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Record W3124217757

Reputation, Diversification and Organizational Explanations of Performance in Professional Service Firms

2004· article· en· W3124217757 on OpenAlexaff
Royston Greenwood, Stan Xiao Li, Rajshree Prakash, David L. Deephouse

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsYork UniversityUniversity of Alberta
Fundersnot available
KeywordsDiversification (marketing strategy)ReputationBusinessProfessional servicesWorkforceCompetitive advantageMarketingService (business)Industrial organizationPublic relationsEconomics
DOInot available

Abstract

fetched live from OpenAlex

Growing interest in knowledge as a competitive asset suggests the benefit of studying professional service firms (PSFs). These firms are highly successful examples of organizations whose ability to manage knowledge is critical to their success. Furthermore, they are worthy of study because they constitute a significant sector of the economy, whether measured by their size, numbers, or influence. Despite their significance, little is known of the determinants of their performance. This paper proposes that the core tasks of PSFs raise unusual strategic and organizational challenges, the resolution of which affects organizational performance. We elaborate the effects of reputation and diversification and contrast them to theory for goods-producing industries. We also hypothesize that PSF managers face a choice in designing structures between the retention and motivation of the professional workforce and transferring knowledge from partners to other professionals. These predictions are tested and supported by data from the largest 100 U.S. accounting firms for the period 1991–2000. The paper thus contributes to a theory of professional service firm management.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.196
Teacher spread0.187 · 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 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

Citations16
Published2004
Admission routes1
Has abstractyes

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