The Influence of Big Data Analytics on SME Performance in China
Abstract
<h2>Cover Page</h2> <p>The Influence of Big Data Analytics on SME Performance in China</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <h2>Conceptual Context of Big Data Analytics and SME Performance in China</h2> <h3>Development and Strategic Relevance of Big Data Analytics</h3> <p>The development and growth of Big Data's popularity in recent years among academics, businesses, and governments has caused considerable concern. The scientific community agrees that Big Data will power the economy in the future (Saleem et al., 2020). A leading management and consulting firm, Huang et al. (2020), claims that Big Data is now integral to every facet of modern business and industry. Given the current state of database management systems, it is challenging to detention, store, procedure, and evaluate Big Data due to the sheer information that would need to be collected, catalogued, processed, and evaluated (Sun et al., 2022). Big Data refers to data sets that are extremely large in volume, making it difficult to analyze and manage with the typical toolsets of centralized database management systems (Yadi et al., 2019). This data is helpful for the business world, manufacturing, and medical study. Input data can be any combination of transaction records, emails, photos, camera logs, activity reports, blog entries, social media posts, and sensor readings (Sun et al., 2022). The exponential growth and increased value of both historical and contemporary data sources are sometimes collectively referred to as Big Data. The analytical skills of today's Internet-based businesses and institutions stand to benefit greatly from the availability and accessibility of big data (Sheng, 2021).</p> <p>In today's rapidly developing technology environment, as well as to accommodate the ever-changing demands of their customer, managers are under growing pressure to make adjustments to their company's procedures (Dong & Yang, 2020). Being highly competitive and quick to adapt to complex dynamics is the best strategy to succeed in an international budget. Businesses operating in the e-commerce space generate massive volumes of data as a byproduct of their everyday activities, which may be mined for insights and utilized to advance the state of the art, get insightful new insights on business processes, and increase efficiency (Song et al., 2021). The term "big data" means the massive amounts of information that have been available in the previous two decades revolutionary source of productivity and opportunity, but harnessing its power has proven to be an enormous challenge for organizations. Since BDA's introduction, traditional methods of doing business have been revolutionized (Wang et al., 2019). This means that BDA has developed an increasingly significant metric for gauging and improving corporate success. When it comes to handling, processing, and analyzing large volumes of data, management in companies faces a wide range of obstacles (Shamim et al., 2019).</p> <p>These days, no company can function without information technology (IT) and business process modeling (BPM) (Huang et al., 2020). BDA used information technology to turn its traditional business into state-of-the-art modules for the cheap capture and storage of enormous volumes of data (Li et al., 2020). Companies, particularly in developing countries, were motivated to alter the nature of their products and operations as a result of the advent of such rapid access to data through technology, since product innovation resulted in 10% greater sales, labor productivity, and growth of SMEs (Bertello et al., 2021). Similarly, when businesses innovate their processes, they are more likely to employ cutting-edge techniques that increase their productivity (Wang et al., 2020). BDA may help SMEs since it encourages product and process innovation, both of which increase productivity. The ability to control massive amounts of data gives businesses a distinct advantage. Large quantities of data and commercial activity are readily available, which contributes to this (Guo et al., 2020).</p> <p>SMEs in Europe continue to be the background of the regional economy, generating the vast majority of new jobs and maintaining competitiveness even when large firms lay off people and cut back on operations (Mangla et al., 2020). After the global recession (2007-2009) together with the European Union's sovereign debt problem (2011-2013), the financial system underwent significant changes, especially in the medium and small-sized enterprise (SME) finance sector (Lu et al., 2022). The significance of SMEs businesses is growing worldwide. Both the European and Chinese economies owe a great deal to the success of their SMEs (Yin et al., 2020). When compared to large corporations, SME had a far more difficult time getting started. They were more likely to encounter issues with funding, taxes, competition, and regulation. For them, money problems are the biggest obstacle. SMB expansion is limited by finance hurdles (Kumar et al., 2019).</p> <p>As a developing nation, China struggles with the fact that fewer people utilize financial services than in more industrialized nations (Liu et al., 2022). Lending services in the nation mostly benefit state-owned companies and other influential economic and political interests (Xu & Li, 2019). To the tune of 80% of urban employment, 50% of fiscal and tax income, and 60% of China's GDP, the country's SMEs are largely to thank (Peredy et al., 2022). The government controls a large portion of China's banking industry, and the state-owned banks that make up this sector have a big branch network (Xu & Li, 2019).</p> <p>Wang et al. (2020) argue that businesses must reevaluate the competitive landscape of their product and process systems as part of their big data strategy. Further, he emphasized that companies which did not take this action would eventually lose market share and their competitive edge. It has been found by Metawa and Metawa (2021) that the formerly exponential improvement in business performance attributable to BDA is beginning to level off. To be more specific, BDA is where companies may get out-of-the-box ideas for improving their businesses. Despite the fact that the influence of BDA on firm performance has been researched separately in the previous literature, there is a need to explore how BDA may assist organizations apply technological innovation (TI) to their operations in order to increase company performance.</p> <h3>Research Problem Concerning Big Data Adoption and SME Sustainability</h3> <p>According to Gao et al. (2019), revealed that the absence of mechanisms to an informal strategic planning process that tracks the success of SMEs was a major contributor to the high failure rate. The China Small Business Administration reports that almost half of SMEs fail during the first year, and that nearly all SMEs fail within five years. According to Kumar et al. (2019), 37% of SMEs with less than 20 workers survive after 4 years, and just 9% survive after 10 years. Despite the lack of dependable data on SME failure rates in China, the country's small and medium-sized enterprises face significant obstacles to achieving long-term success (Liu et al., 2022). With the help of Industry 4.0, SMEs may improve their technological performance and efficiency. There were downward trends in BDA during 2012–2013, (Metawa & Metawa, 2021). The rapid development curve of BDA-based firms was flattening off, yet progressive organizations embraced BDA to gain competitive pressure.</p> <p>A study by Xu & Li (2019), shown that SMEs are lagging behind in their use of BDA technology. As the company begins its transition to digital operations, BDA is an integral part of the process. Companies in China have recently begun to place a premium on analytical tools. The early adopters include both SMEs and more conventional manufacturers (Wang et al., 2020). As the state of the art in computing evolves, the importance of "big data" to both the public and private sectors has only grown. Having additional information may improve analysis quality, which in turn can boost one's confidence in one's conclusions (Sun & Liu, 2020). Better operational efficiency, lower risk, and lower costs are all possible results of making a wise business choice. Even while big data presents significant difficulties for developing nations, it also offers enormous opportunities for enhancing rural communities and bringing about profound changes in economic and social life (Anwar et al., 2018). If China is serious about shaping its economic future, it must increase its big data investment. Companies now have access to vast troves of information because to the proliferation of the Internet, mobile devices with advanced computing capabilities, and social networking. The adoption of BDA in business by SMEs is subject to a number of circumstances and conditions, as was found by (Khan et al., 2020). This study will explore on the influence of big data on the SME’s performance.</p> <h3>Research Objectives on Big Data and SME Performance</h3> <ul> <li>To establish the relationship between big data and SME’s performance in China.</li> <li>To examine the effect of big data on SME’s performance in China.</li> <li>To determine the Big Data analytics issues and challenges faced by SME’s.</li> </ul> <h2>Theoretical and Empirical Foundations of Big Data Analytics in SMEs</h2> <h3>Scope and Structure of the Scholarly Review</h3> <p>This chapter will review studies done that relates to the research objectives as guided by the research topic. The chapter will consist of two section the theoretical analysis and the empirical analysis.</p> <h3>Resource-Based View of Big Data Capabilities and SME Performance</h3> <p>The "resource perspective" provided by Safari and Saleh (2020) suggests that the pace of total growth for firms is related to how effectively they use the various resources at their disposal. To give a more accurate image of RBVT, Dionysus & Arifin (2020) separated a company's resources into two groups: internal heterogeneous resources and external immobile resources. The goal of this research was to determine how factors like big data analytics on the performance of SMEs. According to the RBVT model, a business needs to use everything at its disposal to succeed (Sukaatmadja et al., 2021). The research presents BDA as an informational and technical asset that SMEs may influence for superior performance. Many researchers have utilized RBVT to foresee future results in a company or other entity. For instance, the RBVT was utilized by the crew in order to boost production (Chumphong et al., 2020). They found that most studies aimed at giving businesses an edge in the market focused on three things: the distribution of assets, the cultivation of existing ones, and the discovery of new strategic assets. An increase of 500% in the number of studies using RBVT to improve firm performance across a variety of metrics has been observed over the past decade (Roostika, 2019).</p> <p>According to Ramon-Jeronimo et al. (2019), RBVT is gaining popularity in the fields of operation and strategic management research as a technique of gauging a company's efficiency. Valaei et al. (2021) applied RBVT and discovered that businesses may boost output by embracing an entrepreneurial spirit and making better use of intangible assets like intellectual and social capital. Karedza & Govender (2020) used RBVT to predict the effect of organizational resources (such culture and structure) on performance and discovered a positive association between culture & structure & productivity. However, Shibin et al. (2020) found that putting the client first was crucial to a company's performance while employing RBVT. According to the study's findings, a company's productivity might benefit from a more positive work environment developed with the use of internal resources and technological advantages who developed the RBVT-based Organizational Productivity Integrated Model. In line with these findings, an empirical study using RBVT was undertaken by Chaudhuri, Subramanian, and Dora (2022), who discovered that a company's financial and physical resources considerably give a reasonable advantage, resulting in enhanced performance. Nikmah et al. (2021) also found, utilizing RBVT, a large portion of FP's funding comes from SCF. They went on to say that digitizing trade is a valuable technical asset that helps to fortify the bond between SCF and FP. Using RBVT, Karim et al. (2022) found that external pressure is a significant factor in the use of productive assets (such BD analytics and capabilities) that boost productivity and profitability. To predict the success of SMEs in light of these considerations, we examine data resources (big data analytics) and technological developments (technological resources).