International Journal of Information Management最新文献

Enhancing supply chain resilience: A fit mechanism between key core technology innovations and digital technology applications 增强供应链弹性：关键核心技术创新与数字技术应用的契合机制

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2026-01-31 DOI: 10.1016/j.ijinfomgt.2026.103040

Hui Sun , Yun Song , Ruiqiu Zhang

In the increasingly complex VUCA global environment, supply chain disruptions are becoming more frequent, making the enhancement of supply chain resilience a critical issue for both academia and practice. Grounded in the task-technology fit (TTF) theory and the fit-based perspective, this study empirically examines the impact of key core technology innovations on supply chain resilience, using a sample of 372 Chinese A-share listed companies from three industries over the period 2012–2023. This study explores the complementing fit and balancing fit mechanisms between key core technology innovations and digital technology applications, while also examining the mediating role of supply base concentration. The results indicate that: (1) Key core technology innovations have a significant positive impact on supply chain resilience. (2) The complementing fit shows that the digital technology application depth significantly strengthens the relationship between key core technology innovations and supply chain resilience, while the application breadth does not exhibit a significant moderating effect. (3) The balancing fit demonstrates a nonlinear impact: high balance maximizes resilience, early-stage imbalance can have compensatory effects, and severe imbalance weakens resilience. The negative impact of the “low key core technology innovations - high digital technology applications” combination is notable. (4) Supply base concentration mediates the relationship between the two fits and resilience, and fits help manage risks associated with high concentration. This study challenges the traditional “efficiency-resilience” paradox, extends TTF theory at the strategic level, and provides insights for firms seeking to build highly resilient supply chains through technological synergy.

在日益复杂的VUCA全球环境中，供应链中断变得越来越频繁，使供应链弹性的增强成为学术界和实践中的关键问题。本研究基于任务-技术契合度理论和契合度视角，以2012-2023年三个行业的372家中国a股上市公司为样本，实证检验了关键核心技术创新对供应链弹性的影响。本研究探讨了关键核心技术创新与数字技术应用之间的互补匹配和平衡匹配机制，并考察了供应基地集中度的中介作用。结果表明：(1)关键核心技术创新对供应链弹性具有显著的正向影响。(2)互补拟合表明，数字技术应用深度显著增强了关键核心技术创新与供应链弹性之间的关系，而应用广度不表现出显著的调节作用。(3)平衡拟合呈现非线性影响，高平衡最大化弹性，早期不平衡具有补偿作用，严重不平衡削弱弹性。“低核心技术创新-高数字技术应用”组合的负面影响是显著的。(4)供应基地集中度在两者契合度与弹性之间起中介作用，契合度有助于管理集中度高的风险。本研究挑战了传统的“效率-弹性”悖论，在战略层面扩展了TTF理论，并为寻求通过技术协同建立高弹性供应链的企业提供了见解。

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引用次数: 0

Configuring trust in AI-augmented healthcare: The role of AI interpretability and data privacy in patient adoption of AI-assisted diagnosis 在人工智能增强医疗保健中配置信任：人工智能可解释性和数据隐私在患者采用人工智能辅助诊断中的作用

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2026-01-27 DOI: 10.1016/j.ijinfomgt.2026.103039

Xiangyu Bian , Yitong Chen , Aobo Yang

The integration of artificial intelligence (AI) into healthcare is transforming traditional patient-physician relationships, particularly in online healthcare platforms. This study investigates how patients integrate AI-assisted diagnosis into their healthcare decision-making alongside traditional physician consultation. Drawing on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Information Systems Success Model, an integrated theoretical framework is developed to examine how digital health literacy and digital service propensity influence AI-assisted diagnosis adoption through AI interpretability and perceived data privacy. A sequential mixed-methods approach combining qualitative interviews (n = 30) and quantitative survey data (n = 753) from Chinese online healthcare platform users is used to validate the proposed model and identify disease type as a significant contextual moderator. Findings reveal that AI interpretability and perceived data privacy are associated with mediating relationships between user characteristics and adoption intentions, with these relationships being significantly stronger in chronic disease contexts compared to acute conditions. This study advances theoretical understanding of healthcare AI adoption by integrating individual differences, system characteristics, and contextual factors into a comprehensive framework. Findings provide actionable insights for platform developers to implement context-specific design strategies, for healthcare providers to develop tailored AI introduction programs, and for policymakers to establish differentiated regulatory guidelines based on disease contexts. These contributions help bridge the "trust gap" in AI-assisted healthcare decision-making.

