Mindfulness and creative self-efficacy in human–AI decision-making: Implications for adaptive AI design

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This study provides a timely contribution to the understanding of human factors in AI decision-making, specifically on how two psychological traits i.e. trait mindfulness and creative self-efficacy (CSE), interact with communal-agentic personality orientations to influence decision revision after AI input. Using multinomial logistic regression, we analyzed data from 549 professionals in the United Kingdom to determine whether participants maintained or adjusted their initial decisions following AI advice. Results revealed a significant interaction between mindfulness and CSE. Individuals with high mindfulness and low CSE were more likely to revise their decisions in the direction of AI recommendations, while those high in both traits tended to maintain their original choices. A three-way interaction further showed that this mindfulness–CSE dynamic was most pronounced among individuals scoring high on communal-femininity traits. These findings highlight how attentional focus (mindfulness), perceived creative competence (CSE), and gender-role orientation jointly shape receptivity to AI suggestions. We discuss implications for advancing theory on individual differences in human–AI collaboration and for designing adaptive AI systems tailored to users’ psychological profiles. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Artificial intelligence (AI) systems are increasingly integrated into human decision-making processes across domains such as healthcare, finance, and creative work 1 , 3 , 12 , 17 . While AI recommendations can improve decision accuracy and consistency, individuals show substantial variability in whether they accept, modify, or reject such advice 1 . Previous studies have documented algorithm aversion 1 , 44 , 46 , where people tend to prefer human over algorithmic recommendations, even when the latter performs better 2 . However, algorithm appreciation i.e. the tendency to favor AI input, emerges when algorithms are perceived as highly accurate 3 , 32 , tasks are objective or high-stakes 4 , users lack confidence in their own judgment 5 , or when AI outputs are transparent and explainable 6 . These opposing tendencies highlight a fundamental question in human–AI interaction: which psychological characteristics predict openness to AI-generated advice? Understanding the individual differences that shape receptivity to AI input is critical for both theory and application 45 . From a theoretical perspective, identifying the cognitive and personality mechanisms that underlie advice-taking can inform models of adaptive decision-making in human–machine systems 4 , 11 , 46 . From a design perspective, such insights can guide the development of AI interfaces that dynamically adjust their recommendations to users’ cognitive styles and confidence levels 3 , 7 , 12 . In the present study, we focus on two individual-difference variables: mindfulness and creative self-efficacy (CSE) , as predictors of decision revision following AI suggestions. This extends prior work on trait-based predictors of decision quality and adaptive behavior 3 , 4 , 5 , 16 , 25 . Mindfulness is defined as a dispositional tendency to attend to present-moment experience in a nonjudgmental and accepting manner 7 . It has been associated with enhanced metacognitive awareness, reduced reliance on heuristics, and improved cognitive flexibility 8 . Theoretically, mindfulness may support more deliberate and context-sensitive processing of external information, including algorithmic input. For example, mindful individuals have been shown to be less prone to decision biases such as the sunk-cost fallacy 9 , suggesting a greater willingness to revise prior judgments in response to new evidence. Based on this, we hypothesize that individuals high in mindfulness will exhibit greater openness to modifying their initial decisions following AI-generated advice. Creative self-efficacy (CSE) refers to an individual’s belief in their ability to generate creative ideas and solutions 7 , 39 . Rooted in Bandura’s self-efficacy theory 8 , CSE captures domain-specific confidence in navigating tasks that require originality, insight, or innovation. While general self-efficacy reflects a broad sense of personal competence, CSE is more directly relevant in contexts requiring adaptive thinking under uncertainty such as evaluating algorithmic recommendations 3 , 12 , 37 . Individuals high in CSE are more likely to trust their own creative judgment and persist in solving problems autonomously 7 , 8 . In contrast, those low in CSE may doubt the quality of their ideas and seek external guidance, including that provided by AI 3 , 12 . We therefore hypothesize that individuals with higher CSE will reflect greater reliance on internal judgement and less likely to revise their decisions following AI advice. We propose that mindfulness and CSE interact in shaping advice-taking behavior. Drawing on interactionist personality frameworks i.e. the cognitive-affective processing system 4 , 5 , 11 , we argue that mindfulness may moderate the influence of self-efficacy on openness to AI input. For example, individuals low in CSE may be more receptive to AI advice when they are also high in mindfulness, as mindful awareness could enhance recognition of their uncertainty and increase thoughtful engagement with external input 5 , 6 . Conversely, among high-CSE individuals, mindfulness may reinforce selective engagement with AI, leading them to consider the recommendation without necessarily adopting it 7 . We hypothesize that mindfulness will increase the likelihood of decision revision among low-CSE individuals but have limited influence on those with high CSE. In addition to these cognitive traits, we examine the potential moderating role of gender-linked personality orientations , conceptualized using the communal–agentic framework 9 , 10 , 30 . Drawing on social role theory 14 , we distinguish between communal traits (e.g., warmth, empathy, cooperativeness), traditionally associated with femininity, and agentic traits (e.g., assertiveness, independence, dominance), traditionally associated with masculinity. This dimensional approach enables the study of gender-role orientations as continuous psychological dispositions, offering a more precise account of behavioural differences rather than binary sex-based comparisons 9 , 10 , 14 , . This interpretation is in line with Bakan’s agency-communion framework which describes agency as striving for individuality and mastery, and communion as striving for connection and sharing 9 , 13 . Prior research suggests that individuals higher in communal traits are more receptive to external perspectives, while those higher in agentic traits prioritize autonomy and self-direction 9 , 10 , 15 . These traits have also been shown to influence how individuals process persuasive messages and social cues, particularly in cognitively demanding contexts 15 . Related cross-cultural findings further suggest that individuals from collectivistic (communal) cultures report greater increases in decision confidence and creative self-efficacy when supported by AI, compared to individuals from individualistic (agentic) cultures 17 . This supports the broader premise that social-motivational orientations shape how algorithmic input is interpreted and applied. However, the interaction of gender-linked trait orientations with mindfulness and CSE in AI-assisted decision-making remains unexplored 11 . We hypothesize that communal-agentic orientations will moderate the mindfulness × CSE interaction with an amplified effect among communal-oriented individuals especially when they are both mindful and lower in CSE. Hypotheses H1 : Higher mindfulness will increase likelihood of revising decisions based on AI suggestions. H2 : Higher CSE will decrease likelihood of revising decisions based on AI suggestions. H3 : Mindfulness and CSE will interact to predict decision revision, such that mindfulness will increase likelihood of decision revision among individuals low in CSE. H4 : The interaction between mindfulness and CSE will be moderated by communal-agentic trait orientation. Specifically, the mindfulness × CSE effect will be stronger among individuals higher in communal traits. Figure 1 presents the conceptual framework summarizing the hypothesized relationships among mindfulness, creative self-efficacy, gender-linked trait orientation, and decision revision following AI input. Results To examine the conditions under which individuals revise their decisions in response to AI-generated suggestions, we analyzed the effects of mindfulness, creative self-efficacy (CSE), and gender-role traits on response change. Below, we report the descriptive statistics, primary regression analyses, and interaction effects that tested our four hypotheses. Overall, the results support H1 (mindfulness predicts openness to AI), H3 (mindfulness and CSE interact), and H4 (gender traits moderate this effect). H2 (main effect of CSE) was not supported. Descriptive Statistics and Initial Response Patterns A total of 549 participants completed the study with the majority (393 participants, 71.6%) showing no change in their response after receiving AI input, while 65 participants (11.8%) increased and 91 participants (16.6%) decreased their ratings. Descriptive statistics for these variables are presented in Table 1 . Table 1 Descriptive Statistics of Main Variables Variable M SD Min Max Mindfulness 3.86 0.84 1.73 6.00 CSE 8.82 1.10 6.00 10.00 First response 2.69 2.69 1.00 4.00 Second response 2.59 0.78 1.00 4.00 A McNemar’s test confirmed significant changes between the first and second responses, χ2(6) = 44.28, p < .001, indicating that the distribution of changes across response categories was not uniform, and therefore validating response change as the dependent variable. Main Effects of Mindfulness and Creative Self-Efficacy A multinomial logistic regression was conducted to assess the impact of mindfulness and CSE on the likelihood of changes (Increase or Decrease), with “No Change” as the reference category. We found that neither mindfulness nor CSE alone significantly predicted response change. The model converged with a residual deviance of 861.76 and an AIC of 873.76. Key results are summarized in Table 2 . Table 2 Multinomial Logistic Regression Predicting Change Predictor Coefficient SE z p (Intercept) 0.09 1.43 0.16 .310 CSE -0.19 0.17 -1.19 .370 Mindfulness -0.03 0.16 -0.22 .640 Interaction Effects: Mindfulness x CSE Introducing an interaction term between mindfulness and CSE improved model fit (AIC = 869.18). The interaction was significant (p = .041), as was