Decision Support Systems最新文献

英文中文

Online–offline combined adaptive hotel recommendation system considering attribute importance and group consensus 考虑属性重要性和群体共识的线上线下组合自适应酒店推荐系统

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-08 DOI: 10.1016/j.dss.2025.114503

Peide Liu , Ran Dang , Peng Wang , Yingcheng Xu , Yunfeng Zhang

With the proliferation of tourism websites, online reviews have become indispensable for offline decision-makers when selecting hotels. Solely relying on personal judgment poses risks amid diverse preferences. Thus, this study aimed to create a hotel recommendation system that integrates online reviews and ratings with offline travel groups. First, the sentiment analysis of online reviews was integrated with ratings using heterogeneous reviewer weights, transforming them into probabilistic linguistic term sets. Second, by predicting reviewers' travel types and clustering them, a method was devised to calculate subgroup weights, considering online group size and offline social trust networks. Third, attribute importance was determined via an online–offline method (attribute importance optimization model) considering the intensity and ordinal information. Subsequently, an adaptive consensus optimization model was developed based on a novel measurement method. This study offers personalized recommendations for offline decision-makers, providing essential guidance for travel agencies and platforms to enhance services and holding significant practical value.

随着旅游网站的激增，在线评论已经成为线下决策者在选择酒店时不可或缺的工具。在多样化的偏好中，仅仅依靠个人判断会带来风险。因此，本研究旨在创建一个酒店推荐系统，将在线评论和评分与线下旅游团体相结合。首先，将在线评论的情感分析与使用异构评论者权重的评级相结合，将其转换为概率语言术语集。其次，通过预测评论者的旅行类型并对其进行聚类，设计了一种考虑在线群体规模和离线社会信任网络的子群体权重计算方法。第三，通过考虑强度和序数信息的线上-线下方法（属性重要性优化模型）确定属性重要性。随后，基于一种新的测量方法，建立了自适应共识优化模型。本研究为线下决策者提供个性化的建议，为旅行社和平台提升服务提供必要的指导，具有重要的实用价值。

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引用次数: 0

An exploration and exploitation of value cocreation-based machine learning framework for automated idea screening 基于价值共同创造的机器学习框架的探索和开发，用于自动化想法筛选

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-05 DOI: 10.1016/j.dss.2025.114504

Qian Liu , Qianzhou Du , Chuang Tang , Yili Hong , Weiguo Fan

Idea screening in collaborative crowdsourcing communities poses significant challenges for firms. These challenges are primarily attributable to issues of prediction accuracy and information overload. The rapid expansion of idea pools generates a vast amount of data, making it difficult to effectively identify valuable ideas for new product development. This study introduces an interpretable framework for machine learning that integrates a novel exploration and exploitation perspective within the value cocreation model to enhance idea screening. The framework incorporates six theoretical dimensions of the exploration and exploitation of value cocreation (EEVC): the exploration and exploitation of digital resources, direct interactions, and ideas and their comments. Our evaluation reveals that the EEVC-based idea-screening system significantly outperforms the traditional 3Cs model in terms of prediction accuracy. SHAP value analysis further reveals that the exploration and exploitation of digital resources are the most influential predictors of idea implementation. The EEVC framework advances open innovation theory by clarifying how value cocreation dynamics influence idea implementation. Practically, it proposes a human–machine collaboration system that enhances expert decision-making for more effective idea selection.

协作众包社区的创意筛选对企业构成了重大挑战。这些挑战主要是由于预测准确性和信息过载的问题。创意池的迅速扩大产生了大量的数据，这使得有效地识别新产品开发的有价值的想法变得困难。本研究引入了一个可解释的机器学习框架，该框架在价值共同创造模型中集成了一个新的探索和开发视角，以增强想法筛选。该框架包含了价值共同创造（EEVC）探索和利用的六个理论维度：数字资源的探索和利用、直接互动、想法及其评论。我们的评估表明，基于eevc的想法筛选系统在预测精度方面显著优于传统的3c模型。SHAP值分析进一步揭示了数字资源的探索和利用是创意实施最具影响力的预测因素。EEVC框架通过阐明价值共同创造动态如何影响理念实施来推进开放式创新理论。在实践中，提出了一个人机协作系统，增强专家决策能力，实现更有效的创意选择。

