Towards Better Social Interaction: The Potential for the Design and Usage of IT Artifacts on Improving Performance Efficiency, and Equity of Social Interaction

The advancement of information and communication technology (ICT) has significantly transformed how people communicate and obtain information in both their personal and professional lives. In the realm of digitally-facilitated social interaction, this dissertation calls for attention to the importance of information technology artifacts (IT artifacts) on social welfare by showcasing that the careful design and usage of IT artifacts have the potential to enhance the effectiveness, efficiency, and fairness of social interactions. The first study centers around addressing the cold-start issue that often arises when new products are introduced. Specifically, I investigate how machine-generated content can enhance the equity of new products. Analyzing data from Kaggle.com, my research demonstrates that the use of machine-generated content is effective in tackling the cold-start problem by increasing the adoption of the product in the initial phase. Additionally, my findings reveal that machine-generated content can also reduce information asymmetry for users regarding the datasets or associated providers. As a result, these outcomes provide strong evidence supporting the use of machine-generated content to enhance equity in online communities. The second research investigates the impact of a platform’s decision to impose application fees on enhancing the quality of matching results in an online labor market. Based on data obtained from Freelancer.com, my analysis demonstrates that the implementation of application costs serves as a motivator for workers to submit fewer but more selective bids. This, in turn, increases the likelihood of employers offering contracts, as workers are less likely to apply casually or without much thought. Overall, these results indicate that application costs can enhance the efficiency of the matching process. In the third study, I examine whether gender differences exist in telework adjustment as a response to disasters and to what extent such adjustments can help reduce gender inequality, using the COVID-19 pandemic as an example. The study's findings reveal the following: 1) Female workers exhibit a higher rate of telework adjustment than their male counterparts by 7% after accounting for differences in job sorting, and female workers are more responsive to external constraints. 2) Telework adjustment can help mitigate gender inequality in the labor market.