Contesting Indonesia’s Single Origin Coffee Market: A Dynamic Capabilities Perspective
Bibliographic record
Abstract
Coffee is a commodity that has high value and great demand, then it supply chain tends to be monopolized by big actors certainly for single origin coffee bean. However, increasing number of local coffee shops and consumer awareness of consumed goods, and the emergence of more conscious consumer groups of “coffee lovers” or “connoisseur’s consumers”, sparks an intesive competition among market actors in local level that influence such dominance. This study aims to employ the dynamic capabilities theories (DCs) to analyze how the market actors, namelysmall traders, wholesalers, certified companies, and coffee shop owners build a strategy to secure their coffee supply amidst the tight competition.Selecting the coffee markets in Dampit District, East Java, Indonesia, we find the actors fighting for social space to win the competition by building network, dependency, and legitimacy. Actors build capabilities based on the internal potential to identify opportunities, take the opportunities, and utilize them to transform business organizations to survive rapid environmental changes. Not just looking at dominance behaviors as how it is in the case of asset-based approach, DCs provide a more balanced perspective between the entrepreneur's capacity and asset control. Detailed research to see each actor builds long term strategies is needed in the future to describe in more detail their strategy in maintaining their business sustainability in the more competitive busisness environment.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".