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Designing a fair and inclusive digital asset-based name-image-likeness marketplace 设计一个公平包容的基于数字资产的姓名形象相似市场
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-12-02 DOI: 10.1016/j.dss.2025.114580
Arthur Carvalho , Liudmila Zavolokina , Suman Bhunia , Gerhard Schwabe
Regulatory changes have enabled American student-athletes to profit from their name, image, and likeness (NIL). However, only a fraction of the student-athlete population is actually profiting from their NIL, which raises questions concerning fairness and inclusiveness. Motivated by that scenario, we look at technological solutions capable of sharing a limited amount of financial resources fairly and inclusively. Following a design science methodology, we define design requirements for such technological solutions after interviewing student-athletes, which leads us to establish the inclusive-meritocratic fairness criterion. Subsequently, we determine design principles that artifacts aiming at helping student-athletes should satisfy. We find that a solution that satisfies the proposed design principles is to associate student-athletes with digital collectibles represented as non-fungible tokens (NFTs). The core idea behind our artifact is that student-athletes receive royalties in primary markets after NFTs are randomly minted, plus deterministic royalties in secondary markets whenever a transaction involving their collectibles happens. Interviews with student-athletes validate our design. We conclude the paper by discussing how our ideas give rise to a new NIL design theory.
规章制度的改变使美国学生运动员能够从他们的名字、形象和相似性(NIL)中获利。然而,只有一小部分学生运动员真正从他们的零收入中获利,这引发了有关公平和包容性的问题。在这种情况的推动下,我们着眼于能够公平和包容地分享有限财政资源的技术解决办法。遵循设计科学方法,我们在采访学生运动员后定义了此类技术解决方案的设计要求,这导致我们建立了包容性精英公平标准。随后,我们确定了旨在帮助学生运动员的人工制品应该满足的设计原则。我们发现,满足所提出的设计原则的解决方案是将学生运动员与表示为不可替代代币(nft)的数字收藏品联系起来。我们的作品背后的核心理念是,在nft随机铸造后,学生运动员在一级市场中获得版税,在二级市场中,每当涉及他们的收藏品的交易发生时,他们就会获得确定的版税。与学生运动员的访谈验证了我们的设计。最后,我们讨论了我们的想法如何产生一个新的零值设计理论。
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引用次数: 0
Digital charisma or human appeal: A comparative study on how streamers' multidimensional signals affect viewers' impulsive purchases in live commerce 数字魅力或人类吸引力:直播者的多维信号如何影响观众在商业直播中的冲动购买的比较研究
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-11-30 DOI: 10.1016/j.dss.2025.114581
Qian Wang , Xixi Li , Xiangbin Yan
Despite the immense popularity of live commerce, many streamers struggle in conveying appealing signals to viewers and realizing expected commercial returns. It is also confusing that the same signal often delivers differential impacts on viewers across virtual and human streaming settings. Toward this end, we integrate signaling theory with consumption value theory and propose a comprehensive framework to explain how streamers' multidimensional signals collectively shape viewers' impulsive purchases and how such signaling processes differ across virtual and human streaming contexts. Analyzing survey data from 557 experienced livestreaming shoppers, we observe that aesthetic, social, and task signals all significantly enhance viewers' product value perceptions, which in turn motivate their impulsive purchases. Moreover, aesthetic signal displays no differential impacts on viewers' product value perceptions across virtual and human live-show settings. Social signal and task signal respectively exert a stronger and a weaker influence on product value perceptions in virtual live shows than in human ones. Our findings provide nuanced insights to help optimize streaming strategies and signal investments, and ultimately enhance commercial effectiveness of live-streaming ventures.
