Decision Support Systems最新文献_第4页

Is ambiguity always adverse? Empirical evidence from the wireless emergency alerts during the pandemic 模棱两可总是不利的吗？来自大流行期间无线紧急警报的经验证据

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-02-19 DOI: 10.1016/j.dss.2025.114424

Jaeho Myeong , Yongjin Park , Jae-Hyeon Ahn

引用次数: 0

Metaverse technology in sustainable supply chain management: Experimental findings 可持续供应链管理中的元宇宙技术：实验结果

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-02-14 DOI: 10.1016/j.dss.2025.114423

Kiarash Sadeghi R. , Divesh Ojha , Puneet Kaur , Raj V. Mahto , Amandeep Dhir

{"title":"Metaverse technology in sustainable supply chain management: Experimental findings","authors":"Kiarash Sadeghi R. , Divesh Ojha , Puneet Kaur , Raj V. Mahto , Amandeep Dhir","doi":"10.1016/j.dss.2025.114423","DOIUrl":"10.1016/j.dss.2025.114423","url":null,"abstract":"<div><div>The metaverse is a transformative force in supply chain information systems, particularly in the context of decision-making processes focusing on sustainable development goals. Thus, this study examines: <em>How does the metaverse among stakeholders contribute to the supply chain decision-making processes regarding sustainable development goals</em>? This study is among the first to provide empirical data examining the impact of the metaverse on stakeholders' sustainability assessment. Additionally, we examine how the metaverse influences supply chain collaboration and logistics decisions efficiency, particularly concerning their significant environmental implications. Through the lens of the practice-based theory, we test the proposed relationships using empirical data collected through a scenario-based survey. Furthermore, we analyzed tweets containing the keywords “metaverse” and “supply chain” to identify related topics and sentiments regarding the metaverse. The text mining analysis revealed three primary topics: logistics, digitalization, and sustainability. Results from the sentiment analysis indicated a predominantly positive attitude towards the metaverse within supply chains.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"191 ","pages":"Article 114423"},"PeriodicalIF":6.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the impact of free live-streamed medical consultation on patient engagement and patient satisfaction in the multistage online consultation process: A quasi-experimental design 在多阶段在线咨询过程中探索免费医疗直播对患者参与和患者满意度的影响：一种准实验设计

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-02-12 DOI: 10.1016/j.dss.2025.114422

Haochen Song , Xitong Guo , Tianshi Wu

{"title":"Exploring the impact of free live-streamed medical consultation on patient engagement and patient satisfaction in the multistage online consultation process: A quasi-experimental design","authors":"Haochen Song , Xitong Guo , Tianshi Wu","doi":"10.1016/j.dss.2025.114422","DOIUrl":"10.1016/j.dss.2025.114422","url":null,"abstract":"<div><div>In recent years, many online healthcare communities (OHCs) in China introduced the feature of free live-streamed medical consultations (FLSMC), which allows patients to communicate with physicians and have an interactive consultation for free through live streaming. Despite the rapid growth of FLSMC, little is known about whether FLSMC can bring benefits to patients when they have online consultation needs in the future. Drawing on signaling theory, this study examines the impact of FLSMC on patient engagement and patient satisfaction in the multistage online consultation process. We further explore the moderating effects of physician's owned and earned signals in the pre-consultation stage by integrating social capital theory with signaling theory. We collect a panel data set of 16,151 physicians from a leading OHC in China. Based on the DID method, a quasi-experimental design, and the instrumental variable method, we demonstrate that FLSMC has a positive effect on patient choice, patient messaging, and patient satisfaction. In addition, we find that the physician's title and online rating can positively moderate the effects of FLSMC on patient choice. This study not only sheds light on the literature on online healthcare by identifying the role of signals in the context of FLSMC, but also provides decision support for patients, physicians, and OHC managers.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"191 ","pages":"Article 114422"},"PeriodicalIF":6.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429544","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

