Computers in Human Behavior最新文献_第2页

From iron to diamond: Collaborative behavior development across competitive tiers in League of Legends 从铁到钻石：《英雄联盟》中跨竞争层级的合作行为发展

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2026-06-01 Epub Date: 2026-01-27 DOI: 10.1016/j.chb.2026.108928

Jimoon Kang, Seongcheol Kim

Understanding how individuals develop collaborative competencies in digital environments has become critical as work, education, and recreation increasingly occur through screens. This mixed-methods study examines collaborative behaviors across competitive skill tiers in League of Legends, analyzing 560,000 matches and conducting expert interviews. Quantitative findings reveal that collaborative behaviors—vision control, strategic communication, and assists—differentiate tiers far more strongly than individual performance. Vision control increased over 200 %, strategic communication increased 169 % with forward-looking signals showing the largest effects, and disruptive behaviors declined five-fold across tiers, while individual combat effectiveness remained flat. Hierarchical regression demonstrated that these dimensions developed as integrated systems, together predicting 87.3 % of tier variance. Expert interviews identified bidirectional selection-socialization processes where collaborative predisposition facilitates advancement and higher-tier environments further reinforce capabilities through stricter norms, peer modeling, and reputational consequences, with threshold effects around mid-tiers indicating qualitative environmental shifts. These findings provide large-scale behavioral evidence supporting Social Identity Theory, team cognition theory, and status characteristics theory in naturalistic digital contexts, demonstrate that teamwork theories require adaptation for fluid-membership environments with standardized communication tools, and reveal collaborative competence as integrated systems requiring holistic development.

随着工作、教育和娱乐越来越多地通过屏幕进行，了解个人如何在数字环境中发展协作能力变得至关重要。这项混合方法的研究分析了《英雄联盟》中不同竞技水平的合作行为，分析了56万场比赛并进行了专家访谈。定量研究结果显示，合作行为——视觉控制、战略沟通和协助——比个人表现更能区分层级。视觉控制增加了200%以上，战略沟通增加了169%，前瞻性信号显示出最大的影响，破坏行为在不同层次上下降了五倍，而个人战斗力保持不变。层次回归表明，这些维度是作为一个综合系统发展起来的，共同预测了87.3%的层次方差。专家访谈确定了双向选择社会化过程，其中协作倾向促进了进步，高层环境通过更严格的规范、同伴建模和声誉后果进一步加强了能力，中层周围的阈值效应表明了质量环境的转变。这些研究结果为社会认同理论、团队认知理论和身份特征理论在自然数字环境下的应用提供了大规模的行为证据，表明团队合作理论需要使用标准化的沟通工具来适应流动性成员环境，并揭示了协作能力是一个需要整体发展的集成系统。

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引用次数: 0

How digital literacy shapes the conversion of political awareness into participation: A cross-national comparative study of Mongolia and China 数字素养如何塑造政治意识向参与的转化：蒙古和中国的跨国比较研究

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2026-06-01 Epub Date: 2026-02-12 DOI: 10.1016/j.chb.2026.108946

Gavaa Zanabazar , Qaiser Mohi Ud Din , Tao Hong

Political awareness is widely viewed as a foundation for democratic participation. However, many politically aware citizens remain inactive. Existing research has examined political awareness, civic engagement, and digital literacy separately, with limited attention to how these factors interact to shape participation especially in digital environments. This study addresses that gap by examining digital literacy as a conditional factor in the relationship between political awareness and political participation. This research investigates the comparative effects of political awareness, civic engagement, and digital literacy on political participation in Mongolia and China. Drawing on the Civic Voluntarism Model, the study develops a moderated mediation framework and examines it using two-wave survey data collected from 600 respondents: 295 from Mongolia and 305 from China between October and December 2024. Findings indicate that political awareness is positively associated with political participation primarily through the mediating role of civic engagement in both samples. Digital literacy statistically significantly moderates this indirect pathway in the Mongolian sample; however, no statistically significant moderating effect was observed in the Chinese sample, suggesting that in more digitally integrated contexts, digital literacy may function as a baseline capability rather than a differentiating resource. Overall, the cross-national analysis indicates that while the underlying mechanisms are consistent across both countries, the strength and significance of these effects vary according to the digital and participatory landscape in each context.

