Paradigmatic Explanation of the Impact of Business Intelligence on Activity-Based Costing Systems with Emphasis on Cost Drivers under Conditions of Uncertainty: A Qualitative Approach

Abstract

Objective: This study aimed to develop a paradigmatic qualitative model explaining how Business Intelligence (BI) influences Activity-Based Costing (ABC) systems, with particular emphasis on the management of cost drivers under conditions of uncertainty.

Methods and Materials: This qualitative study employed the systematic grounded theory approach of Strauss and Corbin. Participants consisted of 13 academic and professional experts in management accounting, ABC, BI, information systems, financial management, industrial accounting, and related managerial fields who were selected through purposive judgmental sampling, supplemented where necessary by snowball sampling. Data were collected through in-depth semi-structured interviews and continued until theoretical saturation was achieved after the thirteenth interview. All interviews were recorded with participants’ consent, transcribed verbatim, and analyzed using MAXQDA. Data analysis was conducted through open, axial, and selective coding, constant comparison, memo writing, and organization of the emerging categories within the Strauss and Corbin paradigmatic framework. Credibility and trustworthiness were strengthened through member checking, expert review, triangulation of professional perspectives, continuous comparison, and documentation of the analytical process.

Findings: The analysis yielded five major paradigmatic dimensions comprising 11 axial categories. Environmental uncertainty and dynamism, competitive pressure and the need for cost management, and management and analysis of cost drivers emerged as causal conditions. Integration and quality of organizational data, BI technological infrastructure and capabilities, and human resource capability and readiness formed the contextual conditions. Managerial support and organizational governance constituted the intervening condition. The principal strategies were the design and implementation of a BI-based ABC system and the development of analytical and predictive capabilities. These strategies led to improved decision-making, control and performance optimization, as well as strategic outcomes and organizational resilience. The findings indicate that BI transforms ABC from a largely retrospective costing mechanism into a more dynamic, integrated, predictive, and decision-support-oriented system.

Conclusion: Effective integration of BI with ABC depends on the simultaneous alignment of data quality, technological infrastructure, human capabilities, managerial governance, and predictive analytics, enabling organizations to manage cost drivers more accurately and respond more effectively to uncertainty.