尽管直播商业非常受欢迎,但许多主播在向观众传达有吸引力的信号并实现预期的商业回报方面仍存在困难。同样令人困惑的是,同样的信号往往在虚拟和真人流媒体设置中给观众带来不同的影响。为此,我们将信号理论与消费价值理论相结合,并提出了一个全面的框架来解释流媒体的多维信号如何共同影响观众的冲动购买,以及这些信号过程在虚拟和真人流媒体环境中有何不同。通过分析557名有经验的直播购物者的调查数据,我们发现美学、社交和任务信号都显著增强了观众对产品价值的感知,从而激发了他们的冲动购买。此外,审美信号在虚拟和真人秀场景下对观众的产品价值感知没有差异影响。社交信号和任务信号对虚拟直播中产品价值感知的影响分别强于真人直播和弱于真人直播。我们的研究结果提供了细致入微的见解,有助于优化流媒体策略和信号投资,并最终提高流媒体企业的商业效益。
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引用次数: 0
Emotion vs. information: Understanding the effect of AI-powered call systems on potential customer decision from a field experiment 情感与信息:从现场实验中了解人工智能呼叫系统对潜在客户决策的影响
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-11-26 DOI: 10.1016/j.dss.2025.114579
Zhe Jing, Xin Xu, Yong Jin, Jie Shen
Emerging technologies such as neural networks, cloud computing, big data, and blockchain have paved the way for the development of artificial intelligence (AI), enabling AI to facilitate business operations. In particular, some organizations seek to leverage AI to replace human agents in positions involving sensitive customer information, with the aim of enhancing privacy protection. However, AI-human interaction tends to fall short of expectations in real-world settings due to the difference between humans and AI. To address this, a study will be conducted to explore the effect of implementing an AI-powered call system on potential customers compared to human agent calls. Leveraging a randomized field experiment conducted at a call center of a large securities company and a randomized online experiment, we investigated the mechanism resulting in the different impacts on customer behavior between humans and AI. The results show that voice-based AI calls trade off emotional and informational support: AI's informational advantages can raise intention, but empathy gaps can suppress it. These findings contribute to the literature on the application of technology in organizations and provide guidance to organizations on the effective implementation of AI systems, highlighting both the advantages and limitations of AI in customer-facing roles.
神经网络、云计算、大数据、区块链等新兴技术为人工智能的发展铺平了道路,使人工智能能够为企业运营提供便利。特别是,一些组织试图利用人工智能来取代涉及敏感客户信息的人工代理,目的是加强隐私保护。然而,由于人类和人工智能之间的差异,在现实世界中,人工智能与人类的互动往往达不到预期。为了解决这个问题,将进行一项研究,以探索与人工代理呼叫相比,实施人工智能呼叫系统对潜在客户的影响。利用在一家大型证券公司呼叫中心进行的随机现场实验和随机在线实验,我们研究了导致人类和人工智能对客户行为产生不同影响的机制。结果表明,基于语音的人工智能通话权衡了情感和信息支持:人工智能的信息优势可以提高意愿,但同理心差距会抑制它。这些发现为组织中技术应用的文献做出了贡献,并为组织有效实施人工智能系统提供了指导,突出了人工智能在面向客户角色中的优势和局限性。
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引用次数: 0
“Language is the dress of thought”: A new method for automatic detection of AI-generated text “语言是思想的外衣”:一种人工智能生成文本自动检测的新方法
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-11-25 DOI: 10.1016/j.dss.2025.114578
Zhenhua Wang , Guang Xu , Ming Ren
While AI technologies have garnered widespread attention for their revolutionary text generation capabilities, concerns have arisen regarding the risks associated with AI-generated text (AIGT), especially when used maliciously. Motivated by the recognition that AIGT is generated based on high-probability tokens, a process that inherently differs from the biological-based thought processes underlying human-written text (HWT), we trace and build upon theories of the language latent level to explore the fundamental differences between AIGT and HWT, particularly in terms of potentiality, logicality, and complexity. A novel method named LA2HDetect is proposed for automatic AIGT detection. Specifically, we discover that HWT exhibits higher potentiality than AIGT; AIGT and HWT each possesses unique characteristics in terms of logicality and complexity. These human-AI differences collectively form the decision-making mechanism of LA2HDetect. Extensive experiments on general domain datasets confirm the competitiveness and robustness of LA2HDetect, which outperforms existing methods. In addition, we evaluate the extensibility of LA2HDetect in multiple vertical domains, and explore the insights across progressively advanced AI models.
