Preamble

Why Are Maximizing Patients More Vigilant Toward Doctors? The Mediating Role of Moral Disengagement

XU Zihao, ZHU Dongqing, YAN Xiaomin
(Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing 100048, China)

Abstract

Patients' pervasive vigilance toward doctors often triggers negative behaviors that pose potential threats to doctor-patient relationships. From an interactive perspective, this research investigates how maximizing decision-making influences patient vigilance and its underlying mechanisms. Three studies found that: (1) maximizing decision-making significantly enhances patients' vigilance toward doctors' medical ethics and professional competence; (2) moral disengagement mediates this relationship, such that maximizing decision-making strengthens patient vigilance by activating moral disengagement; and (3) doctor friendly behavior moderates this effect—not only failing to alleviate maximizing patients' vigilance but actually intensifying it through strengthened moral disengagement. By introducing moral disengagement theory, this study constructs a psychological mechanism linking maximizing decision-making to patient vigilance, thereby expanding research on vigilance and maximizing in interpersonal communication while offering new insights for preventing and mitigating doctor-patient conflicts.

Keywords: patient vigilance toward doctors, maximizing decision-making, moral disengagement, doctor-patient interaction
Classification: B849: C91

1. Introduction

Rapid social changes have intensified individuals' defensive psychology (Cheng et al., 2021), manifesting in doctor-patient relationships as declining trust and excessive vigilance (Lü et al., 2019). Research indicates that patient vigilance triggers dual stress responses: cognitively, it produces hostile attribution bias (Li et al., 2015; Liu et al., 2019), and physiologically, it manifests as heightened anxiety (Kimble et al., 2014; Öhman & Mineka, 2001). Though this implicit psychological tension does not directly cause overt conflict, it continuously undermines relationship resilience and increases confrontation risks (Barsade, 2002), representing a key psychological hazard for conflict escalation (Yan, 2018). Studies show that social environmental factors—such as low economic mobility (Zhao et al., 2023), economic inequality (Cheng et al., 2021), collectivist culture (Li et al., 2015; Liu et al., 2019)—and individual personality traits—such as conscientiousness (Rose et al., 2002), avoidant attachment style (Tang et al., 2017), and high trait anxiety (Mogg & Bradley, 1998)—significantly influence vigilance. However, the mechanisms through which individual decision-making characteristics (e.g., maximizing decision-making) affect vigilance, particularly in doctor-patient interactions, remain underexplored. Theoretically, decision-making characteristics (e.g., analytical vs. intuitive thinking) significantly influence error rates (Kozhevnikov et al., 2014), while vigilance optimizes decision-making by enhancing executive control and environmental monitoring (Cohen et al., 1996; Pacheco-Unguetti et al., 2010). Practically, decision-making characteristics widely affect vigilance: overconfidence in organizational management reduces vigilance (Hayward & Hambrick, 1997), while differences in decision-making characteristics between doctors and patients in shared decision-making (e.g., professional vs. value orientation) may intensify conflicts (Elwyn et al., 2012), thereby influencing patient vigilance. Thus, investigating how decision-making characteristics shape vigilance holds both theoretical and practical significance.

This research examines maximizing decision-making—a classic and common decision-making characteristic involving extensive effort to achieve optimal outcomes (Schwartz et al., 2002; Cheek & Schwartz, 2016; Zhu & Xie, 2013)—and integrates moral disengagement theory to explore its impact on patient vigilance, the mediating role of moral disengagement, and the moderating effect of doctor friendly behavior. By revealing the psychological mechanisms through which maximizing decision-making influences patient vigilance, this study not only expands research on vigilance and maximizing in interpersonal communication but also provides practical insights for resolving doctor-patient trust crises and preventing conflicts.

1.1 Patient Vigilance Toward Doctors

When organisms face survival threats, they activate adaptive vigilance mechanisms, manifesting as heightened threat sensitivity (MacDonald & Leary, 2005) and "fight-or-flight" responses (Cannon, 1932). Humans are sensitive not only to physical threats but also to social threats (e.g., rejection, stigmatization), which trigger vigilance systems, leading to excessive hostile attribution and harm anticipation (Li et al., 2015; Liu et al., 2019). This vigilance exists across various social relationships (Cheng et al., 2021; Li et al., 2015; Liu et al., 2019), including doctor-patient interactions. In medical contexts, medical errors, ethical violations, and related negative reports collectively shape patients' threat perception (Su et al., 2010; Shin & Niv, 2021). Research shows that patients often presuppose mistrust toward doctors (Wang et al., 2015), providing indirect evidence for their vigilance.

Previous research has primarily examined how social environmental factors—low economic mobility (Zhao et al., 2023) and economic inequality (Cheng et al., 2021)—increase vigilance, and how collectivist culture makes individuals more likely to develop hostile expectations toward ingroup members (Liu et al., 2019) and close others (Li et al., 2015). Individual traits such as conscientiousness (Rose et al., 2002), avoidant attachment style (Tang et al., 2017), and high trait anxiety (Mogg & Bradley, 1998) also exacerbate vigilance. However, research on decision-making characteristics (e.g., maximizing) and the psychological formation mechanisms of vigilance remains limited. This study addresses this gap by examining how patients' maximizing decision-making enhances their vigilance toward doctors from a moral disengagement perspective.

1.2 Maximizing Decision-Making and Patient Vigilance

Maximizing and satisficing decision-making originate from rational choice models (von Neumann & Morgenstern, 1944) and bounded rationality models (Simon, 1955), describing two distinct decision strategies. Schwartz et al. (2002) conceptualized maximizing decision-making tendency. High maximizers extensively search options to achieve optimal outcomes, while low maximizers (satisficers) stop searching once they encounter an acceptable option, settling for "good enough" results. Maximizing can be measured as a personality trait (Schwartz et al., 2002) or manipulated as a mindset (Ma & Roese, 2014).

According to preparedness theory (Öhman & Mineka, 2001), vigilance correlates closely with individuals' ability to perceive environmental threats—stronger threat perception ability yields higher vigilance (Lick et al., 2015). Individuals with strong threat perception ability typically exhibit: more frequent environmental scanning (Gomes & Semin, 2020), superior threat identification (Mogg & Bradley, 1998), greater cognitive resource allocation to threat processing (Chen et al., 2024), heightened anxiety under potential threats (Kimble et al., 2014), and stronger physiological responses (Öhman & Mineka, 2001). Maximizing decision-making may enhance performance across these dimensions.

First, maximizers scan the environment more frequently than satisficers. They seek more alternatives before deciding (Cheek & Schwartz, 2016; Iyengar et al., 2006), conduct deeper background investigations (Iyengar et al., 2006), attend to more decision-irrelevant information (Zhu et al., 2019), and continue searching for reviews post-decision (Kim, 2022). This indicates that maximizers tend to scan and attend to their surroundings more frequently.

Second, maximizers are better at detecting and identifying threat information than satisficers. They view "non-optimal" options as potential threats to achieving optimal outcomes (Schwartz et al., 2002) and use exhaustive search, option comparison, and social comparison (Schwartz et al., 2002; Weaver et al., 2015) to identify and eliminate more non-optimal options (Polman, 2010). Through practice effects (Du et al., 2013), maximizers develop cognitive advantages in rapidly and accurately eliminating "non-optimal" options, creating proficiency in threat identification.

