The Dynamics of Knowledge Growth in Business Intelligence and Analytics: A Bibliometric Investigation

Violeta Cvetkoska (), Bojan Kitanovikj (), Tina Nartnik (), Jurij Jaklič () and Gordana Savić ()
Additional contact information
Violeta Cvetkoska: Ss. Cyril and Methodius University in Skopje, Faculty of Economics–Skopje
Bojan Kitanovikj: Ss. Cyril and Methodius University in Skopje, Faculty of Economics–Skopje
Tina Nartnik: University of Ljubljana, School of Economics and Business
Jurij Jaklič: University of Ljubljana, School of Economics and Business
Gordana Savić: University of Belgrade, Faculty of Organizational Sciences

A chapter in Advanced Data Analytics, Machine Learning and AI in Business, 2026, pp 18-35 from Springer

Abstract: Abstract Business Intelligence and Analytics (BI&A) has emerged over the last thirty years as a vital domain for driving organizational performance, digital transformation, and competitive advantage. As the field continues to expand rapidly with the rise of AI-augmented analytics, understanding the structure and direction of existing research becomes critical. Without a clear map of intellectual progress and thematic shifts, both academic and practical efforts risk becoming disconnected and misaligned. As a result, our research objective is to provide a systematic bibliometric investigation of the evolution of knowledge within the BI&A research field. The motivation behind this analysis is to bring clarity to a complex and growing body of literature and to identify strategic pathways for future research and practice. By uncovering the foundations, trends, and turning points in the field, we support a more informed and impactful development of BI&A strategies across sectors. We examine a dataset of 2,374 peer-reviewed articles indexed in Scopus, applying a combination of bibliographic coupling, co-authorship exploration, and keyword co-occurrence mapping. Following the PRISMA guidelines and employing text mining, we create key thematic clusters and trace the emergence of influential research streams. The results reveal a strong progression from early technical focus to more integrated, strategic, and domain-specific applications. This transition highlights the importance of cross-disciplinary collaboration and the alignment of analytics with real-world business needs. Practically, the findings help scholars target under-explored clusters, guide organizations in aligning analytics investments with capability and governance needs, and support policymakers and business leaders in fostering cross-disciplinary and cross-country collaboration to reduce fragmentation and accelerate decision-making.

Keywords: Business Intelligence; Analytics; Bibliometric Analysis (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-032-23493-3_2

Ordering information: This item can be ordered from
http://www.springer.com/9783032234933

DOI: 10.1007/978-3-032-23493-3_2

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().