Deer Creek Land Development (DCLD)
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe and analyse the experiences of a small business, Deer Creek Land Developments (DCLD), which has been very successful in negotiating the competitive pressures in a mature industry over time and has built sustainable competitive advantage. The firm has been quite successfully navigating the ups and downs of the market. The case provides an excellent example of how small businesses can open their business models to respond to changes in the external environment, such as an economic downturn, and/or simply to grow. Design/methodology/approach The paper uses a single case study approach. Detailed interviews of the owner and the manager were used to collect data for the case study. Findings DCLD's success is found to be hinged on its ability to consistently enhance operational efficiencies, move to higher valuations by adopting an open business model that exploits core in‐house capabilities and those acquired through contractors and partner organizations. Practical implications The paper provides several interesting insights useful for small business managers and entrepreneurs. Small businesses can use openness of both types, as demonstrated in the case, to create strategic differentiation and also to reduce operating costs. Originality/value This paper initiates a rich field enquiry, which provides some interesting insights to small business managers. The case study is used to demonstrate how a small business can effectively use an open business model to negotiate competitive and environmental pressures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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; 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".