Bibliographic record
Abstract
Citation (2016), "List of Contributors", Resource Redeployment and Corporate Strategy (Advances in Strategic Management, Vol. 35), Emerald Group Publishing Limited, Bingley, pp. vii-viii. https://doi.org/10.1108/S0742-332220160000035018 Publisher: Emerald Group Publishing Limited Copyright © 2016 Emerald Group Publishing Limited Gautam Ahuja University of Michigan, Ann Arbor, MI, USA Jaideep Anand The Ohio State University, Columbus, OH, USA Josep M. Argilés-Bosch Universitat de Barcelona, Barcelona, Spain Joel Blit University of Waterloo, Waterloo, ON, Canada Robert P. Bremner Stanford University, Palo Alto, CA, USA Ari Dothan Interdisciplinary Center Herzliya (IDC), Herzliya, Israel Gary Dushnitsky London Business School, London, UK Kathleen M. Eisenhardt Stanford University, Palo Alto, CA, USA Timothy B. Folta University of Connecticut, Storrs, CT, USA; University of Strasbourg Institute for Advanced Study, Strasbourg, France Josep Garcia-Blandon Universitat Ramon Llull, Barcelona, Spain Douglas P. Hannah University of Texas at Austin, Austin, TX, USA Constance E. Helfat Dartmouth College, Hanover, NH, USA Samina Karim Northeastern University, Boston, MA, USA Hyunseob Kim The Ohio State University, Columbus, OH, USA Thomas Klueter IESE Business School, Barcelona, Spain Dovev Lavie Technion – Israel Institute of Technology, Haifa, Israel Gwendolyn K. Lee University of Florida, Gainesville, FL, USA Christopher C. Liu University of Toronto, Toronto, ON, Canada Shaohua Lu Tulane University, New Orleans, LA, USA Mónica Martinez-Blasco Universitat Ramon Llull, Barcelona, Spain Luis L. Martins University of Texas at Austin, Austin, TX, USA Patia J. McGrath University of Pennsylvania, Philadelphia, PA, USA Douglas J. Miller Rutgers, The State University of New Jersey, New Brunswick, NJ, USA Will Mitchell University of Toronto, Toronto, ON, Canada Elena Novelli City University London, London, UK Srikanth Parachuri The Pennsylvania State University, State College, PA, USA Violina P. Rindova University of Texas at Austin, Austin, TX, USA Harbir Singh University of Pennsylvania, Philadelphia, PA, USA Hsiao-shan Yang University of Illinois at Urbana-Champaign, Champaign, IL, USA Adrian Yeow SIM University, Singapore Book Chapters Resource Redeployment and Corporate Strategy Advances in Strategic Management Resource Redeployment and Corporate Strategy Copyright Page List of Contributors Examining Resource Redeployment in Multi-Business Firms Resource Redeployment in Business Ecosystems Product Turnover: Simultaneous Product Market Entry and Exit Resource Redeployment through Exit and Entry: Threats of Substitution as Inducements Incumbent Responses to an Entrant with a New Business Model: Resource Co-Deployment and Resource Re-Deployment Strategies Resource Characteristics and Redeployment Strategies: Toward a Theoretical Synthesis What Goes on Beneath the Surface of Reconfiguration? The Impact of Redeployment via Activity Addition and Subtraction on Firm Scope and Turnover Resource Reconfiguration and Transactions across Firm Boundaries: The Roles of Firm Capabilities and Market Factors The Hare and the Fast Tortoise: Dynamic Resource Reconfiguration and the Pursuit of New Growth Opportunities by Yahoo and Google (1995–2007) Linking Technologies to Applications – Insights from Online Markets for Technology Resource Reconfiguration: Learning from Performance Feedback The Impact of Absorbed and Unabsorbed Slack on Firm Profitability: Implications for Resource Redeployment About the Authors
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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.730 | 0.760 |
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; the direct Gemma label and the distilled Codex classifier 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".