</p> <h3>Empirical Evidence on Big Data Analytics and SME Outcomes</h3> <h3>Conceptual Characteristics and Business Value of Big Data</h3> <p>When talking about Big Data, Ngiam & Khor (2019), defined as any massive amount of information that requires cutting-edge methods of data collection and storage. As a rapidly expanding sector of the ICT industry, "Big Data" (or "Data Science") is subject to constant change and development. Technologies at the cutting edge of the information age, such semantic technology, speech and voice processing, and networking, generate or make use of massive volumes of data, making them inextricably related to Big Data (Wang et al., 2020). This ambiguity is fueled in part by the lightning-fast evolution of technology; for instance, Big Data stores information that was unavailable to commonplace PCs and mobile devices until recently, but is now easily controllable by these tools (Allam & Dhunny, 2019). Although Big Data marks a revolutionary shift in the way organizations handle their data, many still haven't caught on to its full potential. In fact, 18% of executives in a recent IBM survey regarded Big Data as little more than a source of massive volumes of data (Price, & Cohen, 2019).</p> <p>There are three main characteristics that can be generalized about the data processed by Big Data. Firstly, the volume of Big Data is too large to effectively process in a short time frame. Second, it includes data that is not typically stored in a computer, such as text, video, and audio files, as well as data that is being collected in real time by hundreds of cameras and satellites to track traffic (Duan et al., 2019). Third, Big Data means a large quantity of data, which poses significant practical challenges when attempting to handle it. According to Roh et al. (2019), Big Data is the next-generation technology and architecture that the Internet Data Center (IDC) has developed to handle high-capacity, high-frequency and different types of data at a reasonable cost. The term "Big Data" is commonly associated with technological advances and the new information era explosion that has resulted in vast volumes of data (Zhu et al., 2018). Integrating and analyzing data from several sources efficiently allows for the identification of patterns and the development of more accurate forecasts of future occurrences. By utilizing IBM's Big Data platform, businesses may have access to vast troves of data that are crucial to their operations (Li et al., 2018). This platform takes use of time-tested techniques while also providing access to state-of-the-art technologies that place a premium on productivity, versatility, accuracy in data search and analysis, and speed when working with both organized and unstructured information (Wachter & Mittelstadt, 2019). To begin, it's complex, which means it includes both unstructured and structured information. For instance, data on the various forms of images, text messages and GPS signals that travel across the Internet and other networks is gathered. Data is acquired so quickly that it needs to be received and processed in real time or very close to it in order for organizations to successfully exploit new opportunities and respond to changing market conditions (Oussous et al., 2018). The third advantage is that it demonstrates the trustworthiness of the data by making it possible to get skewed results from the many data processes that, taken together, generate vast quantities of skewed or distorted data (such as social network data) (Grover et al., 2018). The volume represents the massive amount of data that has been gathered for analysis and must be housed in a single database to facilitate the organization's summary process (Xu & Duan, 2019).</p> <p>Big Data has considerable worth from the information technology industry point of view. The next generation of the IT sector will revolve on big data, propelled in large part by cloud computing, mobile Internet, and social networks. There will be a $5.3 trillion market for the third IT platform by 2020, and it will be responsible for 90% of the IT industry's growth from 2013 to 2020, say Wang et al. (2018) at IDC. The analytics-as-a-service (AaaS) paradigm is set to disrupt the IT sector, and Big Data firms will revise their infrastructures accordingly. IBM, Google, Microsoft, and Oracle are just a few of the companies that have development initiatives connected to Big Data in the works (Zhou et al., 2020).</p> <h3>Adoption of Big Data Analytics Among Chinese SMEs</h3> <p>Big data analytics (BDA) refers to the process of bringing together two different types of technology, specifically, sophisticated analytics and massive amounts of data. Organizations having links to analytics-based firms can track and manage BDA more effectively (Mangla et al., 2020). With data only expected to increase in size, Maroufkhani et al. (2020) suggested that BDA and data analytics may be viewed as synonymous. BDA is a term coined by and used to describe a methodology for processing and interpreting large datasets to improve business operations. Dong and Yang (2020) state that BDA includes everything from the consolidation of several data sources into a single location to the timely and effective management, analysis, and retrieval of the resultant enormous data set. According to the description offered by Iqbal et al. (2018), BDA is an all-encompassing process that relies upon the knowledge of many various sectors to deliver actionable insights, create value for the organization, and boost competition. Chinese SMEs are ranked third in terms of their utilization of Industry 4.0 technologies. 67% of Chinese SMEs have used some form of data analysis and statistical process automation (BDA), mobile payment system, or software for process automation (Wang & Wang, 2020). Since 2013, China has been investing in its BDA ecosystem, giving it a leg up on its ASEAN neighbors like Indonesia and Thailand. While most countries have yet to fully appreciate big data's potential benefits, China is one of the few with a concrete BDA strategy for doing so (Maroufkhani et al., 2019). At the start of 2016, China has the most developed ecosystem for technological infrastructure in all of Southeast Asia, according to Mohd Selamat et al. (2018) of innovation Capital Director. SME for you is an approachable platform with the intention of encouraging SMEs to adopt BDA, initiate an ecommerce strategy, and advertise their goods and services in both local and international markets. The Chinese government has invested heavily on domestic BDA innovation and adoption (Shabbir, & Gardezi, 2020).</p> <h3>Relationship Between Big Data Analytics and SME Performance</h3> <p>Since the turn of the decade, academics and corporate executives have shown a keen interest in big data analytics (BDA) as a mechanism by which companies may more effectively manage, process, and analyze massive volumes of data in order to sharpen their competitive edge (Mubarak et al., 2019). A departure from traditional data warehouses, the volume of BDA, estimated in Exabytes, consists of a variety of data types, including but not limited to: unstructured, structured, and semi-structured (Mikalef et al., 2019). Online social media sites like Google, Youtube, and Facebook are the key sources for this information. A definition and explanation of big data were provided by for instance, using the 5 V attributes of volume, veracity, velocity, velocity and verity. Big data is described similarly by Ferraris et al. (2018) as a massive volume of data that is difficult to measure using traditional methodologies and equipment. Further, BDA was emphasized by Saleem et al. (2020), as an emerging class of frameworks and approaches for efficiently keeping tabs on and analyzing huge amounts of data, both unstructured and structured. The importance of big data to organizations has increased dramatically as a result of recent technological developments (Maroufkhani et al., 2020). BDA is receiving a lot of attention from upper management considering it may lead to a 4-6% boost in output (Mangla et al., 2020). 85-91% of organizations, based on an analysis of one thousand enterprises, have begun investing in big data analytics initiatives in the recent several years. Recent work by Shabbir and Gardezi (2020) distinguishes between predictive and prescriptive BDA to emphasize the value of each in determining whether or not a company would adopt the method. Big data analytics was shown to improve business processes as a whole.</p> <p>Predictive BDA is a phrase for using historical data to anticipate future events, their causes, and outcomes (Dong & Yang, 2020). You can use predictive BDA as an analytical model to discover what's really causing an issue (Sun et al., 2020). Alternatively, prescriptive BDA is a form of data analysis that suggests courses of action and justifications based on existing conditions (Ferraris et al., 2018). The use of big data predictive analysis as a strategic tool to boost company results was found by Mubarak et al. (2019). It has been demonstrated by other researchers that predictive analysis can boost a company's overall performance by as much as 76% across a number of metrics (sales, profits, satisfied customers, growing markets, and new products). The research of Bouwman et al. (2019) supports our own findings that big data predictive analysis can boost organizational performance and help businesses stand out from the competition. Prescriptive BDA was found to be a significant predictor of BDA adoption, which in turn improved business process performance, according to researchers at. The capabilities of BDA human talent, information technology, mathematics, and statistics need to be in harmony with the three pillars of BDA description, prediction, and prescription discussed in. Bag et al. (2020) that managers may learn what drives their company's success by using big data's predictive and prescriptive analysis. That's why experts in academia and business have been studying BDA so intensively. The study's main objective is to determine whether or not small and medium-sized firms will be profitable in the future within the context of the Chinese economy using big data analytics and technological advancements.</p> <h3>Effects of Big Data Analytics on SME Performance</h3> <p>These findings reveal major potential and hazards associated with Big Data applications for SMEs, laying the framework for future research. They also suggested some guidelines for how small and medium-sized businesses might make the most of Big Data (Bouwman et al., 2019). A study shows that firms who have invested in BDA-related technologies and management have an upper hand in the marketplace (Haar et al., 2022). One more research has stressed the need for reliable infrastructure to promote wider use of big data. The research employed text analysis to uncover 26 factors affecting company adoption of big data, and then used those findings to inform a framework for internal BDA decision-making (Ardito et al., 2021). Another study that looked at the rate of big data adoption among Chinese SMEs found that the industry was eager to use analytical approaches. China's financial markets have been reshaped by the advent of novel BD-spawned financial and monetary services and instruments, which Popovi, Puklavec, and Oliveira (2018) argue could serve as a future economic strength for the country. There might be major overhauls of the current system, which was something they expected. Kiyabo & Isaga's (2020) study on SMEs in China that embraced "mobile technology" (MT) and "big data-based social media marketing" (SMM). To better understand what influences the pace at which SMEs use social media platforms built on big data, they studied SMM for MT adoption amongst various business sizes (Asad et al., 2022). Another research investigated how firms might benefit from adopting a "data-driven innovation orientation" thanks to big data (Maroufkhani et al., 2019). Based on the findings, it is clear that big data operations boost organizational performance and equip decision-makers with novel approaches to addressing challenges (Ferraris et al., 2018). Yadegaridehkordi et al. (2020), conducted an online poll of managers and three separate case studies to collect data on the resources and contextual elements that lead to performance benefits as a result of investment in big data analytics.