No abstract

No abstract

No abstract

Extant studies suggest implementing a business intelligence (BI) system is a costly, resource-intensive and complex undertaking. Literature draws attention to the critical success factors (CSFs) for implementation of BI systems. Leveraging case studies of seven large organizations and blending them with Yeoh and Koronios's (2010) BI CSFs framework, our empirical study gives evidence to support this notion of CSFs and provides better contextual understanding of the CSFs in BI implementation domain. Cross-case analysis suggests that organizational factors play the most crucial role in determining the success of a BI system implementation. Hence, BI stakeholders should prioritize on the organizational dimension ahead of other factors. Our findings allow BI stakeholders to holistically understand the CSFs and the associated contextual issues that impact on implementation of BI systems. IntroductionThe successful implementation of information technology (IT) innovations remains both a theoretical and a managerial challenge. Many IT implementation projects have a high-risk profile (Harrington & Guimaraes, 2005;Kutsch, Denyer, Hall, & Lee-Kelley, 2013) and various IT innovations introduced by organizations are either rejected or underused by end users (Sharma & Yetton, 2003).Business intelligence (BI) technologies have recently received considerable attention from both industry and acareceived (Chen, Chiang, & Storey, 2012). In a worldwide survey of IT spending (Gartner, 2013b), BI-related technologies (still) ranked among the top technology priorities of many chief information officers, with global BI software spending expected to grow by 7% over the previous year (Gartner, 2013a). Such enthusiasm can be attributed to the rising importance of BI systems, which have regularly been viewed as "a broad category of technologies, applications, and processes for gathering, storing, accessing, and analyzing data to help its users make better decisions" (Wixom & Watson, 2010, p. 14). Yet implementing a BI system does not only entail the purchase of a combination of software and hardware; rather, it is a complex undertaking requiring appropriate infrastructure and resources over a lengthy period of time (Yeoh & Koronios, 2010). In fact, cases have previously been reported where large investments in various BI initiatives over lengthier periods resulted in little or no benefits for the organizations implementing them (Williams & Williams, 2007).Despite the vibrant BI market and the complexities surrounding the implementation of BI systems, the critical success factors (CSFs) of BI system implementation initiatives remain poorly understood. A typical BI system implementation involves multifaceted technological, organizational, and process issues, sharing similar characteristics with other intelligence system (IS) infrastructural projects like enterprise resource planning (ERP) systems implementation (Popovič, Hackney, Coelho, & Jaklič, 2012). Still, most existing CSF studies have focused on identifying lists of CSFs and little con...

Purpose The purpose of this paper is to provide a better understanding of the determinants of business intelligence system (BIS) adoption stages. It develops and empirically tests a conceptual model for assessing the determinants of BIS diffusion on the evaluation, adoption, and use stages in the context of small and medium enterprises (SMEs). Design/methodology/approach Drawing on data from 181 SMEs the influence of technological, organizational, and environmental factors on BIS adoption stages were analyzed using the PLS-SEM method. Findings The paper provides empirical insights about how technological, organizational, and environmental factors affect individual BIS adoption stages. Practical implications The paper includes implications for managers and solution providers to understand the influence of various determinants to more effectively conclude the adoption process. Originality/value This study represents important progress in the theoretical understanding of the role of technological, organizational, and environmental factors across the different BIS adoption stages.

International audienceThe information systems (IS) literature has long emphasized the importance of user acceptance of computer-based IS. Evaluating the determinants of acceptance of information technology (IT) is vital to address the problem of underutilization and leverage the benefits of IT investments, especially for more radical technologies. This study examines individual acceptance of biometric identification techniques in a voluntary environment, measuring the intention to accept and further recommend the technology resulting from a carefully selected set of variables. Drawing on elements of technology acceptance model (TAM), diffusion of innovations (DOI) and unified theory of acceptance and use of technology (UTAUT) along with the trust-privacy research field, we propose an integrated approach that is both theoretically and empirically grounded. By testing some of the most relevant and well-tested elements from previous models along with new antecedents to biometric system adoption, this study produces results which are both sturdy and innovative. We first confirm the influence of renowned technology acceptance variables such as compatibility, perceived usefulness, facilitating conditions on biometrics systems acceptance and further recommendation. Second, prior factors such as concern for privacy, trust in the technology, and innovativeness also prove to have an influence. Third, unless innovativeness, the most important drivers to explain biometrics acceptance and recommendation are not from the traditional adoption models (TAM, DOI, and UTAUT) but from the trust and privacy literature (trust in technology and perceived risk)

No abstract

No abstract