Information Processing & Management最新文献_第2页

PERStance: Personality-guided enhanced multimodal stance detection 持久性：个性导向的增强多模态姿态检测

IF 6.9 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2026-01-23 DOI: 10.1016/j.ipm.2025.104593

Guoqi Geng, Qianyi Zhan, Heng-Yang Lu

Stance detection aims to identify users’ attitudes toward specific targets in social media, playing a crucial role in information processing and public opinion management. However, existing research on stance detection often overlooks the potential influence of user personality traits on stance expression. In view of the shortcomings of existing stance detection methods, this paper studies the impact of personality traits on stance judgment. To this end, we propose PERStance, a personality-guided enhanced multimodal zero-shot stance detection method. Specifically, PERStance uses a Large Language Model (LLM) to infer users’ personality traits from a multi-dimensional perspective, thereby more accurately understanding users’ potential stances on specific targets. To mitigate issues such as LLM hallucinations and reasoning confusion, we incorporate the Chain-of-Thought framework in the stance detection stage and optimize its reasoning path. Experimental results on multiple multimodal stance detection datasets show that the PERStance method proposed in this paper achieves the best performance in stance detection, with an average increase of 23.88% in the Macro-F1 score. Ablation experiments verify the effectiveness of each module in this method. The source code of our proposed framework is released at https://github.com/jncsnlp/PERStance.

姿态检测旨在识别用户在社交媒体中对特定目标的态度，在信息处理和舆论管理中起着至关重要的作用。然而，现有的姿态检测研究往往忽略了用户人格特征对姿态表达的潜在影响。针对现有姿态检测方法的不足，本文研究了人格特征对姿态判断的影响。为此，我们提出了一种人格引导的增强多模态零射击姿态检测方法PERStance。具体而言，PERStance使用LLM （Large Language Model）从多维度角度推断用户的人格特征，从而更准确地了解用户对特定目标的潜在立场。为了减轻LLM幻觉和推理混乱等问题，我们将思维链框架纳入姿态检测阶段，并优化其推理路径。在多个多模态姿态检测数据集上的实验结果表明，本文提出的PERStance方法在姿态检测中表现最佳，其Macro-F1得分平均提高23.88%。烧蚀实验验证了该方法中各个模块的有效性。我们提出的框架的源代码发布在https://github.com/jncsnlp/PERStance。

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引用次数: 0

Validating generative agent-Based modeling in social media simulations through the lens of the friendship paradox 通过友谊悖论的镜头验证社交媒体模拟中基于生成代理的建模

IF 6.9 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2026-01-23 DOI: 10.1016/j.ipm.2026.104636

Gian Marco Orlando , Valerio La Gatta , Diego Russo , Vincenzo Moscato

Generative Agent-Based Modeling (GABM) is an emerging simulation paradigm that integrates the reasoning capabilities of Large Language Models (LLMs) with traditional Agent-Based Modeling to replicate complex social behaviors, including user interactions on social media platforms. While GABM has been employed to study localized phenomena on social media, such as opinion formation and information propagation, its capacity to capture global network-level phenomena remains underexplored. In this paper, we address this gap by investigating whether GABM-based social media simulations exhibit the Friendship Paradox (FP) – a counterintuitive network phenomenon where individuals, on average, have fewer friends than their friends. We design a GABM-based framework for social media simulation, featuring generative agents that emulate real users by incorporating distinct personalities, interests, and behaviors. Leveraging three real-world Twitter datasets centered on the US 2020 Election, UK Brexit, and the QAnon conspiracy, we demonstrate that the FP and its generalized forms emerge in GABM-based simulations. Consistent with real-world social media, we observe a hierarchical structure where generative agents preferentially connect with others exhibiting superior attributes, such as greater activity or influence, without being instructed with any behavioral rules. Furthermore, our analysis reveals that infrequent connections with highly connected agents primarily drive the Friendship Paradox, mirroring established patterns in real-world networks. Overall, our findings validate the ability of GABM to replicate global social media phenomena, highlighting its potential as a robust framework for modeling and analyzing complex social behaviors at scale.

