Impact of Influencer Marketing on Consumer Purchase Behavior during the Pandemic
Bibliographic record
Abstract
Social Media has turned from our regular photos and thought dumping platform to a marketing space mainly led by influencers. Influencers, the ones who influence, hold a firm grasp on people all over social media through their content, views, thoughts, and uniqueness that they have to offer. These influencers are known to impact people especially the younger generations. We can mark them as a new form of marketing that works beyond traditional marketing and is not limited to just selling a product but broadens the horizon to building a brand identity and creating a trustful relation between the audience, the brand, and the influencer. As the number of people who use social media grow, so grows the number of influencers and so does the number of companies choosing to use influencer marketing. The scope is big, the audience is endless and the influencers are professionals at creating engaging marketing content that is a long-term investment for any company big or small. The aim of this paper is to bring to light the recent uproar of Influencer Marketing on social media during the pandemic and how it has had an impact on companies and on the audience’s purchase behavior. The data for this paper has been taken through a small research survey that has also been done on a sample size of 50 consumers to study the impact of influencer marketing on their purchase behavior and decisions. All data used is particular to the pandemic and hence data post-2020 to now has been used.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".