Decision Support Systems最新文献

{"title":"Impact of categorization autonomy on effective use and adoption intentions","authors":"Arash Saghafi ,&nbsp;Poonacha Medappa ,&nbsp;Ariton Debrliev","doi":"10.1016/j.dss.2025.114499","DOIUrl":"10.1016/j.dss.2025.114499","url":null,"abstract":"<div><div>Category tree view is an omnipresent element in graphical user interfaces where it captures information in terms of a hierarchical structure. These categorization trees facilitate human users' cognitive economy and decision-making. While previous research has investigated the utilities of using unstructured data compared to pre-categorized information by business users, the effectiveness of allowing users the autonomy to create their own categorization hierarchies from generic object types remains unexplored. This paper evaluates the benefits of categorization autonomy in terms of search precision, as an objective measure, as well as subjective intentions to use the system. We examined users' interactions with a platform in information seeking tasks with 201 subjects. Our findings indicate that categorization autonomy leads to superior results, both in terms of effective use and behavioral perceptions. We also found that the impact of categorization autonomy is moderated by task flexibility, such that the benefits are more apparent in tasks that necessitate open-ended search approaches. By focusing on how user-driven categorization influences system interaction, our study contributes to the design of decision support systems that are better aligned with users' cognitive structures and task demands.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114499"},"PeriodicalIF":6.7,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503391","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}

{"title":"Corrigendum to “Impact of multidimensional presence on user well-being in metaverse communities”","authors":"Arslan Rafi ,&nbsp;Sanjit K. Roy ,&nbsp;Mohsin Abdur Rehman ,&nbsp;Muhammad Junaid Shahid Hasni","doi":"10.1016/j.dss.2025.114497","DOIUrl":"10.1016/j.dss.2025.114497","url":null,"abstract":"<div><div>In the original article, we examined various factors, including social, spatial, and self-presence, influencing user well-being in metaverse communities. We intended to examine the symmetrical and asymmetrical relationships between types of presence and user well-being. However, discrepancies emerged in reporting the final measurement items and their validity assessment. We provide details on how we corrected the errors in the article.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114497"},"PeriodicalIF":6.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503547","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}

{"title":"The more aesthetic, the better? The impact of photo aesthetics on perceived review helpfulness","authors":"Yu Han ,&nbsp;Ziqiong Zhang ,&nbsp;Carol X.J. Ou ,&nbsp;Zili Zhang","doi":"10.1016/j.dss.2025.114496","DOIUrl":"10.1016/j.dss.2025.114496","url":null,"abstract":"<div><div>Review helpfulness is crucial for assessing the quality of online reviews and mitigating information overload. Although numerous studies have explored the impact of textual and reviewer characteristics on review helpfulness, the role of photo aesthetics remains important but underexplored. This study addresses this gap by investigating the impact of photo aesthetics on perceived review helpfulness and its underlying mediating effects. The hotel review data from <span><span>TripAdvisor.com</span><svg><path></path></svg></span> exhibit an inverted U-shaped effect of photo aesthetics on perceived review helpfulness, in which review text length moderates this relationship. To further validate this causal relationship and explore the underlying mediating effects, an experimental study is conducted. The experimental results confirm the causal impact of photo aesthetics on perceived review helpfulness and reveal that perceived pleasure, reviewer effort and review authenticity mediate the relationship. These novel insights challenge the notion that “the more aesthetic, the better” for review photos, offering new theoretical and practical implications.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114496"},"PeriodicalIF":6.7,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502471","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}

{"title":"The risk-risk trade-off (R2T) framework: Examining contact [cash] versus contactless [mobile] payment usage","authors":"Abhipsa Pal ,&nbsp;Rahul Dé ,&nbsp;H. Raghav Rao","doi":"10.1016/j.dss.2025.114495","DOIUrl":"10.1016/j.dss.2025.114495","url":null,"abstract":"<div><div>Although the diffusion of mobile payment technology has been historically governed by contextual events that trigger anxiety, accentuating either the risks of mobile payments or the risks of its conflicting alternative, cash, literature neglects the importance of examining the risks associated with the alternatives. To address this gap, we develop the risk-risk trade-off (R²T) framework, drawing from the theory of substitutes of hazardous substances, and examine how individuals make usage decisions by balancing two sets of risks – for mobile payments and cash, respectively. On one side, the framework weighs contactless [mobile payment] risks related to potential thefts and losses, heightened by the rise in cybercrime. Conversely, on the other side, it weighs the risks from its substitute, contact [cash] payment, carrying the health hazard of infectious disease transmission through contact, with this risk magnified during the global pandemic. To validate the model, we used survey responses from 1403 participants in India and triangulated the quantitative results using their qualitative comments. This study theoretically contributes to the mobile payment usage literature by moving beyond technology risks as the sole risks to be considered for usage decision-making and includes the analysis of risks of the technology's substitute, cash, as well. The framework can support analysis of users' decisions towards consciously choosing the technology against its alternatives, in various risky contexts.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114495"},"PeriodicalIF":6.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335630","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}

