When AI Feels Supportive: Psychological Safety, Satisfaction, and Turnover Among Healthcare Professionals

When AI Feels Supportive: Psychological Safety, Satisfaction, and Turnover Among Healthcare Professionals | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 October 2025 V1 Latest version Share on When AI Feels Supportive: Psychological Safety, Satisfaction, and Turnover Among Healthcare Professionals Author : Heungsok Cha 0000-0001-6696-7669 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176007254.45491764/v1 456 views 240 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study investigated how healthcare professionals' perceptions of AI integration influence job satisfaction and turnover intention, with psychological safety as a mediating mechanism. Based on a three-wave survey of 360 hospital and clinic employees in China, analyzed through confirmatory factor analysis and structural equation modeling, the findings show that when AI is perceived as transparent, useful, and supportive, clinicians report higher satisfaction and lower turnover intention. Psychological safety explains these effects, as favorable AI appraisals foster a climate where staff feel safe to voice concerns and take risks, enhancing satisfaction and reducing quitting intentions. While conducted in a collectivist context, the results highlight the need for cross-national validation. Practically, AI implementation should be paired with training, career development, and error-tolerant cultures to ensure adoption strengthens retention. The study contributes by integrating technology-acceptance and Job Demands--Resources perspectives, positioning psychological safety as a key mechanism linking AI to workforce outcomes. When AI Feels Supportive: Psychological Safety, Satisfaction, and Turnover Among Healthcare Professionals Dr. Cha, Heungsok Associate Professor of International Commerce Joongbu University in South Korea [email protected] ABSTRACT This study investigated how healthcare professionals’ perceptions of AI integration influence job satisfaction and turnover intention, with psychological safety as a mediating mechanism. Based on a three-wave survey of 360 hospital and clinic employees in China, analyzed through confirmatory factor analysis and structural equation modeling, the findings show that when AI is perceived as transparent, useful, and supportive, clinicians report higher satisfaction and lower turnover intention. Psychological safety explains these effects, as favorable AI appraisals foster a climate where staff feel safe to voice concerns and take risks, enhancing satisfaction and reducing quitting intentions. While conducted in a collectivist context, the results highlight the need for cross-national validation. Practically, AI implementation should be paired with training, career development, and error-tolerant cultures to ensure adoption strengthens retention. The study contributes by integrating technology-acceptance and Job Demands–Resources perspectives, positioning psychological safety as a key mechanism linking AI to workforce outcomes. Keywords : AI integration; psychological safety; job satisfaction; turnover intention Highlights • Favorable AI integration boosts clinicians’ job satisfaction and lowers turnover. • Psychological safety mediates the link between AI perception and work outcomes. • Transparent, skill-building AI rollouts enhance safety and retention. • Integrating JD–R and technology-acceptance perspectives advances theory on AI and work. 1. Introduction Artificial intelligence (AI) has moved from isolated pilots to a core layer of clinical infrastructure, supporting diagnostics, triage, and documentation across hospitals (OECD, 2024; U.S. Department of Health and Human Services [HHS], 2025; Lanyi et al., 2025; CADTH, 2025). Horizon scans and policy briefs anticipate continued double-digit growth in AI investment over the next five years—driven by workforce shortages and value-based-care mandates—making clinicians’ acceptance of AI systems a strategic imperative for health services planning (OECD, 2024; HHS, 2025; Lanyi et al., 2025; CADTH, 2025). Clinicians’ appraisals of this rollout—hereafter AI integration perception (AIP)—are linked to attitudes and well-being at work (Huo et al., 2025; Kim et al., 2025). Favorable AIP aligns with greater basic-needs satisfaction and engagement, whereas negative appraisals of organizational AI adoption are associated with strain outcomes, including depressive symptoms (Huo et al., 2025; Kim et al., 2025). In nursing samples, positive AI knowledge, beliefs, and attitudes are associated with stronger intentions to remain in the profession and to integrate AI into practice, underscoring the practical stakes of shaping clinicians’ cognitive appraisals of AI (Oweidat et al., 2025; Paré et al., 2025). Psychological safety—the shared belief that speaking up, erring, or experimenting is acceptable—emerges as a pivotal climate resource during digital transformation (Vöcking et al., 2024). Design-ethnographic and review evidence indicates that technology rollouts can unsettle trust and learning norms unless teams cultivate open communication and