MétaCan
Menu
Back to cohort
Record W6991741443

The influence of innovation strategy on enterprise: a pedagogical case study of Lego

2020· dissertation· en· W6991741443 on OpenAlexaboutno aff

Bibliographic record

VenueRepositório do ISCTE-IUL · 2020
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsnot available
Fundersnot available
KeywordsOrder (exchange)Face (sociological concept)The InternetFellAmbidexterityControl (management)Disruptive innovationSales forceSales management
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.325
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRepositório do ISCTE-IULSame topicManagement and Marketing EducationFrench-language works237,207