Decision Support Systems最新文献

英文中文

How does artificial intelligence-generated content reshape user-generated content? An empirical study from TripAdvisor 人工智能生成内容（AIGC）如何重塑用户生成内容（UGC）？来自TripAdvisor的一项实证研究

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-16 DOI: 10.1016/j.dss.2026.114623

Zidong Li, Youngsok Bang

Generative AI has revolutionized content creation across digital ecosystems, yet its broader implications for user-generated content (UGC) remain underexplored. To address this gap, we examine TripAdvisor's introduction of AI-Generated Content (AIGC) summaries and investigate how this feature influences user review behavior. Drawing on a taxonomy of online review-writing motivations, we propose that AIGC fulfills multiple motivations to share experiences, reducing users' incentives to contribute new content. Utilizing a natural experiment with hotel reviews in Singapore, our difference-in-differences analysis reveals that overall review volume declines significantly after AIGC implementation, with high-rated reviews exhibiting a sharper decrease than low-rated ones. This effect is more pronounced for lower-tier hotels than higher-tier hotels. We also observe that reviewers compose longer reviews and assign slightly lower ratings post-AIGC. Our structural topic modeling also reveals a significant shift in review content from general to specific topics. These findings demonstrate how generative AI reshapes UGC dynamics and highlight practical considerations for platform managers seeking to leverage AI innovation while maintaining the authenticity and diversity of user feedback.

生成式人工智能已经彻底改变了整个数字生态系统的内容创作，但其对用户生成内容（UGC）的更广泛影响仍未得到充分探索。为了解决这一差距，我们研究了TripAdvisor引入的ai生成内容（AIGC）摘要，并研究了这一功能如何影响用户的评论行为。根据在线评论写作动机的分类，我们提出AIGC满足了分享体验的多重动机，减少了用户贡献新内容的动机。利用新加坡酒店评论的自然实验，我们的差异中差异分析显示，在AIGC实施后，总体评论量显著下降，高评价的评论比低评价的评论下降幅度更大。这种影响在低级别酒店中比在高级别酒店中更为明显。我们还观察到，审稿人撰写的审稿时间更长，并且在aigc之后分配的评分略低。我们的结构化主题建模还揭示了复习内容从一般主题到特定主题的重大转变。这些发现展示了生成式人工智能如何重塑UGC动态，并强调了平台管理者在寻求利用人工智能创新的同时保持用户反馈的真实性和多样性的实际考虑。

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引用次数: 0

AI-generated fake review detection 人工智能生成的虚假评论检测

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-30 DOI: 10.1016/j.dss.2026.114628

Jiwei Luo , Guofang Nan , Dahui Li

Online reviews of e-commerce platforms have long been recognized as a major factor that influences a consumer’s purchasing decisions. However, the emergence of generative artificial intelligence (GAI) has accelerated the proliferation of fake online reviews, which can significantly reduce consumer trust in these platforms. This study proposes a novel supervised learning approach that can be flexibly integrated into decision support system to help platforms effectively detect AI-generated fake reviews. In the approach, we first construct two types of variables to distinguish between human-written genuine reviews and AI-generated fake reviews. Then, we introduce an outlier detection method based on cumulative probability density to calculate the probability that a fake review generated by AI. Finally, we train the AdaBoost model using the cumulative probability density values of reviews computed above to obtain classifier that can accurately detect AI-generated fake reviews. Numerical experiments demonstrate that the proposed method can produce more accurate detections of AI-generated fake review than several state-of-the-art baseline methods. We contribute to the related literature by the exploitation of statistical theory, which posits that outliers, as small probability events, are typically located at the tails of feature distributions, a principle effectively employed in detecting AI-generated fake reviews.

