Decision Support Systems最新文献_第3页

Alice in land of games: Investigating behavior spillover effects of users' engagement and social connections across games 游戏领域的Alice：调查游戏中用户粘性和社交关系的行为溢出效应

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

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2026-01-02 DOI: 10.1016/j.dss.2025.114611

Yue Li , Haowen Deng , Cheng Zhang

The rapid growth of digital gaming allows players to move seamlessly between virtual worlds, raising questions about how behaviors in one gaming world transfer to another. Drawing on operant conditioning theory, we posit that engagement—driven by reinforced action–reward loops—forms internalized play habits that spill over positively across games, while social connections create coordination costs that hinder cross-game transitions. Using a panel of 25,164 users observed across two same-type games, we employ user-day-level panel regressions, structural equation modeling, and latent-class analysis to test these hypotheses. Our results reveal three core insights. First, higher engagement in the initial game significantly increases engagement in the subsequent game, whereas stronger social connections in the first game reduce later engagement, confirming positive and negative behavior spillover effects, respectively. Second, both spillover effects are intensified under collaborative play context, where teamwork amplifies reinforcement for highly engaged players but deepens “lock-in” for socially embedded players. Third, engagement-driven users exhibit more consistent cross-game routines, shorter switching intervals, and higher daily switch frequencies than their social-driven counterparts. These findings deepen our understanding of cross-game engagement and offer actionable guidance for optimizing event timing, social features, and retention strategies across game portfolios.

数字游戏的快速发展使玩家能够在虚拟世界之间无缝移动，这引发了一个问题，即一个游戏世界中的行为如何转移到另一个游戏世界。根据操作性条件反射理论，我们认为，在强化的行动奖励循环的驱动下，用户粘性形成了内化的游戏习惯，并在整个游戏中产生积极的影响，而社交联系则产生了阻碍跨游戏过渡的协调成本。通过观察两款同类型游戏的25164名用户，我们使用用户日水平面板回归、结构方程模型和潜在类分析来测试这些假设。我们的研究结果揭示了三个核心见解。首先，在初始游戏中较高的参与度会显著提高后续游戏的参与度，而在初始游戏中较强的社交联系会降低后续游戏的参与度，这分别证实了积极和消极的行为溢出效应。其次，这两种溢出效应在合作游戏情境下都得到强化，团队合作强化了高度投入的玩家，但加深了社交玩家的“锁定”。第三，用户粘性驱动型用户比社交驱动型用户表现出更一致的跨游戏习惯、更短的切换间隔和更高的每日切换频率。这些发现加深了我们对跨游戏粘性的理解，并为优化游戏组合中的活动时间、社交功能和留存策略提供了可行的指导。

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引用次数: 0

Hierarchy-team appointment implementation and physician service productivity: The moderating role of individual and team characteristics 层级团队预约执行与医师服务生产力：个人与团队特质的调节作用

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

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2025-12-26 DOI: 10.1016/j.dss.2025.114598

Hang Xie , Ji Wu , Doris Chenguang Wu , Xiaomei Zeng

The uneven distribution of patient flow among physicians—marked by overburdened specialists and underutilized junior practitioners—perpetuates systemic inefficiencies in outpatient healthcare systems. This study examines the impact of Hierarchical Team Appointment (HTA) system, an innovative appointment scheduling function that probabilistically allocates patients across tiers, on individual physician's patient-volume-based productivity (PVBP). While prior research focuses on individual physicians' adoption of digital channels, the role of team-based systems like HTA in enhancing PVBP through operational efficiency remains underexplored. In this study, we examine the impact of HTA implementation on individual physician's PVBP, mediated by improvements in efficiency. We also explore how this impact varies across specialties, teams and physicians. Using a quasi-experimental design, we analyzed data from 201 physicians at a leading ophthalmology specialty hospital in China, tracking their weekly HTA engagement. Our findings reveal that HTA participation significantly boosts physician PVBP, particularly for junior physicians and in less complexity specialties. Moreover, HTA mitigates patient flow disparities (6.6 %) between team members. The HTA implementation demonstrates potential for managing patient flow and improving physician PVBP, offering valuable insights for healthcare decision support.