</p> <p>Last but not least, four distinct configurations of components in big data analytic resources that contribute to high performance were found using a "fuzzy-set qualitative comparative analysis" approach. Researchers in Romania analyzed the effect of big data analytics on the supply chain competitiveness of local companies (Dong & Yang, 2020). Businesses want to improve the efficiency of their supply chains, so they are looking into novel resources like cloud computing and security technologies. To determine what influences the use of big data in business and what kinds of performance can be improved with big data, Liu et al. (2020) conducted a systematic review of analytics studies on big data. The potential benefits of big data for SMEs to improve performance and competitiveness, as well as the barriers to adoption that SMEs may face, were investigated by Ramon-Jeronimo et al. (2019). As well as highlighting the obstacles that prevent SMEs from successfully adopting big data, this research suggests a data analytics maturity model for small and medium enterprises.</p> <h3>Adoption Barriers and Operational Challenges in Big Data Analytics</h3> <p>It is widely believed that Big Data is a major factor in the development of new technologies and the growth of SMEs (Maroufkhani et al., 2020). For SMEs, the advent of Big Data and its attendant capabilities for tracking and predicting market and customer behavior is a game-changer. If used properly, it has the potential to enhance productivity, adaptability, reactivity, and the ability to meet customer expectations through enhanced decision making (Dong & Yang, 2020). According to Sun, Zhao, & Sun (2020), the overall opinion was that a focus on innovation might aid in the growth of SMEs. Data loading and data exploitation strategies are being considered by businesses of all sizes, from multinational corporations to fledgling enterprises. Using BDA technology, they make adjustments to existing business practices (Shabbir, & Gardezi, 2020). There is a strong call for small and medium-sized organizations to embrace Big Data strategies in order to address the enormous data challenges they confront. The SMEs Business Development Act (BDA) provides a number of avenues for achieving aggressive basic leadership (Ferraris et al., 2018).</p> <p>The development of more complex methods for managing and analyzing data has led directly to the expansion of many enterprises. The long-term benefits of big data may be realized by small and medium-sized businesses (Ngiam & Khor, 2019). Their size and flexibility mean that even a small shift can have a big impact on their growth. It's inaccurate to assume that small and medium-sized enterprises (SMEs) can't afford high-end computer systems, but cost is always a factor (and might be a barrier) when a business is thinking about buying software, especially if it's expensive (Zhu et al., 2018). Although the system may provide the required functionality, it may not be cost-effective to install (Grover et al., 2018). Keep in mind that many SMEs lack the expertise or resources necessary to select, setup, roll out, and maintain complex IT systems. Small and medium-sized businesses may struggle with BDA if these factors are present (Mehta & Pandit, 2018). Another issue is that very few Chinese SMEs have any idea what big data is, and even fewer are willing to venture into an area where they have no track record (Duan et al., 2019). It's not unexpected that many SMEs in both developing and developed countries lack the competence of big data. There is considerable uncertainty among SMEs regarding the existence of big data (Price & Cohen, 2019). Consequently, there is skepticism among SMEs about the positive outcomes of big data analytics. It has been suggested that small and medium-sized businesses (SMEs) may utilize big data to collaborate with one another and find real-time answers to the challenges they face. But for that to happen, SMEs would need to implement more open decision-making processes (Tiwari et al., 2018).</p> <p>Problems with Data Security: The lack of trust in big data's security is a major barrier for small and medium-sized businesses. Compared to large organizations, SMEs worry more about their data being compromised (Yang et al., 2019). This is because SMEs lack the resources of larger firms in terms of infrastructure and IT, making it difficult for them to compete (Li et al., 2018). Many SMEs IT infrastructures are vulnerable due to the use of antiquated database management systems for which maintenance has been discontinued by the software companies that created them (Roh et al., 2019). As a result, there is a significant danger of cyber-attacks, infiltration, and data leakage, especially for SMEs. In a large data setting, these complications become even more severe. Data in the millions of bytes range travels over multi-user channels. Most SMEs need to outsource data analytics because they lack the internal capacity to do so. As a consequence, fewer SMEs will have complete control over their data. Using cloud services increases already present concerns about data privacy and protection.</p> <p>Simply put, the company doesn't have enough in-house Big Data specialists to handle the workload. There is a severe shortage of in-house data analysts. Without assistance, SMEs struggle to complete big data analytics. The reasons for this data specialist shortage are manifold (Lähnemann et al., 2020). Problems with BDA include insufficient knowledge to manage it, a lack of competent personnel, and high staffing costs. As a result of a skills gap, many companies in the developed world have been slow to adopt big data. In the business world, big data analysts are in short supply. It was estimated that by 2018, the demand for data analytics specialists in China would have increased by 243%, but only about 198,000 people were working in the field. When compared to large corporations, small and medium businesses may not have the same latitude in terms of task delegation. Businesses of all sizes, but especially those in the SME sector, require employees who can fill multiple roles. Even fewer opportunities exist in the labor market for people with such skills and experience.</p> <h2>Research Design and Analytical Procedures for Evaluating Big Data in Chinese SMEs</h2> <h3>Methodological Framework and Research Onion Structure</h3> <p>A methodical and theoretical analysis of the procedures that will be used to carry out an empirical investigation seems to make up the dissertation's methodology. According to Mohajan (2018), the methodology chapter explains the researcher's approach to data collecting, analysis, and the enhancement of the reliability and validity of the data.</p> <p>The Research Onion framework, developed initially by Saunders, Lewis, and Thornhill, would be used to structure this approach (2009). Visualization of this structure is shown below.</p> <p>Figure 1. Research Onion (Saunders, et al., 2009)</p> <p>In order to assess the efficacy of data gathering and analysis, this framework offers to characterize a number of crucial steps in the research process. Philosophy, approach, strategy, design, timeline, and methodology are all components of a research process.</p> <h3>Interpretivist Philosophical Orientation</h3> <p>Research philosophy, as defined by Padilla-Daz (2015), is a collection of shared convictions regarding the veracity of the research's subject matter. Several prominent ideologies, including positivism, interpretivism, realism, and objectivism, were characterized by Crossan (2003). Avgousti (2013) notes that besides positivism and interpretivism, many more ideologies may be found in academic writing. Thus, the research philosophy chosen has a direct bearing on the field of study. Positivism, in its broadest sense, is predicated on the hypothetical presence of research objectives existing outside of reality. Positivist researchers, on the other hand, use an objective, numerical approach to collection and analysis of data. Consequently, quantitative analysis and a deductive approach are frequently used in accordance with this ideology. Interpretativism, on the other hand, necessitates an interpretation of study materials, hence it presupposes that human reality affects research objectives (Singh, 2015). This philosophy tends to be employed in qualitative studies due to its inherent subjectivity. The philosophy of interpretivism will be employed throughout this dissertation.</p> <h3>Deductive and Qualitative Research Approach</h3> <p>Deductive and inductive reasoning are two of the key research methods identified by Azungah (2018). The primary distinction, according to Woiceshyn & Daellenbach (2018), is in the timing and method of applying theory to empirical data. The deductive method involves forming a hypothesis in light of previously established theory and then putting that hypothesis to the test. In other words, it is the natural conclusion of "the specific to the general" method, which involves digging into a well-established theory or phenomena in order to put it to the test.</p> <p>It would appear that the inductive technique can be used as an alternate to the deductive one. The "generic to the specific" method is based on the premise that theory may be derived from preliminary observations and results that lay the groundwork for a larger investigation. Since this dissertation is on a well-known research phenomenon—big data in China's SMEs—a literature review will come first, followed by an empirical study. It saves time, gets us right to the heart of the research question, and has easily explicable principles; these are the main benefits of a deductive method.</p> <p>There is also a common categorization of research methods into quantitative and qualitative categories. The validity of the results may be checked using the quantitative method's reliance on numerical data (respondent numbers and the frequency with which they answered) and a set of generally accepted statistical norms. Researchers who hope to generalize their findings frequently choose this strategy, as the study of such data is founded on the application of statistical tools and quantitative procedures. Contrarily, the qualitative method is based on the analysis and collection of in-depth, in-depth information on social phenomena and the responses of respondents, including their thoughts and emotions. In contrast to the high sample sizes often used in quantitative research, qualitative studies typically involve fewer participants but more in-depth analysis. To better understand the impact of big data on the success of Chinese SMEs, this dissertation will take a qualitative approach.</p> <h3>Case Study Strategy Focused on Chinese SMEs</h3> <p>There are a plethora of methods available to the researcher, and Pathak, Jena, and Kalra (2013) have catalogued many of them. Among these methods are experiments, case studies, surveys, and applications of grounded theory. Aurini, Heath, and Howells (2016) claim that the purpose of a case study approach is to get insight into the nature of a research topic by analyzing one thing and drawing parallels to other entities. Researchers in the social sciences often use this strategy because it enables them to zero down on a particular problem rather than surveying a massive sample size of individuals or institutions. This dissertation is dedicated to China's micro, small, and medium-sized businesses it follows that the case study method will be employed. The paper will draw from a wide variety of secondary qualitative sources, including books, academic journals, and industry reports, to investigate Big data analysis in China and its application to this case.