Mapping Corporate Tax Planning and Corporate Social Responsibility: A Hybrid Method of Category Analysis
Bibliographic record
Abstract
The relationship between corporate tax planning (CTP) and corporate social responsibility (CSR) is complex, with various perspectives, and a detailed scientific analysis of this relationship is required. This complexity arises from the conflicting interests of maximizing shareholder value through tax strategies while meeting societal expectations of ethical behaviour and transparency. So, the main objective of this research is to reveal the state of the art regarding the relationship between these two concepts. To achieve this goal and map the scientific literature relating to CTP and CSR, the Scopus and Web of Science (WoS) databases were used, resulting in a screening process identifying 47 relevant articles. The methodology employed is hybrid, combining a systematic review and category analysis. The main results reveal a strong relationship between corporate tax planning and CSR. Tax avoidance is the focus, followed by tax aggressiveness due to the conflict between shareholder benefits and social obligations. In addition, the most tested theory is risk management. This study highlights the interdisciplinary nature of CTP and CSR research, integrating accounting, business ethics, and management for a holistic understanding of corporate behaviour. The focus on tax avoidance underscores its key role in the CTP-CSR relationship, reinforcing theories that link tax practices to corporate ethics and suggesting aggressive tax strategies can undermine CSR efforts. As the main practical implication, the study suggests that policymakers should promote transparency in companies’ tax practices and encourage CSR activities, aligning companies’ behaviour with society’s expectations and improving compliance with tax obligations.
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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 | 0.023 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.045 | 0.035 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".