人工智能（AI）与医疗保健的整合正在改变传统的医患关系，特别是在在线医疗保健平台中。本研究调查了患者如何将人工智能辅助诊断与传统的医生咨询结合到他们的医疗保健决策中。利用技术接受和使用统一理论2 （UTAUT2）和信息系统成功模型，开发了一个综合理论框架，以研究数字健康素养和数字服务倾向如何通过人工智能可解释性和感知数据隐私影响人工智能辅助诊断的采用。采用顺序混合方法，结合中国在线医疗平台用户的定性访谈（n = 30）和定量调查数据（n = 753）来验证所提出的模型，并确定疾病类型是一个重要的语境调节因子。研究结果显示，人工智能的可解释性和感知到的数据隐私与用户特征和采用意图之间的中介关系相关，与急性疾病相比，慢性疾病背景下这些关系明显更强。本研究通过将个体差异、系统特征和背景因素整合到一个综合框架中，推进了对医疗保健人工智能采用的理论理解。研究结果为平台开发人员实施特定环境的设计策略，为医疗保健提供者开发量身定制的人工智能引入计划，以及为政策制定者根据疾病环境建立差异化的监管指南提供了可操作的见解。这些贡献有助于弥合人工智能辅助医疗保健决策中的“信任鸿沟”。

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引用次数: 0

Seeking help from AI: Understanding patient use of intelligent guidance applications 向AI寻求帮助：了解患者对智能引导应用程序的使用情况

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2026-01-15 DOI: 10.1016/j.ijinfomgt.2026.103032

Tailai Wu , Ruihan Li , Zhaohua Deng , Lulu Zhou , Lingfei Lu

Intelligent guidance applications (IGAs) have emerged with a profound impact on the patient’s experience of using healthcare services in and out of hospitals. However, the implementation of IGAs faces challenges, including low popularity and acceptance as well as uneven use of hospitals in different regions. Meanwhile, little research has examined the factors of IGAs use. To promote patient use of IGAs, this study focuses on identifying the factors of patient use of IGAs. A research model was developed to examine the factors and articulate their relationships with IGAs use based on the help-seeking model. We validated our research model through a two-stage survey and analyzed the collected data using a multi-analytical approach, including structural equation modeling (SEM) and artificial neural network (ANN). The SEM analysis results indicate that accuracy, personalization, anthropomorphism, and openness all significantly impact patients’ use intention and behavior of IGAs through distress. Self-concealment not only affects the above four attributes but also influences distress and attitudes to IGAs. Meanwhile, the impacts of both distress and attitudes to IGAs on intention to use IGAs are moderated by health consciousness. Besides, the ANN analysis results show that intention to use is the strongest predictor of IGAs use, while distress is the strongest predictor of intention to use IGAs. These findings not only provide a solid theoretical understanding of the factors of IGAs use but also have several managerial implications for hospitals and managers of IGAs to help them make effective decisions about using IGAs.

智能引导应用程序（IGAs）的出现对患者在医院内外使用医疗保健服务的体验产生了深远的影响。然而，IGAs的实施面临着挑战，包括普及程度和接受度低，以及不同地区医院的使用不平衡。与此同时，很少有研究调查iga使用的因素。为了促进患者对IGAs的使用，本研究侧重于确定患者使用IGAs的因素。开发了一个研究模型来检查这些因素，并根据求助模型阐明它们与iga使用的关系。我们通过两阶段的调查验证了我们的研究模型，并使用结构方程模型（SEM）和人工神经网络（ANN）等多分析方法分析了收集到的数据。扫描电镜分析结果表明，准确性、个性化、拟人化和开放性均显著影响患者通过痛苦使用iga的意愿和行为。自我隐藏不仅影响上述四个属性，还影响对IGAs的痛苦和态度。同时，健康意识调节了心理压力和对iga的态度对iga使用意向的影响。此外，人工神经网络分析结果表明，使用意向是使用iga的最强预测因子，而痛苦是使用iga的最强预测因子。这些发现不仅为IGAs使用的因素提供了坚实的理论理解，而且还为医院和IGAs管理人员提供了一些管理意义，以帮助他们做出关于使用IGAs的有效决策。

