The Study of Nostalgia-Oriented Strategy Aimed at Millennials on The Example of The Lego Group
Bibliographic record
Abstract
Abstract Objective: The purpose of this paper was to examine the effect that nostalgia-oriented strategy has on the Millennials’ perception of the LEGO brand. Methodology: The methodology was based on past research in the field and used a modified version of a questionnaire developed by Pascal et al. (2002). Findings: The research was conducted among 203 young respondents in the second quarter of 2019. During the realization of research, the indirect method of gathering information, using a survey technique was applied. The survey was conducted with the application of the techniques of electronic survey. The research methodology was based on past research in the field and a modified version of a questionnaire developed by Pascal et al. (2002) was used. Value Added: This paper is the first to have found that companies operating in the toy industry are using nostalgia with aim of sustaining the brand loyalty. Recommendations: The toy industry has become pretty much an unfair place to do business these days, as the biggest toymakers are involved in a fierce fight for the next generations of kids enamoured with the latest high-tech wonders. This paper demonstrates how LEGO ® ’s efforts reaped dividends as they have begun to address Millennials. It can be said, then, that the future of marketing in the following months would involve nostalgia as a major tool accelerating all the strategic endeavours in this clash of brands as the trend described hereinafter does not seem to slow down.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".