Determinants of Corporate Sponsorship: Evidence From Consumer Product and Services Sector
Bibliographic record
Abstract
The role of corporate sponsorship as part of the corporate philanthropy of a business has been promoted to ensure the awareness of its products or services.In order to get insights on their motivation, this study investigates the determinants of the corporate sponsorships, specifically the companies' financial characteristics and marketing intensity in Malaysian consumer products and services industry.Using annual reports, this study examined five largest listed companies in consumer products and services industry from the years 2000 -2018, which consisted of 95 firm-year observations.Panel data regression analysis was carried out using the panel regression fixed effects model to analyse the effect of companies' financial characteristics and marketing intensity on corporate sponsorship.Financial characteristics were represented by debt ratio, return on assets, and return on equity while marketing intensity was represented by marketing expenses to total sales.The findings showed insignificant relationships between all variables and corporate sponsorship.The results may suggest that financial characteristics and marketing intensity are not the most important determinants of corporate involvement in sponsorship activities.This research would provide insights for management that, corporate sponsorships are done to promote their products and services and not necessarily have influenced by their financial and marketing agenda.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".