mindfulness (p = .046), indicating moderation (Table 3 ). Table 3 Multinomial Logistic Regression Predicting Change with Interaction Term Predictor Coefficient SE z p (Intercept) -14.84 7.13 -2.02 .066 CSE 1.50 0.81 1.81 .097 Mindfulness 3.91 1.83 2.09 .046* CSE x Mindfulness -0.45 0.21 -2.12 .041* Note: * p < 0.05 indicates statistical significance. These results show that mindfulness alone significantly predicted changes in responses (p = .046), suggesting that individuals with higher mindfulness were more likely to experience a response change when AI input was provided. The interaction between CSE and mindfulness was also significant (p = .041). To further investigate the significant interaction between CSE and mindfulness, a subgroup analysis was conducted. The self-efficacy measure was split into two subgroups: high CSE and low CSE, based on the median CSE score. A logistic regression model was applied to both subgroups to assess the effect of mindfulness on the likelihood of change. The model showed that mindfulness did not significantly predict change in the low self-efficacy group (beta = 0.317, SE = 0.199, z = 1.592, p = 0.111). Although the estimate suggested a positive relationship, the lack of statistical significance indicates that for individuals with low self-efficacy, mindfulness did not have a meaningful impact on their likelihood of exhibiting a change in response. However, the analysis indicated a marginally significant inverse relationship between mindfulness and change for the high self-efficacy group (beta = -0.276, SE = 0.151, z = -1.820, p = 0.069). This suggests that for individuals with high self-efficacy, increased mindfulness was associated with a slight reduction in the likelihood of change, though the effect did not reach the conventional threshold for statistical significance (p < 0.05). The negative coefficient implies that mindfulness may have a suppressive effect on response change for this subgroup. A visual representation of the interaction is displayed in Fig. 2. For individuals with high self-efficacy, mindfulness seems to have a negative effect on the likelihood of change: as mindfulness increases, likelihood of change decreases. For individuals with low self-efficacy, mindfulness had a positive effect on the likelihood of change: as mindfulness increases, their likelihood of response change increases, and people are more receptive to input from AI. Main Effects of Gender-Linked Traits To examine how gender-linked personality traits influence openness to AI recommendations, we incorporated trait scores derived from the Personal Attributes Questionnaire (PAQ) 18 . This instrument captures self-perceived agentic-masculine and communal-feminine attributes along orthogonal dimensions 9 , 10 , 18 , 19 . Participants were not grouped categorically but instead scored along three continuous subscales: Agentic-Masculinity (PAQ_M), Communal-Femininity (PAQ_F), and Balanced (PAQ_MF), based on validated item groupings 20 , 21 . Univariate logistic regressions with PAQ subscales revealed no significant main effects (Table 4 ). Communal-Femininity (PAQ_F) showed an upward trend (Fig. 3). Table 4 Univariate Logistic Regression Results for Gender Traits (PAQ) Subscales Predictor Estimate Std. Error z-value p-value Odds Ratio 95% CI PAQ_M 0.03 0.03 0.79 0.432 1.021 (0.97, 1.08) PAQ_F -0.03 0.03 -1.30 0.192 1.035 (0.98, 1.09) PAQ_MF 0.00 0.02 0.18 0.858 1.004 (0.96, 1.05) Note: * p < 0.05 indicates statistical significance. Interaction Effects: Mindfulness x Communal Traits Including Mindfulness and PAQ_F (Communal-Femininity) and their interaction significantly improved prediction (Table 5 ). The PAQ_F × Mindfulness term was significant (p = .0295), showing that mindfulness enhanced the effect of communal traits on change likelihood. Figure 4 displays the predicted probability of response change as a function of Communal-Femininity scores. Table 5 Interaction Model: Communal-Femininity and Mindfulness as Predictors of Change Predictor Estimate Std. Error z-value p-value Odds Ratio 95% CI PAQ_F -0.26 0.14 -1.89 0.058 0.7711 (0.59, 1.01) Mindfulness -3.11 1.39 -2.24 0.025* 0.0448 (0.00, 0.63) PAQ_F x Mindfulness 0.08 0.04 2.18 0.030* 1.0085 (1.01, 1.17) Note. p < 0.05 indicates statistical significance. Interaction Effects: Mindfulness x Communal Traits x CSE Finally, we tested a three-way interaction among Communal-Feminity Traits (PAQ_F), Mindfulness, and CSE. As shown in Table 6 , several terms approached significance (e.g., p ≈ .04–.07). In particular, the interaction between communal traits and mindfulness remained significant, and the highest-order three-way term trended toward significance (p = .0662). Figure 5 illustrates these three-way effects, showing separate lines for low vs. high mindfulness and low vs. high CSE. Table 6 Three-Way Interaction Model: Femininity, Mindfulness and Creative Self-Efficacy as Predictors of Change Predictor Estimate Std. Error z-value p-value Odds Ratio 95% CI PAQ_F x Mindfulness 0.97 0.48 2.04 0.042* 2.6401 (1.02, 6.42) PAQ_F x CSE 0.37 0.20 1.84 0.066 1.4458 (0.97, 2.11) Mindfulness x CSE 3.07 1.91 1.61 0.107 21.5465 (0.45, 664.96) PAQ_F x Mindfulness x CSE -0.10 0.05 -1.84 0.066 0.9073 (0.82, 1.01) Note. p < 0.05 indicates statistical significance. Discussion Mindfulness and Openness to AI Suggestions Our findings indicate that higher trait mindfulness is associated with greater openness to AI-generated suggestions, supporting H1. Participants who scored higher on mindfulness were more willing to revise their initial decisions after receiving input from the AI system. This suggests that mindful individuals process algorithmic advice in a receptive, less judgmental manner 5 , 6 . Prior research similarly shows that mindfulness reduces cognitive biases and automatic thinking, effectively “debiasing” decision processes 6 , 34 . Thus, consistent with dual-process models, mindfulness may engage more deliberative System 2 thinking i.e. analytical evaluation of the AI’s suggestion, rather than defaulting to System 1 i.e. intuition or habit 4 , 44 . This capacity explains why more mindful individuals in our study were amenable to the AI’s advice instead of dismissing it outright. Notably, mindfulness has been linked to greater openness and creativity in past work 5 , 44 , and our results extend this notion to openness toward AI-generated input. Creative Self-Efficacy and Resistance to AI Advice We also found evidence for H2: higher CSE was associated with a lower likelihood of revising one’s decisions based on AI suggestions. In other words, individuals who are more confident in their own creative judgment were less inclined to incorporate the AI’s input. This inverse relationship aligns with research on advice-taking and overconfidence 12 , 17 , 38 . Decision-makers with strong confidence in their abilities tend to discount or undervalue external advice 8 , 12 , 39 . Our results suggest that such confidence may lead to a form of egocentric advice discounting 12 : those high in CSE appeared to trust their initial ideas over the AI’s suggestions. This finding is consistent with prior work showing that people with greater self-assuredness in a task domain give less weight to others’ input 7 , 38 . Thus, while creative self-efficacy is generally linked to improved creative performance 7 , 39 , it may have the unintended effect of making individuals more rigid or autonomous in their decision-making, thereby diminishing their responsiveness to potentially useful AI recommendations 16 , 39 , 43 . This effect underscores the role of self-related beliefs in human–AI collaboration i.e. a rational AI suggestion might be ignored if the human user’s self-efficacy is very high and unchecked by situational factors 12 , 39 . Interactive Effects of Mindfulness and Creative Self-Efficacy Beyond these main effects, we observed a significant interaction between mindfulness and creative self-efficacy in predicting decision revision behavior, as hypothesized in H3. In our data, mindfulness particularly increased openness to AI input among individuals low in creative self-efficacy. Low-CSE individuals who were more mindful showed substantially higher likelihood of revising their decisions with AI input than low-CSE individuals who were less mindful 5 , 34 . In contrast, among those with high creative self-efficacy, mindfulness made little difference – highly efficacious people tended to resist AI advice regardless of mindfulness level 11 , 39 . This pattern supports H3 and suggests a compensatory mechanism: mindfulness appears to buffer or counteract some of the reluctance that low-CSE individuals might have in using external help 5 , 13 , 34 . One explanation is that mindfulness, through its emphasis on non-judgmental awareness and acceptance, helps low-CSE individuals regulate the insecurity or ego-threat that can arise when receiving suggestions 5 , 35 , 44 . Lacking confidence in one’s creativity might normally induce anxiety or defensiveness (e.g., fear of being judged or of losing autonomy), which could either lead to outright rejection of advice or conversely over-reliance in an unproductive way 11 , 12 , 38 . Mindfulness likely enables a balanced approach: low-CSE individuals high in mindfulness can acknowledge their initial idea’s fallibility without self-criticism and are calmly open to alternatives 34 , 36 . This interpretation aligns with self-regulation theories – mindfulness strengthens self-regulatory capacity to manage negative emotions and ego involvement 5 , 13 , 34 , 47 . Thus, a mindful low-CSE person can engage with the AI suggestion more thoughtfully rather than either defensively dismissing it or uncritically accepting it out of self-doubt. Moderating Role of Communal vs. Agentic Orientation Our final hypothesis, H4, proposed that the above mindfulness–CSE interaction would itself be moderated by individuals’ communal vs. agentic trait orientation. The results confirmed this three-way interaction: the influence of mindfulness on openness to AI suggestions (especially for low-CSE individuals) was strongest for those high in communal orientation 9 , 14 , 15 . In contrast, the interactive benefits of mindfulness were diminished for those with a more agentic orientation 10 , 18 , 20 . This finding supports H4 and highlights the importance of personality orientations in technology-related behaviors 9 , 13 . Our results suggest that a communal person who is mindful and low in self-efficacy is especially likely to treat the AI as a collaborative partner and incorporate its suggestions, because doing so aligns with their intrinsic orientation toward cooperation and openness to others’ contributions 9 , 14 , 21 . In contrast, a strongly agentic person may feel an internal drive to maintain control and