The role of digital marketing, CSR policy and green marketing in brand development
Bibliographic record
Abstract
Corporate Social Responsibility (CSR) policy, digital marketing and green marketing are considered as some of the most emerging topics. However, the major problem is associated with the lack of CSR policies, development and adaptation of green marketing in the companies operating in manufacturing companies in the UK. In this manner, this study aimed to determine the role of digital marketing, CSR policies and green marketing in brand development. Concerning this, the case of UK’s manufacturing companies was considered which can help the manufacturing companies operating in the UK to make the development of brand more effective, as the consumers would perceive the brand which complies with the environmental laws. To attain the aim, the researchers utilized a quantitative method of data collection where a close-ended survey questionnaire was utilized. The data was collected from the concerned participants working in the manufacturing sector of the UK and the sample size considered for the analysis was based on 404 participants. The analysis was conducted using Structural Equation Modeling (SEM) on Smart PLS. The analysis revealed that the overall impact of green marketing, CSR policy and digital marketing was statistically significant on the brand development of UK’s manufacturing companies. Considering this, it has been recommended to the manufacturing companies in the UK to focus on environmental disclosure, green innovation, green alliance and promotional activity for the purpose of ensuring brand development. However, this study is limited to the geographical bounds of the UK; therefore, it has a certain room for future research.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.000 | 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".