长期以来，电子商务平台的在线评论一直被认为是影响消费者购买决策的主要因素。然而，生成式人工智能（GAI）的出现加速了虚假在线评论的扩散，这可能会大大降低消费者对这些平台的信任。本研究提出了一种新的监督学习方法，可以灵活地集成到决策支持系统中，帮助平台有效地检测人工智能生成的虚假评论。在该方法中，我们首先构建两种类型的变量来区分人工编写的真实评论和人工智能生成的虚假评论。然后，我们引入了一种基于累积概率密度的离群点检测方法来计算人工智能生成虚假评论的概率。最后，我们使用上述计算的评论的累积概率密度值来训练AdaBoost模型，以获得能够准确检测人工智能生成的虚假评论的分类器。数值实验表明，该方法可以比几种最先进的基线方法更准确地检测人工智能生成的虚假评论。我们利用统计理论为相关文献做出了贡献，该理论认为，异常值作为小概率事件，通常位于特征分布的尾部，这一原则有效地用于检测人工智能生成的虚假评论。

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引用次数: 0

From prompts to parameters: A prompt-free GLLM framework to measure digital activities through corporate disclosures 从提示到参数：一个无提示的GLLM框架，通过公司披露来衡量数字活动

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-03-24 DOI: 10.1016/j.dss.2026.114666

Sihang Chen, Lijian Wei, Lei Shi

引用次数: 0

Editorial for the special issue: Empowering bright internet and bright AI 特刊社论：赋能光明互联网与光明人工智能

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-03-11 DOI: 10.1016/j.dss.2026.114663

Daegon Cho, Shan Liu, Dan J. Kim

引用次数: 0

What makes a good image? Exploring patients' physician selection behavior leveraging large language models and scenario experiments 是什么造就了一个好的形象？利用大型语言模型和场景实验探索患者的医生选择行为

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.dss.2025.114608

Shan Liu , Qingshan Liu , Kezhen Wei , Guangsen Si , Chenze Wang , Muyu Zhang

As important information cues for patients' selection, physicians' online profile images have received limited attention. We explore the effects of visual cues—image feature (image clarity) and image contents (smile intensity and medical professionalism) on patients' selection behavior, while also examining the moderating effect of consultation price. Leveraging large language models, we annotate visual cues to facilitate empirical analysis. This analysis demonstrates that image clarity, smile intensity, and medical professionalism positively affect patients' selection behavior, with consultation price amplifying the effect of image clarity. We further conduct scenario-based experiments to examine the underlying mechanism from perspectives of information foraging and perceived diagnosticity. This study enriches theoretical insights into patients' selection behavior by mining physicians' image information. It also advances the empirical methodological paradigm by integrating the large language model with empirical analysis. Our findings help physicians and platform managers strategically optimize profile images and consultation prices to improve physicians' popularity in online health market.

作为患者选择的重要信息线索，医生的在线个人资料图片受到的关注有限。我们探讨了视觉线索-图像特征（图像清晰度）和图像内容（微笑强度和医疗专业度）对患者选择行为的影响，同时考察了咨询价格的调节作用。利用大型语言模型，我们注释视觉线索以促进实证分析。分析表明，图像清晰度、微笑强度和医疗专业精神正向影响患者的选择行为，且咨询价格放大了图像清晰度的影响。我们进一步从信息觅食和感知诊断的角度进行了基于场景的实验来研究其潜在机制。本研究通过对医生影像信息的挖掘，丰富了对患者选择行为的理论认识。它还通过将大语言模型与实证分析相结合，推进了实证方法论范式。我们的研究结果有助于医生和平台管理者战略性地优化个人资料图像和咨询价格，以提高医生在在线医疗市场的知名度。

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引用次数: 0

Corrigendum to “‘Decoding LLMs’ verbal deception in online reviews” [Decision Support Systems 200 (2026) 114529]. “解码法学硕士在线评论中的口头欺骗”的勘误表[决策支持系统200(2026)114529]。

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI: 10.1016/j.dss.2025.114594

Yinghui Huang , Jinyi Zhou , Wanghao Dong , Weiqing Li , Maomao Chi , Changbin Jiang , Weijun Wang , Shasha Deng

引用次数: 0

Brand crisis and recovery in livestream commerce: A psychological contract violation theory perspective 直播商业中的品牌危机与复苏：一个心理契约违约理论的视角

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.dss.2025.114597

Jiaqi Liu , Xiang Gong , Zhenxin Xiao , Xiaoxiao Liu , Matthew K.O. Lee , Hongwei Wang

