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Record W4408865290 · doi:10.1016/j.bar.2025.101643

Linkage between strategy and financial performance disclosure in annual reports: A new reporting path for organizational learning

2025· article· en· W4408865290 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe British Accounting Review · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsUniversity of LethbridgeSaint Mary's University
FundersUniversity of ManchesterSt Mary's UniversitySaint Mary’s UniversityAthens University of Economics and BusinessVaasan yliopisto
KeywordsLinkage (software)Path (computing)BusinessAccountingPath analysis (statistics)Computer scienceMachine learning

Abstract

fetched live from OpenAlex

We examine the reporting practice of linkage between strategy disclosures (management discussion of firm strategy and the business model) and financial performance disclosures in annual reports as a path for organizational learning. For identification, we use the UK Company Law Amendment mandating a Strategic Report as a separate section of the annual report so that strategy disclosures provide sufficient context for financial statements. We confirm that the linkage between strategy and performance disclosures in annual reports increases incrementally after the amendment for firms that are more strongly affected by the Company Law Amendment. For these firms, we document an incremental rise in measures associated with organizational learning (e.g., measures of organizational changes and workforce engagement). Our study has important policy implications for the structure of textual disclosure in annual reports.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.233
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it