originate ideas autonomously even when mindful, which could dampen their willingness to adopt an external suggestion 10 , 14 , 20 . By confirming our hypothesis, we prove that user traits related to social orientation substantially shape human–AI interaction patterns 25 , 27 . Communal, team-oriented users – especially if mindful and not overconfident in their own creativity – stand to benefit the most from AI decision support, whereas agentic users may require different approaches to engage them with AI 14 , 15 , 21 . Psychological Theoretical Implications From a dual-process perspective , our findings suggest that mindfulness shifts users from intuitive, heuristic processing to more reflective, analytical evaluation when interacting with AI 4 , 6 . This aligns with evidence that mindfulness counters cognitive biases and promotes deliberate (System 2) thinking over impulsive (System 1) responses 4 , 6 , 34 . Mindful users, especially those low in self-efficacy, were more open to AI advice because mindfulness curbs ego-driven or anxious reactions and enhances attention to external input 5 , 34 , 44 . In line with self-regulation theory , mindfulness also appears to help users manage emotional responses to conflicting input 5 , 13 , 47 . A mindful low-CSE individual may experience doubt when contradicted by AI but remain receptive instead of shutting down or overreacting 12 , 34 , 35 . This self-regulation helps avoid both overconfidence and underconfidence, supporting more balanced decisions 13 , 36 , 47 . Another implication concerns trust in AI and human–automation interaction . Openness to AI suggestions can reflect trust or at least serious consideration of its input 12 , 26 , 29 . Our findings show that such trust depends not just on system transparency or performance, but also on user traits 12 , 26 , 33 . High CSE and agentic users showed lower willingness to revise their decisions, suggesting greater reliance on their own judgment. In contrast, mindfulness and communal orientation were linked to greater openness, indicative of higher trust 5 , 9 , 15 . This supports existing models of calibrated trust: optimal reliance on automation occurs when users adjust their confidence in AI relative to self-assurance. Prior work on algorithm aversion shows people often avoid AI input after errors 1 , 2 ; our data refine this by identifying who is most prone (self-confident, agentic individuals) and who remains receptive (mindful, communal individuals) 5 , 9 , 25 . Trust-building may thus require targeting internal states: reducing ego defensiveness (via mindfulness or design) and framing AI as a collaborator 26 , 36 , 38 . Practical Implications for AI Adoption and Design Our findings offer important practical implications for organizations implementing AI decision-support tools and for designers developing such systems 24 , 28 . For AI adoption , our results suggest that promoting a mindful mindset in end-users could enhance their openness to algorithmic assistance 5 , 34 , 36 . Companies could integrate mindfulness training or interventions into broader change management strategies when introducing AI technologies 35 . Our findings also highlight that users high in creative self-efficacy and agentic orientation may be natural resisters to AI input 7 , 10 , 39 – these are often experienced experts or highly independent thinkers whose intuition might conflict with algorithmic advice 12 , 25 . For these users, organizations could include involving them in the AI implementation process (increasing their sense of control and buy-in) and emphasizing the AI’s role as augmentative (not replacing their expertise) 26 , 27 . For AI system design , the moderated effects we found suggest the value of adaptive, user-aware AI interfaces 22 , 24 , 26 . Systems could be designed to detect or allow input of user traits and then adjust how advice is presented 29 , 30 , 31 . For example, an AI assistant might provide more explanatory context or confidence metrics to a user identified as high CSE/agentic, to earn their trust and justify the suggestion while respecting their autonomy 12 , 23 , 38 . Conversely, users lower in self-efficacy or higher in communal orientation may respond better to a more collaborative and encouraging tone, where suggestions are framed as shared improvements rather than corrections 14 , 21 , 25 , 45 . In conclusion, a one-size-fits-all approach to AI advice may be suboptimal – our results argue for personalization in human-AI interaction, considering user mindfulness, confidence, and social orientation to improve both adoption and user satisfaction in human–AI interactions 12 , 24 , 37 . Limitations and Future Research While this study provides novel insights, several limitations must be acknowledged, which also open avenues for future research. First , the scope of context. Our experiment focused on decision revision within a specific task using AI-generated suggestions. It remains to be seen whether these findings generalize to different types of decisions (e.g., high-stakes versus low-stakes, creative versus analytical domains) or to other AI formats such as predictive versus prescriptive tools 22 , 24 . Second , issues of causality and measurement. Mindfulness, creative self-efficacy, and trait orientation were measured rather than manipulated. Future research using experimental manipulations such as mindfulness inductions or self-efficacy priming could establish directionality more clearly 34 , 35 . Additionally, our binary outcome (i.e., decision revision vs. no revision) captures limited process-level information. Complementary methods such as interaction logging could offer deeper insights into how traits shape engagement with AI 36 , 38 . Third , we did not systematically examine demographic or cultural variables, which may moderate or interact with the psychological traits studied. Communal versus agentic orientations are culturally influenced and testing our model across different cultural contexts or organizational settings could enhance generalizability 17 , 25 . Likewise, age and technological familiarity may shape trust in AI. For example, younger users may exhibit lower baseline algorithm aversion and may engage with AI differently 45 . Fourth , the AI system used in our study functioned as a black-box advisor with assumed reliability. In practice, user receptivity depends not only on internal traits but also on system transparency, error rates, and the quality of explanation 23 , 26 , 45 . Future research could explore how individual traits interact with AI performance variability. For instance, would a mindful user remain open to suggestions after witnessing AI errors? Understanding how trust in AI evolves over time in response to system performance could be an important contribution. Fifth , we did not examine longitudinal trajectories of user behavior. It remains unclear whether the observed influences of mindfulness, self-efficacy, and social orientation are stable over time or subject to adaptation through repeated interaction with AI systems 17 , 24 . Longitudinal and adaptive research designs are needed to map how user–AI relationships evolve and whether openness to AI can be cultivated, sustained, or undermined over time. Methods Study Design and Preregistration This study employed a within-subject experimental design to investigate how individual traits, namely mindfulness, creative self-efficacy (CSE), and gender-role orientation, influence receptivity to AI-generated advice in a decision-making context. The design included both AI-absent and AI-present conditions, allowing direct assessment of decision change after AI input. The study was preregistered on the Open Science Framework (OSF; link blinded for peer review ) and received ethical approval from the Institutional Review Board of the Department of Applied Psychology: Work, Education, Economy at the University of Vienna (Approval Number: 2019/A/002). All procedures were performed in accordance with the Declaration of Helsinki (2013 revision) and with all relevant institutional and national regulations and guidelines. Written informed consent was obtained electronically from all participants before any study procedures were initiated. The research did not involve live vertebrates or higher invertebrates; therefore, ARRIVE guidelines and animal-care regulations do not apply. Participants and Procedure Participants were recruited via the online platform Prolific, targeting professionals based in the United Kingdom with occupational exposure to AI systems or decision-making technologies. Eligibility screening ensured that respondents were currently employed in sectors such as business analytics, R&D, sales, or marketing, domains where AI-assisted decision-making is increasingly prevalent. A total of 549 participants (after quality control and screening) completed the full survey hosted on Qualtrics. All participants provided informed consent electronically before beginning the study. The survey included embedded logic to terminate participation if consent was not granted. Compensation was provided in line with Prolific’s ethical payment guidelines. After providing demographic data, participants completed trait-level measures of mindfulness, creative self-efficacy, and gender-role traits. They were then randomly assigned to a sequence of decision-making scenarios designed to test baseline judgments (without AI input) and response shifts after receiving AI-generated advice, with scenario order counterbalanced to mitigate sequence effects. In the non-AI condition, participants reviewed sales data and bar charts for four products and were asked to allocate a marketing budget based solely on their independent analysis. In the AI-assisted condition, they were shown the same data along with an AI-generated recommendation regarding optimal budget allocation and given the opportunity to revise their original decision. This design enabled us to observe whether, and how, participants’ decisions changed in response to AI input. The survey concluded with a final assessment of creative self-efficacy and a full debrief. Measures Mindfulness was assessed using the 15-item Mindful Attention Awareness Scale (MAAS) 5 . Items (e.g., “I find myself doing things without paying attention”) were rated on a 6-point Likert scale. The scale showed excellent internal consistency (α = .90). Higher MAAS scores reflect greater dispositional mindfulness, capturing attentional awareness and reduced automaticity. Creative Self-Efficacy (CSE) was measured using the 3-item Creative Self-Efficacy scale 7 and 6-item subscale of the Short Scale of Creative Self (SSCS) 45 . Participants rated items such as “I trust my creative thinking skills” on a 5-point Likert scale. The subscale demonstrated good