Decision Support Systems最新文献_第4页

Excessive use in the metaverse: The role of multisensory interaction 虚拟世界中的过度使用：多感官互动的作用

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

Decision Support Systems

Pub Date : 2024-12-24 DOI: 10.1016/j.dss.2024.114390

Chongyang Chen , Yao-Yu Wang , Kem Z.K. Zhang , Fenghua Xie

The metaverse allows users to interact with the real and virtual worlds naturally by stimulating multimodal sensations. Meanwhile, the attractive environments created by the metaverse may also bring challenges such as excessive use. There is a great deal of uncertainty about the undesirable risks of the metaverse. Therefore, this study makes efforts to introduce a theoretical framework and explain why the advanced design of multisensory interaction can result in excessive use in the context of metaverse games. Considering the influence of multisensory interaction, we point out the important yet little investigated role of feelings in this research, especially when previous studies mostly focus on the effects of cognition. We thus apply the theory of feelings-as-information as our theoretical basis. We first systematically identify specific types of sensation stimulation in multisensory interaction. Then, we interpret the process that a desirable characteristic (multisensory interaction), which contributes to realistic feelings (plausibility illusion and place illusion), may affect users to generate maladaptive judgment (i.e., time distortion) and finally lead to unexpected outcome of excessive use. Our research model is tested with a scenario-based survey method. The empirical data confirms the proposed model. This study provides noteworthy insights on the potential dangers of the metaverse. The implications are discussed.

虚拟世界允许用户通过刺激多模态感觉自然地与真实世界和虚拟世界进行交互。同时，虚拟世界创造的诱人环境也可能带来过度使用等挑战。关于虚拟世界的不良风险存在大量的不确定性。因此，本研究试图引入一个理论框架，并解释为什么多感官互动的先进设计会导致在虚拟世界游戏中过度使用。考虑到多感官相互作用的影响，我们指出了情感在本研究中的重要作用，但很少被研究，特别是在以往的研究主要集中在认知的影响。因此，我们将“感觉即信息”理论作为我们的理论基础。我们首先系统地确定了多感觉相互作用中的特定类型的感觉刺激。然后，我们解释了一个令人满意的特征（多感官交互），它有助于产生真实感（合理性错觉和地点错觉），可能会影响用户产生不适应的判断（即时间扭曲），最终导致过度使用的意外结果的过程。我们的研究模型用基于场景的调查方法进行了测试。实证数据证实了所提出的模型。这项研究对超宇宙的潜在危险提供了值得注意的见解。讨论了其含义。

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引用次数: 0

Decision support through deep reinforcement learning for maximizing a courier's monetary gain in a meal delivery environment

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

Decision Support Systems

Pub Date : 2024-12-20 DOI: 10.1016/j.dss.2024.114388

Weiwen Zhou, Hossein Fotouhi, Elise Miller-Hooks

Meal delivery is a fast-growing industry supported by couriers participating in the gig economy. This paper takes a single courier's perspective and provides decision support for an individual courier who works at will in repositioning between jobs and order-taking to optimize her profit during a work period. A hybrid discrete-time, discrete-event simulation environment was developed based on data from a real-world meal delivery environment to replicate daily operations. The single courier's repositioning and order-taking decision problem is formulated as a Markov decision process. Two classes of deep reinforcement learning (DRL) methodologies, value-based and policy-gradient algorithms, were implemented to determine the courier's best decisions to take as the courier's work shift progresses. In numerical experiments, the best optimal policy resulting from the DRL algorithms is shown to outperform all considered static policies in all demand environments. Insights from studying the decisions suggested by the best of the DRL methods were employed to create a promising static policy by generating decision trees for relocation and order-taking. The results indicate that as couriers find more intelligent strategies for maximizing their rewards, the meal delivery platform will have even greater need to incentivize couriers to fulfill less attractive orders, especially in surge periods. Finally, the impact of a multi-courier DRL environment, where multiple couriers have the advantage of the DRL strategy, was studied. For this purpose, a multi-agent DRL was implemented and numerical experiments were conducted to investigate the tradeoffs between individual courier gains and system-level performance. Findings from this multi-agent extension show the negative impacts of selfish behavior on not only the system, but the couriers themselves.