</p> <h3>Descriptive Single-Method Qualitative Design</h3> <p>Mason and Ide (2014) state that there are three study designs to choose from: single-method, multiple-method, and mixed-method studies. The overarching plan for completing a study is known as the research design. Since this article will solely employ a qualitative research strategy based on the collection and analysis of secondary data related to Big data analysis in China.</p> <p>Descriptive research methodology will form the basis of this study. To acquire knowledge to comprehensively characterize a phenomena, population, or circumstance is the overarching goal of descriptive research. Auriacombe & Schurink (2010). The where, how, and when of the study's purpose are all questions that the research plan attempts to address. In order to assess whether or not the study goals have been met, a descriptive research design may employ many research methods. This study relies on numerical data, hence it is the best fit. A qualitative approach will form the basis of the study. Data for qualitative studies are typically collected from secondary resources like scholarly publications.</p> <p>Facts that are calculated, carefully specified, and accurately depicted can be acquired using a qualitative research technique and used to construct vivid representations of the events being reported. Evaluations of beliefs, norms, practices, and other such factors are conducted using qualitative research methodologies. Argument construction and the discovery of research patterns both call for the use of qualitative evidence (Pechlaner & Volgger, 2012). It reveals how well-known explanations and points of view are grasped when a qualitative investigation is conducted. This helps in both conceptualizing the issue at hand and formulating analytical, quantitative views on the matter. Using quantitative research, we may be able to better identify opinion and thinking patterns and proceed with additional examination of the issue. "Goodell, Stage, and Cooke" (2016), Analyzing social or human problems may be done through qualitative research by painting a vivid picture with words, providing in-depth viewpoints on the facts, and conducting tests in their natural settings.</p> <h3>Longitudinal Time Horizon for Big Data Analysis</h3> <p>According to Reilly et al. (2016), there are two possible study time horizons: longitudinal and cross-sectional. If a cross-sectional horizon is selected, the research will concentrate on examining the objective at a particular moment in time, whereas a longitudinal horizon would compare the two at different times. This dissertation will utilize a lengthy time frame to examine the entire Big data analysis process in China.</p> <h3>Secondary Data Collection and Qualitative Analysis Procedures</h3> <p>This section will elaborate on what goes into gathering and analyzing data. This study will employ a qualitative research strategy, gathering and analyzing secondary data, as was previously described. The literature on the topic of big data in Chinese SMEs will be sifted via books, reputable online business magazines, peer-reviewed articles, academic journals, annual reports, PhD dissertations and other sources.</p> <p>The researcher will use a qualitative data analysis framework developed by Corbin and Strauss to make sense of the qualitative data collected. Their method presupposes that analysis of data may be completed in three steps, including reduction, presentation, and conclusion drawing. Consequently, the researcher will initially analyze and summarize the data. As a second measure, he plans to use visual aids like tables and charts to clarify the material. Finally, he'll draw some broad conclusions regarding the significance of the facts.</p> <h3>Procedures for Ensuring Data Validity and Reliability</h3> <p>Ultimately, it is important to note that the researcher's major focus throughout the study process will be on increasing the validity and dependability of the data obtained. To achieve this goal, one must adhere to the university's ethical guidelines, use research papers that have not expired, and make appropriate use of the academic literature.</p> <h3>Ethical Use, Interpretation, and Storage of Secondary Evidence</h3> <p>When completed, this dissertation will adhere to the university's most stringent guidelines and thus meet the criteria for ethical research and writing. The researcher will rely exclusively on reputable secondary sources. As a result, the author will try to rely on academic sources that were published no later than 2005, which should increase the results' validity and reliability. Furthermore, if necessary, the author will draw on industry reports available on the official company websites. After the information has been correctly interpreted and stored on the author's computer, it will be password-protected.</p> <h3>Methodological Strengths and Limitations of Secondary Qualitative Research</h3> <p>The research has certain merits, chief among them being the low overhead expense of data collection thanks to the researcher's assessment of previously collected data. The researcher had to hit the stacks to learn more about the topic at hand and examine the work of other scholars (Queirós, Faria & Almeida, 2017). Since the information was easily accessible from secondary sources, the data was also dependable, consistent, and exact. Data reliability and accuracy are ensured by the fact that information was gleaned through a thorough examination of the published works of previous researchers in the field. Extend your explanations of complicated topics to include more specifics. Some seemingly intractable issues turned out to be trivial to describe, thanks to the fact that they had already been covered extensively in earlier studies. Munthe-Kaas et al. (2019), knowledge on several advanced topics was both accessible and comprehensive. Quantitative data may be improved using the knowledge gleaned through qualitative research, since the data collected can be used to bolster secondary data. This approach also made it possible to collect data in a variety of ways, including on taboo topics. The method does not rely on a small subset of research, but rather draws from a large pool of studies.</p> <p>Since the data was secondary, the researcher found that the study's conclusions couldn't be applied to a specific group of people or geographical area. Since no participants were included in the study, no conclusions can be drawn about the population as a whole (Pepper et al., 2018). Because the study's findings didn't correspond to any pre-existing categories, analysis was challenging. Data collection was also a weakness of the study due to its length. A longer period of time was required for data collection due to the researcher's requirement to go through relevant books, journals, and online resources. Longer was needed to analyze the data and compile relevant information for the study. In order to amass a sufficient amount of potentially relevant and reliable data, the researcher had to read as many studies as possible. It was difficult to get to some of the source material we needed because of time constraints, and reading the work of other researchers would have taken up too much of that very little we had (Smith & Zajda, 2018).</p> <h2>Empirical Findings and Interpretation of Big Data Effects on Chinese SMEs</h2> <h3>Evidence Derived from the Secondary Data Analysis</h3> <h3>Correlation Between Big Data Analytics and SME Performance in China</h3> <p>The Chinese economy has undergone significant change over the past three decades, shifting from a centrally controlled model to one based on free market principles. China's GDP grew at an average yearly rate of 9.3 percent from 1978 to 1993, 9.0 percent from 1993 to 2004, and 9.3 percent from 2005 to 2016. In 2012, SMEs made up 99.4 percent of all Chinese businesses, 59 percent of China's GDP, and 60 percent of China's total sales, based on the National Bureau of Statistics of China (Huang et al., 2020). One of the most notable results of China's economic reforms is the fact that the country's massive development was driven primarily by private sector SMEs. However, the financial services sector is less developed in China and other emerging nations. Companies of all sizes, especially those in developing countries, take competition from multinational corporations more seriously and face more challenges in emerging economies. Limited success has been shown in the areas of cost-effective and sustained demand from general consumers, commercial financing organizations, and microfinance institutions.</p> <p>Data gathering and analysis have become vital organizational strategies in light of the exponential growth in data volume and complexity brought on by new, cutting-edge technology and the variety of today's marketplaces. This is becoming a major issue for SMEs, especially in countries with poor infrastructure and limited resources. As a result, we set out to investigate how implementing BDA might improve the productivity of SMEs. To do this, we first conducted a literature analysis to identify the most important determinants of BDA found in prior research. After gathering the necessary information from specialists, the effects of BDA on performance were assessed.</p> <p>Finding the ways in which BDA affects the efficiency of SMEs' operations is a significant contribution made by this research. A company's operational and organizational performance improves almost immediately in response to its financial success, but its social performance is unaffected. The effectiveness of an organization is positively and significantly influenced by its operational and social performance. These findings suggest that senior management buy-in to BDA is necessary to boost key performance indicators including ENP, OPP, SOP, and ORP. Because BD use presents numerous strategic and economic possibilities for SMEs to flourish in a business ecosystem where innovation and competition are major drivers. This research highlights the significance of understanding the benefits of BD in order to make more informed decisions and achieve better results. SMEs may take advantage of BD by employing novel data types to increase their agility, find solutions to difficult challenges, and boost their organization's performance and outcomes. As a result, businesses undergo significant shifts in their operations and performance, and they increasingly rely on data-driven analysis and modeling. To this end, SMEs would be well-served to make use of BD as a tool for the development of new sources of value, knowledge, and processes.</p> <p>While Sun, Zhao, and Sun (2020) attempted to investigate the effects of big data adoption on productivity, they probably failed to take SMEs into account. On top of that, our suggested paradigm takes into account almost all the elements impacting big data adoption, whereas earlier studies only took into account a subset of these aspects. Consistent with our findings, discovered that both technological and personal big data skills had a strong favorable influence on business performance. Furthermore, this view was backed by Shamim et al., (2020), It contended that SME prosperity was significantly influenced by organizational factors. Xu & Li (2019) looked at BDA's management elements as potential drivers of change in how an organization performs. Prior research has also uncovered the significance and beneficial impacts of financial considerations in the spread of big data. The procedures for data security and privacy, as well as the quality of the data, remain major factors in deliberations on whether or not to utilize big data. Although embracing big data's benefits might boost organizational and personal productivity, security and privacy worries have slowed its uptake. As a result, it is important for businesses to take into account privacy and security while implementing big data. Similarly to Sun, Zhao & Sun (2020), environmental considerations were also taken into account as a key component of BDA. The extent to which an organization is prepared to engage in BDA is mostly determined by external circumstances, which are well acknowledged.