identifies psychological safety as a recurring determinant of digital-health adoption success, with links to withdrawal cognitions during technological change in nursing (Minartz et al., 2024; Schlicht et al., 2025; Vöcking et al., 2024). Despite these insights, psychological safety has rarely been modeled as a mediator between AIP and downstream job attitudes using temporally separated designs or datasets spanning multiple professional groups (Huo et al., 2025; Kim et al., 2025; Paré et al., 2025). Guided by Job Demands–Resources theory, positive AIP is framed as a job resource that fosters psychological safety, which in turn elevates job satisfaction and reduces turnover intention (Bakker & Demerouti, 2017). To test this account, a three-wave panel survey of hospital professionals in China was implemented (T1 AIP → T2 psychological safety → T3 job satisfaction and turnover intention), with two focal hypotheses: H1 , AIP positively predicts job satisfaction; and H2 , AIP negatively predicts turnover intention (Bakker & Demerouti, 2017). 2. Literature review 2.1 AI integration perception and job satisfaction The Job Demands–Resources (JD–R) model positions favorable AI integration perception (AIP) as a job resource that expands capabilities, streamlines effort, and supports motivation, thereby improving work engagement and satisfaction (Chuang et al., 2025; Kassa, 2025; Soulami et al., 2024). Complementing JD–R, the Technology Acceptance Model (TAM) and allied acceptance frameworks show that perceived usefulness, performance expectancy, and trust reliably forecast positive AI attitudes and downstream workplace outcomes, including satisfaction (Almeida, 2025; Hughes, 2019; Kelly et al., 2023). Related perspectives—transactional stress and self-perception theories—further suggest that when AI is appraised as a challenge (rather than a hindrance) and implemented in collaboration with human judgment (rather than as a substitution strategy), employees infer competence, agency, and value, reinforcing satisfaction (Huang & Gursoy, 2024; Sadeghi, 2024; Xu et al., 2024). A growing empirical base aligns with these propositions across methods, sectors, and cultural settings. Syntheses of hundreds of studies report that AI-enabled redesign can elevate job satisfaction, reduce stress, and shift effort toward more meaningful tasks as routine work is automated (Kassa, 2025; Soulami et al., 2024; Hughes, 2019). Large-sample surveys and field experiments likewise find that collaborative AI strategies enhance perceived career achievement and fairness, while AI-supported performance systems are associated with higher satisfaction when they are transparent and seen as supportive rather than punitive (Chuang et al., 2025; Nath et al., 2025; Sadeghi, 2024; Xu et al., 2024). Cross-cultural acceptance studies converge on the same pattern: positive AIP correlates with better attitudinal outcomes, especially when implementation is participatory and organizational supports are visible (Almeida, 2025; Huang & Gursoy, 2024; Kelly et al., 2023). Mechanistically, positive AIP operates through three interlinked pathways. First, efficiency and augmentation: AI reduces time on routine tasks and augments decision quality, freeing attention for intrinsically rewarding work (Chuang et al., 2025; Huang & Gursoy, 2024; Kassa, 2025; Sadeghi, 2024). Second, autonomy and information quality: better decision support and flexible workflows increase perceived control and fit with preferences (Hughes, 2019; Soulami et al., 2024; Xu et al., 2024). Third, strain reduction and growth prospects: lower cognitive load and newly created skill pathways support competence and career development, further elevating satisfaction (Chuang et al., 2025; Nath et al., 2025; Sadeghi, 2024; Xu et al., 2024). These effects are contingent on context; innovation climate and a collaborative (vs. substitution) implementation approach consistently strengthen the AIP–satisfaction link. H1 . AI integration perception will be positively associated with job satisfaction. 2.2 AI integration perception and turnover intention AI integration perception (AIP) captures the extent to which employees experience organizational AI as useful, fair, and developmental—rather than opaque, surveillant, or threatening—and, by extension, whether it adds resources (autonomy, learning) or amplifies demands (insecurity, anxiety) under the Job Demands–Resources and technology-acceptance lenses (Bakker & Demerouti, 2017; Kelly, Kaye, & Oviedo-Trespalacios, 2023). When AI is framed as augmenting growth and competence, employees tend to reciprocate with stronger attachment and lower turnover intention; when it is framed as substitutional or punitive, threat appraisals and withdrawal cognitions increase (Bakker & Demerouti, 2017; Kelly et al., 2023). Accumulating evidence across sectors supports this mechanism. AI-induced job insecurity has been shown to erode psychological safety and trigger maladaptive behaviors, a climate pathway closely linked to exit cognitions (Kim, 2024). In multi-firm samples, AI anxiety positively predicts turnover intention, with quiet quitting partially mediating the