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Record W7023735993

The Outcome of Design Innovation and the Antecedents of Design Activities: Insights from Canadian Manufacturing Industries

2023· dissertation· en· W7023735993 on OpenAlexfundaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Ottawa
KeywordsProduct innovationNew product developmentProduct designInnovation managementProduct (mathematics)Design technologyFunction (biology)Empirical researchService (business)Outcome (game theory)
DOInot available

Abstract

fetched live from OpenAlex

The importance of product design has been getting attention in the past decade from scholars and practitioners. Design plays a critical role in firms’ product development and business strategies. In recent years, scholars began to see design innovation as another vital innovation element of a new product. A new product should encompass at least two innovation elements: technology innovation and design innovation. While technology points to the function of a product, design points to the form of a product. Despite the advocacy of scholarly examination of design innovation, there are few studies of design innovation. In this dissertation, two empirical studies have been conducted to examine the outcome of design innovation and the antecedents of design activities, respectively. Study 1 examines the effect of design innovation (as well as technology and service innovation) on new product performance. Additionally, the study examines the roles of marketing innovation and process innovation in mediating the relationships between these innovation activities and new product performance. Study 2 examines how firms’ absorptive capacity, competitive responsiveness, and product development resources drive design and R&D activities. Design and R&D activities typically lead to the introduction of design and technology innovation. Regarding the findings from this dissertation, the first study shows that design, technology, and service innovation (which, argued by this study, are the three main innovation elements of a new product) all contribute to new product performance. Additionally, marketing innovation and process innovation are found to mediate the relationship between these innovation elements and performance. The second study shows that a firm’s competitor responsiveness, absorptive capacity (captured by “institutional sources” and “market sources of information”), and product development resources (captured by “cross-functional design team”, “design or information control technologies”, and “concurrent engineering”) are positively related to firms’ design and R&D activities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.543

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.006
Science and technology studies0.0100.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.211
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes2
Has abstractyes

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