Decision Support Systems最新文献_第8页

Boosting the underdogs: Unraveling how prevailing streamer visits drive revenue for emerging streamers on livestreaming entertainment platforms 推动弱者：揭示主流流媒体访问如何推动直播娱乐平台上新兴流媒体的收入

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

Decision Support Systems

Pub Date : 2025-07-25 DOI: 10.1016/j.dss.2025.114511

Huijing Guo , Xin Bao , Le Wang , Xin (Robert) Luo

Livestreaming entertainment (LSE) platforms have become increasingly popular for real-time social interaction. While high-status actors (prevailing streamers) attract large audiences, new streamers often struggle with visibility and earnings. This study examines how social capital transmission from high-status actors affect emerging streamers' live revenue, using Social Capital Theory and Arousal Theories as frameworks. We analyzed data from 52,010 emerging streamers over two weeks on a major LSE platform. The research shows that visits from established streamers significantly increase new streamers' revenue. This positive effect is notably stronger when new streamers have shown good past performance and belong to top guilds and visiting established streamers have strong performance records and actively interact during their visits. Our findings contribute to LSE platform research by highlighting the supportive role of established streamers. These insights can help platforms develop strategies to enhance platform vitality, diversify content, support emerging streamers' growth, and foster a more sustainable streaming ecosystem.

直播娱乐（LSE）平台在实时社交互动方面越来越受欢迎。虽然高地位的演员（流行的流媒体）吸引了大量的观众，但新的流媒体经常在知名度和收入方面挣扎。本研究以社会资本理论和激励理论为框架，探讨了社会资本传播对新兴流媒体直播收入的影响。我们在LSE的一个主要平台上分析了两周内来自52010个新兴流媒体的数据。研究表明，来自老牌主播的访问量显著增加了新主播的收入。当新的主播过去表现良好，并且属于顶级公会，并且访问的老牌主播有良好的表现记录并在访问期间积极互动时，这种积极的影响就会明显增强。我们的研究结果通过强调知名主播的支持作用，为伦敦政治经济学院的平台研究做出了贡献。这些见解可以帮助平台制定战略，增强平台活力，使内容多样化，支持新兴流媒体的发展，并培养一个更可持续的流媒体生态系统。

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引用次数: 0

Sentiment-aware cross-modal semantic interaction model for harmful meme detection 有害模因检测的情感感知跨模态语义交互模型

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

Decision Support Systems

Pub Date : 2025-07-25 DOI: 10.1016/j.dss.2025.114509

Yuxiao Duan, Xiang Zhao, Hao Guo

The increasing proliferation of harmful memes has a serious negative impact on society, rendering the detection of such memes a formidable challenge. Prior research has predominantly concentrated on the modal and semantic attributes of memes while neglecting the significance of cross-modal interactions and detailed semantic information. Although some approaches have incorporated large language models, they often have the problem of harmful avoidance due to ethical constraints. To address these issues, we propose a novel sentiment-aware cross-modal semantic interaction detector, which delves into the profound implications through three principal dimensions: semantic extraction, modal interaction, and sentiment polarity assessment. In the semantic extraction module, Visual Question-Answering is utilized to incorporate detailed knowledge and descriptions. For modal interaction, the positional relationships between meme objects and texts are investigated, and a distance-based attentional multimodal detector is established. In the sentiment polarity module, the sentiment polarity of the text is judged. These components are integrated to form a cohesive joint detection system. Extensive experiments across three benchmark datasets demonstrate SSID significantly outperforms state-of-the-art baselines, enhancing detection accuracy and exhibiting robustness.

有害模因的日益泛滥对社会产生了严重的负面影响，对这些模因的检测是一项艰巨的挑战。以往的研究主要集中在模因的模态和语义属性上，而忽视了模因跨模态交互作用和详细语义信息的重要性。尽管一些方法结合了大型语言模型，但由于伦理约束，它们往往存在有害回避的问题。为了解决这些问题，我们提出了一种新的情感感知跨模态语义交互检测器，该检测器通过三个主要维度：语义提取、模态交互和情感极性评估来深入研究其深远影响。在语义提取模块中，采用可视化问答的方式，将详细的知识和描述融合在一起。对于模态交互，研究模因对象与文本之间的位置关系，建立基于距离的注意多模态检测器。在情感极性模块中，判断文本的情感极性。这些组件被整合成一个有凝聚力的联合检测系统。在三个基准数据集上进行的广泛实验表明，SSID显著优于最先进的基线，提高了检测精度并表现出鲁棒性。

