Decision Support Systems最新文献_第2页

Reassessing taxonomy-based data clustering: Unveiling insights and guidelines for application 重新评估基于分类法的数据聚类：揭示应用见解和指导原则

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

Decision Support Systems

Pub Date : 2024-10-01 DOI: 10.1016/j.dss.2024.114344

Maximilian Heumann , Tobias Kraschewski , Oliver Werth , Michael H. Breitner

Clustering for taxonomy-based archetype identification has become an established method in Information Systems (IS) research, aiding strategic decision-making across diverse research and business domains. However, the effectiveness of the approach depends critically on the compatibility of clustering methods and algorithms with the specific data characteristics. This study, based on a comprehensive review of 87 articles employing taxonomy-based clustering in IS research, reveals a notable mismatch between the chosen clustering algorithms and the nature of the data, particularly in the context of archetype development from taxonomy-based data. To address these methodological inconsistencies, we introduce a set of clustering guidelines tailored to the unique requirements of archetype development from taxonomy-based data. These guidelines are informed by a computational study involving seven identified datasets from the taxonomy-building literature, ensuring their practical applicability and scientific relevance. Our guidelines are designed to enhance the robustness and scientific validity of insights and decisions derived from taxonomy-based clustering. By improving the methodological rigor of clustering methods, our research addresses a critical mismatch in current practices and contributes to enhancing the quality of decision-making informed by taxonomy-based analysis in IS research.

基于分类法的原型识别聚类方法已成为信息系统（IS）研究中的一种成熟方法，可帮助不同研究和业务领域做出战略决策。然而，该方法的有效性关键取决于聚类方法和算法与特定数据特征的兼容性。本研究基于对 87 篇在信息系统研究中采用基于分类法聚类的文章的全面回顾，揭示了所选聚类算法与数据性质之间的明显不匹配，特别是在从基于分类法的数据中进行原型开发的情况下。为了解决这些方法上的不一致，我们提出了一套聚类指南，以适应从分类法数据中开发原型的独特要求。这些指南参考了一项计算研究，涉及分类法建设文献中的七个已确定数据集，确保了它们的实际适用性和科学相关性。我们的指导原则旨在提高基于分类法聚类得出的见解和决策的稳健性和科学性。通过提高聚类方法的方法论严谨性，我们的研究解决了当前实践中的一个关键不匹配问题，并有助于提高信息系统研究中基于分类法分析的决策质量。

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引用次数: 0

The impact of emotional expression by artificial intelligence recommendation chatbots on perceived humanness and social interactivity 人工智能推荐聊天机器人的情感表达对人性化感知和社交互动的影响

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

Decision Support Systems

Pub Date : 2024-09-30 DOI: 10.1016/j.dss.2024.114347

Junbo Zhang , Xiaolei Wang , Jiandong Lu , Luning Liu , Yuqiang Feng

Artificial intelligence-powered chatbots capable of expressing emotions have gained significant popularity in the realm of customer service. Although previous studies have explored the impact of emotional expression in chatbots, there is a lack of understanding regarding the precise effects of different emotional cues. In this study, we drew upon social presence theory to investigate how different emotional cues conveyed by recommendation chatbots affect perceived humanness, social interactivity, and social presence. We conducted a series of scenario-based online experiments to shed light on these dynamics. We found that all three emotional cues (text, emoticons, and images) employed by chatbots can increase perceived humanness and social interactivity. Social presence appears to be an underlying mechanism for these positive relationships. We also observed two-way interactions for any pair of emotional cues and a three-way interaction for all three emotional cues. Ultimately, to elicit the most favorable customer perception, we propose that a mode of emotional expression using either text or emoticons alone is most appropriate. These findings deepen our understanding of the impact of emotional expressions in chatbots and offer novel insights into how to deploy chatbots in customer service.

能表达情感的人工智能聊天机器人在客户服务领域大受欢迎。虽然以前的研究已经探讨了聊天机器人中情感表达的影响，但对不同情感线索的确切影响还缺乏了解。在本研究中，我们借鉴了社会存在理论，研究了推荐聊天机器人传达的不同情感线索如何影响人性化感知、社会互动性和社会存在。我们进行了一系列基于场景的在线实验来揭示这些动态变化。我们发现，聊天机器人使用的所有三种情感线索（文本、表情符号和图像）都能提高人性化感知和社交互动性。社交存在感似乎是这些积极关系的潜在机制。我们还观察到任何一对情感线索的双向互动，以及所有三种情感线索的三向互动。最终，我们提出，要想获得最有利的客户感知，仅使用文本或表情符号的情感表达方式是最合适的。这些发现加深了我们对聊天机器人中情感表达影响的理解，并为如何在客户服务中部署聊天机器人提供了新的见解。