Brand streamer crisis (BSC) is a growing concern in livestream commerce due to its accidental, adverse, and uncontrollable consequences. Drawing on psychological contract violation (PCV) theory, we examine the effect of BSC and its recovery strategies on brand performance. In study 1, we conducted a natural experiment with a synthetic difference-in-differences (SDID) model and found that BSC reduces product sales (i.e., financial performance) and follower increments (i.e., relational performance). In Study 2, we performed an observational study with an interrupted time series (ITS) analysis and revealed that the defensive recovery strategy has positive effects on product sales and follower increments. Additionally, the offensive recovery strategy has a positive effect on product sales, while it has a nonsignificant effect on follower increments. Our study contributes to the literature by developing a PCV perspective of brand crisis and offers effective recovery strategies for practitioners in livestream commerce.

品牌主播危机（Brand streamer crisis，简称BSC）由于其偶然性、不利性和不可控的后果，在直播商业中日益受到关注。运用心理契约违约理论，研究平衡记分卡及其恢复策略对品牌绩效的影响。在研究1中，我们使用合成差异中差异（SDID）模型进行了自然实验，发现平衡计分卡降低了产品销售（即财务绩效）和追随者增量（即关系绩效）。在研究2中，我们使用中断时间序列（ITS）分析进行了观察性研究，发现防御性恢复策略对产品销售和追随者增量有积极影响。此外，进攻性恢复策略对产品销售有正向影响，而对追随者增量的影响不显著。我们的研究通过发展品牌危机的PCV视角为文献做出了贡献，并为直播商业从业者提供了有效的恢复策略。

引用次数: 0

At your fingertips: Do augmented reality gestures reveal product-related emotion? 触手可及：增强现实手势是否揭示了与产品相关的情感？

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1016/j.dss.2025.114595

Pratik Tarafdar , Alvin Chung Man Leung , Wei Thoo Yue , Indranil Bose

Advances in immersive technologies give online retailers an opportunity to integrate augmented reality (AR) experiences for their customers. With AR, product presentations shift from static images to interactive virtual experiences. This interaction allows online retailers to identify product-related emotions through affective computing. In mobile AR, customers use touch gestures for virtual interaction. Drawing from theories of immersive media and affective computing, we hypothesize that touch movements and pressure in AR-based mobile applications relate to positive emotions during product interactions. We conducted an observational study in a controlled laboratory setting to test our hypotheses and found that these variables can predict emotional responses. To ensure robustness, we applied explainable AI methods, including Shapley Additive Explanations (SHAP), to interpret the contribution of each touch gesture. We found that specific gestures, including the number of pan movements, the average time for pinch movements, touch pressure, and the number of rotate movements, strongly predict positive emotional responses, highlighting the importance of haptic engagement in immersive shopping experiences. These findings have important theoretical and practical implications. We explain how touch behavior can predict product-related emotions and demonstrate how online retailers can implement emotion analytics in AR shopping applications.

沉浸式技术的进步让在线零售商有机会为他们的客户整合增强现实（AR）体验。有了AR，产品展示从静态图像转变为交互式虚拟体验。这种互动允许在线零售商通过情感计算来识别与产品相关的情感。在移动增强现实中，客户使用触摸手势进行虚拟交互。根据沉浸式媒体和情感计算理论，我们假设基于ar的移动应用程序中的触摸运动和压力与产品交互过程中的积极情绪有关。我们在一个受控的实验室环境中进行了一项观察性研究，以检验我们的假设，发现这些变量可以预测情绪反应。为了确保鲁棒性，我们应用了可解释的人工智能方法，包括Shapley加性解释（SHAP），来解释每个触摸手势的贡献。我们发现，特定的手势，包括平移动作的次数、按压动作的平均时间、触摸压力和旋转动作的次数，强烈地预测了积极的情绪反应，强调了触觉参与在沉浸式购物体验中的重要性。这些发现具有重要的理论和实践意义。我们解释了触摸行为如何预测与产品相关的情感，并展示了在线零售商如何在AR购物应用程序中实现情感分析。

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引用次数: 0

Expectancy as a critical factor of IT adoption for learning toward a successful BMD scholar model implementation in a digital divide context 期望是在数字鸿沟背景下成功实现BMD学者模型的关键因素

