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

Jurij Jaklič, Tanja Grublješič, Aleš Popovič

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)305-318
Number of pages14
JournalInternational Journal Of Information Management
Publication statusPublished - 1 Dec 2018


  • Business intelligence & analytics
  • Compatibility
  • Result demonstrability
  • Social influence
  • Socio-organizational drivers
  • Task-technology fit
  • Use intentions
  • Work style


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