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Record W1964312016 · doi:10.1016/j.jom.2006.05.011

Process innovativeness in technology services organizations: Roles of differentiation strategy, operational autonomy and risk‐taking propensity

2006· article· en· W1964312016 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

VenueJournal of Operations Management · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsAutonomyBusinessProcess (computing)MarketingIndustrial organizationComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Abstract This paper examines the effect of differentiation strategy on process innovativeness in technology services organization (TSOs). In addition it examines the direct and moderating effects of two organizational constructs— operational autonomy and risk‐taking propensity . Analysis of data from 102 firms in the mid‐Atlantic region of the USA indicates that both differentiation strategy and operational autonomy are positively related with process innovativeness, while an organization's risk‐taking propensity has no such relationship. In addition, operational autonomy moderates the relationship between differentiation strategy and process innovativeness, while no evidence was found for the moderating effect of risk‐taking propensity on this relationship. Further sub‐group analysis shows that in TSOs with high levels of operational autonomy, risk‐taking propensity has a positive moderating effect on the above relationship. Post hoc analysis also establishes positive links among process innovativeness and 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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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