Media sentiments and firm’s sales growth: the moderating role of offering characteristics
Bibliographic record
Abstract
Purpose Although the interest in firm media sentiment has been increasing, the impact of news media sentiments on consumers’ perception of firms’ offerings and, subsequently, their sales remain unknown. This study aims to address this research question in this study. Furthermore, the authors consider the role of two boundary conditions, i.e., offerings’ similarity and offerings’ service ratio, that moderate the main relationship. Design/methodology/approach Using a comprehensive and novel data set of over 900 firms between 2009 and 2019 from multiple sources, this study addresses the research questions. The authors use a fixed effects panel regression model to estimate the model. Findings A firm’s news media sentiments can influence consumers’ perception of the corporate brand, thereby driving sales growth. This study finds that when a firm’s offerings are not differentiated from its competitors, news media sentiments become more important and so does when a firm offers more services than a product. Research limitations/implications To the best of the authors’ knowledge, this study is the first to assess customers’ responses as manifested in the sales growth of a firm’s offerings, using both primary and secondary data and analysis. Practical implications The findings provide actionable insights to managers by identifying specific offerings-related attributes – similarity and service ratio – where media sentiments play a critical role in influencing sales growth. Originality/value While existing studies in marketing have primarily considered user-generated social media sentiments, this study departs from this literature by investigating earned media sentiments through traditional media outlets such as newspapers and business magazines, which have rarely been studied in marketing.
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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 | 0.013 | 0.021 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".