Performance measurement through corporate communication: evidence from Indian manufacturing firms
Bibliographic record
Abstract
Purpose This study aims to address the limited understanding of how linguistic signals in corporate disclosures influence firm performance in emerging economies, where unique ownership structures, market concentration and policy uncertainties shape disclosure practices. By exploring these dynamics, the study provides insights into the complex interplay between communication strategies and financial outcomes. Design/methodology/approach The study uses annual reports to extract the embedded tones/signals using natural language processing (NLP) techniques. Specifically, this paper performs the sentence segmentation, preprocessing and parsing of textual content of the annual reports using Gensim, Spacy and the Regular Expression package in Python at several phases of parsing the text. Furthermore, we test our proposed hypotheses using a panel data regression approach. Findings The findings report that uncertain, litigious and financial constraint signals have a negative and significant effect on the firm’s financial outcomes. It highlights that the uncertain or litigation-related content in the annual report disclosure deteriorates the financial outcomes. Most importantly, the study reveals that promoter ownership and the Herfindahl index positively moderate the relationship between embedded signals and financial performance. However, policy uncertainties and business group (BG) affiliation negatively moderate the relationship between embedded signals and financial performance. Originality/value This study uniquely contributes to the literature by employing advanced NLP techniques to decode embedded signals in corporate disclosures and assess their impact on financial performance. It provides novel insights into the moderating roles of promoter ownership, market concentration, EPU and BG – factors that are especially pertinent in the context of emerging economies.
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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 | 0.001 | 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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".