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引用次数: 0

Does warm care matter? Exploring the effects of service characteristics on organizational impression in smart retail stores 温暖的关怀重要吗？探讨服务特征对智慧零售商店组织印象的影响

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

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

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

Given its context orientation, service quality is a core issue in the study of smart retail. This paper examines service quality in smart retail through the lens of the cues–images–impressions model. The objective is to analyze the influence of service characteristics of smart retail stores (SRSs) on customers' perceived service quality and organizational impressions. Using a mixed-methods design and a fuzzy-set qualitative comparative analysis approach, the study highlights customer orientation, SRS employee characteristics, and the SRS servicescape as mechanisms driving service quality and enabling positive organizational impression. The findings have both theoretical and practical implications for future research.

服务质量是智能零售研究的核心问题，它具有上下文导向。本文通过线索-图像-印象模型来考察智能零售中的服务质量。目的是分析智能零售商店的服务特征对顾客感知服务质量和组织印象的影响。本研究采用混合方法设计和模糊集定性比较分析方法，强调客户导向、SRS员工特征和SRS服务逃逸是驱动服务质量和产生积极组织印象的机制。这些发现对未来的研究具有理论和实践意义。

引用次数: 0

Flight delay dynamics: Unraveling the impact of airport-network-spilled propagation on airline on-time performance 航班延误动力学：揭示机场网络溢出传播对航空公司准点率的影响

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

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

Yi Tan , Yajun Lu , Lu Wang

Flight delay prediction has attracted increasing attention in airline operations. Early identification of potential flight delays is crucial for improving airport scheduling and airline operations while mitigating associated costs. This study investigates the influence of the potential propagation of flight delays throughout the airport network via interconnected flights, a mechanism we term Airport-Network-Spilled Propagation (ANSP). To model the ANSP mechanism, we develop a novel time-dependent, network-based approach that decays the importance of past delays. From this network, we extract a real-time ANSP score for each airport to measure the influence of propagated delays. To evaluate our proposed approach, we employ four state-of-the-art machine learning models using domestic airline on-time performance data from the 30 Large Hub airports in the United States. The results demonstrate that integrating the ANSP score with established features from airline operations literature significantly enhances flight departure delay prediction performance, achieving an increase in AUC of up to 5.49%. Furthermore, we conduct an explainable AI analysis using Shapley additive explanations (SHAP), which reveals that our ANSP score ranks as the most important predictor among all features tested.

航班延误预测在航空公司运营中越来越受到关注。及早发现潜在的航班延误对于改善机场调度和航空公司运营，同时降低相关成本至关重要。本研究探讨了航班延误通过互联航班在整个机场网络中潜在传播的影响，我们称之为机场-网络溢出传播（ANSP）机制。为了对ANSP机制进行建模，我们开发了一种新的基于时间的网络方法，该方法降低了过去延迟的重要性。从这个网络中，我们提取了每个机场的实时ANSP分数，以衡量传播延迟的影响。为了评估我们提出的方法，我们采用了四种最先进的机器学习模型，使用了来自美国30个大型枢纽机场的国内航空公司准点率数据。结果表明，将ANSP得分与航空公司运营文献中已建立的特征相结合，显著提高了航班离港延误预测的性能，AUC提高了5.49%。此外，我们使用Shapley加性解释（SHAP）进行了可解释的人工智能分析，这表明我们的ANSP分数是所有测试特征中最重要的预测因子。

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引用次数: 0

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

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

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

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

To improve its economic and environmental performance, Carrefour, a major European retailer, restructured the distribution of logistic flows from its small and medium suppliers by introducing consolidation centers to expand flows and optimize resource sharing. The success of such an innovative supply chain (SC) largely depends on the number of suppliers deciding to adopt it without reverting to the previous SC. This specific context prompted us to propose a multi-agent model to analyze how the success of SC restructuring evolves as a function of delivery costs, information system (IS) integration and assimilation, and institutional pressures. Simulation results show first that, the lower IS integration in both the extant and the new SC, the more firms switch to and stay in the new SC. Second, a high level of IS assimilation in the new SC structure combined with coercive pressures fosters the success of SC restructuring.

