The dissertation explores how digital labor platforms, particularly crowdworking environments, can be improved by incorporating Persuasive System Design (PSD) elements to address challenges faced by crowdworkers. Crowdworking, characterized by flexibility and autonomy, often lacks the social interactions and acknowledgment typical in traditional work environments, leading to feelings of isolation, demotivation, and decreased work quality.
To mitigate these negative effects, the dissertation investigates how gamification and conversational agents (CA) can influence motivation and task performance in digital labor environments. Specifically, it focuses on how these design elements can promote well-being, improve motivation, and enhance task-related behaviors among workers by analyzing their pro-task and pro-user effects.
Findings:
Conversational agents create a sense of social presence, helping to alleviate isolation, but can also induce performance pressure.
Gamification elements like badges and leaderboards can improve task performance but need careful implementation to avoid overperformance.
A well-balanced integration of pro-task and pro-user elements is essential to improve worker satisfaction and retain platform performance.
The Acknowledgment-Matching Framework developed in the dissertation helps understand the effects of match/mismatch of the internal performance perception with the external acknowledgments concerning individuals behavior and cognition.
Conclusion:
The dissertation concludes that designing digital work environments with well-integrated PSD elements, such as gamification and CAs, can significantly improve motivation, well-being, and performance among crowdworkers. The work provides insights for scholars and practitioners to design digital labor platforms that promote positive psychological and behavioral outcomes while minimizing negative effects like isolation and demotivation.
