Decision Support Systems最新文献

英文中文

Exploring the motivations behind behavior: A theory-driven deep-learning framework for cyberviolence behavior detection

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

Decision Support Systems

Pub Date : 2025-01-28 DOI: 10.1016/j.dss.2025.114409

Xuelong Chen , Yiping Chen , Guojie Yin

The anonymity and convenience of social media platforms enable the public to express and even vent themselves, which drives a surge of cyberviolence behaviors (CVB). Recent advances in machine learning, especially in deep learning, have drastically benefited CVB detection. However, despite the wide use of state-of-the-art deep-learning models, previous studies only analyzed each post/comment for the presence of (obfuscated) abusive text, which is not comprehensive and exact because the content posted online may not necessarily include negative words. In complex and conflicting situations, people may overlook implicit violence, leading to failures in situational judgment. Herein, we designed a well-grounded and explainable deep-learning framework based on the theory of planned behavior (TPB) to explore the motivations behind CVB to better detect it. Specifically, we constructed a systematic and comprehensive suite of computable features grounded in TPB and then proposed a novel model named Multilevel and Multiattribute Embedding CVB detection model considering Dual-view Contextual Information. Our framework detected implicit and explicit CVB with macro F1 scores of >88.67 %, outperforming state-of-the-art methods. We further provided differentiated strategies according to the scale and distribution of different classes of CVB and proposed related management implications. Our study sheds light on platform operations in managing online content and mitigating the risk of governance cost wastage and deterioration of the cyber ecosystem.

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引用次数: 0

Does the most popular answer lead to the best answer: The moderating roles of tenure, social closeness, and cultural tightness

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

Decision Support Systems

Pub Date : 2025-01-27 DOI: 10.1016/j.dss.2025.114405

Yuxin Cai , Xiayu Chen , Shaobo Wei

Online question and answer (Q&A) communities rely on the general audience or the question asker to determine the best answer. However, limited attention has been directed toward understanding the influence of the general audience-favored answer (i.e., most popular answer) on the question asker-selected best answer (i.e., best answer). This study examines whether and how the general audience-favored answer influences the question asker-selected best answer. Drawing upon uncertainty reduction theory (URT), this paper investigates how three uncertainty reduction strategies (i.e. question askers' tenure, social closeness between the question asker and question answerer, and cultural tightness of the question asker's region) moderate the relationship between the general audience-favored answer and the question asker-selected best answer. To test the theoretical model, we used a dataset from an online Q&A community comprising 161,695 observations. Our results reveal that the general audience-favored answer more likely leads to the question asker-selected best answer. Furthermore, we find that the question asker's tenure and the social closeness between the question asker and question answerer negatively moderate the above relationship, while the cultural tightness of question asker's region positively moderates the above relationship. This research offers a new perspective on the mechanisms through which the general audience-favored answer leads to question asker-selected best answer. By identifying the critical roles of uncertainty reduction strategies during the best answer selection process, our research provides valuable insights for online Q&A community managers to optimize user engagement and satisfaction.

在线问答（Q&A）社区依靠广大受众或提问者来确定最佳答案。然而，人们对一般受众喜爱的答案（即最受欢迎的答案）对提问者选择的最佳答案（即最佳答案）的影响的了解还很有限。本研究探讨了受众喜爱的答案是否以及如何影响提问者选出的最佳答案。本文借鉴不确定性降低理论（URT），研究了三种不确定性降低策略（即提问者的任期、提问者与回答者之间的社会亲密度以及提问者所在地区的文化亲密度）如何调节普通受众喜爱的答案与提问者选择的最佳答案之间的关系。为了检验该理论模型，我们使用了一个在线问答社区的数据集，其中包含 161,695 个观察结果。我们的结果表明，一般受众喜爱的答案更有可能导致提问者选择最佳答案。此外，我们还发现，提问者的任期和提问者与回答者之间的社会亲密度对上述关系有负向调节作用，而提问者所在地区的文化紧密度对上述关系有正向调节作用。这项研究为一般受众喜爱的答案导致提问者选择最佳答案的机制提供了一个新的视角。通过确定减少不确定性策略在最佳答案选择过程中的关键作用，我们的研究为在线问答社区管理者优化用户参与度和满意度提供了有价值的见解。

