Critical Success Factors for Implementing Business Intelligence Systems in Small and Medium Enterprises on the Example of Upper Silesia, Poland

Abstract

The main objective of this article is to identify the critical success factors (CSFs) for Business Intelligence (BI) systems implementation in small and medium enterprises (SMEs).The structure of the article is subordinated to this objective. The paper identifies the term Business Intelligence, characteristics of Business Intelligence systems, and various perspectives of their development. Then, the existing CSFs of IT projects and BI projects proposed by various authors in literature are reviewed. Next, on the basis of statistical data and literature, the role of SMEs in the economy and the barriers to their development are assessed. The obtained results allowed us to determine that one of the barriers to the SMEs development is the implementation and use of IT. Subsequently, using in-depth interviews with SMEs, the SMEs need for BI systems as well as the determinants and barriers to their implementation are recognized. Based on the findings, using critical thinking and inductive reasoning, the authors of the article along with the researched enterprises have defined CSFs that are crucial for implementing BI systems in SMEs. The results obtained may be useful for managers, policy makers, business analysts, and IT specialists in dealing with planning and implementation of BI systems in SMEs.

An integrated model for determining business intelligence systems adoption and post-adoption benefits in banking sector

“…Therefore, organizations tend to havea higher adoption level, if they appoint a project champion who has a related background to the innovation under consideration (Chong et al, 2009;Puklavec & Oliveira, 2014). Many studies have consistently found that the presence of a champion enabled the adoption, is a determinant and played a significant role (Gu et al, 2012;Hwang et al, 2004; C.-P. Lee & Shim, 2007;C. Olszak & Ziemba, 2012;Puklavec & Oliveira, 2014;Yeoh & Koronios, 2010).…”

“…In addition, extant literature shows that there are few empirical studies when it comes to organizational adoption of BI Systems based on adoption theories (DOI, TOE, INT) as many have been done at the individual level (Grublješič & Jaklič, 2015;Hou, 2014;Hou, 2012;Kester & Preko, 2015;Pilz & Ferraz, 2013;Yoon, Ghosh, & Bong-Keun Jeong, 2014). Also, most of the existing studies have focused mainly on Maturity Models (MMs) for measuring the current state of BI Systems as well as Critical Success Factors (CSFs) for BI Systems implementation in large organizations and Small Medium Enterprises (SMEs) in different parts of the world especially advanced economies (Dawson & Van Belle, 2013;Fedouaki & El Alami, 2013;Hawking, Jovanovic, & Sellitto, 2011;Hawking, 2013;Khojasteh, 2013;Mungree et al, 2013;Olbrich et al, 2012;Olszak & Ziemba, 2012;Olszak, 2013;Ong & Siew, 2013;Hribar Rajterič, 2010;Yeoh et al, 2008; ISSN: 2414-309X DOI: 10.20474/jabs-2.2.4 TAF Publishing Yeoh & Koronios, 2010). Again, there are very few studies that have looked at the post-adoption effects of BI Systems on organizational performance (Elbashir, Collier, & Davern, 2008;Hou, 2015).…”

“…Therefore, one key technology that had received a lot of attention from academia and practitioners recently is Business Intelligence (BI) Systems (Olszak & Ziemba, 2012). BI Systems can leverage a company's IT Systems and other technologies such as Enterprise Resource plann-these benefits are giving these organizations competitive advantage over their competitors (Matei & Bank, 2010;Rob & Coronel, 2009;Negash, 2004;Davenport, 2006;Hočevar & Jaklič, 2008).…”

“…As a result, a lot of organizations have implemented BI Systems using Critical Success Factors (CSFs) and Maturity Models (MMs) (Dawson & Van Belle, 2013;Fedouaki & El Alami, 2013;Mungree, Rudra & Morien, 2013;Olbrich, Pöppelbuß & Niehaves, 2012;Olszak & Ziemba, 2012;Ong & Siew, 2013;Hribar Rajterič, 2010;Yeoh, Koronios, & Gao, 2008;Yeoh & Koronios, 2010). Currently, many organizations are using BI tools (for example,online analytical processing (OLAP), Data warehouse, Dashboards, Data Mining, Reporting, and so on) (Zaman, 2005) to deal with both structured and semi-structured data (Blumberg & Atre, 2003;Rudin & Cressy, 2003) to present value-added and meaningful reports which can then aid management in decision making.…”

Analyzing business intelligence maturity

“…Other published work on critical success factors (Yeoh & Koronios (2010) and Olszak & Ziemba (2012)) focuses on CSFs for implementing BI systems; however Shanks & Bekmamedova (2012) have presented results from a project where they studied a large Australian financial services firm and found critical success factors that helped increase analytical maturity. In addition, when appropriate, we also use complementary theory as a means to introduce different perspectives and for being able to make the discussion concerning our case study as detailed and extensive as possible.…”