Impact of Intellectual Capital on Mergers and Acquisitions: Evidence from Developed and Emerging Capital Markets
Bibliographic record
Abstract
In this article, we analyse the influence of intellectual capital on M&A performance in developed and emerging capital markets with the use of the event studies and regression analysis methodologies. In contrast to previous research studies in this area, we assess the impact of the components of intellectual capital (human, structural, and relational capital) on firm value as a result of mergers and broaden the scarce level literature on this specific topic. We additionally present a comparative analysis of the influence of intellectual capital components on M&A performance vis-à-vis the performance of acquirers from developed and emerging capital markets.Our research sample consists of 194 cross-border deals closed in the period 2010–2018. We compare developed markets based on firms from USA, Canada, Germany, Great Britain, France, Italy and Japan and emerging markets based on firms from China, India, Brazil and Malaysia.Our findings contribute to the literature in several ways. Firstly, we document a positive and significant dependence between the level of intellectual capital of the target firm and the M&A performance level of the acquirer, irrespective of the market where the acquirer operates. We provide empirical support for the postulation that the higher the level of intellectual capital of the target firm, the higher M&A performance of the acquirer will be in both developed and emerging markets. Secondly, we empirically prove that each of the components of intellectual capital of the target firm increases M&A performance: the higher the level of human, structural or relational capital of the target firm, the higher the M&A performance level of the acquirer in both developed and emerging capital markets. Thirdly, we show that the level of impact of human capital on M&A performance is higher for emerging market acquirers, and the impact of structural capital is higher for developed market acquirers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".