病人流动在医生之间的不均匀分布——以负担过重的专科医生和未充分利用的初级医生为标志——使门诊医疗系统的系统性效率低下。本研究考察了分层团队预约（HTA）系统的影响，这是一种创新的预约调度功能，可以跨层概率地分配患者，对个体医生的基于患者量的生产力（PVBP）。虽然先前的研究主要集中在个体医生对数字渠道的采用上，但基于团队的系统（如HTA）在通过操作效率提高PVBP方面的作用仍未得到充分探索。在这项研究中，我们研究了HTA的实施对个体医生的PVBP的影响，通过提高效率来调节。我们还探讨了这种影响在不同专业、团队和医生之间的差异。采用准实验设计，我们分析了来自中国一家领先眼科专科医院的201名医生的数据，跟踪他们每周的HTA参与情况。我们的研究结果表明，HTA的参与显著提高了医生的PVBP，特别是对于初级医生和不太复杂的专业。此外，HTA减轻了团队成员之间的患者流量差异（6.6%）。HTA的实现显示了管理患者流量和改善医生PVBP的潜力，为医疗保健决策支持提供了有价值的见解。

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引用次数: 0

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

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.dss.2025.114599

Hongke Zhao , Zhichen Xiang , Likang Wu , Yanhong Guo

Recommendation systems are pivotal in personalizing user experiences across e-commerce platforms. Yet, they often suffer from inherent biases, such as popularity bias. Popularity bias refers to the tendency of recommendation systems to favor overrepresented items, leading to homogenized suggestions that limit diversity and fail to address the needs of users with unique or less common preferences. While large language models (LLMs) promise to enhance recommendation quality through semantic understanding, they struggle with biased data and insufficient collaborative knowledge. To address these challenges, we propose Causal-Enhanced LLM-based Debiased Recommender System (CLD-Rec), a novel two-stage framework that synergizes causal inference and LLMs to mitigate biases and improve recommendation robustness. In the first stage, causal reasoning identifies and eliminates biases (e.g., popularity bias) from user–item interactions, thereby generating debiased collaborative knowledge. In the second stage, this knowledge is integrated with LLMs; by leveraging the latter’s robust language comprehension capabilities, the system generates personalized recommendations that not only align with user preferences but also ensure fairness. Experimental results show that CLD-Rec consistently outperforms state-of-the-art models across multiple datasets, including Games, Toys, and Sports. Additionally, CLD-Rec demonstrates superior fairness by effectively mitigating popularity bias, leading to more balanced and diverse recommendations.

推荐系统是跨电子商务平台个性化用户体验的关键。然而，他们经常受到固有偏见的影响，比如受欢迎程度偏见。流行偏差是指推荐系统倾向于支持代表性过高的项目，导致同质化的建议，限制了多样性，无法满足具有独特或不太常见偏好的用户的需求。虽然大型语言模型（llm）承诺通过语义理解来提高推荐质量，但它们与有偏见的数据和协作知识不足作斗争。为了解决这些挑战，我们提出了基于因果增强的llm的去偏见推荐系统（CLD-Rec），这是一个新的两阶段框架，可以协同因果推理和llm来减轻偏见并提高推荐的鲁棒性。在第一阶段，因果推理从用户-物品交互中识别并消除偏见（例如，流行偏见），从而产生无偏见的协作知识。在第二阶段，这些知识与法学硕士相结合；通过利用后者强大的语言理解能力，系统生成个性化的推荐，不仅符合用户偏好，而且确保公平性。实验结果表明，CLD-Rec在多个数据集（包括游戏、玩具和体育）上始终优于最先进的模型。此外，CLD-Rec通过有效地减轻人气偏见，展示了卓越的公平性，从而导致更平衡和多样化的推荐。

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引用次数: 0

Does emotional consistency matter? A study of cover image and narrative text in loan-based crowdfunding 情感一致性重要吗？贷款众筹中的封面形象与叙事文本研究

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

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2025-12-26 DOI: 10.1016/j.dss.2025.114596

Haohao Li , Weitao He , Luning Liu , Yuqiang Feng

In multimodal communication environments, facial emotions in images and emotional expressions in narrative text shape viewers' decision-making processes. However, there have been limited systematic investigations into how these two emotional cues affect crowdfunding outcomes. This study proposes a novel theoretical construct regarding image-text emotional consistency and quantifies it by integrating computer vision and text analysis techniques. A theoretical perspective of information processing indicates an inverted U-shaped relationship between image-text emotional consistency and crowdfunding performance. This nonlinear relationship is enhanced when fundraisers are female but weakened as the project risk level increases. An analysis of a large-scale dataset containing 1,588,821 valid crowdfunding projects on the Kiva platform from 2006 to 2023 supports these theoretical expectations. The findings presented herein enrich theoretical knowledge about the effects of multimodal emotional expressions in crowdfunding and provide practical guidance for designing effective crowdfunding campaigns.

