Decision Support Systems最新文献_第4页

Investigating the cross-channel impact of live streaming on retail sales: Evidence from a live streaming e-commerce platform in China 调查直播对零售销售的跨渠道影响：来自中国直播电子商务平台的证据

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.dss.2025.114591

Lixia Hu , Jiahui Mo , Qingfei Min , Xin (Robert) Luo

The rapid advancement of live streaming technology has given rise to a novel selling channel, enabling real-time, two-way interaction between retailers and consumers and thereby transforming the operational landscape of e-commerce channels. When considering the adoption of live streaming, retailers must carefully evaluate potential cross-channel spillover effects, as these can significantly impact overall sales performance after implementation. However, the presence, magnitude, and underlying mechanisms of such spillover effects remain poorly understood and warrant further empirical investigation. This research aims to evaluate the overall effect and the cross-channel spillover effects generated by the introduction of live streaming channels, along with their underlying mechanisms. Using a difference-in-differences combined with propensity score matching approach and panel data from a leading e-commerce platform in China. We find that the introduction of live streaming channels significantly enhances overall shop sales performance. Furthermore, it not only enhances the sales performance of live-streamed products on traditional online channel (inward cross-channel spillover effect) but also drives increased sales of non-live-streamed products within the same shop on traditional online channel (outward cross-channel spillover effect). Additionally, the implementation of live streaming channels substantially improves a shop's reputation and expands its fan base, indicating that live streaming plays a key role in building brand equity.

直播技术的快速发展催生了一种全新的销售渠道，使零售商和消费者之间实现了实时、双向的互动，从而改变了电子商务渠道的运营格局。在考虑采用直播时，零售商必须仔细评估潜在的跨渠道溢出效应，因为这些效应在实施后会对整体销售业绩产生重大影响。然而，这种溢出效应的存在、程度和潜在机制仍然知之甚少，需要进一步的实证研究。本研究旨在评估引入直播渠道所产生的整体效应和跨渠道溢出效应，以及其潜在机制。采用差异中的差异结合倾向得分匹配方法和来自中国领先电子商务平台的面板数据。我们发现，直播渠道的引入显著提升了店铺的整体销售业绩。不仅提升了传统线上渠道直播产品的销售业绩（向内跨渠道溢出效应），还带动了传统线上渠道同店非直播产品的销售增长（向外跨渠道溢出效应）。此外，直播渠道的实施大大提高了商店的声誉并扩大了其粉丝基础，这表明直播在建立品牌资产方面发挥了关键作用。

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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

Decision Support Systems

Pub Date : 2026-02-01 Epub 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

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

Decision Support Systems

Pub Date : 2026-02-01 Epub 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

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

Decision Support Systems

Pub Date : 2026-02-01 Epub 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

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

Decision Support Systems

Pub Date : 2026-01-01 Epub 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

AI nudging and decision quality: Evidence from randomized experiments in online recommendation setting 人工智能推动和决策质量：来自在线推荐设置随机实验的证据

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-01-01 Epub Date: 2025-11-04 DOI: 10.1016/j.dss.2025.114565

Yuxiao Luo , Nanda Kumar , Adel Yazdanmehr

This study explores the impacts of AI nudging on customer purchase decisions. Digital nudging is a well-established technique used to alter people's behaviors in a predictable way. With the rapid development of Artificial Intelligence/Machine Learning (AI/ML) and the widespread integration of the “black box” algorithm in the digital choice architecture, personalized targeting nudges can vastly influence individual and collective behaviors and lead to undesired consequences. AI nudge refers to the situation when human outsources developing and implementing nudges to AI/ML systems. Drawing upon the literature on nudge and recommendation agents/systems in IS, this study investigated the impact of two types of recommendation badges on user decision quality: AI nudge (e.g., Amazon's Choice) and non-AI nudge (e.g., Best Seller). We found that these two badges can lead to different user perceptions of transparency and thus affect the choice confidence of product selection. In addition, the effect of perceived transparency on choice confidence is contingent upon the mismatch/match between the recommendation and users' preferences, with perceived transparency exerting significantly higher influence on choice confidence in the preference match condition. We tested our research model using a randomized experiment and post-task survey data collected from 837 US-based college students with online shopping experience. This is the first empirical study examining the impact of AI nudging on user decision-making on e-commerce platforms and will contribute to the nudge literature and biased recommendation research in IS. The study also brings ethical implications to the use of AI/ML models and calls for careful oversight on delegating the power of nudging to AI in guiding online user behavior.

