers conducted both online and hard-copy forms to distribute these questionnaires to the respondents within three months in the central cities of Saudi Arabia. The survey identified the demographic profile and included five variables, current position of employees, company field, company size by annual revenue, company size by employees, and company experience in the field of business. According to the survey's findings, the percentage of employees who responded to the questionnaire were workers in CIO/IT director/Technology director, and their percentage reached about 77.1%. In addition, most of the companies that responded to the questionnaire were companies working in the field of Transport/ logistics/post, as their percentage reached 31.3%. Moreover, the percentage of responding companies in terms of annual revenues (85-150 million SAR) reached 52.1%. In the end, the responding companies met the employee size range (50-100 employees) (63.2%).

METHOD
The questionnaire was divided into two sections, factors of adoption and firm performance.

RESULTS
The measurement model was assessed by evaluating outer loadings, composite reliability and validity, Cronbach's alpha (α), average variance extracted (AVE), and factor loadings, as shown in Table  1. All the constructs had Cronbach's alpha (α) and CR greater than 0.70, meeting construct reliability criteria. All average variance extracted (AVE) values were more significant than 0.50, meeting convergent validity criteria. And the factor loadings, greater than 0.70. The structural model was assessed by evaluating collinearity statistics (VIF), coefficient of determination (R 2 ), path coefficients (β), effect size (f 2 ), and predictive relevance (Q 2 ). The VIF values were less than five, confirming collinearity. Furthermore, the path coefficients and total effects were analyzed and supported four out of ten hypotheses. The estimated model in this research study could explain 73.8% of the variance in the plan, 81.5% of the variance in source, 77.8% of the variance in the making, 67.7% of the variance in delivery, and 70.8% of the variance in return. Predictive relevance (Q 2 ) values were more outstanding than zero, indicating that the estimated model was predictive. Table 1 shows the results, indicating that the importance of the path coefficient is proven using t statistics and p-value evaluation at the .05, .01, and .001 confidence interval levels.

Discussion of the relationship between technical contexts and SCM performance
This section discusses how to evaluate hypotheses H1, H2, H3, and H4. H1: Relative advantage is positively related to supply chain management performance at Saudi firms.
The data analysis results did not support H1, implying that there is no association between relative advantage and business performance. The study's findings on the link between relative advantage and business performance are less significant in the setting of Big Data adoption. Agrawal (2015) and Nam et al. (2015) found no evidence for relative advantage in studies of Big Data adoption, hinting that relative advantage may be redundant in such investigations. However, the lack of awareness of how big data analytics technology may be used to improve The employee of a business feels that big data analytics technology offers several benefits, including improving the firm's performance. As a result, the benefits of using big data analytics technologies are obvious to business decision-makers and staff.
H2: Complexity is positively related to supply chain management performance at Saudi firms.
The findings on the influence of complexity on business performance indicated a positive link, corroborating H2. This study suggested that the complexity of the firm had a positive correlation with its success. While earlier research indicates that complexity has a detrimental effect on BDA acceptance (Verma & Chaurasia, 2019; Agrawal, 2015), a few further studies indicate that complexity does not affect BDA (Lai et al., 2018) or other IT innovation adoption (Maduku et al., 2016). In comparison, the degree of complexity is mostly determined by the users who will interact with technology, as the descriptive demographics revealed that the majority of respondents (77.1 percent) specialize in information technology. As a result, users will have no difficulty interacting with this technology, since it is simple to grasp, engaging with it is clear and intelligible, and it is simple to become proficient at utilizing it.
H3: Compatibility is positively related to supply chain management performance at Saudi firms.
Analyses of the data indicate a negative correlation between compatibility and company performance. This means that the infrastructure compatibility for adopting big data analytics technology negatively affects the firm performance. Hence, H3 is not supported. Previous studies have shown an insignificant effect of compatibility on BDA adoption (Maroufkhani et al., 2020). Furthermore, managers' decisions on BDA adoption are influenced by compatibility. Organizational decision-makers will be more receptive to adopting and using BDA technology in various aspects of supply chain operations if they believe it is consistent with the data acquired, current operating procedures, IT infrastructure, and company values and beliefs.
H4: Security is positively related to supply chain management performance at Saudi firms.
H4 is corroborated by the path coefficient values. As a result, the security factor and SCM performance have a favorable correlation. This result suggested that security was associated with company performance. In contrast, earlier research has established a negative correlation between security and the use of Big Data technologies (Salleh & Janczewski, 2016; Nguyen & Petersen, 2017). The survey resulted in a high mean security factor (4.40), indicating that businesses possess the necessary security capabilities to adopt this technology, possess the necessary skills to ensure data security when utilizing this BDA technology, can easily integrate security policies for technology, have adequate tools and mechanisms in place to ensure effective data protection when utilizing this BDA technology, and possess the necessary security capabilities to adopt this technology. As a consequence, businesses can protect their customers' data and prevent security concerns. The customers should have no concerns about security.

