International Journal of Information Management最新文献

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

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

Generative AI in academic research activities: The hidden side of self-detrimental consumption 学术研究活动中的生成式人工智能：自我有害消费的隐藏一面

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

International Journal of Information Management

Pub Date : 2025-12-23 DOI: 10.1016/j.ijinfomgt.2025.103024

Mai Nguyen , Yunen Zhang , Yi Bu , Russell Belk

Generative AI (GenAI) is increasingly embedded in academic research activities undertaken by researchers (including research-active educators) and research students. While GenAI can raise efficiency, it may also foster self-detrimental consumption for short-term convenience that erodes long-term research integrity and capability. To map this “hidden side”, we conducted a netnography of discussions on X-platform (formerly Twitter) by self-identified researchers, research-active educators and research students (between October and November 2024; Study 1), alongside semi-structured interviews with 19 Australia-based researchers (aged 19–45; Study 2). Across the data, we identified five key themes: user misuse, environmental facilitators, usage barriers, GenAI limitations, and challenges, along with related sub-themes. Integrating both studies, we propose the GenAI Self-Detrimental Consumption (GAI-SDC) framework, which explicates how these factors interrelate within academic research contexts. The framework offers a focused lens for analyzing GenAI-related behaviors by examining how factors interact in academic research activities. The practical contribution includes actionable strategies from the framework, providing tangible measures for institutions, researchers, and developers to mitigate self-detrimental use and promote responsible GenAI integration in academic research activities.

生成人工智能（GenAI）越来越多地嵌入到研究人员（包括积极从事研究的教育工作者）和研究学生的学术研究活动中。虽然GenAI可以提高效率，但它也可能助长为了短期便利而自我损害的消费，从而侵蚀长期的研究完整性和能力。为了描绘这一“隐藏的一面”，我们在x平台（以前的Twitter）上进行了一项由自我认同的研究人员、研究活跃的教育工作者和研究生（2024年10月至11月；研究1）进行的讨论网络图，同时对19名澳大利亚研究人员（年龄19 - 45岁；研究2）进行了半结构化访谈。通过这些数据，我们确定了五个关键主题：用户滥用、环境促进因素、使用障碍、GenAI限制和挑战，以及相关的子主题。结合这两项研究，我们提出了基因自我有害消耗（GAI-SDC）框架，该框架解释了这些因素在学术研究背景下如何相互关联。该框架通过考察各种因素在学术研究活动中的相互作用，为分析基因相关行为提供了一个聚焦的视角。实际贡献包括来自框架的可操作策略，为机构、研究人员和开发人员提供切实的措施，以减轻对自身有害的使用，并促进学术研究活动中负责任的GenAI集成。

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引用次数: 0

How does artificial intelligence capacity enhance the production system resilience and operational performance? A human-organization-technology fit perspective 人工智能能力如何提高生产系统的弹性和运行性能？人-组织-技术契合的视角

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

International Journal of Information Management

Pub Date : 2025-12-23 DOI: 10.1016/j.ijinfomgt.2025.103023

Junbin Wang , Yangyan Shi , Xinyu Jiang , V.G. Venkatesh

Artificial Intelligence (AI) capabilities are increasingly pivotal for enhancing production system resilience in today's volatile business environments. However, the integration of AI technologies with established organizational information processing and decision-making frameworks remains inadequately understood. Grounded in the Human-Organization-Technology (HOT) fit theory, this study investigates how AI capacity positively influences a firm’s operational performance. Using multi-wave survey data collected from 305 manufacturing firms via a professional online platform during the COVID-19 pandemic, we identify critical factors that reinforce this positive effect and elucidate its underlying mechanisms, with particular emphasis on how AI reconfigures organizational information flows and knowledge practices. Partial least squares-based structural equation modeling was employed to test the hypothesized model. The findings reveal a significant positive impact of AI capacity on production system resilience. Furthermore, production system resilience itself exerts a strong positive influence on operational performance. Crucially, production system resilience serves as a key mediating mechanism, through which AI capacity indirectly enhances operational performance. Finally, the degree of fit, conceptualized across task-tool, human-tool, and data-tool dimensions, moderates the positive effect of AI capacity on production system resilience. This research is contextualized within the Chinese manufacturing sector, a major global production hub, and enriches the theoretical discourse on AI capacity and production system resilience from an information management perspective, highlighting its transformative role in organizational information flows, knowledge creation, and data-driven decision processes.

在当今多变的商业环境中，人工智能（AI）能力对于增强生产系统的弹性越来越重要。然而，人工智能技术与已建立的组织信息处理和决策框架的集成仍然没有得到充分的理解。基于人-组织-技术（HOT）契合理论，本研究探讨了人工智能能力如何积极影响企业的运营绩效。利用2019冠状病毒病大流行期间通过专业在线平台从305家制造企业收集的多波调查数据，我们确定了加强这种积极影响的关键因素，并阐明了其潜在机制，特别强调了人工智能如何重新配置组织信息流和知识实践。采用偏最小二乘结构方程模型对假设模型进行检验。研究结果揭示了人工智能能力对生产系统弹性的显著积极影响。此外，生产系统弹性本身对运营绩效有很强的正向影响。至关重要的是，生产系统弹性是关键的中介机制，通过该机制，人工智能能力间接提高了运营绩效。最后，跨任务-工具、人-工具和数据-工具维度概念化的契合度调节了人工智能能力对生产系统弹性的积极影响。本研究以全球主要生产中心中国制造业为背景，从信息管理的角度丰富了人工智能能力和生产系统弹性的理论论述，突出了其在组织信息流、知识创造和数据驱动决策过程中的变革作用。

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引用次数: 0

B&S2Vec: Mapping market structure in two-sided platform based on consumers’ purchase trajectories B&S2Vec：基于消费者购买轨迹的双边平台市场结构映射

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

International Journal of Information Management

Pub Date : 2025-12-23 DOI: 10.1016/j.ijinfomgt.2025.103025

Peng Wu , Shansen Wei , Xian Cheng , Runshi Liu

Platform companies must identify their market structure to develop effective growth strategies. This study introduces a method to vector buyers and sellers (B&S2Vec), using network representation learning to automatically extract latent buyer and seller attributes derived from the buyer’s purchase trajectories among thousands of sellers on a two-sided platform. We first construct a large-scale bipartite buyer-seller network by purchase trajectories; and then we compress the network into a low-dimensional representation space to learn complex patterns from the bipartite network by using B&S2Vec; we use t-SNE to obtain market structure visualization by reducing the learned representation vector to obtain the associated 2-dimensional visualization map. Our theoretical and simulation studies show that B&S2Vec effectively identifies market structures. In addition, we demonstrate its efficiency in optimizing marketing campaigns with budget constraints on a real platform. This study contributes to the advancement of research in two-sided platform marketing and market structure analysis.

平台公司必须确定自己的市场结构，以制定有效的增长战略。本研究引入了一种向量买家和卖家的方法（B&S2Vec），利用网络表示学习在双边平台上的数千个卖家中自动提取买家购买轨迹中衍生的潜在买家和卖家属性。首先利用购买轨迹构造了一个大规模的二部买卖网络；然后利用B&；S2Vec算法将网络压缩到低维表示空间，从二部网络中学习复杂模式；我们使用t-SNE通过减少学习到的表示向量来获得相关的二维可视化图，从而获得市场结构可视化。我们的理论和模拟研究表明，B&；S2Vec有效地识别了市场结构。此外，我们在真实平台上展示了它在预算约束下优化营销活动的效率。本研究有助于推进双边平台营销和市场结构分析的研究。

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引用次数: 0