基于生成代理的建模（GABM）是一种新兴的仿真范式，它将大型语言模型（llm）的推理能力与传统的基于代理的建模相结合，以复制复杂的社会行为，包括社交媒体平台上的用户交互。虽然GABM已被用于研究社交媒体上的局部现象，如意见形成和信息传播，但其捕捉全球网络层面现象的能力仍未得到充分探索。在本文中，我们通过调查基于gabm的社交媒体模拟是否表现出友谊悖论（FP）来解决这一差距。FP是一种反直觉的网络现象，平均而言，个人的朋友比他们的朋友少。我们设计了一个基于gabm的社交媒体模拟框架，其特点是生成代理通过结合不同的个性、兴趣和行为来模拟真实用户。利用以2020年美国大选、英国脱欧和QAnon阴谋为中心的三个真实世界的Twitter数据集，我们证明了FP及其广义形式出现在基于gabm的模拟中。与现实世界的社交媒体一致，我们观察到一种层次结构，在这种结构中，生成智能体优先与表现出更优越属性的其他智能体连接，比如更活跃或更有影响力，而不受任何行为规则的指导。此外，我们的分析表明，与高度连接的代理之间的不频繁连接主要驱动了友谊悖论，反映了现实世界网络中的既定模式。总的来说，我们的研究结果验证了GABM复制全球社交媒体现象的能力，突出了它作为大规模建模和分析复杂社会行为的强大框架的潜力。

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引用次数: 0

Conversational generative retrieval with contextual denoising 会话生成检索与上下文去噪

IF 6.9 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2026-01-23 DOI: 10.1016/j.ipm.2026.104642

Wenhao Zhang , Yuhan Wang , Yi Bai , Mengqi Zhang , Zhaochun Ren , Zhumin Chen , Pengjie Ren

Conversational search needs an understanding of the interaction history between users and systems to retrieve information that aligns with the user’s current query. A prominent challenge in this task lies in addressing irrelevant context, which can obscure the user’s true intent and hinder retrieval accuracy. Existing methods for conversational search rely on the dense retrieval architecture, which encodes the context into fixed-length vectors. However, these approaches are constrained by the inherent limitations of the bi-encoder architecture, making it difficult to mitigate interference from irrelevant contexts effectively. In this paper, we propose Conversational Generative Retrieval for Context Denoising (CGR4CD), a novel framework that leverages a sequence-to-sequence generative retrieval architecture with cross-attention layers to dynamically capture key information during decoding. Our method introduces innovative proposition-based docids and a two-stage training strategy, combining generation and rank losses for enhanced denoising and ranking. Extensive evaluations on three conversational search benchmarks (TopiOCQA, QReCC, and OR-QuAC) demonstrate that CGR4CD achieves state-of-the-art performance, outperforming baselines in 8 out of 9 evaluation metrics. On TopiOCQA, which is characterized by topic switching and high noise, CGR4CD achieves an average improvement of 8.2% over baselines of similar size and surpasses the LLM-based Chatretriever by 4.4%, highlighting its superior context denoising capability. Our approach also maintains parameter efficiency, achieving these results with a T5-large backbone compared to larger LLM-based retrievers. Detailed analysis further validates its superior capability in contextual denoising, particularly in noisy scenarios.

会话搜索需要了解用户和系统之间的交互历史，以便检索与用户当前查询一致的信息。该任务的一个突出挑战在于处理不相关的上下文，这可能会模糊用户的真实意图并影响检索的准确性。现有的会话搜索方法依赖于密集检索架构，该架构将上下文编码为固定长度的向量。然而，这些方法受到双编码器架构固有局限性的限制，使得难以有效地减轻来自不相关上下文的干扰。在本文中，我们提出了上下文去噪的会话生成检索（CGR4CD），这是一个新颖的框架，它利用具有跨注意层的序列到序列生成检索架构来动态捕获解码过程中的关键信息。我们的方法引入了创新的基于命题的docids和两阶段训练策略，结合了生成和秩损失来增强去噪和排序。对三个会话搜索基准（TopiOCQA、QReCC和OR-QuAC）的广泛评估表明，CGR4CD达到了最先进的性能，在9个评估指标中有8个优于基线。在主题切换和高噪声特征的TopiOCQA上，CGR4CD比相似大小的基线平均提高了8.2%，比基于llm的Chatretriever高出4.4%，突出了其优越的上下文去噪能力。我们的方法还保持了参数效率，与基于llm的大型检索器相比，使用t5大的主干获得了这些结果。详细的分析进一步验证了其在上下文去噪方面的优越能力，特别是在嘈杂的场景下。

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引用次数: 0

DS_HURNSP: An effective method for mining high utility repeated negative sequential patterns from data streams DS_HURNSP：从数据流中挖掘高效用重复负序列模式的有效方法