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引用次数: 0

Disclosure of IT-related risk factors in corporate filings

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

Decision Support Systems

Pub Date : 2025-01-25 DOI: 10.1016/j.dss.2025.114403

Alfred Z. Liu , Angela Xia Liu , Kexin Zhao

This research investigates the disclosure of IT-related risk factors in U.S. public firms' periodic SEC filings. Drawing upon the Resource-Based View theory, we propose that a firm's IT capability determines the disclosure of its overall IT-related risk factors. We employ a machine learning-enhanced dictionary that captures emerging IT keywords from newly filed corporate reports to quantify the scope and specificity of such disclosures. Our findings indicate that IT capability enhances IT-related risk factor disclosures in general and specifically in response to adverse IT events, such as data breaches. We also find that disclosing IT-related risk factors reduces a firm's perceived risk and enhances its shareholder value. Our research underscores the critical yet under-researched role of IT capability in shaping disclosures of IT-related risk factors and highlights such disclosures' informational value to investors.

引用次数: 0

Enhanced digital embeddedness and bubble mitigation in NFT marketplaces: The impact of rarity rank on user trading behavior

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

Decision Support Systems

Pub Date : 2025-01-23 DOI: 10.1016/j.dss.2025.114407

Lin Yuan , Chaoyue Gao , Alvin Chung Man Leung , Qiang Ye

As a nascent market in recent years, the NFT market has been widely scrutinized for its significant market bubble. To help investors make more informed trading decisions, several NFT marketplaces have introduced features that display the rarity information of NFTs directly on their interfaces. Existing literature on the rarity effect suggests that this feature generally increases trading activity. However, in the unique context of the NFT marketplace, its impact on user trading behavior remains an open question. This study focuses on the event where Rarible began displaying rarity information for profile picture (PFP) NFTs on its platform. Utilizing the theoretical perspective of dual process theory, we conceptualize the introduction of the rarity label as enhanced digital embeddedness. By using other NFT collections on the platform that have rarity information but do not display rarity rank labels as the control group, this study employs a rigorous Difference-in-Differences design. We find that this event leads to a decrease in both trading volume and trading price, primarily for lower-ranked NFTs, small-size collections, recent NFTs rather than top-ranked, large-size collections, established NFTs. Additional time-varying analysis also explains the asynchronous changes in price and trading volume. This study enriches the literature on the NFT marketplaces and the rarity effect, extends the application of dual process theory, and provides practical decision support for market regulators, managers, and platform users.

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引用次数: 0

An interpretable imbalance ensemble classification method for readmission risk assessment incorporating multi-view perturbation and SHAP analysis

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

Decision Support Systems

Pub Date : 2025-01-23 DOI: 10.1016/j.dss.2025.114404

Shaoze Cui , Ruize Gao , Junwei Kuang , Liang Yang , Huaxin Qiu , Xiaowen Wei

In the domain of medical services, patients are frequently readmitted shortly after discharge due to inadequate discharge planning or relapses of their illnesses. Such occurrences not only deplete valuable medical resources but also compromise patient satisfaction with the medical care they receive. To address this issue, we propose an interpretable imbalance ensemble classification method incorporating multi-view perturbation to evaluate the risk of patient readmission. Our study introduces a novel multi-view perturbation technique to bolster the model's generalization capabilities. Furthermore, we propose a more robust ensemble strategy based on Evidential Reasoning (EVR) rules, which enhances the stability of the ensemble learning model's fusion outcomes. Additionally, recognizing the impact of sensitive parameters on model performance, we present a parameter optimization approach utilizing the Differential Evolution (DE) algorithm, which balances model predictive accuracy and computational efficiency within the fitness function. Empirical results using real-world medical data indicate that our proposed method accurately identifies patients at high risk of readmission and surpasses current state-of-the-art methods in risk assessment.

