نیکا جامعی زاده

Information Systems (IS) research has recently shown increasing attention to the phenomenon of Big Data; however, the manner in which theory development has occurred within this literature has not yet been systematically investigated. The objective of this research is to delineate the state of theory development in the Big Data literature within the IS domain and to identify the dominant theoretical patterning approaches. To this end, a systematic review of ۶۶ articles published in A* and A-ranked journals in the IS field, according to the ABDC list, was conducted. The data were analyzed inductively using thematic analysis. Findings reveal that the Big Data literature in this domain is structured around four core thematic areas: strategic use of Big Data, Big Data organizational readiness and considerations, Big Data-enabled operational excellence, and product leadership. Distinct patterns of research questions were identified within each of these domains. Nevertheless, the distribution of research attention across these domains is unbalanced, with a dominant focus observed on strategic and innovative dimensions. Furthermore, ۳۵ theories were identified in this literature, the analysis of which indicates the dominance of Resource-Based View (RBV) and Dynamic Capabilities View (DCV) theories. This dominance has led to a kind of theoretical path dependence, which, while contributing to conceptual coherence in the literature, has limited the scope for theoretical innovation. The examination of theory development processes and tactics further shows that theorizing in this field has primarily been evolutionary and incremental, focusing more on the specification and extension of constructs. Consequently, the capacity of Big Data to challenge theoretical assumptions and construct new theories has been underutilized. Amidst this, the identification of the emerging tactic of “Synergistic Relationships” as an innovation of the present research suggests that moving toward more dynamic theorizing can unlock new theoretical pathways in the IS Big Data literature. Overall, the findings indicate that for this literature to transition from Contribution of Theory to Contribution to Theory and Theory building, a critical reconsideration of research question formulation, critical inquiry into dominant assumptions, and diversification of the theoretical frameworks employed are essential.