{"title":"Knowledge-based Context-aware Group Recommender System for Point of Interest recommendation","authors":"Nargis Pervin ,&nbsp;Abhishek Kulkarni ,&nbsp;Ayush Adarsh ,&nbsp;Shreya Som","doi":"10.1016/j.dss.2025.114485","DOIUrl":"10.1016/j.dss.2025.114485","url":null,"abstract":"<div><div>The rise of Location-based Social Networking (LBSN) platforms has transformed the way users explore Points of Interest (POIs), increasingly relying on group-based recommendations. However, recommending POIs to groups presents unique challenges due to conflicting preferences among members. Traditional group recommendation algorithms often prioritize aggregated methods or explicit preference extraction, overlooking latent domain-specific information and the dynamic nature of group decision-making. To address these gaps, we propose a novel Knowledge-based Context-Aware Group Recommender System (KCGRS) designed to support decision-making processes within groups. KCGRS operates in two key stages: first, it utilizes a knowledge graph to learn domain-specific embeddings for both users and POIs, ensuring that implicit preferences and contextual factors are incorporated. In the second stage, these embeddings are enhanced with contextual information using a feed-forward transformer model, allowing for a more nuanced understanding of real-time preferences. The decision-making process is further refined by generating a group embedding, which is computed by applying a weighted aggregate of the context-infused embeddings of individual group members. This approach models group dynamics and decision processes more accurately, ensuring that the final recommendation reflects the collective preferences of the group. Experiments on real-world Yelp data show that KCGRS significantly outperforms five state-of-the-art baselines, delivering up to an average of 14.15% improvement in Hit ratio and a 13.07% increase in NDCG compared to the next best method while also maintaining competitive runtime efficiency. Furthermore, KCGRS demonstrates enhanced diversity and coverage in recommendations, ensuring that POI suggestions cater to a broader range of user preferences while avoiding over-personalization. This balance between accuracy, diversity, and efficiency highlights KCGRS’s effectiveness in supporting group decision-making and its potential to enhance collaborative recommendations in LBSN platforms. Finally, a user study with 144 participants was conducted that resulted in statistically significant levels of user satisfaction and trust in the recommendations, thereby supporting the practical effectiveness of the KCGRS system.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114485"},"PeriodicalIF":6.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322516","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}

{"title":"Handling imperfection: A taxonomy for machine learning on data with data quality defects","authors":"Michael Hagn,&nbsp;Bernd Heinrich,&nbsp;Thomas Krapf,&nbsp;Alexander Schiller","doi":"10.1016/j.dss.2025.114493","DOIUrl":"10.1016/j.dss.2025.114493","url":null,"abstract":"<div><div>In recent years, machine learning (ML) has become ubiquitous in sectors including transportation, security, health, and finance to analyze large amounts of data and support decision-making. However, real-world datasets used in ML often exhibit various data quality (DQ) defects that can significantly impair the performance and validity of ML models and thus also the decisions derived from them. Therefore, a plethora of methods across various research strands have been proposed to address DQ defects and mitigate their negative impact on ML-based data analysis and decision support. This has resulted in a fragmented research landscape, where comparisons and classifications of methods dealing with ML on data with DQ defects are very challenging for both researchers and practitioners. Thus, based on a structured design process, we develop and present a taxonomy for this research field. The taxonomy serves as a systematic framework to classify and organize existing research and methods according to relevant dimensions and facilitates future work in this area. Its reliability, understandability, completeness, and usefulness are supported by an evaluation with external researchers and practitioners. Finally, we identify current trends and research gaps and derive challenges and directions for future research.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114493"},"PeriodicalIF":6.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338972","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}

{"title":"An explainable lesion detection transformer model for medical imaging diagnosis decision support: Design science research","authors":"Xinwei Wang ,&nbsp;Yi Feng ,&nbsp;Sutong Wang ,&nbsp;Dujuan Wang ,&nbsp;T.C.E. Cheng","doi":"10.1016/j.dss.2025.114492","DOIUrl":"10.1016/j.dss.2025.114492","url":null,"abstract":"<div><div>Utilizing machine learning methods for auxiliary decision support in medical imaging significantly reduces missed detections and unnecessary expenses. However, the strict accuracy and transparency requirements in the medical field pose challenges for deep learning applications based on neural networks. To address these issues, we propose a novel artificial intelligence artifact guided by the design science research methodology for lesion detection decision support in medical images, called Explainable Lesion DEtection TRansformer (EL-DETR). This approach features an explainable separate attention mechanism in the decoder that highlights the attention weights of content and location queries, providing insights into the inference process through attention mapping visualizations. In addition, we introduce a hybrid matching query strategy to enhance the learning of positive samples and develop an adaptive efficient compound loss function to optimize training. We demonstrate EL-DETR's superior accuracy, robustness, and interpretability using four real-world datasets, establishing it as a reliable tool for clinical diagnosis and treatment decision support based on medical imaging. The code and original data are available at <span><span>https://github.com/weimingai/EL-DETR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114492"},"PeriodicalIF":6.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291619","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}