association, underscoring the role of disengagement in translating threat into exit plans (Uygungil-Erdogan, Cücük, & Erdoğan, 2025). Related work documents that heightened AI awareness as threat correlates with stronger quitting plans unless organizations buffer with training and upskilling (Lin, He, & Wang, 2024). Broader syntheses likewise note that perceived technological disruption elevates turnover intention, especially when trust and human involvement are low (Jin et al., 2024; Khalifa et al., 2025). Healthcare patterns are consistent with these dynamics. Among nurses, positive AI attitudes and practices are associated with a stronger intent to stay, indicating that supportive beliefs around AI can stabilize retention (Oweidat et al., 2025). Conceptual and commentary work in nursing highlights that well-designed AI can relieve administrative burden and reduce burnout—conditions that typically precede turnover—provided implementation protects care quality and voice (Rony et al., 2024; Yakusheva, 2025). Industry estimates similarly project meaningful documentation relief for frontline staff, reinforcing the plausibility of retention benefits when AI is integrated transparently and with adequate support (MedCity News, 2025). H2. AI integration perception will be negatively associated with turnover intention. 2.3 The mediating role of psychological safety A substantial literature identifies psychological safety as a reliable antecedent of positive work attitudes, including job satisfaction; a meta-analysis across 117 samples links higher psychological safety to stronger affect and satisfaction, and recent survey evidence with team-level psychological safety as among the strongest determinants of job satisfaction after accounting for workload and tenure (Frazier et al., 2017; Etti et al., 2025). Because organizational AI can either bolster or erode psychological safety—through signals of augmentation versus replacement—supportive AI integration is expected to enhance psychological safety, which in turn elevates satisfaction; evidence from AI contexts indicates that AI-induced job insecurity undermines psychological safety and that psychological safety can operate as a mediator in AI–outcome linkages (Kim, 2024; Kim, 2025). Psychological safety also shapes retention. Longitudinal analyses of more than 27,000 U.S. healthcare workers identify psychological safety as an enduring resource that buffers resource constraints and reduces turnover intention over time (Bahadurzada et al., 2024). In parallel, research on digital transformation shows that when psychological safety is low, quiet-quitting behaviors escalate and are associated with higher turnover intention, underscoring psychological safety’s role in mitigating withdrawal cognitions (Kim & Tae, 2024). Collectively, these findings support a process in which psychological safety transmits the effects of AI integration perception onto downstream attitudes, decreasing withdrawal while improving satisfaction (Bahadurzada et al., 2024; Frazier et al., 2017). H3. Psychological safety mediates the relationship between AI integration perception and job satisfaction. H4. Psychological safety mediates the relationship between AI integration perception and turnover intention. Figure 1. Proposed research mode 3. Methodology 3.1 Research design To strengthen causal inference for the hypothesized indirect effects, a three-wave panel survey with two-month lags was implemented (T1 = Month 0; T2 = Month 2; T3 = Month 4). A 60-day interval is sufficient for attitudinal change (e.g., shifts in psychological safety) while limiting threats from history and maturation. The spacing aligns with longitudinal studies in hospital settings that commonly use 2–3-month lags. Surveys were administered to healthcare professionals nationwide. All constructs were measured on a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree). Participants were matched across waves using unique codes; responses were anonymous and confidential. Because respondents were nested within hospitals and professions, subsequent analyses included site clustering and prespecified controls (age, tenure, gender, AI hours/week). Time separation, anonymity, and matched IDs were used to mitigate common-method variance, with an additional latent-method/marker check planned in the measurement model. Table 1. Three-Wave Survey Timeline T1 Month 0 AI integration perception (+ demographics & controls, not counted as items) 6 5–7 min T2 Month 2 Psychological safety 5 3 min T3 Month 4 Job satisfaction + Turnover intention 10 (7 + 3) 4–5 min Note. Scale items exclude demographics/control fields. Total scale burden ≈ 22 items (<15 minutes) plus brief control fields at T1. 3.2 Measurement AI integration perception was measured with six adapted items from Davis (1989) capturing perceived usefulness, ease of use, and task–technology fit (e.g., “AI helps me accomplish tasks more effectively”). Psychological safety was assessed with five items from Edmondson (1999) (e.g., “Mistakes are not held against me on this team”). Job satisfaction was measured with seven items from Spector (1985) (e.g., “Pay is fair for the work performed”). Turnover intention was measured with three items from Mobley et al. (1978) (e.g., “I intend to look for another job”). All items used a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree), were contextually adapted to hospital settings, and were reviewed by academic experts and healthcare practitioners to ensure clarity and relevance. Higher scores indicate higher levels of each construct. 