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Record W2981894309 · doi:10.5539/ibr.v12n11p57

Can a Leader’s Positive Evaluation Improve Occupational Satisfaction of Employees? —— Based on the Empirical Investigation of a Real Estate Enterprise G Province Branch

2019· article· en· W2981894309 on OpenAlexvenueno aff
Chengmeng Zhang, Meiqing Leng, Shuting Xu

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEstateJob satisfactionIncentiveWork (physics)MarketingBusiness administrationManagementFinanceEconomicsMicroeconomicsEngineering

Abstract

fetched live from OpenAlex

Employees are the creators of corporate benefits. The level of occupational satisfaction is directly related to the performance of employees and the survival and development of enterprises. Through the analysis of the questionnaire data of 538 employees of the B group G province branch, it is found that the employee's "self-efficacy" is the most affecting the occupational satisfaction of the real estate employees if and only if all the variables work together on the employee occupational satisfaction. There is no significant correlation between "employee and leadership and co-worker relationship", "leading positive evaluation of employees" and occupational satisfaction. The results show that as a new type of enterprise entity, real estate enterprises should pay attention to the incentives for employee performance, continuously promote the management system to be scientific and reasonable, and achieve the organizational recognition of employees and the improvement of occupational satisfaction.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.053
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.377
Teacher spread0.277 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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