本研究探讨了人工智能推动对客户购买决策的影响。数字轻推是一种成熟的技术，用于以可预测的方式改变人们的行为。随着人工智能/机器学习（AI/ML）的快速发展以及“黑箱”算法在数字选择架构中的广泛集成，个性化的目标推动可以极大地影响个人和集体的行为，并导致意想不到的后果。AI助推是指人类将开发和实施助推外包给AI/ML系统的情况。借鉴IS中助推和推荐代理/系统的文献，本研究调查了两种类型的推荐徽章对用户决策质量的影响：人工智能助推（例如亚马逊的选择）和非人工智能助推（例如畅销书）。我们发现这两个徽章可以导致不同的用户对透明度的感知，从而影响产品选择的选择信心。此外，感知透明度对选择信心的影响取决于推荐与用户偏好之间的不匹配/匹配，在偏好匹配条件下，感知透明度对选择信心的影响显著更高。我们使用随机实验和从837名有网购经历的美国大学生中收集的任务后调查数据来测试我们的研究模型。这是第一个研究人工智能助推对电子商务平台用户决策影响的实证研究，将有助于is领域的助推文献和偏见推荐研究。该研究还对人工智能/机器学习模型的使用提出了伦理问题，并呼吁对委托人工智能指导在线用户行为的权力进行仔细监督。

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引用次数: 0

Decoding LLMs' verbal deception in online reviews 破解法学硕士在线评论中的口头欺骗

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-01-01 Epub Date: 2025-09-02 DOI: 10.1016/j.dss.2025.114529

Yinghui Huang , Jinyi Zhou , Wanghao Dong , Weiqing Li , Maomao Chi , Changbin Jiang , Weijun Wang , Shasha Deng

The proliferation of fake online reviews, a long-standing threat to platform trust, is now exacerbated by large language models (LLMs) capable of generating highly convincing deceptive text. Understanding the linguistic strategies LLMs employ is crucial for developing effective mitigation. To address this gap, we develop an explainable artificial intelligence (XAI)-based computational framework, grounded in deception detection theories, to analyze and distinguish the deceptive patterns of LLMs. A core component of our methodology is a novel Turing-style test designed for LLM-generated online reviews. When applied to three purpose-built datasets, our framework not only achieves high detection accuracy for both human-authored fakes (96.57 %) and LLM-generated fakes (96.13 %)—substantially outperforming two current general-purpose detectors—but also indicates that LLMs possess a human-level deceptive capability (metric gaps <0.72 %). The analysis reveals that while cues related to cognitive load and perceptual-contextual details are powerful discriminators for both human and machine deception, certainty uniquely signals LLM-generated text, whereas emotion is a primary predictor only for human fakes. These findings support a central dissociation hypothesis between linguistic generation and cognitive representation: LLM deception is characterized by strategies like surface-level fluency, content realism without experiential grounding, and positivity bias. This study probes the mechanistic differences between human and machine deception, delivers a robust computational detection framework, and advances the theoretical discourse on AI's capacity for deceit.

虚假在线评论的激增是对平台信任的长期威胁，如今，能够生成高度令人信服的欺骗性文本的大型语言模型（llm）加剧了这种威胁。理解法学硕士采用的语言策略对于制定有效的缓解措施至关重要。为了解决这一差距，我们开发了一个可解释的基于人工智能（XAI）的计算框架，以欺骗检测理论为基础，分析和区分法学硕士的欺骗模式。我们方法论的一个核心组成部分是为法学硕士生成的在线评论设计的新颖的图灵风格测试。当应用于三个专门构建的数据集时，我们的框架不仅对人为伪造（96.57%）和llm生成的伪造（96.13%）都达到了很高的检测精度——大大优于目前的两种通用检测器——而且还表明llm具有人类水平的欺骗能力（度量差距<； 0.72%）。分析表明，虽然与认知负荷和感知上下文细节相关的线索是人类和机器欺骗的强大判别器，但确定性唯一地表明llm生成的文本，而情感仅是人类骗局的主要预测因素。这些发现支持了语言生成和认知表征之间的中心分离假设：法学硕士欺骗的特点是表面流利、没有经验基础的内容现实主义和积极偏见等策略。本研究探讨了人类和机器欺骗之间的机制差异，提供了一个强大的计算检测框架，并推进了人工智能欺骗能力的理论论述。