</p> <p>The study's results offer supervisors a useful roadmap for settling BDA-related questions. It's also helpful for business owners and managers since it provides them with actionable advice they can put into practice right away to boost productivity inside their companies. As an example, they may observe the effects of BD's many facets on a company's productivity. Additionally, this study may be used to test a number of hypotheses of interest to researchers and enhance the reliability of future investigations. To verify the suggested concept, more study can create adaptable scales of assessment. Since this is likely one of the first models to investigate the effects of BDA on the efficiency of SMEs in such depth, it could be worthwhile to reevaluate it using data collected from samples. Possible areas of focus for future studies relating to SMEs and big data utilization include the creation of a culture that is driven by data, the expansion of knowledge management (KM) capabilities, and the improvement of employee technical skills through the provision of big data-specific education and training. There are two major limitations to this study, which could be addressed in future research. Because mediator and moderator factors, such access to funds, were not examined here, future researchers are encouraged to do so. Since Iranian SMEs are the subject of this research, additional factors including industry, firm size, and staff count may be included as controls.</p> <h3>Effects of Big Data Analytics on SME Performance in China</h3> <p>Researchers and business leaders have paid more attention to analytics of big data over the past decade. BDA, or business data analytics, is the process of analyzing large data sets for the purpose of making better business choices. Different academics, such as Sun et al., (2022), have already characterized BDA, defining it as a divergence from traditional data warehouses due to its blend of unstructured, structured data and semi-structured, with its bulk measured in Exabytes. Typically, one may rely on social media platforms to provide this kind of content. For example, here's an explanation of big data that takes into consideration the five Vs: (Shamim et al., 2020). Guo et al. (2020) agree with this definition, describing big data as a significant volume of data that is difficult to examine with traditional methodologies and tools. Furthermore, BDA was emphasized as a new generation of procedures and architectures used to quickly measure and analyze enormous volumes of organized and unstructured data.</p> <p>Because of these technological advancements, the worth of big data to businesses has increased dramatically. The possibility of 4-6% gains in productivity has managers and executives placing a greater emphasis on BDA. Based on research conducted over the course of several years, it was determined that between 85% and 91% of firms have allocated resources toward big data analytics projects (Liu et al., 2020). Important factors of BDA adoption were found to be both predictive and prescriptive BDA. They also found that commercial activities might be run more efficiently with the use of analytics of big data. Predictive BDA can also employ an analytical model for a deeper dive into the whys of problems. Prescriptive BDA, on the other hand, seeks answers to issues like "what should we do," "why should we do it," and "what should be a positive conclusion in this situation?" (ii) what would constitute a successful outcome given the existing circumstances. In a 2015 study conducted by Parnell, Long, and Lester, they found that using predictive analysis on large datasets led to significant gains efficiency in operations and performance. Consistent with the results of earlier research. According to a study conducted by Iqbal et al. (2018), predictive analysis can boost business output by as much as 76%. (including but not limited to: revenue and sales, customer happiness, market growth, and product creation). Similar to our own findings, Parnell, Long & Lester (2015) revealed a good link between organization performance and big data predictive analysis. They also pointed out that this link allows organizations to strategically differentiate themselves and increase productivity. Prescriptive BDA is a powerful predictor of whether or not an organization would adopt BDA, which in turn improves the efficiency of business processes, as revealed by researchers at. The three tenets of BDA that Huang et al. (2020) laid forth are description, prediction, and prescription, and they should be paired with BDA capabilities such for example human talent, IT statistics tools and mathematics, to glean comprehensive understanding from current data. The use of big data predictive and prescriptive analysis is proposed by Sun, Zhao, and Sun (2020) as a way for managers to identify and isolate the factors that contribute to the firm's overall success. As a result, BDA has garnered a lot of emphasis from academics and professionals. The primary objective of our research is to predict the success or failure of medium and small-sized firms in China by utilizing big data analytics and technological development.</p> <p>Our research intended to predict the performance of SMEs through BDA and technological innovation, with the resource-based approach as a theoretical foundation. Our study's overarching objective was to elucidate the mechanisms by which business development activity (BDA) contributes to the flourishing of medium and small-sized enterprises (SMEs) through technological innovation (product and process). The study's findings corroborate those of other studies which had found that both forms of BDA helped SMEs innovate their products and processes. The same is true for SMEs; higher performance may be attained by product and process innovation. Business dynamics analysis and the success of small and medium-sized enterprises (SMEs) are linked in a complex way, which is mediated in large part by technological innovation.</p> <p>Also, it was discovered that predictive BDA has a positive and statistically significant correlation with both process and product innovation. a study indicated that using data analytics derived from customers' blogs improved the quality of business choices. In a similar vein, found that using data to make decisions outperformed other organizational characteristics in increasing productivity by up to 5%. Additionally, Chen, Wang, and Huang's (2019) study showed that OI has a direct impact on FP.</p> <p>This research also shows that there is a favorable and statistically significant link between prescriptive BDA and new product and process development. Consistent with the results of a research, this result suggests that data-driven technologies have a positive effect on IT adoption and supply chain efficiency. Xu & Li (2019) found that business process analysis (BPA) has a substantial direct impact on service innovation, and noted that most financial institutions are improving customer service facilities as a direct result of BPA.</p> <p>Consistent with prior research, we discovered that innovation in both products and processes significantly boosted the profitability of small and medium-sized enterprises (SMEs). A favorable relationship between technical progress and productivity was discovered by Huang et al. (2020). Authors of a study examining the correlation between TI and IP in the banking industry reached the conclusion that TI improved IP performance. Ren, Eisingerich, and Tsai (2015) revealed that as compared to non-technology businesses, technological innovation considerably boosted organizational productivity.</p> <p>To confirm the findings of other researchers, we observed that technological innovation moderates the connection between business development activities and the success of small and medium-sized firms. In particular, they found that innovative abilities mediated the link between market orientation and export success for businesses. How China's small and medium-sized enterprises (SMEs) are affected by big data's impact on business success. The significant mediation impact of innovation performance between enabling excellence and firm success was also found by Sen, Ozturk, and Vayvay (2016).</p> <h3>Big Data Analytics Issues and Challenges Faced by SMEs</h3> <h3>Data Security Risks Affecting Big Data Adoption</h3> <p>According to Coleman et al. (2016), a lack of a secure network for storing and transmitting corporate data is the primary factor preventing mainstream use of big data. Del Vecchio et al. (2018) voiced concerns about data security, saying that firms run significant risks while storing data on popular cloud services. Therefore, agreed, pointing out that keeping private information safe in the cloud is a significant difficulty. Though cloud computing has numerous advantages, some people are wary of its use because of worries about their data's privacy, performance, and reliability. It has been argued, however, that small and medium-sized enterprises (SMEs) face greater risks to their data than large corporations do because of their lower resources and less extensive expertise in information security.</p> <h3>Unclear Data Ownership and Ineffective Data Governance</h3> <p>Establishing a big data governance maturity standard is the first step in assessing the current status of data management inside a business (Bi & Cochran, 2014). According to, in order for organizations to maximize the value of big data analytics, they must establish company-wide data governance policies, rules, and practices. However, this might be difficult since large data requires interacting with data from several sources, each of which could have different data collection and display standards. This makes it challenging to identify and account for variances in the value of a single variable.</p> <p>Inadequate data governance, according to some specialists, can cause a wide range of specific and competitive problems for SMEs, and they cite examples from large corporations such as (Ferraris et al., 2018). Due to their lack of ubiquity, data governance frameworks are unlikely to be adaptable enough to meet the changing demands of SMEs as they expand. Additionally, it was said that small organizations generally lack the data-driven worries and understanding required to handle and grasp technology. One further issue with SME is that there is no clear division of labor for managing data.</p> <h3>Privacy and Personal Information Protection Concerns</h3> <p>The issue of data privacy in the context of big data is gaining prominence. Ulas (2019) claims that concerns about the misuse of private data are prevalent. Concerns regarding data security and privacy in the cloud have been cited as an obstacle to the widespread use of big data by philanthropic organizations, universities, and hospitals. That hinders progress in the field of big data. Arunachalam, Kumar, and Kawalek (2018) emphasize the necessity for innovative solutions paying attention to confidentiality difficulties in encryption and safe data transit as the big data age increasingly relies on the cloud in addition to traditional hardware. People are wary about storing data clusters on publicly accessible servers (Sorensen et al., 2010), according to a separate body of studies. Due to the complexity and lack of intuitiveness of the existing EU data protection regulations and its impacts, it might be difficult for SMEs to affordably retain legal counsel. Another research came to the same conclusion, stating that legal representation was out of reach for most small and medium-sized enterprises.</p> <h3>Financial Costs of Large-Scale Data Analysis and Storage</h3> <p>As stated by, most businesses need to make a large investment in order to successfully absorb innovation. However, research has revealed that financial restrictions are a major barrier to big data's widespread adoption in enterprises. Similarly, research shows that inadequate capital is a significant barrier to growth for small and medium-sized enterprises (SMEs). According to Wang, Kung, and Byrd (2018), the initial and total investment budget have an effect on a company's purpose to use big data. Additionally, another research by emphasized the fact that compared to large enterprises, SMEs had less access to debt financing. This disparity was mostly attributable to misinformation or asymmetry in the information available to SMEs and financial institutions. Small and medium-sized enterprises (SMEs) are hesitant to venture beyond their areas of expertise due to a lack of financial resources (Mikalef et al., 2019).