Third, maximizers allocate more cognitive resources to processing threat information than satisficers. They attend to undiscovered options and their potential losses (Iyengar et al., 2006) and eliminate more non-optimal options (Polman, 2010). Even after decision completion, they attempt to change possible non-optimal outcomes (Chowdhury et al., 2009) and retain power to alter decisions (Shiner, 2015). This demonstrates greater cognitive resource consumption and higher cognitive load (Misuraca & Teuscher, 2013; Simon, 1955; Zhu et al., 2019). Additionally, maximizers tend to save more to cope with future risks (Brannon, 2021), further indicating they expend more effort processing threat information.

Fourth, maximizers experience greater anxiety than satisficers when facing potential threats. Their decision times are longer (Chowdhury et al., 2009; Misuraca & Teuscher, 2013; Schwartz et al., 2002), and they perceive greater time pressure (Chowdhury et al., 2009), which may induce anxiety (Gärling et al., 2016). Since eliminating numerous non-optimal options involves processing more threat information, maximizers may experience stronger anxiety when confronting non-optimal options (Iyengar et al., 2006; Schwartz et al., 2002).

Fifth, maximizers show stronger physiological responses than satisficers under potential threats. Sustained high arousal and attention represent key physiological characteristics of vigilance (Mackworth, 1948). Maximizers have longer decision times and greater time pressure, resulting in higher arousal (Adam et al., 2015). Moreover, they must continuously search and compare more options over extended periods to select optimal outcomes, whereas satisficers stop searching upon encountering satisfactory results (Cheek & Schwartz, 2016; Schwartz et al., 2002; Simon, 1955). Simultaneously, maximizers must maintain attention to change possible non-optimal outcomes (Chowdhury et al., 2009). This indicates that maximizers better match the characteristic of maintaining high attention over prolonged periods. Therefore, when facing threat information (i.e., non-optimal options), maximizers likely exhibit stronger physiological responses.

In summary, maximizers' consistent patterns across behavioral-cognitive dimensions (environmental scanning, threat detection, threat information processing) and emotional-physiological dimensions (anxiety, arousal, attention) indicate stronger ability to perceive potential threats. During doctor-patient interactions, both doctors' professional incompetence and ethical violations trigger patient threat perception (Shin & Niv, 2021; Su et al., 2010). Compared to satisficers, maximizing patients may be more sensitive to potential threats, thus exhibiting stronger defensive psychology and higher vigilance toward doctors. Based on this, we propose:

Hypothesis 1: Maximizing patients exhibit higher vigilance toward doctors than satisficing patients.

1.3 The Mediating Role of Moral Disengagement

Social cognitive theory posits that people use cognitive mechanisms to escape moral constraints and engage in immoral behavior (Bandura, 1991). Moral disengagement theory (Bandura, 1986, 2002) further proposes that individuals normally rely on self-regulatory systems to inhibit immoral behavior, but moral disengagement weakens this mechanism, allowing individuals to rationalize inappropriate actions without violating their moral self-conceptions (Bandura, 2002; Detert et al., 2008; Duffy et al., 2012; Moore et al., 2012). Early research treated moral disengagement as a stable trait (Moore et al., 2012), while recent studies emphasize its situational dependency (Zhu et al., 2021; Chen & Liang, 2017). For example, job insecurity triggers employees' moral disengagement, increasing workplace deviance (Huang et al., 2017). This study proposes that when patients perceive potential threats from doctors, moral disengagement may mediate the effect of maximizing decision-making on vigilance.

According to social exchange theory (Blau, 1964), maximizing decision-making more readily activates moral disengagement. Potential threats from doctors trigger negative experiences such as cognitive depletion and anxiety (Smith et al., 2019). Individuals tend to partially attribute negative states to others (Miller & Ross, 1975). Consequently, patients may blame doctors for their threat perception and negative experiences. Based on social exchange theory (Blau, 1964), this attribution may lead patients to believe doctors have violated the "implicit contract" of social exchange (Rousseau, 1995), causing their suffering and thereby activating moral disengagement mechanisms. Since maximizing decision-making enhances patients' ability to perceive potential threats, maximizing patients more easily attribute threat-induced negative experiences to doctors violating reciprocal psychological contracts, thus freeing themselves from moral standards and activating moral disengagement.

Although direct evidence linking maximizing decision-making and moral disengagement is lacking, multiple indirect studies support their association. On one hand, research shows individuals tend to maximize in valued domains (Zhu et al., 2022), and maximizing positively correlates with greed (Seuntjens et al., 2015), indicating that maximizers highly focus on their own goals and interests. On the other hand, maximizers not only extensively evaluate various information (e.g., existing options, potential alternatives, rejected options, and additional information) (Hughes & Scholer, 2017; Zhu et al., 2019) but also more frequently change decision outcomes (Chowdhury et al., 2009; Ma & Roese, 2014; Shiner, 2015), demonstrating high cognitive flexibility. Highly goal-oriented individuals tend to actively employ moral disengagement mechanisms through cognitive flexibility, using self-justification and behavioral rationalization to achieve personal interests (Shalvi et al., 2015). Furthermore, maximizers often adopt an egocentric perspective when making decisions for others (Luan et al., 2018) and are perceived as lacking warmth in interpersonal interactions (Chen et al., 2023), a low-empathy trait that highly matches characteristics of moral disengagers (Detert et al., 2008; Moore et al., 2012). Thus, maximizing decision-making may stimulate individuals' moral disengagement.

Integrating social exchange theory (Blau, 1964) and moral disengagement theory (Bandura, 1986), moral disengagement mechanisms not only foster immoral behavior but may also increase patient vigilance toward doctors. Although vigilance itself is not immoral behavior, since not all doctors harm patients (Haque & Waytz, 2012), patient vigilance may violate doctors' expectations of prosocial norms (Blau, 1964; Eisenberg et al., 2010) and even be perceived as relationship betrayal (Saeed et al., 2024). Therefore, vigilant patients may experience guilt and self-condemnation similar to negative emotions following immoral behavior (Cohen et al., 2012; Tangney et al., 2007). According to moral disengagement theory (Bandura, 1986), a normally functioning moral self-regulatory system would inhibit patient vigilance. However, when moral disengagement mechanisms activate, patients can escape cognitive dissonance caused by vigilance through reinterpreting vigilance, ignoring its harm to doctors, or attributing blame to doctors, thereby more likely exhibiting high vigilance. This psychological process exists not only in morally relevant behaviors (e.g., social undermining; Duffy et al., 2012) and decisions (e.g., unethical decisions; Detert et al., 2008) but also in behaviors not directly related to morality (e.g., employee turnover; Huang et al., 2017) or experiences (e.g., psychological well-being; Aftab & Malik, 2021).

In summary, compared to satisficing patients, maximizing patients are more likely to use moral disengagement mechanisms to justify their vigilance toward doctors, thereby maintaining higher vigilance. Specifically, they may view vigilance as a legitimate defense to protect their own interests, believe vigilance causes no substantial harm to doctors, or attribute potential harm to doctors. Thus, we propose:

Hypothesis 2: Moral disengagement mediates the relationship between maximizing decision-making and patient vigilance toward doctors.