为了改善其经济和环境绩效，欧洲主要零售商家乐福通过引入整合中心来扩大流量和优化资源共享，重组了中小型供应商的物流流分布。这种创新供应链（SC）的成功在很大程度上取决于决定采用它而不回到以前的供应链的供应商的数量。这一特定背景促使我们提出一个多智能体模型来分析供应链重组的成功是如何作为交付成本、信息系统（IS）集成和同化以及制度压力的函数演变的。模拟结果表明，首先，在现有和新的供应链中，越低的信息系统整合，越多的公司转向并留在新的供应链中。其次，在新的供应链结构中，高水平的信息系统同化与强制压力相结合，促进了供应链重组的成功。

引用次数: 0

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

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

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

Sen Yan, Zhiyi Wang, David Dobolyi

The recent development of generative AI (GenAI) algorithms has allowed machines to create new content in a realistic way, driving the spread of AI-generated content (AIGC) on the Internet. However, generative AI models and AIGC have exacerbated several societal challenges such as security threats (e.g., misinformation), trust issues, ethical concerns, and intellectual property regulation, calling for effective detection methods and a better understanding of AI-generated vs. human-written content. In this paper, we focus on AI-generated texts produced by large language models (LLMs) and extend prior detection methods by proposing a novel framework that combines semantic information and linguistic features. Based on potential semantic and linguistic differences in AI vs. human writing, we design our Semantic-Linguistic-Detector (SemLinDetector) framework by integrating a transformer-based semantic encoder and a linguistic encoder with parallel linguistic representations. By comparing a series of benchmark models on datasets collected from various LLMs and human writers in multiple domains, our experiments show that the proposed detection framework outperforms other benchmarks in a consistent and robust manner. Moreover, our model interpretability analysis showcases our framework's potential to help understand the reasoning behind prediction outcomes and identify patterns of differences in AI-generated and human-written content. Our research adds to the growing space of GenAI by proposing an effective and responsible detection system to address the risks and challenges of GenAI, offering implications for researchers and practitioners to better understand and regulate AIGC.

最近，生成式人工智能（GenAI）算法的发展使机器能够以逼真的方式创造新内容，从而推动了人工智能生成内容（AIGC）在互联网上的传播。然而，生成式人工智能模型和AIGC加剧了一些社会挑战，如安全威胁（例如，错误信息）、信任问题、道德问题和知识产权监管，这需要有效的检测方法，并更好地理解人工智能生成的内容与人类编写的内容。在本文中，我们将重点放在由大型语言模型（llm）生成的人工智能生成文本上，并通过提出一个结合语义信息和语言特征的新框架来扩展先验检测方法。基于人工智能与人类写作中潜在的语义和语言差异，我们通过集成基于转换器的语义编码器和具有并行语言表示的语言编码器来设计语义-语言-检测器（SemLinDetector）框架。通过比较从多个领域的各种法学硕士和人类作家收集的数据集上的一系列基准模型，我们的实验表明，所提出的检测框架以一致和稳健的方式优于其他基准。此外，我们的模型可解释性分析展示了我们的框架的潜力，可以帮助理解预测结果背后的原因，并识别人工智能生成和人工编写内容的差异模式。本研究提出了一种有效的、负责任的检测系统来应对GenAI的风险和挑战，为研究人员和从业人员更好地理解和监管AIGC提供了启示，为GenAI的发展提供了空间。

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引用次数: 0

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

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

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

Shweta Kumari Choudhary, Arpan Kumar Kar

In today's digital environment, organizations face security challenges like intentional breaches influenced by their specific policies and structures. As emerging technologies like Generative Artificial Intelligence (GAI) become more integrated into organizational processes, the adoption of GAI moderates organizational contextual conditions and rule characteristics, which affects the perceived risk of violating security rules. We extend the SOIPSV model to analyze cybersecurity practices and the strategic use of GAI in enhancing organizational resilience against security breaches. We establish the direct and moderating impacts of contextual conditions and rule characteristics, along with interactions in complex organizational cyber security. Our first study uses text mining for inferential and configurational analysis. Our second qualitative study explained the model of dynamic interplay between GAI and organizational factors. Our findings have implications for perceived risk management and managers redesigning business processes to manage security breaches.

