Dimensions of Compensation as Predictors of Tertiary Educators' Performance

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{"abstract":"This study investigated the impact of compensation dimensions on the work performance of tertiary educators at a private university in Jiangxi Province, China. Using a quantitative descriptive–predictive design, data were collected from 385 educators through a structured questionnaire measuring demographic characteristics, extrinsic compensation (salary, incentives, rewards, insurance), intrinsic compensation (career advancement, task importance, job autonomy), and performance dimensions (instruction, scientific research, social service). Statistical analyses, including descriptive statistics, correlation, and multiple linear regression, were conducted using SPSS. Results revealed significant demographic differences and a strong relationship between compensation and performance indicators. Satisfaction with extrinsic compensation increased with age, educational attainment, and income, while intrinsic compensation correlated more with academic rank. Incentives exhibited the highest correlation with scientific research performance (r = 0.72), whereas task importance was most associated with instructional performance (r = 0.64). Regression results indicated that intrinsic compensation had stronger predictive power than extrinsic compensation, with career advancement emerging as the most influential positive predictor of research and social service outcomes. The study concludes that educators’ performance is primarily driven by intrinsic motivational factors, suggesting the need for a composite incentive system that balances financial and developmental rewards. The findings contribute to the improvement of faculty performance management and compensation frameworks in applied private universities.","abstract_source":"original","authors":["Weiwei Li","Althea Denuevo"],"date":"2025-11-19","doi":"10.70838/pemj.490310","id":6726,"issue":3,"journal":{"issn":"2822-4353","name":"Psychology and Education: A Multidisciplinary Journal"},"pdf_sha256":"23fbbc17045a1e0fb0efd316cbe1e49e713132acdbec00ec6006e696aaa85e43","schema":"scimatic-article/1","title":"Dimensions of Compensation as Predictors of Tertiary Educators' Performance","volume":49}

Check it yourself

1. The record, from the token's own metadata:

printf '%s' "$(curl -s https://scimatic.org/nft/articles/6726.json | jq -r .canonical_record)" | sha256sum

2. The hash stored on chain (contentHash(6726)):

curl -s https://rpc.scimatic.net -H 'content-type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"eth_call","params":[{"to":"0x0E596B4bb924937aA35D9b342A90347ed6F05eF8","data":"0xf3e0c2900000000000000000000000000000000000000000000000000000000000001a46"},"latest"]}'

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