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引用次数: 0

The impact of enterprise and public social media use on guanxi formation and task performance 企业和公众使用社交媒体对关系形成和任务绩效的影响

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2026-01-13 DOI: 10.1016/j.ijinfomgt.2026.103030

Evelyn Ng , Robert M. Davison , Louie Wong , Barney Tan , Jingzhu Hong

This study examines the differential impacts of enterprise social media (ESM) and public social media (PSM) on guanxi formation and task performance in the Chinese workplace. Guanxi, a key cultural concept in Chinese society, encompasses interpersonal relationships that significantly influence organizational dynamics. Using a sample of 214 employees from a Guangzhou branch of a global logistics firm, we explored how ESM and PSM contribute to guanxi development, and how guanxi, in turn, affects extra-role behavior (ERB) and team identification, ultimately impacting task performance. The study found that ESM is more effective for work-related communications, fostering initial guanxi development, while PSM plays a crucial role in deepening social guanxi. These findings, further validated with analysis of a supplementary dataset comprised of 683 valid responses from employees of a China-based IT service provider, suggest that organizations should consider the distinct roles of ESM and PSM in workplace communication strategies, particularly in contexts where guanxi is pivotal. Furthermore, the research demonstrates that guanxi, developed through both enterprise and public social media interactions, plays an important role in fostering ERB and team identification, which collectively enhance task performance. The study offers theoretical contributions to the understanding of guanxi in digital environments and practical implications for managing social media use in Chinese organizations.

本研究考察了企业社交媒体（ESM）和公共社交媒体（PSM）对中国职场关系形成和任务绩效的差异影响。关系是中国社会的一个重要文化概念，它包含了对组织动态有重大影响的人际关系。我们以一家跨国物流公司广州分公司的214名员工为样本，探讨了ESM和PSM如何促进关系发展，以及关系如何反过来影响角色外行为（ERB）和团队认同，最终影响任务绩效。研究发现，ESM在与工作相关的沟通中更有效，有助于建立初步的关系，而PSM在加深社会关系方面起着至关重要的作用。通过对中国IT服务提供商员工的683份有效回复的补充数据集的分析，进一步验证了这些发现，表明组织应该考虑ESM和PSM在工作场所沟通策略中的不同角色，特别是在关系至关重要的情况下。此外，研究还表明，通过企业和公众社交媒体互动而形成的关系在促进ERB和团队认同方面发挥着重要作用，两者共同提升了任务绩效。该研究为理解数字环境中的关系提供了理论贡献，并为管理中国组织的社交媒体使用提供了实践意义。

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引用次数: 0

Knowledge workers’ trust and reception of generative AI’s advice in complex tasks 知识工作者在复杂任务中对生成式人工智能建议的信任和接受

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2026-01-12 DOI: 10.1016/j.ijinfomgt.2026.103031

Alireza Amrollahi , Jiaqi Yang , Syed Muhammad Fazal-e-Hasan , Basma Badreddine

Building on the prior literature that suggests knowledge workers are generally averse to algorithmic advice, this study explores the differences in reception of and trust in generative AI (GAI) advice compared to human advice, particularly among various reception groups engaged in complex and professional tasks, such as software development. Studies 1 and 2 explore preferences between human and GAI advice sources and assess the impact of users’ reception to GAI. The findings reveal that programmers appreciate GAI advice more than the equivalent advice from human experts. Furthermore, the reception type significantly influences advice-taking behaviour; programmers with a dominant reception of GAI exhibit greater acceptance, while those with an oppositional reception show less acceptance. In Study 3, we develop a nomological model through survey data to verify the complex relationships among technological innovativeness, various forms of trust in GAI, and advice-taking behaviour, noting variations among the different reception groups. We also conduct a complementary configurational analysis to examine how users’ trust in GAI is influenced by factors outside the main domain of study, such as task complexity, perceived security risks, and past exposure to GAI. Our research challenges the widely held belief of algorithm aversion among knowledge workers and contributes to information systems literature by highlighting the impact of the critical factors such as individual reception, past exposure, and innovativeness on knowledge workers’ advice-taking from GAI. Practically, it offers insights for organisations to develop human-centric GAI implementation strategies that embrace individual differences.