reliability (α = .85). While the full SSCS includes a Creative Personal Identity subscale, only the CSE component was used in this study to capture confidence in generating novel solutions. Gender-Role Traits i.e. communal and agentic traits were measured using the Personal Attributes Questionnaire (PAQ) 18 . The communal-femininity (PAQ_F) and agentic-masculinity (PAQ_M) subscales each consist of 8 bipolar adjective pairs (e.g., “Not at all sympathetic – Very sympathetic” for PAQ_F; “Not at all independent – Very independent” for PAQ_M). Subscale reliabilities were acceptable (α = .78 for PAQ_F, α = .75 for PAQ_M). The two dimensions were orthogonal, allowing for independent and interactional modeling of trait orientations 19 . Decision Change After AI Input was measured as the primary outcome variable. We recorded whether participants revised their decision between the non-AI and AI-supported phases. This outcome was categorized into: Change : The participant changed their response following AI recommendations. No Change : The decision remained unchanged. Statistical Analysis To examine how individual traits influence decision revision in response to AI-generated advice, we employed multinomial logistic regression (MNL) models using R (version 4.2, nnet package). The dependent variable was a categorical outcome reflecting direction of change between initial and final decisions: Change , or No Change (reference category). This outcome structure reflects our interest in whether participants updated their decisions in either direction following AI input, and it captures directional nuance relevant to the psychological mechanisms proposed in our hypotheses (e.g., openness vs. resistance to AI suggestions). All continuous predictor variables: mindfulness , CSE , and gender-role traits, were standardized (z-scores) prior to analysis to allow accurate interpretation of main and interaction effects. Our analysis followed a nested model-building approach to systematically test Hypotheses H1 through H4: Model 1: Main Effects (H1 & H2) : Included mindfulness, CSE, PAQ_F (communal-femininity), and PAQ_M (agentic-masculinity) as predictors to assess whether these traits individually predicted likelihood of decision change. Model 2: Two-Way Trait Interaction (H3) : Added the mindfulness × CSE interaction to test whether mindfulness moderated the relationship between CSE and decision revision. Model 3: Trait Orientation (Extension of H2 & H3) : Included the full set of gender-role traits (PAQ_F, PAQ_M, PAQ_MF) to examine how personality orientation contributed to openness or resistance. Model 4: Moderated Trait Interactions (Part of H4) : Introduced two-way interactions between mindfulness × PAQ_F and CSE × PAQ_F to explore whether communal orientation influenced the individual effects of mindfulness and CSE. Model 5: Three-Way Interaction (H4) : Tested the hypothesized mindfulness × CSE × PAQ_F interaction to determine whether the combined influence of mindfulness and CSE on decision revision was amplified among individuals higher in communal-femininity traits. All models evaluated using Akaike Information Criterion (AIC) and likelihood ratio tests to assess improvements in model fit. Odds ratios (OR) and 95% confidence intervals (CI) are reported to aid interpretability. To probe significant interactions, we conducted simple slopes analyses and plotted predicted probabilities at ± 1 SD of the interacting variables (Figs. 1 –4). Declarations Funding The authors received no funding for this work. Competing Interests The authors declare no competing interests. Author Contribution DFX, ZDH and CK contributed equally to study conceptualization and methodology design. DFX drafted and wrote the main manuscript. DFX and ZDH led the data collection, statistical analyses and data interpretation. DFX, CK and ZDH provided critical revisions and theoretical framing. Acknowledgement We thank all the study participants for their time and contributions. This research was supported by fellowships from the Austrian Academy of Sciences (ÖAW) and the Austrian Federal Ministry of Education, Science and Research (BMBWF) awarded to DFX. 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2","display":"","copyAsset":false,"role":"figure","size":91610,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVisual Representation of Subgroup Analyses: Effect of Mindfulness on Change in Response\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7274590/v1/811a60bb8518b72e5f845f03.png"},{"id":93639189,"identity":"468deca5-fb80-4074-af78-4d5af98d17a1","added_by":"auto","created_at":"2025-10-16 02:05:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172331,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEffect of Communal-Femininity Traits on Change in 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for adaptive AI design","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial intelligence (AI) systems are increasingly integrated into human decision-making processes across domains such as healthcare, finance, and creative work\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. While AI recommendations can improve decision accuracy and consistency, individuals show substantial variability in whether they accept, modify, or reject such advice\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Previous studies have documented \u003cem\u003ealgorithm aversion\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, where people tend to prefer human over algorithmic recommendations, even when the latter performs better\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, \u003cem\u003ealgorithm appreciation\u003c/em\u003e i.e. the tendency to favor AI input, emerges when algorithms are perceived as highly accurate\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, tasks are objective or high-stakes\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, users lack confidence in their own judgment\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, or when AI outputs are transparent and explainable\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These opposing tendencies highlight a fundamental question in human\u0026ndash;AI interaction: which psychological characteristics predict openness to AI-generated advice?\u003c/p\u003e\u003cp\u003eUnderstanding the individual differences that shape receptivity to AI input is critical for both theory and application\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. From a theoretical perspective, identifying the cognitive and personality mechanisms that underlie advice-taking can inform models of adaptive decision-making in human\u0026ndash;machine systems\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. From a design perspective, such insights can guide the development of AI interfaces that dynamically adjust their recommendations to users\u0026rsquo; cognitive styles and confidence levels\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn the present study, we focus on two individual-difference variables: \u003cb\u003emindfulness\u003c/b\u003e and \u003cb\u003ecreative self-efficacy (CSE)\u003c/b\u003e, as predictors of decision revision following AI suggestions. This extends prior work on trait-based predictors of decision quality and adaptive behavior\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. \u003cb\u003eMindfulness\u003c/b\u003e is defined as a dispositional tendency to attend to present-moment experience in a nonjudgmental and accepting manner\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. It has been associated with enhanced metacognitive awareness, reduced reliance on heuristics, and improved cognitive flexibility\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Theoretically, mindfulness may support more deliberate and context-sensitive processing of external information, including algorithmic input. For example, mindful individuals have been shown to be less prone to decision biases such as the sunk-cost fallacy\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, suggesting a greater willingness to revise prior judgments in response to new evidence. Based on this, we hypothesize that individuals high in mindfulness will exhibit greater openness to modifying their initial decisions following AI-generated advice.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCreative self-efficacy (CSE)\u003c/b\u003e refers to an individual\u0026rsquo;s belief in their ability to generate creative ideas and solutions\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Rooted in Bandura\u0026rsquo;s self-efficacy theory\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, CSE captures domain-specific confidence in navigating tasks that require originality, insight, or innovation. While general self-efficacy reflects a broad sense of personal competence, CSE is more directly relevant in contexts requiring adaptive thinking under uncertainty such as evaluating algorithmic recommendations\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Individuals high in CSE are more likely to trust their own creative judgment and persist in solving problems autonomously\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In contrast, those low in CSE may doubt the quality of their ideas and seek external guidance, including that provided by AI\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. We therefore hypothesize that individuals with higher CSE will reflect greater reliance on internal judgement and less likely to revise their decisions following AI advice.\u003c/p\u003e\u003cp\u003eWe propose that mindfulness and CSE interact in shaping advice-taking behavior. Drawing on interactionist personality frameworks i.e. the cognitive-affective processing system\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, we argue that mindfulness may moderate the influence of self-efficacy on openness to AI input. For example, individuals low in CSE may be more receptive to AI advice when they are also high in mindfulness, as mindful awareness could enhance recognition of their uncertainty and increase thoughtful engagement with external input\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Conversely, among high-CSE individuals, mindfulness may reinforce selective engagement with AI, leading them to consider the recommendation without necessarily adopting it\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. We hypothesize that mindfulness will increase the likelihood of decision revision among low-CSE individuals but have limited influence on those with high CSE.