在多模态传播环境中，图像中的面部情绪和叙事文本中的情感表达塑造了观众的决策过程。然而，关于这两种情绪线索如何影响众筹结果的系统调查却非常有限。本研究提出了一种新的关于图像-文本情感一致性的理论结构，并通过计算机视觉和文本分析技术对其进行量化。从信息加工的理论角度来看，图文情感一致性与众筹绩效之间存在倒u型关系。当筹资人是女性时，这种非线性关系会增强，但随着项目风险水平的增加，这种非线性关系会减弱。对Kiva平台上从2006年到2023年的1,588,821个有效众筹项目的大规模数据集的分析支持了这些理论预期。本文的研究结果丰富了关于多模态情感表达在众筹中的作用的理论知识，为设计有效的众筹活动提供了实践指导。

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引用次数: 0

Dynamic hypergraph neural networks for consumer purchase path prediction: Integrating promotions, experiences, and store heterogeneity 用于消费者购买路径预测的动态超图神经网络：整合促销、体验和商店异质性

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

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2026-01-05 DOI: 10.1016/j.dss.2026.114614

Juntao Wu , Quan Liu , Lifan Chen , Wenxiang Zhao , Hefu Liu

With the rise of mobile and online food and beverage services, consumer behavior has become increasingly dynamic, multichannel, and personalized. To support improved decision making, restaurant operators need systems that not only predict purchasing behavior but also capture the complex interactions among promotions, consumer experiences, and historical actions. This paper proposes a Behavioral Economics-informed Hyper Graph Network (BEHGN) framework, which integrates Expected Utility Theory and Mental Accounting Theory to model both short-term promotional responses and long-term experience effects. BEHGN employs a hypergraph structure to represent consumers, stores, products, and coupons, and uses large language models to extract experiential features from online reviews. This design enables the system to capture cross-store behavior dynamics and store heterogeneity in a unified decision support model. Experiments on real-world data from two major food and beverage chains demonstrate that BEHGN outperforms existing models in predicting consumer purchase paths, offering higher accuracy and adaptability. The results highlight the potential of BEHGN to enhance food and beverage decision support, contributing to better strategy formulation and performance outcomes.

随着移动和在线餐饮服务的兴起，消费者行为变得越来越动态、多渠道和个性化。为了支持改进的决策制定，餐厅经营者需要的系统不仅要预测购买行为，还要捕捉促销、消费者体验和历史行为之间复杂的相互作用。本文提出了一个基于行为经济学的超图网络（BEHGN）框架，该框架将期望效用理论和心理会计理论结合起来，对短期促销反应和长期体验效应进行建模。BEHGN采用超图结构来表示消费者、商店、产品和优惠券，并使用大型语言模型从在线评论中提取体验特征。这种设计使系统能够在统一的决策支持模型中捕获跨商店行为动态和商店异质性。对两家主要食品和饮料连锁店的真实数据进行的实验表明，BEHGN在预测消费者购买路径方面优于现有模型，具有更高的准确性和适应性。结果突出了BEHGN在提高食品和饮料决策支持方面的潜力，有助于更好的战略制定和绩效结果。

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引用次数: 0

Understanding cyberloafing in video-conferencing-enabled online learning: A resource-based perspective 理解视频会议在线学习中的网络闲逛：基于资源的视角

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

Decision Support Systems

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.dss.2025.114607

Xusen Cheng , Yanyue Ran , Bo Yang , Shuang Zhang , Jose Benitez

Video-conferencing (VC)-enabled online learning has become a norm in schools and institutions. However, the multitasking capabilities of VC often lead users to exhibit cyberloafing behaviors in learning contexts. Despite its prevalence, there is limited understanding of such deviant behaviors in VC-enabled online learning and how to address them. To tackle this gap, we draw on conservation of resources (COR) theory to investigate the evolutionary logic of effort investment behaviors. Using a sequential mixed-methods approach, we first conducted semi-structured interviews, which revealed two constraining resources and one empowering resource of VC in learning contexts. We propose that an individual's consumption of such environmental resources, along with their individual resources, leads to gains in and losses of self-regulated learning (SRL) within VC-enabled online learning, which in turn affect subsequent cyberloafing behaviors. To test our research model and hypotheses, we conducted a survey with 309 participants, followed by a post-survey interview to triangulate the findings. Our study offers valuable insights into the design and implementation of VC in learning contexts to mitigate cyberloafing behaviors, contributing to both theory and practice.

