The effect of font boldness, noise disturbance and time pressure on human error in the context of cloud change operation

Abstract

This study investigated the number of operator errors, task completion time, and workload of subjects at different levels by imposing conditions such as focused text boldness, noise disturbance, and time pressure to simulate a realistic cloud change business process in the laboratory. Results of the study showed that the text bolding of important content reduced the number of errors, whereas noise interference increased the number of errors. Text boldness only reduced the number of corrected errors, and noise interference only increased the number of uncorrected errors. Moreover, bolding was found to have different effects on the number of errors under different noise levels and time pressure levels, with text boldness significantly reducing the number of total errors only in quiet or low time pressure states. Time pressure had no effect on cloud change task error counts, but high time pressure resulted in higher subjective workload.

Practitioner Summary

Operator error is one of the main causes of service failure, and reducing operator error in cloud change operations is of practical importance. In this study, we found focused text boldness could reduce operator errors, while noise could increase the number of errors. High time pressure would lead to a high workload.

Keywords:

• Human error
• workload
• cloud change operation
• text boldness
• noise

Acknowledgements

We appreciate the support from the National Natural Science Foundation of China with the Main Research Project on Machine Behavior and Human-Machine Collaborated Decision Making Methodology (Project Number: 72192820), and the Second Research Project on Human Behavior in Human-Machine Collaboration (Project Number: 72192824).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China with the Main Research Project on Machine Behaviour and Human-Machine Collaborated Decision Making Methodology (Project Number: 72192820), and the Second Research Project on Human Behaviour in Human-Machine Collaboration (Project Number: 72192824).