DECEN: A deep learning model enhanced by depressive emotions for depression detection from social media content DECEN：一种由抑郁情绪增强的深度学习模型，用于从社交媒体内容中检测抑郁

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-02-10 DOI: 10.1016/j.dss.2025.114421

Zhijun Yan , Fei Peng , Dongsong Zhang

{"title":"DECEN: A deep learning model enhanced by depressive emotions for depression detection from social media content","authors":"Zhijun Yan , Fei Peng , Dongsong Zhang","doi":"10.1016/j.dss.2025.114421","DOIUrl":"10.1016/j.dss.2025.114421","url":null,"abstract":"<div><div>Depression is a serious and recurrent mental illness that significantly affects an individual's life and the society as a whole. Automatic detection of depression is crucial for early intervention and minimizing negative consequences. Existing studies on building deep learning models for automated depression detection have mainly used post-level emotion polarity (i.e., positive and negative emotions) and word embeddings as predictive features. Few have considered depressive emotions (e.g., anhedonia) expressed in those posts, despite that depressive emotions are essential to clinical depression diagnosis. Moreover, existing approaches for depression detection often ignore the relationship between emotions and their context. This study proposes a Depressive Emotion-Context Enhanced Network (DECEN) that consists of a pre-trained depressive emotion recognition module and an emotion-context enhanced representation module to address those limitations. DECEN first integrates semantic and syntactic structure representations of textual content of social media posts to identify depressive emotions conveyed through terms either explicitly or implicitly, rather than general emotion words. Furthermore, we propose an emotion-context enhanced representation method to enhance the role of the context of depressive emotions in depression detection. The evaluation using real social media data demonstrates that DECEN outperforms the state-of-the-art models in depression detection. The results of an ablation experiment also reveal that the proposed depressive emotion recognition and emotion-context enhanced representation modules, the two novel design artifacts, improve model performance. This study contributes to depression diagnostic decisions by introducing a novel method and providing new technical and practical insights for detecting depression from social media content.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"191 ","pages":"Article 114421"},"PeriodicalIF":6.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395755","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

Understanding physicians' noncompliance use of AI-aided diagnosis—A mixed-methods approach 了解医生对人工智能辅助诊断的不依从性-混合方法方法

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-02-10 DOI: 10.1016/j.dss.2025.114420

Jiaoyang Li , Xixi Li , Cheng Zhang

{"title":"Understanding physicians' noncompliance use of AI-aided diagnosis—A mixed-methods approach","authors":"Jiaoyang Li , Xixi Li , Cheng Zhang","doi":"10.1016/j.dss.2025.114420","DOIUrl":"10.1016/j.dss.2025.114420","url":null,"abstract":"<div><div>Despite the pervasiveness of artificial intelligence (AI) technologies in the healthcare industry, physicians are reluctant to follow the recommendations suggested by AI-aided diagnostic systems. We conceptualize physicians' noncompliance use of AI-aided diagnostic systems and draw on the technology threat avoidance theory (TTAT) to investigate the phenomenon of interest. Specifically, we leverage a mixed-methods approach to develop and test a comprehensive research model of physicians' noncompliance use of AI under the overarching theory of TTAT. With an exploratory qualitative study by interviewing ten physicians with experience in using AI-aided diagnostic systems, we observe that (1) physicians experience two distinct types of threats imposed by AI, namely AI threats to diagnostic process and outcome, (2) physicians' resistance to AI-aided diagnostic systems is the underlying psychological mechanism that turns their AI threat perceptions into noncompliance usage behavior, and (3) physicians' professional capital serves as an essential boundary condition in understanding the impacts of AI threats on resistance. In a confirmatory quantitative survey with 160 physicians, we find that (1) both AI threats to diagnostic process and outcome arouse physicians' psychological resistance, (2) such resistance to AI-aided diagnosis leads to noncompliance usage behavior, (3) noncompliance use of AI-aided diagnosis decreases physicians' diagnostic performance enhanced by AI, and (4) physicians' professional capital weakens the positive impact of AI threat to diagnostic process on resistance, but strengthens the positive impact of AI threat to diagnostic outcome on resistance. Our research advances the understanding of post-adoption noncompliance use of AI technology and enriches TTAT in health AI use. Our empirical findings offer practical suggestions for implementing and managing AI technology in the healthcare industry.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"191 ","pages":"Article 114420"},"PeriodicalIF":6.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520356","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

