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Nexius Partner Business Intelligence Designer
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By Mladen Pancić Mladen Pancić Scilit Preprints.org Google Scholar *, † , Dražen Ćućić Dražen Ćućić Scilit Preprints.org Google Scholar † and Hrvoje Serdarušić Hrvoje Serdarušić Scilit Preprints.org Google Scholar †
Using Nexus For Design Reviews In Machine Shops
Received: 31 December 2022 / Revised: 23 February 2023 / Accepted: 13 March 2023 / Published: 21 March 2023
The analysis of the reasons or drivers for the adoption of big data analytics and blockchain and their subsequent influence on company performance has become a major need as a direct result of the rapidly growing popularity of business intelligence. The purpose of this research is to present a model that examines the direct and indirect influence of business intelligence on firm performance through the mediating roles of the adoption of big data analytics and blockchain. The analysis is based on data collected from a representative sample of 387 employees from 12 IT companies operating in Croatia. The study examines these relationships using structural equation modeling. The results showed that business intelligence has a direct and significant impact on company performance. In addition, business intelligence significantly and positively impacted the adoption of big data analytics and blockchain and, in turn, company performance. In addition, the adoption of big data analytics and blockchain technology signified and mediated the relationship between business intelligence and company performance. Both mediations were partial. Finally, the study also provides managerial implications, limitations and future directions.
Business intelligence combines all news sources into something beyond the sum of their components. It does this by leveraging the operational data provided by enterprise resource planning systems and transforming it into meaningful intelligence that directly supports the company’s strategic goals (Al-Mobaideen 2014). Business intelligence (BI) is widely recognized as the art of deriving business value from data; Consequently, BI systems and communication infrastructure are required to integrate disparate data sources into a consistent standard framework to facilitate fact-checking and in-depth analysis across the enterprise. By recognizing the company’s information systems, such as customer data, purchasing information, personnel information, production data, marketing and advertising activity data, and any additional references to critical data (Khan 2019; Muntean and Cabau 2011), business intelligence tools have had the ability to make more smart judgments more efficient (Sharda et al. 2014). Undoubtedly, the accuracy of the data on which a company’s decisions are based determines the quality of these assessments (Kilani 2022). When managers consider both the company’s internal operations and the external environment in which it operates, they can make both productive and profitable decisions. This requires constantly seizing new opportunities, taking calculated risks, and maintaining a flexible stance in response to various new demands (Muntean et al. 2010; Shi and Lu 2010).
Business intelligence initiatives help decision makers solve business problems to maximize business value. The primary goal of these initiatives is to increase profitability and productivity. According to Zeng et al. (2012), the solution to a business challenge usually consists of a process that also involves business information, while business information itself is rarely a sufficient response to the company’s needs. Business intelligence vendors are busy offering suitable solutions for administrators, business intelligence solutions that are competent in implementing balanced scorecards, business reports and performance dashboards (Khatibi et al. 2020). This is related to managerial visions and a strategic planning tool that offers a global view of a company, transforming its strategy and mission into concrete and quantifiable goals (Muntean et al. 2010; Silahtaroğlu and Alayoğlu 2016).
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With the availability of “big data” and blockchain technology in intelligent machines, the idea of ”business intelligence” (BI) has emerged as an increasingly important one (Agarwal and Dhar 2014). Over the past two generations, the importance of the closely related fields of business intelligence, blockchain, and big data analytics has grown significantly in both the academic and commercial worlds (Chen et al. 2012; Daneshvar Kakhki and Palvia 2016). When integrated with big data and blockchain technology, these forms of business intelligence can perform operations and actions that are both more timely and more relevant than those performed by humans. Business intelligence is used in both test and production environments by IT development companies (Wamba-Taguimdje et al. 2020). The term “machine learning” refers to the process by which business intelligence can gain new tools to examine big data and automate decisions. The term “business intelligence” is most often used as an umbrella term to represent a system (Shollo and Kautz 2010) or methods and concepts (Sabherwal and Becerra-Fernandez 2013) that improve decision-making by using reality-support networks. Many concepts (such as “business intelligence”, “business analytics” and “big data”) are often interchangeable in research. Authors have described business intelligence in a variety of ways, including as “a process and a brand” (Jourdan et al. 2008, p. 121), “a process, a brand and a combination of methods, or a mixture of such ” (Shollo and Kautz 2010, p. 87) or as “a good or service alone” (Seddon et al. 2017). Several of these findings come from a study conducted by Accenture and General Electric. According to the study, 89 percent of companies believe that if they do not integrate big data and blockchain, they will lose market share (Columbus 2014).
Business intelligence (BI), blockchain technology, cloud services, big data, and fifth-generation (5G) wireless networks are the five primary trends that are currently leading and influencing business (enterprise) performance. The term “big data” refers to the attempt to find technologies that can analyze the huge amounts of information that are consistently produced. Big data uses computers to process information to gain insights or advantages over competitors. Big data analytics encompasses a wide range of software programs, hardware technologies, and business procedures that are all somehow connected to the phases of collecting, storing, accessing, and analyzing large amounts of data (Bayrak 2015). “Big data” refers to the enormity of a large amount of unorganized data collected as part of the process of developing big data analytics. This type of data can only be analyzed and understood using specialized software and hardware (Bayrak 2015). By analyzing social media data, crucial aspects of marketing strategies can be controlled automatically using big data analytics and blockchain technology (Tan et al. 2013). These factors include customers’ opinions about a brand, service or organization. On the other hand, access to big data presents practitioners and academics with new obstacles, even as it opens up previously unimaginable opportunities for marketing intelligence. The analysis of large amounts of data focuses mainly on overcoming three different types of difficulties: storage, management and processing (Kaisler et al. 2013).
Wang et al. (2022) noted that firm performance is affected by the capability and reliability of Business Intelligence (BI). In addition, performance affects a company’s competitive advantage. In addition, BI capability affects BI reliability. Companies are actively contributing to the rapid development of big data technologies and are becoming more interested in the possibilities of big data. According to the Organization for Economic Co-operation and Development (OECD), big data promises to produce increased value in various business activities and it has been singled out as the next big thing in technological advancement (Gunasekaran et al. 2017). Against this background, a recent study claims that “big data is more than just a technical issue and for big data to be fully effective, it needs to become an integral part of organizations” (Braganza et al. 2017). Recent research by Ji-fan Ren et al. (2016) investigated the relationship between
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