Decision Support Systems最新文献

AI art in the gig economy: Investigating the effects of non-copyrightability in online labor markets 零工经济中的人工智能艺术：调查非版权性对在线劳动力市场的影响

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2025-09-27 DOI: 10.1016/j.dss.2025.114545

Lan Li, Noelle Li Ying Cheah, Seung Hyun Kim

{"title":"AI art in the gig economy: Investigating the effects of non-copyrightability in online labor markets","authors":"Lan Li, Noelle Li Ying Cheah, Seung Hyun Kim","doi":"10.1016/j.dss.2025.114545","DOIUrl":"10.1016/j.dss.2025.114545","url":null,"abstract":"<div><div>As generative AI continues to transform industries, including the creative sector, it has become critical to understand how it interacts with legal frameworks. This study aims to investigate the effect of the landmark ruling issued by the U.S. District Court on August 18, 2023, which declared AI-generated art uncopyrightable to provide clarity to previously ambiguous legal standards on the AI-related services in online labor markets. Our findings reveal that prices for AI-related gigs on an online freelancer platform dropped by 32.97 % following the ruling, suggesting that the lack of copyright may have reduced the perceived value by limiting clients' residual rights. Furthermore, our research indicates that both freelancer experience and communication efficiency significantly moderate the relationship between AI art non-copyrightability and project pricing. In addition, the results show that large corporate clients were more affected by the ruling than individual clients. In contrast, prices for projects commissioned by small and mid-sized corporate clients did not change significantly. This suggests that large firms are more sensitive to intellectual property uncertainties because they rely heavily on formal rights to secure control and revenue from creative assets. This research contributes to a nuanced understanding of how legal frameworks for AI may shape the gig economy's AI art-related creative services, offering valuable guidelines for more informed decision-making by freelancers, clients, platform owners, and policymakers in this evolving landscape.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"199 ","pages":"Article 114545"},"PeriodicalIF":6.8,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236140","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

Being responsible or affable: Investigating the effects of AI error correction behaviors on user engagement 负责任或和蔼可亲：调查AI纠错行为对用户粘性的影响

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2025-09-17 DOI: 10.1016/j.dss.2025.114542

Yunchang Zhu, Xianghua Lu

{"title":"Being responsible or affable: Investigating the effects of AI error correction behaviors on user engagement","authors":"Yunchang Zhu, Xianghua Lu","doi":"10.1016/j.dss.2025.114542","DOIUrl":"10.1016/j.dss.2025.114542","url":null,"abstract":"<div><div>Affable design is increasingly employed in AI conversational agents to foster smoother interaction and enhance user experience. However, a growing concern is that this overemphasis on social appeal often overlooks corrective interventions, particularly when users hold false or biased beliefs. Such omissions carry the risk of reinforcing user misconceptions and ultimately undermining the effectiveness of human–AI collaboration. Drawing upon the attribution theory, this study investigates whether the error-correction behavior of AI agents offset these risks and improve user engagement. Empirical evidence from three experimental studies verifies that AI agents' error-correction behavior indeed enhances users' perceived responsibility of AI agents and strengthens their engagement intentions. This effect does not appear to compromise social comfort, especially in the context where responsibility takes precedence, such as healthcare. This study further finds that the high expertise of AI agents amplifies the positive effects of error-correction behavior, while high entitativity diminishes these effects by blurring AI agents' responsibility. These findings offer important guidance for designing responsible AI agents and highlight the value of AI error-correction behaviors in human-AI interaction.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114542"},"PeriodicalIF":6.8,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158579","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

Corporate credit scoring method based on unlabeled data and multi-source data 基于无标记数据和多源数据的企业信用评分方法

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2025-09-16 DOI: 10.1016/j.dss.2025.114543

