Exploring the Link of Real Options Theory with Dynamic Capabilities Framework in Open Innovation-Type Merger and Acquisition Deals
Bibliographic record
Abstract
Although it is well established that acquisition-based dynamic capabilities have important consequences for merger and acquisition (M&A) processes, direct evidence on how real option applications can measure a dynamic capability-based synergy in open innovation-type M&A deals has been scarce. This study draws from seminal research on real options theory to explore some of these benefits and limits to value a synergy in one recent highly strategic acquisition. To strengthen the identification of causal effects, the paper develops the proposition that justifies the role of dynamic capabilities as antecedents of the success of open innovation-type M&A deals in the ICT industry and demonstrates real options’ application to measure M&A synergies. To test the internal and external validity of the proposition, the explorative case study on Samsung’s acquisition of Harman International Industries was analyzed and interpreted. This study contributes important empirical evidence to bear on the literature on open innovation theory, dynamic capabilities framework, and real options theory.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".