IF 6.9 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2026-01-22 DOI: 10.1016/j.ipm.2026.104637

Xiangjun Dong , Yicong Zhen , Ping Qiu , Jing Chi , Lei Guo , Wenpeng Lu , Long Zhao , Yongshun Gong , Yuhai Zhao

Mining high utility repeated negative sequential patterns (HURNSPs) from data streams is an important method for data stream analysis. However, the existing methods in this topic don’t consider negative events and repeated events, which results in weak decision-making effectiveness. So in this paper, we propose an effective algorithm DS_HURNSP for mining HURNSPs from data streams based on a sliding window model. First, we propose an effective utility-list prefix tree structure to store high utility repeated positive sequential patterns (HURPSPs). Second, we construct a utility mapping set based on a hash-table structure to enable rapid querying of HURPSPs information. Finally, we propose a two-stage computation method to compute the utility of high utility repeated negative sequential candidates (HURNSCs) by mapping them to the set of HURPSPs, avoiding rescanning database. Extensive experiments on six datasets show that the DS_HURNSP algorithm generates tens to thousands of times as many HURNSPs as the baseline method, and reduces the average runtime by more than half.

从数据流中挖掘高效用重复负序列模式（HURNSPs）是数据流分析的重要方法。然而，本课题现有的方法没有考虑负面事件和重复事件，导致决策有效性较弱。因此，本文提出了一种基于滑动窗口模型的有效算法DS_HURNSP从数据流中挖掘hurnsp。首先，我们提出了一种有效的实用列表前缀树结构来存储高实用重复正序列模式（hurpsp）。其次，基于散列表结构构造实用映射集，实现对hurpsp信息的快速查询。最后，我们提出了一种两阶段的计算方法，通过将高效用重复负序候选（HURNSCs）映射到hurpsp集合来计算它们的效用，从而避免重新扫描数据库。在6个数据集上的大量实验表明，DS_HURNSP算法产生的hurnsp数是基线方法的几十到几千倍，平均运行时间减少了一半以上。

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引用次数: 0

SEGA: Selective cross-lingual representation via sparse guided attention for low-resource multilingual named entity recognition SEGA：基于稀疏引导注意力的低资源多语言命名实体识别的选择性跨语言表示

IF 6.9 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2026-01-22 DOI: 10.1016/j.ipm.2026.104627

Paerhati Tulajiang , Jinzhong Ning , Yuanyuan Sun , Liang Yang , Yuanyu Zhang , Kelaiti Xiao , Zhixing Lu , Yijia Zhang , Hongfei Lin

Multilingual named entity recognition (NER) is especially challenging in low-resource and typologically diverse languages, where translation drift, morphological variation, and noisy alignments degrade performance. Existing encoder-based methods often rely on dense attention or uniform alignment, which tends to propagate irrelevant signals across languages. We present SEGA, a lightweight and typology-aware framework that incorporates sparse guided attention to select auxiliary signals, alongside a weighted fusion layer that balances representations between cross-lingual and monolingual contexts. Unlike prior approaches, SEGA requires no parallel corpora and supports fully monolingual inference. We evaluate SEGA on six multilingual NER benchmarks spanning over 60 languages, including CoNLL, WikiANN, MasakhaNER 2.0, XTREME-40, WikiNEuRal, and MultiNERD. SEGA achieves new state-of-the-art results on five datasets, with absolute gains of up to +24.2 F1 over strong encoder baselines, and outperforming prompt-based large language models by up to +18.9 F1 in low-resource scenarios. Efficiency analyses show that SEGA adds only  ∼ 30M parameters beyond a standard dual encoder, making it lightweight and deployable on a single GPU. Comprehensive ablation, visualization, and error analyses confirm that SEGA is robust to alignment noise, morphological complexity, and boundary ambiguity, offering a practical and scalable solution for real-world multilingual NER.

