Decision Support Systems最新文献

Modeling evolving user interests and engagement on short video sharing platforms: An attention-based deep generative approach 短视频分享平台上不断变化的用户兴趣和参与度建模：基于注意力的深度生成方法

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2026-04-01 Epub Date: 2026-02-02 DOI: 10.1016/j.dss.2026.114629

Jinnan Huang , Jiapeng Liu , Zice Ru , Xiuwu Liao

{"title":"Modeling evolving user interests and engagement on short video sharing platforms: An attention-based deep generative approach","authors":"Jinnan Huang , Jiapeng Liu , Zice Ru , Xiuwu Liao","doi":"10.1016/j.dss.2026.114629","DOIUrl":"10.1016/j.dss.2026.114629","url":null,"abstract":"<div><div>The rise of short video sharing platforms (SVSPs) has fundamentally transformed online content consumption. However, the unique characteristics of users’ short video consumption on SVSPs, including fine-grained temporal dependencies in rapid interest evolution, complex engagement state dynamics, and heterogeneous user–content attributes, pose significant challenges for understanding user behavior on SVSPs. To address these issues, we propose a Dynamic Interest and Engagement Model (DIEM), an attention-based deep generative model grounded in the Stimulus–Organism–Response (S–O–R) theoretical framework. Unlike conventional recommender models that represent users and content as static embeddings with heuristic interaction functions, DIEM models their interaction through a generative latent engagement process parameterized by a causal Transformer encoder and a bidirectional self-attention amortized inference network. By enforcing causal temporal structure in the encoder and leveraging bidirectional contextual information for posterior inference over engagement states, these architectures go beyond prevailing sequence models and effectively operationalize the “organism” component of S–O–R. We evaluated our model on the large-scale KuaiRand-1K dataset from Kuaishou platform. Experimental results demonstrate that DIEM significantly outperforms representative baselines across multiple evaluation metrics while providing interpretable insights into temporal interest evolution and personalized attention patterns. This research advances both theoretical understanding and practical applications for optimizing content delivery and enhancing user experience on SVSPs.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"203 ","pages":"Article 114629"},"PeriodicalIF":6.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146109875","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

How to leverage digital platforms in enhancing organizational resilience: The roles of supply chain integration and market orientation 如何利用数字平台增强组织弹性：供应链整合和市场导向的作用

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2026-04-01 Epub Date: 2026-01-07 DOI: 10.1016/j.dss.2026.114612

Qinyao Zheng , Jiabao Lin , Jose Benitez

{"title":"How to leverage digital platforms in enhancing organizational resilience: The roles of supply chain integration and market orientation","authors":"Qinyao Zheng , Jiabao Lin , Jose Benitez","doi":"10.1016/j.dss.2026.114612","DOIUrl":"10.1016/j.dss.2026.114612","url":null,"abstract":"<div><div>Despite the potential of digital platforms in promoting organizational resilience, the intermediate mechanisms and contextual contingencies of this association remain inadequately explored. Drawing on dynamic capability theory, we investigate how digital platform use influences organizational resilience through supply chain integration (SCI), with market orientation serving as a critical contingency factor. Using a sample of 178 Chinese agribusinesses, we find that both digital platform exploitative use and digital platform explorative use significantly improve SCI, which subsequently enhances organizational resilience. SCI exerts as a partial mediator in the association of digital platform exploitative use with organizational resilience, whereas acts as a full mediator in the association of digital platform explorative use with organizational resilience. Notably, market orientation strengthens the positive association of digital platform exploitative use with SCI, thus amplifying the positive mediating effect of SCI in the association of digital platform exploitative use with organizational resilience. Conversely, market orientation diminishes the favorable influence of digital platform explorative use on SCI, thereby impairing the positive mediating effect of SCI in the association of digital platform explorative use with organizational resilience. This study enriches the IS literature on the business implications of digital platforms by providing theoretical illustrations and empirical evidence on how digital platform use helps agribusinesses develop organizational resilience.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"203 ","pages":"Article 114612"},"PeriodicalIF":6.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981461","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

Extracting declarative constraints for process modeling from natural language descriptions with large language models 从具有大型语言模型的自然语言描述中提取用于流程建模的声明性约束

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2026-04-01 Epub Date: 2026-01-30 DOI: 10.1016/j.dss.2026.114627