{"title":"Investigating the impact of differential privacy obfuscation on users’ data disclosure decisions","authors":"Michael Khavkin,&nbsp;Eran Toch","doi":"10.1016/j.dss.2025.114474","DOIUrl":"10.1016/j.dss.2025.114474","url":null,"abstract":"<div><div>Differential Privacy (DP) has emerged as the standard for privacy-preserving analysis of individual-level data. Despite growing attention in the research community to the operationalization of DP through the selection of the privacy budget <span><math><mi>ɛ</mi></math></span>, little is known about how DP obfuscation affects users’ disclosure decisions in data market scenarios. These decisions may be context-specific and vary with privacy preferences, eliciting disparate data valuations across individuals. Through a choice-based conjoint analysis (<span><math><mrow><msub><mrow><mi>N</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><mn>588</mn></mrow></math></span>), simulating realistic data markets, we analyzed how varying DP protection levels influence individual decision-making of participation in data collection under DP. Our findings show that personal reward and the guaranteed DP protection had the strongest influence on participants’ selection of a data collection scenario. Surprisingly, the type of disclosed data had the least influence on participants’ decisions to disclose personal data, a trend consistent across participants from different countries. Furthermore, increasing the DP protection level by a single unit reduced the preferred compensation price by over 60% for the same level of user utility, with marginal effects diminishing exponentially at higher DP levels. Our results were then confirmed in an online study (<span><math><mrow><msub><mrow><mi>N</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><mn>146</mn></mrow></math></span>) involving real data disclosure with actual payments, using our original scenario framing. Our findings can support context-specific DP configuration and help data practitioners improve decision-making associated with privacy protection in differentially private systems, balancing the trade-off between DP and compensation costs.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114474"},"PeriodicalIF":6.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254153","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}

{"title":"Social media meets FinTech platforms: How do online emotions support credit risk decision-making?","authors":"Zenan Zhou ,&nbsp;Zhichen Chen ,&nbsp;Yingjie Zhang ,&nbsp;Tian Lu ,&nbsp;Xianghua Lu","doi":"10.1016/j.dss.2025.114471","DOIUrl":"10.1016/j.dss.2025.114471","url":null,"abstract":"<div><div>As emerging FinTech platforms face pressure in efficiently managing credit risk, the human emotional spectrum of FinTech platform borrowers within social media becomes a potential source for gaining insight into and evaluating their financial behaviors. Collaborating with an Asian FinTech platform, we investigate the impact of social media emotions on a platform’s loan-approval decisions and repayment-reminder interventions before due dates. We demonstrate that anger at the pre-approval stage has a U-shaped relationship with platform borrowers’ default probability. We reveal what we call “<em>a bright side of anger</em>” with respect to curbing financial credit risk: moderate intensity of anger at the pre-approval stage suggests a lower loan default probability. We also find that the average happiness tendency of platform delinquent borrowers’ at the pre-maturity stage becomes informative and valuable, as it shows a U-shaped relationship with loan default; as for anger, it does not work therein. Furthermore, our field experiment indicates that a positive-expectation reminder is useful for prompting repayment when delinquent borrowers are in strong emotional intensities, regardless of anger or happiness. However, a negative-consequence reminder results in a higher default probability for delinquent borrowers who maintain high immediate happiness before the loan maturity dates. We draw on the classical appraisal theory of emotions and the feelings-as-information theory to interpret our findings. We offer non-trivial theoretical and practical implications to support FinTech platform credit risk decision-making by investigating the value of social media emotions and advocating for cross-functional coordination between debt approval and debt collection departments.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"195 ","pages":"Article 114471"},"PeriodicalIF":6.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167967","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}

{"title":"Multi-interest transfer using contrastive learning for cross-domain recommendation","authors":"Yu-Lin Lai ,&nbsp;Szu-Hao Huang ,&nbsp;Chiao-Ting Chen ,&nbsp;Cheng-Jhang Wu","doi":"10.1016/j.dss.2025.114473","DOIUrl":"10.1016/j.dss.2025.114473","url":null,"abstract":"<div><div>With advancements in information technology, deep learning techniques have been widely applied to recommendation systems, substantially assisting businesses and users in making better decisions. However, it still faces some intractable limitations, such as the cold-start problem and data sparsity. Hence, cross-domain recommendations are proposed to address these problems by referring to the domains with richer data. Existing models usually apply domain- or user-level transferal to exchange information between domains. For domain-level transferals, information is transferred directly using a straightforward transformation without filtering. In contrast, user-level transferal sets trainable parameters to control the ratio of user embedding from two domains. The former is insufficiently precise for every user, and the latter encounters generalization issues. For these reasons, these methods ameliorate the cold-start problem but create a new problem: negative transfer. Thus, we propose an interest-level transferal called multi-interest transferal to more precisely extract multiple interests and transfer related ones according to the target items. Nevertheless, it is not easy to model interest correlations of different domains. We, therefore, devise three self-supervised learning tasks to model the correlations and extract discriminant information. The experimental results reveal that this model outperforms other state-of-the-art methods by about 7% to 10%. Through multi-interest and contrastive learning techniques, our approach can model the decision-making process more effectively in cross-domain recommendation.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"195 ","pages":"Article 114473"},"PeriodicalIF":6.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185389","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}