3.3 Data collection Recruitment was facilitated by doctoral researchers in South Korea who contacted healthcare professionals in China. A three-wave online survey was administered at two-month intervals, with confidentiality assured and unique identification codes used to match responses across waves, thereby mitigating common-method bias through temporal separation. After excluding incomplete submissions and failed attention checks, 360 matched cases were retained for analysis. Multivariate normality of the composite variables was evaluated using the Henze–Zirkler test (Henze & Zirkler, 1990), which failed to reject the null hypothesis (HZ = 0.41, p = .995), indicating approximate normality of the data. 3.4 Data analysis Analyses used SPSS 23 for descriptives and reliability and AMOS 22 for CFA/SEM. First, demographic characteristics were summarized. Second, internal consistency and convergent validity were examined by computing Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE) (derived from CFA standardized loadings). Discriminant validity was assessed via the Fornell–Larcker criterion and heterotrait–monotrait (HTMT) ratios. Third, confirmatory factor analyses (CFA) evaluated the measurement model; structural equation modeling (SEM) tested the theorized relationships. Model adequacy was judged using χ²/df, CFI, TLI, RMSEA (with 90% CI), and SRMR. To address potential common-method variance, the three-wave design and confidentiality were supplemented by a latent common-method factor check. Missing data was handled with full information maximum likelihood (FIML), and standard errors were cluster-robust at the hospital level. Finally, hypotheses were tested within the SEM framework, and indirect effects were evaluated using bias-corrected bootstrapping with 5,000 resamples to obtain confidence intervals. 4. Results 4.1 Profile of respondents In Table 2, the final sample consisted of 360 healthcare professionals. In terms of gender, 65.3% were female and 34.7% were male. With respect to age, the largest group was between 31 and 40 years old (47.8%), followed by those above 40 years (25.6%), 26–30 years (15.0%), and 25 years or below (11.7%). Regarding working experience, over one-third reported 6–10 years of tenure (36.7%), while 25.8% had more than 10 years, 16.4% had 3–6 years, and smaller proportions reported 1–3 years (10.3%) or less than 1 year (10.8%). For professional roles, the sample included nurses (42.2%), doctors (34.4%), allied health staff (17.2%), and a smaller proportion of administrative staff (6.1%). Departmental distribution showed notable representation from Internal Medicine (19.2%), Surgery (18.6%), Pediatrics (15.8%), Emergency (13.6%), and Oncology (12.2%), with the remaining participants in Administrative (20.6%). Departmental distribution showed notable representation from Internal Medicine (19.2%), Surgery (18.6%), Pediatrics (15.8%), Emergency (13.6%), and Oncology (12.2%), with the remaining participants in Administrative (20.6%). Table 2 Demographic profile of the respondents ( N = 360). Gender Female 235 65.28 Male 125 34.72 Age 25 years or below 42 11.67 26–30 years 54 15.00 31–40 years 172 47.78 Above 40 years 92 25.56 Working Experience Less than 1 year 39 10.83 1–3 years 37 10.28 3–6 years 59 16.39 6–10 years 132 36.67 Above 10 years 93 25.83 Profession Admin 22 6.11 Allied health staff 62 17.22 Doctor 124 34.44 Nurse 152 42.22 Department Admin 74 20.56 Emergency 49 13.61 Internal Medicine 69 19.17 Oncology 44 12.22 Pediatrics 57 15.83 Surgery 67 18.61 Note. Percentages may not sum to exactly 100% due to rounding. 4.2 Correlations, means, standard deviation, reliability and validity In Table 3, the sample (N = 360) comprised predominantly female staff (M for gender coding = 0.64, SD = 0.48), with a mean age of 35.97 years (SD = 7.60) and mean tenure of 7.04 years (SD = 4.75). Average weekly AI use was 3.07 hours (SD = 2.19). Construct means were moderately favorable: AI integration (M = 4.78, SD = 0.89), psychological safety (M = 4.98, SD = 0.89), and job satisfaction (M = 5.07, SD = 0.82); turnover intention was below the midpoint (M = 3.39, SD = 0.99). Correlations were in expected directions and small-to-moderate in magnitude: AI integration correlated positively with psychological safety (r = .26, p < .001) and job satisfaction (r = .22, p < .001), and negatively with turnover intention (r = −.22, p < .001). Psychological safety was positively related to job satisfaction (r = .29, p < .001) and negatively to turnover intention (r = −.26, p < .001); job satisfaction related modestly and negatively to turnover intention (r = −.11, p < .05). Among controls, older age and longer tenure showed small positive associations with AI integration (rs = .17 and .15, respectively), and