Local Adaptations of Generic Application Systems: The Case of Veiling Holambra in Brazil
Bibliographic record
Abstract
This paper focuses on local adaptations, referring to the significant or subtle changes local firms make in their local business processes and rules in order to fit with a generic application system, and to the changes they make in the features of a generic application system. Local adaptations are therefore bidirectional in nature. Although several studies stress the importance of local adaptations for the overall success of information technologies (IT) used across locations, more research is needed regarding what kind of local adaptations are required for a particular generic application system to work well in particular localities. The nature and extent of local adaptations are still poorly understood. This paper provides a concrete illustration of a historically situated local adaptation: the case of Veiling Holambra. This Brazilian cooperative has imported a generic auction marketplace model from Holland and adapted it to local conditions, to succeed in a globalized and competitive flower market. Using concepts drawn from studies on globalization, cross-cultural implementations, and IT-based organizational change literature, we put forward three propositions that help to explain the success of local adaptations. The results of our case study indicate that the immigration of Dutch people was critical for bringing knowledge of cooperative structure and flower production to Holambra and led to a relatively small design-use gap. The ability to take local, contextual requirements into account without neglecting the ‘generic’ knowledge led to the successful implementation of the generic auction model. This mutual influence was particularly enabled by the Brazilian culture of improvization.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".