Yunhong Xu, Yitong Chen, Li Sun, Yu Chen

{"title":"Corporate credit scoring method based on unlabeled data and multi-source data","authors":"Yunhong Xu, Yitong Chen, Li Sun, Yu Chen","doi":"10.1016/j.dss.2025.114543","DOIUrl":"10.1016/j.dss.2025.114543","url":null,"abstract":"<div><div>Unlabeled data and multi-source data provide unprecedented opportunities for the financial industry to improve credit scoring accuracy. When utilizing unlabeled data, existing credit scoring methods often suffer from unreliability issues due to improper clustering or the introduction of noise when predicting labels. When utilizing multi-source data, existing credit scoring methods based on federated learning frameworks fail to tailor models for different data distributions of different data sources due to the limitations of relying on a single global model. Moreover, recent studies have explored the individual value of unlabeled data and multi-source data, but they often fail to utilize both. To address these issues, we propose UMDCS (Unlabeled and Multi-Source data Driven Credit Scoring), a self-supervised credit scoring method that utilizes both unlabeled and multi-source data simultaneously. To utilize unlabeled data, we propose a novel sample masking function to generate pseudo-labels for unlabeled data and pre-train the encoder using the pretext tasks. To utilize multi-source data, we employ a horizontal federated learning framework to aggregate local encoders into a global model while preserving data privacy. The global encoder is concatenated with personalized predictors to form personalized credit scoring models for each data source. Five experiments and statistical significance tests show that UMDCS outperforms other baseline methods.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114543"},"PeriodicalIF":6.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120987","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

Emotion aware session based news recommender systems 基于情感感知会话的新闻推荐系统

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2025-09-15 DOI: 10.1016/j.dss.2025.114540

Benjamin Gundersen , Saikishore Kalloori , Abhishek Srivastava

{"title":"Emotion aware session based news recommender systems","authors":"Benjamin Gundersen , Saikishore Kalloori , Abhishek Srivastava","doi":"10.1016/j.dss.2025.114540","DOIUrl":"10.1016/j.dss.2025.114540","url":null,"abstract":"<div><div>News recommender systems are decision support systems that exploit user-article interactions over a short duration of time to discover users’ interests and predict unseen news articles to generate a ranking of news articles that are relevant and interesting. In the news recommendation scenario, the relevance of articles decays quickly, and fresh articles are generated daily. Session based models are proposed using time-aware approaches to exploit interactions sequentially. Prior news recommender systems do not consider emotional information expressed in news articles within sessions for recommendations. Emotions play a key role in supporting decision-making and emotionally charged headlines can evoke curiosity or urgency, prompting users to click on certain articles. This paper presents an innovative decision support system for session based news recommendation, using expressed emotions from news articles, such as expressed in the title, abstract, and text, to improve user decision-making. We introduce a novel methodology that incorporates expressed emotions into three session based news recommendation models. Our results demonstrate that expressed emotion carries valuable information to improve session based news recommenders on various ranking metrics significantly and proved especially beneficial in scenarios with limited user interaction history, addressing the cold-start problem. The results show significant improvements in ranking metrics, emphasizing the utility of emotional features for dynamic decision-making support.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114540"},"PeriodicalIF":6.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120986","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

Enhancing cybersecurity risk assessment using temporal knowledge graph-based explainable decision support system 基于时态知识图的可解释决策支持系统增强网络安全风险评估

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2025-09-13 DOI: 10.1016/j.dss.2025.114526

Subhajit Bag , Sobhan Sarkar , Indranil Bose

{"title":"Enhancing cybersecurity risk assessment using temporal knowledge graph-based explainable decision support system","authors":"Subhajit Bag , Sobhan Sarkar , Indranil Bose","doi":"10.1016/j.dss.2025.114526","DOIUrl":"10.1016/j.dss.2025.114526","url":null,"abstract":"<div><div>Assessing cybersecurity policies is crucial for any organization to combat evolving cyber threats. The absence of a comprehensive dataset has prevented previous studies from analyzing the risk of organizations’ cybersecurity policies. Past studies have not considered temporal information in the policies. Analysis of cybersecurity policies using attention mechanism requires automated determination of optimal number of attention units which remains unaddressed. Moreover, absence of interpretation in cybersecurity studies creates a barrier to understanding policy vulnerabilities and developing targeted solutions. To address these challenges, we develop a decision support system which (i) enhances risk classification of organization’s cybersecurity policies, (ii) develops a comprehensive cybersecurity policy dataset from the websites of 190 companies, transformed into a knowledge graph to capture entity relationships among various policies, (iii) integrates temporal information into the knowledge graph by incorporating time stamps from event sequences in cyberattack information, (iv) develops Explainable Factor Analysis based Multi-Head Attention mechanism, which automates the determination of the optimal number of attention units and optimizes data allocation across attention units using factor analysis, and (v) utilizes attention heatmaps and shapley values for interpretability. Our cybersecurity policy dataset is used as a case study with four benchmark datasets for further validation. Results reveal that our model outperforms the other state-of-the-art, achieving an 87.78% <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> score, followed by robustness checking and statistical significance testing. Finally, Shapley values are used to interpret the model’s output to identify vulnerabilities within the organizational policies, providing crucial insights enabling decision-makers to enhance their cybersecurity policies and mitigate potential threats.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114526"},"PeriodicalIF":6.8,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097012","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