政治意识被广泛视为民主参与的基础。然而，许多有政治意识的公民仍然不活跃。现有的研究分别考察了政治意识、公民参与和数字素养，对这些因素如何相互作用来塑造参与（尤其是在数字环境中）的关注有限。本研究通过考察数字素养作为政治意识和政治参与之间关系的一个条件因素来解决这一差距。本研究考察了政治意识、公民参与和数字素养对蒙古和中国政治参与的比较影响。根据公民志愿主义模型，该研究开发了一个有调节的中介框架，并使用从600名受访者中收集的两波调查数据对其进行了检验：2024年10月至12月期间，蒙古295人，中国305人。研究结果表明，在两个样本中，政治意识主要通过公民参与的中介作用与政治参与呈正相关。在蒙古样本中，数字素养在统计上显著地调节了这种间接途径；然而，在中国样本中没有观察到统计学上显著的调节效应，这表明在更数字化整合的环境中，数字素养可能是一种基线能力，而不是一种差异化资源。总体而言，跨国分析表明，尽管两国的潜在机制是一致的，但这些影响的强度和重要性因各国的数字和参与性环境而异。

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引用次数: 0

Attention hijacked: How social media notifications disrupt cognitive processing 注意力被劫持：社交媒体通知如何扰乱认知过程

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2026-06-01 Epub Date: 2026-01-29 DOI: 10.1016/j.chb.2026.108926

Hippolyte Fournier , Arnaud Fournel , François Osiurak , Olivier Koenig , Flora Pâris , Vivien Gaujoux , Fabien Ringeval

Digital notifications are ubiquitous in modern life, yet their impact on cognitive performance remains poorly understood. Here, we introduce an ecologically valid paradigm to examine how social media notifications disrupt ongoing task performance. Participants completed a Stroop task while receiving smartphone-style notifications, allowing us to track both behavioral and physiological responses. Notifications triggered a transient slowdown in cognitive processing lasting approximately seven seconds, driven by the combined influence of perceptual salience, learned associations, and relevance appraisal. The magnitude of this disruption was predicted by the inferred relevance of notifications and by individual patterns of smartphone use—specifically, the frequency of interaction (notification volume and checking behavior) rather than total time spent on the device. These findings were mirrored in changes in pupil dilation, a physiological marker of arousal, confirming the convergent impact of notification relevance and smartphone habits. Together, our results show that modern digital cues can hijack attentional resources, even in the absence of explicit personal relevance. They underscore the need to account for notification frequency — not merely screen time — when evaluating the cognitive cost of pervasive digital environments, and raise broader concerns about the long-term impact of habitual device interaction on attentional functioning.

数字通知在现代生活中无处不在，但它们对认知表现的影响却鲜为人知。在这里，我们引入了一个生态有效的范式来研究社交媒体通知是如何破坏正在进行的任务执行的。参与者在完成Stroop任务的同时会收到智能手机式的通知，这样我们就可以跟踪他们的行为和生理反应。在感知显著性、学习关联和相关性评估的综合影响下，通知触发了认知处理的短暂放缓，持续约7秒。这种破坏的程度是通过通知的推断相关性和智能手机使用的个人模式来预测的——特别是互动的频率（通知量和检查行为），而不是花在设备上的总时间。这些发现反映在瞳孔扩张的变化上，瞳孔扩张是觉醒的生理标志，证实了通知相关性和智能手机习惯的趋同影响。总之，我们的研究结果表明，即使在没有明确的个人关联的情况下，现代数字线索也可以劫持注意力资源。他们强调，在评估无处不在的数字环境的认知成本时，需要考虑通知频率，而不仅仅是屏幕时间，并引发了对习惯性设备交互对注意力功能的长期影响的更广泛关注。

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引用次数: 0

Stimulated or saturated? Biometric analysis of augmented sport experiences among young adults: The role of hedonic innovativeness and repeated exposure 刺激型还是饱和型？年轻人增强运动体验的生物计量分析：享乐创新和重复暴露的作用

