The impact of IT on the growth and development of insular firms
Bibliographic record
Abstract
Purpose The purpose of this paper is to show if the use of information technology (IT) strengthens the growth and development of insular firms. It investigates IT usage in an ultra‐peripheral territory of the Economic Union and aims to gain a better understand between the use of the IT and the growth and development of insular firms. Design/methodology/approach This research examines 118 of the largest Réunionese firms and several interesting factors are identified. Certain characteristics such as size, age, diversification of activities or lack of diversification, status as a subsidiary of a larger firm, degree of computerization and if electronic communications influence IT use were specifically examined. Also looked at were certain factors that could illustrate the relationship between IT use and firm growth and development. Findings This paper presents findings and discusses these in terms of the degree of utilization of IT by insular firms, the development of external relationships and the importance of developing IT investment to reduce transaction costs within Réunionese enterprises. From this research, there is an assumption that the use of IT may have an impact on the growth and development of insular firms. Research limitations/implications This research reported in this paper focused only on Réunion Island and not on other insular contexts. Further European ultra peripheral regions must be investigated to support the findings. Practical implications The findings can help governments, public and private actors to increase the ICT investments of insular firms in order to reduce costs and improve the growth and development of local firms. Originality/value The originality of this paper is the focus on the role of IT on the growth and development of insular firms.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".