The influence of innovation strategy on enterprise: a pedagogical case study of Lego
Bibliographic record
Abstract
Toys are an indispensable partner in the growth of children, which can give children boundless joy. Toys can not only satisfy children's playful nature, but also be an important tool to cultivate children's good intelligence and healthy psychology. The growth of the toy industry has given rise to big toy companies such as Lego, Hasbro and Mattel. With the progress of the Internet and the development of electronic games, the traditional toy industry has been impacted to some extent. In 2019, in 13 major markets around the world toy sales fell by 3%, Australia, Belgium, Brazil, Canada, France and the United States have all seen sales decline and even closed large retail stores in 2019. In the face of the current depressed market situation in the toy industry, Lego Group, on the contrary, has achieved sustained profits. Recent data show that Lego saw significant growth in both sales and profits in the first half of 2020, reporting a 14% rise in sales and a 7% surge in revenues. The purpose of this paper is to provide a pedagogical case study on the Lego company to the audience – management undergraduate students. The case allows students to apply important strategic tools like Porter's Five Forces model and the VRIO model, and concepts related to innovation and ambidexterity to the case, in order to combine theory with practice, which is hoped will have a positive impact on their understanding of the matters and ability to apply the concepts in new settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".