1.4 The Moderating Role of Doctor Friendly Behavior

Doctor friendly behavior refers to caring, respectful, and supportive actions doctors actively take during interactions (Doyle et al., 2013), such as patient listening, detailed condition explanations, or prescribing more effective medications. Since individuals' threat perception often relates to others' behavioral expressions (Bandura, 1999) and moral disengagement is typically triggered by threat perception (e.g., job insecurity; Huang et al., 2017), the degree to which maximizing decision-making triggers patient moral disengagement may closely relate to doctors' friendly behavior.

Specifically, patients' threat perception toward doctors easily forms negative stereotypes (Zuo et al., 2006). According to construal level theory (CLT; Trope & Liberman, 2010), when individuals have greater psychological distance from actors, their perception relies more on generalized or stereotypical impressions of the actor's group rather than specific behaviors (McCrea et al., 2012). Therefore, when perceiving individual doctors, patients more likely depend on negative stereotypes of the doctor group to form negative expectations, as doctors and patients belong to different groups with greater psychological distance (Fu et al., 2020). Research shows the human brain tends to believe information consistent with original expectations and doubt inconsistent information (Nickerson, 1998). When doctors exhibit friendly behavior, this positive expression contradicts patients' negative expectations, breaking their stereotypes (Hilton & von Hippel, 1996), causing patients to suspect doctors' motives (Mayer et al., 1995) and believe friendly behavior may hide negative intentions (e.g., recommending new drugs for extra economic benefits). Once patients believe doctors' friendly behavior serves negative intentions, potential threats from doctors become further confirmed and amplified (Shin & Niv, 2021).

Moreover, research indicates that perceived intentionality plays an important role in evaluating negative behavioral consequences (Morewedge, 2009). When behaviors cause negative outcomes, people tend to believe actors acted intentionally (Knobe, 2003). This suggests intentionality may serve as a cue for negative outcomes. When doctors actively display friendliness, patients perceive their intentionality (Bonaccio et al., 2016), then infer friendly behavior may lead to negative consequences, strengthening perception of doctors' potential threats.

Combining characteristics of maximizing and satisficing decision-making, since maximizing patients have stronger ability to perceive potential threats, doctors' friendly behavior may further intensify their threat perception. Consequently, maximizing patients are more likely to activate moral disengagement mechanisms to rationalize their vigilance toward doctors.

Hypothesis 3: Doctor friendly behavior moderates the relationship between maximizing decision-making and patient moral disengagement. This relationship is stronger when doctors exhibit friendly behavior and weaker when they do not.

[FIGURE:1] Theoretical Framework

Based on this, we propose that doctor friendly behavior moderates the indirect effect of maximizing decision-making on patient vigilance through moral disengagement (Figure 1). Specifically, when doctors exhibit friendly behavior, the mediating effect of moral disengagement becomes more pronounced. Therefore, we propose:

Hypothesis 4: Doctor friendly behavior moderates the indirect relationship between maximizing decision-making and patient vigilance through moral disengagement. This indirect relationship is stronger when doctors exhibit friendly behavior and weaker when they do not.

2. Study 1

2.1 Purpose

Study 1 sampled patients with recent medical experiences but outside hospital settings, aiming to preliminarily explore the effect of maximizing decision-making on patient vigilance and verify the mediating role of moral disengagement, thereby testing Hypotheses 1 and 2. Additionally, this study examined common ingroup identity as an alternative explanation. Research shows that maximizers' stronger egocentric decision-making perspective (Luan et al., 2018) hinders common ingroup identity formation (Brewer & Chen, 2007), while doctor-patient common ingroup identity effectively alleviates patients' competitive victimhood (Deng et al., 2023), thereby reducing defensive vigilance. Thus, common ingroup identity may also serve as a mechanism through which maximizing affects vigilance. This study measured common ingroup identity to exclude its role in the maximizing effect.

2.2 Method

2.2.1 Participants

The independent variable was maximizing decision-making, the mediator was moral disengagement, and the dependent variable was patient vigilance toward doctors. Using G*Power software for chi-square test sample size estimation (setting statistical power 1−β = 0.80, significance level α = 0.05, effect size φ = 0.3), the minimum required sample size was 88. This study recruited 140 eligible participants through the Credamo platform (95 females, 45 males; age: M = 35.59 years, SD = 5.07). Inclusion criteria were adults without medical professional backgrounds who had medical experiences within the past six months. To ensure sample quality, screening items asked "Have you received medical professional education?" and "Have you had any medical visits in the past six months?" Unqualified participants were automatically excluded by the platform, and recruitment continued until the target sample size was reached.

2.2.2 Procedure and Materials

Participants sequentially completed measures of maximizing decision-making, patient vigilance toward doctors, moral disengagement, common ingroup identity, and demographic variables. Upon completion, participants received small gifts. For measuring patient vigilance, the study adopted patients' subjective predictions of target doctors' potential behaviors in typical medical scenarios. All other variables were measured using validated scales published in authoritative domestic and international journals. All scales underwent rigorous back-translation procedures and were appropriately adapted for Chinese contexts. Specific measures were as follows:

Maximizing Decision-Making. The 13-item Maximization Scale developed by Schwartz et al. (2002) was used. Sample items include "No matter what it takes, I always try to choose the best" and "I don't like to settle for 'good enough' options." The scale used 7-point ratings (1 = strongly disagree, 7 = strongly agree; Cronbach's α = 0.80), with higher scores indicating stronger maximizing tendencies (M = 4.10, 95% CI = [3.98, 4.26]). Following Luan and Li's (2017) classification criteria, participants scoring above the median were classified as maximizers (n = 70), and those below as satisficers (n = 70).

Patient Vigilance Toward Doctors. Drawing on Liu et al.'s (2019) method for measuring vigilance toward ingroup members, this study measured vigilance through patients' subjective predictions of doctors' potential behaviors in typical medical scenarios. Previous research indicates that doctors' incompetence and ethical violations are primary causes of patient mistrust (Lü et al., 2019), and Chinese doctor-patient conflicts mainly concentrate on six aspects: treatment outcomes, professional skills, service attitude, medical costs, consultation duration, and medical integrity (Su et al., 2010). Based on this, vigilance was measured across two dimensions: medical competence and medical ethics, with competence further divided into professional ability and work attitude, and ethics divided into economic interests and resource allocation.

By adapting and organizing specific examples from relevant research, this study designed eight typical medical scenarios (see Appendix 1), each corresponding to sub-dimensions: professional ability (performing high-difficulty surgery, treating rare diseases), work attitude (working emergency night shifts, seeing high-volume outpatients), economic interests (prescribing medications, handling patient red envelopes), and resource allocation (allocating special medications, treating high-power patients). For example, the high-difficulty surgery scenario: "Dr. Wang is an orthopedic surgeon about to perform an extremely technically difficult and complex surgery for a patient. In this situation, what behaviors do you think Dr. Wang might exhibit?" The medication prescription scenario: "Dr. Shen is a respiratory specialist who needs to prescribe appropriate medications based on diagnosis. In this situation, what behaviors do you think Dr. Shen might exhibit?" To avoid interference from individual doctor characteristics, only doctors' surnames were mentioned. Patients listed at least two possible behaviors in each scenario and rated the valence of listed behaviors on a 7-point scale ("Is this behavior positive or negative?"; −3 = very negative, 0 = ambiguous, 3 = very positive).

