A Telecommuting Experience Service Design Decision Model Based on BP Neural Network.

Area of Science:

• Management Science
• Artificial Intelligence
• Organizational Psychology

Background:

• The COVID-19 pandemic significantly impacted telecommuting experience and job performance.
• Ensuring job performance stability for remote employees is a critical concern for organizations.

Purpose of the Study:

• To construct a decision model for telecommuting experience service design.
• To provide a theoretical basis for evaluating telework performance and employee psychological health.

Main Methods:

• Analytic Hierarchy Process (AHP) for stakeholder identification.
• Grey Relational Analysis (GRA) and NASA Task Load Index (NASA-TLX) for factor measurement.
• Backpropagation (BP) neural network for predictive modeling of telecommuting experience.

Main Results:

• A service system map was created based on model predictions to evaluate telework performance enhancement.
• Diverse factors influence telecommuting, with emotions being dominant.
• Positive correlations exist between emotional impact, temporal perception, execution difficulty, and communication barriers.

Conclusions:

• The decision model accurately predicts telecommuting efficiency.
• The model offers an effective approach for innovative remote management strategies.
• Understanding emotional impact is key to optimizing the telecommuting experience.