Bibliographic record
Abstract
Purpose The paper seeks to explore the development of an intellectual capital flow statement based on a framework that harnesses contemporary research on intellectual capital. Design/methodology/approach Case studies of wireless technology companies based in Canada are adopted to examine the interrelationship between intellectual capital components with a resource‐based view as well as deficiencies in their current financial reporting with respect to intellectual capital. An intellectual capital flow statement is proposed in order to capture the necessary characteristics. Findings This study confirms the inter‐relationship between components of intellectual capital and business growth performance among the selected cases of wireless technology companies. It suggests an “add‐on” disclosure of intellectual capital flow that would enhance the usefulness and predictability of performance. Research limitations/implications This study is based on case studies of six wireless technology companies and may not be generalisable to other technology‐based companies. Practical implications The paper suggests a disclosure method for intellectual capital that mitigates problems with information asymmetry in technology‐based companies while maintaining harmony with current financial reporting practice. Originality/value This paper integrates prior studies and concepts in intellectual capital, technology management and financial accounting theory, aiming to develop an integrated framework for the disclosure of intellectual capital.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".