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引用次数: 0

Artificial intelligence agents or human agents? Impact of online customer service agents on crowdfunding performance 人工智能代理还是人类代理？在线客服代理对众筹绩效的影响

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-01-01 Epub Date: 2025-10-30 DOI: 10.1016/j.dss.2025.114562

Wei Wang , Yao Tong , Jian Mou

Although Artificial Intelligence (AI) agents are being increasingly deployed in crowdfunding platforms to address labor shortages, knowledge about their scope and limits is still limited. Across a secondary data analysis and three experiments (total N = 1027), we reveal that AI (vs. human) agents are more effective in reward-based (vs. donation-based) crowdfunding. This effect can be parallelly mediated by perceptions of warmth and competence, with AI agents evoking higher competence but weaker warmth perceptions. Importantly, anthropomorphic AI agents serve as an effective intervention to alleviate AI's negative impact on donation-based crowdfunding by enhancing warmth perceptions. Finally, we show that human agents outperform AI agents in boosting donation-based funding performance only for those with an interdependent versus independent self-construal. Overall, these findings expand the theoretical framework on AI applications in crowdfunding and offer actionable insights for fundraisers and platform operators to optimize agent deployment.

尽管人工智能（AI）代理越来越多地部署在众筹平台上，以解决劳动力短缺问题，但对其范围和限制的了解仍然有限。通过二次数据分析和三个实验（总N = 1027），我们发现人工智能（相对于人类）代理在基于奖励（相对于基于捐赠）的众筹中更有效。这种效应可以通过对温暖和能力的感知来平行调节，人工智能代理唤起更高的能力，但更弱的温暖感知。重要的是，拟人化人工智能代理通过增强温暖感知，可以有效地缓解人工智能对捐赠型众筹的负面影响。最后，我们表明，只有对于那些具有相互依赖与独立自我构造的人，人类代理在提高基于捐赠的融资绩效方面才优于人工智能代理。总的来说，这些发现扩展了人工智能在众筹中的应用的理论框架，并为筹款人和平台运营商优化代理部署提供了可操作的见解。

引用次数: 0

Leveraging large language models for enhanced process model comprehension 利用大型语言模型来增强流程模型的理解能力

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-01-01 Epub Date: 2025-11-04 DOI: 10.1016/j.dss.2025.114563

Humam Kourani , Alessandro Berti , Jasmin Hennrich , Wolfgang Kratsch , Robin Weidlich , Chiao-Yun Li , Ahmad Arslan , Wil M.P. van der Aalst , Daniel Schuster

In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges, particularly as organizations scale and processes become more complex. This paper introduces a novel framework utilizing the advanced capabilities of Large Language Models (LLMs) to enhance the comprehension of complex process models. We present different methods for abstracting business process models into a format accessible to LLMs, and we implement advanced prompting strategies specifically designed to optimize LLM performance within our framework. Additionally, we present a tool, AIPA, that implements our proposed framework and allows for conversational process querying. We evaluate our framework and tool through: i) an automatic evaluation comparing different LLMs, model abstractions, and prompting strategies; ii) a qualitative analysis assessing the ability to identify critical quality issues in process models; and iii) a user study designed to assess AIPA’s effectiveness comprehensively. Results demonstrate our framework’s ability to improve the comprehension and understanding of process models, pioneering new pathways for integrating AI technologies into the BPM field.

在业务流程管理（BPM）中，有效地理解流程模型是至关重要的，但也带来了重大挑战，特别是在组织规模扩大和流程变得更加复杂的情况下。本文介绍了一种利用大型语言模型（llm）的高级功能来增强对复杂过程模型的理解的新框架。我们提出了将业务流程模型抽象为LLM可访问的格式的不同方法，并实现了专门设计用于在我们的框架内优化LLM性能的高级提示策略。此外，我们还提供了一个工具AIPA，它实现了我们提出的框架，并允许会话过程查询。我们通过以下方式评估我们的框架和工具：i)比较不同llm、模型抽象和提示策略的自动评估；Ii)定性分析，评估识别过程模型中关键质量问题的能力；iii)旨在全面评估AIPA有效性的用户研究。结果表明，我们的框架能够提高对流程模型的理解和理解，为将AI技术集成到BPM领域开辟了新的途径。

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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

Decision Support Systems

Pub Date : 2026-01-01 Epub 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