The Saliency of Second Level Agenda-setting Theory Effects on the Corporate Reputation of Business Organizations in Nigeria
Bibliographic record
Abstract
Purpose: This paper examines how business news reports on the activities of business organizations in Nigeria set agenda for the opinions, attitudes and perceptions of their stakeholders.Design/method/approach: Content analysis of business newspapers and semi-structured interviews were employed to test the validity of four theoretical propositions on the second level agenda-setting effects.Findings: Our findings confirm existing theories that the amount of news coverage received by a corporation connects positively to public awareness and perception of the corporation; that the amount of news coverage dedicated to specific attributes of some business organizations in Nigeria connects to the proportion of the stakeholders who define the corporation by these attributes; that the more media cover specific corporate attributes from a positive perspective, the more positively will the general public perceive these attributes; and that the more negative that the media coverage is for some specific corporate attributes, the more negative will the general public perceive those attributes.Theoretical implication: One can reasonably argue that based on the outcomes of this study, corporate reputation theories as propounded in international marketing studies bear significant corollary to reputation studies in Nigeria.Practical implication: The study suggests the need for reputation managers in Nigeria and in the western world to learn from one another by forming strategic alliances.Limitation of study: This study is devoted to the Nigerian business environment. Whilst this may prevent the generation of a theory, it offers an opportunity to examine the subject on comparative national study.
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.
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".