Mergers and Acquisitions : Creating Integrative Knowledge
Bibliographic record
Abstract
List of Figures. List of Tables. Lists of Contributors. Introduction: Amy L. Pablo And Mansour Javidan. Part I: M&A Performance:. 1. Mind The Gap: Key Success Factors In Cross--Border Acquisitions: Noa Erez--Rein (Australian Graduate School of Mangement), Miriam Erez (Faculty of Industrial Engineering and Management, Technion, Israel Institute of Technology),. 2. The Secrets Of M&A Success: A Co--Competence & Motivational Approach To Synergy Realization: Rickard Larsson (School of Economics and Management, Technion, Israel Institute of Technology), Kenneth R. Brousseau (Decision Dynamics LLC) Michael Driver (Marshall School of Business, University of Southern California), Patrick Sweet (Decision Dynamics AB). Part II: M&A Strategy:. 3. Cross--Border Mergers And Acquisitions: Challenges And Opportunities: Michael Hitt (Mays School of Business, Texas A&M University) And Vicenzo Pisano (Arizona State University). 4. Firm Competitiveness And Acquisition: The Role Of Competitive Strategy And Operational Effectiveness In M&A's: Paul Mudde (Siedman School of Business) And Thomas Brush (Krannert Graduate School of Mangement, Purdue University). 5. Acquisitions Of Entrepreneurial Firms: A Comparison Of Private And Public: Jung--Chin Shen (Strategy and Management Department, INSEAD) And Jeffrey Reuer (Fisher College of Business, Ohio State University). Part III: Merger Implementation And Integration:. 6.The Role Of The Corporate Academy In Mergers And Acquisitions: Shlomo Ben--Hur And Todd Thomas (DaimlerChrysler Services Academy). 7. Managing The Acquisition Process: Do Differences Actually Exist Across Integration Approaches: Kimberly Ellis (Eli Broad Graduate School of Mangement, Michigan State University). Part IV: Culture And Leadership In M&A's:. 8. The Neglected Role Of Leadership In Successful M&A: Sim Sitkin (Center for Organizational Research at the Fuqua School of Business) And Amy L. Pablo (Haskayne School of Business, University of Calgary). 9. The Role Of CEO Charismatic Leadership In Effective Implementation: David Waldman (School of Managbement at Arizona State University -- West). 10. The Impact Of Culture Differences On Strategy Realization: Vera Hertog (Vlerick Leuven Gent Management School, Universiteit Gent). Part V: M&A Knowledge Transfer And Learning:. 11. Technology Based Industries: Danna Greenberg (Faculty of Management, Babson College). 12.Does It Pay To Capture Intangible Assets? Asli Arikan (Boston University). Part VI: Research In M&A's:. 13.What Have We Learned From M&A Research? Joseph Bower ( Harvard University). 14. Where We've Been And Where We're Going: Amy L. Pablo , Mansour Javidan(Haskayne School of Business, University of Calgary), Harbir Singh (The Wharton School, The University of Pennsylvania), Michael Hitt (Mays School of Business, Texas A&M University) And Dave Jemison (McCombs School of Business, University of Texas at Austin). Index
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".