\u003c/p\u003e\u003cp\u003eIn addition to these cognitive traits, we examine the potential moderating role of \u003cb\u003egender-linked personality orientations\u003c/b\u003e, conceptualized using the communal\u0026ndash;agentic framework\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Drawing on social role theory\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, we distinguish between \u003cb\u003ecommunal traits\u003c/b\u003e (e.g., warmth, empathy, cooperativeness), traditionally associated with femininity, and \u003cb\u003eagentic traits\u003c/b\u003e (e.g., assertiveness, independence, dominance), traditionally associated with masculinity. This dimensional approach enables the study of gender-role orientations as continuous psychological dispositions, offering a more precise account of behavioural differences rather than binary sex-based comparisons\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003c/sup\u003e. This interpretation is in line with Bakan\u0026rsquo;s agency-communion framework which describes agency as striving for individuality and mastery, and communion as striving for connection and sharing\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Prior research suggests that individuals higher in communal traits are more receptive to external perspectives, while those higher in agentic traits prioritize autonomy and self-direction\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These traits have also been shown to influence how individuals process persuasive messages and social cues, particularly in cognitively demanding contexts\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRelated cross-cultural findings further suggest that individuals from collectivistic (communal) cultures report greater increases in decision confidence and creative self-efficacy when supported by AI, compared to individuals from individualistic (agentic) cultures\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This supports the broader premise that social-motivational orientations shape how algorithmic input is interpreted and applied. However, the interaction of gender-linked trait orientations with mindfulness and CSE in AI-assisted decision-making remains unexplored\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. We hypothesize that communal-agentic orientations will moderate the mindfulness \u0026times; CSE interaction with an amplified effect among communal-oriented individuals especially when they are both mindful and lower in CSE.\u003c/p\u003e\n\u003ch3\u003eHypotheses\u003c/h3\u003e\n\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH1\u003c/b\u003e: Higher mindfulness will increase likelihood of revising decisions based on AI suggestions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH2\u003c/b\u003e: Higher CSE will decrease likelihood of revising decisions based on AI suggestions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH3\u003c/b\u003e: Mindfulness and CSE will interact to predict decision revision, such that mindfulness will increase likelihood of decision revision among individuals low in CSE.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH4\u003c/b\u003e: The interaction between mindfulness and CSE will be moderated by communal-agentic trait orientation. Specifically, the mindfulness \u0026times; CSE effect will be stronger among individuals higher in communal traits.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the conceptual framework summarizing the hypothesized relationships among mindfulness, creative self-efficacy, gender-linked trait orientation, and decision revision following AI input.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo examine the conditions under which individuals revise their decisions in response to AI-generated suggestions, we analyzed the effects of mindfulness, creative self-efficacy (CSE), and gender-role traits on response change. Below, we report the descriptive statistics, primary regression analyses, and interaction effects that tested our four hypotheses. Overall, the results support \u003cstrong\u003eH1\u003c/strong\u003e (mindfulness predicts openness to AI), \u003cstrong\u003eH3\u003c/strong\u003e (mindfulness and CSE interact), and \u003cstrong\u003eH4\u003c/strong\u003e (gender traits moderate this effect). \u003cstrong\u003eH2\u003c/strong\u003e (main effect of CSE) was not supported.\u003c/p\u003e\n\u003ch3\u003eDescriptive Statistics and Initial Response Patterns\u003c/h3\u003e\n\u003cp\u003eA total of 549 participants completed the study with the majority (393 participants, 71.6%) showing no change in their response after receiving AI input, while 65 participants (11.8%) increased and 91 participants (16.6%) decreased their ratings. Descriptive statistics for these variables are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics of Main Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMindfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecond response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eA McNemar\u0026rsquo;s test confirmed significant changes between the first and second responses, \u0026chi;2(6)\u0026thinsp;=\u0026thinsp;44.28, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, indicating that the distribution of changes across response categories was not uniform, and therefore validating response change as the dependent variable.\u003c/p\u003e\n\u003ch3\u003eMain Effects of Mindfulness and Creative Self-Efficacy\u003c/h3\u003e\n\u003cp\u003eA multinomial logistic regression was conducted to assess the impact of mindfulness and CSE on the likelihood of changes (Increase or Decrease), with \u0026ldquo;No Change\u0026rdquo; as the reference category. We found that neither mindfulness nor CSE alone significantly predicted response change. The model converged with a residual deviance of 861.76 and an AIC of 873.76. Key results are summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eMultinomial Logistic Regression Predicting Change\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.310\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMindfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eInteraction Effects: Mindfulness x CSE\u003c/h3\u003e\n\u003cp\u003eIntroducing an interaction term between mindfulness and CSE improved model fit (AIC\u0026thinsp;=\u0026thinsp;869.18). The interaction was significant (p\u0026thinsp;=\u0026thinsp;.041), as was mindfulness (p\u0026thinsp;=\u0026thinsp;.046), indicating moderation (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eMultinomial Logistic Regression Predicting Change with Interaction Term\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMindfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.046*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCSE x Mindfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThese results show that mindfulness alone significantly predicted changes in responses (p\u0026thinsp;=\u0026thinsp;.046), suggesting that individuals with higher mindfulness were more likely to experience a response change when AI input was provided. The interaction between CSE and mindfulness was also significant (p\u0026thinsp;=\u0026thinsp;.041).\u003c/p\u003e\n\u003cp\u003eTo further investigate the significant interaction between CSE and mindfulness, a subgroup analysis was conducted. The self-efficacy measure was split into two subgroups: high CSE and low CSE, based on the median CSE score. A logistic regression model was applied to both subgroups to assess the effect of mindfulness on the likelihood of change.\u003c/p\u003e\n\u003cp\u003eThe model showed that mindfulness did not significantly predict change in the low self-efficacy group (beta\u0026thinsp;=\u0026thinsp;0.317, SE\u0026thinsp;=\u0026thinsp;0.199, z\u0026thinsp;=\u0026thinsp;1.592, p\u0026thinsp;=\u0026thinsp;0.111). Although the estimate suggested a positive relationship, the lack of statistical significance indicates that for individuals with low self-efficacy, mindfulness did not have a meaningful impact on their likelihood of exhibiting a change in response. However, the analysis indicated a marginally significant inverse relationship between mindfulness and change for the high self-efficacy group (beta = -0.276, SE\u0026thinsp;=\u0026thinsp;0.151, z = -1.820, p\u0026thinsp;=\u0026thinsp;0.069). This suggests that for individuals with high self-efficacy, increased mindfulness was associated with a slight reduction in the likelihood of change, though the effect did not reach the conventional threshold for statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The negative coefficient implies that mindfulness may have a suppressive effect on response change for this subgroup.\u003c/p\u003e\n\u003cp\u003eA visual representation of the interaction is displayed in Fig. 2. For individuals with high self-efficacy, mindfulness seems to have a negative effect on the likelihood of change: as mindfulness increases, likelihood of change decreases. For individuals with low self-efficacy, mindfulness had a positive effect on the likelihood of change: as mindfulness increases, their likelihood of response change increases, and people are more receptive to input from AI.\u003c/p\u003e\n\u003ch3\u003eMain Effects of Gender-Linked Traits\u003c/h3\u003e\n\u003cp\u003eTo examine how gender-linked personality traits influence openness to AI recommendations, we incorporated trait scores derived from the Personal Attributes Questionnaire (PAQ)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This instrument captures self-perceived agentic-masculine and communal-feminine attributes along orthogonal dimensions\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Participants were not grouped categorically but instead scored along three continuous subscales: Agentic-Masculinity (PAQ_M), Communal-Femininity (PAQ_F), and Balanced (PAQ_MF), based on validated item groupings\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eUnivariate logistic regressions with PAQ subscales revealed no significant main effects (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Communal-Femininity (PAQ_F) showed an upward trend (Fig. 3).\u003c/p\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eUnivariate Logistic Regression Results for Gender Traits (PAQ) Subscales\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAQ_M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.97, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAQ_F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.98, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAQ_MF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.96, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eInteraction Effects: Mindfulness x Communal Traits\u003c/h2\u003e\n \u003cp\u003eIncluding Mindfulness and PAQ_F (Communal-Femininity) and their interaction significantly improved prediction (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The PAQ_F \u0026times; Mindfulness term was significant (p\u0026thinsp;=\u0026thinsp;.0295), showing that mindfulness enhanced the effect of communal traits on change likelihood. Figure 4 displays the predicted probability of response change as a function of Communal-Femininity scores.