Bibliographic record
Abstract
Mazzarol, T. and S. Reboud. 2011. Strategic Innovation in Small Firms. Edward Elgar: Cheltenham. Strategic Innovation in Small Firms is edited by Tim Mazzarol at the University of Western Australia Business School and Sophie Reboud from the Burgundy School of Business in Dijon, France. The preface explains, briefly, the purpose of the book as exploring how small firms in developed countries manage innovation and commercialize new products or processes based on data from a common survey and case study protocol applied by researchers in 11 OECD countries. The authors of the 14 chapters, who come mainly from business schools around the world, worked together on a multinational research project to provide perspectives on small business innovation and commercialization on a country-by-country basis. The topics of chapters are efficiently arranged in a matrix-like style. Each chapter generally takes an element of the overall topic (success measures, management practices) and examines that topic within a particular national context. For example, a chapter on the success of innovation development in the United States is followed by a chapter on innovation management practices in Canadian small businesses. Due mainly to this content strategy, the book should be of interest to those researching small business systems in the OECD, those researching innovation and commercialization management in general and, of course, those researching innovation and commercialization in small businesses. In the first chapter, the editors define the key terms used in the subsequent chapters and describe the common frameworks that were used by the other researchers. A multilevel approach combining the task environment, the organizational environment, and the individual/managerial environment created the conceptual skeleton for examining and reporting the data in a consistent way regardless of the national context or factors of interest. Working within this framework, the authors collectively point out some interesting conclusions: for example, that Flemish small businesses in the creative sector may be less profitable because they do not commercialize their innovations very well and that Australian high-tech small businesses commercialize best when they maintain strong relationships with existing customers. It was relatively easy to integrate findings cumulatively across the book and I found myself wondering at the conclusion of each chapter whether, for instance, Flemish businesses in the creative sector might improve their commercialization efforts by borrowing from the customer-centric focus of small Australian businesses in high-tech. The common framework organization of the edited book made these thought experiments easy to construct and intriguing to follow through. Two issues became apparent early on in reading the book that I would have liked the editors to address in some way. …
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".