Moral Disengagement. The 8-item Propensity to Morally Disengage Scale by Moore et al. (2012) was used. Sample items include "It is okay to spread rumors to protect people you care about." The scale used 7-point ratings (1 = strongly disagree, 7 = strongly agree; Cronbach's α = 0.83), with higher scores indicating greater moral disengagement.

Common Ingroup Identity. The 2-item scale by Deng et al. (2023) measured patients' identification with a common doctor-patient ingroup. Items were "Doctors and patients together form a group fighting disease, and I belong to this group" and "I feel proud to belong to the disease-fighting group." Items used 7-point ratings (1 = strongly disagree, 7 = strongly agree), with higher scores indicating stronger common ingroup identity.

2.2.3 Coding of Patient Vigilance

Following previous research methods (Liu et al., 2019; Study 1), two research assistants unaware of the hypotheses coded patients' descriptions of possible doctor behaviors (−1 = negative behavior, 0 = ambiguous behavior, 1 = positive behavior). Positive behaviors included "actively preparing for surgery to ensure success" and "prescribing appropriate medications for the condition." Ambiguous behaviors included "having another expert perform the surgery" and "prescribing more expensive but more effective medications." Negative behaviors included "making minor mistakes during surgery due to excessive nervousness" and "prescribing more medications from partnered pharmaceutical companies to obtain extra commissions." Unclear behaviors were excluded from analysis. The two coders showed high consistency (ICC = 0.95), with disagreements resolved through discussion. Since negative and ambiguous behaviors are more threatening than positive behaviors, negative and ambiguous behaviors indicated vigilance, while positive behaviors indicated no vigilance.

Given that vigilance is fundamentally a subjective experience, researchers further used patients' self-ratings of doctors' possible behaviors to verify coding accuracy. Results showed high correlation between patients' self-ratings and research assistants' coding (r = 0.76, p < 0.001), indicating high accuracy and validity of the coding.

2.3 Results

2.3.1 Effect of Maximizing Decision-Making on Patient Vigilance

Chi-square tests analyzed the effect of maximizing decision-making on patient vigilance. The independent variable was a dichotomous variable of maximizers (scores above median) vs. satisficers (scores below median), and dependent variables were proportions of positive, negative, and ambiguous behaviors. Results showed (Figure 2 [FIGURE:2]) that compared to satisficers, maximizers believed doctors would exhibit fewer positive behaviors (maximizers: 64.02% vs. satisficers: 76.36%; χ²(1, N = 1280) = 18.18, p < 0.001, φ = 0.12) and more negative behaviors (maximizers: 17.98% vs. satisficers: 8.97%; χ²(1, N = 1280) = 16.36, p < 0.001, φ = −0.11). The difference in ambiguous behaviors was not significant (maximizers: 17.98% vs. satisficers: 14.67%; χ²(1, N = 1280) = 2.03, p = 0.154).

Logistic regression further analyzed the effect of maximizing decision-making (continuous variable) on patient vigilance (dichotomous variable; 0 = positive behavior representing no vigilance, 1 = negative and ambiguous behaviors representing vigilance). Results showed maximizing decision-making significantly predicted patient vigilance (B = 0.34, SE = 0.06, Wald χ² = 27.09, p < 0.001, Exp(B) = 1.40, 95% CI [1.23, 1.58]). Thus, Hypothesis 1 was supported.

2.3.2 Mediating Role of Moral Disengagement

Regression analysis tested the mediating role of moral disengagement. The independent variable was maximizing decision-making (continuous), the mediator was moral disengagement (continuous), and the dependent variable was patient vigilance (dichotomous; 0 = positive behavior representing no vigilance, 1 = negative and ambiguous behaviors representing vigilance). Results showed (Figure 3 [FIGURE:3]) that maximizing decision-making significantly positively affected moral disengagement (B = 0.40, SE = 0.03, p < 0.001, 95% CI [0.35, 0.45]). Logistic regression indicated moral disengagement significantly positively affected patient vigilance (B = 0.30, SE = 0.05, Wald χ² = 35.72, p < 0.001, Exp(B) = 1.35, 95% CI [1.23, 1.49]). When both maximizing decision-making and moral disengagement were entered simultaneously, moral disengagement remained a significant positive predictor (B = 0.25, SE = 0.05, p < 0.001, 95% CI [0.14, 0.35]), while the effect of maximizing decision-making weakened (B = 0.24, SE = 0.07, p < 0.001, 95% CI [0.11, 0.37]). Following Hayes's (2012) approach for dichotomous outcomes, bootstrap analysis with 5,000 resamples further tested the mediation effect. Results showed the indirect effect of maximizing decision-making on patient vigilance through moral disengagement was significant (B = 0.10, SE = 0.02, 95% CI [0.06, 0.14]). Thus, Hypothesis 2 was supported.

An alternative explanation for the main effect of maximizing decision-making might be that, compared to satisficers, maximizers fail to establish a common superordinate identity with doctors and do not form common ingroup identity, with their high vigilance stemming from distrust of outgroup members. To exclude this possibility, t-tests analyzed the effect of maximizing decision-making on common ingroup identity. The independent variable was the maximizer/satisficer dichotomy, and the dependent variable was common ingroup identity (continuous). Results surprisingly showed maximizers had significantly higher common ingroup identity than satisficers (M = 5.82, SD = 0.89 vs. M = 5.28, SD = 1.23; t(138) = 2.91, p = 0.004, Cohen's d = 0.50). Regression analysis further showed maximizing decision-making significantly positively predicted common ingroup identity (β = 0.24, SE = 0.19, p = 0.004). These results excluded the possibility of lower common ingroup identity among maximizers, supporting our hypotheses.

2.3.3 Additional Analysis

Chi-square tests further analyzed the effect of maximizing decision-making on vigilance toward doctors' competence and ethics. The independent variable was the maximizer/satisficer dichotomy, and dependent variables were proportions of vigilance (negative and ambiguous behaviors) in competence or ethics domains. Results showed maximizers exhibited significantly higher vigilance toward competence than satisficers (17.90% vs. 9.87%; χ²(1, N = 186) = 23.42, p < 0.001, φ = 0.35) and toward ethics (17.19% vs. 14.80%; χ²(1, N = 211) = 4.56, p = 0.033, φ = 0.15), though the ethics difference was smaller. Additionally, satisficers showed significantly higher vigilance toward ethics than competence (14.80% vs. 9.87%; χ²(1, N = 150) = 6.00, p = 0.014, φ = 0.07), while maximizers showed no significant difference between competence and ethics vigilance (17.90% vs. 17.19%; χ²(1, N = 247) = 0.101, p = 0.750, φ = 0.01).

2.4 Discussion

Study 1 preliminarily validated the main effect of maximizing decision-making and the mediating effect of moral disengagement among patients with recent medical experiences but outside hospital settings. Results showed maximizing decision-making significantly enhanced patient vigilance toward doctors, with moral disengagement playing a significant mediating role. Specifically, compared to satisficers, maximizers showed higher vigilance toward doctors, primarily attributable to their higher moral disengagement rather than lower common ingroup identity.

