In an age rapidly shaped by artificial intelligence, understanding how AI influences consumer behavior, especially in health-related choices, remains a pressing scientific endeavor. A recent study spearheaded by researchers Bi, Cui, Sun, and their team delves into the nuanced ways different types of AI—categorized as “thinking AI” and “feeling AI”—impact consumers’ willingness to purchase healthy food. By meticulously examining these dynamics across diverse consumption contexts, the study enriches current discourse on AI’s role as a behavioral nudge and its potential to foster healthier eating habits.
The research pivots on a conceptually sophisticated experimental framework that distinguishes between two fundamental AI archetypes. “Thinking AI” embodies a rational, data-driven agent designed to appeal to consumers’ cognitive faculties, offering logical analyses and evidence-based suggestions. In contrast, “feeling AI” operates through emotional engagement, seeking to elicit affective responses that might sway consumer choices through empathy and affect rather than raw data. The authors hypothesized that these distinct AI modes would differentially influence purchase intentions depending on whether consumers were in public or personal consumption contexts.
To rigorously test these hypotheses, the team employed the WellEat-AI product, which parallels the Nutri-AI system but with slight functional refinements that enhance its applicability. This tool offers a robust platform for recreating interactions between AI and consumers, providing valuable insights into how the dual constructs of “thinking” and “feeling” AI manifest in real-world decision-making scenarios. The methodical approach taken ensures that findings gained from this robustness check maintain high credibility and reproducibility within the research community.
The experiment engaged 109 university students from China, of which 90 participants provided valid data in the final analysis. These students were randomly allocated into two distinct consumption contexts: public and personal. Within these settings, participants interacted with thinking AI and feeling AI to examine how each AI type affected their willingness to purchase healthy food items. This randomized design permitted an unbiased evaluation of causal relationships between AI modalities and consumer intentions, mitigating confounding variables that frequently hamper behavioral research.
Results from a rigorous two-way analysis of variance revealed compelling context-dependent divergences in consumer behavior. Within public consumption contexts, participants exhibited a markedly higher willingness to purchase healthy food when influenced by thinking AI, with a mean willingness score of 5.47 (SD = 0.495). This contrasted starkly with feeling AI’s influence, which yielded a mean of 2.37 (SD = 0.607). Statistically, this difference was highly significant (F(1, 176) = 1418.66, p < 0.001), underscoring the potency of cognitively oriented AI nudges in socially observable settings.
Conversely, in personal consumption contexts—settings likely characterized by greater privacy and less social accountability—the opposite pattern emerged. Participants’ willingness to purchase healthy foods was substantially higher when influenced by feeling AI (M = 5.52, SD = 0.518) compared to thinking AI (M = 2.41, SD = 0.587), with this difference also reaching high statistical significance (F(1, 176) = 1418.66, p < 0.001). This intimate context clearly favored the affectively driven AI, signaling that emotional engagement resonates more deeply on an individual level when social pressures are minimized.
These dichotomous results elucidate an important reality: AI’s efficacy in guiding consumer health choices is not monolithic but contextually mediated. In public forums where social perception plays a critical role, consumers seem more receptive to logical, data-rich AI communications that reinforce health-conscious behaviors. In contrast, when making choices in private, emotional appeals leveraging empathy and affective resonance appear more persuasive. Such findings extend and confirm the central hypothesis, H1, that different AI product types shape purchase intentions in alignment with consumption context.
To deepen the analysis, the researchers explored the underlying psychological mechanisms mediating these effects by employing advanced statistical bootstrap procedures. These analyses aimed to disentangle whether cognitive or affective responses principally accounted for AI’s influence in each context. In public contexts, cognitive responses were found to significantly mediate the effect, with confidence intervals for the mediation effect excluding zero (BootLLCI = −3.0129; BootULCI = −1.7075), while affective mediation was non-significant. This confirms that analytical processing drives AI’s behavioral impact in settings where external perception matters.
In personal consumption contexts, however, the pattern inverted. Affective responses served as significant mediators of AI’s effect on willingness to purchase healthy food (BootLLCI = 2.5228; BootULCI = 3.4507). Cognitive mediation did not reach significance in this scenario (BootLLCI = −0.0845; BootULCI = 0.1497). This highlights how emotional resonance fosters internalization of healthy purchasing decisions when the social gaze is absent, accentuating the distinct psychological pathways through which AI exerts influence.
By untangling the cognitive-affective mediatory roles, the study not only verifies its initial hypotheses but also offers practical implications for the design of AI-driven health nudges. For instance, developers of AI health advisors might strategically tailor the AI’s communication style according to the consumer’s situational context—deploying fact-based, logical AI in public or communal settings and affectively rich AI in private contexts—to optimize persuasion and efficacy.
This nuanced understanding speaks volumes about AI’s transformative potential in public health promotion. The differential impacts underscore that effective AI nudges must move beyond one-size-fits-all models to embrace context-aware strategies, thereby maximizing consumer receptiveness and ultimately contributing to healthier lifestyle choices at scale. Such an approach aligns well with current trends prioritizing personalized health interventions.
Furthermore, the study’s rigorous experimental design, with its random assignment and comprehensive statistical validation—including robustness checks replicating findings with an alternate AI product—strengthens confidence in the replicability and generalizability of these insights. This is critical in a field often challenged by inconsistent findings due to varying AI implementations and psychological variables.
While these results open promising avenues, they also highlight new research questions. Future investigations might examine whether cultural factors modulate these patterns, given the sample’s concentration within a Chinese university setting. Additionally, exploring long-term behavioral outcomes beyond willingness to purchase—such as actual purchasing behavior or sustained dietary changes—could further elucidate AI’s real-world impact.
Moreover, as AI technology evolves toward increasing sophistication and hybridity, future models may integrate both thinking and feeling AI characteristics, potentially generating even more potent nudges. Understanding how these hybrid AI agents interact with context and mediators could revolutionize AI’s role in shaping health behavior.
In summary, this pioneering study offers a compelling narrative on how distinct AI types differentially engage cognitive and affective pathways to influence health-related consumer choices within context-sensitive parameters. Such granular insights will inform scholars, practitioners, and policymakers seeking to harness AI’s persuasive power responsibly and effectively to promote public health.
The study’s findings underscore that tailoring AI strategies to the social context in which consumption decisions occur is paramount. Whether public benchmarks demand rational justifications or private moments call for emotional encouragement, AI’s flexible deployment could revolutionize health promotion efforts. This research marks a significant step forward in decoding the complex interplay between AI, psychology, and consumer behavior, illuminating pathways to healthier societies shaped by intelligent, empathic technology.
Subject of Research: The influence of distinct types of AI (thinking AI vs. feeling AI) on consumers’ willingness to purchase healthy food, analyzed across public and personal consumption contexts with mediation by cognitive and affective responses.
Article Title: Thinking AI or feeling AI? The effect of AI on consumers’ willingness to purchase healthy food from the perspective of nudge.
Article References:
Bi, C., Cui, X., Sun, Z. et al. Thinking AI or feeling AI? The effect of AI on consumers’ willingness to purchase healthy food from the perspective of nudge. Humanit Soc Sci Commun 12, 1032 (2025). https://doi.org/10.1057/s41599-025-05391-w
Image Credits: AI Generated