Human-Robo-advisor collaboration in decision-making: Evidence from a multiphase mixed methods experimental study 人-机器人顾问在决策中的协作：来自多阶段混合方法实验研究的证据

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2025-09-12 DOI: 10.1016/j.dss.2025.114541

Hasan Mahmud , Najmul Islam , Satish Krishnan

{"title":"Human-Robo-advisor collaboration in decision-making: Evidence from a multiphase mixed methods experimental study","authors":"Hasan Mahmud , Najmul Islam , Satish Krishnan","doi":"10.1016/j.dss.2025.114541","DOIUrl":"10.1016/j.dss.2025.114541","url":null,"abstract":"<div><div>Robo-advisors (RAs) are cost-effective, bias-resistant alternatives to human financial advisors, yet adoption remains limited. While prior research has examined user interactions with RAs, less is known about how individuals interpret RA roles and integrate their advice into decision-making. To address this gap, this study employs a multiphase mixed methods design integrating a behavioral experiment (<em>N</em> = 334), thematic analysis, and follow-up quantitative testing. Findings suggest that people tend to rely on RAs, with reliance shaped by information about RA performance and the framing of advice as gains or losses. Thematic analysis reveals three RA roles in decision-making and four user types, each reflecting distinct patterns of advice integration. In addition, a 2 × 2 typology categorizes antecedents of acceptance into enablers and inhibitors at both the individual and algorithmic levels. By combining behavioral, interpretive, and confirmatory evidence, this study advances understanding of human–RA collaboration and provides actionable insights for designing more trustworthy and adaptive RA systems.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114541"},"PeriodicalIF":6.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221256","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

Social capital matters: Towards comprehensive user preference for product recommendation with deep learning 社会资本问题：通过深度学习实现产品推荐的综合用户偏好

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2025-09-08 DOI: 10.1016/j.dss.2025.114527

Weiyue Li , Ming Gao , Bowei Chen , Jingmin An , Yeming Gong

{"title":"Social capital matters: Towards comprehensive user preference for product recommendation with deep learning","authors":"Weiyue Li , Ming Gao , Bowei Chen , Jingmin An , Yeming Gong","doi":"10.1016/j.dss.2025.114527","DOIUrl":"10.1016/j.dss.2025.114527","url":null,"abstract":"<div><div>Social recommender systems help address data sparsity in user–product interactions by leveraging social relationships to infer user preferences. However, existing models often overlook the role of social capital that influence decision-making in social commerce. Social capital consists of structural, relational, and cognitive dimensions, all of which shape user preferences. To better understand these influences, we propose a multi-task learning framework named DeepSC that integrates social capital theory into preference modeling. Its user preference learning module extracts structural features through graph-based pre-training, learns relational features from dynamic user embeddings, and models cognitive features using a hypergraph attention network. Additionally, the dual graph-based product feature learning module enhances cognitive feature extraction by incorporating product co-interactions. DeepSC is optimized through a joint learning objective, combining point-wise and pair-wise learning with an auxiliary social link prediction task to refine user representations. Experiments on three e-commerce datasets demonstrate that DeepSC significantly outperforms the state-of-the-art recommendation models, highlighting the effectiveness of integrating social capital into social preference learning. Our research advances social recommendation by providing a social capital theory-driven approach to modeling user behavior in digital commerce.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114527"},"PeriodicalIF":6.8,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097011","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 hybrid firm relationships with graph neural networks for stock investment decisions 基于图神经网络的混合企业关系股票投资决策建模

