IT Industry Analysts: A Review and Two Research Agendas
Bibliographic record
Abstract
The firms involved in analyzing the information technology industry (IT), such as Gartner, Forrester, and IDC, are reputed to have a major impact on both IT vendors and IT adopters through their influence over how IT actually is acquired and used. The purpose of this article is to take stock of the nascent stream of research on industry analysts that has developed in recent years in order to shed some light on the IT analysis industry―to analyze the IT industry analysts, if you will. Using an organizational field-level lens, we look at the business models of the firms that operate in this industry. We examine the main institutional work that the analysts in these firms perform as status arbiters, institutional carriers, network brokers, IT fashion setters, and knowledge entrepreneurs. We examine the competitive and institutional pressures faced by analysts in these firms. Finally, we propose two research agendas: (1) to study the impact that this industry has had, and could continue to have, on the IT industry as a whole, and (2) to study how the relationship between the academic information systems community and the IT analysis industry might co-evolve.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".