Gyunam Park , Julian Kofferath , Minsu Cho

{"title":"Extracting declarative constraints for process modeling from natural language descriptions with large language models","authors":"Gyunam Park , Julian Kofferath , Minsu Cho","doi":"10.1016/j.dss.2026.114627","DOIUrl":"10.1016/j.dss.2026.114627","url":null,"abstract":"<div><div>With the growing availability of unstructured text data in organizations, automating the extraction of process models from natural language descriptions has become increasingly crucial. Traditional rule-based techniques face challenges such as limited generalization and constraints to narrow sets of control-flow constructs. We present a systematic empirical study of Large Language Models (LLMs) for translating natural language sentences into declarative process constraints using the <em>Declare</em> modeling language. Through comprehensive evaluation across seven LLM architectures, we demonstrate that fine-tuned models achieve 97.8% template accuracy compared to 53.8% for existing rule-based approaches. Our fine-tuning approach substantially outperforms prompting techniques (99.4% vs. 56.5% template accuracy), establishing clear guidance for practical deployment. We contribute a benchmark dataset of 969 sentence-constraint pairs across 11 <em>Declare</em> templates, direction-sensitive evaluation metrics, and complete reproducibility materials. The extracted constraints enable practical decision support applications including compliance monitoring, conformance checking, and automated governance dashboards.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"203 ","pages":"Article 114627"},"PeriodicalIF":6.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089536","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

Financial statement fraud detection using topic-driven financial sentiment analysis 基于主题驱动的财务情绪分析的财务报表舞弊检测

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2026-04-01 Epub Date: 2026-01-08 DOI: 10.1016/j.dss.2026.114615

Petr Hajek , Josef Novotny , Michal Munk

{"title":"Financial statement fraud detection using topic-driven financial sentiment analysis","authors":"Petr Hajek , Josef Novotny , Michal Munk","doi":"10.1016/j.dss.2026.114615","DOIUrl":"10.1016/j.dss.2026.114615","url":null,"abstract":"<div><div>Financial statement fraud undermines market integrity and incurs substantial costs for investors, regulators, and companies. Text-based detection methods have emerged as useful complements to traditional financial indicators, but many fail to incorporate domain-specific topics or sentiment cues, often missing subtle changes in deceptive communication. To overcome this problem, this study proposes a topic-driven financial sentiment analysis (TDFSA) model that detects corporate fraud by analyzing linguistic patterns in the Management Discussion & Analysis (MD&A) sections of annual reports. Our approach captures contextual sentiment within financially relevant topics using FinBERT embeddings. To evaluate these signals in fraud detection, we integrate the TDFSA outputs into a broader cost-sensitive evaluation framework. This framework combines text-based indicators with financial ratios to balance the need to avoid false alarms with the high cost of undetected fraud. Using data from U.S. firms flagged in SEC Accounting and Auditing Enforcement Releases from 2014 to 2024 and matched non-fraud peers, we examine trends in financial ratios, textual complexity, and sentiment dynamics in the three years preceding fraud events. The results show that models leveraging TDFSA achieve higher detection accuracy and lower cost than dictionary-based sentiment, generic topic models, and deep learning baselines.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"203 ","pages":"Article 114615"},"PeriodicalIF":6.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947956","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

MediHC: An AI-powered framework for hierarchical disease classification using multi-head attention and contrastive learning MediHC：使用多头注意和对比学习进行分层疾病分类的人工智能框架

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2026-04-01 Epub Date: 2026-01-13 DOI: 10.1016/j.dss.2025.114592

Yechi Xu , Shaokun Fan , Hongxun Jiang

{"title":"MediHC: An AI-powered framework for hierarchical disease classification using multi-head attention and contrastive learning","authors":"Yechi Xu , Shaokun Fan , Hongxun Jiang","doi":"10.1016/j.dss.2025.114592","DOIUrl":"10.1016/j.dss.2025.114592","url":null,"abstract":"<div><div>Timely and accurate diagnosis of complex diseases, particularly those with atypical symptoms, is crucial for reducing patient suffering and healthcare costs. However, current early-stage diagnostic accuracy is often compromised by unstructured patient narratives and limited clinical exposure to rare cases. To address these challenges, we introduce the Medical Hierarchy Classifier (MediHC), an AI-powered framework designed to enhance clinical decision-making by analyzing patient–doctor conversations. MediHC comprises three novel modules: a Language Processing Module using large language models (LLMs) and BioClinicalBERT for medical text embeddings; a Hierarchical Multi-Head Attention Module for modeling disease taxonomy dependencies; and a Multi-Level Prediction Module with Contrastive Learning to distinguish between similar diseases. A composite loss function jointly optimizes predictive performance and feature quality. Extensive experiments on real-world clinical datasets demonstrate that MediHC significantly outperforms existing methods, achieving superior accuracy across multiple levels of disease classification. These results underscore MediHC’s substantial potential to advance diagnostic strategies in challenging clinical contexts.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"203 ","pages":"Article 114592"},"PeriodicalIF":6.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961667","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

Not all questions are equal: Unveiling the varied effects of question types on product popularity 并非所有问题都是一样的：揭示问题类型对产品受欢迎程度的不同影响

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2026-04-01 Epub Date: 2026-02-02 DOI: 10.1016/j.dss.2026.114625

