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Record W2565566430 · doi:10.1111/jpim.12362

Configurations of Innovations across Domains: An Organizational Ambidexterity View

2016· article· en· W2565566430 on OpenAlexaff
Zhang Feng, Yonggui Wang, Dahui Li, Victor Cui

Bibliographic record

VenueJournal of Product Innovation Management · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsAmbidexterityIndustrial organizationBusinessBalance (ability)Service (business)MarketingKnowledge managementComputer science

Abstract

fetched live from OpenAlex

How do firms balance explorative and exploitative innovation for superior firm performance? While most prior studies have approached this issue by focusing on technology‐related innovation, the role of balancing exploration and exploitation in other important organizational domains, i.e., marketing, and the interaction effect of ambidexterity across different domains have been overlooked. This study contributes to this line of research by investigating how firms simultaneously balance exploration and exploitation across two critical domains, namely technology innovation and market innovation. The study distinguishes four types of configurations: market leveraging (technology exploration and market exploitation), technology leveraging (technology exploitation and market exploration), pure exploitation (technology exploitation and market exploitation), and pure exploration (technology exploration and market exploration). From an organizational ambidexterity perspective, the current work investigates whether and how these different combinations exert distinctive effects on firm performance. Specifically, the article posits that (a) technology exploration and market exploitation complement each other, and (b) technology exploitation and market exploration also complement each other, such that both market leveraging and technology leveraging strategies have positive effects on firm performance. The article also maintains that such positive relationships are fully mediated by differentiation and low cost advantages. Conversely, it is argued that (c) technology exploration and market exploration conflict with each other, and (d) so do technology exploitation and market exploitation, such that both pure exploration and pure exploitation have negative effects on firm performance. Hypotheses were tested using survey data collected from 292 manufacturing and service firms in China. The results supported most of the hypotheses, except that pure exploration demonstrated no significant relationship with firm performance.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0010.008
Scholarly communication0.0060.005
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.286
Teacher spread0.258 · 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 designNot applicable
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

Citations70
Published2016
Admission routes1
Has abstractyes

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