The role of compatibility in predicting business intelligence and analytics use intentions

Jurij Jaklič, Tanja Grublješič and Aleš Popovič

International Journal of Information Management, 2018, vol. 43, issue C, 305-318

Abstract: Research shows that data-driven decision-making using Business Intelligence and Analytics (BI&A) can create competitive advantages for organizations. However, this can only happen if users successfully accept BI&A and use it effectively. Analytical decision processes are often characterized by non-routine and ill-structured tasks and decisions, making individuals’ work styles more pronounced. Aligning on one hand what a BI&A solution can offer and, on the other, the changing needs and expectations of users, the way they like to work – their work style, can thus be difficult. This illustrates the importance of compatibility evaluations in the BI&A context, including perceptions of the technology fit with the user’s work needs and style, along with the fit with the organizational decision processes and organizational values when deciding to use BI&A. These issues have not yet been thoroughly researched in the existing BI&A literature. In response, we conduct a quantitative survey-based study to examine the interrelated role of compatibility in predicting BI&A use intentions. The model is empirically tested with the partial least squares (PLS) approach through to structural equation modeling (SEM). Our results show that compatibility perceptions have a direct positive impact on use intentions, mediate the impact of performance perceptions on use intentions, while the socio-organizational considerations of result demonstrability and social influence have interaction effects by positively strengthening the perceived relevance of compatibility in impacting use intentions.

Keywords: Compatibility; Business intelligence & analytics; Socio-organizational drivers; Use intentions; Social influence; Result demonstrability; Work style; Task-technology fit (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:43:y:2018:i:c:p:305-318

DOI: 10.1016/j.ijinfomgt.2018.08.017

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