在先前的文献表明知识工作者普遍反对算法建议的基础上，本研究探讨了与人类建议相比，对生成式人工智能（GAI）建议的接受和信任的差异，特别是在从事复杂和专业任务（如软件开发）的各种接受群体中。研究1和2探讨了人类和GAI建议来源之间的偏好，并评估了用户接受GAI的影响。研究结果表明，程序员更欣赏GAI的建议，而不是来自人类专家的同等建议。此外，接受类型显著影响建议采纳行为；接受GAI的占主导地位的程序员表现出更高的接受度，而接受GAI的持反对意见的程序员则表现出更低的接受度。在研究3中，我们通过调查数据建立了一个规律模型，以验证技术创新、GAI中各种形式的信任和建议采纳行为之间的复杂关系，并注意到不同接受群体之间的差异。我们还进行了补充的配置分析，以检查用户对GAI的信任如何受到主要研究领域之外的因素的影响，例如任务复杂性、感知的安全风险和过去对GAI的暴露。我们的研究挑战了知识工作者对算法厌恶的普遍看法，并通过强调个人接受、过去接触和创新等关键因素对知识工作者从GAI中获得建议的影响，为信息系统文献做出了贡献。实际上，它为组织开发以人为中心的GAI实施策略提供了见解，这些策略包含了个体差异。

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引用次数: 0

Organizational learning for exploring Generative AI: CORE-sandbox experiments 探索生成式人工智能的组织学习：核心沙盒实验

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2026-01-07 DOI: 10.1016/j.ijinfomgt.2026.103029

Dov Te’eni , Myriam Raymond , Frantz Rowe , Etienne Thénoz , Philippe Trimborn

Generative AI (GenAI) holds potential for organizations, offering transformative opportunities while simultaneously raising concerns about its associated risks. Like many emerging technologies, GenAI presents organizations with a significant challenge: navigating uncertainty before making large-scale decisions about which systems to adopt and how to implement and leverage them. Managers cannot rely solely on general knowledge of GenAI; they require insights tailored to their specific organizational context. Drawing on an 18-month study of sandbox experiments conducted within a large international service organization, this paper presents CORE-sandbox experiments as a structured framework for systematically learning about the critical dimensions of uncertainty surrounding GenAI. The framework organizes learning into four key domains: Capabilities, Opportunities, Risks, and Ecosystem. The paper also advances the discourse on organizational learning and dynamic capabilities by demonstrating how in-situ and ex-situ learning cycles reinforce one another and how second and third-order organizational learning emerge under conditions of high uncertainty before GenAI rollout decisions are made.

生成式人工智能（GenAI）为组织提供了潜在的变革机会，同时也引起了对相关风险的关注。像许多新兴技术一样，GenAI向组织提出了一个重大的挑战：在做出关于采用哪些系统以及如何实现和利用它们的大规模决策之前，导航不确定性。管理者不能仅仅依赖GenAI的一般知识；他们需要针对其特定组织环境量身定制的洞察力。在一个大型国际服务组织进行的为期18个月的沙盒实验研究中，本文提出了核心沙盒实验作为一个结构化框架，用于系统地了解围绕GenAI的不确定性的关键维度。该框架将学习分为四个关键领域：能力、机会、风险和生态系统。本文还通过展示原位和非原位学习周期如何相互加强，以及在GenAI推出决策之前，在高度不确定的条件下，二级和三级组织学习如何出现，推进了组织学习和动态能力的论述。

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引用次数: 0

Exploring information sharing intention of employees through privacy calculus perspective: A mixed-methods approach 基于隐私演算视角的员工信息共享意愿研究：一种混合方法研究

