The impact of social media influencer marketing on purchasing decisions of Millennials in Finland
Bibliographic record
Abstract
In the first quarter of 2021, the number of internet users worldwide was more than 4.6 billion, and the number of social media users had also passed the 3.8 billion mark. These numbers indicate that almost 60 percent of the people worldwide are now online, and numerous studies propose that more than half of the world population will be using social media by the end of the year 2021. The power and impact of social media is indisputable. \n \nThe main purpose for this research was to examine how social media and the marketing power of its influencers effect on buying demeanours of Millennials in Finland. Because of social media, people are more connected than ever, and they can access to any sort of information with their smart devices. For the brands social media has offered a new way to stay connected with their customers, but at the same time the competition between brands has increased. Social media has taken the marketing game of brands to another level and this on the other hand has affected on the decision making of consumers. \n \nSocial media is changing and developing daily. This means that if something was epoch-making this week, there is a chance it will be old news next week. In the marketing point of view, this means that the social media strategy for brands should be strong but also adjustable. In strategy building more focus should be put on understanding consumer purchasing behaviour. This research introduces several kinds of factors influencing the purchasing behavior of people, along with the most general types of buying behaviors. \n \nConcepts like social media, social media platforms, newest social media trends, social media marketing, and influencer marketing were analysed and discussed in the literature part of this study. For the research part a qualitative research was performed in order to receive more realisation and understanding of the underlying reasons and drive of people. All the participants of this research were familiar with the concept of influencer marketing and most of them were following influencers on their social media platforms. Theme interviews were conducted and after the interviews the data was analyzed. \n \nThe data collected during this research is showing that Millennials see influencers more like acquaintances in social media, but when the advertising is executed in the right manner, influencers have an impact on the purchasing decisions and information seeking of Millennials in Finland. When the influencer marketing is implemented properly it has long-lasting consequences and this is something brands should put their focus on. The significance of social media on consumers purchasing behaviour is very apparent and assumably only increases in the future with new technologies emerging in the market.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".