</p> <h3>Technical and Consulting Challenges in Data Storage</h3> <p>It is estimated by IDC's Digital Universe journal that each two years, the total amount of data stored in IT systems throughout the world doubles. Data expansion outpaces computing capability, as Gandomi & Haider (2015) pointed out. CPU speeds do not increase. A business's options for data storage include the cloud, on-premises, and hybrid solutions, as explained by. However, the data suggests that for SMEs, cloud storage really results in cost savings. As the value of on-premises data storage decreases, both large and small businesses (SMEs) and corporate organizations (COs) experience similar difficulties while storing data in the cloud. A dependable consulting agency with expertise in data storage difficulties is needed by businesses. Without a trustworthy consulting business, the problem of data storage will become obvious. That is to say, the difficulty of keeping data is linked to the paucity of consulting services. An outstanding examination of data storage was completed by Anagnostopoulos, Agwu, and Emeti (2014). They discovered that hyper-converged infrastructure and converged as well as software-defined storage, may help firms scale their hardware. Compression, deduplication, and tiering are just a few of the techniques that can reduce the size and expense of big data storage. This creates a window of opportunity for SMEs to adopt big data solutions. This approach, however, is incredibly challenging for SMEs, as stated by Parnell, Long, and Lester (2015).</p> <h3>Interpretation of Findings in Relation to Existing Scholarship</h3> <p>The study found that China's GDP grew by 9.3 percent year from 1978 to 1993, 9.0 percent annually from 1993 to 2004, and 9.3 percent annually from 2005 to 2016. In 2012, small and medium-sized businesses made up 99.4% of all Chinese firms, 59% of China's GDP, and 60% of China's total sales, as reported by the National Bureau of Statistics of China (Sun & Liu, 2020). The fact that private sector SMEs were primarily responsible for China's quick development is one of the most notable outcomes of its economic reforms. However, the financial services sectors in China and other emerging economies are not as well developed as those in more mature economies. Global competition is taken more seriously and more problems are experienced by businesses of all sizes in rising economies.</p> <p>Bottom line: BDA needs buy-in from the C-suite to boost key performance indicators including ENP, OPP, SOP, and ORP. BD use presents numerous strategic and economic opportunities for SMEs to thrive in a business ecosystem where competition and innovation are major drivers, and this study highlights the importance of understanding these advantages for improved decision making and performance improvement, in agreement with the findings of (Ngiam & Khor, 2019). Utilizing novel data kinds, BD might help SMEs boost their adaptability, find solutions to difficult situations, and boost their performance and bottom line. As a result, there have been substantial shifts in how businesses run, with an increased reliance on data-driven modeling and analysis.</p> <p>Although embracing big data's advantages could boost business and individual productivity, worries about security and privacy have delayed its acceptance. Companies must thus give serious thought to data security and privacy measures before integrating big data. In BDA, environmental considerations played a crucial role. The extent to which a firm engages in BDA is mostly determined by circumstances outside of the organization that are public knowledge.</p> <p>Flexible evaluation scales may be developed via further study, lending credence to the suggested method. It would be interesting to re-evaluate the model using data from additional samples, as this is likely one of the first models that explores the influence of BDA on the performance of SMEs in a way that is entirely compatible with the findings of previous research (Shabbir, & Gardezi, 2020). Future research on small and medium-sized enterprises (SMEs) and big data utilization may concentrate on topics such as the institutionalization of a data-driven culture, the upskilling of personnel through big data-specific education and training, and the growth of knowledge management (KM) capabilities.</p> <p>The majority of this information comes from social media websites. As a framework, the five Vs are used to talk about big data (Haar et al., 2022). Ngiam & Khor (2019) describe big data similarly, as a large amount of unstructured and structured information that is challenging to analyse with conventional methods and tools. BDA was also emphasized as a new generation of architectures and techniques used to swiftly measure and analyze large amounts of un-structured and structured data.</p> <p>The study also concluded that using predictive analysis of big data as a strategic tool improves both business output and productivity. Using a range of measurements, predictive analysis has the capacity to enhance a company's productivity by up to 76%. (including but not limited to: sales and income, customer delight, market expansion, and product innovation). Our findings are consistent with those of Mikalef et al. (2019), who found a favorable correlation between big data predictive analysis and organizational efficiency. They went on to say that this connection aids in strategic planning, which in turn helps businesses get an edge in the market and improve operations. Researchers at found that the adoption of BDA may be accurately predicted by the degree to which prescriptive BDA is used to enhance the effectiveness of business processes.</p> <p>In line with previous studies such as (Popovi, Puklavec, & Oliveira, 2018), it was shown that technological innovation affects the relationship between business development activities and the performance of small and medium-sized enterprises. In particular, they discovered that innovation abilities positively mediated the connection between export performance and market orientation for businesses.</p> <h2>Integrated Conclusions and Strategic Recommendations for SME Big Data Adoption</h2> <h3>Integrated Conclusions on Big Data Analytics and SME Performance</h3> <p>Decision-makers can use the study's results as a practical guide when it comes to BDA. Managers and business owners may benefit from it as well, because it provides them with actionable solutions and advice they can implement and prioritize to boost the efficiency and effectiveness of their organizations. They may see, for instance, the effects of BD on a company's productivity as a whole. This study may be used by researchers to test a number of hypotheses of interest, therefore enhancing the reliability of future investigations. The suggested model may be verified by further study by creating variable measurement scales. Given that our model is perhaps one of the first to conduct a thorough analysis of BDA's effect on the performance of SMEs, it may be interesting to re-evaluate the model using data collected from different samples. Developing a data-driven culture, knowledge management capabilities, and staff technical skills in terms of training and education relevant to big data-specific competences are all areas that might benefit from more study. There are two key caveats to this study that might be remedied in subsequent studies for more solid findings. Although not done so here, subsequent studies may want to take into account potential mediator and moderator characteristics like access to funding. Since Iranian SMEs are the subject of this research, additional factors including industry, company size, and headcount might be included as controls.</p> <p>This study aimed to answer two research questions in order to investigate the potential advantages and disadvantages of using big data by small and medium-sized enterprises in developing economies. To what extent can small and medium-sized enterprises in developing economies use big data? Moreover, RQ2: What barriers prevent SMEs in developing economies from using big data? The study used a desk-review technique and an exploratory method to achieve its three goals: 1. identifying benefits for adopting big data in SMEs; 2. identifying issues encountered by SMEs; and 3. identifying hurdles for adopting and exploiting big data in SMEs. In this process, we read a wide range of articles, government papers, and other relevant publications. It was found that small and medium-sized enterprises have trouble making use of big data tools and technologies because they lack the personnel with the necessary skills and the financial resources to acquire them. However, large and small businesses alike who employ big data analytics are finding success and growing their bottom lines. The report advised that custom-designed training be made available for SMEs. Institutions of higher education have a responsibility to aid in the dissemination of such initiatives. To help small and medium-sized businesses afford and put to use technologies and big data tools, the government must give both non-financial and financial support. Research on improving big data tools and technology for SMEs is needed moving forward. Consequently, SMEs would have access to more usable technology and resources. An action research strategy would be valuable when such technologies and tools are produced, so that they may be tested on SMEs.</p> <p>In sum, this study illuminates the difficulties small and medium-sized enterprises in China may have when they attempt to incorporate big data. Small and medium-sized firms that rely on conventional techniques are attempting to incorporate big data in order to compete with multinational corporations (MNCs) in today's rapidly transforming industrial environment, where digitization is one of the most major driving factors. However, there are difficulties that they must overcome in order to complete the adoption.</p> <p>The greatest barrier to widespread use of this resource is the ever-increasing need for skilled data analysts. This is because small and medium-sized businesses in China aren't contributing as much as large ones to the fight against growing wages. Concerns about data privacy and protection, concerns about security of data, and a lack of clarity over who owns what data or how it should be used are just a few of the management difficulties preventing the widespread of big data adoption in China's small and medium-sized enterprises. Not only that, but concerns about privacy and protection of data and concerns about data security scored third and fourth among the twenty difficulties of big data adoption in SMEs. This finding substantiates the worries regarding data security raised by Coleman et al. (2016) in their study of European SMEs. Additionally, Chinese SMEs worry that they would have been better off not sharing their data for fear of a problem.</p> <p>The two most significant technological challenges preventing small and medium-sized enterprises (SMEs) in China from adopting big data are a lack of infrastructure to process enormous amounts of data quickly and a lack of analytical tools. Three key obstacles connected to skill are a lack of in-house data analysis experience, the complexity of analytics solutions in the software business, and the limitations of employees' abilities to work with big data. The second and fifth most often reported challenges are a lack of in-house data analysis expertise and the complexity of implementing software-based analytics solutions, respectively.</p> <p>It follows that only the rate of adoption from these five groups of concerns significantly affects cultural difficulties. Because of this, fundamental problems confronting China's SMEs may be traced back to the country's cultural norms. Using a one-sample t-test, we found that Chinese SMEs' lack of big data expertise was the main factor holding back wider adoption. The broad use of big data in China's SMEs is hampered by cultural barriers. Therefore, the result of the one-sample t-test corroborate the result of the logistic regression study.