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2026-06-01 Epub Date: 2026-02-05 DOI: 10.1016/j.chb.2026.108932

Yongjae Kim , Jin Woo Ahn

The rise of augmented reality (AR) in sport broadcasting represents a promising strategy to counter declining interest among young adult consumers, a demographic characterized by digital fluency yet reduced engagement with traditional sports. This study examines how exposure to AR-enhanced highlights influence young adult's cognitive, affective, behavioral responses, particularly in relation to hedonic innovativeness, sport involvement, and repeated exposure. Two mixed-factorial experimental designs with 156 college students compare AR and non-AR NFL highlights, employing biometric measures (eye-tracking, galvanic skin response, facial expression analysis), self-reports, and Post-Experience Eye-Tracked Protocol (PEEP) interviews. Results show that AR heightened cognitive engagement by directing attention to digital overlays but did not directly enhance affective engagement or behavioral intention. Moderation analyses revealed that sport involvement increased AR's effect on affective engagement, while hedonic innovativeness predicted stronger behavioral intentions. Repeated-exposure analyses further showed clear habituation effects, with both affective and cognitive engagement declining across successive AR sequences, though hedonic innovators maintained relatively higher engagement levels. Heatmaps confirmed novelty decay, as visual attention shifted from AR graphics to core gameplay over time. Findings extend theories of consumer innovation and habituation while offering practical guidance for tailoring AR to young sport audiences.

增强现实（AR）在体育广播中的兴起代表了一种有希望的战略，可以应对年轻成年消费者兴趣下降的趋势，这一人群的特点是数字流畅，但对传统体育的参与度却有所降低。本研究探讨了暴露于ar增强亮点如何影响年轻人的认知、情感和行为反应，特别是与享乐创新、运动参与和重复暴露有关。两个混合因子实验设计采用生物特征测量（眼球追踪、皮肤电反应、面部表情分析）、自我报告和体验后眼球追踪协议（PEEP）访谈，对156名大学生进行了AR和非AR NFL亮点的比较。结果表明，AR通过将注意力引导到数字覆盖来提高认知参与，但没有直接提高情感参与或行为意图。适度分析显示，运动参与增加了AR对情感投入的影响，而享乐创新预测了更强的行为意图。重复暴露分析进一步显示了明显的习惯效应，在连续的AR序列中，情感和认知参与度都有所下降，尽管享乐创新者保持了相对较高的参与度。随着时间的推移，视觉注意力从AR图像转移到核心游戏玩法，热图证实了新鲜感的衰减。研究结果扩展了消费者创新和习惯的理论，同时为为年轻的体育观众量身定制AR提供了实践指导。

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引用次数: 0

User cognitive fit in human-AI interaction: Exploring the link between input representation and generative output complexity 人机交互中的用户认知契合：探索输入表示与生成输出复杂性之间的联系

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2026-06-01 Epub Date: 2026-01-21 DOI: 10.1016/j.chb.2026.108927

Hechang Cai , Jinlai Zhou

Generative artificial intelligence (AI) has transformed knowledge production into an interactive process in which user inputs actively influence generative outputs. Yet, little is known about how different forms of input representation affect the complexity of generative. Building on cognitive fit theory, this study examines how the characteristics of user input jointly determine output complexity in human–AI interaction. Using a large corpus of over sixty thousand conversations, we adopt a multimethod approach that combines fixed-effects modeling with interpretable machine learning (XGBoost, SHAP, and time-sensitive pattern analysis). The results reveal a nonlinear and dynamic relationship between input representation fit and output complexity, indicating that both low-fit and high-fit inputs can increase output complexity through distinct mechanisms. We further demonstrate that these effects differ according to interaction depth and temporal context, suggesting that representational alignment is not static but dynamically recalibrated during iterative exchanges. The study enhances the cognitive fit theory by conceptualizing fit as an adaptive alignment process in generative environments instead of a fixed match between human cognition and system representation. The findings highlight the necessity for AI systems that support adaptive prompting and context-aware feedback to enhance collaborative generative improvement.