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2026-01-03 DOI: 10.1016/j.ijinfomgt.2025.103028

Abdul Khader V , Sreejith S S

The contemporary business world demands a high volume of data in each of its functional areas, including human resources. Despite the availability of various data extraction options, it is imperative to directly obtain information from employees for making decisions using human resources analytics. We primarily aim to investigate employees’ perspectives on voluntarily sharing their personal information through a respecified privacy calculus model (PCM) to ensure contextual validity and theoretical coherence. We conducted sequential exploratory mixed-methods research, consisting of two stages. The first stage involved qualitative interviews with 23 employees, while the second stage included quantitative survey data collected from 511 employees, aiming to gain a comprehensive understanding of employees’ perceptions of personal information sharing. In the first stage, we identified six influential themes using thematic analysis and developed a conceptual model based on the privacy calculus perspective. In the second stage, we used covariance-based structural equation modeling (CB-SEM) to analyze the survey data to validate the model. Findings confirmed the explanatory power of PCM and respecified it in the context of employee personal information sharing. We offer recommendations to organizations on how to collect and manage HR information, taking into account the perspectives of employees.

当代商业世界在每个功能领域都需要大量的数据，包括人力资源。尽管有各种数据提取选项，但必须直接从员工那里获取信息，以便使用人力资源分析做出决策。本研究的主要目的是通过一个重新定义的隐私演算模型（PCM）来研究员工自愿分享个人信息的观点，以确保语境的有效性和理论的连贯性。我们进行了顺序探索性混合方法研究，包括两个阶段。第一阶段对23名员工进行了定性访谈，第二阶段对511名员工进行了定量调查，旨在全面了解员工对个人信息共享的看法。在第一阶段，我们使用主题分析确定了六个有影响力的主题，并基于隐私微积分的视角建立了一个概念模型。在第二阶段，我们使用基于协方差的结构方程模型（CB-SEM）对调查数据进行分析，以验证模型。研究结果证实了PCM的解释力，并在员工个人信息共享的背景下重新定义了PCM。我们向组织提供关于如何收集和管理人力资源信息的建议，同时考虑到员工的观点。

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引用次数: 0

Corrigendum to “Reflecting the impact of customer participation in digital era: The role of data analytics capability and organization coupling” [International Journal of Information Management 87 (2026) 103022] “反映数字时代客户参与的影响：数据分析能力和组织耦合的作用”的勘误表[国际信息管理杂志87 (2026)103022]

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2026-01-02 DOI: 10.1016/j.ijinfomgt.2025.103027

Zhenghao Michael Xia , Yangsong Hu , Xiaodong Marcus Li , Kang Xie , Jinghua Xiao

引用次数: 0

Bound to disclosure: An assessment of secondary data use concerns 约束披露：对二级数据使用问题的评估

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-12-27 DOI: 10.1016/j.ijinfomgt.2025.103026

Stephen Flowerday , Jake Mead , Rene Moquin

The sudden and complete dominance of social media by a few select companies has often led users to feel at odds with the rapidly changing business strategies in digital environments. One practice, the secondary use of personal information, has received limited attention in privacy behavior research. While many people remain unaware of how much of their data is collected, the secondary use of personal information, using personal information for reasons beyond the original transaction, is an increasing concern among social media users. Grounded in privacy calculus theory, this study aimed to propose and empirically test a research model regarding user concerns about secondary data use and its impact on self-disclosure intentions on Facebook. Privacy calculus research seeks to explain the privacy paradox, which refers to the disconnect between individuals' privacy concerns and their actual behavior. We posit that users are not perfectly rational but rather operate under conditions of bounded rationality, shaped by both real-world and engineered constraints, particularly evident in secondary data use practices. The findings demonstrate that concerns about the secondary use of personal information significantly diminish users' perceived benefits and heighten their perceived risks. Despite this, users continue to perceive that the benefits of information disclosure outweigh the risks. Our findings suggest that the opaque, multilayered nature of secondary data use on social media platforms exemplifies the conditions of bounded rationality under which users operate. Faced with limited information, cognitive constraints, and complex data ecosystems, individuals engage in satisficing behaviors that inadvertently increase their vulnerability to exploitation. Building on this observation, we extend privacy calculus by modeling disclosure decisions under bounded rationality and by centering secondary data use as the key driver of privacy concerns.

