Organizational Ambidexterity: How Balanced Scorecard (BSC) and Activity-Based Costing (ABC) Enable Exploration–Exploitation Synergy and Sustainable Performance
Bibliographic record
Abstract
This study investigates how management accounting systems (MASs), specifically the balanced scorecard (BSC) and activity-based costing (ABC), foster organizational ambidexterity (OA) and, in turn, enhance firm performance. Drawing on the resource-based view, theory of constraints, and the ambidexterity literature, this research explores whether BSC and ABC act as enablers of the exploration–exploitation balance necessary for long-term competitiveness. A quantitative survey design was employed, targeting large- and medium-sized organizations in Saudi Arabia. Data from 186 valid responses were analyzed using structural equation modeling (SEM) to test the proposed relationships. The results provide robust empirical evidence that both BSC and ABC significantly contribute to OA. While BSC indirectly enhances organizational performance through OA, ABC exerts a direct positive effect on performance. Furthermore, higher levels of OA were found to significantly improve business outcomes, confirming its role as a critical mediator of strategic and operational success. The study makes three key contributions: First, it validates the role of MASs in building ambidextrous capabilities that enable firms to balance efficiency with innovation. Second, it demonstrates the complementary effects of BSC and ABC in driving superior organizational performance. Third, it highlights the strategic value of MASs in aligning organizational practices with sustainable development goals, thereby reconciling short-term profitability with long-term growth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".