The introduction posited that maximizing decision-making strengthens patient threat perception by enhancing their ability to perceive potential threats, thereby triggering moral disengagement mechanisms. Thus, enhanced threat perception from doctors constitutes a key prerequisite for moral disengagement to function. Since previous research has not revealed the association between maximizing decision-making and threat perception, this study validated this prerequisite through a follow-up study. Additionally, although Study 1 excluded common ingroup identity as an alternative explanation, other potential confounds needed consideration, such as education level, immediate emotional states, and previous consultation experiences. To test the threat perception prerequisite and control for confounds, researchers conducted a study with 120 adults recruited from Credamo without medical backgrounds who had medical experiences within six months. Participants sequentially completed measures of maximizing decision-making (Maximization Scale; Schwartz et al., 2002), threat perception (Trust in Physician Scale; Anderson & Dedrick, 1990), education level (years of education), immediate emotional states (PANAS; Watson et al., 1988), and consultation experience ("How was your last consultation experience?" 1 = very unpleasant, 7 = very pleasant). Results showed maximizers perceived significantly higher threat from doctors than satisficers (M = 3.42, SD = 1.23 vs. M = 2.40, SD = 1.14; t(118) = 4.71, p < 0.001). Regression analysis further showed maximizing decision-making significantly positively predicted threat perception (β = 0.53, SE = 0.12, p < 0.001). Additionally, the two groups did not differ significantly in education level (maximizers: M = 11.85, SD = 2.87; satisficers: M = 12.15, SD = 3.35; t(118) = 0.53, p = 0.600), positive affect (Cronbach's α = 0.90; maximizers: M = 2.93, SD = 1.38; satisficers: M = 3.15, SD = 1.39; t(118) = 0.86, p = 0.392), negative affect (Cronbach's α = 0.88; maximizers: M = 3.08, SD = 1.42; satisficers: M = 2.75, SD = 1.50; t(118) = −1.25, p = 0.214), or consultation experience (maximizers: M = 4.25, SD = 2.06; satisficers: M = 3.95, SD = 1.99; t(118) = −0.81, p = 0.420). These results not only provide a key prerequisite for maximizing decision-making to activate moral disengagement mechanisms but also rule out potential confounding variables.

3. Study 2

3.1 Purpose

Study 2 employed a field survey method with real patients in hospital settings to further examine the relationships among maximizing decision-making, moral disengagement, and patient vigilance, while exploring the moderating role of doctor friendly behavior to validate the full model and its ecological validity.

3.2 Method

3.2.1 Sample

This study was conducted at a nephrology specialty hospital in northern China. With department heads' assistance, researchers selected a male doctor in his late 30s as the target doctor and required him to withhold all personal information during consultations. To control for potential confounds (e.g., treatment effects, long-term contact, treatment costs), the sample was limited to first-time outpatients seeing this doctor under general registration with no prior contact. During informed consent, researchers promised participants: strict confidentiality and anonymity; unconditional withdrawal rights; and full ethical compliance. This study was approved by Capital Normal University School of Psychology Ethics Committee. A total of 375 patients were recruited, and after excluding those who failed to complete post-consultation questionnaires or whose pre-post questionnaires could not be matched, 327 valid questionnaires remained (155 females, 172 males; age: M = 33.71 years, SD = 9.24), yielding an 87.20% valid response rate.

3.2.2 Procedure and Materials

Data were collected at two time points: before and after consultation. Before consultation, patients reported maximizing decision-making (independent variable) and demographics; after consultation, they reported doctor friendly behavior (moderator), vigilance toward doctors (dependent variable), moral disengagement (mediator), and control variables. Patients completed questionnaires in the waiting area outside consultation rooms and received small gifts upon return. Measures were as follows:

Maximizing Decision-Making. The same 13-item scale as Study 1 was used (Schwartz et al., 2002; Cronbach's α = 0.82).

Doctor Friendly Behavior. Patients answered questions based on their actual experiences and feelings about doctor friendly behavior: "Reflecting on the just-completed consultation, did the doctor actively perform positive behaviors beyond routine procedures that benefited you based on your personal needs or special circumstances? For example, recommending more suitable doctors or medical institutions based on your condition, or prescribing newly developed and more effective special medications?" If patients reported no friendly behavior, they described the consultation process; if they reported friendly behavior, they described both the process and specific friendly actions. Patients' subjective descriptions not only prompted deep reflection but also provided basis for researchers to verify and revise judgments of doctor friendly behavior. Results showed high consistency between researchers' and patients' judgments (ICC = 0.94).

Patient Vigilance Toward Doctors. The same subjective prediction method as Study 1 was used. Two research assistants coded doctors' possible behaviors similarly (0 = positive behavior representing no vigilance, 1 = negative and ambiguous behaviors representing vigilance; ICC = 0.93).

Moral Disengagement. The same 8-item scale as Study 1 was used (Moore et al., 2012; Cronbach's α = 0.86).

Control Variables. Beyond demographics, researchers controlled for: prior attitudes toward doctors using an 11-item scale (e.g., "Sometimes doctors care more about their own convenience than patients' medical needs"; Hall et al., 2002; α = 0.83); perceived competition in doctor-patient relationships using three adapted items (e.g., "The more resources doctors get, the less I get," "If doctors' situation improves, mine worsens," and "Doctors may take what I have"; α = 0.80), as competition perception may affect vigilance (Liu et al., 2019); consultation quality ("How was the quality of this consultation?" 1 = low quality, 7 = high quality); and disease severity ("How severe is your illness?" 1 = not at all severe, 7 = very severe).

3.3 Results

3.3.1 Descriptive Statistics

[TABLE:1] presents means, standard deviations, and correlations among variables. Results showed maximizing decision-making significantly positively correlated with moral disengagement (r = 0.68, p < 0.001) and vigilance toward doctors (r = 0.40, p = 0.002). Moral disengagement also significantly positively correlated with vigilance (r = 0.44, p = 0.001).

3.3.2 Hypothesis Testing

A regression model tested hypotheses with maximizing decision-making (continuous) as independent variable, doctor friendly behavior (dichotomous; 0 = no friendly behavior, 1 = friendly behavior) as moderator, moral disengagement (continuous) as mediator, and patient vigilance (dichotomous; 0 = no vigilance, 1 = vigilance) as dependent variable. Results appear in Table 2 [TABLE:2].

To test Hypothesis 1, Model 6 showed that after controlling for relevant variables, maximizing decision-making significantly positively predicted patient vigilance (B = 0.45, SE = 0.04, Wald χ² = 142.67, p < 0.001, Exp(B) = 1.57, 95% CI [1.46, 1.69]), supporting Hypothesis 1.