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引用次数: 0

Flowing together or alone: Impact of collaboration in the metaverse 同流还是独流？元宇宙中合作的影响

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

Decision Support Systems

Pub Date : 2024-09-30 DOI: 10.1016/j.dss.2024.114346

Fiona Fui-Hoon Nah , Brenda Eschenbrenner , Langtao Chen

The metaverse is the next-generation Internet (Web3) that facilitates social connections and collaborations in a virtual world environment. Given the potential of the metaverse to provide more satisfying and effective means of remote collaborations, exploring the possibility of leveraging the metaverse for these endeavors is warranted. Therefore, an important question to address is whether greater engagement occurs when tasks are completed collaboratively versus individually in the metaverse. We address this question by drawing on flow and transportation theories to hypothesize the effect of carrying out a creative task in the metaverse collaboratively versus alone on one's cognitive absorption, a contextually relevant proxy for the flow experience. In the context of the metaverse, cognitive absorption refers to the heightened enjoyment experienced when one is immersed and “transported” into the metaverse while maintaining a sense of curiosity and control as well as perceiving a distorted sense of time. We conducted a laboratory experiment to test our research hypotheses. The results indicate that collaborations in the metaverse enhance cognitive absorption. Cognitive absorption, in turn, increases outcome satisfaction and intention to use the metaverse. The findings provide theoretical contributions by enhancing the nomological network of cognitive absorption as well as explaining how computer-mediated collaborations can facilitate the virtual transportation of users into the metaverse. The findings also offer insights and guidance for enhancing cognitive absorption and outcome satisfaction in the metaverse as well as the intention to use the metaverse.

元宇宙是下一代互联网（Web3），可促进虚拟世界环境中的社会联系与合作。鉴于元虚拟世界有可能提供更令人满意和更有效的远程协作手段，因此有必要探索利用元虚拟世界开展这些工作的可能性。因此，需要解决的一个重要问题是，在元虚拟环境中，合作完成任务与单独完成任务是否会产生更大的参与度。为了解决这个问题，我们借鉴了流动和运输理论，假设在元海外合作完成一项创造性任务与单独完成一项创造性任务对一个人的认知吸收（一种与流动体验相关的情境代理）的影响。在元宇宙中，认知吸收指的是当一个人沉浸在元宇宙中并被 "传送 "到其中，同时保持好奇心和控制感，以及感知到扭曲的时间感时所体验到的更高的愉悦感。我们进行了一项实验室实验来验证我们的研究假设。结果表明，元宇宙中的合作能增强认知吸收。认知吸收反过来又会提高结果满意度和使用元宇宙的意愿。研究结果加强了认知吸收的名义网络，并解释了以计算机为媒介的协作如何促进用户进入元宇宙的虚拟交通，从而为理论研究做出了贡献。研究结果还为提高认知吸收、元宇宙中的结果满意度以及使用元宇宙的意愿提供了见解和指导。

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引用次数: 0

Team formation in large organizations: A deep reinforcement learning approach 大型组织中的团队组建：深度强化学习方法

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

Decision Support Systems

Pub Date : 2024-09-26 DOI: 10.1016/j.dss.2024.114343

Bing Lv , Junji Jiang , Likang Wu , Hongke Zhao

Efficient team formation is critical to human resource management, particularly as large enterprise organizations continue to flatten and are increasingly driven by projects. Efficiently scheduling internal departments and reducing employee scheduling costs are essential objectives. This paper addresses the challenge of extracting employees from the existing network who possess the necessary skills to meet project requirements while minimizing the disruption to the original department network. To tackle this problem, we model the organization as a graph, where each employee is a node, and edges represent communication between them. We formulate team formation as a combinatorial optimization problem on the graph. We first innovatively design the employee replacement and organizational measures for changing structures on the graph. To overcome the complexity of team formation under vast organizational structures and resource constraints, we propose the Graph Combinatorial Optimization DQN framework. This novel approach combines reinforcement learning and graph neural networks. By leveraging graph neural networks, we learn employee representations based on their basic information, skills, and communication patterns with other employees. Furthermore, during testing, we enable the agent to continuously improve its solutions through learning and avoid the pitfall of optimizing early decisions that may hinder the modification of later decisions. This is achieved by incrementally building subsets of solutions. We demonstrate the superiority of the GCO-DQN framework using both the real-world enterprise dataset and a synthetic dataset by comparing GCO-DQN with five state-of-the-art methods.