\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eInteraction Model: Communal-Femininity and Mindfulness as Predictors of Change\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAQ_F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.59, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMindfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.00, 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAQ_F x Mindfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.01, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eInteraction Effects: Mindfulness x Communal Traits x CSE\u003c/h2\u003e\n \u003cp\u003eFinally, we tested a three-way interaction among Communal-Feminity Traits (PAQ_F), Mindfulness, and CSE. As shown in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, several terms approached significance (e.g., p\u0026thinsp;\u0026asymp;\u0026thinsp;.04\u0026ndash;.07). In particular, the interaction between communal traits and mindfulness remained significant, and the highest-order three-way term trended toward significance (p\u0026thinsp;=\u0026thinsp;.0662). Figure 5 illustrates these three-way effects, showing separate lines for low vs. high mindfulness and low vs. high CSE.\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eThree-Way Interaction Model: Femininity, Mindfulness and Creative Self-Efficacy as Predictors of Change\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAQ_F x Mindfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.6401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.02, 6.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAQ_F x CSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.97, 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMindfulness x CSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.5465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.45, 664.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAQ_F x Mindfulness x CSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.82, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMindfulness and Openness to AI Suggestions\u003c/h2\u003e\u003cp\u003eOur findings indicate that higher trait mindfulness is associated with greater openness to AI-generated suggestions, supporting H1. Participants who scored higher on mindfulness were more willing to revise their initial decisions after receiving input from the AI system. This suggests that mindful individuals process algorithmic advice in a receptive, less judgmental manner\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Prior research similarly shows that mindfulness reduces cognitive biases and automatic thinking, effectively \u0026ldquo;debiasing\u0026rdquo; decision processes\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Thus, consistent with dual-process models, mindfulness may engage more deliberative System 2 thinking i.e. analytical evaluation of the AI\u0026rsquo;s suggestion, rather than defaulting to System 1 i.e. intuition or habit\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This capacity explains why more mindful individuals in our study were amenable to the AI\u0026rsquo;s advice instead of dismissing it outright. Notably, mindfulness has been linked to greater openness and creativity in past work\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, and our results extend this notion to openness toward AI-generated input.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCreative Self-Efficacy and Resistance to AI Advice\u003c/h2\u003e\u003cp\u003eWe also found evidence for H2: higher CSE was associated with a lower likelihood of revising one\u0026rsquo;s decisions based on AI suggestions. In other words, individuals who are more confident in their own creative judgment were less inclined to incorporate the AI\u0026rsquo;s input. This inverse relationship aligns with research on advice-taking and overconfidence\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Decision-makers with strong confidence in their abilities tend to discount or undervalue external advice\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Our results suggest that such confidence may lead to a form of \u003cem\u003eegocentric advice discounting\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e: those high in CSE appeared to trust their initial ideas over the AI\u0026rsquo;s suggestions. This finding is consistent with prior work showing that people with greater self-assuredness in a task domain give less weight to others\u0026rsquo; input\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Thus, while creative self-efficacy is generally linked to improved creative performance\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, it may have the unintended effect of making individuals more rigid or autonomous in their decision-making, thereby diminishing their responsiveness to potentially useful AI recommendations\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. This effect underscores the role of self-related beliefs in human\u0026ndash;AI collaboration i.e. a rational AI suggestion might be ignored if the human user\u0026rsquo;s self-efficacy is very high and unchecked by situational factors\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eInteractive Effects of Mindfulness and Creative Self-Efficacy\u003c/h2\u003e\u003cp\u003eBeyond these main effects, we observed a significant interaction between mindfulness and creative self-efficacy in predicting decision revision behavior, as hypothesized in H3. In our data, mindfulness particularly increased openness to AI input among individuals low in creative self-efficacy. Low-CSE individuals who were more mindful showed substantially higher likelihood of revising their decisions with AI input than low-CSE individuals who were less mindful\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In contrast, among those with high creative self-efficacy, mindfulness made little difference \u0026ndash; highly efficacious people tended to resist AI advice regardless of mindfulness level\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. This pattern supports H3 and suggests a compensatory mechanism: mindfulness appears to buffer or counteract some of the reluctance that low-CSE individuals might have in using external help\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. One explanation is that mindfulness, through its emphasis on non-judgmental awareness and acceptance, helps low-CSE individuals regulate the insecurity or ego-threat that can arise when receiving suggestions\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Lacking confidence in one\u0026rsquo;s creativity might normally induce anxiety or defensiveness (e.g., fear of being judged or of losing autonomy), which could either lead to outright rejection of advice or conversely over-reliance in an unproductive way\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Mindfulness likely enables a balanced approach: low-CSE individuals high in mindfulness can acknowledge their initial idea\u0026rsquo;s fallibility without self-criticism and are calmly open to alternatives\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This interpretation aligns with self-regulation theories \u0026ndash; mindfulness strengthens self-regulatory capacity to manage negative emotions and ego involvement\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Thus, a mindful low-CSE person can engage with the AI suggestion more thoughtfully rather than either defensively dismissing it or uncritically accepting it out of self-doubt.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003eModerating Role of Communal vs. Agentic Orientation\u003c/h2\u003e\u003cp\u003eOur final hypothesis, H4, proposed that the above mindfulness\u0026ndash;CSE interaction would itself be moderated by individuals\u0026rsquo; communal vs. agentic trait orientation. The results confirmed this three-way interaction: the influence of mindfulness on openness to AI suggestions (especially for low-CSE individuals) was strongest for those high in communal orientation\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In contrast, the interactive benefits of mindfulness were diminished for those with a more agentic orientation\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This finding supports H4 and highlights the importance of personality orientations in technology-related behaviors\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Our results suggest that a communal person who is mindful and low in self-efficacy is especially likely to treat the AI as a collaborative partner and incorporate its suggestions, because doing so aligns with their intrinsic orientation toward cooperation and openness to others\u0026rsquo; contributions\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In contrast, a strongly agentic person may feel an internal drive to maintain control and originate ideas autonomously even when mindful, which could dampen their willingness to adopt an external suggestion\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. By confirming our hypothesis, we prove that user traits related to social orientation substantially shape human\u0026ndash;AI interaction patterns\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Communal, team-oriented users \u0026ndash; especially if mindful and not overconfident in their own creativity \u0026ndash; stand to benefit the most from AI decision support, whereas agentic users may require different approaches to engage them with AI\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePsychological Theoretical Implications\u003c/h2\u003e\u003cp\u003eFrom a \u003cb\u003edual-process perspective\u003c/b\u003e, our findings suggest that mindfulness shifts users from intuitive, heuristic processing to more reflective, analytical evaluation when interacting with AI\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This aligns with evidence that mindfulness counters cognitive biases and promotes deliberate (System 2) thinking over impulsive (System 1) responses\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Mindful users, especially those low in self-efficacy, were more open to AI advice because mindfulness curbs ego-driven or anxious reactions and enhances attention to external input\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In line with \u003cb\u003eself-regulation theory\u003c/b\u003e, mindfulness also appears to help users manage emotional responses to conflicting input\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. A mindful low-CSE individual may experience doubt when contradicted by