Train controller interface design: factors influencing mental workload

1999 ◽  
Author(s):  
W.A. MacDonald
Keyword(s):  
Interface Design ◽  
Mental Workload ◽  
Factors Influencing ◽  
Design Factors

Localized User Interface for Improving Cell Phone Users' Device Competency

2010 ◽  
pp. 129-143 ◽  
Author(s):  
D. Krisnawati Lucia ◽  
Restyandito
Keyword(s):  
User Interface ◽  
Interface Design ◽  
Cell Phone ◽  
Procedural Knowledge ◽  
User Interface Design ◽  
Rote Learning ◽  
Usability Tests ◽  
Factors Influencing ◽  
Design Culture ◽  
A Cell

This study tried to examine how cell phone users who undergo a technology leap acquire their procedural knowledge of operating a cell phone and to find out which factors can improve their device competency. Using interviews, usability tests, and a questionnaire, this study found out that many respondents use unstructured means such as asking other cell phone users or rote learning in gaining their procedural knowledge. Some factors influencing users’ device competencies that were found in this study are classified into three categories: user interface design, culture, and the users themselves. In order to improve users’ device competency, elements in those three categories must be integrated. One realization of such integration is the attempt of localizing user interface through the user’s culture, not the culture where the cell phone is designed and manufactured.

scholarly journals Work Performance Measurement of Data Entry Employees in E-Commerce Industry Based on Mental Workload Value

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Iwan Aang Soenandi
Keyword(s):  
Work Performance ◽  
Mental Workload ◽  
Data Entry ◽  
Time Measurement ◽  
Task Load ◽  
Factors Influencing ◽  
Before And After ◽  
Reaction Time Measurement ◽  
Nasa Tlx ◽  
Electroencephalogram Eeg

This reseach aimed to measure the mental workload of data entry processing tasks in the e-commerce industry based on mental workload value. It was to determine the factors influencing mental workload mainly induced by the data entry process. The experiments without work instruction and with two types of work instruction were conducted to diagnose the mental workload. The measurement of the initial mental workload condition of data entry employees was conducted in the laboratory. Then, the Electroencephalogram (EEG) measurement using sensors from Emotiv was performed every 30 minutes, and the data of EEG measurements (focus, engagement, and stress) were collected using the laptop. Meanwhile, pulse measurement (heart rate) was measured before and after the work. Raw National Aeronautics and Space Administration Task Load Index (NASA-TLX) and reaction time measurement were conducted after the work. Through these experiments, the researchers identify that mental effort and fatigue are the significant determinants of mental workload value in the data entry process of the e-commerce industry. In respect of the results of work performance analysis, it is recommended that the placement of work instruction should be near the employee. Then, the task demand (minimum completion target) should be adjusted according to each employee’s capacity.

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