虽然人工智能技术因其革命性的文本生成能力而受到广泛关注,但人们也开始关注与人工智能生成文本(AIGT)相关的风险,尤其是在恶意使用时。由于认识到AIGT是基于高概率令牌生成的,这一过程本质上不同于人类书面文本(HWT)背后基于生物的思维过程,我们追踪并建立了语言潜在水平的理论,以探索AIGT和HWT之间的根本差异,特别是在潜力、逻辑性和复杂性方面。提出了一种新的AIGT自动检测方法LA2HDetect。具体而言,我们发现HWT比AIGT表现出更高的电位;AIGT和HWT在逻辑性和复杂性方面各具特色。这些人类与人工智能的差异共同形成了LA2HDetect的决策机制。在一般领域数据集上的大量实验证实了LA2HDetect的竞争力和鲁棒性,优于现有方法。此外,我们评估了LA2HDetect在多个垂直领域的可扩展性,并探索了跨逐步先进的人工智能模型的见解。
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引用次数: 0
A novel method for testing adverse selection with IoT data: Evidence from China's auto insurance market 用物联网数据测试逆向选择的新方法:来自中国车险市场的证据
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-11-19 DOI: 10.1016/j.dss.2025.114576
Esther Yanfei Jin , Wei Jiang , Zhiqiang Zheng
Adverse selection remains a significant challenge in the insurance industry, often resulting in substantial financial losses for insurers. The primary hurdle in addressing the issue lies in accurately identifying and quantifying adverse selection. Traditional methods often fail to adequately account for the heterogeneity of insurance purchasers and the endogenous nature of their insurance decisions. This study introduces an innovative approach that integrates the Gaussian Mixture Model and the regression-based model from Dionne et al. [18] to assess adverse selection, addressing the limitations of previous methods. Through comprehensive simulations, we demonstrate that our method yields unbiased estimates, outperforming existing approaches. Applied to China's automobile insurance market, this method leverages IoT-based telematics data to capture risk heterogeneity among policyholders more effectively than relying solely on traditional policy information. The results offer robust evidence of adverse selection, in contrast to conventional methods that fail to detect this phenomenon due to their inability to account for underlying risk and insurance choice heterogeneity. Our approach offers insurers a robust framework for identifying information asymmetries in the market, thereby enabling the development of more targeted policy interventions and risk management strategies.
逆向选择仍然是保险业面临的一个重大挑战,通常会给保险公司带来巨大的经济损失。解决这个问题的主要障碍在于准确地识别和量化逆向选择。传统的方法往往不能充分考虑保险购买者的异质性和他们的保险决策的内生性质。本研究引入了一种创新的方法,该方法集成了高斯混合模型和Dionne等人的基于回归的模型来评估逆向选择,解决了以前方法的局限性。通过综合模拟,我们证明了我们的方法产生无偏估计,优于现有的方法。该方法应用于中国车险市场,利用基于物联网的远程信息处理数据,比仅仅依靠传统的保单信息更有效地捕捉投保人之间的风险异质性。研究结果为逆向选择提供了有力的证据,而传统方法由于无法解释潜在风险和保险选择异质性而无法检测到这一现象。我们的方法为保险公司提供了一个强有力的框架,用于识别市场中的信息不对称,从而能够制定更有针对性的政策干预和风险管理策略。
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引用次数: 0
Published content vs. live-streamed: Empirical from digital content activities in online healthcare communities 发布内容与直播:来自在线医疗保健社区数字内容活动的经验
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-11-19 DOI: 10.1016/j.dss.2025.114577
Liuan Wang , Yuke Luo , Linan Zhang
Online Health Communities (OHCs) serve as a platform for individuals seeking health support, where the exchange of health information harbors the potential to generate substantial social value. Existing literature reveals that previous research has primarily focused on the incentives for doctors' information sharing. However, the mechanism through which this information sharing translates into tangible benefits for hospitals remains unclear. To address these gaps, this study interprets it as a kind of digital content activity (DCA) and delves into its impact on hospitals' online demand in OHC. Analyzing data from over 2000 active hospitals on a leading OHC in China, our findings indicate that hospitals' published content activity consistently increases their online demand in OHC. In contrast, live-streamed content activity decreases online demand in the short term, yet this impact turns positive in the long term. Furthermore, a hospital's organizational capital enhances the impact of live-streamed content activity, while the hospital's reputation strengthens the long-term positive impact of published content activity. This study offers a novel perspective for understanding knowledge sharing within OHCs, providing practical insights for OHCs and hospitals.
在线卫生社区(OHCs)是寻求卫生支持的个人的平台,其中卫生信息的交换具有产生巨大社会价值的潜力。现有文献显示,以往的研究主要集中在医生信息共享的激励机制上。然而,这种信息共享转化为医院切实利益的机制尚不清楚。为了解决这些差距,本研究将其解释为一种数字内容活动(DCA),并深入研究其对OHC中医院在线需求的影响。通过分析中国一家领先的OHC上2000多家活跃医院的数据,我们的研究结果表明,医院发布的内容活动持续增加了他们对OHC的在线需求。相比之下,直播内容活动在短期内会减少在线需求,但从长期来看,这种影响会转为积极。此外,医院的组织资本增强了直播内容活动的影响力,而医院的声誉增强了发布内容活动的长期积极影响。本研究为理解OHCs内部的知识共享提供了一个新的视角,为OHCs和医院提供了实用的见解。
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引用次数: 0
What makes a well-performing NFT collection initial offering campaign: Evidence from OpenSea Drop 是什么让NFT系列的首次发行活动表现良好:来自OpenSea的证据掉落
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-11-19 DOI: 10.1016/j.dss.2025.114575
Zhichao Wu , Xi Zhao , Xiaoni Lu
The Drop feature on OpenSea provides creators with a standardized tool for designing NFT collection (NFTC) initial offering campaigns. This study examines the impact of campaign design elements on sales performance. Analyzing 693 NFTCs, we reveal an inverted U-shaped relationship between target size and sales outcomes, attributable to the balance between social proof and scarcity. Additionally, we observe a positive effect of incorporating pre-sale stages, which is driven by social proof. Notably, OpenSea's official certification, as a significant credibility signal, moderates these effects. This research advances the understanding of social proof theory within the Web3.0 context, offering actionable insights for NFT creators to optimize campaign strategies and for platform managers to enhance the effectiveness of the Drop feature.
