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Record W2093771625 · doi:10.5539/ibr.v7n9p119

How Employees’ Loyalty Programs Impact Organizational Performance within Jordanian Banks?

2014· article· en· W2093771625 on OpenAlexvenueno aff
Wasfi Alrawabdeh

Bibliographic record

VenueInternational Business Research · 2014
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsLoyaltyBusinessMarketingTest (biology)Sample (material)Performance appraisalProfit (economics)Training and developmentManagementEconomics

Abstract

fetched live from OpenAlex

This study aimed at examining the factors that influence and impact employees’ performance levels within Jordanian Banks. This study is based on quantitative research to test the research hypotheses and reveal the important factors that impact loyalty programs. A convenient sample of 377 employees was selected to represent the population which includes all employees working in the banking sector in Jordan. Based on previous studies and literature, a framework was developed to test the loyalty factors against organization performance. Loyalty factors involve employees’ satisfaction, financial rewards, motivation, performance appraisal, employees’ training and development, employees’ empowerment, internal communication, and work environment. Organizational performance indicators include sales, market share, profit, demand, decision making efficiency, and customers’ satisfaction level. Results of the study revealed that only six factors have significant relationships between loyalty levels and employees' performance. These include financial rewards, employees’ satisfaction, motivation, performance appraisal, internal communication, and employees’ training and development. Financial rewards that involve salaries, bonuses, and commission are the most important factor.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
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 head0.037
GPT teacher head0.312
Teacher spread0.275 · 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 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

Citations10
Published2014
Admission routes1
Has abstractyes

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