在当今的数字环境中，组织面临着安全挑战，例如受其特定策略和结构影响的故意破坏。随着像生成式人工智能（GAI）这样的新兴技术越来越多地集成到组织流程中，GAI的采用缓和了组织的上下文条件和规则特征，这些条件和规则特征会影响违反安全规则的感知风险。我们扩展了SOIPSV模型，以分析网络安全实践和GAI在增强组织抵御安全漏洞方面的战略应用。我们建立了上下文条件和规则特征的直接和调节影响，以及复杂组织网络安全中的相互作用。我们的第一项研究使用文本挖掘进行推理和配置分析。我们的第二个定性研究解释了GAI与组织因素之间动态相互作用的模型。我们的发现对感知风险管理和管理人员重新设计业务流程以管理安全漏洞具有启示意义。

引用次数: 0

Impact of categorization autonomy on effective use and adoption intentions 分类自主性对有效使用和采用意图的影响

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-06-25 DOI: 10.1016/j.dss.2025.114499

Arash Saghafi , Poonacha Medappa , Ariton Debrliev

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.

类别树视图是图形用户界面中无处不在的元素，它根据层次结构捕获信息。这些分类树有利于人类用户的认知经济和决策。虽然以前的研究已经调查了使用非结构化数据与业务用户预分类信息的效用，但允许用户从通用对象类型中自主创建自己的分类层次结构的有效性仍未得到探索。本文从搜索精度（作为一种客观衡量标准）和使用该系统的主观意愿两方面来评估分类自治的好处。我们研究了201个主题的用户在信息搜索任务中与平台的交互。我们的研究结果表明，无论是在有效使用方面还是在行为感知方面，分类自主都能带来更好的结果。我们还发现，分类自主性的影响受到任务灵活性的调节，因此，在需要开放式搜索方法的任务中，其好处更为明显。通过关注用户驱动的分类如何影响系统交互，我们的研究有助于设计更符合用户认知结构和任务需求的决策支持系统。

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引用次数: 0

Corrigendum to “Impact of multidimensional presence on user well-being in metaverse communities” “多维存在对虚拟社区用户福祉的影响”的更正

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-06-24 DOI: 10.1016/j.dss.2025.114497

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

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.

在最初的文章中，我们研究了影响虚拟社区用户幸福感的各种因素，包括社会、空间和自我存在。我们打算研究存在类型和用户幸福感之间的对称和不对称关系。然而，在报告最终测量项目及其效度评估中出现了差异。我们提供了如何在文章中纠正错误的详细信息。

引用次数: 0

The more aesthetic, the better? The impact of photo aesthetics on perceived review helpfulness 越美观越好？照片美学对感知评论有用性的影响

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-06-22 DOI: 10.1016/j.dss.2025.114496

Yu Han , Ziqiong Zhang , Carol X.J. Ou , Zili Zhang

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 TripAdvisor.com 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.

评论的帮助性对于评估在线评论的质量和减轻信息过载至关重要。虽然许多研究已经探讨了文本和审稿人特征对审稿有用性的影响，但照片美学的作用仍然很重要，但尚未得到充分的探索。本研究通过调查照片美学对感知评论帮助性的影响及其潜在的中介作用来解决这一差距。TripAdvisor.com的酒店评论数据显示，照片美学对感知评论有用性的影响呈倒u型，其中评论文字长度调节了这一关系。为了进一步验证这一因果关系并探索潜在的中介效应，我们进行了一项实验研究。实验结果证实了照片美学对评论帮助感的因果影响，并揭示了感知愉悦、评论者努力和评论真实性在这一关系中起中介作用。这些新颖的见解挑战了评论照片“越美越好”的观念，提供了新的理论和实践意义。

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