OpenSea的Drop功能为创建者提供了一个标准化的工具来设计NFTC首次发行活动。本研究探讨活动设计元素对销售绩效的影响。通过对693个国家的分析,我们发现目标规模与销售结果之间存在倒u型关系,这可归因于社会认同与稀缺性之间的平衡。此外,我们观察到加入预售阶段的积极影响,这是由社会认同驱动的。值得注意的是,OpenSea的官方认证作为一个重要的可信度信号,缓和了这些影响。这项研究促进了对Web3.0背景下社会认同理论的理解,为NFT创作者优化活动策略和平台管理者提高Drop功能的有效性提供了可操作的见解。
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引用次数: 0
Breaking boundaries: Investigating the formation of cross-domain collaboration on social media platforms 打破边界:调查社交媒体平台上跨领域协作的形成
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-11-13 DOI: 10.1016/j.dss.2025.114574
Mengxiao Zhu , Lin Liu , Chunke Su
Creators on social media platforms are increasingly engaging in collaborative content generation. Given the recognized value of integrating diverse perspectives and expertise from different domains, such as fostering innovation, improving content quality, and expanding audience engagement, this study aims to investigate the decision-making dynamics among creators involved in cross-domain collaboration. Drawing on social identity theory, we examine the effect of content domain differentiation on the formation of collaborative relationships and how creators' attributes of content diversity and influencing power alter these effects. Our data were collected from Bilibili, one of the largest Chinese video-sharing platforms, which offers a joint submission feature allowing multiple creators to publish their generated videos. We employ exponential random graph models (ERGMs) to analyze the formation of a collaboration network comprising 2490 creators. The findings reveal that content domain differentiation is negatively related to the formation of collaborative relationships, indicating that cross-domain collaborative relationships are less likely to occur compared to within-domain ones on social media. Furthermore, content diversity mitigates the negative effect of content domain differentiation, suggesting that creators with higher content diversity are more inclined to engage in cross-domain collaborations. Regarding influencing power, creators with less reach and activeness are more likely to participate in cross-domain collaboration. Interestingly, creators with institutional authority are less likely to form cross-domain collaborations, whereas those with individual authority are more likely, compared to non-authority creators. This study highlights the challenges in fostering cross-domain collaborative relationships on social media and elucidates actionable strategies to promote such collaborations.
社交媒体平台上的创作者越来越多地参与到协作内容生成中。鉴于整合来自不同领域的不同观点和专业知识的公认价值,例如促进创新、提高内容质量和扩大受众参与度,本研究旨在调查参与跨领域合作的创作者之间的决策动态。本文以社会认同理论为基础,考察了内容领域分化对协作关系形成的影响,以及创作者的内容多样性属性和影响力如何改变这些影响。我们的数据来自Bilibili,这是中国最大的视频分享平台之一,该平台提供联合提交功能,允许多个创作者发布自己制作的视频。我们使用指数随机图模型(ergm)来分析由2490个创建者组成的协作网络的形成。研究发现,内容领域分化与协作关系的形成呈负相关,表明在社交媒体上,跨领域的协作关系比领域内的协作关系更不容易发生。此外,内容多样性可以缓解内容领域分化的负面影响,表明内容多样性越高的创作者更倾向于进行跨领域合作。在影响力方面,覆盖面和活跃度较低的创作者更有可能参与跨领域合作。有趣的是,与非权威的创造者相比,拥有机构权威的创造者不太可能形成跨领域合作,而拥有个人权威的创造者则更有可能形成跨领域合作。本研究强调了在社交媒体上培养跨领域合作关系所面临的挑战,并阐明了促进这种合作的可行策略。
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引用次数: 0
ProMatch: A novel dynamic process-unpacking approach for two-way proactive recruitment ProMatch:一种新颖的动态流程拆解方法,用于双向主动招聘
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-11-13 DOI: 10.1016/j.dss.2025.114564
Xiaowei Shi , Cong Wang , Qiang Wei
Online recruitment platforms have revolutionized labor markets by enabling bidirectional engagement between job seekers and employers, but this transformation has also introduced complex decision-making challenges due to information overload and parallel decision processes. Existing research and algorithms often focus on static and one-way models, neglecting the dynamic feedback loops and preference adjustments inherent in two-way proactive recruitment. This study introduces ProMatch, a novel person-job matching approach designed to support decision-making for both sides. ProMatch formalizes recruitment as a multi-stage process involving intention formation, preference updates, and bilateral matching, capturing the sequential dependencies between decision outcomes. It also incorporates a dynamic preference learning mechanism grounded in self-regulation theory, which iteratively refines preferences using textual profiles, historical interactions, and feedback. Validation using a real-world IT enterprise dataset and a two-week field experiment demonstrates ProMatch’s effectiveness. Results show a 9% increase in click-through rates and a 20% improvement in interview-through rates, highlighting its ability to enhance prediction accuracy by dynamically modeling evolving preferences. ProMatch’s innovations offer actionable decision support for both job seekers and employers, ultimately improving recruitment efficiency and cost-effectiveness in modern recruitment ecosystems.