</p> <p>Both large and small businesses are utilizing big data technology to make sense of this deluge of data and get actionable insights from it. Small and Medium-Sized Enterprises (SMEs) can benefit from BDA since it helps them spot new changes in their units by analyzing data and detecting connections between items. Considering all the data, figures, and opportunities we've laid out in this article, it's clear that BDA is essential to their company's success. Small and medium-sized businesses (SMEs) must also undergo a cultural transition if they are to reap the benefits of Big Data. As a result, they must be open to incorporating Big Data into their decision-making processes and prepared to go beyond the traditional methods they've been using to handle data. They must be ready to dive headfirst into the vast ocean of information that awaiting them on the Internet. Small and medium-sized businesses (SMEs) need more than just encouragement to begin using data analytics. All of the aforementioned communities face a formidable obstacle in the form of the previously identified problems.</p> <h3>Strategic Recommendations for Big Data Adoption in Chinese SMEs</h3> <p>Based on Lessig's Four Modalities—law, infrastructure, society, and the market—this analysis assessed the difficulties confronted by China's SMEs. Reviewing the relevant literature reveals the importance of drawing attention to a more all-encompassing framework for pinpointing the problems encountered by China's small and medium-sized enterprises. The government and non-government organizations must devote significant resources to the discussion of common difficulties for small and medium-sized firms if these businesses are to overcome them and grow to their full potential. The current literature has also discussed the problems that exist in China's SMEs, regardless of the social, architectural, legal, and commercial challenges. In China, there has been an increase in case studies because of the pressing need to boost the success rate of small and medium-sized enterprises and, by extension, their productivity.</p> <p>Inadequate empirical research exists on the barriers of big data adoption in SMEs, as was noted before in this work. More investigation into this expanding field is required, however this study did a good job of providing an overview of the status quo of the obstacles to adopting big data in China's SMEs. The findings might be further studied in further studies. This study's questionnaire found that a lack of knowledge of big data was the most major factor preventing small and medium-sized enterprises in China from adopting the technology. The incomprehensibility of massive data might be the subject of future quantitative or qualitative study. Additionally, data warehousing firms in China are growing in popularity and they might provide more in-depth information on this subject. Other researchers will be able to perform better work on this subject if they have access to data from these firms.</p> <h2>Reference List</h2> <p>Agwu, M. O., & Emeti, C. I. (2014). Issues, challenges and prospects of small and medium scale enterprises (SMEs) in Port-Harcourt city. European journal of sustainable development, 3(1), 101-101.</p> <p>Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80-91.</p> <p>Anwar, M., Khan, S. Z., & Shah, S. Z. A. (2018). Big data capabilities and firm’s performance: a mediating role of competitive advantage. Journal of Information & Knowledge Management, 17(04), 1850045.</p> <p>Ardito, L., Raby, S., Albino, V., & Bertoldi, B. (2021). The duality of digital and environmental orientations in the context of SMEs: Implications for innovation performance. Journal of Business Research, 123, 44-56.</p> <p>Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416-436.</p> <p>Asad, M., Kashif, M., Sheikh, U. A., Asif, M. U., George, S., & Khan, G. U. H. (2022). Synergetic effect of safety culture and safety climate on safety performance in SMEs: does transformation leadership have a moderating role?. International journal of occupational safety and ergonomics, 28(3), 1858-1864.</p> <p>Aurini, J. D., Heath, M., & Howells, S. (2016). The how to of qualitative research: Strategies for executing high quality projects. Sage.</p> <p>Avgousti, K. (2013). Research philosophy, methodology, quantitative and qualitative methods. The Cyprus Journal of Sciences, 11, 33.</p> <p>Azungah, T. (2018). Qualitative research: deductive and inductive approaches to data analysis. Qualitative research journal.</p> <p>Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 153, 104559.</p> <p>Bertello, A., Ferraris, A., Bresciani, S., & De Bernardi, P. (2021). Big data analytics (BDA) and degree of internationalization: the interplay between governance of BDA infrastructure and BDA capabilities. Journal of Management and Governance, 25(4), 1035-1055.</p> <p>Bi, Z., & Cochran, D. (2014). Big data analytics with applications. Journal of Management Analytics, 1(4), 249-265.</p> <p>Bouwman, H., Nikou, S., & de Reuver, M. (2019). Digitalization, business models, and SMEs: How do business model innovation practices improve performance of digitalizing SMEs?. Telecommunications Policy, 43(9), 101828.</p> <p>Chaudhuri, A., Subramanian, N., & Dora, M. (2022). Circular economy and digital capabilities of SMEs for providing value to customers: Combined resource-based view and ambidexterity perspective. Journal of Business Research, 142, 32-44.</p> <p>Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1883.</p> <p>Chumphong, O., Srimai, S., & Potipiroon, W. (2020). The Resource-Based View, Dynamic Capabilities and SME Performance for SMEs to Become Smart Enterprises. ABAC ODI Journal Vision. Action. Outcome, 7(2), 129.</p> <p>Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort‐Martorell, X., & Reis, M. S. (2016). How can SMEs benefit from big data? Challenges and a path forward. Quality and Reliability Engineering International, 32(6), 2151-2164.</p> <p>Corbin, J., & Strauss, A. (2014). Basics of qualitative research: Techniques and procedures for developing grounded theory. Sage publications.</p> <p>Crossan, F. (2003). Research philosophy: towards an understanding. Nurse Researcher (through 2013), 11(1), 46.</p> <p>Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1-25.</p> <p>Del Vecchio, P., Di Minin, A., Petruzzelli, A. M., Panniello, U., & Pirri, S. (2018). Big data for open innovation in SMEs and large corporations: Trends, opportunities, and challenges. Creativity and Innovation Management, 27(1), 6-22.</p> <p>Dionysus, R., & Arifin, A. Z. (2020). Strategic Orientation on Performance: The Resource Based View Theory Approach. Jurnal Akuntansi, 24(1), 136-153.</p> <p>Dong, J. Q., & Yang, C. H. (2020). Business value of big data analytics: A systems-theoretic approach and empirical test. Information & Management, 57(1), 103124.</p> <p>Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71.</p> <p>Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., ... & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, 107599.</p> <p>Elijah, O., Rahman, T. A., Orikumhi, I., Leow, C. Y., & Hindia, M. N. (2018). An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet of things Journal, 5(5), 3758-3773.</p> <p>Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2018). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision.</p> <p>Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.</p> <p>Gao, S., Wang, H., Yuan, X., & Lin, L. (2019). Cooperative mechanism of SME growth in the mesoscopic structure with strategic and nonstrategic partners. IEEE Intelligent Systems, 35(3), 7-18.</p> <p>Goodell, L. S., Stage, V. C., & Cooke, N. K. (2016). Practical qualitative research strategies: Training interviewers and coders. Journal of Nutrition Education and Behavior, 48(8), 578-585.</p> <p>Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of management information systems, 35(2), 388-423.</p> <p>Guo, H., Yang, Z., Huang, R., & Guo, A. (2020). The digitalization and public crisis responses of small and medium enterprises: Implications from a COVID-19 survey. Frontiers of Business Research in China, 14(1), 1-25.</p> <p>Haar, J., O’Kane, C., & Daellenbach, U. (2022). High performance work systems and innovation in New Zealand SMEs: testing firm size and competitive environment effects. The International Journal of Human Resource Management, 33(16), 3324-3352.</p> <p>Huang, Y., Lin, C., Wang, P., & Xu, Z. (2020). Saving China from the coronavirus and economic meltdown: Experiences and lessons. Available at SSRN 3570696.</p> <p>Huang, Y., Zhang, L., Li, Z., Qiu, H., Sun, T., & Wang, X. (2020). Fintech credit risk assessment for SMEs: Evidence from China.</p> <p>Hussin, H., Salleh, N. A., Suhaimi, M. A., Rahman, M. M., & Ali, A. M. (2018). A model to assess the impacts of cloud computing use on sme performance: a resource-based view. Advanced Science Letters, 24(3), 1800-1804.</p> <p>Iqbal, M., Kazmi, S. H. A., Manzoor, A., Soomrani, A. R., Butt, S. H., & Shaikh, K. A. (2018, March). A study of big data for business growth in SMEs: Opportunities & challenges. In 2018 International conference on computing, mathematics and engineering technologies (iCoMET) (pp. 1-7). IEEE.</p> <p>Karedza, G., & Govender, K. K. (2020). The impact of organizational capabilities on SMEs export performance: Application of the resource based view theory. International Journal of Entrepreneurial Research, 3(3), 68-75.</p> <p>Karim, M. S., Nahar, S., & Demirbag, M. (2022). Resource-Based Perspective on ICT Use and Firm Performance: A Meta-analysis Investigating the Moderating Role of Cross-Country ICT Development Status. Technological Forecasting and Social Change, 179, 121626.</p> <p>Khan, K. U., Atlas, F., Ghani, U., Akhtar, S., & Khan, F. (2020). Impact of intangible resources (dominant logic) on SMEs innovation performance, the mediating role of dynamic managerial capabilities: evidence from China. European Journal of Innovation Management.</p> <p>Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE access, 6, 32328-32338.</p> <p>Kim, H. K., So, W. H., & Je, S. M. (2019). A big data framework for network security of small and medium enterprises for future computing. The Journal of Supercomputing, 75(6), 3334-3367.</p> <p>Kiyabo, K., & Isaga, N. (2020). Entrepreneurial orientation, competitive advantage, and SMEs’ performance: application of firm growth and personal wealth measures. Journal of Innovation and Entrepreneurship, 9(1), 1-15.</p> <p>Kumar, N., Brint, A., Shi, E., Upadhyay, A., & Ruan, X. (2019). Integrating sustainable supply chain practices with operational performance: an exploratory study of Chinese SMEs. Production Planning & Control, 30(5-6), 464-478.</p> <p>Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., ... & Schönhuth, A. (2020). Eleven grand challenges in single-cell data science. Genome biology, 21(1), 1-35.</p> <p>Li, G., Luo, Z., Anwar, M., Lu, Y., Wang, X., & Liu, X. (2020). Intellectual capital and the efficiency of SMEs in the transition economy China; Do financial resources strengthen the routes?. PloS one, 15(7), e0235462.</p> <p>Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism management, 68, 301-323.</p> <p>Lim, C., Kim, K. J., & Maglio, P. P. (2018). Smart cities with big data: Reference models, challenges, and considerations. Cities, 82, 86-99.</p> <p>Liu, W., Liang, Y., Bao, X., Qin, J., & Lim, M. K. (2022). China's logistics development trends in the post COVID-19 era. International Journal of Logistics Research and Applications, 25(6), 965-976.</p> <p>Liu, Y., Soroka, A., Han, L., Jian, J., & Tang, M. (2020). Cloud-based big data analytics for customer insight-driven design innovation in SMEs. International Journal of Information Management, 51, 102034.</p> <p>Lu, Z., Wu, J., Li, H., & Nguyen, D. K. (2022). Local bank, digital financial inclusion and SME financing constraints: Empirical evidence from China. Emerging Markets Finance and Trade, 58(6), 1712-1725.</p> <p>Mangla, S. K., Raut, R., Narwane, V. S., & Zhang, Z. J. (2020). Mediating effect of big data analytics on project performance of small and medium enterprises. Journal of Enterprise Information Management.