weekly AI hours related positively to AI integration (r = .25, p < .001) and negatively to turnover intention (r = −.14, p < .01). Gender exhibited trivial correlations with focal constructs. In Table 4, all four reflective scales exhibited acceptable to strong internal consistency (α = .81–.88) and convergent validity (AVE = .55–.73; all ≥ .50). For AI integration perception, standardized loadings ranged from .71 to .84 with α = .88 and AVE = .62, indicating a coherent, unidimensional construct anchored in task effectiveness, relevance, quality, and ease-of-use items. Psychological safety showed comparably strong loadings (.75–.82), α = .85, and AVE = .63, supporting that teams allow risk taking, error tolerance, and open voice. Job satisfaction—a multifaceted construct—displayed adequate loadings (.69–.78), α = .86, and AVE = .55, consistent with a reliable composite across pay, promotion, supervision, benefits, coworkers, work, and communication facets. Turnover intention presented the strongest convergence (.77–.86), α = .81, and AVE = .73, reflecting a tight three-item scale capturing job search/quit intentions. Taken together, the magnitude and homogeneity of loadings (no item < .69) indicate sound indicator quality and support forming composite scores. Further, the square roots of AVE (AI ≈ .79, PS ≈ .79, JS ≈ .74, TI ≈ .85) exceed the largest observed inter-construct correlation (|r| ≤ .29), providing evidence for discriminant validity under the Fornell–Larcker criterion. Table 3. Correlations, means and standard deviations Age (years) 35.97 7.60 — Tenure (years) 7.04 4.75 0.02 — Gender (1 = Female) 0.64 0.48 0.02 −0.02 — AI hours/week 3.07 2.19 0.01 0.12* −0.02 — AI integration (T1) 4.78 0.89 0.17** 0.15** −0.00 0.25*** — Psychological Safety (T2) 4.98 0.89 0.14** 0.07 −0.05 0.06 0.26*** — Job satisfaction (T3) 5.07 0.82 0.11* 0.08 −0.01 0.09 0.22*** 0.29*** — Turnover intention (T3) 3.39 0.99 −0.05 −0.05 −0.01 −0.14** −0.22*** −0.26*** −0.11* — Note. N = 360. SD = standard deviation. *p < .05; **p < .01; ***p < .001 Table 4. Item measurement properties AI Integration Perception (α = .88, AVE = .62) AI helps me accomplish tasks more effectively. .80 AI functions are relevant to my key responsibilities. .84 AI-generated information is high quality for my needs. .76 It is easy to show others that AI improves my outcomes. .81 Learning required AI applications is straightforward. .81 Interacting with AI requires little mental effort. .71 Psychological Safety (α = .85, AVE = .63) Mistakes are not held against me on this team. .82 Difficult issues can be raised with the team. .78 Members are accepted even if they are different. .75 It is safe to take a risk on this team. .82 No one would deliberately undermine my efforts. .76 Job Satisfaction (α = .86, AVE = .55) Pay is fair for the work performed. .69 Opportunities for promotion are adequate. .73 My supervisor is competent in their job. .78 The benefits I receive are satisfactory. .71 I like the people I work with. .76 The work itself is enjoyable. .74 Communication in the organization is effective. .71 Turnover Intention (α = .81, AVE = .73) I intend to look for another job. .86 I am actively seeking employment elsewhere. .78 I plan to leave this organization. .77 Notes. SL = standardized factor loadings from a confirmatory factor analysis (CFA) on each construct’s item set. AVE and CR were computed from CFA loadings. Item stems are paraphrased for brevity and aligned with Davis (1989), Edmondson (1999), Spector (1985), and Mobley et al. (1978). Values are computed from the current dataset (N = 360). 4.3 Tests of model fit 4.3.1 Measurement model. Confirmatory factor analyses (CFA) were conducted for each scale and for the full four-factor measurement model. Item quality was acceptable (all standardized loadings ≥ .69; Cronbach’s α = .81–.88; AVE = .55–.73). Model adequacy was evaluated using χ²/df, CFI, TLI, RMSEA (90% CI), and SRMR (GFI is not reported). The single-construct CFAs—AI integration perception (6 items), psychological safety (5), job satisfaction (7), and turnover intention (3)—met conventional thresholds (CFI/TLI ≥ .95; RMSEA ≤ .06; SRMR ≤ .08), supporting unidimensionality. The four-factor model specifying correlated latent variables (AI_T1, PS_T2, JS_T3, TI_T3) also fit well with the same criteria. Discriminant validity was supported (Fornell–Larcker: √AVE exceeded inter-construct correlations; HTMT < .85). To address common-method bias, a latent method factor added to the omnibus CFA left substantive loadings and factor correlations essentially unchanged (ΔCFI < .01). 4.3.2 Structural model. The longitudinal structural model (AI_T1 → PS_T2 → JS_T3, TI_T3, with direct paths from AI_T1 to T3 outcomes) was estimated in SEM using FIML with hospital-clustered robust standard errors. Overall fit met recommended thresholds (CFI/TLI ≥ .95; RMSEA ≤ .06; SRMR ≤ .08). Because RMSEA can be unstable with low degrees of freedom, emphasis is placed on CFI/TLI and SRMR for near-saturated specifications. Controls were included as exogenous predictors of PS_T2 and the T3 outcomes; including controls did not materially change the substantive paths. 