Automated Author Profile程进凯
Sichuan University
程进凯
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.7 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Telecommuting has witnessed a remarkable surge in development, propelled by the advent of cutting-edge technologies and the outbreak of the COVID-19 pandemic. Nevertheless, there has been relatively limited research exploring how to motivate employees to voluntarily engage in telecommuting arrangements. Drawing on the social identity approach, this study delves into the influence of artificial intelligence (AI) use at work on an individual’s telecommuting voluntariness. Additionally, by integrating the perspective of flexibility stigma, we examine whether an individual’s perception of flexibility stigma can moderate the relationship above. In a time-lagged field study (Study 1) involving 218 employees in China, the results indicated that AI use at work was positively associated with telecommuting voluntariness through job virtuality. In a scenario experiment (Study 2) with 159 students from a large public university in China, we manipulated the variables of flexibility stigma and job virtuality. The findings supported our hypothesis that for participants with lower perceived flexibility stigma, job virtuality showed a significantly stronger positive relationship with telecommuting voluntariness, whereas for those with higher perceived flexibility stigma, this relationship was negated. Collectively, both studies illustrate that AI use at work can effectively promote telecommuting arrangements, particularly in organizations where the perception of flexibility stigma is relatively low.
Authors
- 程进凯