[FIGURE:4] Interaction Effect of Maximizing Decision-Making and Doctor Friendly Behavior on Moral Disengagement (Study 2)

To test Hypothesis 2, Model 2 showed maximizing decision-making significantly positively affected moral disengagement (B = 0.69, SE = 0.01, p < 0.001). Model 7 indicated moral disengagement significantly positively affected patient vigilance (B = 0.44, SE = 0.03, Wald χ² = 175.22, p < 0.001, Exp(B) = 1.55, 95% CI [1.45, 1.65]). In Model 8, after adding moral disengagement, its effect on vigilance remained significant (B = 0.30, SE = 0.04, Wald χ² = 50.68, p < 0.001, Exp(B) = 1.36, 95% CI [1.25, 1.47]), while maximizing decision-making's effect weakened (B = 0.23, SE = 0.05, Wald χ² = 23.06, p < 0.001, Exp(B) = 1.26, 95% CI [1.15, 1.38]). Bootstrap analysis with 5,000 resamples showed the indirect effect of maximizing decision-making on vigilance through moral disengagement was significant (B = 0.22, SE = 0.03, 95% CI [0.16, 0.28]), supporting Hypothesis 2.

To test Hypothesis 3, Model 4 showed the interaction between maximizing decision-making and doctor friendly behavior significantly positively predicted moral disengagement (B = 0.21, SE = 0.02, p < 0.001). As shown in Figure 4 [FIGURE:4], when doctors exhibited no friendly behavior, maximizing decision-making significantly affected moral disengagement (B = 0.55, SE = 0.02, p < 0.001); when doctors exhibited friendly behavior, this effect strengthened further (B = 0.74, SE = 0.01, p < 0.001). This indicates doctor friendly behavior significantly moderated the relationship between maximizing decision-making and moral disengagement, supporting Hypothesis 3.

To test Hypothesis 4, bootstrap analysis with 5,000 resamples calculated the mediated effect when doctors did and did not exhibit friendly behavior. Results showed the mediation effect was significant when doctors exhibited no friendly behavior (B = 0.16, SE = 0.02, 95% CI [0.12, 0.21]) and significantly stronger when they exhibited friendly behavior (B = 0.22, SE = 0.03, 95% CI [0.16, 0.28]). The difference between these two mediation effects was significant (Δβ = 0.06, SE = 0.01, 95% CI [0.05, 0.09]), supporting Hypothesis 4.

3.4 Discussion

Study 2 confirmed the significant moderating role of doctor friendly behavior in the moral disengagement pathway between maximizing decision-making and patient vigilance, providing support for the full model. Although Study 2 had high external validity by replicating variable relationships in real-world settings beyond the laboratory, limitations remained: first, the design could not establish causal inference; second, most patients described heterogeneous doctor friendly behaviors, which might interfere with results. To address these limitations, Study 3 used experimental design to directly manipulate maximizing decision-making and doctor friendly behavior, thereby further verifying causal relationships among variables.

4. Study 3

4.1 Purpose

Study 3 used experimental design to directly manipulate maximizing decision-making and doctor friendly behavior, replicating the full model and providing causal evidence for variable relationships.

4.2 Method

4.2.1 Participants and Design

This study employed a 2 (maximizing decision-making: maximizing/satisficing) × 2 (doctor friendly behavior: present/absent) between-subjects design, with moral disengagement as mediator and patient vigilance as dependent variable. Using G*Power for two-way ANOVA sample size estimation (setting statistical power 1−β = 0.80, two-tailed α = 0.05, effect size f = 0.40, four groups), the minimum required sample size was 73. Researchers recruited 300 patients from three Beijing hospitals who voluntarily participated after detailed informed consent. To ensure data quality, an attention check asked: "Did the doctor in the above medical scenario exhibit friendly behavior?" After excluding 28 participants who failed to complete or pass the attention check, the final sample comprised 272 participants (170 females, 102 males; age: M = 33.71 years, SD = 9.24).

4.2.2 Procedure and Materials

Participants were randomly assigned to maximizing or satisficing groups, completing corresponding priming tasks and manipulation checks. They then randomly completed vigilance measurement tasks with or without doctor friendly behavior and attention checks. Finally, participants completed the moral disengagement scale, control variables, and demographic information. Participants received small gifts after completing questionnaires in hospital waiting areas.

Maximizing Decision-Making Manipulation. Following previous priming procedures (Luan & Li, 2017; Ma & Roese, 2014; Zhu et al., 2019), the maximizing group answered five decision questions about books, travel destinations, universities, jobs, and pets, each requiring selection of the best option from five alternatives (e.g., "Please select the university you believe provides the best education: A. Harvard; B. Yale; C. Princeton; D. Pennsylvania; E. Columbia"). The satisficing group answered similar five questions but selected satisfactory options (e.g., "Please select the university you believe provides satisfactory education: A. Harvard; B. Yale; C. Princeton; D. Pennsylvania; E. Columbia"). All participants then completed manipulation checks ("To what extent was your choice motivated by 'satisfactory'/'best' considerations?" 1 = not at all, 9 = completely).

Patient Vigilance Measurement. Participants randomly completed vigilance measurement tasks with or without doctor friendly behavior. In the no-friendly-behavior condition, following Cheng et al.'s (2021) method, participants read the eight medical scenarios from Study 1 (without doctor friendly behaviors) and predicted the likelihood of doctors engaging in behaviors that could harm patients' interests (1 = completely impossible, 7 = very possible; α = 0.92). In the friendly-behavior condition, following Liu et al.'s (2019) method for measuring vigilance when targets exhibit friendly behavior, researchers added doctors' friendly behaviors to the eight scenarios from Study 1 (see Appendix 2). Participants read the eight scenarios with doctor friendly behaviors and predicted the likelihood of doctors harming patients' interests (1 = completely impossible, 7 = very possible; α = 0.85). Example scenario with friendly behavior: "Dr. Wang is an orthopedic surgeon about to perform an extremely technically difficult and complex surgery. Dr. Wang explained key surgical steps, potential risks, and possible consequences in detail before surgery. How likely do you think Dr. Wang is to engage in behaviors that could harm patients' interests?" The medication scenario: "Dr. Shen is a respiratory specialist who needs to prescribe appropriate medications. Dr. Shen chose the latest developed and more effective special medication when writing the prescription. How likely do you think Dr. Shen is to engage in behaviors that could harm patients' interests?" Higher predicted likelihood indicated stronger vigilance.

Moral Disengagement. The same 8-item scale as Study 1 was used (Moore et al., 2012; Cronbach's α = 0.86).

Control Variables. To control for potential confounds, participants completed measures of emotional experience (PANAS; Watson et al., 1988), health literacy (Wu et al., 2020; α = 0.74–0.82), psychological distance from doctors ("Overall, how psychologically close do you feel to doctors?" 1 = very close, 7 = very distant), and perceived consultation fee reasonableness ("How reasonable do you think this consultation fee is?" 1 = very unreasonable, 7 = very reasonable).

4.3 Results

4.3.1 Maximizing Decision-Making Manipulation Check

Independent samples t-tests analyzed differences between maximizing and satisficing groups. Results showed the maximizing group rated their choices as significantly more best-motivated (M = 6.16, SD = 0.32) than the satisficing group (M = 3.85, SD = 0.90; t(270) = 28.05, p < 0.001), and significantly less satisfactory-motivated (M = 3.86, SD = 0.92) than the satisficing group (M = 5.88, SD = 0.42; t(270) = 23.25, p < 0.001). This confirmed successful manipulation.