Qingxian An , Dan Li , Pengkun Wu , Zhixiang Zhou

{"title":"Not all questions are equal: Unveiling the varied effects of question types on product popularity","authors":"Qingxian An , Dan Li , Pengkun Wu , Zhixiang Zhou","doi":"10.1016/j.dss.2026.114625","DOIUrl":"10.1016/j.dss.2026.114625","url":null,"abstract":"<div><div>The widespread adoption of online question and answer (Q&A) systems by e-commerce platforms has sparked interest in understanding their potential impacts. Nevertheless, little is known about how various types of consumer questions influence product popularity. Using a dataset from <span><span>JD.com</span><svg><path></path></svg></span>, this study categorizes consumer questions along the content and temporal dimensions and investigates how different types of questions influence product popularity. The findings reveal that questions concerning core, auxiliary, and peripheral attributes are generally associated with higher product popularity. By comparison, post-purchase questions, particularly those focused on core attributes, are associated with lower product popularity. Further analysis suggests that factors such as question length, product price, and product type moderate these relationships. Specifically, for high-priced search products, longer post-purchase core attribute questions are linked to stronger negative associations with product popularity, whereas longer post-purchase peripheral attribute questions show more positive associations. Questions regarding auxiliary attributes do not show similar patterns. These findings provide crucial theoretical and practical insights for e-commerce platforms and merchants in optimizing Q&A strategies.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"203 ","pages":"Article 114625"},"PeriodicalIF":6.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189598","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

Financial reinforcement learning under concept drift based on knowledge distillation and curriculum learning 基于知识升华和课程学习的概念漂移下的金融强化学习

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2026-04-01 Epub Date: 2026-01-23 DOI: 10.1016/j.dss.2026.114624

Chang-An Wang , Szu-Hao Huang , Chiao-Ting Chen , Yi-Tang Fang

{"title":"Financial reinforcement learning under concept drift based on knowledge distillation and curriculum learning","authors":"Chang-An Wang , Szu-Hao Huang , Chiao-Ting Chen , Yi-Tang Fang","doi":"10.1016/j.dss.2026.114624","DOIUrl":"10.1016/j.dss.2026.114624","url":null,"abstract":"<div><div>Market makers provide financial market liquidity by continuously offering buy and sell orders at publicly quoted prices, while simultaneously earning profits from the bid–ask spread in the process. Various deep reinforcement learning algorithms have been proposed to address such high-frequency sequential decision-making application. However, identifying and resolving the traditional concept drift problem of machine learning system in highly dynamic and complex financial environments has always been a very challenging task. In this paper, a novel reinforcement learning framework with environmental sentiment awareness incorporating curriculum learning and knowledge distillation is proposed. With the aid of a sudden concept drift detector based on market sentiment analysis, our trading model will restructure itself during significant market changes. Additionally, a novel curriculum learning method has been designed to enhance learning efficiency in diverse time segments comprising extensive learning environments. Furthermore, knowledge distillation is adopted to refine the agent’s adaptive capabilities for handling daily gradual concept drift. Experiments with TAIEX Options (TXO) data demonstrate that our method outperforms traditional models, achieving a 38.17% increase in PnL-MAP and a 0.07 increase in Sharpe ratio, while maintaining comparable inventory risk. During testing, sudden concept drift events were detected approximately once every five market-making trading days (i.e., about once per week). This also validates that our proposed market-making strategy based on a sentiment-aware reinforcement learning framework effectively enhances trading performance by modeling sudden and gradual concept drifts.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"203 ","pages":"Article 114624"},"PeriodicalIF":6.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146032785","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 decision support framework for estimating the impact of covariate shift in machine learning systems 用于估计机器学习系统中协变量移位影响的决策支持框架

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2026-04-01 Epub Date: 2026-02-08 DOI: 10.1016/j.dss.2026.114632