多语言命名实体识别（NER）在低资源和类型多样化的语言中尤其具有挑战性，其中翻译漂移、形态变化和噪声对齐会降低性能。现有的基于编码器的方法通常依赖于密集关注或均匀对齐，这倾向于跨语言传播不相关的信号。我们提出了一种轻量级的类型感知框架SEGA，它结合了稀疏引导注意力来选择辅助信号，以及一个加权融合层，以平衡跨语言和单语言上下文之间的表示。与之前的方法不同，SEGA不需要并行语料库，并且完全支持单语言推理。我们对世嘉进行了六种多语言NER基准测试，涵盖60多种语言，包括CoNLL、WikiANN、MasakhaNER 2.0、XTREME-40、WikiNEuRal和multierd。世嘉在五个数据集上实现了新的最先进的结果，在强大的编码器基线上的绝对增益高达+24.2 F1，在低资源场景下优于基于提示的大型语言模型高达+18.9 F1。效率分析表明，世嘉在标准双编码器之外仅增加了 ~ 30M参数，使其重量轻，可部署在单个GPU上。综合消融、可视化和误差分析证实，SEGA对对齐噪声、形态复杂性和边界模糊具有鲁棒性，为现实世界的多语言NER提供了实用且可扩展的解决方案。

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引用次数: 0

TaNSP: An efficient target pattern mining algorithm based on negative sequential pattern TaNSP：一种基于负序模式的高效目标模式挖掘算法

IF 6.9 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2026-01-22 DOI: 10.1016/j.ipm.2026.104643

Xiaowen Cui , Xue Dong , Ping Qiu , Chuanhou Sun , Yuhai Zhao , Wenpeng Lu , Xiangjun Dong

Target pattern mining (TPM) aims to return sets of target patterns related to a user-queried target sequence. However, existing TPM research is confined to positive sequential patterns, overlooking negative sequential patterns, which results in limited decision support capabilities. Moreover, introducing negative sequential patterns faces challenges of low mining efficiency and limited pruning techniques. To address these issues, we propose an efficient target pattern mining algorithm based on negative sequential pattern, called TaNSP, to achieve TPM for negative sequence as the user-queried target sequence and output negative sequential patterns containing the target query sequence, while also supporting positive sequential patterns. Specifically, we propose a pruning strategy based on a triple bitmap to guide pattern generation and improve mining efficiency. Then, we propose a pruning strategy to address the limitations of pruning techniques when the negative sequence is the target query sequence. The experimental results on six datasets demonstrate that, compared to the baseline method, TaNSP can increase operational efficiency by more than twice, demonstrating excellent scalability and practicality.

目标模式挖掘（TPM）旨在返回与用户查询的目标序列相关的目标模式集。然而，现有的TPM研究仅限于积极的顺序模式，忽视了消极的顺序模式，导致决策支持能力有限。此外，引入负序模式还面临着挖掘效率低和修剪技术有限的挑战。为了解决这些问题，我们提出了一种高效的基于负序列模式的目标模式挖掘算法TaNSP，将负序列作为用户查询的目标序列实现TPM，输出包含目标查询序列的负序列模式，同时也支持正序列模式。具体来说，我们提出了一种基于三重位图的剪枝策略来指导模式生成，提高挖掘效率。然后，我们提出了一种剪枝策略，以解决当负序列是目标查询序列时剪枝技术的局限性。在6个数据集上的实验结果表明，与基线方法相比，TaNSP的运算效率提高了2倍以上，具有良好的可扩展性和实用性。

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引用次数: 0

From personalized learning to explainable prediction: A data-driven framework for patient no-shows 从个性化学习到可解释的预测：患者缺席的数据驱动框架

IF 6.9 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2026-01-21 DOI: 10.1016/j.ipm.2026.104633

Wenbo Zhang , Xi Chen , Mingwei Wang , Rui Hou , Ning Li

The prediction of patient no-shows has emerged as a critical research topic in healthcare service management. To address the challenges of behavioral heterogeneity and factor diversity in patient no-shows, this study proposes a data-driven three-stage decision analytics method integrating personalized preference learning and explainable prediction. First, to capture patient no-show behavioral heterogeneity, we applied community detection algorithms to identify distinct patient subgroups, enabling subgroup-specific exploration of no-show patterns. Second, within each subgroup, we employ a preference learning model to quantify the relative importance of the influencing factors tailored to each group’s characteristics. Finally, we developed an XGBoost predictive model based on SHAP to achieve highly accurate and interpretable no-show probability predictions. Experimental results based on 2431 real outpatient appointment records from the Department of Ophthalmology at General Medical Harbin 242 Hospital showed that air quality emerged as a critical factor influencing patient attendance, with poorer air quality significantly increasing the likelihood of no-shows. In addition, the proposed PL-XGBoost-SHAP model achieved an average accuracy of 90.08%, precision of 92.36%, recall of 95.65% and F1 score of 93.96% across the four subgroups. These results showed an accuracy improvement of 2–12% compared with the five baseline models, demonstrating the scientific validity and feasibility of the proposed approach. Sensitivity analyses and statistical tests confirmed the robustness and generalizability of the proposed method. Consequently, this study offers significant practical implications for healthcare providers, enabling the design of personalized appointment reminders and effective allocate resources.

