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Record W7071791787

Three essays on the origins and consequences of product exploration

2023· dissertation· en· W7071791787 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2023
Typedissertation
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsMcGill University
FundersMcGill University
KeywordsProduct (mathematics)Production (economics)Government (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

The first essay of this dissertation is a review of the literature from leading management journals on product exploration (i.e., a firm's creation of new products different from those in its existing product portfolio).This review shows a strong base of studies on the antecedents and consequences of product exploration, although the latter has received comparatively less attention.It further reveals five opportunities for future research, two of which motivate the second and third essays of this dissertation.The first of the two opportunities that I focus on is determining how the elements that lead to product exploration operate through the new product development process.The second is to identify whether and when exploratory products are riskier than exploitative products.In the second essay, we 2 extend research on technological exploration as an antecedent to product exploration.Prior research shows that firms can improve their technological exploration through their organizational structure, particularly by implementing structural separation.We consider the downstream effects of structural separation as firms turn technological exploration into product exploration, which requires the difficult translation of technological inventions into commercial products.As a baseline, we predict that technological exploration increases a firm's product exploration rate.However, we also predict that the delicate transition from technological exploration to product exploration is impeded by barriers stemming from structural separation.We identify three types of structural separation -divisional compartmentalization, geographic dispersion, and internal network fragmentation -as barriers that weaken the baseline relationship.An empirical analysis in the American medical devices industry provides robust support for our 1 French version to follow. 2 Essays 2 and 3 are co-authored with Martin Goossen.hypotheses.The findings of this study contribute to the literatures on ambidexterity, organizational exploration, and organizational R&D structures.In the third essay, we advance research on the performance implications of product exploration.New product introductions can be a source of firm renewal and growth or precipitate a firm's downfall.We use the concepts of exploration-exploitation and second-order learning from the organizational learning literature to explain this variability.We predict that exploratory products have more variable performance outcomes than exploitative products.This is because they have the potential for superior effectiveness or to create new lines of business, but they also require costly organizational changes and increase the likelihood of mistakes.Further, we hypothesize that this effect is augmented if the product also addresses a new-to-the-firm market segment and is attenuated when the firm has greater experience in marketing exploratory products.Using a large-scale study on stock market reactions to the FDA approval of new medical devices between 2000 and 2015, we find partial support for our hypotheses.The findings of this study contribute to the literatures on organizational learning, new product introductions, and market reactions to strategic actions.John-Paul Ferguson, Corey Phelps, and Patrick Cohendet.John-Paul, you gave me inspiration at the toughest moments in the program.Corey, your attention to detail has sharpened all aspects of my work.Patrick, you have always been able to introduce a new perspective and expand the ways I think about

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)0.006
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.148
GPT teacher head0.342
Teacher spread0.194 · 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 designTheoretical or conceptual
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
Published2023
Admission routes2
Has abstractyes

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