Global Market Potential For Information Technology Products and Services
Bibliographic record
Abstract
This report describes the use of market potential analysis as a strategic tool to identify market opportunities and make resource investments in countries and regions where they have the greatest potential long-term return.This tool is used to categorize leading and potential growth markets, identify drivers and barriers to growth, and quantify market potential for a set of IT products and services by country and region. Highlights of the report include:IT market opportunities are closely related to national wealth, yet there is a great deal of variance among countries at any given income level.These differences can be seen as evidence of inherent differences among countries that make some leaders and others laggards.But they also can be seen as evidence of untapped market potential.While developed countries such as Canada, Germany and Britain have more or less saturated PC markets, others such as Italy and Spain still have break-out potential.More important is the potential in emerging markets such as India, Indonesia and especially China, which could add another 50 million PCs to its installed base at its current income level.Relative penetration rates vary by country and product.The U.S. is a leader in PC adoption, but lags in cell phone and broadband, where Korea, Canada and others lead.Household market potential for IT products and services depends on average income but also on the distribution of income.Using product penetration curves over the income distribution, we can estimate actual and potential household market size for different products.Over 80 million households in the developing Asian economies will pass the $35,000 income level between 2000 and 2010, creating a massive new middle class of consumers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".