The research team, led by Yuting Zhang, set out to test a question borrowed from organizational psychology and applied to the political arena. In workplaces, employees manage the impressions colleagues form of them through two broad families of tactics. Promotive tactics highlight achievements, competence, and positive outcomes, painting the actor in an aspirational light. Protective tactics, by contrast, defend against blame, offering excuses, justifications, or apologies when things go wrong. Governments, the researchers reasoned, deploy the same repertoire when they broadcast policy successes or explain away failures, and young audiences steeped in social media may be especially sensitive to these signals.

To disentangle cause from correlation, the team combined a controlled experiment with a large-scale survey, a methodological pairing that allows each approach to shore up the weaknesses of the other. In the experimental phase, participants were randomly exposed to government messages framed as either promotive or protective impression management, and their subsequent trust in government was measured. The survey phase then captured naturally occurring perceptions of government communication among a broader youth sample, along with measures of the psychological mechanisms and cultural values the researchers believed would moderate the effects.

The experimental results were unambiguous on the first question: both types of tactics increased youth trust in government. When young participants read government communications that emphasized accomplishments and capability, their trust rose. When the communications instead acknowledged problems and offered protective framing—explaining constraints, taking responsibility, or contextualizing setbacks—trust rose as well. Neither strategy was inherently superior; both moved the needle, suggesting that the act of strategic self-presentation itself, regardless of direction, engages citizens’ evaluative machinery in ways that silence or generic messaging does not.

But the study’s most intriguing contribution lies in the mechanism it identified. Drawing on the philosopher John Rawls’s concept of an overlapping consensus—the area of agreement that emerges when people with different values and worldviews nonetheless converge on shared principles—the researchers proposed that impression management works by building this consensus between the government and its young audience. When a government message resonates with what youth already believe is fair, competent, or legitimate, it creates a zone of moral overlap. The survey data supported this pathway statistically: the relationship between both promotive and protective tactics and trust in government was mediated by overlapping consensus, meaning the tactics boosted trust at least partly by convincing young people that government and citizen share common ground.

Culture, it turns out, decides which tactic works best for whom. The researchers measured two dimensions of cultural values at the individual level, adapted from Hofstede’s classic framework: collectivism, the tendency to prioritize group harmony and in-group loyalty over individual goals, and long-term orientation, a future-focused perspective that values persistence, thrift, and sustained effort over immediate gratification. These two values played strikingly different moderating roles for the two tactic types.

Collectivism amplified the power of promotive impression management. Among young Chinese participants who scored high on collectivism, the positive relationship between achievement-focused government messaging and overlapping consensus was significantly stronger, which in turn produced a larger indirect boost to trust. This makes intuitive sense: citizens who see themselves as part of a collective enterprise are more receptive to narratives of shared accomplishment and national progress, and promotive messages tap directly into that identity. For this group, a government showcasing what it has built and achieved reads not as bragging but as reporting on a common project they belong to.

Long-term orientation produced a result the researchers did not anticipate. The original hypothesis predicted that a strong future orientation would weaken the effectiveness of protective impression management, on the logic that forward-looking individuals might be impatient with excuses and defensive framing. The data said the opposite. Long-term orientation strengthened the positive relationship between protective tactics and overlapping consensus, and the indirect effect on trust was stronger at higher levels of this value. One plausible interpretation is that young people who think in long horizons are more willing to accept that governments face constraints, trade-offs, and delayed payoffs, and therefore find protective explanations—acknowledging difficulties while committing to eventual resolution—to be credible rather than evasive. Rather than dismissing defensive communication, patient, future-oriented youth appear to read it as evidence of prudence.

The findings arrive amid a rich literature documenting China’s distinctive trust landscape. Previous surveys have shown that political trust in China is often hierarchical, with citizens expressing more confidence in the central government than in local authorities, and that trust levels shift across birth cohorts as generational experiences diverge. Scholars have also documented how Chinese officials actively construct their public image online, and how government microblogs worked to sustain trust during the COVID-19 pandemic. The new study adds a psychological micro-foundation to this macro picture: it specifies not only that government communication matters, but through what cognitive route and under which cultural conditions it matters most.