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引用次数: 0

Sparse-enhanced additive interaction neural network for interpretable credit decision 可解释信用决策的稀疏增强加性交互神经网络

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

Decision Support Systems

Pub Date : 2025-07-22 DOI: 10.1016/j.dss.2025.114507

Xingyu Lan , Hong Fan , Wanan Liu , Meng Xia , Kai Guo

Intelligent credit decision systems are crucial for financial institutions’ risk management, aiming to mitigate credit risk. While deep learning models offer high predictive accuracy, their opacity hinders decision support. Neural Additive Models (NAMs) offer feature-level interpretability but fail to capture complex interactions among credit risk factors. To enhance both accuracy and interpretability, we propose the Sparse-Enhanced Additive Interaction Neural Network (SAINTNet) for explainable credit scoring. SAINTNet advances NAM’s framework with dual-node additive modules and adaptive sparse feature selection, enabling autonomous feature learning. Leveraging entmax sparsity and optimized temperature settings, SAINTNet: (1) maintains interpretability, particularly for credit feature interactions; (2) achieves superior accuracy compared to black-box models. Experiments on four credit datasets demonstrate SAINTNet’s superior performance and systematic interpretability through global feature importance, local decision analysis, and interaction visualization, improving decision audits in high-risk credit scenarios.

智能信贷决策系统是金融机构风险管理的关键，其目的是降低信贷风险。虽然深度学习模型提供了很高的预测准确性，但它们的不透明性阻碍了决策支持。神经加性模型（NAMs）提供特征级的可解释性，但无法捕获信用风险因素之间复杂的相互作用。为了提高准确性和可解释性，我们提出了稀疏增强的加性交互神经网络（SAINTNet）用于可解释的信用评分。SAINTNet通过双节点加性模块和自适应稀疏特征选择改进了NAM框架，实现了自主特征学习。利用entmax稀疏性和优化的温度设置，SAINTNet:(1)保持可解释性，特别是对于信用特征交互；(2)与黑箱模型相比，精度更高。在四个信用数据集上进行的实验表明，SAINTNet通过全局特征重要性、局部决策分析和交互可视化等方法，具有卓越的性能和系统的可解释性，改善了高风险信用场景下的决策审计。

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引用次数: 0

A novel fuzzy nonparallel support vector machine for identifying helpful online reviews 一种新的模糊非并行支持向量机用于在线评论识别

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-21 DOI: 10.1016/j.dss.2025.114506

Yan Zhang , Guofang Nan , Jian Luo , Jing Zhang

Online review datasets are always imbalanced and contain numerous outliers or noise, making the accurate and efficient identification of helpful reviews a critical challenge in the digital age. To address this issue, the optimal feature set is first obtained from numerous constructed possible features (including ones based on the knowledge adoption model) by a feature selection method, and then a novel fuzzy nonparallel quadratic surface support vector machine (FNQSSVM) model is proposed for identifying helpful online reviews in this study. For well handling the imbalanced data with outliers or noise, a novel fuzzy membership function is first developed based on the K-nearest neighbor method with respect to the cosine distance, and then incorporated with the kernel-free nonlinear and nonparallel separating ideas to propose the FNQSSVM model by directly using two nonparallel quadratic surfaces for nonlinear classification. Computational results on three crawled real-life datasets in different domains show that the proposed FNQSSVM model outperforms the well-known and state-of-the-art classification methods in terms of classification accuracy for identifying helpful online reviews, within competitive computational time. The proposed method can be integrated into the decision support systems to assess the helpfulness of online reviews and facilitate the ranking of helpful reviews. Our findings can provide valuable managerial insights for online platforms, merchants and customers.