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.dss.2025.114593

Jean Robert Kala Kamdjoug , Samuel Fosso Wamba , Serge-Lopez Wamba-Taguimdje , Pascal Koko Bashengezi

When introducing new educational systems, governments must consider the expectations of end beneficiaries to ensure alignment between stated objectives and intended outcomes. A notable example is the implementation of the Bachelor–Master–Doctorate (BMD) system in higher education in developing countries. This large-scale reform places particular emphasis on integrating information technologies for learning, commonly referred to as e-learning. However, existing literature on e-learning adoption as a decision support system rarely examines the policies and strategies that shape its integration into educational systems. This study analyzes the factors driving e-learning adoption by higher education institutions in a developing country within the BMD framework. A mixed-methods approach was employed, combining a survey-based study, exploratory qualitative interviews and reports, and a literature review to develop a questionnaire grounded in expectancy–performance theory. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The results indicate positive relationships between technological context factors (network speed, network coverage, and device performance), expected academic performance factors (student motivation, course design, learning outcomes, learning assistance, and community-building support), and students' intention to use information technology for learning.

在引入新的教育体系时，政府必须考虑最终受益者的期望，以确保既定目标与预期结果之间的一致性。一个显著的例子是在发展中国家高等教育中实施学士-硕士-博士（BMD）制度。这种大规模的改革特别强调整合学习的信息技术，通常被称为电子学习。然而，现有的关于采用电子学习作为决策支持系统的文献很少研究将其整合到教育系统中的政策和策略。本研究在BMD框架下分析了推动发展中国家高等教育机构采用电子学习的因素。采用混合方法，结合基于调查的研究，探索性质的访谈和报告，以及文献综述来开发基于期望-绩效理论的问卷。采用偏最小二乘结构方程模型（PLS-SEM）和模糊集定性比较分析（fsQCA）对数据进行分析。结果表明，技术环境因素（网络速度、网络覆盖和设备性能）、期望学习成绩因素（学生动机、课程设计、学习成果、学习辅助和社区建设支持）与学生使用信息技术学习意愿之间存在正相关关系。

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引用次数: 0

Learning user preferences in livestreaming market: A graphical model considering temporal effect 直播市场中用户偏好的学习：一个考虑时间效应的图形模型

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2026-01-05 DOI: 10.1016/j.dss.2025.114600

Qingyuan Lin , Yijun Li , Miłosz Kadziński , Mengzhuo Guo

The livestreaming market has experienced rapid growth, making effective recommendation systems essential for enhancing user engagement and marketing strategies. Traditional models often fall short in simultaneously capturing user preferences, host popularity, and the temporal dynamics inherent in livestreaming platforms. To address these challenges, we propose an interpretable graphical model that integrates Poisson Factorization with hierarchical structures and explicit temporal effects. Our model jointly learns user preferences and host popularity while accounting for temporal variations. We develop a variational Bayesian inference algorithm for efficient parameter estimation. Using real-world data from a leading livestreaming platform, we demonstrate that our model outperforms several baseline methods in predicting viewing volumes and capturing user–host interactions before, during, and after a public vacation. Additionally, the learned low-dimensional representations enhance predictive tasks, such as payment behavior prediction, and enable effective profiling and segmentation of users and hosts. Our findings provide insights for decision-makers aiming to optimize recommendation systems and marketing strategies in the dynamic livestreaming market.

直播市场经历了快速增长，有效的推荐系统对于提高用户参与度和营销策略至关重要。传统模型在同时捕捉用户偏好、主持人受欢迎程度和直播平台固有的时间动态方面往往存在不足。为了解决这些挑战，我们提出了一个可解释的图形模型，该模型将泊松分解与层次结构和显式时间效应相结合。我们的模型在考虑时间变化的同时，共同学习用户偏好和主机受欢迎程度。我们开发了一种变分贝叶斯推理算法，用于有效的参数估计。使用来自领先直播平台的真实世界数据，我们证明了我们的模型在预测观看量和捕获公共假期之前，期间和之后的用户-主机交互方面优于几种基线方法。此外，学习到的低维表示增强了预测任务，如支付行为预测，并能够有效地分析和分割用户和主机。我们的研究结果为决策者在动态直播市场中优化推荐系统和营销策略提供了见解。

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