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Record W4413178698 · doi:10.1111/jpim.70000

A Brave New World: The Impact of Technology on Innovation Management

2025· article· en· W4413178698 on OpenAlexfundno aff
Dhruv Grewal, Praveen K. Kopalle, Dominik Mahr

Bibliographic record

VenueJournal of Product Innovation Management · 2025
Typearticle
Languageen
FieldComputer Science
TopicAI in Service Interactions
Canadian institutionsnot available
FundersUniversity at BuffaloUniversity of Illinois at Urbana-ChampaignKing's College LondonUniversidad del AtlánticoSingapore Management UniversityYork UniversityFlorida Atlantic UniversityArizona State UniversityMcGill UniversityQueensland University of TechnologyUniversiteit MaastrichtFlorida State UniversityOklahoma State UniversityCardiff UniversityState University of New York
KeywordsBusinessIndustrial organizationTechnology managementKnowledge managementMarketingComputer science

Abstract

fetched live from OpenAlex

ABSTRACT This special issue—focused on the transformative impact of emerging technologies (e.g., artificial intelligence, Internet of Things, service robots, blockchain) on innovation management—identifies the ways that firms are rethinking how they generate ideas, develop products, and engage with stakeholders across organizational and societal levels. The articles in this special issue span multiple domains, including ride‐sharing, smart homes, health care, and digital communication. In addition, they demonstrate how new methods are reshaping new product development (NPD) processes. These contributions highlight how emerging technologies enable novel ideation through tools like large language models and topic modeling, as well as how they support agile, data‐informed NPD and dynamic commercialization strategies. Leveraging these insights, this editorial proposes a conceptual framework that encompasses a simplified, three‐step NPD process (ideation, development, and commercialization), along with key drivers of innovation and their implications for individuals, firms, society, and academia.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.018
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.020
GPT teacher head0.341
Teacher spread0.321 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2025
Admission routes1
Has abstractyes

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