支持视频会议（VC）的在线学习已经成为学校和机构的一种规范。然而，VC的多任务处理能力经常导致用户在学习环境中表现出网络闲逛行为。尽管它很普遍，但人们对风险投资支持的在线学习中的这种越轨行为以及如何解决它们的理解有限。为了解决这一问题，我们利用资源守恒理论来研究努力投资行为的进化逻辑。采用顺序混合方法，我们首先进行了半结构化访谈，揭示了学习环境中风险投资的两个约束资源和一个授权资源。我们认为，个人对这些环境资源的消耗，以及他们的个人资源，导致了在风险投资支持的在线学习中自我调节学习（SRL）的收益和损失，这反过来影响了随后的网络闲逛行为。为了检验我们的研究模型和假设，我们对309名参与者进行了调查，随后进行了调查后的访谈，以三角测量结果。我们的研究为在学习环境中设计和实现风险投资以减轻网络闲逛行为提供了有价值的见解，对理论和实践都有贡献。

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引用次数: 0

Optimal selling mode for real-time data marketplace: Subscription, subscription-based free sample or subscription-based one-off purchase 实时数据市场的最优销售模式：订阅、订阅免费样品或订阅一次性购买

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2026-02-15 DOI: 10.1016/j.dss.2026.114636

Shufeng Lei, Guofang Nan, Guowei Liu

Firms are increasingly using real-time data for decision-making, contributing to the rapid growth of real-time data marketplaces. Data vendors typically offer one of three selling modes: subscription, subscription-based free sample, or subscription-based one-off purchase. In the subscription mode, data vendors charge buyers for access to the entire dataset, including both historical and real-time data. In the subscription-based free sample mode, data vendors provide free data samples alongside the subscription. In the subscription-based one-off purchase mode, data vendors allow the purchase of historical data alongside the subscription. We develop an analytical model to identify the optimal selling mode for a monopoly data vendor, in which buyers are heterogeneous in both their valuations for the datasets (high-type and low-type) and their expertise (informed and uninformed). Our analysis shows that if the proportion of informed buyers is low and the high-type buyers' valuation is high, the subscription-based one-off purchase mode is optimal. In this case, uninformed buyers first purchase historical data, and those who realize they are high-type proceed to subscribe to the entire dataset. If the proportion of informed buyers is moderate, the subscription-based free sample mode becomes optimal. Specifically, when the high-type buyers' valuation is low, the data vendor provides part of the historical data as a free sample, leading to a win-win outcome compared to the subscription mode. When the valuation is high, all historical data are provided as a free sample. Otherwise, the subscription mode is optimal. These results provide strategic guidance for data vendors to make selling modes.

企业越来越多地使用实时数据进行决策，这促进了实时数据市场的快速增长。数据供应商通常提供三种销售模式中的一种：订阅、基于订阅的免费样品或基于订阅的一次性购买。在订阅模式下，数据供应商向购买者收取访问整个数据集的费用，包括历史数据和实时数据。在基于订阅的免费样本模式中，数据供应商在订阅的同时提供免费的数据样本。在基于订阅的一次性购买模式中，数据供应商允许在购买订阅的同时购买历史数据。我们开发了一个分析模型来确定垄断数据供应商的最佳销售模式，其中买家对数据集的估值（高类型和低类型）和他们的专业知识（知情和不知情）都是异质的。我们的分析表明，当知情买家比例较低且高类型买家估值较高时，基于订阅的一次性购买模式是最优的。在这种情况下，不知情的买家首先购买历史数据，而那些意识到自己是high-type的人继续订阅整个数据集。当知情购买者的比例适中时，基于订阅的免费抽样模式是最优的。具体而言，当高类型买家估值较低时，数据供应商提供部分历史数据作为免费样本，与订阅模式相比，实现了双赢。当估值较高时，所有历史数据作为免费样本提供。否则，订阅模式为最优。这些结果为数据供应商制定销售模式提供了战略指导。