生成式人工智能（AI）将知识生产转变为用户输入积极影响生成式输出的交互过程。然而，人们对不同形式的输入表示如何影响生成的复杂性知之甚少。在认知契合理论的基础上，本研究考察了用户输入的特征如何共同决定人机交互中的输出复杂性。使用超过6万个对话的大型语料库，我们采用了一种多方法方法，将固定效应建模与可解释的机器学习（XGBoost、SHAP和时间敏感模式分析）相结合。研究结果揭示了输入表示拟合与输出复杂度之间的非线性动态关系，表明低拟合和高拟合输入都可以通过不同的机制增加输出复杂度。我们进一步证明，这些影响根据交互深度和时间背景而不同，这表明表征对齐不是静态的，而是在迭代交换中动态重新校准的。本研究将认知契合定义为生成环境中的自适应对齐过程，而不是人类认知与系统表征之间的固定匹配，从而增强了认知契合理论。研究结果强调了支持自适应提示和上下文感知反馈的人工智能系统的必要性，以加强协作生成改进。

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引用次数: 0

Corrigendum to ‘Interplay of agenda setters in the digital age: The associative issue network between news organizations and political YouTube channels’ [Computers in Human Behavior 155 (2024), 108169] “数字时代议程设置者的相互作用：新闻机构和政治YouTube频道之间的关联问题网络”的勘误表[Computers in Human Behavior 155 (2024), 108169]

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2026-06-01 Epub Date: 2026-02-16 DOI: 10.1016/j.chb.2026.108948

Bumsoo Kim , Lin Han , Yonghwan Kim

引用次数: 0

Rethinking targeting strategies for SMEs: How artificial intelligence and audience breadth drive advertising performance 重新思考中小企业的目标定位策略：人工智能和受众广度如何推动广告效果

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2026-06-01 Epub Date: 2026-02-03 DOI: 10.1016/j.chb.2026.108930

Minjeong Ham , Jaeyoung Park

Despite the proliferation of AI-powered advertising systems promising superior targeting capabilities, empirical evidence comparing their performance to human judgment remains scarce. Drawing on bounded rationality and human-AI complementarity theories, this study examines 400 Facebook advertisements from a European beauty company to investigate when and how AI-powered targeting outperforms traditional approaches. Using a generalized linear model with a binomial family and logit link function, we discover a counterintuitive precision paradox: AI-powered targeting is associated with 88% higher click-through rates (CTR) when combined with broad targeting, yet produces non-significant performance gains when constrained by narrow targeting. The results suggest that effective human-AI collaboration may require a fundamental shift in human roles, from tactical specification to strategic boundary setting. This research contributes to the growing literature on human-AI interaction by identifying boundary conditions for complementarity and providing actionable insights for the digital advertising industry. The findings of this research have broader implications for understanding how advertisers and advertising platforms can effectively deploy AI systems in decision-making contexts where human intuition may conflict with algorithmic optimization.

尽管人工智能驱动的广告系统承诺具有卓越的定位能力，但将其表现与人类判断进行比较的经验证据仍然很少。利用有限理性和人类-人工智能互补理论，这项研究调查了一家欧洲美容公司的400个Facebook广告，以调查人工智能定位何时以及如何优于传统方法。使用具有二项式族和logit链接函数的广义线性模型，我们发现了一个违反直觉的精度悖论：当与广泛的目标定位相结合时，人工智能驱动的目标定位与88%的高点击率（CTR）相关，但当受到狭隘目标定位的限制时，产生的性能提升并不显著。结果表明，有效的人类与人工智能协作可能需要人类角色的根本转变，从战术规范到战略边界设置。这项研究通过确定互补性的边界条件并为数字广告行业提供可操作的见解，为人类与人工智能互动的文献增长做出了贡献。这项研究的发现对于理解广告商和广告平台如何在人类直觉可能与算法优化相冲突的决策环境中有效地部署人工智能系统具有更广泛的意义。