在线招聘平台通过实现求职者和雇主之间的双向互动,彻底改变了劳动力市场,但这种转变也带来了复杂的决策挑战,因为信息过载和决策过程并行。现有的研究和算法往往侧重于静态和单向模型,而忽略了双向主动招聘中固有的动态反馈循环和偏好调整。本研究引入ProMatch,一种新颖的个人-工作匹配方法,旨在支持双方的决策。ProMatch将招聘形式化为一个多阶段的过程,包括意向形成、偏好更新和双边匹配,捕捉决策结果之间的顺序依赖关系。它还结合了基于自我调节理论的动态偏好学习机制,该机制使用文本概要、历史交互和反馈迭代地改进偏好。使用真实的IT企业数据集和为期两周的现场实验验证了ProMatch的有效性。结果显示,点击率提高了9%,采访通过率提高了20%,突出了通过动态建模不断变化的偏好来提高预测准确性的能力。ProMatch的创新为求职者和雇主提供可操作的决策支持,最终提高现代招聘生态系统的招聘效率和成本效益。
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引用次数: 0
Effects of artificial intelligence usage and knowledge-based dynamic capabilities on organizational innovation: A configurational approach 人工智能使用和基于知识的动态能力对组织创新的影响:一种配置方法
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-11-06 DOI: 10.1016/j.dss.2025.114573
Meng An , Jiabao Lin , Jose Benitez
Many antecedents of organizational innovation have been examined in isolation, overlooking their synergistic and threshold effects. To address this gap, this study draws on resource orchestration theory to investigate how AI usage and knowledge-based dynamic capabilities, i.e., knowledge generation capability, knowledge acquisition capability, and market-sensing capability, jointly drive exploratory and exploitative innovation. Using survey data from 218 Chinese firms, we apply fuzzy-set qualitative comparative analysis (fsQCA) to identify multiple sufficient configurations that generate high innovation, highlighting heterogeneous pathways shaped by firm size and industry context. To complement these findings, we conduct necessary condition analysis (NCA), which reveals critical threshold levels for AI usage and knowledge capabilities that should be met regardless of the chosen configuration. Furthermore, we map fsQCA results with three types of interdependencies among AI usage and knowledge-based capabilities—complementarity, contingency, and substitution—to form configurations that lead to different organizational innovations. This study enriches configurational theory on organizational innovation, expands the theoretical boundaries of AI-enabled innovation, and provides actionable decision support for resource allocation and capability development under digital transformation.
许多组织创新的前因被孤立地考察,忽略了它们的协同效应和门槛效应。为了解决这一差距,本研究借鉴资源编排理论,探讨人工智能的使用和基于知识的动态能力,即知识生成能力、知识获取能力和市场感知能力,如何共同推动探索性和开发性创新。利用218家中国企业的调查数据,我们运用模糊集定性比较分析(fsQCA)来识别产生高创新的多种充分配置,突出了由企业规模和行业背景形成的异质路径。为了补充这些发现,我们进行了必要条件分析(NCA),揭示了人工智能使用和知识能力的关键阈值水平,无论所选择的配置如何,都应该满足这些阈值水平。此外,我们将fsQCA结果与人工智能使用和基于知识的能力之间的三种相互依赖关系——互补性、偶然性和替代性——进行映射,以形成导致不同组织创新的配置。本研究丰富了组织创新的构型理论,拓展了人工智能创新的理论边界,为数字化转型下的资源配置和能力发展提供了可操作的决策支持。
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引用次数: 0
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Decision Support Systems
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