The AI Augmented Crowd: How Human Crowdvoters Adopt AI (or Not)

Abstract:

To date, innovation management research on idea evaluation has focused on human experts and crowd evaluators. With recent advances in artificial intelligence (AI), idea evaluation and selection processes need to keep up. As a result, the potential role of AI-enabled systems in idea evaluation has become an important topic in innovation management research and practice. While AI can help overcome human capacity constraints and biases, prior research has identified also aversive behaviors of humans towards AI. However, research has also shown lay people’s appreciation of AI. This study focuses on human crowdvoters’ AI adoption behavior. More precisely, we focus on gig workers, who despite often lacking expert knowledge are frequently engaged in crowdvoting. To investigate crowdvoters’ AI adoption behavior, we conducted a behavioral experimental study (n= 629) with incentive-compatible rewards in a human-AI augmentation scenario. The participants had to predict the success or failure of crowd-generated ideas. In multiple rounds, participants could opt to delegate their decisions to an AI-enabled system or to make their own evaluations. Our findings contribute to the innovation management literature on open innovation, more specifically crowdvoting, by observing how human crowdvoters engage with AI. In addition to showing that the lay status of gig workers does not lead to an appreciation of AI, we identify factors that foster AI adoption in this specific innovation context. We hereby find mixed support for influencing factors previously identified in other contexts, including financial incentives, social incentives, and the provision of information about AI-enabled system’s functionality. A second novel contribution of our empirical study is, however, the fading of crowdvoters’ aversive behavior over time.