Bibliographic record
Abstract
In the global environment, organizations around the world are constantly facing challenges to build competitive advantage. “The global world is characterized by more competition, diverse workforce, continuously changing customer needs, and new technology changes”(Gupta, 2011, p. 510). Given the constantly changing landscape, and the opportunity for firms to expand their domestically successful business models throughout the world, why do we continue to see such high failure rates to realize return of capital through mergers and acquisitions? “Researches have indicated that approximately 70-80% of Mergers and Acquisitions (M&A) fail to create significant value above the annual cost of capital” (Bruner, 2002). To explain these high failure rates, research has focused on building better integration models, improving the process to merge cultural difference, and a straight calculation of the potential future returns. Given that global expansion is a crucial element of growth for many firms, this study will prove that an improved due diligence process will enhance their likelihood for return on their investment. “Due diligence all too often becomes an exercise in verifying the target’s financial statements rather than conducting a fair analysis of the deal’s strategic logic and the acquirer’s ability to realize value from it” (Cullinan et al., 2004). A due diligence process should be able to answer the key questions, analyze the driving forces, and understand the areas and abilities a firm has to generate enhanced value. Of course, an enhanced due diligence process is not as exciting for firms and can be seen today as getting in the way of global expansion, but will firms continue to succeed in this global environment if their expansion strategies continue to create little value for their stakeholders?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".