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Record W3038303692 · doi:10.3168/jds.2019-18025

Effects of employer management on employee recruitment, satisfaction, engagement, and retention on large US dairy farms

2020· article· en· W3038303692 on OpenAlexaff
Stanley J. Moore, Phillip T. Durst, Caroline Ritter, Diego B. Nóbrega, Herman W. Barkema

Bibliographic record

VenueJournal of Dairy Science · 2020
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Calgary
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsJob satisfactionEmployee engagementEmployee retentionBusinessTurnoverEmployee resource groupsHuman resource managementMarketingPerceptionWork (physics)Retention ManagementPerformance appraisalPsychologyEmployee researchPublic relationsManagementSocial psychologyEconomicsPolitical science

Abstract

fetched live from OpenAlex

Lack of employee engagement and satisfaction and high turnover rate of employees are major problems on some US dairy farms. Farm adoption of human resource management (HRM) practices varies widely. Using feedback from 168 employees from 12 large dairy farms, our objectives were to understand employee perceptions of HRM strengths and weaknesses and their influence on (1) employee satisfaction, (2) retention of employees, (3) willingness of employees to recommend the farm as a place to work, and (4) level of employee engagement on the farm. Employees who rated their employers higher for almost any management-related question (MRQ) were more satisfied in their jobs and more likely to recommend their place of work to other potential employees. Employees reported a higher intention to stay on the farm when employers scored higher on MRQs connected to employer-employee relationships. Employees who rated "Relationships" (a component consisting of 5 MRQs) higher were more likely to have a higher rate of satisfaction, were more likely to intend to stay at their job, and were more likely to recommend their place of work to others. No single management question was positively related to employee engagement (as measured by employees having and sharing ideas to improve the business); however, ethnicity, gender, job role, duration of employment, and employee's self-appraisal of their desire to learn and commitment to the farm were each associated with engagement. Female employees were less likely to provide ideas to their employers (compared with male employees), as were Spanish-speaking employees (compared with English-speaking employees). Differences between Spanish- and English-speaking employees were also present in measures such as intention to stay (shorter) and willingness to recommend the farm as a place to work (higher). Employees who rated themselves higher on their desire to learn and commitment to the farm were more likely to provide ideas to their employers, as were longer-term employees. In conclusion, we inferred that dairy farm management can improve employee retention and engagement through improved use of HRM practices.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.116
GPT teacher head0.356
Teacher spread0.239 · 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

Citations40
Published2020
Admission routes1
Has abstractyes

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