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Cross-Cultural Issues in Industrial, Work, and Organizational Psychology

2022· reference-entry· en· W4297899711 on OpenAlexaff
Sharon Glazer, Catherine T. Kwantes

Bibliographic record

VenueOxford Research Encyclopedia of Psychology · 2022
Typereference-entry
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsOrganizational cultureMultinational corporationPublic relationsSociologyIndustrial and organizational psychologyGlobalizationPolitical scienceSocial psychologyPsychology

Abstract

fetched live from OpenAlex

Abstract For the first half century of industrial, work, and organizational psychology’s (IWOP’s) existence, the role of culture has been ambiguous at best. Attention to culture in IWOP started to take form in the 1970s, and in the last 20 years culture has begun to be integrated into IWOP research more generally. This integration has led to explorations of culture in the workplace far beyond the initial focus on organizational management theories and practices to looking at cultures’ relationships with all aspects of organizational and employee experiences. Most cross-cultural studies in IWOP research clearly show the importance of understanding societal culture’s impact on organizations, institutions, work, and workers. Merely transferring existing theories and practices of IWOP to new contexts without a clear understanding of whether those theories and practices make sense across cultures is unlikely to be successful. Through globalization and the multiculturalization of workforces, employees are increasingly interacting with people of varying cultural backgrounds on a far more regular basis than in the past. This change has spurred attention to how culture has impacted theories, research, and practices in some key IWOP theoretical domains, including leadership; occupational safety, stress, and health; precarious and decent work; trust and trustworthiness; and diversity, equity, and inclusion in the multinational organization.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.648
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0270.000

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 head0.096
GPT teacher head0.416
Teacher spread0.320 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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