Discussion of correlation between organizational contexts and SCM performance
This section explains the methodology for evaluating hypotheses H5, H6, and H7.
H5: Top management support is positively related to supply chain management performance at Saudi firms. H6: IT expertise is positively related to supply chain management performance at Saudi firms.
The effect of IT expertise on firm performance revealed a positive relationship, therefore, supporting H6. On the other hand, these results implied that there is a positive relationship between IT expertise and firm performance. Therefore, empirical evidence indicates that IT competence has a beneficial effect on IT adoption across a range of technologies, including Big Data adoption (Nam et al., 2015). Expertise in information technology is regarded as one of the company's most valuable assets. Companies can provide all facilities to adopt big data analytics technology such as IT infrastructure, but they are facing big problems in providing big data technology specialists. Moreover, especially in the Middle East countries, the existence of IT expertise in BDA is very little, and it is not easy to attract them from other countries, so the presence of this expertise will greatly help the company's decisions in adopting big data technology to raise the performance of the company.
H7: Organizational resources are positively related to supply chain management performance at Saudi firms.
H7 is not supported based on the results of the path coefficient. As a result, the association between organizational resources and SCM performance is minimal. The findings corroborate prior studies ( Hung et al., 2016). In light of the hypothesis's findings, the existence of vendors and agents in the Saudi market working to provide support and training for companies will motivate firms to adopt big data analytics technology, which will reflect positively on the firm's performance. As a result, the more external help available, the more probable it is that Big Data technologies will be embraced.
H10: Privacy is positively related to supply chain management performance at Saudi firms.
H10 is not supported based on the results of the path coefficient. Therefore, there is an insignificant relationship between privacy and SCM Performance. This finding suggested that security was related to the firm's performance. According to previous studies, the major concerns of business owners are when it comes to deploying data-related technologies, privacy and security are paramount (Priyadarshinee et al., 2017). Fear of losing control of personal information and the danger of information being leaked to competitors may hinder BDA adoption. Additionally, Nam et al. (2015) uncovered evidence for a link between privacy regulations and Big Data usage in a prior study on the issue. Privacy issues are among the most important issues in Saudi society. Due to the nature of the conservative society, it is not easy to obtain all the information about customers because there is a belief among customers that it is possible to use this information for other things that go beyond marketing studies. In addition, there is a prevailing belief in the society of Saudi Arabia that their data is vulnerable to penetration or distortion or distribution to other parties. Despite the existence of regulations and legislation that punishes anyone who infringes the privacy of others with deterrent punishments, society still has a fear of sharing data with others. As a result, the presumed challenges associated with complying with privacy requirements decrease the likelihood that Big Data technologies will be implemented.

CONCLUSION
The purpose of this study was to investigate the relationship between technical, organizational, and environmental adoption factors, and SCM performance at medium-large supply chain firms in Saudi Arabia. Two crucial implications may be drawn from the PLS results. First, complexity, security, IT expertise, and external support factors demonstrate their positive effect on BDA adoption, which improves firm performance. In contrast, relative advantage, compatibility, top management support, organizational resources, competitive pressure, and privacy factors have been shown to negatively affect the adoption of BDA, thus decreasing firm performance.
Based on the findings, the study recommended that firms prioritize spreading and growing awareness inside the organization about the key advantages of BDA technology to all stakeholders. Additionally, firms must think about the challenges of implementing BDA technology as well as additional elements that might affect acceptance, such resistance to change, data quality and integration, organizational learning culture, and decision-making culture. Therefore, firms are required to develop appropriate plans on how to overcome these challenges during the planning stages for the deployment of technology.
Future studies should incorporate the suggested paradigm for big data analytics technology adoption with other theories, such as RBV, TTF, and TAM. The framework's ability to explain previously unrecognized features of big data analytics technology adoption and deployment may be enhanced by including more theory-based context. In the end, the study was limited to medium-sized Saudi Arabian supply chain companies. To generalize the study's results to the Saudi market, a framework that can be evaluated at small-to medium-sized businesses in Saudi Arabia would be needed.