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引用次数: 0

What happens when platforms disclose the purchase history associated with product reviews? 当平台披露与产品评论相关的购买记录时会发生什么？

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

Decision Support Systems

Pub Date : 2024-11-14 DOI: 10.1016/j.dss.2024.114367

Miaomiao Liu , Xiaohua Zeng , Cheng Zhang , Yong Liu

In striking a balance between attracting more product reviews versus maintaining review quality, online platforms have started to label reviews with whether they are associated with verifiable purchases. This paper examines the impact of such disclosure policy on the strategic behavior of review writers and the helpfulness of verified reviews (VRs) and non-verified reviews (NVRs) for review users. We propose that the introduction of the verified purchase tag induces two competing effects for VRs, increased credibility and concerns for acquisition bias, which in turn influence the behaviors of both writers and users. By exploiting the exogenous shock resulting from a policy change on Amazon, we find that, after the disclosure, NVRs became longer in length and VRs started to contain more unique information. Surprisingly, we find strong evidence that VRs receive fewer helpfulness votes than NVRs. We further explore the underlying mechanism, namely review users' concerns about acquisition bias associated with VRs, and identify conditions under which these unexpected effects can be mitigated. Our findings generate important implications for online platforms seeking to design a more effective review ecosystem and for review writers aiming to produce more helpful content.

为了在吸引更多产品评论和保持评论质量之间取得平衡，在线平台开始标注评论是否与可验证的购买相关联。本文研究了这种披露政策对评论作者策略行为的影响，以及已验证评论（VR）和未验证评论（NVR）对评论用户的帮助。我们认为，经过验证的购买标签的引入会对 VR 产生两种竞争效应：可信度的提高和对获取偏差的担忧，这反过来又会影响撰写者和用户的行为。通过利用亚马逊政策变化带来的外生冲击，我们发现，在政策披露后，NVR 的长度变长，VR 开始包含更多独特信息。令人惊讶的是，我们发现了强有力的证据，表明 VR 比 NVR 获得的有用性投票更少。我们进一步探讨了其背后的机制，即评论用户对与 VR 相关的获取偏差的担忧，并确定了可以减轻这些意外影响的条件。我们的发现对寻求设计更有效的评论生态系统的在线平台和旨在制作更有帮助内容的评论作者具有重要意义。

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引用次数: 0

A comparative analysis of the effect of initiative risk statement versus passive risk disclosure on the financing performance of Kickstarter campaigns 主动风险声明与被动风险披露对 Kickstarter 活动融资绩效影响的比较分析

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

Decision Support Systems

Pub Date : 2024-11-09 DOI: 10.1016/j.dss.2024.114366

Wei Wang , Ying Li , Jian Mou , Kevin Zhu

Extending the theory of perceived risk, this study examines how risk perception, a vital factor in determining investment decisions, comprising both initiative risk statement generated by fundraisers and passive risk disclosure published by backers, influences crowdfunding financing performance. Utilizing a corpus of 126,593 innovative projects from Kickstarter, text analytics is employed to classify risks into controllable and uncontrollable types for an empirical comparative examination. The results show that initiative risk statement negatively impacts financing performance, while passive risk disclosure has a positive influence. Comparatively, passive risk disclosure is superior to initiative risk statement. Uncontrollable (controllable) risks in initiative (passive) risk statement are superior to controllable (uncontrollable) ones. Additionally, a textual cognitive load negatively impacted initiative risk statement and passive risk disclosure. Multiple additional tests, including continuous and discrete measurements of risk, endogeneity correction, and dynamic effects over time, demonstrate the robustness of the results. This study contributes to extending the understanding of online financing risks and providing practical implications for fundraisers and backers in innovative online projects.

本研究对感知风险理论进行了扩展，探讨了风险感知这一决定投资决策的重要因素如何影响众筹融资绩效，其中包括筹款人生成的主动风险声明和支持者发布的被动风险披露。利用 Kickstarter 上 126593 个创新项目的语料库，采用文本分析法将风险分为可控和不可控类型，进行实证比较研究。结果表明，主动风险声明对融资绩效有负面影响，而被动风险披露则有正面影响。相对而言，被动风险披露优于主动风险声明。主动（被动）风险声明中的不可控（可控）风险优于可控（不可控）风险。此外，文本认知负荷对主动风险声明和被动风险披露有负面影响。其他多项测试，包括风险的连续和离散测量、内生性校正和随时间变化的动态效应，都证明了研究结果的稳健性。本研究有助于扩展对在线融资风险的理解，并为创新在线项目的筹款人和支持者提供实际意义。