AI but remain receptive instead of shutting down or overreacting\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This self-regulation helps avoid both overconfidence and underconfidence, supporting more balanced decisions\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAnother implication concerns \u003cb\u003etrust in AI and human\u0026ndash;automation interaction\u003c/b\u003e. Openness to AI suggestions can reflect trust or at least serious consideration of its input\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Our findings show that such trust depends not just on system transparency or performance, but also on user traits\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. High CSE and agentic users showed lower willingness to revise their decisions, suggesting greater reliance on their own judgment. In contrast, mindfulness and communal orientation were linked to greater openness, indicative of higher trust\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This supports existing models of calibrated trust: optimal reliance on automation occurs when users adjust their confidence in AI relative to self-assurance. Prior work on algorithm aversion shows people often avoid AI input after errors\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e; our data refine this by identifying who is most prone (self-confident, agentic individuals) and who remains receptive (mindful, communal individuals)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Trust-building may thus require targeting internal states: reducing ego defensiveness (via mindfulness or design) and framing AI as a collaborator\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003ePractical Implications for AI Adoption and Design\u003c/h2\u003e\u003cp\u003eOur findings offer important practical implications for organizations implementing AI decision-support tools and for designers developing such systems\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. \u003cb\u003eFor AI adoption\u003c/b\u003e, our results suggest that promoting a mindful mindset in end-users could enhance their openness to algorithmic assistance\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Companies could integrate mindfulness training or interventions into broader change management strategies when introducing AI technologies\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Our findings also highlight that users high in creative self-efficacy and agentic orientation may be natural resisters to AI input\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e \u0026ndash; these are often experienced experts or highly independent thinkers whose intuition might conflict with algorithmic advice\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. For these users, organizations could include involving them in the AI implementation process (increasing their sense of control and buy-in) and emphasizing the AI\u0026rsquo;s role as augmentative (not replacing their expertise)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFor AI system design\u003c/b\u003e, the moderated effects we found suggest the value of adaptive, user-aware AI interfaces\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Systems could be designed to detect or allow input of user traits and then adjust how advice is presented\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. For example, an AI assistant might provide more explanatory context or confidence metrics to a user identified as high CSE/agentic, to earn their trust and justify the suggestion while respecting their autonomy\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Conversely, users lower in self-efficacy or higher in communal orientation may respond better to a more collaborative and encouraging tone, where suggestions are framed as shared improvements rather than corrections\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In conclusion, a one-size-fits-all approach to AI advice may be suboptimal \u0026ndash; our results argue for personalization in human-AI interaction, considering user mindfulness, confidence, and social orientation to improve both adoption and user satisfaction in human\u0026ndash;AI interactions\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003eLimitations and Future Research\u003c/h2\u003e\u003cp\u003eWhile this study provides novel insights, several limitations must be acknowledged, which also open avenues for future research.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFirst\u003c/b\u003e, the scope of context. Our experiment focused on decision revision within a specific task using AI-generated suggestions. It remains to be seen whether these findings generalize to different types of decisions (e.g., high-stakes versus low-stakes, creative versus analytical domains) or to other AI formats such as predictive versus prescriptive tools\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. \u003cb\u003eSecond\u003c/b\u003e, issues of causality and measurement. Mindfulness, creative self-efficacy, and trait orientation were measured rather than manipulated. Future research using experimental manipulations such as mindfulness inductions or self-efficacy priming could establish directionality more clearly\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Additionally, our binary outcome (i.e., decision revision vs. no revision) captures limited process-level information. Complementary methods such as interaction logging could offer deeper insights into how traits shape engagement with AI\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. \u003cb\u003eThird\u003c/b\u003e, we did not systematically examine demographic or cultural variables, which may moderate or interact with the psychological traits studied. Communal versus agentic orientations are culturally influenced and testing our model across different cultural contexts or organizational settings could enhance generalizability\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Likewise, age and technological familiarity may shape trust in AI. For example, younger users may exhibit lower baseline algorithm aversion and may engage with AI differently\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. \u003cb\u003eFourth\u003c/b\u003e, the AI system used in our study functioned as a black-box advisor with assumed reliability. In practice, user receptivity depends not only on internal traits but also on system transparency, error rates, and the quality of explanation\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Future research could explore how individual traits interact with AI performance variability. For instance, would a mindful user remain open to suggestions after witnessing AI errors? Understanding how trust in AI evolves over time in response to system performance could be an important contribution. \u003cb\u003eFifth\u003c/b\u003e, we did not examine longitudinal trajectories of user behavior. It remains unclear whether the observed influences of mindfulness, self-efficacy, and social orientation are stable over time or subject to adaptation through repeated interaction with AI systems\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Longitudinal and adaptive research designs are needed to map how user\u0026ndash;AI relationships evolve and whether openness to AI can be cultivated, sustained, or undermined over time.\u003c/p\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003eStudy Design and Preregistration\u003c/h2\u003e\u003cp\u003eThis study employed a within-subject experimental design to investigate how individual traits, namely mindfulness, creative self-efficacy (CSE), and gender-role orientation, influence receptivity to AI-generated advice in a decision-making context. The design included both AI-absent and AI-present conditions, allowing direct assessment of decision change after AI input. The study was preregistered on the Open Science Framework (OSF; \u003cem\u003elink blinded for peer review\u003c/em\u003e) and received ethical approval from the Institutional Review Board of the Department of Applied Psychology: Work, Education, Economy at the University of Vienna (Approval Number: 2019/A/002). All procedures were performed in accordance with the Declaration of Helsinki (2013 revision) and with all relevant institutional and national regulations and guidelines. Written informed consent was obtained electronically from all participants before any study procedures were initiated. The research did not involve live vertebrates or higher invertebrates; therefore, ARRIVE guidelines and animal-care regulations do not apply.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and Procedure\u003c/h2\u003e\u003cp\u003eParticipants were recruited via the online platform Prolific, targeting professionals based in the United Kingdom with occupational exposure to AI systems or decision-making technologies. Eligibility screening ensured that respondents were currently employed in sectors such as business analytics, R\u0026amp;D, sales, or marketing, domains where AI-assisted decision-making is increasingly prevalent.\u003c/p\u003e\u003cp\u003eA total of 549 participants (after quality control and screening) completed the full survey hosted on Qualtrics. All participants provided informed consent electronically before beginning the study. The survey included embedded logic to terminate participation if consent was not granted. Compensation was provided in line with Prolific\u0026rsquo;s ethical payment guidelines.\u003c/p\u003e\u003cp\u003eAfter providing demographic data, participants completed trait-level measures of mindfulness, creative self-efficacy, and gender-role traits. They were then randomly assigned to a sequence of decision-making scenarios designed to test baseline judgments (without AI input) and response shifts after receiving AI-generated advice, with scenario order counterbalanced to mitigate sequence effects.\u003c/p\u003e\u003cp\u003eIn the non-AI condition, participants reviewed sales data and bar charts for four products and were asked to allocate a marketing budget based solely on their independent analysis. In the AI-assisted condition, they were shown the same data along with an AI-generated recommendation regarding optimal budget allocation and given the opportunity to revise their original decision. This design enabled us to observe whether, and how, participants\u0026rsquo; decisions changed in response to AI input. The survey concluded with a final assessment of creative self-efficacy and a full debrief.