4.4 Hypotheses testing 4.4.1 Tests of direct effects. Table 5 summarizes standardized path coefficients (β), critical ratios, and significance. Results indicate positive effects of AI integration on psychological safety and job satisfaction, and negative effects on turnover intention. Psychological safety positively predicts job satisfaction and negatively predicts turnover intention. Thus, H1 (AI → Job satisfaction) and H2 (AI → Turnover intention) are supported; paths involving psychological safety are included as part of the theorized mediation mechanism. Table 5. Hypotheses testing of direct effects H1: AI integration → Job satisfaction 0.12 2.40* Supported H2: AI integration → Turnover intention −0.13 −2.60** Supported AI integration → Psychological safety 0.26 5.10*** — Psychological safety → Job satisfaction 0.34 6.70*** — Psychological safety → Turnover intention −0.28 −5.50*** — Note. Standardized coefficients (β) reported. Two-tailed tests: *p < .05; **p < .01; ***p < .001. 4.4.2 Tests of indirect effects . As shown in Table 6, bias-corrected bootstrapping (5,000 resamples) was used to test mediation. Both indirect effects were statistically significant, with confidence intervals excluding zero, indicating that psychological safety transmits the influence of AI integration to both outcomes. Table 6. Hypotheses testing of mediation effects H3: AI integration → Psychological safety → Job satisfaction 0.09 [0.04, 0.15] Supported H4: AI integration → Psychological safety → Turnover intention −0.07 [−0.12, −0.03] Supported Note. BC = bias-corrected confidence interval. Indirect β ≈ a×b (AI→PS × PS→outcome). 5. Discussion and implications 5.1 Discussion The evidence supports all four hypotheses. Employees who appraise their organization’s AI rollout as transparent, skill-enhancing, and well aligned with daily workflows report higher job satisfaction (H1) and lower turnover intention (H2). Framed through the Job Demands–Resources lens, favorable AI integration perception functions as a motivational resource—expanding autonomy, decision support, and efficiency—thereby elevating satisfaction while dampening withdrawal cognitions. These patterns are consistent with large multi-context surveys and field studies that link constructive AI appraisals to greater work meaningfulness and reduced quitting inclinations. Psychological safety emerges as the primary conduit through which these benefits materialize. When AI adoption is accompanied by open dialogue, error-tolerant learning norms, and responsive support, psychological safety rises; in turn, psychologically safe teams consistently display stronger well-being and lower intent to leave. The mediation tests echo this mechanism: positive AI integration perception increases the felt sense that “it is safe here to try, err, and learn,” and that climate translates technological optimism into higher satisfaction and lower turnover intention (H3–H4). The indirect effects are small to moderate yet practically meaningful, indicating partial mediation—AI influences outcomes both directly and indirectly via psychological safety. Taken together, the results outline a virtuous cycle: well-communicated, development-oriented AI initiatives strengthen psychological safety; stronger safety transforms AI’s resource potential into attitudinal gains (greater satisfaction) and retention benefits (lower turnover intention). In settings facing persistent staffing pressures, even modest effect sizes accrue at scale, underscoring the strategic value of pairing technical deployment with climate-building practices that sustain learning and voice. 5.2 Theoretical Implications The results extend technology-acceptance theorizing by linking favorable AI integration perception to distal attitudinal outcomes—higher job satisfaction and lower turnover intention—that have long anchored organizational behavior research, thereby moving beyond usage intentions to core evaluations of work and attachment (Davis, 1989; Venkatesh et al., 2003; Locke, 1976; Mobley et al., 1979). In doing so, the evidence positions AI appraisal not only as a predictor of adoption but as a motivational signal about role quality and employment prospects. Job satisfaction—classically tied to evaluations of task characteristics and work context (Locke, 1976)—and turnover intention—central in process models of withdrawal (Mobley et al., 1979)—both appear sensitive to how AI is framed and experienced at the point of care, thereby broadening the criterion space typically associated with acceptance models (Davis, 1989; Venkatesh et al., 2003). The findings also foreground psychological safety as the socio-emotional mechanism through which AI perceptions translate into these outcomes, integrating human–AI systems perspectives with the Job Demands–Resources (JD–R) framework (Huang & Rust, 2021; Bakker & Demerouti, 2007; Edmondson, 1999). Psychological safety functions as a resource that attenuates change-related strain and enables the motivational potential of AI to materialize: where voice, error-tolerant learning, and experimentation are normative, employees can appropriate AI’s informational and efficiency gains into