在线评论数据集总是不平衡的，并且包含许多异常值或噪声，这使得准确有效地识别有用的评论成为数字时代的关键挑战。为了解决这一问题，首先通过特征选择方法从大量构建的可能特征（包括基于知识采用模型的特征）中获得最优特征集，然后提出一种新的模糊非并行二次曲面支持向量机（FNQSSVM）模型来识别有用的在线评论。为了更好地处理带有异常值或噪声的不平衡数据，首先基于余弦距离的k近邻方法建立了一种新的模糊隶属函数，然后结合无核非线性和非并行分离思想，提出了直接使用两个非并行二次曲面进行非线性分类的FNQSSVM模型。在不同领域的三个抓取的真实数据集上的计算结果表明，在竞争性的计算时间内，所提出的FNQSSVM模型在识别有用的在线评论的分类精度方面优于已知的和最先进的分类方法。该方法可以集成到决策支持系统中，以评估在线评论的有用性，并促进有用评论的排名。我们的研究结果可以为在线平台、商家和客户提供有价值的管理见解。

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引用次数: 0

Stock habitats and information flow: How do different co-attention behaviors in online communities shape market reactions? 股票生境与信息流：网络社区中不同的共同关注行为如何影响市场反应？

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-13 DOI: 10.1016/j.dss.2025.114508

Yuhong Zhan , Chaoyue Gao , Alvin Chung Man Leung , Qiang Ye

Investors increasingly use online investment communities to acquire financial market information before making trading decisions to reduce the cost of information acquisition and get more abundant content. Due to limited attention, investors tend to focus their trading only on a subset of assets that align with their personal investment preferences. Thus, the attention behavior of investors in the communities can reflect their focus trends and indicate future stock movements. Unlike previous research that mainly focused on investor common search and viewing behaviors, we constructed stock clusters based on different common attention behaviors data (i.e., common follow behavior by investors and common mention behavior by content contributors) and compared their predictive capabilities on stock returns. After controlling for some deterministic factors, we verified the existence of comovement among stocks within the clusters (i.e., stock habitats) and found that investors' common attention behaviors can better predict stock returns compared to content contributors. To explore the mechanism, we found a possible direction of information flow between different stock habitats and revealed the leading role of content contributors in online investment communities. This study enriches the literature on stock habitats and information diffusion in online investment communities and provides practical decision support on portfolio management for investors. Moreover, online platform managers can also use our conclusions to provide better decision-making assistance for market participants.

投资者越来越多地利用在线投资社区在进行交易决策前获取金融市场信息，以降低信息获取成本，获得更丰富的内容。由于注意力有限，投资者倾向于只关注与他们个人投资偏好相符的资产子集。因此，投资者在社区中的关注行为可以反映其关注趋势，预示未来的股票走势。与以往的研究主要关注投资者的常见搜索和观看行为不同，我们基于不同的常见关注行为数据（即投资者的常见关注行为和内容贡献者的常见提及行为）构建了股票聚类，并比较了它们对股票收益的预测能力。在控制了一些确定性因素后，我们验证了集群内股票（即股票栖息地）之间存在共动，发现投资者的共同关注行为比内容贡献者更能预测股票收益。为了探索这一机制，我们发现了不同股票生境之间信息流动的可能方向，并揭示了在线投资社区中内容贡献者的主导作用。本研究丰富了网上投资社区中股票生境与信息扩散的相关文献，为投资者的投资组合管理提供了实用的决策支持。此外，网络平台管理者也可以利用我们的结论为市场参与者提供更好的决策辅助。

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引用次数: 0

Gamified giving: Contingent effects of leaderboard rankings on donation behavior in online medical crowdfunding 游戏化捐赠：排行榜排名对在线医疗众筹捐赠行为的偶然影响

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-08 DOI: 10.1016/j.dss.2025.114505

Yi Wu , Leping Xiao , Zhongtao Hu , Na Liu , Nan Feng

Online medical crowdfunding has emerged as a vital resource for patients seeking public assistance. As a typical gamification design, leaderboards play a crucial role in boosting users' donation. Grounded in motivational affordance and social influence theories, this study investigates how different leaderboard types and rankings influence donation through the underlying mechanism of sense of self-worth. A 2 (leaderboard ranking: high vs. low) × 2 (leaderboard type: public vs. social) between-subject experiment was conducted to validate our research model. The results reveal that high rankings enhance users' donation intentions by boosting their sense of self-worth. This positive effect is more pronounced in public leaderboards than in social ones. Additionally, donation experience weakens the positive effect of sense of self-worth on donation intention. This study contributes to the decision support systems literatures on online crowdfunding and gamification design with practical implications for fundraising strategies.

