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Record W2598160996 · doi:10.1002/sej.1252

Employee‐based Innovation in Organizations: <scp>O</scp> vercoming Strategic Risks from Opportunism and Governance

2017· article· en· W2598160996 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStrategic Entrepreneurship Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsOpportunismIncentiveCorporate governanceBusinessValue (mathematics)Quality (philosophy)Industrial organizationFlexibility (engineering)MarketingSalientEconomicsMicroeconomicsFinanceManagement

Abstract

fetched live from OpenAlex

Research summary E mployees performing everyday tasks frequently acquire valuable new ideas and knowledge. Our formal analysis studies how organizations can benefit from employee‐driven innovation by using incentives to overcome strategic risk from opportunism and governance. We use a game theory framework to analyze the strategic interactions involved and identify incentives under which valuable ideas will be revealed without appropriation (in equilibrium). Our analysis considers both the short run and the long run, where governance can be adjusted to maximize expected future innovation profits. Innovation value, frequency, governance quality, and employee contestation costs are shown to play a salient role in determining the innovation incentives and equilibrium. Overall, our analysis and results provide a number of insights on how organizations can overcome frictions from strategic innovation risks to more fully mobilize their innovation potential and knowledge‐based resources. Managerial summary I dea theft can occur in organizations when employees find it beneficial to present a valuable idea of another employee as theirs. If employees engaged in everyday tasks believe this will likely happen or that they will not be rewarded enough, they may not reveal them. We analyze the design of appropriate innovation rewards that will prevent stealing of innovative ideas and allow organizations to capture value from employee‐driven innovation. We show that governance quality, innovation value, and costs related to contestation play a salient role in determining appropriate innovation rewards and the organization's innovation capacity. “Flatter” organizations can deter idea theft more effectively and need to pay lower innovation rewards. In the long term, we show that all organizations can become more innovative by adjusting their governance quality and reducing employee contestation costs. Further, the ones with higher innovation potential and contestation costs will move toward higher quality governance and seek more entrepreneurial employees, as this raises long‐run innovation profits. Copyright © 2017 Strategic Management Society.

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.

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 categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
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
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
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 head0.161
GPT teacher head0.369
Teacher spread0.208 · 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