高效的团队组建对人力资源管理至关重要，尤其是在大型企业组织不断扁平化并日益由项目驱动的情况下。高效安排内部部门和降低员工调度成本是必不可少的目标。本文要解决的难题是，如何从现有网络中挑选出具备必要技能的员工来满足项目要求，同时尽量减少对原有部门网络的干扰。为了解决这个问题，我们将组织建模为一个图，其中每个员工都是一个节点，边代表他们之间的通信。我们将团队组建表述为图上的组合优化问题。我们首先创新性地设计了员工替换和组织措施，以改变图上的结构。为了克服在庞大的组织结构和资源限制下组建团队的复杂性，我们提出了图组合优化 DQN 框架。这种新方法结合了强化学习和图神经网络。通过利用图神经网络，我们可以根据员工的基本信息、技能以及与其他员工的交流模式来学习他们的表征。此外，在测试过程中，我们还能让代理通过学习不断改进其解决方案，避免因优化早期决策而阻碍后期决策的修改。这是通过逐步建立解决方案子集来实现的。通过将 GCO-DQN 与五种最先进的方法进行比较，我们使用真实世界的企业数据集和合成数据集证明了 GCO-DQN 框架的优越性。

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引用次数: 0

Addressing staffing challenges through improved planning: Demand-driven course schedule planning and instructor assignment in higher education 通过改进规划应对人员配置挑战：高等教育中以需求为导向的课程表规划和教师分配

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

Decision Support Systems

Pub Date : 2024-09-25 DOI: 10.1016/j.dss.2024.114345

Guisen Xue, O. Felix Offodile, Rouzbeh Razavi, Dong-Heon Kwak, Jose Benitez

This paper presents a novel decision support system (DSS) to address the University Course Timetabling Problem (UCTP). The solution decomposes the NP-complete UCTP into two sub-problems, allowing a structured approach to addressing the complexities inherent in the UCTP process. A mixed integer linear programming (MILP) model is proposed to integrate academic year course schedule planning and instructor assignment, accommodating various constraints to meet student demands. The model optimizes the number of course sections and strategically schedules instructors, aiming to reduce the number of new and distinct courses assigned to them. Historical data from an academic department encompassing multiple disciplines, including Computer Information Systems, Business Management, and Business Analytics, at a large public university in the U.S. is used to develop the model, and the results are compared with the actual course schedule and instructor assignment. The results demonstrate that the proposed DSS would result in a 14 % reduction in the number of course sections offered, translating to approximately $130,000 in annual savings. Additionally, it could significantly reduce the number of new courses assigned to instructors by up to 81 % and the number of distinct course sections assigned to them by 29 %.

本文介绍了一种新型决策支持系统（DSS），用于解决大学课程时间安排问题（UCTP）。该解决方案将 NP 完备的 UCTP 分解为两个子问题，从而以结构化的方法解决 UCTP 过程中固有的复杂问题。我们提出了一个混合整数线性规划（MILP）模型，用于整合学年课程表规划和教师分配，同时考虑各种约束条件以满足学生需求。该模型优化了课程部分的数量，并对讲师进行了战略性安排，旨在减少分配给讲师的新课程和不同课程的数量。该模型的开发使用了美国一所大型公立大学一个包含多个学科（包括计算机信息系统、商业管理和商业分析）的学术部门的历史数据，并将结果与实际课程安排和讲师分配进行了比较。结果表明，拟议的教学支持系统将使所提供的课程节数减少 14%，每年可节省约 13 万美元。此外，该系统还可将分配给教师的新课程数量大幅减少 81%，将分配给教师的不同课节数量减少 29%。

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引用次数: 0

“Why do you find similar reviews helpful?”: Psychological mechanisms of the effect of linguistic style matching on review helpfulness "为什么您觉得类似评论对您有帮助？语言风格匹配对评论有用性影响的心理机制

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

Decision Support Systems

Pub Date : 2024-09-20 DOI: 10.1016/j.dss.2024.114340

David Sugianto Lie , Ali Gohary , Pei-Yu Chien , Bach To Nhu Truong

Although previous studies have examined the relationship between Language Style Matching (LSM) and review helpfulness, little research has been devoted to exploring the underlying psychological mechanism of the effect. The current research was conducted to investigate the effect of LSM on review helpfulness, and to introduce perceived credibility as the psychological mechanism that explains the effect. The findings from three experimental studies have shown that perceived credibility explains the positive effect of LSM on review helpfulness. Moreover, consumers find that reviews with high LSM are more helpful and credible when experts rather than peers provide the reviews. This research contributes to the literature on LSM and the helpfulness of reviews by showing how congruency in the language of the review provided by experts increases the reviews' appeal and offers practical suggestions for managers and marketers to better manage their product and service reviews.