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引用次数: 0

Evaluating human-based and large language model-based content analysis: A case study on negative word-of-mouth classification in social media 评估基于人类和基于大语言模型的内容分析：社交媒体负面口碑分类的案例研究

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2026-06-01 Epub Date: 2026-01-20 DOI: 10.1016/j.chb.2026.108904

Yolande Yunhsiou Yang , Chih-Chien Wang , Pau-Lin Chou , Chieh-Yu Tien , Jia-Ci Wen , Nien-Hsin Chen

This main purpose of this study is to examine the effectiveness of human annotation versus large language model (LLM)-based automated content analysis in classifying negative word-of-mouth (NWOM) on social media. Using ChatGPT as the representative LLM, we evaluated its performance in categorizing comments into five sentiment types: positive, neutral, negative, irony, and boycott. Through three experiments, we compared ChatGPT's classification accuracy, precision, recall, and F1 score with human annotators under varying conditions, including the provision of classification definitions and keywords, as well as parameter adjustments such as temperature and Top_p. The findings reveal that while ChatGPT demonstrated strong recall in detecting negative comments, it struggles with nuanced classifications such as irony and boycott, where human annotators exhibit higher precision. Incorporating explicit definitions and keywords significantly enhanced ChatGPT's classification accuracy and consistency, particularly in distinguishing boycott-related comments. However, adjustments to temperature and Top_p yield only marginal improvements. These results highlight the potential of integrating LLMs with human annotation to optimize large-scale content analysis while addressing the limitations of automated sentiment classification. The study provides theoretical and practical insights into AI-assisted content analysis, with implications for social media monitoring, brand crisis management, and influencer engagement strategies.

本研究的主要目的是检验人工注释与基于大语言模型（LLM）的自动内容分析在对社交媒体上的负面口碑（NWOM）进行分类方面的有效性。使用ChatGPT作为代表性LLM，我们评估了它在将评论分为五种情绪类型方面的表现：积极，中立，消极，讽刺和抵制。通过三个实验，我们比较了ChatGPT在不同条件下的分类准确率、精密度、召回率和F1分数与人类注释器的比较，包括提供分类定义和关键词，以及温度和Top_p等参数的调整。研究结果表明，虽然ChatGPT在检测负面评论方面表现出很强的召回能力，但它在诸如讽刺和抵制等细微分类方面表现得很差，而人类注释者在这些方面表现出更高的准确性。结合明确的定义和关键词显著提高了ChatGPT的分类准确性和一致性，特别是在区分抵制相关评论方面。然而，温度和Top_p的调整只产生了微小的改善。这些结果突出了将llm与人类注释集成在一起以优化大规模内容分析的潜力，同时解决了自动情感分类的局限性。该研究为人工智能辅助内容分析提供了理论和实践见解，对社交媒体监控、品牌危机管理和网红参与策略具有重要意义。

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引用次数: 0

Personal cancer worry and Systemic Cancer Concern: Pathways to health behaviors via social media and emotional well-being 个人癌症担忧和系统癌症关注：通过社交媒体和情感健康的健康行为途径

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2026-05-01 Epub Date: 2026-01-17 DOI: 10.1016/j.chb.2026.108918

Chi-Chin Hsiao , Hsuan-Wei Lee

Cancer worry and concern are associated with how individuals engage with and interpret digital health environments, yet existing research has not distinguished between personal worry about individual risk and systemic concerns regarding institutional responses. This study examines how personal worry and systemic cancer concern relate to digital health behaviors through distinct affective and trust-related pathways. Drawing on an integrated theoretical framework, we analyze nationally representative U.S. data (HINTS 6; N = 6252) using structural equation modeling. Personal cancer worry is associated with increased social media engagement and reduced emotional well-being, with emotional vulnerability in particular aligning with greater involvement in harmful behaviors (e.g., smoking), whereas social media engagement shows more selective behavioral associations. In contrast, systemic cancer concern, marked by institutional distrust, corresponds to limited or passive digital engagement and diminished emotional well-being, patterns that relate to lower participation in positive health behaviors. Social media engagement is associated with perceiving less health misinformation, showing modest behavioral associations. Overall, the findings distinguish two forms of cancer-related psychological responses: a pattern of active but emotionally vulnerable engagement associated with personal worry, and a pattern of trust-sensitive, low-engagement interpretation associated with systemic concern. Findings inform psychologically responsive digital health design. Emotion-sensitive features may support users with heightened vulnerability, whereas credibility cues may address institutional distrust.