患者缺席预测已成为医疗服务管理领域的一个重要研究课题。为了解决患者失诊的行为异质性和因素多样性的挑战，本研究提出了一种数据驱动的三阶段决策分析方法，该方法将个性化偏好学习和可解释预测相结合。首先，为了捕捉患者的失诊行为异质性，我们应用社区检测算法来识别不同的患者亚组，从而实现针对亚组的失诊模式探索。其次，在每个子群体中，我们采用偏好学习模型来量化针对每个群体特征的影响因素的相对重要性。最后，我们开发了一个基于SHAP的XGBoost预测模型，以实现高精度和可解释的缺席概率预测。基于哈尔滨242总医院眼科2431份真实门诊预约记录的实验结果表明，空气质量成为影响患者出诊的关键因素，空气质量越差，患者失诊的可能性显著增加。此外，所提出的PL-XGBoost-SHAP模型在4个亚组中的平均准确率为90.08%，精密度为92.36%，召回率为95.65%，F1得分为93.96%。结果表明，与5种基线模型相比，该方法的精度提高了2-12%，证明了该方法的科学有效性和可行性。敏感性分析和统计检验证实了该方法的稳健性和可推广性。因此，本研究为医疗保健提供者提供了重要的实际意义，使个性化预约提醒的设计和有效分配资源成为可能。

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引用次数: 0

CausalLog: Log parsing using LLMs with causal intervention for bias mitigation CausalLog：使用带有因果干预的llm进行日志解析，以减轻偏差

IF 6.9 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2026-01-21 DOI: 10.1016/j.ipm.2026.104609

Yuan Tian, Shi Ying, Tiangang Li

Log parsing transforms unstructured log messages into structured formats, serving as a critical step for various log analysis tasks. Large language models (LLMs) have recently shown strong performance in this task. However, they tend to rely on their experiential knowledge as shortcuts, introducing bias and reducing parsing accuracy. To address this issue, we propose CausalLog, a lightweight and flexible debiasing framework for log parsing. CausalLog is inspired by the Structural Causal Model and the front-door adjustment principle. On this basis, counterfactual rewriting is implemented through tailored prompt engineering, aiming to mitigate biases without accessing LLM internals. In addition, n-gram statistics of log data are integrated as a bias-free reference for an adjustment score, which helps improve both parsing accuracy and interpretability. Experiments on public log datasets show that CausalLog outperforms state-of-the-art methods, providing observational evidence that it improves both log grouping and template extraction accuracy.

日志解析将非结构化的日志消息转换为结构化的格式，是执行各种日志分析任务的关键步骤。大型语言模型（llm）最近在这项任务中表现出了强大的性能。然而，他们往往依赖于他们的经验知识作为捷径，引入偏见和降低解析的准确性。为了解决这个问题，我们提出了CausalLog，这是一个轻量级的、灵活的日志解析去偏框架。CausalLog的灵感来源于结构因果模型和前门调整原则。在此基础上，通过定制的提示工程实现反事实重写，旨在在不访问LLM内部的情况下减轻偏见。此外，将日志数据的n-gram统计信息集成为调整分数的无偏差参考，有助于提高解析精度和可解释性。在公共日志数据集上的实验表明，CausalLog优于最先进的方法，提供了观察证据，表明它提高了日志分组和模板提取的准确性。

引用次数: 0

Neural embeddings of collaboration networks predict citation impact and innovation 协作网络的神经嵌入预测引文影响和创新

IF 6.9 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2026-01-20 DOI: 10.1016/j.ipm.2026.104634