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引用次数: 0

Panoramic sales insight: Using multimodal fusion to improve the effectiveness of flash sales

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

Decision Support Systems

Pub Date : 2025-01-18 DOI: 10.1016/j.dss.2025.114401

Haoran Wang , Zhen-Song Chen , Mingjie Fang , Yilong Wang , Feng Liu

Flash sales are a widely adopted e-commerce marketing strategy that operate over a brief period, offering limited-time discounts, special promotions, or clearance items to create a sense of urgency and promote rapid sales. This study proposes panoramic sales insight (PSI), a multimodal revenue forecasting framework designed to improve the accuracy of revenue predictions for flash sales. Using historical flash sales data from the fast fashion retailer Shein, the proposed PSI framework integrates both structured and unstructured data, utilizing a text–image fusion module to fuse features from product images and text descriptions and a deep neural network to forecast revenue. The text features are extracted using bidirectional encoder representations from transformers (BERT), the product image features are extracted using a vision transformer (ViT), and review keyword extraction is conducted using Fumeus. Multimodal fusion then integrates these features to deliver accurate revenue forecasting. Controlled experiments evaluate the performance of each module within the PSI framework, while ablation analysis confirms the robustness of PSI. This study provides valuable insights for managers, enabling more accurate revenue forecasting and improving the effectiveness of flash sales.

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引用次数: 0

Willing and able: Task recommendation with a trade-off of the bilateral benefits for knowledge-intensive crowdsourcing

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

Decision Support Systems

Pub Date : 2025-01-03 DOI: 10.1016/j.dss.2025.114400

Xicheng Yin , Jing Li , Kevin Zhu , Wei Wang , Hongwei Wang

Given the “profit-seeking” behavior of task solvers and the “quality-seeking” focus of solution seekers in knowledge-intensive crowdsourcing contests, task recommender systems must manage the trade-off between their respective benefits. This study proposes a multitask deep learning model with a multigate hybrid expert structure to jointly model solver preference and ability, thereby balancing bilateral benefits. The knowledge source for participation and performance prediction tasks are grounded in expectancy theory and performance theory, respectively. Linear and deep neural network (DNN) modules are integrated to enhance both memorization and generalization capabilities. By incorporating gating networks, the model effectively captures correlations between the two prediction tasks, balances intertask weights, and allows each task to learn features in different ways using linear and DNN modules. Additionally, our method addresses sample selection bias and data sparsity issues through feature transfer learning, leveraging the sequential pattern between participation and winning. Cross-validation experiments on Kaggle data demonstrate the model effectiveness, provide data-driven decision support for task recommendation and resource allocation in knowledge-intensive crowdsourcing platforms.

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引用次数: 0

Can causal machine learning reveal individual bid responses of bank customers? — A study on mortgage loan applications in Belgium

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.2024.114378

Christopher Bockel-Rickermann , Sam Verboven , Tim Verdonck , Wouter Verbeke

Personal loan pricing requires accurate estimates of individual customer behavior, such as the willingness to take out a loan at a given price, the “bid response”. This is challenging due to the nonlinearity of responses hindering the discretionary definition of models, as well as the confoundedness of observational training data. This paper investigates the application of data-driven and machine learning (ML) methods to estimate individual bid responses. We argue that framing bid response modeling as a problem of causal inference is crucial for accurate modeling and understanding of challenging factors. We test established ML algorithms and state-of-the-art causal ML methods on a dataset on mortgage loan applications in Belgium and investigate the effects of different levels of confounding in the data. Our results demonstrate that methods that address confounding can improve bid response estimation, especially when established non-causal methods are negatively affected.

引用次数: 0

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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