Is seeing the same as doing? An evaluation of vicarious experiences in the metaverse 看和做是一样的吗？对虚拟世界中替代体验的评价

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-02-06 DOI: 10.1016/j.dss.2025.114419

Caleb Krieger , Andy Luse , Ghazal Abdolhossein Khani , Rathindra Sarathy

引用次数: 0

A deep learning–based method to predict the length of stay for patients with traumatic fall injuries in support of physicians' clinical decisions and patient management 一种基于深度学习的方法来预测创伤性跌倒损伤患者的住院时间，以支持医生的临床决策和患者管理

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-02-03 DOI: 10.1016/j.dss.2025.114411

Jiaxuan Peng , Da Xu , Paul Jen-Hwa Hu , Jessica Qiuhua Sheng , Ting-Shuo Huang

{"title":"A deep learning–based method to predict the length of stay for patients with traumatic fall injuries in support of physicians' clinical decisions and patient management","authors":"Jiaxuan Peng , Da Xu , Paul Jen-Hwa Hu , Jessica Qiuhua Sheng , Ting-Shuo Huang","doi":"10.1016/j.dss.2025.114411","DOIUrl":"10.1016/j.dss.2025.114411","url":null,"abstract":"<div><div>Accurate estimates of the length of stay (LOS) for patients who suffer traumatic fall injuries are crucial to inform physicians' clinical decisions and patient management. They also have important implications for resource utilization efficiency and cost containment efforts by healthcare organizations. Effective predictions should consider essential relationships across different variables pertaining to patient demographics, clinical history, injury severity, and physiology. A proposed deep learning–based method incorporates these relationships and can predict LOS more accurately, as demonstrated by a comparative evaluation involving 3722 patients who suffered traumatic fall injuries between 2011 and 2017. The results show the superior performance of the proposed method, relative to eleven prevalent methods that represent different analytics approaches. Our method demonstrates superior predictive performance, as manifested by the highest F-measure values and area under the curve. It is particularly efficacious for patients likely in need of longer LOS, which is relatively more important to physicians and healthcare organizations. This study underscores the value of incorporating important relationships and interactions among distinct patient variables to estimate LOS, with a particular emphasis on the inter-disease relationships, physiology-severity interactions, and patient information in clinical notes. The proposed method can be implemented as a decision support system to enhance physicians' clinical decisions and patient management, and improve healthcare organizations' resource planning and utilization efficiency, with nontrivial cost containment implications.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"191 ","pages":"Article 114411"},"PeriodicalIF":6.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350672","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

Contending with coronaries: May HIT be with you 与冠状动脉抗争：愿它与你同在

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-02-02 DOI: 10.1016/j.dss.2025.114410