For practitioners, the implications are concrete. Governments seeking to rebuild credibility with skeptical young audiences should not choose between boasting and apologizing—both can work—but should instead match the tactic to the audience’s cultural profile. Achievement-oriented messaging gains traction with collectivist youth, while candid, protective communication resonates with those who take the long view. More fundamentally, the study suggests that the goal of government communication should not be persuasion in the shallow sense but the cultivation of overlapping consensus: demonstrating that government actions align with values young citizens already hold. Whether these dynamics, observed in the Chinese context, generalize to societies with different media environments and political cultures remains an open question, and the authors note that fostering consensus and accounting for cultural differences among youth groups offers practical guidance for governments everywhere. As trust in institutions becomes a defining political currency of the digital age, the psychology of how governments present themselves—and how young citizens decode those presentations—may prove as consequential as the policies being presented.

Subject of Research: The effects of government impression management tactics on youth trust in government in China, mediated by overlapping consensus and moderated by cultural values

Article Title: The influence of government impression management on youth trust in government: evidence from China

Article References: Zhang, Y., Guo, J., Wang, F., Pan, Y., & Wen, H. (2026). The influence of government impression management on youth trust in government: evidence from China. Current Psychology, 45(17), Article 1434. https://doi.org/10.1007/s12144-026-09907-8

Image Credits: AI Generated

DOI: 10.1007/s12144-026-09907-8

Keywords: government trust, impression management, youth, China, overlapping consensus, collectivism, long-term orientation, political psychology, cultural values, government communication, Current Psychology, trust in government

The research team, led by Lei Lin of the Southwest University of Political Science and Law, together with Jing Tan of Chongqing University and Di Zheng of the Sichuan Academy of Social Sciences, recruited 599 urban residents in China for two randomized online experiments. The choice of population was deliberate. China has more than 900 million urban permanent residents, a group that overlaps heavily with the audiences of official government microblogs, weather applications, and other digital warning channels, and these users are among the most frequent adopters of AI-enabled services. The experimenters used Credamo, a survey platform with verified users, and applied strict quality controls: eligibility was restricted to the highest credit ratings, duplicate submissions from the same IP address or device were blocked, minimum completion times were enforced, and attention and comprehension checks filtered out careless respondents. Of 636 initial responses, 37 were excluded under preregistered criteria, leaving a final sample whose demographic profile shifted negligibly after cleaning.

The scenario at the heart of both experiments was a heavy rainfall warning issued during peak flood season in a Chinese city, a hazard chosen for its familiarity, its low political sensitivity, and the fact that both government agencies and AI platforms plausibly issue such alerts. Warning severity was communicated through the standardized color-coded signals of the China Meteorological Administration, ranging from blue for general risk through yellow and orange to red for extreme risk. All experimental materials were identical in content, length, and style; only the source cue varied. Pilot testing confirmed that participants could accurately identify who was said to have issued each warning and perceived the scenarios as realistic.

The first experiment employed a three-group between-subjects design comparing a government-only warning, an AI-only warning, and a consistent joint warning issued by both. Three hundred valid participants were randomly assigned, 101 to the government condition, 100 to the AI condition, and 99 to the joint condition, and balance checks confirmed no pretreatment differences across groups on gender, age, education, income, occupation, or disaster experience. A one-way analysis of variance revealed a substantial effect of warning source on information trust, with a partial eta squared of 0.215, a medium-to-large effect. Post hoc comparisons using Tukey’s honestly significant difference test showed that trust in government warnings significantly exceeded trust in AI warnings, with a mean difference of 0.29 points on the five-point scale and a Cohen’s d of roughly 0.45.

More striking still was the performance of the joint condition. Warnings attributed jointly to the meteorological station and an AI-driven warning platform produced the highest trust of all, exceeding the government-only condition by 0.26 points and the AI-only condition by 0.55 points, the latter corresponding to a Cohen’s d of approximately 0.85, a large effect. The authors interpret this as evidence that credibility emerges not from any single source but from the combination of institutional authority and technological channels. When AI alerts appear alongside official government communication, they are more likely to be perceived as complementary rather than autonomous, easing concerns about algorithmic opacity and unclear responsibility. The finding echoes the long-standing ‘speak with one voice’ principle in risk communication, which emphasizes coherent messaging across sources to sustain public confidence.

