Self-Service Business Intelligence Adoption in Business Enterprises: The Effects of Information Quality, System Quality, and Analysis Quality

Self-Service Business Intelligence Adoption in Business Enterprises: The Effects of Information Quality, System Quality, and Analysis Quality

Mohammad Daradkeh (Yarmouk University, Jordan) and Radwan Moh'd Al-Dwairi (Yarmouk University, Jordan)
DOI: 10.4018/978-1-5225-3909-4.ch050
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Despite the growing popularity of self-service business intelligence (SSBI) tools, empirical research that investigates their acceptance by business professionals is still scarce. This paper presents and tests an integrated model of the antecedents of users' acceptance of SSBI tools in business enterprises. The proposed model is developed based on the technology acceptance model (TAM) and incorporating information and system quality from DeLone and McLean IS success model. It also includes an important factor from the business intelligence literature called analysis quality. To test the model, data were collected through a questionnaire survey from 331 business users working in a variety of industries in Jordan. Data were analysed using structural equation modeling (SEM) techniques. The results demonstrated that the three quality factors– information quality, system quality and analysis quality – are key antecedents of perceived usefulness and ease of use, which in turn were found to be strong predictors of users' intention to use SSBI tools. The findings of this study provide several implications for research and practice, and thus should help in the design and deployment of more user-accepted SSBI tools.
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In today’s analytics-driven business milieu, organizations must use business intelligence (BI) technologies to improve their business processes and support their decision-making activities. Yet, decisions in most organizations are still not based on BI because of the inability to keep up with the fast-growing demand for information and analytics (Imhoff & White, 2011). This demand, combined with the inability of IT department to meet the analytical needs of business professionals, has resulted in shifting the BI from highly governed and IT-centric platforms to more decentralized BI deployments empowering business users with self-service analytical and data discovery capabilities (Meulen & Rivera, 2015; Stodder, 2015).

Self-service business intelligence (SSBI) has emerged as a technological innovation providing an environment that enables business users to become more self-reliant and less dependent on the IT organization (Imhoff & White, 2011). This environment is intended to extend the reach and scope of BI applications to address a wider range of business needs and problems. Such extension must support the needs of business users for personalized and collaborative decision-making environment (Alpar, Engler, & Schulz, 2015; Imhoff & White, 2011; Logi Analytics, 2015). At the same time, the IT department will be freed from the burden of satisfying users’ requests of routine reports and analysis, so that it can focus its efforts on developing more advanced analytics capabilities and strategic initiatives (Schlesinger & Rahman, 2016). Logi Analytics (2015) found that establishing a SSBI environment in an organization can reduce IT requests by 47% when business users are empowered to accomplish tasks autonomously.

Over the last years, SSBI has received considerable attention from both business communities and academia. Major research companies such as Gartner, Logi Analysis, and The Data Warehousing Institute (TDWI) have focused much of their recent research on SSBI and data analytics capabilities (Imhoff & White, 2011; Logi Analytics, 2015; Meulen & Rivera, 2015; Stodder, 2015). Gartner predicted that by 2018, most business users in organizations will have access to SSBI tools to fulfill their information and analytics needs. Already business owners are focusing their BI investments into decentralized, self-serviced analytical tools with the intention to expand the reach and scope of BI within organizations to broader range of consumers and non-traditional BI users (Meulen & Rivera, 2015). According to a recent survey by Logi Analytics (2015), 95% of business organizations plan to invest in SSBI in the very near future. The key business drivers influencing companies to adopt and invest into SSBI are:

  • 1.

    Constantly changing business needs

  • 2.

    IT’s inability to satisfy new requirements in timely manner

  • 3.

    The need for more analytical-driven organizations

  • 4.

    Slow information access (Imhoff & White, 2011; Logi Analytics, 2015)

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