MétaCan
Menu
Back to cohort
Record W4399479636 · doi:10.2308/jmar-2023-041

Navigating Unprecedented Times: How Managers’ Empathetic Adjustments in a Crisis Influence Employee Effort in a Competitive Environment

2024· article· en· W4399479636 on OpenAlex
L. L. Berger, Lan Guo, Sara Wick

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Management Accounting Research · 2024
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsBusinessCompetitive advantageMarketingIndustrial organizationPublic relationsMicroeconomicsPsychologyEconomicsPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT When organizational crises arise, one way that managers can help employees cope is to provide empathetic adjustments, where managers adjust downward performance expectations for all employees while communicating the adjustment with empathy. In a competitive environment, we explore whether providing an empathetic adjustment to employees during a crisis affects their postcrisis effort. We conduct an experiment and observe that an empathetic adjustment significantly improves the postcrisis effort of top and bottom performers. The increase in postcrisis effort of top performers can be attributed to the effect of the adjustment, whereas the increase in postcrisis effort of bottom performers can be attributed to the effect of empathy. In a supplemental survey, we find a range of positive effects of empathetic adjustment, including increased engagement, reduced burnout, and lower turnover intentions. Data Availability: Data are available from the authors upon request. JEL Classifications: G31; G32; G33; M21.

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.

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)0.005
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 score0.093
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.050
GPT teacher head0.411
Teacher spread0.362 · 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