## 1 Introduction  
Business environments are becoming complex in the era of Industry 4.0. Therefore, enterprises need advanced innovations and technologies for quick response to the dynamic markets [1; 2]. The emergence of Business Intelligence Systems (BIS) is led by the rapid growth of technology and diffusion of the internet in the mid-90s [3; 4]. BIS is generally known as a complete suite of tools, techniques, and methodologies that enable the firms to integrate and analyze large data sets in order to identify their weakness, strengths, and opportunities [5; 6; 7]. BIS is an information system (IS) that facilitates decision-making by (i) management, aggregation and integration of unstructured and structured data, (ii) handling with large datasets such as big data, (iii) offering ad-hoc queries, reporting, forecasting, and analysis solutions, (iv) end-users support with advanced processing abilities to explore new knowledge [3; 8]. BIS has the ability to boost the internationalization process of organizations by sorting, summarizing, filtering and data integration from multiple channels [9], such as competitors, host markets, and local government [10]. In contemporary trade, due to hyper-competition and technological advances with big data [11; 12]. BI technology is still positioned among the best technological priorities of several decision-making authorities including business owners, chief executive officers (CEO) and chief information officers [3; 13; 14]. To compete in the traditional markets or online business, the BI solutions have attained great attention of industry practitioners for delivering better products and services with improved processes and managerial practices [15]. It is depicted by a drastic increase of the worldwide BIS market that increased almost 7.3% in 2017, with revenues up to $18.3 and it is expected to reach $22.8 billion by the end of 2020 [3; 16]. It is evident by previous studies, that enterprises are failed to harness the true benefits from the adoption of BIS [3; 14; 17; 18]. In spite of its extreme importance, great market expansion and growing investments, more than 70% BI projects have failed to give the expected results [3; 17; 19]. Enterprises are struggling hard to find the best determinants to make their BIS and big data integration successful by yielding the maximum benefits from the BIS [18; 20]. The academic research related to Big Data and BIS has thrived [21; 22]. In a large number of published studies, the practitioners and scholars are still discussing the tactical, management, and strategic approaches to the successful integration and adoption of BIS [11; 23]. However, the existing body of knowledge is insufficient in this perspective. For example, [PERSON] and [PERSON] have published a bibliometric study from 1990 to 2017 to identify emerging topics of BIS and big data analytics [18]. Whereas, [PERSON] et al. have conducted a systematic literature review of BIS adoption studies during the period of 2009 to 2015, they reviewed the theories which are used for BIS adoption in small and medium enterprises (SME) [24]. A systematic literature review of two decades (2000-2019) was conducted recently by [PERSON] et al., they discussed the BIS adoption, utilization and success comprehensively [3]. It is evident by literature review and bibliometrics studies on BI analytics that an extensive stream of BIS studies has been published till date [18; 25]. However, to date, no comprehensive review is available in the perspective of adoption/acceptance with most significant determinants, theories and models at individual level or organizational levels. It is also suggested in the literature that BIS adoption decisions are influenced deeply by considering the appropriate determinants [14; 26]. Enterprises also require to focus on user acceptance challenges to enhance the success of BIS projects [3].