Abstract:

With the rapid development of artificial intelligence (AI), AI coaching is becoming widely used in intelligent education, smart fitness, and other fields. Given this backdrop, many new service forms have emerged, such as AI teachers and coaches. However, how do consumers’ attitudes towards AI coaching differ from those toward human guidance? Is AI coaching effective in helping consumers achieve long-term goals? What are the differences in consumers’ evaluations of AI coaching? This project explores the impact of AI coaching on consumers’ goal-seeking behavior from the dynamic perspective of goal management by combining the characteristics of AI coaching in terms of operability, feedback, and emotionality. This study was conducted in three stages: preselection, promotion, and evaluation.

First, in the preselection stage, when the goal is set for the near (vs. distant) future, consumers choose AI coaching (vs. human guidance) more often. At this stage, consumers have a more concrete (vs. abstract) mindset, leading them to prefer the highly methodical and diverse characteristics of AI coaching. Additionally, the higher the degree of the coaching AI’s anthropomorphism, the weaker the difference in consumers’ preferences for using AI coaching (vs. human guidance) when they set goals for the near (vs. distant) future.

Second, in the promotion stage, the effect of AI coaching (vs. human guidance) on improving consumers’ skills is modulated by their skill levels. Specifically, for consumers with higher level skills, more constructive criticism is needed. However, because of the limited ability of AI coaching to provide detailed negative feedback, highly skilled consumers cannot receive beneficial guidance for their shortcomings, resulting in lower performance improvement effects from AI coaching (vs. human guidance). However, for consumers with lower skill levels, there is no significant difference in the performance improvement effect between AI and human coaching on consumers’ goal pursuit. This is because when consumers' skills are low, they need more positive feedback, and both AI and human coaches can provide timely positive feedback to encourage consumers to continue pursuing their goals.

Third, in the evaluation stage, after achieving the goal, consumers are less likely to share positive word-of-mouth evaluations about AI coaching compared to humans when the training guidance is successfully completed. This is because consumers have lower expectations for positive emotional returns from AI coaching than they do from human guidance. Thus, it is not worthwhile for these consumers to make recommendations through word-of-mouth or engage in other prosocial behaviors. Conversely, when the training guidance is unsuccessful, consumers are more likely to share negative word-of-mouth evaluations about AI coaching than of human guidance. This is because consumers have lower anticipated guilt when providing negative evaluations for AI coaching compared to human coaching.

Overall, this study overcomes the bottlenecks of previous studies from three perspectives to conduct a theoretical construction. First, it focuses on previous studies’ one-time contact AI service scenarios and shifts attention to the impact of long-term cooperative relationships with AI coaching. Second, we overcome the simple passive contact scenarios between consumers and AI that have been explored in previous studies and explore consumer learning scenarios in which consumers learn specific skills from AI coaching. In addition, unlike existing research on AI coaching, which focuses on interactions with service providers, this project explores scenarios in which AI coaches interact directly with end consumers. Finally, in contrast to previous studies that mainly concentrated on the negative impacts of AI, we focused on the positive impacts of AI coaching on consumer well-being. Based on the dynamic perspective of goal management, this study explores how AI acts as a guide to help consumers pursue long-term goals in long-term cooperative relationships. Our anticipated findings will help companies achieve a win-win situation in technology implementation and commercial realization, effectively promoting the deep layout and rapid development of artificial intelligence in the intelligent education, smart fitness, and healthcare fields.

Key words: service technology, artificial intelligence, coach, consumer behavior, goal regulation