尽管以往的研究已经考察了语言风格匹配（LSM）与评论有用性之间的关系，但很少有研究致力于探索该效应的潜在心理机制。本研究旨在探究语言风格匹配对评论有用性的影响，并引入感知可信度作为解释该影响的心理机制。三项实验研究的结果表明，感知可信度解释了LSM对评论有用性的积极影响。此外，消费者发现，当专家而非同行提供评论时，高LSM的评论更有帮助、更可信。这项研究显示了专家提供的评论语言的一致性如何增加评论的吸引力，为管理者和营销人员更好地管理他们的产品和服务评论提供了实用建议，从而为有关 LSM 和评论有用性的文献做出了贡献。

引用次数: 0

Competency or investment? The impact of NFT design features on product performance 能力还是投资？NFT 设计特点对产品性能的影响

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

Decision Support Systems

Pub Date : 2024-09-20 DOI: 10.1016/j.dss.2024.114341

Yanxin Wang, Jingzhao An, Xi Zhao, Xiaoni Lu

This study investigates how NFT design features affect project performance. From the consumption perspective, NFT design features are divided into competency-related (image complexity, consistency) and investment-related (initial price, royalty). Using transaction data of 3297 NFT projects, we find that image complexity has an inverted U-shaped effect on long-term performance, while consistency boosts both short-term and long-term performance by formulating brand symbolism. Royalty as investment cost negatively affects short-term performance, while royalty and initial price exhibit inverted U-shaped impacts on long-term performance due to their mixed roles of costs and quality signals. Market uncertainty amplifies the impacts of complexity and royalties while diminishing the initial price impact in the long term. Findings support NFT project design, enhancing Web3 consumer behavior understandings.

本研究探讨了 NFT 设计特征如何影响项目绩效。从消费角度看，NFT 设计特征分为与能力相关的特征（形象复杂性、一致性）和与投资相关的特征（初始价格、特许权使用费）。利用 3297 个 NFT 项目的交易数据，我们发现形象复杂性对长期绩效有倒 U 型影响，而一致性则通过形成品牌象征性来促进短期和长期绩效。作为投资成本的特许权使用费会对短期绩效产生负面影响，而特许权使用费和初始价格则会对长期绩效产生倒 U 型影响，这是因为它们同时扮演着成本和质量信号的角色。市场的不确定性扩大了复杂性和特许权使用费的影响，而降低了初始价格的长期影响。研究结果支持 NFT 项目设计，增强了对 Web3 消费者行为的理解。

引用次数: 0

Session context data integration to address the cold start problem in e-commerce recommender systems 整合会话上下文数据，解决电子商务推荐系统中的冷启动问题

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

Decision Support Systems

Pub Date : 2024-09-19 DOI: 10.1016/j.dss.2024.114339

Ramazan Esmeli , Hassana Abdullahi , Mohamed Bader-El-Den , Ali Selcuk Can

Recommender systems play an important role in identifying and filtering relevant products based on the behaviours of users. Nevertheless, recommender systems suffer from the ‘cold-start’ problem, which occurs when no prior information about a new session or a user is available. Many approaches to solving the cold-start problem have been presented in the literature. However, there is still room for improving the performance of recommender systems in the cold-start stage. In this article, we present a novel method to alleviate the cold-start problem in session-based recommender systems. The purpose of this work is to develop a session similarity-based cold-start session alleviation approach for recommendation systems. The developed method uses previous sessions’ contextual and temporal features to find sessions similar to the newly started one. Our results on three different datasets show that, based on the provided Mean Average Precision and Normalised Discounted Cumulative Gain scores, the Session Similarity-based Framework consistently outperforms baseline models in terms of recommendation relevance and ranking quality across three used datasets. Our approach can be used to address the challenges associated with cold start sessions where no previously interacted items are present.