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引用次数: 0

Revisiting time-varying dynamics in stock market forecasting: A multi-source sentiment analysis approach with large language model

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

Decision Support Systems

Pub Date : 2024-11-08 DOI: 10.1016/j.dss.2024.114362

Zhiqi Shao , Xusheng Yao , Feng Chen , Ze Wang , Junbin Gao

This paper presents the Heterogeneous Dynamic Seemingly Unrelated Regression with Dynamic Linear Models (HD-SURDLM), an innovative framework for stock return prediction that combines cutting-edge sentiment analysis with dynamic financial modeling. The model integrates sentiment data from 2.5 million Twitter posts and various news sources, utilizing state-of-the-art sentiment analysis tools such as VADER, TextBlob, and RoBERTa. HD-SURDLM refines Gibbs sampling for enhanced numerical stability and efficiency while capturing cross-sectional dependencies across multiple assets such as a portfolio. The model consistently outperforms traditional methods like LSTM, Random Forest, and RNN in forecasting accuracy. Empirical results show a 1.02% improvement in 1-day horizon forecasts, a 0.42% gain for 20-day predictions, and a 0.36% increase for 50-day forecasts. By effectively merging public sentiment with dynamic asset modeling, HD-SURDLM offers substantial improvements in short- and long-term prediction accuracy. Its capacity to capture both cross-sectional insights and temporal dynamics makes it an invaluable tool for investors, traders, and financial institutions navigating sentiment-driven markets. HD-SURDLM not only enhances predictive accuracy but also provides a robust decision-support system for financial stakeholders.

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引用次数: 0

DeepSecure: A computational design science approach for interpretable threat hunting in cybersecurity decision making DeepSecure：在网络安全决策中采用可解释的威胁猎取计算设计科学方法

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

Decision Support Systems

Pub Date : 2024-11-06 DOI: 10.1016/j.dss.2024.114351

Prabhat Kumar , Danish Javeed , A.K.M. Najmul Islam , Xin (Robert) Luo

Businesses and industries are placing a greater emphasis on information systems for cybersecurity decision-making due to the rising cybersecurity threat landscape and the critical need to protect their digital assets. Threat hunting provides a data-driven and proactive approach to cybersecurity, enabling organizations to efficiently detect, analyze, and respond to cyber threats in real-time. Despite playing a crucial role, these systems face several obstacles, including the manual analysis of technical threat intelligence, the non-Gaussian nature of real-world data, the high rate of false positives produced during threat hunting, and the lack of interpretation and justification for these complex models. This article adopts the computational design science paradigm to develop a novel IT artifact for threat-hunting named DeepSecure. First, to automatically extract latent patterns from multivariate time series datasets, we propose a dynamic vector quantized variational autoencoder technique. Second, a multiscale hierarchical attention bi-directional gated recurrent unit-based threat-hunting mechanism is designed. Finally, we provide the visualization of attention scores to aid in model interpretation. We evaluate the DeepSecure against state-of-the-art benchmarks on two publicly available datasets, namely, ToN-IoT and CSE-CIC-IDS2018. The experimental evaluation proves that our model can efficiently identify threat types. Beyond demonstrating practical utility, the proposed framework can help address the lack of interpretation and justification for complex models in cyber threat detection and will allow organizations to respond to potential security incidents quickly.

由于网络安全威胁的不断增加以及保护数字资产的迫切需要，各行各业都更加重视信息系统的网络安全决策。威胁猎取系统为网络安全提供了一种数据驱动的前瞻性方法，使企业能够高效地实时检测、分析和应对网络威胁。尽管这些系统发挥着至关重要的作用，但也面临着一些障碍，包括技术威胁情报的人工分析、现实世界数据的非高斯性、威胁猎取过程中产生的高误报率，以及缺乏对这些复杂模型的解释和论证。本文采用计算设计科学范式，开发了一种名为 DeepSecure 的新型 IT 工件，用于威胁猎取。首先，为了从多元时间序列数据集中自动提取潜在模式，我们提出了一种动态向量量化变分自动编码器技术。其次，我们设计了一种基于多尺度分层注意力双向门控递归单元的威胁猎捕机制。最后，我们提供了注意力分数的可视化，以帮助解释模型。我们在两个公开数据集（即 ToN-IoT 和 CSE-CIC-IDS2018）上对照最先进的基准对 DeepSecure 进行了评估。实验评估证明，我们的模型可以有效识别威胁类型。除了展示实际效用外，所提出的框架还有助于解决网络威胁检测中复杂模型缺乏解释和论证的问题，并使企业能够快速应对潜在的安全事件。

