Competition, Competitive Advantage, and Clusters: The Ideas of Michael Porter
Bibliographic record
Abstract
Harvard professor, Michael Porter has been one of the most influential figures in strategic management research over the last three decades. He infused a rigorous theoretical framework of industrial organization economics with the then still embryonic field of strategic management and elevated it to its current status as an academic discipline. Porter's outstanding career is also characterized by its cross-disciplinary nature. Following his most important work on strategic management, he then made a leap to the policy side and dealt with a completely different set of analytical units. More recently he has made a foray into inner city development, environmental regulations, and health care services. Throughout these explorations Porter has maintained his integrative approach, seeking a road that links management case studies and the general model building of mainstream economics. With expert contributors from a range of disciplines including strategic management, economic development, economic geography, and planning, this book assesses the contribution Michael Porter has made to these respective disciplines. It clarifies the sources of tension and controversy relating to all the major strands of Porter's work, and provides academics, students, and practitioners with a critical guide for the application of Porter's models. The book highlights that while many of the criticisms of Porter's ideas are valid, they are almost an inevitable outcome for a scholar who has sought to build bridges across wide disciplinary valleys. His work has provided others with a set of frameworks to explore in more depth the nature of competition, competitive advantage, and clusters from a range of vantage points. Available in OSO: http://www.oxfordscholarship.com/oso/public/content/management/9780199578030/toc.html Contributors to this volume - Robert Huggins, Cardiff School of Management, University of Cardiff Hiro Izushi, Aston Business School Jay B. Barney, Ohio State University J.-C. Spender, Lund/ESADE Nicolai J. Foss, Copenhagen Business School Robert E. Hoskisson, Arizona State University Michael A. Hitt, Texas A&M University William P. Wan, Texas Tech University Daphne Yiu, Chinese University of Hong Kong Omar Aktouf, HEC Montreal Miloud Chennoufi, Canadian Forces College W. David Holford, University of Quebec at Montreal Robert M. Grant, Bocconi University Jan Fagerberg, University of Oslo Brian Snowdon, Durham University Christian H. M. Ketels, Harvard Business School Edward J. Malecki, the Ohio State University Ron Martin, University of Cambridge Peter Sunley, University of Southampton
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".