推荐系统在根据用户行为识别和筛选相关产品方面发挥着重要作用。然而，推荐系统也存在 "冷启动 "问题，即在没有关于新会话或用户的事先信息时出现的问题。文献中提出了许多解决冷启动问题的方法。然而，推荐系统在冷启动阶段的性能仍有提升空间。在本文中，我们提出了一种新方法来缓解基于会话的推荐系统中的冷启动问题。这项工作的目的是为推荐系统开发一种基于会话相似性的冷启动会话缓解方法。所开发的方法利用以前会话的上下文和时间特征来查找与新启动会话相似的会话。我们在三个不同数据集上的研究结果表明，根据所提供的平均精确度（Mean Average Precision）和归一化累计收益（Normalised Discounted Cumulative Gain）分数，基于会话相似性的框架在三个数据集的推荐相关性和排名质量方面始终优于基准模型。我们的方法可用于应对与冷启动会话相关的挑战，因为在冷启动会话中没有以前互动过的项目。

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引用次数: 0

Shaping innovation pathways: Metaverse application configurations in high-technology small- and medium-sized enterprises 塑造创新之路：高科技中小企业的元数据应用配置

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

Decision Support Systems

Pub Date : 2024-09-19 DOI: 10.1016/j.dss.2024.114336

Jianwen Zheng , Justin Zuopeng Zhang , Kai Ming Au , Veda C. Storey , Huan Wang , Yifan Yang

The emergence of Industry 4.0, characterized by rapid technological change and fierce competition, challenges technology firms to make strategic innovation decisions. Central to this is the metaverse, a hybrid virtual space combining virtual reality, augmented reality, and the internet. Recognizing that the implications of metaverse applications extend beyond individual organizations, this research examines its adoption configurations. Using the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, we analyze survey data from 116 high-technology small and medium-sized enterprises in China using fuzzy-set qualitative comparative analysis (fsQCA). Our research reveals that no isolated factor within the TAM-TOE framework solely affects innovation decision-making. Instead, we identify three configurations that enhance decision-making quality and three that increase its speed, leading to improved innovation performance. In this way, this research advances the understanding of technology adoption configurations in the innovation processes of young tech firms.

以技术快速变革和激烈竞争为特征的工业 4.0 的出现，对技术公司的战略创新决策提出了挑战。其中的核心是元宇宙，这是一个结合了虚拟现实、增强现实和互联网的混合虚拟空间。本研究认识到元宇宙应用的影响超出了单个组织的范围，因此对其采用配置进行了研究。利用技术接受模型（TAM）和技术-组织-环境（TOE）框架，我们使用模糊集定性比较分析法（fsQCA）分析了来自中国 116 家高科技中小企业的调查数据。我们的研究发现，TAM-TOE 框架中没有任何一个孤立的因素会单独影响创新决策。相反，我们确定了三种提高决策质量的配置和三种提高决策速度的配置，从而提高了创新绩效。通过这种方式，本研究推进了对年轻科技企业创新过程中技术采用配置的理解。

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引用次数: 0

Responsible metaverse: Ethical metaverse principles for guiding decision-making and maintaining complex relationships for businesses in 3D virtual spaces 负责任的元宇宙：指导三维虚拟空间中企业决策和维护复杂关系的元宇宙伦理原则

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

Decision Support Systems

Pub Date : 2024-09-18 DOI: 10.1016/j.dss.2024.114337

Rajat Kumar Behera , Marijn Janssen , Nripendra P. Rana , Pradip Kumar Bala , Debarun Chakraborty

A metaverse is a three-dimensional virtual space (3D VS) where businesses and individuals worldwide can engage, interact, communicate, transact, and exchange information in real-time through an immersive and collaborative platform. These interactions can create complex relationships influenced by the decision-making processes of businesses. Such complexity can lead to challenges in maintaining relationships, ensuring exclusiveness, preventing misuse, and addressing other ethical issues. Therefore, this study aims to identify ethical principles within the metaverse to guide decision-making and maintain complex relationships between users and businesses. Both qualitative and quantitative data were collected for analysis, and simple random sampling was employed for primary data collection. The empirical analysis was conducted using a mixed-method approach. The study identified four ethical principles that guide complex relationships within the metaverse: business benefit evaluation, fairness, explainability, and reliability principles. These principles positively influence decision-making, which, in turn, positively affects the maintenance of complex relationships within 3D VS.

元宇宙是一个三维虚拟空间（3D VS），世界各地的企业和个人可以通过一个身临其境的协作平台，在这个虚拟空间中参与、互动、沟通、交易和实时交换信息。这些互动可以创建受企业决策过程影响的复杂关系。这种复杂性可能导致在维护关系、确保排他性、防止滥用和解决其他伦理问题方面的挑战。因此，本研究旨在确定元宇宙中的伦理原则，以指导决策并维护用户与企业之间的复杂关系。本研究收集了定性和定量数据用于分析，并采用简单随机抽样的方法收集原始数据。实证分析采用混合方法进行。研究确定了指导元宇宙中复杂关系的四项伦理原则：商业利益评估、公平性、可解释性和可靠性原则。这些原则对决策产生了积极影响，进而对 3D VS 中复杂关系的维护产生了积极影响。

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引用次数: 0