在线医疗众筹已成为寻求公共援助的患者的重要资源。作为一种典型的游戏化设计，排行榜在促进用户捐赠方面发挥着至关重要的作用。本研究以动机启示理论和社会影响理论为基础，探讨了不同排行榜类型和排名如何通过自我价值感的潜在机制影响捐赠。为了验证我们的研究模型，我们进行了2（排行榜排名：高vs低）× 2（排行榜类型：公共vs社交）受试者间实验。结果显示，高排名通过提升用户的自我价值感来增强他们的捐赠意愿。这种积极影响在公共排行榜中比在社交排行榜中更为明显。此外，捐赠经历削弱了自我价值感对捐赠意愿的正向作用。本研究对网络众筹和游戏化设计的决策支持系统文献有一定的借鉴意义。

引用次数: 0

Online–offline combined adaptive hotel recommendation system considering attribute importance and group consensus 考虑属性重要性和群体共识的线上线下组合自适应酒店推荐系统

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-08 DOI: 10.1016/j.dss.2025.114503

Peide Liu , Ran Dang , Peng Wang , Yingcheng Xu , Yunfeng Zhang

With the proliferation of tourism websites, online reviews have become indispensable for offline decision-makers when selecting hotels. Solely relying on personal judgment poses risks amid diverse preferences. Thus, this study aimed to create a hotel recommendation system that integrates online reviews and ratings with offline travel groups. First, the sentiment analysis of online reviews was integrated with ratings using heterogeneous reviewer weights, transforming them into probabilistic linguistic term sets. Second, by predicting reviewers' travel types and clustering them, a method was devised to calculate subgroup weights, considering online group size and offline social trust networks. Third, attribute importance was determined via an online–offline method (attribute importance optimization model) considering the intensity and ordinal information. Subsequently, an adaptive consensus optimization model was developed based on a novel measurement method. This study offers personalized recommendations for offline decision-makers, providing essential guidance for travel agencies and platforms to enhance services and holding significant practical value.

随着旅游网站的激增，在线评论已经成为线下决策者在选择酒店时不可或缺的工具。在多样化的偏好中，仅仅依靠个人判断会带来风险。因此，本研究旨在创建一个酒店推荐系统，将在线评论和评分与线下旅游团体相结合。首先，将在线评论的情感分析与使用异构评论者权重的评级相结合，将其转换为概率语言术语集。其次，通过预测评论者的旅行类型并对其进行聚类，设计了一种考虑在线群体规模和离线社会信任网络的子群体权重计算方法。第三，通过考虑强度和序数信息的线上-线下方法（属性重要性优化模型）确定属性重要性。随后，基于一种新的测量方法，建立了自适应共识优化模型。本研究为线下决策者提供个性化的建议，为旅行社和平台提升服务提供必要的指导，具有重要的实用价值。

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引用次数: 0

An exploration and exploitation of value cocreation-based machine learning framework for automated idea screening 基于价值共同创造的机器学习框架的探索和开发，用于自动化想法筛选

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-05 DOI: 10.1016/j.dss.2025.114504

Qian Liu , Qianzhou Du , Chuang Tang , Yili Hong , Weiguo Fan

Idea screening in collaborative crowdsourcing communities poses significant challenges for firms. These challenges are primarily attributable to issues of prediction accuracy and information overload. The rapid expansion of idea pools generates a vast amount of data, making it difficult to effectively identify valuable ideas for new product development. This study introduces an interpretable framework for machine learning that integrates a novel exploration and exploitation perspective within the value cocreation model to enhance idea screening. The framework incorporates six theoretical dimensions of the exploration and exploitation of value cocreation (EEVC): the exploration and exploitation of digital resources, direct interactions, and ideas and their comments. Our evaluation reveals that the EEVC-based idea-screening system significantly outperforms the traditional 3Cs model in terms of prediction accuracy. SHAP value analysis further reveals that the exploration and exploitation of digital resources are the most influential predictors of idea implementation. The EEVC framework advances open innovation theory by clarifying how value cocreation dynamics influence idea implementation. Practically, it proposes a human–machine collaboration system that enhances expert decision-making for more effective idea selection.

