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Record W3110140986 · doi:10.1002/hrdq.21414

Breaking the mold: Retention strategies for generations X and Y in a prototypical accounting firm

2020· article· en· W3110140986 on OpenAlexaffabout
Sandra J. Nelson, Linda Duxbury

Bibliographic record

VenueHuman Resource Development Quarterly · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsCarleton University
Fundersnot available
KeywordsWorkforceIdentity (music)General partnershipBusinessWork (physics)AccountingMarketingPublic relationsEconomicsPolitical scienceFinance

Abstract

fetched live from OpenAlex

Abstract The success of Canadian accounting firms depends on their ability to adapt their long‐standing HR structures and HRD practices to meet the expectations of today's workforce. Generational identity theory and theory explaining employee expectations informed our analysis of a multi‐method (interviews and appreciate inquiry workshops) case study which employed an action research approach. Data were collected from 36 Gen X and Y knowledge workers who were part of the firm's talent pool to determine how closely the work and career expectations of today's (i.e., Generation X and Generation Y) accountants align to the prototypical HR structures and practices of professional partnership (P2) accounting firms. Three main contributions arise out of our qualitative analysis. First, it contributes to generational identity theory by providing a comparison between Generation X and Y in terms of their career and work expectations. Second, it demonstrates why firms with traditional practices and structures may be having difficulty retaining talent in today's labor market. Third, it provides evidence as to why prototypical accounting firms need to change and suggestions on how they can change their HR practices and HRD strategies to enhance retention and better meet the expectations of today's workforce.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.245
Teacher spread0.204 · 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

Citations16
Published2020
Admission routes2
Has abstractyes

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