In recent years industries are shifting towards the age of Industry 4.0 or digital transformation. This transformation results from contributions from various engineering and manufacturing disciplines and merging the technologies, including artificial intelligence and machine learning, neuro-technologies, mobile and cloud computing, sensing, and other “exponential technologies”. In the era of Industry 4.0, growing exponentially, data significantly impact obtaining new knowledge, fostering business innovation, and achieving competitive advantage. Accordingly, the utilization of big data with distinct capabilities, features, and specifications has grown rapidly in industrial contexts because of engineering developments and, more specifically, in computer science and information technology. Big data with developing data mining techniques supports the so-called smartness of everything everywhere by facilitating real-time, dynamic, self-adaptive, and precise control capabilities (Ramezani & Camarinha-Matos, 2019; Kopanakis, Vassakis, & Mastorakis, 2016; Chatterjee, Fornasiero, Ramezani & Ferrada, 2019).
Theoretically, there is a wide-ranging discussion about the impact of information technology (IT) on the organization's performance. Although in such a trend, IT contributes to firms' competitiveness, but its role is still relatively unclear (Kohli, & Grover, 2008). In conditions that information flows in the internet and virtual networks are established with high speed and intensity, the level of information of individuals as the main audiences of this data flow is affected by this trend. Thus, the emergence of new ideas and creativity from each person and staff (Bondarouk & Ruel, 2009) and consequently, the necessity creation of a new structure for the current organizations in the global society is inevitable and worthy of serious review and contemplation.
In addition to the hardware available in each job system that requires to be updated in line with the current turbulent era, the software related to each job also needs to be reviewed and updated. On the other hand, “Knowledge management cycle (KMC)” models became essential issues for organizations in the volatile and globalized word in order to make valuable decisions and achieve business outcomes. KMC is a strategic tool in fostering the (symbiosis of implicit and explicit) knowledge and creating a proper perception of it in organizations (Kopanakis, Vassakis, & Mastorakis, 2016).
Big data is one of the topics that has been considered by managers and decision-makers nowadays due to the high speed and volume of data exchange to develop productivity in industries (Nasrollahi, & Ramezani, 2020). Although, in recent years, several studies have been conducted on various methods of data analysis and storage, particularly in the field of industry, and big data has become an important issue in different sectors (Yadegaridehkordi et al., 2020). Still, in small and medium enterprises (SMEs), less attention has been paid to it, and it is a relatively unknown issue for the managers of these organizations. Nevertheless, studies have shown that technology and innovation are strategic priorities for SMEs' growth, and among them, big data will be one of the main drivers (Sen, Ozturk, & Vayvay, 2016).
Since the competitive market becomes more complicated day by day, the organizational decision-makers increasingly rely more and more upon big data for the firm's utilization, understanding process performance, product performance analysis, inventory management optimization, and so on (Hazen et al. 2014). Hence, it is necessary to ensure the quality and reliability of the used data. Lack of adequate and reliable data may lead to inappropriate decisions, missing organizational plans and opportunities, and damage to the organization (Warth, Kaiser, & Kügler, 2011).
Big data adoption (BDA) enables organizations to create a clear picture of customers and their demands in order to make well-informed decisions in designing marketing strategies (Manyika et al., 2011). SMEs, in turn, also require big data to analyse the market and predict customer behavior. Big data in SMEs can lead to increased flexibility, efficiency, responsiveness, and the ability to anticipate and meet customer needs, thus providing organizations a competitive edge (Sen et al., 2016). Adoption of big data is dependent upon technological, organization, managerial, and environmental factors and positively influences firm performance (Frisk & Bannister, 2017; Verma & Bhattacharyya, 2017).
Notably, developed countries have conducted most research on BDA, so we need more studies in developing countries (Baig, Shuib, & Yadegaridehkordi, 2019; Brock & Khan, 2017; Yadegaridehkordi et al., 2020). Therefore, in the present study, we try to evaluate the impact of big data adoption on SMEs’ performance in developing countries, an issue that has not been studied yet.
Thereupon, in this study, besides developing a comprehensive model and describing how big data components affect performance, it is helped formulate theoretical basics in this field and confirm previous studies. Generally, one of the aims of this study is to demonstrate the importance of adopting big data in SMEs as a strategic requirement. To achieve this goal, the remainder of this article is structured as follows. The second section provides a brief overview of the theoretical basis and related works in the literature to achieve this goal. The third section describes the research method, statistical population, and the samples under study. In the fourth section, the research findings are presented. Finally, in the last section, the conclusion and suggestions for further research are presented.