</p> <p>Maroufkhani, P., Ismail, W. K. W., & Ghobakhloo, M. (2020). Big data analytics adoption model for small and medium enterprises. Journal of Science and Technology Policy Management, 11(4), 483-513.</p> <p>Maroufkhani, P., Wagner, R., Wan Ismail, W. K., Baroto, M. B., & Nourani, M. (2019). Big data analytics and firm performance: A systematic review. Information, 10(7), 226.</p> <p>Mason, D. M., & Ide, B. (2014). Adapting qualitative research strategies to technology savvy adolescents. Nurse researcher, 21(5).</p> <p>Mehta, N., & Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. International journal of medical informatics, 114, 57-65.</p> <p>Metawa, N., & Metawa, S. (2021). Internet financial risk early warning based on big data analysis. American Journal of Business and Operations Research, 3(1), 48-60.</p> <p>Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276.</p> <p>Mohajan, H. K. (2018). Qualitative research methodology in social sciences and related subjects. Journal of economic development, environment and people, 7(1), 23-48.</p> <p>Mohd Selamat, S. A., Prakoonwit, S., Sahandi, R., Khan, W., & Ramachandran, M. (2018). Big data analytics—A review of data‐mining models for small and medium enterprises in the transportation sector. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(3), e1238.</p> <p>Mubarak, M. F., Shaikh, F. A., Mubarik, M., Samo, K. A., & Mastoi, S. (2019). The impact of digital transformation on business performance: A study of Pakistani SMEs. Engineering technology & applied science research, 9(6), 5056-5061.</p> <p>Munthe-Kaas, H. M., Glenton, C., Booth, A., Noyes, J., & Lewin, S. (2019). Systematic mapping of existing tools to appraise methodological strengths and limitations of qualitative research: First stage in the development of the CAMELOT tool. BMC medical research methodology, 19(1), 1-13.</p> <p>Ngiam, K. Y., & Khor, W. (2019). Big data and machine learning algorithms for health-care delivery. The Lancet Oncology, 20(5), e262-e273.</p> <p>Nikmah, F., Rahmawati, R., & Sukma, E. A. (2021). Resource-based view: Implementation in Indonesia SMEs to achieve competitive advantage. European Journal of Research and Reflection in Management Sciences Vol, 9(1).</p> <p>Oncioiu, I., Bunget, O. C., Türkeș, M. C., Căpușneanu, S., Topor, D. I., Tamaș, A. S., ... & Hint, M. Ș. (2019). The impact of big data analytics on company performance in supply chain management. Sustainability, 11(18), 4864.</p> <p>Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2018). Big Data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 30(4), 431-448.</p> <p>Padilla-Díaz, M. (2015). Phenomenology in educational qualitative research: Philosophy as science or philosophical science. International journal of educational excellence, 1(2), 101-110.</p> <p>Parnell, J. A., Long, Z., & Lester, D. (2015). Competitive strategy, capabilities and uncertainty in small and medium sized enterprises (SMEs) in China and the United States. Management Decision.</p> <p>Pathak, V., Jena, B., & Kalra, S. (2013). Qualitative research. Perspectives in clinical research, 4(3).</p> <p>Pechlaner, H., & Volgger, M. (2012). How to promote cooperation in the hospitality industry: Generating practitioner‐relevant knowledge using the GABEK qualitative research strategy. International Journal of Contemporary Hospitality Management.</p> <p>Peredy, Z., Yaouki, X., & Laki, B. (2022). Challenges of the Innovative Chinese Small and Medium Sized Enterprises (SME's) in the Last Decade. ACTA PERIODICA (EDUTUS), 24, 19-35.</p> <p>Popovič, A., Puklavec, B., & Oliveira, T. (2018). Justifying business intelligence systems adoption in SMEs: Impact of systems use on firm performance. Industrial Management & Data Systems.</p> <p>Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature medicine, 25(1), 37-43.</p> <p>Qalati, S. A., Yuan, L. W., Khan, M. A. S., & Anwar, F. (2021). A mediated model on the adoption of social media and SMEs’ performance in developing countries. Technology in Society, 64, 101513.</p> <p>Queirós, A., Faria, D., & Almeida, F. (2017). Strengths and limitations of qualitative and quantitative research methods. European journal of education studies.</p> <p>Ramon-Jeronimo, J. M., Florez-Lopez, R., & Araujo-Pinzon, P. (2019). Resource-based view and SMEs performance exporting through foreign intermediaries: The mediating effect of management controls. Sustainability, 11(12), 3241.</p> <p>Reilly, G., Souder, D., & Ranucci, R. (2016). Time horizon of investments in the resource allocation process: Review and framework for next steps. Journal of Management, 42(5), 1169-1194.</p> <p>Ren, S., Eisingerich, A. B., & Tsai, H. T. (2015). How do marketing, research and development capabilities, and degree of internationalization synergistically affect the innovation performance of small and medium-sized enterprises (SMEs)? A panel data study of Chinese SMEs. International Business Review, 24(4), 642-651.</p> <p>Roh, Y., Heo, G., & Whang, S. E. (2019). A survey on data collection for machine learning: a big data-ai integration perspective. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1328-1347.</p> <p>Roostika, R. (2019). SMEs craft industry application of resource based view: capabilities role of SMEs performance. Review of Integrative Business and Economics Research, 8, 423-440.</p> <p>Safari, A., & Saleh, A. S. (2020). Key determinants of SMEs’ export performance: a resource-based view and contingency theory approach using potential mediators. Journal of Business & Industrial Marketing.</p> <p>Saleem, H., Li, Y., Ali, Z., Ayyoub, M., Wang, Y., & Mehreen, A. (2020). Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation. Journal of Enterprise Information Management.</p> <p>Saleem, H., Li, Y., Ali, Z., Mehreen, A., & Mansoor, M. S. (2020). An empirical investigation on how big data analytics influence China SMEs performance: do product and process innovation matter?. Asia Pacific Business Review, 26(5), 537-562.</p> <p>Schurink, E., & Auriacombe, C. J. (2010). Theory development: Enhancing the quality of the case study as research strategy in qualitative research. Journal of public administration, 45(3-1), 435-455.</p> <p>Sen, D., Ozturk, M., & Vayvay, O. (2016). An overview of big data for growth in SMEs. Procedia-Social and Behavioral Sciences, 235, 159-167.</p> <p>Shabbir, M. Q., & Gardezi, S. B. W. (2020). Application of big data analytics and organizational performance: the mediating role of knowledge management practices. Journal of Big Data, 7(1), 1-17.</p> <p>Shamim, S., Zeng, J., Khan, Z., & Zia, N. U. (2020). Big data analytics capability and decision making performance in emerging market firms: The role of contractual and relational governance mechanisms. Technological Forecasting and Social Change, 161, 120315.</p> <p>Shamim, S., Zeng, J., Shariq, S. M., & Khan, Z. (2019). Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view. Information & Management, 56(6), 103135.</p> <p>Sheng, T. (2021). The effect of fintech on banks’ credit provision to SMEs: Evidence from China. Finance Research Letters, 39, 101558.</p> <p>Shibin, K. T., Dubey, R., Gunasekaran, A., Hazen, B., Roubaud, D., Gupta, S., & Foropon, C. (2020). Examining sustainable supply chain management of SMEs using resource based view and institutional theory. Annals of Operations Research, 290(1), 301-326.</p> <p>Singh, K. D. (2015). Creating your own qualitative research approach: Selecting, integrating and operationalizing philosophy, methodology and methods. Vision, 19(2), 132-146.</p> <p>Smith, K., & Zajda, J. (2018). Qualitative and quantitative methodologies: A minimalist view. Education and Society, 36(1), 73-83.</p> <p>Song, H., Li, M., & Yu, K. (2021). Big data analytics in digital platforms: how do financial service providers customise supply chain finance?. International journal of operations & production management.</p> <p>Sukaatmadja, I., Yasa, N., Rahyuda, H., Setini, M., & Dharmanegara, I. (2021). Competitive advantage to enhance internationalization and marketing performance woodcraft industry: A perspective of resource-based view theory. Journal of Project Management, 6(1), 45-56.</p> <p>Sun, B., & Liu, Y. (2020). Business model designs, big data analytics capabilities and new product development performance: Evidence from China. European Journal of Innovation Management.</p> <p>Sun, T., Zhang, W. W., Dinca, M. S., & Raza, M. (2022). Determining the impact of Covid-19 on the business norms and performance of SMEs in China. Economic Research-Ekonomska Istraživanja, 35(1), 2234-2253.</p> <p>Sun, W., Zhao, Y., & Sun, L. (2020). Big data analytics for venture capital application: towards innovation performance improvement. International Journal of Information Management, 50, 557-565.</p> <p>Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, 319-330.</p> <p>Ulas, D. (2019). Digital transformation process and SMEs. Procedia Computer Science, 158, 662-671.</p> <p>Valaei, N., Rezaei, S., Bressolles, G., & Dent, M. M. (2021). Indispensable components of creativity, innovation, and FMCG companies' competitive performance: a resource-based view (RBV) of the firm. Asia-Pacific Journal of Business Administration.</p> <p>Wachter, S., & Mittelstadt, B. (2019). A right to reasonable inferences: re-thinking data protection law in the age of big data and AI. Colum. Bus. L. Rev., 494.</p> <p>Wang, C. J., Ng, C. Y., & Brook, R. H. (2020). Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. Jama, 323(14), 1341-1342.</p> <p>Wang, F., Ding, L., Yu, H., & Zhao, Y. (2020). Big data analytics on enterprise credit risk evaluation of e-Business platform. Information Systems and e-Business Management, 18(3), 311-350.</p> <p>Wang, S., & Wang, H. (2020). Big data for small and medium-sized enterprises (SME): A knowledge management model. Journal of Knowledge Management, 24(4), 881-897.</p> <p>Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological forecasting and social change, 126, 3-13.</p> <p>Wang, Z., Rafait Mahmood, M., Ullah, H., Hanif, I., Abbas, Q., & Mohsin, M. (2020). Multidimensional perspective of firms’ IT capability between digital business strategy and firms’ efficiency: A case of Chinese SMEs. SAGE Open, 10(4), 2158244020970564.</p> <p>Wang, Z., Shou, M., Wang, S., Dai, R., & Wang, K. (2019). An empirical study on the key factors of intelligent upgrade of small and medium-sized enterprises in China. Sustainability, 11(3), 619.</p> <p>Woiceshyn, J., & Daellenbach, U. (2018). Evaluating inductive vs deductive research in management studies: Implications for authors, editors, and reviewers. Qualitative research in organizations and management: An International Journal, 13(2), 183-195.</p> <p>Xu, J., & Li, J. (2019). The impact of intellectual capital on SMEs’ performance in China: Empirical evidence from non-high-tech vs. high-tech SMEs. Journal of Intellectual Capital.</p> <p>Xu, L. D., & Duan, L. (2019). Big data for cyber physical systems in industry 4.0: a survey. Enterprise Information Systems, 13(2), 148-169.</p> <p>Yadegaridehkordi, E., Nilashi, M., Shuib, L., Nasir, M. H. N. B. M., Asadi, S., Samad, S., & Awang, N. F. (2020). The impact of big data on firm performance in hotel industry. Electronic Commerce Research and Applications, 40, 100921.</p> <p>Yadi, L. I. U., Yuning, S. O. N. G., Jiayue, Y. U., Yingfa, X. I. E., Yiyuan, W. A. N. G., & Xiaoping, Z. E. N. G. (2019). Big-data-driven model construction and empirical analysis of SMEs credit assessment in China. Procedia computer science, 147, 613-619.</p> <p>Yang, R., Yu, F. R., Si, P., Yang, Z., & Zhang, Y. (2019). Integrated blockchain and edge computing systems: A survey, some research issues and challenges. IEEE Communications Surveys & Tutorials, 21(2), 1508-1532.</p> <p>Yin, C., Jiang, C., Jain, H. K., & Wang, Z. (2020). Evaluating the credit risk of SMEs using legal judgments. Decision Support Systems, 136, 113364.</p> <p>Zhou, C., Su, F., Pei, T., Zhang, A., Du, Y., Luo, B., ... & Xiao, H. (2020). COVID-19: challenges to GIS with big data. Geography and sustainability, 1(1), 77-87.</p> <p>Zhu, L., Yu, F. R., Wang, Y., Ning, B., & Tang, T. (2018). Big data analytics in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 20(1), 383-398.</p>