Matthijs Meire, Steven Hoornaert, Arno De Caigny, Kristof Coussement

{"title":"A decision support framework for estimating the impact of covariate shift in machine learning systems","authors":"Matthijs Meire, Steven Hoornaert, Arno De Caigny, Kristof Coussement","doi":"10.1016/j.dss.2026.114632","DOIUrl":"10.1016/j.dss.2026.114632","url":null,"abstract":"<div><div>Covariate shift arises when the distribution of input features differs between the Source (training) and Target (deployment) sets, under the assumption of equal posterior distributions. This issue often leads to significant risks and challenges in decision-making systems due to biased machine learning predictions. However, organizations lack effective tools to determine when retraining is warranted. This paper proposes a practical, data-driven framework that supports post-deployment decision-making by estimating model performance under covariate shift. Our study addresses covariate shifts in binary classification by examining methods to monitor bias, detect malignant shifts, and identify boundary conditions that hamper accurate estimations. We evaluate several weighting methodologies, including nearest neighbor matching, domain classifiers, and density ratio fittings, to derive weighted performance indicators that guide decisions on model maintenance. Through simulations and five real-world datasets, we find that nearest neighbor matching outperforms the more commonly proposed density ratio fittings, yielding an 84.69% improvement under single covariate shifts. While multiple covariate shifts complicate estimation of real Target set performance, more severe shifts largely offset this finding. Applying our approach to a real-world study on customer churn further validates its effectiveness. Our proposed framework can be embedded within broader decision support systems and enables robust performance estimation without requiring Target labels or retraining, which facilitates more informed decisions about model lifecycle management. Finally, we provide recommendations for future research on how to investigate and update models under covariate shift.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"203 ","pages":"Article 114632"},"PeriodicalIF":6.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138278","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

Should amazon display product Q&As more prominently? The informational role of Q&As and reviews, and the moderating effect of product involvement 亚马逊是否应该更突出地展示产品问答？问答和评审的信息作用，以及产品参与的调节作用

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2026-04-01 Epub Date: 2026-01-09 DOI: 10.1016/j.dss.2026.114613

Gaurav Jetley , Shivendu Shivendu

{"title":"Should amazon display product Q&As more prominently? The informational role of Q&As and reviews, and the moderating effect of product involvement","authors":"Gaurav Jetley , Shivendu Shivendu","doi":"10.1016/j.dss.2026.114613","DOIUrl":"10.1016/j.dss.2026.114613","url":null,"abstract":"<div><div>Amazon recently demoted on-page Q&As by moving them off the main product page and launched Rufus, an AI assistant trained on reviews, Q&As, and catalog data. This redesign foregrounds a core interface question: which information should be surfaced to consumers, and when? We study how the thematic overlap and novelty between the visible “top” reviews and Q&As relate to product sales, and whether these relationships differ by product involvement. Leveraging a large panel dataset of high- and low-involvement products, we use machine learning techniques to quantify the thematic overlap and divergence in content and apply an instrumental variable approach with fixed effects estimator to analyze their impact on sales. Our findings reveal that for high-involvement products, novel information between reviews and Q&As significantly enhances sales by reducing consumer uncertainty. Conversely, for low-involvement products, overlapping information across these sources facilitates purchasing decisions, leading to increased sales. A counterfactual analysis indicates that adding a single, strategically chosen review or Q&A to the visible head can lift sales, especially for low-performing items. We translate these findings into involvement-aware rules for placement and ranking: preserve co-located, complementary Q&As for high-involvement decisions; surface concise cross-source confirmations for low-involvement ones. Our study contributes to the broader understanding of how UGC can be optimized to support consumer decision-making and improve operational effectiveness in e-commerce.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"203 ","pages":"Article 114613"},"PeriodicalIF":6.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981682","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

From text boxes to talking faces: Comparing chatbots and digital humans for online review collection 从文本框到说话的面孔：比较聊天机器人和数字人类的在线评论收集

IF 6.8 1区计算机科学

Decision Support Systems Pub Date : 2026-04-01 Epub Date: 2026-01-29 DOI: 10.1016/j.dss.2026.114626

Warren Rosengren , Agrim Sachdeva , Antino Kim , Alan R. Dennis

{"title":"From text boxes to talking faces: Comparing chatbots and digital humans for online review collection","authors":"Warren Rosengren , Agrim Sachdeva , Antino Kim , Alan R. Dennis","doi":"10.1016/j.dss.2026.114626","DOIUrl":"10.1016/j.dss.2026.114626","url":null,"abstract":"<div><div>Digital humans (agents controlled by artificial intelligence with highly realistic faces and voices) are beginning to appear in place of text-based chatbots in routine customer service roles, such as soliciting feedback and reviews. There are two fundamental ways digital humans may transform the process of providing online reviews. First, with highly human-like visual appearances, digital humans can significantly enhance the perceived humanness of agents collecting reviews compared to traditional text-based chatbots. Second, by enabling spoken rather than typed communication, the interaction may take on a more casual, conversational nature, making the process feel like an informal conversation. We conducted an online experiment to investigate how users respond to digital humans and text-based chatbots when providing online reviews of restaurants. Our findings suggest that digital humans enhance the perceived humanness of the agent, making the review process feel more like a casual conversation. This perceived humanness and casual conversational nature mediate the impact of digital humans on outcomes, increasing perceived effectiveness, efficiency, satisfaction, and usage intention. Our study demonstrates that replacing a text-based chatbot with a verbally interactive digital human has medium to large effects on improving key outcome measures, suggesting that deploying digital humans can help firms increase the volume of online product and service reviews.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"203 ","pages":"Article 114626"},"PeriodicalIF":6.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071489","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