meaningful work and professional growth (Edmondson, 1999; Bakker & Demerouti, 2007). Conceptually, this mechanism clarifies that AI is not inherently morale-enhancing or threatening; its effects depend on whether the surrounding climate affords safe engagement with new tools (Kahn, 1990; Nembhard & Edmondson, 2006). Demonstrating both direct and mediated effects helps reconcile polarized narratives predicting either uplift or erosion in work quality under AI adoption (Acemoglu & Restrepo, 2020; Makridakis, 2017). A direct path from AI appraisal to attitudes is consistent with acceptance-derived notions of perceived usefulness and performance expectancy (Davis, 1989; Venkatesh et al., 2003), whereas an indirect path via psychological safety reflects the socio-relational conditions required for learning and adaptation (Edmondson, 1999; Bakker & Demerouti, 2007). This dual-path specification explains when and how optimistic versus skeptical narratives may hold: uplift is more likely under climates of inclusiveness and team learning; erosion is more likely where error-voice is risky and change is experienced as surveillance or substitution (Nembhard & Edmondson, 2006; Huang & Rust, 2021). The framework further suggests multi-level theorizing. Psychological safety is inherently collective, often residing at the unit or team level, while AI appraisal and satisfaction are frequently measured at the individual level (Edmondson, 1999; Kahn, 1990). Embedding cross-level paths—team psychological safety shaping individual satisfaction and withdrawal—can align micro acceptance processes with meso climate dynamics and illuminate variance decomposition across levels (Edmondson, 1999; Bakker & Demerouti, 2007). In parallel, boundary conditions deserve explicit modeling: leadership style may amplify or dampen the safety pathway (Piccolo & Colquitt, 2006), fairness perceptions may condition whether AI signals support versus threat (Colquitt, 2001), and national culture may moderate preferences for augmentation versus substitution and tolerance for algorithmic guidance (Hofstede, 2001). Finally, the human–AI systems lens implies content-specific contingencies (Huang & Rust, 2021). Distinguishing augmentative from automating use cases can refine predictions about non-linearities—for example, potential inverted-U relations between AI intensity and satisfaction when marginal gains are offset by autonomy loss. Mapping tasks by complexity and interdependence can specify when AI most readily becomes a resource in JD–R terms and when it risks becoming a demand (Bakker & Demerouti, 2007). Collectively, these extensions encourage models of digital transformation that place relational climate variables alongside traditional acceptance beliefs and that test moderators such as leadership, justice, and culture to explain heterogeneity in satisfaction and retention outcomes (Colquitt, 2001; Hofstede, 2001; Piccolo & Colquitt, 2006; Davis, 1989; Venkatesh et al., 2003; Locke, 1976; Mobley et al., 1979; Huang & Rust, 2021; Bakker & Demerouti, 2007; Edmondson, 1999; Kahn, 1990; Nembhard & Edmondson, 2006; Acemoglu & Restrepo, 2020; Makridakis, 2017). 5.3 Practical Implications Clinicians respond most positively when AI is positioned as an augmentation of clinical judgment rather than a substitute for it. Framing matters: when an initiative is introduced with a clear account of bedside benefits—more minutes reclaimed from documentation, faster access to diagnostic cues on complex cases, fewer redundant clicks—job satisfaction rises, and withdrawal cognitions recede. The most effective rollouts treat communication with the same rigor as protocol design. Short “clinical impact” briefings that translate technical features into patient-care improvements, paired with a one-page workflow map that shows exactly which steps will be shortened and for whom, anchor the change in day-to-day practice rather than in abstract promise. Skill investment signals credibility and reduces uncertainty. Launches work best when accompanied by protected learning time and visible development pathways: micro-credentials in AI-supported diagnostics and documentation, mentored rotations in data-driven quality improvement, and co-design sprints where nurses and physicians help shape decision-support dashboards. When these activities are tied to recognition, promotion criteria, or pay differentials, AI adoption becomes legible as career growth instead of a moving target. Psychological safety is the quiet engine that converts constructive AI perceptions into durable gains. Each technical “go-live” benefits from an equivalent cultural milestone: routine debrief huddles where staff can surface model errors, bias concerns, or workflow friction without blame; named AI peer champions who can facilitate open dialogue on the wards; and public acknowledgment of early error reporting as professional excellence. Feedback must travel somewhere: an AI incident log with time-stamped remediation, plus release notes that explain what changed, why it changed, and how users will notice the difference, close the loop and build trust. Sustained improvement also depends on governance that guards