癌症的担忧和关注与个人如何参与和解释数字健康环境有关，然而现有的研究并没有区分个人对个人风险的担忧和对机构反应的系统性担忧。本研究考察了个人担忧和系统性癌症担忧如何通过不同的情感和信任相关途径与数字健康行为相关。利用一个完整的理论框架，我们使用结构方程模型分析了具有全国代表性的美国数据（HINTS 6; N = 6252）。个人癌症担忧与社交媒体参与度的增加和情绪幸福感的降低有关，尤其是情绪脆弱性与更多的有害行为（如吸烟）有关，而社交媒体参与度则显示出更多的选择性行为关联。相比之下，以机构不信任为特征的系统性癌症关注，对应于有限或被动的数字参与和情绪幸福感的减少，这些模式与积极健康行为的参与度较低有关。社交媒体参与与感知较少的健康错误信息有关，表现出适度的行为关联。总体而言，研究结果区分了两种与癌症相关的心理反应：一种是与个人担忧相关的积极但情感上脆弱的投入模式，另一种是与系统担忧相关的信任敏感、低投入解释模式。研究结果为心理响应型数字健康设计提供了依据。情绪敏感的特征可能支持具有高度脆弱性的用户，而可信度线索可能解决机构不信任。

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引用次数: 0

Bounty design in online communities: Uneven effects on prosocial behavior across user groups 在线社区的赏金设计：不同用户群体对亲社会行为的不均匀影响

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2026-05-01 Epub Date: 2026-01-14 DOI: 10.1016/j.chb.2026.108922

Jing Xu , Jianwei Liu , Kee-Hung Lai , Xu Gao , Yahe Yu , Dong Jing

User-led bounty design has emerged as a novel mechanism in online communities, enabling users to offer monetary incentives to boost engagement. Nevertheless, as an extrinsic stimulus, bounties can exert complex influences on users' prosocial behavior, affecting the motivation for user participation on online platforms and thereby generating spillover or crowding-out effects. This study employs a difference-in-differences (DID) model to examine how bounty design affects prosocial behavior across different user groups in online communities. The results reveal a significant decline in overall prosocial behavior, particularly among users with high extrinsic motivation. Conversely, users with high intrinsic motivation exhibit increased prosocial behavior. Additionally, bounty amount and historical comment sentiment significantly moderate these effects. Innovatively, this study focuses on the heterogeneous outcomes of bounty incentives on groups with different motivational levels, indicating that motivational level is a crucial factor influencing the effectiveness of user incentives—users with different motivational levels may even exhibit completely opposite responses to bounty incentives. This study provides insights into the complex mechanisms of bounty design and emphasizes the importance of accounting for different user groups in online community design. These findings may also help inform the optimization of incentive mechanisms.

用户主导的赏金设计已经成为在线社区的一种新机制，允许用户提供金钱奖励来提高参与度。然而，作为一种外在刺激，奖励会对用户的亲社会行为产生复杂的影响，影响用户参与网络平台的动机，从而产生溢出效应或挤出效应。本研究采用差异中的差异（DID）模型来研究赏金设计如何影响网络社区中不同用户群体的亲社会行为。结果显示，整体亲社会行为显著下降，特别是在具有高外在动机的用户中。相反，具有高内在动机的用户表现出更多的亲社会行为。此外，赏金数额和历史评论情绪显著调节了这些影响。创新之处在于，本研究关注赏金激励对不同动机水平群体的异质性结果，表明动机水平是影响用户激励有效性的关键因素，不同动机水平的用户甚至可能对赏金激励表现出完全相反的反应。这项研究为赏金设计的复杂机制提供了见解，并强调了在在线社区设计中考虑不同用户群体的重要性。这些发现也有助于激励机制的优化。

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引用次数: 0