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Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

Job compensable factors and factor weights derived from job analysis data.

Chia-Fen Chi¹, Tin-Chang Chang, Ping-Ling Hsia

¹Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan. Chris@mail.ntust.edu.tw

Perceptual and Motor Skills

|September 21, 2007

Summary

Job analysis in Taiwan revealed that specific job attributes significantly predict wages. Key factors like education, experience, and working conditions explain a portion of pay variance, highlighting areas for improved compensation models.

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Last Updated: Jul 11, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

Area of Science:

Human Resources
Labor Economics
Occupational Psychology

Background:

Understanding the determinants of wages is crucial for fair compensation and labor market analysis.
Previous studies have explored various job attributes influencing pay, but comprehensive analyses across diverse job titles are needed.

Purpose of the Study:

To investigate the relationship between job attributes and compensation for 1,039 job titles in Taiwan.
To identify key predictors of monthly wages using statistical analysis of job analysis data.

Main Methods:

Analysis of government data for 1,039 job titles, coding 79 variables across six classes.
Factor and multiple regression analysis were applied to 23 variables significantly related to pay.
A 4-factor solution and a 9-item multiple regression model were developed to predict monthly wages.

Main Results:

A 4-factor solution (occupational knowledge, human relations, work schedule hardships, physical hardships) explained 43.8% of variance but predicted only 23.7% of pay.
Multiple regression using 9 job analysis items (education, experience, certifications, leadership, shift work) better predicted pay, explaining 32.5% of variance.
Current job analysis data did not measure mental effort and responsibility (accountability).

Conclusions:

Job attributes like education, experience, and working conditions are significant predictors of wages in Taiwan.
Existing job analysis data may not fully capture all factors influencing compensation, such as mental effort.
Further cross-validation of job evaluation factors with wage rates is recommended for accurate calibration.