协作众包社区的创意筛选对企业构成了重大挑战。这些挑战主要是由于预测准确性和信息过载的问题。创意池的迅速扩大产生了大量的数据，这使得有效地识别新产品开发的有价值的想法变得困难。本研究引入了一个可解释的机器学习框架，该框架在价值共同创造模型中集成了一个新的探索和开发视角，以增强想法筛选。该框架包含了价值共同创造（EEVC）探索和利用的六个理论维度：数字资源的探索和利用、直接互动、想法及其评论。我们的评估表明，基于eevc的想法筛选系统在预测精度方面显著优于传统的3c模型。SHAP值分析进一步揭示了数字资源的探索和利用是创意实施最具影响力的预测因素。EEVC框架通过阐明价值共同创造动态如何影响理念实施来推进开放式创新理论。在实践中，提出了一个人机协作系统，增强专家决策能力，实现更有效的创意选择。

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引用次数: 0

Does warm care matter? Exploring the effects of service characteristics on organizational impression in smart retail stores 温暖的关怀重要吗？探讨服务特征对智慧零售商店组织印象的影响

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-03 DOI: 10.1016/j.dss.2025.114502

Sheng-Wei Lin , Shin-Yuan Hung , Kai-Teng Cheng

Given its context orientation, service quality is a core issue in the study of smart retail. This paper examines service quality in smart retail through the lens of the cues–images–impressions model. The objective is to analyze the influence of service characteristics of smart retail stores (SRSs) on customers' perceived service quality and organizational impressions. Using a mixed-methods design and a fuzzy-set qualitative comparative analysis approach, the study highlights customer orientation, SRS employee characteristics, and the SRS servicescape as mechanisms driving service quality and enabling positive organizational impression. The findings have both theoretical and practical implications for future research.

服务质量是智能零售研究的核心问题，它具有上下文导向。本文通过线索-图像-印象模型来考察智能零售中的服务质量。目的是分析智能零售商店的服务特征对顾客感知服务质量和组织印象的影响。本研究采用混合方法设计和模糊集定性比较分析方法，强调客户导向、SRS员工特征和SRS服务逃逸是驱动服务质量和产生积极组织印象的机制。这些发现对未来的研究具有理论和实践意义。

引用次数: 0

Flight delay dynamics: Unraveling the impact of airport-network-spilled propagation on airline on-time performance 航班延误动力学：揭示机场网络溢出传播对航空公司准点率的影响

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-01 DOI: 10.1016/j.dss.2025.114494

Yi Tan , Yajun Lu , Lu Wang

Flight delay prediction has attracted increasing attention in airline operations. Early identification of potential flight delays is crucial for improving airport scheduling and airline operations while mitigating associated costs. This study investigates the influence of the potential propagation of flight delays throughout the airport network via interconnected flights, a mechanism we term Airport-Network-Spilled Propagation (ANSP). To model the ANSP mechanism, we develop a novel time-dependent, network-based approach that decays the importance of past delays. From this network, we extract a real-time ANSP score for each airport to measure the influence of propagated delays. To evaluate our proposed approach, we employ four state-of-the-art machine learning models using domestic airline on-time performance data from the 30 Large Hub airports in the United States. The results demonstrate that integrating the ANSP score with established features from airline operations literature significantly enhances flight departure delay prediction performance, achieving an increase in AUC of up to 5.49%. Furthermore, we conduct an explainable AI analysis using Shapley additive explanations (SHAP), which reveals that our ANSP score ranks as the most important predictor among all features tested.

航班延误预测在航空公司运营中越来越受到关注。及早发现潜在的航班延误对于改善机场调度和航空公司运营，同时降低相关成本至关重要。本研究探讨了航班延误通过互联航班在整个机场网络中潜在传播的影响，我们称之为机场-网络溢出传播（ANSP）机制。为了对ANSP机制进行建模，我们开发了一种新的基于时间的网络方法，该方法降低了过去延迟的重要性。从这个网络中，我们提取了每个机场的实时ANSP分数，以衡量传播延迟的影响。为了评估我们提出的方法，我们采用了四种最先进的机器学习模型，使用了来自美国30个大型枢纽机场的国内航空公司准点率数据。结果表明，将ANSP得分与航空公司运营文献中已建立的特征相结合，显著提高了航班离港延误预测的性能，AUC提高了5.49%。此外，我们使用Shapley加性解释（SHAP）进行了可解释的人工智能分析，这表明我们的ANSP分数是所有测试特征中最重要的预测因子。

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引用次数: 0