Decision Support Systems最新文献_第3页

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

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

Yan Zhang , Guofang Nan , Jian Luo , Jing Zhang

{"title":"A novel fuzzy nonparallel support vector machine for identifying helpful online reviews","authors":"Yan Zhang , Guofang Nan , Jian Luo , Jing Zhang","doi":"10.1016/j.dss.2025.114506","DOIUrl":"10.1016/j.dss.2025.114506","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114506"},"PeriodicalIF":6.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Stock habitats and information flow: How do different co-attention behaviors in online communities shape market reactions?","authors":"Yuhong Zhan , Chaoyue Gao , Alvin Chung Man Leung , Qiang Ye","doi":"10.1016/j.dss.2025.114508","DOIUrl":"10.1016/j.dss.2025.114508","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114508"},"PeriodicalIF":6.7,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

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

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

{"title":"Online–offline combined adaptive hotel recommendation system considering attribute importance and group consensus","authors":"Peide Liu , Ran Dang , Peng Wang , Yingcheng Xu , Yunfeng Zhang","doi":"10.1016/j.dss.2025.114503","DOIUrl":"10.1016/j.dss.2025.114503","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114503"},"PeriodicalIF":6.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"An exploration and exploitation of value cocreation-based machine learning framework for automated idea screening","authors":"Qian Liu , Qianzhou Du , Chuang Tang , Yili Hong , Weiguo Fan","doi":"10.1016/j.dss.2025.114504","DOIUrl":"10.1016/j.dss.2025.114504","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114504"},"PeriodicalIF":6.7,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

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

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

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

Yi Tan , Yajun Lu , Lu Wang

{"title":"Flight delay dynamics: Unraveling the impact of airport-network-spilled propagation on airline on-time performance","authors":"Yi Tan , Yajun Lu , Lu Wang","doi":"10.1016/j.dss.2025.114494","DOIUrl":"10.1016/j.dss.2025.114494","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114494"},"PeriodicalIF":6.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An agent-based model to analyze the influence of IS integration and IS assimilation on the adoption dynamics of a green supply chain: The case of regional consolidation centers 基于agent的信息系统整合和信息系统同化对绿色供应链采用动态影响分析模型——以区域整合中心为例

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-06-30 DOI: 10.1016/j.dss.2025.114501

François de Corbière , Hirotoshi Takeda , Johanna Habib , Frantz Rowe , Daniel Thiel

引用次数: 0

An explainable framework for assisting the detection of AI-generated textual content 一个可解释的框架，用于协助检测人工智能生成的文本内容

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-06-28 DOI: 10.1016/j.dss.2025.114498

Sen Yan, Zhiyi Wang, David Dobolyi

{"title":"An explainable framework for assisting the detection of AI-generated textual content","authors":"Sen Yan, Zhiyi Wang, David Dobolyi","doi":"10.1016/j.dss.2025.114498","DOIUrl":"10.1016/j.dss.2025.114498","url":null,"abstract":"<div><div>The recent development of generative AI (GenAI) algorithms has allowed machines to create new content in a realistic way, driving the spread of AI-generated content (AIGC) on the Internet. However, generative AI models and AIGC have exacerbated several societal challenges such as security threats (e.g., misinformation), trust issues, ethical concerns, and intellectual property regulation, calling for effective detection methods and a better understanding of AI-generated vs. human-written content. In this paper, we focus on AI-generated texts produced by large language models (LLMs) and extend prior detection methods by proposing a novel framework that combines semantic information and linguistic features. Based on potential semantic and linguistic differences in AI vs. human writing, we design our Semantic-Linguistic-Detector (SemLinDetector) framework by integrating a transformer-based semantic encoder and a linguistic encoder with parallel linguistic representations. By comparing a series of benchmark models on datasets collected from various LLMs and human writers in multiple domains, our experiments show that the proposed detection framework outperforms other benchmarks in a consistent and robust manner. Moreover, our model interpretability analysis showcases our framework's potential to help understand the reasoning behind prediction outcomes and identify patterns of differences in AI-generated and human-written content. Our research adds to the growing space of GenAI by proposing an effective and responsible detection system to address the risks and challenges of GenAI, offering implications for researchers and practitioners to better understand and regulate AIGC.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114498"},"PeriodicalIF":6.7,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling the role of generative AI in organizational privacy and security 生成式人工智能在组织隐私和安全中的作用建模

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-06-27 DOI: 10.1016/j.dss.2025.114500

Shweta Kumari Choudhary, Arpan Kumar Kar

引用次数: 0