Bibliographic record
Abstract
Purpose To propose a conceptual framework that facilitates the benchmarking of strategic processes necessary for entrepreneurial survival and success. Design/methodology/approach Drawing on extant literature on entrepreneurial survival, this paper considers the chaotic and emergent nature of the entrepreneurial organization and how benchmarking can contribute to a newly established firm's chances for survival and prosperity. The paper incorporates the concept of a sustainable competitive advantage in the discussion, and offers organizational culture as being the imperfectly imitable element which will contribute to the entrepreneurial firm's success. Findings Four key processes are identified that contribute to entrepreneurial viability – cooperation, sharing founder's vision, time management, and developing organizational competencies – and suggestions are offered for developing appropriate benchmarks for these processes. The paper also highlights two instruments that may be useful in this endeavor. Research limitations/implications The paper draws attention to the usefulness of benchmarking processes and not just metrics in fostering entrepreneurial survival. Key processes are identified, and suggestions are provided for researchers to begin work on developing the necessary benchmarks. Practical implications The paper not only offers a theoretical discussion of the usefulness of benchmarking processes as opposed to focusing only on outcomes, but also helps the practitioner to implement such benchmarking activities by highlighting practical instruments for this purpose. Originality/value This paper brings to bear literature from several streams of research. It takes benchmarking from its metric‐oriented focus to a more process‐focused approach, and applies it in the context of entrepreneurial survival.
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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 | 0.035 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".