Strategic use of Influencer Marketing in Bangladesh: A Study of Bangladeshi YouTubers
Bibliographic record
Abstract
In recent years, influencer marketing (marketing strategies involving Social Media Influencers/SMIs) has become an increasingly popular and effective marketing method for businesses globally. Despite this growth in popularity, there needs to be more research on Bangladeshi SMIs or the methodologies that Bangladeshi marketing firms use to recruit and work with them. My research aims to gain an understanding of Bangladeshi influencer culture, with an emphasis on the perspectives of Bangladeshi YouTubers. This exploratory study first uses relevant theoretical and academic literature to review and comprehend the prior research on SMI marketing. It then addresses a gap in research about Bangladeshi influencer culture through a comparative qualitative analysis of videos produced by YouTubers in Bangladesh and Canada and semi-structured interviews with influencers. The research investigates Bangladeshi YouTubers' characteristics and their intrinsic and extrinsic motivations for working as SMIs. Self-determination theory (SDT) (Deci & Ryan, 1985) is used to compare and contrast their experiences with those of North American (Canadian) YouTubers. This research also analyses the perspectives of marketing executives based in Bangladesh to understand further how businesses approach influencer marketing. The findings show how incorporating culture and language into content delivery by Bangladeshi SMIs adds to their identity and motivation, separating them from a universal global definition of social media influencers. Based on these findings, this thesis recommends how Bangladeshi businesses may effectively employ influencer marketing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".