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引用次数: 0

Effects of visual-preview and information-sidedness features on website persuasiveness 视觉预览和信息片面性特征对网站说服力的影响

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

Decision Support Systems

Pub Date : 2024-11-01 DOI: 10.1016/j.dss.2024.114361

Yi-Chen Lee , Chih-Hung Peng , Choon-Ling Sia , Weiling Ke

Enhancing a website's persuasiveness and improving users' satisfaction and intention are critical for companies and website designers. Based on the Fogg Behavior Model (FBM), this study explores the perspective of persuasive technology in the context of a website. We identify and design two types of persuasive features: a visual-preview feature and an information-sidedness feature. We propose that websites with these persuasive features are perceived as more persuasive than their counterparts. We further propose that website persuasiveness is positively related to user satisfaction and behavior intention. Data collected from an experimental study lend support to our hypotheses. Theoretical contribution and managerial implications of this study are discussed.

对于企业和网站设计者来说，增强网站的说服力、提高用户的满意度和意向至关重要。本研究基于福格行为模型（Fogg Behavior Model，FBM），从网站说服技术的角度进行了探讨。我们确定并设计了两类有说服力的功能：视觉预览功能和信息片面性功能。我们认为，与同类网站相比，具有这些说服功能的网站更具有说服力。我们进一步提出，网站的说服力与用户满意度和行为意向呈正相关。实验研究收集的数据支持了我们的假设。我们还讨论了本研究的理论贡献和管理意义。

引用次数: 0

The evolution of organizations and stakeholders for metaverse ecosystems: Editorial for the special issue on metaverse part 1 元宇宙生态系统的组织和利益相关者的演变：为元宇宙特刊第 1 部分撰写的社论

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

Decision Support Systems

Pub Date : 2024-10-29 DOI: 10.1016/j.dss.2024.114353

Arpan Kumar Kar , Patrick Mikalef , Rohit Nishant , Xin (Robert) Luo , Manish Gupta

Metaverse ecosystems are fast growing platforms which are witnessing wide adoption. Different digital platforms like social media are trying to evolve into metaverse ecosystems which are perceived to enhance the overall experiences of different users. However there is a lack of impactful empirical literature which have attempted to document diverse socio-technical perspectives surrounding these emerging digital platforms. We highlight an overview of current literature in information systems, whereby discourse in metaverse is currently situated. Our editorial also introduces the studies which have been published in the special issue on metaverse, whereby many of the unique socio-technical elements of design, adoption, usage and impacts of metaverse platforms have been discussed. The studies included in the special issue also highlight specific areas of future research, surrounding metaverse platforms. We conclude by showcasing how research in metaverse may evolve to become more impactful over time.

元宇宙生态系统是一种快速发展的平台，正在被广泛采用。不同的数字平台（如社交媒体）正试图发展成元宇宙生态系统，以提升不同用户的整体体验。然而，目前缺乏有影响力的实证文献，试图记录围绕这些新兴数字平台的各种社会技术观点。我们重点概述了当前信息系统领域的文献，其中包括有关元宇宙的论述。我们的社论还介绍了发表在元海外特刊上的研究，其中讨论了元海外平台的设计、采用、使用和影响等许多独特的社会技术要素。特刊中的研究还强调了围绕元海外平台的未来研究的具体领域。最后，我们还展示了元海外研究如何随着时间的推移不断发展，变得更具影响力。

引用次数: 0

Know where to go: Make LLM a relevant, responsible, and trustworthy searchers 知道去哪里：让 LLM 成为相关的、负责任的、值得信赖的搜索者