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2025-09-07 DOI: 10.1016/j.dss.2025.114528

Yang Du , Biao Li , Zhichen Lu , Gang Kou

引用次数: 0

Decision support for integrated trade agent's procurement and sales planning under uncertainty 不确定条件下综合贸易代理采购销售计划的决策支持

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2025-09-05 DOI: 10.1016/j.dss.2025.114537

An Liu , Xinyu Wang , Jiafu Tang

{"title":"Decision support for integrated trade agent's procurement and sales planning under uncertainty","authors":"An Liu , Xinyu Wang , Jiafu Tang","doi":"10.1016/j.dss.2025.114537","DOIUrl":"10.1016/j.dss.2025.114537","url":null,"abstract":"<div><div>This paper investigates a trade agent decision optimization problem (TADOP), in which a trade agent (TA) selects a subset of retailers and suppliers to maximize its profit under uncertain demand and spot price. The TA operates between suppliers and retailers as a third-party platform and decide which subset of retailers to serve, taking into account capacity reservations with option suppliers in advance. Once demand and spot price are realized, the TA decides how much to procure from each channel to fulfill retailers' demand. The problem is formulated as a two-stage stochastic program. Due to the high complexity and large number of scenarios, we reformulate the problem as a set-partition model, where the master problem (MP) selects the combination of retailers to serve, and the subproblem (SP) identifies the optimal procurement plans, thus reducing the number of variables and constraints. To further enhance tractability, the SP is transformed into an equivalent shortest-path problem (SPP) to address issues of non-linearity and non-convexity. Experimental results demonstrate the effectiveness of the decomposition approach, providing TAs with a practical decision-making tool for procurement and sales. Furthermore, the insights gained into TAs' procurement and sales strategies across various scenarios offer valuable guidance for decision-making in uncertain supply chain environments.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114537"},"PeriodicalIF":6.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027230","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

A field study on the impact of the counter ad-blocking wall strategy on user engagement 反广告拦截墙策略对用户粘性影响的实地研究

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2025-09-02 DOI: 10.1016/j.dss.2025.114525

Michael K. Chen , Shuai Zhao , Cristian Borcea , Yi Chen

{"title":"A field study on the impact of the counter ad-blocking wall strategy on user engagement","authors":"Michael K. Chen , Shuai Zhao , Cristian Borcea , Yi Chen","doi":"10.1016/j.dss.2025.114525","DOIUrl":"10.1016/j.dss.2025.114525","url":null,"abstract":"<div><div>Ad-blocking tools prevent ads from being shown to web users. Their increasingly widespread usage poses an existential risk to online publishers who provide free content and rely on display ads for revenue. Studies on counter ad-blocking strategies taken by publishers are limited, especially with regard to how these strategies affect user engagement, thus posing additional uncertainties to the selection of a suitable counter ad-blocking strategy. Through a randomized field experiment with a large global publisher, our study seeks to understand how the two most common counter ad-blocking strategies, (i) Wall and (ii) Acceptable Ads Exchange (AAX), affect user engagement differently. Our results show that the Wall strategy causes a lower overall engagement compared to AAX, mainly due to users who refuse to whitelist and leave the website. Over time, the negative impact increases, albeit at a slower speed. Furthermore, heavier users, identified based on the amount of engagement in the pre-treatment period, are less affected by the Wall strategy than lighter users; instrumental users, who read for practical purposes, are less affected than entertainment users. Finally, the Wall strategy has a bigger negative impact on the engagement of popular and new articles, compared to niche and old articles, respectively, as observed by a longer tail in engagement distribution with respect to content. These results on the heterogeneous effects of counter ad-blocking strategies on engagement offer novel and important managerial implications on a publisher’s choice of counter ad-blocking strategy and editorial decisions.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114525"},"PeriodicalIF":6.8,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097010","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