Nirup Menon, Amitava Dutta, Sidhartha Das

{"title":"Contending with coronaries: May HIT be with you","authors":"Nirup Menon, Amitava Dutta, Sidhartha Das","doi":"10.1016/j.dss.2025.114410","DOIUrl":"10.1016/j.dss.2025.114410","url":null,"abstract":"<div><div>Health Information Technology (HIT) is revolutionizing healthcare by serving as the backbone for various decision support activities across the healthcare continuum, particularly within hospital settings. While existing literature highlights its positive impact on patient satisfaction, costs, and quality, its role in complementing other crucial hospital inputs to influence clinical healthcare outcomes has been relatively understudied. In this study, we explore the complementary effects of a specific type of HIT, Clinical Decision Support Systems (CDSS) on cardiac mortality rates (CMR) in hospitals. Though hospital personnel and cardiac medical services (CMS) are pivotal in reducing CMR, CDSS plays a complementary role by providing information and decision support throughout the cardiac care delivery process. Leveraging panel data spanning from 2016 to 2020, our analysis reveals that CDSS complements CMS and hospital personnel in mitigating CMR. These findings provide theoretical insights into the benefits facilitated by CDSS in cardiac care and hold managerial implications for the effective deployment of this technology within hospital settings. Through our analysis, we aim to elucidate the synergistic effects of CDSS, cardiac medical services, and healthcare personnel in improving clinical healthcare outcomes, particularly in the management of cardiac disease.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"191 ","pages":"Article 114410"},"PeriodicalIF":6.7,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372582","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

Impact of multidimensional presence on user well-being in metaverse communities 多维存在对虚拟社区用户幸福感的影响

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-02-01 DOI: 10.1016/j.dss.2024.114382

Arslan Rafi , Sanjit K. Roy , Mohsin Abdur Rehman , Muhammad Junaid Shahid Hasni

{"title":"Impact of multidimensional presence on user well-being in metaverse communities","authors":"Arslan Rafi , Sanjit K. Roy , Mohsin Abdur Rehman , Muhammad Junaid Shahid Hasni","doi":"10.1016/j.dss.2024.114382","DOIUrl":"10.1016/j.dss.2024.114382","url":null,"abstract":"<div><div>In metaverse communities, users engage in various activities, such as socializing, gaming, and exploration. Presence in such communities refers to the feeling of being there and being fully immersed. This study examines the role of various dimensions of presence (e.g., social, spatial, and self) in driving user well-being in metaverse communities. Moreover, the study tests the mediating roles of social connectedness and social support. The data set comprised responses from 357 metaverse users. Data were analyzed using multigroup analysis (MGA), necessary condition analysis, partial least squares path modeling, and artificial neural network analysis. Results show that social presence, spatial presence, and self-presence affect users' well-being in metaverse communities. Further, they indicate that spatial presence has a particularly strong impact on user well-being among extroverts and introverts. This study is one of the first to investigate how aspects of presence and user well-being interact inside metaverse communities. The practical implications of the results are discussed.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"189 ","pages":"Article 114382"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IS/IT Backsourcing decision making - A design science research approach 信息系统/信息技术外包决策——一种设计科学研究方法

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-02-01 DOI: 10.1016/j.dss.2024.114379

Jacky Akoka , Isabelle Comyn-Wattiau

{"title":"IS/IT Backsourcing decision making - A design science research approach","authors":"Jacky Akoka , Isabelle Comyn-Wattiau","doi":"10.1016/j.dss.2024.114379","DOIUrl":"10.1016/j.dss.2024.114379","url":null,"abstract":"<div><div>Many organizations implement outsourcing solutions for information systems and/or information technology (IS/IT). Some of them face problems resulting from dissatisfaction with these outsourcing decisions. Reasons for dissatisfaction include an outsourcing agreement that did not meet expectations, organizational changes, and a loss of control over the business. As a result, companies are rethinking their existing contracts by adopting a backsourcing solution. IS/IT backsourcing refers to the full or partial repatriation of previously outsourced activities. Given the strategic stakes of backsourcing, it is imperative to develop a decision-making process based on a solid framework. Based on an extensive literature review, we propose a design science research (DSR) approach consisting of four artifacts: a set of requirements, a conceptual framework, a decision model, and a prototype to support decision makers considering IS/IT backsourcing issues. A validation is presented, consisting of an illustrative scenario, a logical argument, and an expert evaluation. In addition, the approach was compared with recommendations found in the literature. Finally, the comments of a panel of IS/IT experts were collected. Our decision support system can be used by decision makers to investigate the backsourcing of IS/IT activities, choosing between three scenarios: backsource, renegotiate the contract, or change vendor.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"189 ","pages":"Article 114379"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170850","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