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Data : Journal of Information Systems and Management
Published by PT Penerbit Ilmiah Indonesia
ISSN : -     EISSN : 30310008     DOI : https://doi.org/10.61978/data
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Data : Journal of Information Systems and Management with ISSN Number 3031-0008 (Online) published by Indonesian Scientific Publication, is a leading open-access and peer-reviewed scientific journal dedicated to publishing high-quality research in the field of information systems and management. Since its establishment, Data has been committed to advancing knowledge and understanding of the integration between information systems and management in a global context. The journal publishes research articles, technical papers, theoretical studies, and case studies that undergo rigorous peer review to ensure the highest standards of academic integrity and originality.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 1 Documents
 1 
Search results for , issue "Vol. 4 No. 1 (2026): January 2026" : 1 Documents clear
History of Research on Big Data Analytic Capability of the Firm Jae; Arash; Zahra; Leonid
Data : Journal of Information Systems and Management Vol. 4 No. 1 (2026): January 2026
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/data.v4i1.1318

Abstract

The rapid growth of data and the increasing strategic importance of analytics have positioned Big Data Analytics Capability (BDAC) as a critical organizational competence. Despite substantial interest in its antecedents and performance outcomes, the historical evolution and intellectual development of BDAC research remain underexplored. This study addresses this gap by applying Citation Sequence Analysis (CSA) to examine the longitudinal citation trajectories of BDAC-focused publications. Using a curated dataset of 119 peer-reviewed records from Scopus, CSA classifies cited references into three trajectory types: sleeping beauties, reflecting delayed recognition; hot papers, indicating immediate but short-lived impact; and constant performers, representing sustained scholarly influence. A transparent methodological protocol, including detailed search queries, inclusion/exclusion criteria, citation normalization, and reliability verification, is provided. Findings reveal foundational works, transitional studies, and emerging contributions and offer a trajectory-based framework for guiding future research. By integrating CSA with trajectory classification, this study advances cumulative knowledge building, provides a historically grounded understanding of BDAC, and informs theory development and strategic practice in analytics deployment.

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