The experiment also confirmed that trust translates into action. In a hierarchical regression controlling for gender, age, education, prior disaster experience, AI usage, institutional trust, and risk perception, adding information trust to the model raised the explained variance in protective intentions from a nonsignificant 2.9 percent to 12.7 percent. Information trust was the strongest predictor of intentions to monitor hazard information, move vehicles and belongings to safety, identify emergency shelters, and stockpile supplies, with a standardized coefficient of 0.416. Risk perception retained a smaller but significant effect. Protective intention items showed acceptable internal consistency, with Cronbach’s alpha of 0.72, while the trust and anticipated regret composites reached 0.82 and 0.84 respectively.

The second experiment turned to the harder question: what happens when government and AI disagree? Four conditions were constructed. In one, both sources issued a low-level blue warning; in a second, both issued an orange warning, serving as a reference and replicating the joint condition from the first study. In the two conflict conditions, one source issued orange while the other issued blue, with the order of severity reversed between the two versions. After excluding invalid responses, 387 participants were distributed almost evenly across the four groups, and randomization checks again showed no meaningful imbalances. A pretest had verified that a one-level discrepancy was perceived as a genuine conflict and that orange was consistently read as more severe than blue.

Conflict, it turned out, produced a powerful emotional response. A one-way analysis of variance revealed a large effect of warning consistency on anticipated regret, with a partial eta squared of 0.349, and a nonparametric Kruskal-Wallis test corroborated the result. Participants in both conflict conditions reported significantly higher anticipated regret than those in either consistent condition, exceeding the consistent-low group by roughly 0.85 to 0.91 points and the consistent-high group by 0.33 to 0.39 points, all differences highly significant. Crucially, the two conflict groups did not differ from each other, meaning it did not matter whether the government or the AI issued the more severe alert. When sources diverged, people temporarily set aside questions of institutional authority and focused instead on avoiding the cost of underestimating the threat, a pattern consistent with worst-case thinking described in regret theory and loss aversion research.

Anticipated regret also proved to be a formidable driver of behavior. In a hierarchical regression, adding anticipated regret to the controls raised explained variance in protective intentions from a nonsignificant 1.7 percent to 26.8 percent, with a standardized coefficient of 0.502, meaning a one-unit increase in expected regret corresponded to roughly half a unit more protective intention, all else held constant. Together, the two experiments support a dual-pathway framework of decision making in multi-source warning environments: under consistent messaging, responses are structured by cognitive trust grounded in institutional authority and technological support; under conflicting messages, responses are instead guided by the emotional motivation to avoid future regret.

The implications for warning system design are substantial. Because joint government-AI releases enhance confidence, the authors suggest that policymakers develop coordination mechanisms between meteorological agencies and AI service providers, aligning update schedules, severity thresholds, and release timing to reduce discrepancies before they reach the public. At the same time, they caution against exploiting regret as a persuasive tool, which could breed anxiety or distrust; instead, communication strategies should explain why different systems produce different alerts and offer graded recommendations for how to respond. The study acknowledges limitations: the simulated scenarios cannot fully reproduce the urgency of real disasters, the sample of urban online users limits generalizability, and the two experiments examined trust and regret in separate randomized conditions rather than estimating a unified causal pathway. Nonetheless, as AI becomes an increasingly visible participant in public warning dissemination, the research makes clear that institutional credibility remains the anchor of public confidence, and that when machines and governments speak in harmony, people listen most closely of all.

Subject of Research: Public trust and behavioral responses to disaster warnings issued by governments versus artificial intelligence systems.

Article Title: Public Responses to Disaster Warnings from Government Versus AI: Evidence from Behavioral Experiments

Article References: Lin, L., Tan, J., & Zheng, D. (2026). Public Responses to Disaster Warnings from Government Versus AI: Evidence from Behavioral Experiments. International Journal of Disaster Risk Science. https://doi.org/10.1007/s13753-026-00766-2

Image Credits: AI Generated

DOI: 10.1007/s13753-026-00766-2

Keywords: disaster warnings, artificial intelligence, government trust, anticipated regret, risk communication, behavioral experiments, early warning systems, protective behavior, algorithm aversion, flood warnings, China, decision making