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Record W2010983673 · doi:10.1177/1548051813515513

Organizational Psychological Capital of Family Franchise Firms Through the Lens of the Leader–Member Exchange Theory

2013· article· en· W2010983673 on OpenAlexaff
Esra Memili, Dianne H.B. Welsh, Eugène Kaciak

Bibliographic record

VenueJournal of Leadership & Organizational Studies · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsBrock University
Fundersnot available
KeywordsFranchiseTransgenerational epigeneticsPositive psychological capitalBusinessFamily businessCapital (architecture)ObligationPsychologySocial psychologyMarketingPolitical scienceLaw

Abstract

fetched live from OpenAlex

We explore organizational psychological capital (PsyCap) of family franchise firms by drawing on PsyCap and leader–member exchange (LMX) theories and family business literature. We suggest that unique family firm LMXs characterized by trust, respect, and obligation can foster organizational PsyCap of family franchise firms, in turn affecting their innovativeness. We also suggest that transgenerational succession intentions moderate the impact of the LMXs on the development of organizational PsyCap of family franchise firms as well as the consequent effects on innovativeness. We supplement these theoretical conjectures with two exploratory analyses based on survey data—a stepwise regression and correlational analysis. We also discuss implications for future research and practice.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.115
GPT teacher head0.284
Teacher spread0.169 · 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 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

Citations41
Published2013
Admission routes1
Has abstractyes

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