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Record W2336799315 · doi:10.20472/iac.2016.021.025

INFLUENCES ON EMPLOYEE REWARD STRATEGIES IN INTERNATIONAL ORGANIZATIONS

2016· preprint· en· W2336799315 on OpenAlexaff
Carolan McLarney, James T. Hansen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Development and Management Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaslow's hierarchy of needsHofstede's cultural dimensions theoryUncertainty avoidanceIndividualismPsychologySocial psychologyConformityMarketingEmployee motivationCollectivismPublic relationsBusinessPolitical science

Abstract

fetched live from OpenAlex

It is of great importance that organizations seek to have a stable, productive, and motivated workforce.The primary way to accomplish this is through effective reward strategies to compensate employees for their efforts.The challenge for the global organization is to ensure that the rewards offered provide motivation for employees and generate workplace commitment, regardless of location.Three notable influences on reward strategies were summarized, the first being Maslow's Hierarchy of Needs.Maslow stated that all people have the same needs and are motivated to fulfill these needs as they increase in complexity (Maslow, 1943, p. 370).The second influence was Herzberg's two factor theory, which identified two factors that provide motivation for employees, motivators (job growth, advancement) and hygiene factors (policies, compensation) (Herzberg, 1968, p. 56).The final influence studied was culture, which emphasized Hofstede's cultural dimensions: power distance, individualism versus collectivism, masculinity versus femininity, uncertainty avoidance, long term orientation, and indulgence versus restraint (Hofstede, 1994, pp.2-5; Hofstede, Hofstede, & Minkov, 2010, p. 281).The evidence showed that using these influences as indicators along with other factors noted in research, such as organizational goals and demographic employee data, will enable a company to make a more balanced decision with respect to international employee reward strategies.Thus, a variety of factors must be considered when creating or revising reward strategies to ensure that irrespective of location, employees will be motivated by the rewards.Three examples were noted of companies who have faced the challenge of implementing an international reward strategy.Both Colgate-Palmolive and RBC were found to have completed analysis with their reward strategies to ensure their international policies were motivating for staff.Lincoln-Electric was identified as a company who failed in their international reward strategy; they incorrectly assumed the rewards that worked in the U.S. would work overseas, which contributed to losses in their European division and required drastic efforts to correct (Hastings, 1999, p. 171).To support leaders in these decisions, a model for assessing reward strategies in the international environment was presented and discussed.Leaders will find the model useful, as it consolidates the key influences that must be considered when reviewing international reward strategies and can be customized to include additional factors as required.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.254
Teacher spread0.213 · 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 designNot applicable
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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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