against workload creep and surveillance anxiety. A multidisciplinary steering group—spanning clinical leadership, nursing, IT, quality, and ethics—should review safety, fairness, and scope-of-practice boundaries, and set explicit “no-punitive-use” guardrails for telemetry or keystroke data. When AI frees time, staffing and task allocation should be adjusted so reclaimed minutes become patient care rather than hidden overtime. Finally, success should be tracked with leading indicators rather than adoption counts alone. A brief psychological-safety index (for example, a few Edmondson-style items) and AI-specific sentiment can be embedded in quarterly engagement or safety surveys; paired with intent-to-stay, burnout risk, AI-related incident reports, and verified time-on-task changes, these metrics provide an honest readout of whether AI is improving care and work. Reporting results on a balanced scorecard—care quality, staff well-being, and efficiency—keeps leadership attention fixed on the human conditions that allow technical investments to translate into higher satisfaction and lower turnover. 5.4 Limitations and Future Research Several boundary conditions should be noted. First, the study was conducted in China, a context with comparatively collectivist norms that may shape technology appraisal and voice differently from many Western settings (Hofstede, 2001). Generalizability would be strengthened by cross-national replications that sample diverse institutional environments and health-system structures, ideally with tests of measurement invariance across countries and professional groups. Second, the explanatory scope was intentionally narrow, modeling psychological safety as a single mediator between AI integration perception and outcomes. Future work could incorporate additional mechanisms (e.g., role clarity, perceived justice, AI self-efficacy, technostress, meaningful work) and moderators (e.g., leadership style, implementation strategy—augmentation vs. substitution, unit climate, national culture) to capture contingencies under which AI improves or erodes attitudes. Third, all focal variables were self-reported, which introduces susceptibility to social desirability and shared-method variance despite temporal separation. Subsequent studies could triangulate with objective or administrative indicators—for example, electronic health record timestamps for documentation time, audit logs of decision-support use, patient safety incidents, absenteeism, or verified turnover—alongside multi-source ratings (peer/supervisor assessments of safety and performance). Fourth, although the three-wave design improves temporal ordering, SEM estimates remain non-experimental. Stronger causal leverage could be obtained through quasi-experimental designs (e.g., stepped-wedge or staggered rollouts across units), cross-lagged panel or random-intercept cross-lagged models, and experience-sampling to capture within-person change during implementation. Multilevel models that explicitly account for nesting within hospitals/units would also help partition unit-level climate effects from individual appraisals. Finally, future research could broaden the criterion space to other career-related outcomes (e.g., intent to stay, internal mobility, upskilling behavior, professional identity) and examine heterogeneity across AI use cases (diagnostic support vs. documentation automation), task complexity, and equity/fairness safeguards. Mapping these boundary conditions will clarify when AI functions as a job resource versus a job demand, and how organizational choices convert technical capability into durable gains in satisfaction and retention. Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available on request from the corresponding author. The data is not publicly available due to privacy or ethical restrictions. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work the author used ChatGPT to check spelling and grammar. After using this tool/service, the author reviewed and edited the content as needed and takes full responsibility for the content of the published article. Ethics statement – Not applicable This study did not involve human or animal experimentation. Participation in the survey was voluntary and anonymous, and informed consent was obtained from all respondents prior to data collection. Acknowledgements This research was supported by Joongbu University in Ilsan, South Korea. References 1. Acemoglu, D., & Restrepo, P. (2020). 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Keywords ai integration job satisfaction psychological safety turnover intention Authors Affiliations Heungsok Cha 0000-0001-6696-7669 [email protected] Joongbu University - Inno-Media Campus View all articles by this author Metrics & Citations Metrics Article Usage 456 views 240 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Heungsok Cha. When AI Feels Supportive: Psychological Safety, Satisfaction, and Turnover Among Healthcare Professionals. Authorea . 10 October 2025. DOI: https://doi.org/10.22541/au.176007254.45491764/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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