Chengyu Li , Wenlong Yang , Meiling Li , Yang Wang

Scientific collaboration is a key driver of breakthroughs in contemporary science. While prior research has primarily focused on team diversity based on individual attributes, quantifying structural diversity based on collaborative relationships at the individual paper level remains underexplored. In this study, we apply neural embedding techniques to quantify structural diversity at the paper level and examine its association with research outcomes. Leveraging the node2vec and GraphSAGE algorithms, we embed each scientist into a dense vector space using the Microsoft Academic Graph dataset, covering 4,772,781 papers written by 5,162,397 scientists from 1995 to 2016. We show that our embedding model effectively classifies scientists across scientific domains and accurately predicts their primary scientific domains. Crucially, our analysis reveals that structural diversity is strongly associated with citation impact, novelty, and disruption at the individual paper level. Specifically, a 1SD increase in structural diversity is associated with a 7.6%, 24.7%, and 2.9% increase in citation impact, and the odds of publishing novel and disruptive papers, respectively. These findings are generalizable across multiple model specifications, time spans, different team sizes, and scientific domains. Finally, our analysis reveals that structural diversity exhibits the strongest correlation with citation impact, novelty, and disruption among all examined diversity measures. Our study highlights the importance of fostering structurally diverse collaborations and has policy implications for institutions, funders, and governments aiming to support impactful and innovative research.

科学合作是当代科学取得突破的关键驱动力。虽然先前的研究主要集中在基于个人属性的团队多样性上，但基于个人论文层面的合作关系的量化结构多样性仍未得到充分探索。在本研究中，我们应用神经嵌入技术在论文层面量化结构多样性，并检验其与研究成果的关系。利用node2vec和GraphSAGE算法，我们使用微软学术图数据集将每位科学家嵌入到密集的向量空间中，该数据集涵盖了1995年至2016年由5,162,397名科学家撰写的4,772,781篇论文。我们的嵌入模型有效地对科学家进行了跨科学领域的分类，并准确地预测了他们的主要科学领域。至关重要的是，我们的分析表明，结构多样性与单个论文的引用影响、新颖性和颠覆性密切相关。具体来说，结构多样性每增加1SD，引用影响力和发表新颖和颠覆性论文的几率分别增加7.6%、24.7%和2.9%。这些发现可以推广到多个模型规范、时间跨度、不同的团队规模和科学领域。最后，我们的分析表明，结构多样性与被引影响、新颖性和颠覆性的相关性最强。我们的研究强调了促进结构多样化合作的重要性，并对旨在支持有影响力和创新研究的机构、资助者和政府具有政策意义。

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引用次数: 0

Artificial intelligence applications and innovation ambidexterity: The “inverted U-shaped” regulating effect of risk-taking 人工智能应用与创新双重性：风险承担的“倒u型”调节效应

IF 6.9 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2026-01-20 DOI: 10.1016/j.ipm.2026.104635

Lihua Fu , Luo Jin , Yaokuang Li

Artificial intelligence (AI) is driving technological and industrial transformation, reshaping enterprise structure and innovation. Despite its importance, research lacks insight into AI’s impact on innovation ambidexterity and the underexplored role of risk-taking. Drawing on information processing theory, this study constructs a theoretical model to examine the impact of AI applications on enterprise exploration, exploitation, and innovation ambidexterity, as well as incorporates the level of risk-taking as a moderating variable. Using Stata 17 to perform panel data regression and a series of robustness tests, we analyze 1,908 firm-year observations from the information transmission, software, and information technology service industries, as well as the scientific research and technology service industries listed on the Shanghai and Shenzhen Stock Exchanges from 2012 to 2022. The empirical analyses propose that AI applications remarkably enhance innovation ambidexterity at the 1 % level. This positive effect is the most pronounced when risk-taking is moderate. This study extends information processing theory to the AI-enabled innovation context and further enriches its boundary conditions by introducing risk-taking as a nonlinear moderator. Managerially, the findings suggest that enterprises should calibrate risk-taking levels to complement AI deployment, enabling a balanced approach to exploration and exploitation.

人工智能（AI）正在推动技术和产业变革，重塑企业结构和创新。尽管它很重要，但研究缺乏对人工智能对创新的影响的洞察力，以及对风险承担作用的探索不足。利用信息处理理论，构建了人工智能应用对企业探索、开发和创新双元性影响的理论模型，并将风险承担水平作为调节变量。利用Stata 17进行面板数据回归和一系列稳健性检验，对2012 - 2022年沪深两市上市的信息传输、软件、信息技术服务行业以及科研和技术服务行业的1908个企业年观测数据进行了分析。实证分析表明，人工智能应用在1%的水平上显著提高了创新的双元性。当冒险适度时，这种积极影响最为明显。本研究将信息处理理论扩展到人工智能驱动的创新环境中，并通过引入风险承担作为非线性调节因子进一步丰富了其边界条件。在管理方面，研究结果表明，企业应调整风险承担水平，以补充人工智能的部署，从而实现勘探和开发的平衡方法。

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引用次数: 0