少数几家公司在社交媒体上突然完全占据主导地位，这常常让用户感到与数字环境中快速变化的商业战略格格不入。个人信息的二次利用这一行为在隐私行为研究中受到的关注有限。虽然许多人仍然不知道他们的数据被收集了多少，但个人信息的二次使用，即出于原始交易之外的原因使用个人信息，越来越受到社交媒体用户的关注。基于隐私演算理论，本研究旨在提出并实证检验一个关于用户对二级数据使用的关注及其对Facebook自我披露意图的影响的研究模型。隐私微积分研究试图解释隐私悖论，即个人对隐私的关注与实际行为之间的脱节。我们假设用户不是完全理性的，而是在有限理性的条件下操作，这是由现实世界和工程约束形成的，特别是在二次数据使用实践中。研究结果表明，对个人信息二次使用的担忧显著降低了用户的感知利益，并增加了他们的感知风险。尽管如此，用户仍然认为信息披露的好处大于风险。我们的研究结果表明，社交媒体平台上二手数据使用的不透明、多层性质体现了用户操作时的有限理性条件。面对有限的信息、认知约束和复杂的数据生态系统，个体参与的满足行为无意中增加了他们被剥削的脆弱性。在此观察的基础上，我们通过在有限理性下建模披露决策并将二手数据使用作为隐私问题的关键驱动因素来扩展隐私演算。

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引用次数: 0

Reflecting the impact of customer participation in digital era: The role of data analytics capability and organization coupling 反映数字时代客户参与的影响：数据分析能力和组织耦合的作用

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-12-26 DOI: 10.1016/j.ijinfomgt.2025.103022

Zhenghao Michael Xia , Yangsong Hu , Xiaodong Marcus Li , Kang Xie , Jinghua Xiao

With the widespread adoption of big data and related technologies in customer participation (CP), exploring the mechanisms through which data analytics enables CP to achieve innovative performance has become an emerging topic in innovation research. However, under different forms of CP, it remains unclear how data analytics technologies and their associated organizational factors will impact firm innovation performance. This study aims to inform this issue, based on match-paired samples of 370 Chinese firms undergoing digital transformation, from an integrated perspective of knowledge production and Socio-technical theory (STT). Our findings show that (1) customer participation as information providers (CPI) and customer participation as co-developers (CPC) have heterogeneous impact paths on innovation performance, in which knowledge production constitutes a complete or partial mediator, respectively. (2) The technical and social aspects of data analytics, namely data analytics capability (DAC) and data and R&D departments coupling (DRDC), respectively, has a linear positive (non-linear inverted U-shaped) moderating effect on the impact paths of CPI (CPC). These results provide more refined evidence for the realization of performance and boundary conditions of CP innovation in the big data era, which helps to enrich the literature on innovation and the practice of data-driven innovation.

随着大数据及相关技术在客户参与（customer participation， CP）中的广泛应用，探索数据分析使客户参与实现创新绩效的机制已成为创新研究的新兴课题。然而，在不同形式的CP下，数据分析技术及其相关组织因素如何影响企业创新绩效尚不清楚。本研究从知识生产和社会技术理论（STT）的综合视角出发，基于370家正在进行数字化转型的中国企业的配对样本，旨在为这一问题提供信息。研究发现：(1)客户作为信息提供者（CPI）和客户作为共同开发者（CPC）对创新绩效的影响路径存在异质性，其中知识生产分别构成完全或部分中介。(2)数据分析的技术层面和社会层面，即数据分析能力（DAC）和数据与研发部门耦合（DRDC）分别对CPI （CPC）的影响路径具有线性正向（非线性倒u型）调节作用。这些研究结果为大数据时代CP创新绩效和边界条件的实现提供了更细化的证据，有助于丰富创新文献和数据驱动创新实践。

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引用次数: 0