The importance of the Ansoff matrix for the study of the information services market
Bibliographic record
Abstract
The purpose of this research is to present the matrix developed by H. Igor Ansoff and reflect on its usefulness for studying the Information Services (IS) market in the context of the information and knowledge society of the 21st century, whose environment is infopolluted and constant changing, making it difficult to access up-to-date, useful, and quality information that would meet the needs of both individuals and organizations. The research was carried out by identifying and consulting scientific literature through EBSCO. The search terms used were Ansoff Matrix, Marketing, Information Science, and Information Services. The chronological period from 1957 to 2023 and the languages Portuguese and English were selected. The results show that information is a valued asset in the 21st century because, together with the Ansoff Matrix, it allows us to understand market needs and create information products and services that help meet consumers' information needs, but also to value and recognize the work done by information services. The Ansoff matrix helps us to understand the best strategy to apply to an information service so that it develops and evolves, contributing to the knowledge and development of the individual and/or organizational consumer, playing an essential role in the innovation and renewal of information products and services. We conclude that in the context of the Information and Knowledge Society, the value of information for human development will contribute to a more informed and, therefore, happier society.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".