4.3.2 Effect of Maximizing Decision-Making on Patient Vigilance

Two-way ANOVA with patient vigilance as dependent variable showed a significant main effect of maximizing decision-making, F(1, 268) = 146.45, p < 0.001, ηp² = 0.35. Specifically, the maximizing group (M = 3.94, SD = 1.56) showed higher vigilance than the satisficing group (M = 2.00, SD = 1.06; t(270) = 12.10, p < 0.001). The main effect of doctor friendly behavior was also significant, F(1, 268) = 4.92, p = 0.027, ηp² = 0.02. The interaction between maximizing decision-making and doctor friendly behavior was significant, F(1, 268) = 4.16, p = 0.043, ηp² = 0.02. To verify robustness, positive affect, negative affect, health literacy, psychological distance from doctors, and perceived consultation fee reasonableness were included as covariates. ANCOVA results still showed higher vigilance in the maximizing group, F(1, 263) = 165.62, p < 0.001, ηp² = 0.39. Results supported Hypothesis 1.

4.3.3 Mediating Effect of Moral Disengagement

Two-way ANOVA with moral disengagement as dependent variable showed a significant main effect of maximizing decision-making, F(1, 268) = 173.75, p < 0.001, ηp² = 0.39. The main effect of friendly behavior was also significant, F(1, 268) = 7.12, p = 0.008, ηp² = 0.03. Specifically, the maximizing group (M = 5.48, SD = 1.32) showed higher moral disengagement than the satisficing group (M = 3.38, SD = 1.30; t(270) = 13.18, p < 0.001).

Bootstrap analysis with 5,000 resamples tested the mediating effect, with maximizing decision-making (dichotomous; 0 = satisficing, 1 = maximizing) as independent variable, moral disengagement (continuous) as mediator, and patient vigilance (continuous) as dependent variable. Results showed the indirect effect through moral disengagement was significant (B = 0.49, SE = 0.13, 95% CI [0.23, 0.73]), supporting Hypothesis 2.

4.3.4 Moderating Effect of Doctor Friendly Behavior

The ANOVA also showed a significant interaction between maximizing decision-making and doctor friendly behavior on moral disengagement, F(1, 268) = 34.22, p < 0.001, ηp² = 0.11. As shown in Figure 5 [FIGURE:5], when doctors exhibited no friendly behavior, the maximizing group (M = 4.78, SD = 1.41) showed significantly higher moral disengagement than the satisficing group (M = 3.54, SD = 1.50; t(134) = 4.94, p < 0.001). When doctors exhibited friendly behavior, the difference between groups increased further (maximizing: M = 6.18, SD = 0.73; satisficing: M = 3.22, SD = 1.05; t(134) = 19.06, p < 0.001). Results supported Hypothesis 3.

4.3.5 Full Model Test

Bootstrap analysis with 5,000 resamples calculated the mediated effect when doctors did and did not exhibit friendly behavior. With maximizing decision-making (dichotomous; 0 = satisficing, 1 = maximizing) as independent variable, doctor friendly behavior (dichotomous; 0 = no friendly behavior, 1 = friendly behavior) as moderator, moral disengagement (continuous) as mediator, and patient vigilance (continuous) as dependent variable, results showed the mediation effect was significant when doctors exhibited no friendly behavior (B = 0.29, SE = 0.09, 95% CI [0.13, 0.47]) and significantly stronger when they exhibited friendly behavior (B = 0.69, SE = 0.18, 95% CI [0.34, 1.05]). The difference between these two mediation effects was significant (Δβ = 0.40, SE = 0.13, 95% CI [0.17, 0.68]), supporting Hypothesis 4. Collectively, results provided empirical support for the theoretical model.

5. General Discussion

This research examined the effect of maximizing decision-making on patient vigilance from a doctor-patient interaction perspective, testing the mediating role of moral disengagement and the moderating effect of doctor friendly behavior. Findings indicate: First, compared to satisficing patients, maximizing patients show higher vigilance toward both doctors' competence and ethics. Second, moral disengagement mediates this relationship—maximizing patients are more likely to use moral disengagement cognitive strategies to justify their high vigilance. Finally, doctor friendly behavior moderates this mediating mechanism. When doctors exhibit friendly behavior, maximizing patients' vigilance does not decrease but instead increases through strengthened moral disengagement. This study combined field surveys and experimental manipulations to provide convergent evidence.

5.1 Theoretical Contributions

This study's theoretical contributions manifest in four aspects. First, it is the first to link maximizing decision-making with patient vigilance toward doctors, significantly expanding the scope and topics of vigilance research. On one hand, previous research has focused on vigilance in interactions with strangers (Zhao et al., 2023) or close others (Li et al., 2015), whereas doctor-patient relationships differ from both stranger and close relationships in trust foundations, emotional bonds, and interaction patterns (Haidet et al., 2002; Street et al., 2009). This study extends vigilance research to the doctor-patient interaction domain, revealing patients' vigilance toward doctors' competence and ethics and its specific manifestations. On the other hand, research shows social environmental factors (Li et al., 2015; Liu et al., 2019) and individual personality traits (Mogg & Bradley, 1998; Rose et al., 2002; Tang et al., 2017) significantly influence vigilance. This study provides a new decision-making characteristic perspective on vigilance antecedents, enriching vigilance research.

Second, this study enriches maximizing decision-making research in interpersonal communication by revealing its effect on patient vigilance. Maximizing research has primarily focused on decision-making characteristics (e.g., option numbers, outcomes, experiences) (Chowdhury et al., 2009; Iyengar et al., 2006; Luan & Li, 2017, 2019; Ma & Roese, 2014; Polman, 2010). Only minimal research has examined how others treat maximizers, finding they are perceived as less warm, receive less social support, and suffer a "maximizing penalty" (Chen et al., 2023). This study reveals how maximizing decision-making significantly affects how individuals treat others—maximizing patients perceive greater threats from doctors and exhibit higher vigilance—further enriching maximizing research in interpersonal communication. This also explains why maximizing correlates negatively with well-being, optimism, self-esteem, and life satisfaction, and positively with depression (Bruine de Bruin et al., 2007; Schwartz et al., 2002). Since maximizers have higher vigilance, and high vigilance-induced tension significantly reduces well-being (Carver & Scheier, 2014), maximizers experience lower well-being.

Third, this study systematically reveals the psychological mechanism through which maximizing decision-making affects patient vigilance based on moral disengagement theory. Previous research identified resource competition (Cheng et al., 2021; Liu et al., 2019; Zhao et al., 2023) or harm from others (Li et al., 2015) as vigilance formation mechanisms. This study illuminates the moral pathway of vigilance by excavating its moral attributes, deepening understanding of vigilance's psychological origins. Moreover, while previous research primarily viewed moral disengagement as a mechanism for immoral behavior (Moore et al., 2012), few studies examined its role in non-moral behaviors, such as job insecurity promoting turnover through moral disengagement (Huang et al., 2017). This study reveals moral disengagement as the psychological mechanism for patient vigilance, providing empirical support for moral disengagement driving behaviors beyond moral domains, breaking theoretical boundaries and expanding applicable contexts.

