Abstract: As artificial intelligence is progressively applied and deployed in enterprises, a growing number of work teams have moved away from traditional goal-setting methods and are attempting to stretch extreme goals to stimulate employee and team creativity within AI-enabled environments. However, the formation mechanism of stretch goals under AI empowerment, as well as their impact on creativity, has yet to be systematically explored and validated. To address this research gap, this paper develops an integrated research framework that systematically elucidates the driving mechanisms and effects of stretch goal setting from an AI empowerment perspective.
The core innovation of this study is to unpack the formation mechanism of stretch goals in the digital intelligence era. Prior studies led by Sitkin identified firm resource constraints and past performance as major predictors of stretch goal setting based on pre-2011 contexts, yet such formation mechanisms have changed dramatically amid digital and intelligent transformation. Managers now recognize that AI integration drives industrial competition from incremental improvement to disruptive innovation, allowing competitors to break traditional limits via AI and gain competitive advantages. In such a competitive environment, merely setting challenging goals is insufficient for firms to cope with growing market pressure. Instead, stretch goals help organizations break traditional thinking and optimize technology and resource allocation, which is essential for organizational survival and development in the AI age. Despite its practical significance, extant literature has largely overlooked how AI technology adoption affects firms’ stretch goal decisions. Against this theoretical and practical gap, this study anchors its analysis in the digital intelligence context. We argue that team-level AI adoption significantly motivates team leaders to prioritize stretch goals over regular challenging goals, and further verify that team leaders’ innovation expectation acts as a critical mediating variable underlying this causal linkage.
Secondly, this paper is the first to propose the empowering role of AI technology in the process through which stretch goals influence individual and team creativity. Existing research on stretch goals has focused only on the boundary effects of “social” factors—such as individual characteristics, individual strategies, work climate, and communication styles—while overlooking how AI can empower employees in pursuing stretch goals. This paper argues that although stretch goals trigger employees’ willingness to explore (i.e., “want to explore”), the technological system determines the extent to which employees are able to explore (i.e., “can explore”). Once a team adopts AI technology, both the perceived usefulness of AI and the human-AI role clarity influence the progression of stretch goal pursuit. Accordingly, drawing on socio‑technical systems theory, Study 2 proposes that perceived AI usefulness plays a significant moderating role in the process through which stretch goals affect individual creativity via employees’ exploratory learning. Study 3 identifies human-AI role clarity as a key contextual variable that shapes how stretch goals facilitate human‑AI collaboration and thereby enhance team creativity.
Finally, this study adopts a multi-level research approach to investigate the consequences of stretch goals. Although existing research has conceptualized stretch goals as a team-level construct, it has only examined their impact on unethical team behavior. How and under what conditions stretch goals foster team creativity in the context of AI empowerment remains largely unexplored. After setting team-level stretch goals, leaders typically communicate the content and requirements of these goals to team members through formal task allocation, team meetings, performance feedback, and other similar channels. In this process, on the one hand, members form individual-level perceptions of stretch goals based on their own understanding and the information received; on the other hand, interactions and sense-making among team members lead to an emergent shared cognition of stretch goals at the team level. Therefore, this study examines the effects of stretch goals on individual creativity and team creativity, as well as the underlying mediating mechanisms, from both individual and team perspectives, thereby contributing to research on stretch goals and creativity.
In summary, this study makes three major theoretical contributions. First, it reveals the formation mechanism of stretch goals in the digital intelligence era, clarifying how team AI adoption and perceived AI intelligence level influence stretch goal setting. Second, it goes beyond the boundary effects of “social” factors emphasized in prior goal research, and proposes the enabling role of AI as a “technological” factor in the process through which stretch goals enhance employee and team creativity, thus advancing stretch goal research. Third, it adopts a multi-level research approach to examine the consequences of stretch goals, investigating their effects on individual and team creativity from both levels, and reveals the mediating role of human‑AI collaboration in the relationship between stretch goals and team creativity. This not only fills the research gap concerning the link between stretch goals and team creativity but also promotes empirical research on human‑AI collaboration.

Key words: stretch goal, artificial intelligence, human-AI cooperation, creativity