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Top1. Introduction
Around 40% of national income in developing countries and 50% of employment worldwide are represented by Small and Medium Enterprise (SME). SME across the world have grown tremendously in recent years and they are expected to grow three times more by 2030. On contrary, some 65% of them fail or exit from market every year, due to several reasons, such as lack of capability, limited funds, and lack of strategic use of resources particularly data (Tarek & Adel, 2016; Wang & Wang, 2020). SME plays a vital role in most of the emerging economies (Ward & Rhodes, 2014). Remarkably, research in the domain of SMEs have shown growing interest among academia, policy makers and industry during last decade. Many research studies have highlighted the importance of SME and their contribution in modern economy. Despite of this, SMEs are vulnerable and lack strength to withstand global competition. Subsequently it is suggested, that to survive and stay competitive, SMEs must act pro-active, observe business trends, and use data tactically (Raj, et. al., 2016; Ojiako, et.al., 2015). Therefore, to build concerted efforts to improve SME competitiveness, data analytics in SME needs attention.
Data analytics in SME, simply put, is the application of statistical methods and technology to process the historical data and draw meaningful results. These results can facilitate strategic decision making, improved performance and better business future (Wang & Wang, 2020). Emerged in late 2000s, data analytics have been highlighted as a critical success factor for many businesses in emerging economies (Nam, et.al., 2021; Liu, Y, et.al., 2020). Data analytics in business acts as a driver of growth in highly competitive business environment (Naeini, et.al., 2019). However, if compared to large enterprises, small enterprise reflects low orientation towards adoption of data analytics in business (Ojiako, et. al, 2015; Boonsiritomachai, et.al., 2016). Surprisingly, few sectors in SME like healthcare, manufacturing, e-commerce, and retail have shown high acceptance of data analytics in business activities (Miyamoto, 2015; Gudfinnsson, et.al., 2019; Maroufkhani, et.al., 2020). Most of the SMEs are using data analytics in report management, financial updates, supply chain function and CRM functions only (Naeini, et.al., 2019; Ajibade.& Mutula, 2019; Gavrila & de Lucas, 2021). As a result, it indicates that even today SMEs are either experiencing discomfort or inhibition in implementation of data analytics. Thus, there exists a huge undefined gap between SMEs not investing in data analytics as against SMEs using data analytics extensively. To identify reasons behind gap, it is essential to identify the inhibitors, restraining forces and enablers of data analytics in SMEs.
Although existing literature illustrate a linear relationship between data analytics and SME performance (O’Connor & Kelly, 2017). But still, lack of data integration, poor IT infrastructure, low technology adoption and shortage of analytics knowledge, leads to ineffective execution of data analytics in SME (Ajibade, et.al, 2019; Lyver & Lu, 2018). With a mix blend of thoroughly examined studies over last decade, one set of studies have been largely reviewed that offers opportunities to move forward and reflect backward whereas another set of studies are unattended. Therefore, a rigorous systematic review will not only integrate the scattered research studies but also draw attention of not-so known research themes in this domain. It will complement the existing studies and bring the loose ends together. Moreover, an exclusive systematic review on data analytics in SME will provide a theoretical background for future research, answer many unaddressed questions by deeper understanding on the matter, and expand the broad topics of research studies in the research domain.