Repetitive Financial Ads on Social Media Shape Next-Gen Future Financial Experience: Why Financial Experts Should be Alert?
Bibliographic record
Abstract
Social media platforms allow financial institutions and advertisers to reach a broad audience and promote their offerings. Consequently, financial ads have become a common feature in social media feeds. Nevertheless, more knowledge is needed regarding the effects of these ads on the Next Gen customer journey (age and culture), who navigates through the complex financial ecosystem trying to make sense of these repetitive financial ads combined with the interference of social media influencers. This qualitative research explores the impact of repetitive financial advertisements on social media and their influence on the general perception of various age groups that compose the Next-Gen. The methodology consisted of an in-depth content analysis of financial influencers on social media and 15 in-depth interviews of participants from different demographic and culture groups who shared their detailed customer journey experiences, including attitudes and perceptions toward repetitive financial ads. The results of this study contribute to providing a better understanding of a) how exposure to repetitive financial ads shapes the customer’s attitude towards financial products and services, b) how variations in perceptions may depend on the customer’s age groups and cultural background, and c) how social media influencers combined with social media platforms selection may impact the customer’s financial literacy and perception on financial products and services. From a managerial perspective, the power of influencers and social media platforms within financial services should be evaluated beyond \n \nrobotic information gathered by big data. Although the qualitative research limits the \naggregate results based on the snowball recruitment and the number of interviewees, it enriches the perspective of putting the human first by listening to the storytelling; it is possible to grasp how the customer engages in sharing, reading, and commenting about financial services and products on various social media platforms, and how the customer is influenced by “financial experts” who promote their expertise on these platforms. Ethical guidance is needed at all levels (e.g., customers, financial institutions, financial advertisers, marketers) to develop and achieve social media efficacy while tailoring educative financial communication strategies toward specific age and cultural groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".