Finally, this study introduces doctor friendly behavior as a moderator of the maximizing-moral disengagement-vigilance pathway. Although friendly behavior is generally believed to benefit doctor-patient relationships (Kim et al., 2020; Street et al., 2009), this study found counterproductive effects: doctor friendly behavior not only failed to reduce maximizing patients' vigilance but strengthened the maximizing-moral disengagement relationship, thereby intensifying vigilance. This resonates with Liu et al.'s (2019) finding that Chinese individuals more easily interpret friendly behavior as hypocrisy in ambiguous situations, while providing new empirical evidence for potential negative effects of friendly behavior (Cialdini & Goldstein, 2004).

5.2 Practical Implications

This research offers positive practical insights for preventing doctor-patient conflicts. Compared to post-conflict resolution, prevention has gained increasing importance, as institutionally reflected in the State Council's Regulations on the Prevention and Settlement of Medical Disputes (2018). Patient vigilance toward doctors, as a key "psychological latent risk" in doctor-patient relationships, represents a prevention priority. Patients' maximizing decision-making characteristics serve as effective indicators for identifying and predicting vigilance, holding important practical significance for prevention efforts. Specifically, when constructing doctor-patient conflict early-warning systems, medical institutions should incorporate patients' maximizing decision-making characteristics alongside interaction features (e.g., agreeableness; Köther et al., 2022). Administrative departments should also include the impact of patient characteristics (including maximizing) on doctor-patient interactions in physicians' communication training. Moreover, since individuals' maximizing tendencies can be adjusted through questioning methods (Zhu et al., 2019), doctors can use explanations and examples during interactions to guide patients toward reducing maximizing tendencies, thereby decreasing vigilance and resolving "psychological latent risks" before conflicts emerge.

Based on the mediating effect of moral disengagement, interventions targeting its cognitive strategies can reduce excessive patient vigilance. Specifically: emphasize that most doctors uphold professional ethics of healing the wounded and practicing benevolence to help patients develop rational cognition and eliminate unnecessary defensive psychology; clarify the negative impacts of excessive precaution on medical staff's psychological experiences and doctor-patient trust to guide patients in recognizing potential harms; share real cases demonstrating doctors' empathy and humanistic care to break patients' stereotypical impressions that doctors deserve blame. Additionally, when doctors exhibit friendly behavior, they should provide clear and specific explanations to avoid misunderstandings from information asymmetry. This "words and deeds combined" communication approach helps convey goodwill, eliminate doubts, and promote harmonious doctor-patient relationships.

5.3 Limitations and Future Directions

This study found maximizing patients showed similar vigilance levels toward doctors' competence and ethics, whereas satisficing patients focused more on ethics. This differs from traditional social cognition perspectives emphasizing warmth over competence in person perception (Wojciszke, 2005). Maximizing patients' pattern appears inconsistent with this conclusion, possibly due to differential benefit associations in interpersonal relationships: traditional research suggests individuals value others' warmth more than competence because they directly benefit from warmth but less from competence (Shang et al., 2021); however, maximizing patients pursue optimal treatment plans and outcomes, fundamentally benefiting from both doctors' competence and ethics, thus maintaining high vigilance toward both. This explanation may generalize to other relational contexts. For instance, individuals focus more on competitors' morality (Fiske et al., 2002) but more on intimate partners' competence (Eastwick et al., 2014), possibly because they respectively benefit from competitors' morality and intimate partners' competence. Future research should test this finding in more diverse and special interpersonal contexts to deepen conclusions.

This study primarily measured patient vigilance through subjective predictions of target doctors' potential behaviors in medical scenarios. While this method captures patients' specific thoughts (e.g., which behaviors and motives they are vigilant about), providing important insights into vigilance's deep causes and eliminating common method bias, vigilance also has significant physiological characteristics. Future research could incorporate physiological indicators for more comprehensive measurement, such as heart rate variability, skin conductance, or multimodal physiological signals (Ding et al., 2022). Behavioral indicators could also serve as effective supplements, such as patients changing hospitals or doctors, seeking multiple consultations through connections, checking doctor reputations through multiple channels before consultations, or verifying doctors' recommendations through various means after consultations. These indicators not only reduce measurement difficulty but also transform categorical data into continuous data, providing richer analytical dimensions. Future research should integrate subjective predictions, physiological indicators, and behavioral indicators to examine patient vigilance toward doctors from multiple dimensions.

This study did not examine potential moderating effects of external factors on maximizing decision-making's effect strength. For example, high-quality medical resources in top-tier hospitals might alleviate vigilance toward competence, while low relational mobility (Yuki et al., 2007) in regions with stable social networks (Friedman, 1991) might enhance patients' psychological security, both potentially weakening maximizing decision-making's effect. Future research should explore these moderating factors for more comprehensive understanding of maximizing decision-making's mechanisms. Additionally, beyond vigilance, other psychological phenomena in doctor-patient interactions deserve attention, such as role ambiguity (Gao, 2023) and competitive victimhood (Deng et al., 2023). Future research should examine whether maximizing decision-making plays similar roles in these psychological risks, and further investigate other patient and doctor characteristics' influence paths on vigilance to more systematically reveal complex psychological mechanisms in doctor-patient interactions.

6. Conclusion

This research systematically examined the effect of maximizing decision-making on patient vigilance and its psychological mechanisms and moderating effects through three studies. Results demonstrate that maximizing decision-making significantly increases patient vigilance toward doctors; moral disengagement mediates this relationship; and doctor friendly behavior not only fails to reduce maximizing patients' vigilance but actually intensifies it through moral disengagement mechanisms.

Appendix 1: Patient Vigilance Measurement (Without Friendly Behavior)

Hello, you will read eight typical medical scenarios commonly encountered in daily life. What do you think the doctor would do in each situation?

(1) Dr. Wang is an orthopedic surgeon about to perform an extremely technically difficult and complex surgery. What behaviors do you think Dr. Wang might exhibit? (List at least 2 possible behaviors):
- Possible behavior 1:
- Possible behavior 2:
- Possible behavior 3:

[Scenarios 2-8 follow the same format for Dr. Zheng (rare diseases), Dr. Xiao (emergency night shifts), Dr. Sun (high outpatient volume), Dr. Shen (prescribing medication), Dr. Li (receiving red envelopes), Dr. Liang (allocating special medications), and Dr. Pan (treating government officials)]

Appendix 2: Patient Vigilance Measurement (With Friendly Behavior)

Hello, you will read eight medical scenarios commonly encountered in daily life. How likely do you think the doctor is to engage in behaviors that could harm patients' interests?

(1) Dr. Wang is an orthopedic surgeon about to perform an extremely technically difficult and complex surgery. Dr. Wang explained key surgical steps, potential risks, and possible consequences in detail before surgery. How likely do you think Dr. Wang is to engage in behaviors that could harm patients' interests?
- Completely impossible ← → Very possible

[Scenarios 2-8 follow the same format, with friendly behaviors including: Dr. Zheng spending extensive time recording patient characteristics; Dr. Xiao arranging intern assistance; Dr. Sun requiring detailed symptom descriptions; Dr. Shen prescribing newly developed special medications; Dr. Li politely refusing red envelopes; Dr. Liang reserving medication based on professional principles; Dr. Pan explaining diagnoses in accessible language]