An exploratory study of the demand side of firms' non-financial information
Bibliographic record
Abstract
Purpose The purpose of the present study is to investigate the demand side of non-financial reporting, especially integrated reporting. We investigate (1) what type of content the users of online integrated reports access most frequently and (2) whether users search for non-financial content and content that integrated reports must contain to comply with the requirements of the International Integrated Reporting Framework (IIRF). Design/methodology/approach We perform big data analysis on the log files associated with user access to five online integrated reports over a period of 12 months to investigate what type of information users access most frequently when they visit online integrated reports. Content analysis enabled us to match the usage data (page impressions) to certain topics within the integrated reports. Our analysis, which is based on data reflecting over one million webpage views, indicates which types of integrated-report content attract the users’ attention and which do not. Findings Our results confirm that integrated reports in general, and specific components in particular, are of significant value to users. In contrast to what other studies suggest, we find no evidence that users seek financial information to a much greater extent than non-financial information. Specifically, our results show that usage between financial and non-financial information is quite evenly distributed and differs depending on the metric under consideration; considering usage by topic impressions financial information is used more frequently, scaling topic impressions by report pages addressing a specific topic, non-financial information displays higher usage. With respect to specific topics, our analysis shows that the topics that attracted most interest from our sample of users are performance, financial statements, company profile and value creation. Furthermore, our findings confirm the importance of integrated reporting from a user perspective. Practical implications The results of our study are of practical value to both standard setters and (potential) preparers for two main reasons: first, they provide user-centric evidence about the importance of non-financial information and empirically confirm that integrated reporting has become widely accepted among users; second, they provide evidence about the topics that are of specific interest for users and therefore should also be considered by preparers and standard setters more carefully. Originality/value The present study enriches the literature on financial and sustainability reporting with informative insights into which of the topics that integrated reports contain attract most interest from users. Moreover, our results are based on actual data on the usage of such reports that reflect real user-journeys, rather than on artificial data, created for research purposes.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".