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

Decision Support Systems

Pub Date : 2024-10-28 DOI: 10.1016/j.dss.2024.114354

Xiang Shi, Jiawei Liu, Yinpeng Liu, Qikai Cheng, Wei Lu

The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches. However, challenges arise in validating the reliability of generated results and the credibility of contributing sources due to the limitations of traditional information retrieval algorithms and the LLM hallucination problem. We aim to transform LLM into a relevant, responsible, and trustworthy searcher in response to these challenges. Rather than following the traditional generative retrieval approach, simply allowing the LLM to summarize the search results, we propose a novel generative retrieval framework leveraging the knowledge of LLMs to foster a direct link between queries and web sources. This framework reforms the retrieval process of the traditional generative retrieval framework by integrating an LLM retriever, and it redesigns the validator while adding an optimizer to ensure the reliability of the retrieved web sources and evidence sentences. Extensive experiments show that our method outperforms several SOTA methods in relevance, responsibility, and trustfulness. It improves search result validity and precision by 2.54 % and 1.05 % over larger-parameter-scale LLM-based systems. Furthermore, it demonstrates significant advantages over traditional frameworks in question-answering and downstream tasks.

大语言模型（LLMs）的出现显示了在网络搜索中提高相关性和提供直接答案的潜力。然而，由于传统信息检索算法的局限性和 LLM 的幻觉问题，在验证生成结果的可靠性和贡献来源的可信度方面出现了挑战。我们的目标是将 LLM 转变为相关、负责和可信的搜索器，以应对这些挑战。我们提出了一个新颖的生成式检索框架，而不是沿用传统的生成式检索方法，简单地让 LLM 总结搜索结果，而是利用 LLM 的知识在查询和网络来源之间建立直接联系。该框架通过整合 LLM 检索器改革了传统生成式检索框架的检索流程，并重新设计了验证器，同时添加了优化器，以确保检索到的网络来源和证据句子的可靠性。大量实验表明，我们的方法在相关性、责任性和可信度方面都优于几种 SOTA 方法。与参数规模更大的基于 LLM 的系统相比，它在搜索结果的有效性和精确性方面分别提高了 2.54 % 和 1.05 %。此外，在问题解答和下游任务方面，它比传统框架具有明显优势。

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引用次数: 0

Returning the “socio” to decision support research: Expanding beyond a purely technical mindset 让决策支持研究回归 "社会"：超越纯技术思维模式

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

Decision Support Systems

Pub Date : 2024-10-28 DOI: 10.1016/j.dss.2024.114352

Cecil Eng Huang Chua , Fred Niederman

This editorial essay argues the design science decision support literature has unduly focused on developing technical systems when organizational problem solving and decision making often require socio-technical ones. Decision making in uncertain environments requires other aspects the technical view actively suppresses, such as effectiveness and innovation. We explore this in a three-step argument. First, we show the necessity of a socio-technical mindset using the example of how cholera was demonstrated to be a waterborne disease in 1854 London in two independent investigations - one technical and one socio-technical. The insights from the socio-technical investigation were ultimately found correct; the technical one arrived at a completely wrong conclusion. Second, we argue authors are discouraged from publishing research on socio-technical design artifacts. We use spreadsheets as an example, and show developers prefer publishing their incremental contributions in other outlets. Puzzlingly, researchers prefer publishing technical design science contributions in DSS journal given their preponderance in our pages. Thus, in our third step, we argue the lack of socio-technical design science research arises from a mismatch of evaluation criteria. We suggest DSS journal cultivate a subset of editorial board members with a socio-technical mindset to apply the appropriate criteria while encouraging submissions of this type.

这篇社论认为，设计科学决策支持文献过度关注技术系统的开发，而组织问题的解决和决策制定往往需要社会技术系统。不确定环境中的决策制定还需要其他一些被技术视角积极压制的方面，如有效性和创新性。我们分三步来探讨这个问题。首先，我们以 1854 年伦敦的霍乱为例，说明社会技术思维方式的必要性，霍乱是通过两项独立的调查--一项是技术调查，一项是社会技术调查--证实的水传播疾病。社会技术调查得出的结论最终被认为是正确的，而技术调查得出的结论则是完全错误的。其次，我们认为作者不愿意发表关于社会技术设计人工制品的研究成果。我们以电子表格为例，说明开发人员更愿意在其他渠道发表他们的增量贡献。令人费解的是，研究人员更愿意在DSS期刊上发表技术设计科学方面的文章，因为这些文章在我们的网页上占了绝大多数。因此，在第三步中，我们认为社会-技术设计科学研究的缺乏源于评价标准的不匹配。我们建议《设计科学》杂志培养一批具有社会技术思维的编辑委员会成员，在鼓励这类投稿的同时，采用适当的标准。

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引用次数: 0