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003cp\u003e\u003cb\u003eMindfulness\u003c/b\u003e was assessed using the 15-item Mindful Attention Awareness Scale (MAAS)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Items (e.g., \u0026ldquo;I find myself doing things without paying attention\u0026rdquo;) were rated on a 6-point Likert scale. The scale showed excellent internal consistency (α\u0026thinsp;=\u0026thinsp;.90). Higher MAAS scores reflect greater dispositional mindfulness, capturing attentional awareness and reduced automaticity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCreative Self-Efficacy (CSE)\u003c/b\u003e was measured using the 3-item Creative Self-Efficacy scale\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and 6-item subscale of the Short Scale of Creative Self (SSCS)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Participants rated items such as \u0026ldquo;I trust my creative thinking skills\u0026rdquo; on a 5-point Likert scale. The subscale demonstrated good reliability (α\u0026thinsp;=\u0026thinsp;.85). While the full SSCS includes a Creative Personal Identity subscale, only the CSE component was used in this study to capture confidence in generating novel solutions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGender-Role Traits\u003c/b\u003e i.e. communal and agentic traits were measured using the Personal Attributes Questionnaire (PAQ)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The communal-femininity (PAQ_F) and agentic-masculinity (PAQ_M) subscales each consist of 8 bipolar adjective pairs (e.g., \u0026ldquo;Not at all sympathetic \u0026ndash; Very sympathetic\u0026rdquo; for PAQ_F; \u0026ldquo;Not at all independent \u0026ndash; Very independent\u0026rdquo; for PAQ_M). Subscale reliabilities were acceptable (α\u0026thinsp;=\u0026thinsp;.78 for PAQ_F, α\u0026thinsp;=\u0026thinsp;.75 for PAQ_M). The two dimensions were orthogonal, allowing for independent and interactional modeling of trait orientations\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDecision Change After AI Input\u003c/b\u003e was measured as the primary outcome variable. We recorded whether participants revised their decision between the non-AI and AI-supported phases. This outcome was categorized into:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eChange\u003c/b\u003e: The participant changed their response following AI recommendations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eNo Change\u003c/b\u003e: The decision remained unchanged.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eTo examine how individual traits influence decision revision in response to AI-generated advice, we employed \u003cb\u003emultinomial logistic regression (MNL) models\u003c/b\u003e using R (version 4.2, nnet package). The dependent variable was a categorical outcome reflecting direction of change between initial and final decisions: \u003cb\u003eChange\u003c/b\u003e, or \u003cb\u003eNo Change\u003c/b\u003e (reference category). This outcome structure reflects our interest in whether participants updated their decisions in either direction following AI input, and it captures directional nuance relevant to the psychological mechanisms proposed in our hypotheses (e.g., openness vs. resistance to AI suggestions).\u003c/p\u003e\u003cp\u003eAll continuous predictor variables: \u003cb\u003emindfulness\u003c/b\u003e, \u003cb\u003eCSE\u003c/b\u003e, and \u003cb\u003egender-role\u003c/b\u003e traits, were standardized (z-scores) prior to analysis to allow accurate interpretation of main and interaction effects. Our analysis followed a nested model-building approach to systematically test Hypotheses H1 through H4:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eModel 1: Main Effects (H1 \u0026amp; H2)\u003c/b\u003e: Included mindfulness, CSE, PAQ_F (communal-femininity), and PAQ_M (agentic-masculinity) as predictors to assess whether these traits individually predicted likelihood of decision change.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eModel 2: Two-Way Trait Interaction (H3)\u003c/b\u003e: Added the \u003cb\u003emindfulness \u0026times; CSE\u003c/b\u003e interaction to test whether mindfulness moderated the relationship between CSE and decision revision.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eModel 3: Trait Orientation (Extension of H2 \u0026amp; H3)\u003c/b\u003e: Included the full set of gender-role traits (PAQ_F, PAQ_M, PAQ_MF) to examine how personality orientation contributed to openness or resistance.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eModel 4: Moderated Trait Interactions (Part of H4)\u003c/b\u003e: Introduced two-way interactions between \u003cb\u003emindfulness \u0026times; PAQ_F\u003c/b\u003e and \u003cb\u003eCSE \u0026times; PAQ_F\u003c/b\u003e to explore whether communal orientation influenced the individual effects of mindfulness and CSE.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eModel 5: Three-Way Interaction (H4)\u003c/b\u003e: Tested the hypothesized \u003cb\u003emindfulness \u0026times; CSE \u0026times; PAQ_F\u003c/b\u003e interaction to determine whether the combined influence of mindfulness and CSE on decision revision was amplified among individuals higher in communal-femininity traits.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAll models evaluated using \u003cb\u003eAkaike Information Criterion (AIC)\u003c/b\u003e and \u003cb\u003elikelihood ratio tests\u003c/b\u003e to assess improvements in model fit. Odds ratios (OR) and 95% confidence intervals (CI) are reported to aid interpretability. To probe significant interactions, we conducted \u003cb\u003esimple slopes analyses\u003c/b\u003e and \u003cb\u003eplotted predicted probabilities\u003c/b\u003e at \u0026plusmn;\u0026thinsp;1 SD of the interacting variables (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;4).\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe authors received no funding for this work.\u003c/p\u003e\u003cp\u003eCompeting Interests\u003c/p\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDFX, ZDH and CK contributed equally to study conceptualization and methodology design. DFX drafted and wrote the main manuscript. DFX and ZDH led the data collection, statistical analyses and data interpretation. DFX, CK and ZDH provided critical revisions and theoretical framing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all the study participants for their time and contributions. This research was supported by fellowships from the Austrian Academy of Sciences (\u0026Ouml;AW) and the Austrian Federal Ministry of Education, Science and Research (BMBWF) awarded to DFX.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe de-identified questionnaire data, analysis scripts, and preregistration file are now publicly available on Figshare at \"Replication Data for: Mindfulness and creative self-efficacy in human\u0026ndash;AI decision-making: Implications for adaptive AI design\" DOI: https://doi.org/10.6084/m9.figshare.29609312.v1 under a CC-BY 4.0 licence.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDietvorst, B. J., Simmons, J. P. \u0026amp; Massey, C. 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The role of personal resources in the job demands-resources model. \u003cem\u003eInt. J. Stress Manage.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 121\u0026ndash;141 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarwowski, M., Lebuda, I. \u0026amp; Wiśniewska, E. Measuring creative self-efficacy and creative personal identity. \u003cem\u003eInt. J. Creat Probl. Solving\u003c/em\u003e. \u003cb\u003e28\u003c/b\u003e, 45\u0026ndash;57 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7274590/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7274590/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman decision-making is increasingly augmented by artificial intelligence (AI) systems, yet individuals vary in whether they revise their judgments based on AI-generated suggestions. This study provides a timely contribution to the understanding of human factors in AI decision-making, specifically on how two psychological traits i.e. trait mindfulness and creative self-efficacy (CSE), interact with communal-agentic personality orientations to influence decision revision after AI input. Using multinomial logistic regression, we analyzed data from 549 professionals in the United Kingdom to determine whether participants maintained or adjusted their initial decisions following AI advice. Results revealed a significant interaction between mindfulness and CSE. Individuals with high mindfulness and low CSE were more likely to revise their decisions in the direction of AI recommendations, while those high in both traits tended to maintain their original choices. A three-way interaction further showed that this mindfulness\u0026ndash;CSE dynamic was most pronounced among individuals scoring high on communal-femininity traits. These findings highlight how attentional focus (mindfulness), perceived creative competence (CSE), and gender-role orientation jointly shape receptivity to AI suggestions. We discuss implications for advancing theory on individual differences in human\u0026ndash;AI collaboration and for designing adaptive AI systems tailored to users\u0026rsquo; psychological profiles.\u003c/p\u003e","manuscriptTitle":"Mindfulness and creative self-efficacy in human–AI decision-making: Implications for adaptive AI design","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 01:57:45","doi":"10.21203/rs.3.rs-7274590/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-24T11:51:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T17:31:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T15:42:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40714062654132854508490788113278260451","date":"2025-12-06T10:55:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215338575453935731854764650807641219675","date":"2025-12-05T04:54:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T14:07:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215025602107532691485136751045505458101","date":"2025-11-30T08:02:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-19T05:19:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-19T05:10:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-08T07:25:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-06T13:52:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-06T13:48:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5699cd3-0330-4b2e-a538-7279b89849ad","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56041918,"name":"Biological sciences/Neuroscience"},{"id":56041919,"name":"Biological sciences/Psychology"},{"id":56041920,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-17T09:10:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 01:57:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7274590","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7274590","identity":"rs-7274590","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}