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The Conceptualization, Measurement, And Influence of a Millennial Career Mindset

2019· article· en· W2965805745 on OpenAlexaffabout
Vanessa Shum, Christopher D. Zatzick, Bin Zhao

Bibliographic record

VenueAcademy of Management Proceedings · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMindsetConceptualizationEmployabilityPsychologyJob satisfactionWorkforceSocial psychologyCareer developmentContext (archaeology)Organizational commitmentPublic relationsPolitical science

Abstract

fetched live from OpenAlex

This study extends research on generational differences beyond stereotypes and demographic differences to the development of a mindset towards careers. Because Millennials grew up in a labor market with less job stability, more contingent work, and a rapidly changing global marketplace, we theorize the development of a multidimensional millennial career mindset (MCM) that encompasses the underlying implicit theories and beliefs that emerge from the millennial context. Specifically, we focus on four dimensions of the MCM: job-hopping norm, self-directed career development, social media embracement, and perceived employability. While all participants in the modern workforce are shaped by the context, we expect the MCM will be stronger for Millennials than non-Millennials. We also hypothesize that the relationship between job satisfaction and withdrawal intentions will be weaker for those individuals possessing a stronger MCM. We collected data from 115 employees at a Canadian organization and found that a MCM moderated the relationships between job satisfaction and organizational commitment and turnover intentions. The findings suggest that job satisfaction is less pertinent to influencing the stay intentions of employees with a strong MCM.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.232

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 head0.030
GPT teacher head0.254
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

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

Citations0
Published2019
Admission routes2
Has abstractyes

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