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引用次数: 0

The effects of tactile kinesics function in live chat systems: The role of interaction comfort and social presence 实时聊天系统中触觉动力学功能的影响：交互舒适和社交存在的作用

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.2025.114589

Xixian Peng , Yating Yu , Qiuyu Hu , Xinwei Wang , Lei Wang

Live chat systems have become a critical tool for customer engagement in today's digital business landscape, particularly with the rise of AI-enabled chatbots. This research explores the effective use of a novel sensory feature—tactile kinesics—in live chat systems. Grounded in the S-O-R framework, we investigate how the implementation of tactile kinesics in live chat systems influences satisfaction through social presence and interaction comfort, while also examining potential boundary conditions. To achieve this, we conducted three experimental studies. Study 1 reveals that tactile kinesics in live chat systems benefit human agents but not chatbots in business-oriented contexts. Study 2 shows that tactile kinesics enhance satisfaction with chatbots when the interaction context is perceived as social-oriented. Study 3 demonstrated that using business-related tactile kinesics (e.g., handshake) increases satisfaction with chatbots in business-oriented contexts. Overall, our research contributes to the existing literature by identifying the generally positive effects of tactile kinesics in live chat systems and highlighting the crucial mediating role of interaction comfort.

在当今的数字商业环境中，实时聊天系统已成为客户参与的关键工具，特别是随着人工智能聊天机器人的兴起。本研究探讨了在实时聊天系统中有效使用一种新的感官特征——触觉动力学。在S-O-R框架的基础上，我们研究了实时聊天系统中触觉动力学的实施如何通过社交存在和互动舒适度影响满意度，同时也研究了潜在的边界条件。为此，我们进行了三项实验研究。研究1表明，实时聊天系统中的触觉运动有利于人类代理，而不是面向商业环境的聊天机器人。研究2表明，当互动环境被认为是面向社交的时，触觉运动可以提高对聊天机器人的满意度。研究3表明，在以商业为导向的环境中，使用与商业相关的触觉动作（例如握手）可以提高聊天机器人的满意度。总的来说，我们的研究通过确定触觉动力学在实时聊天系统中的普遍积极影响，并强调交互舒适性的关键中介作用，为现有文献做出了贡献。

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引用次数: 0

“Language is the dress of thought”: A new method for automatic detection of AI-generated text “语言是思想的外衣”：一种人工智能生成文本自动检测的新方法

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: 2025-11-25 DOI: 10.1016/j.dss.2025.114578

Zhenhua Wang , Guang Xu , Ming Ren

While AI technologies have garnered widespread attention for their revolutionary text generation capabilities, concerns have arisen regarding the risks associated with AI-generated text (AIGT), especially when used maliciously. Motivated by the recognition that AIGT is generated based on high-probability tokens, a process that inherently differs from the biological-based thought processes underlying human-written text (HWT), we trace and build upon theories of the language latent level to explore the fundamental differences between AIGT and HWT, particularly in terms of potentiality, logicality, and complexity. A novel method named LA2HDetect is proposed for automatic AIGT detection. Specifically, we discover that HWT exhibits higher potentiality than AIGT; AIGT and HWT each possesses unique characteristics in terms of logicality and complexity. These human-AI differences collectively form the decision-making mechanism of LA2HDetect. Extensive experiments on general domain datasets confirm the competitiveness and robustness of LA2HDetect, which outperforms existing methods. In addition, we evaluate the extensibility of LA2HDetect in multiple vertical domains, and explore the insights across progressively advanced AI models.

虽然人工智能技术因其革命性的文本生成能力而受到广泛关注，但人们也开始关注与人工智能生成文本（AIGT）相关的风险，尤其是在恶意使用时。由于认识到AIGT是基于高概率令牌生成的，这一过程本质上不同于人类书面文本（HWT）背后基于生物的思维过程，我们追踪并建立了语言潜在水平的理论，以探索AIGT和HWT之间的根本差异，特别是在潜力、逻辑性和复杂性方面。提出了一种新的AIGT自动检测方法LA2HDetect。具体而言，我们发现HWT比AIGT表现出更高的电位；AIGT和HWT在逻辑性和复杂性方面各具特色。这些人类与人工智能的差异共同形成了LA2HDetect的决策机制。在一般领域数据集上的大量实验证实了LA2HDetect的竞争力和鲁棒性，优于现有方法。此外，我们评估了LA2HDetect在多个垂直领域的可扩展性，并探索了跨逐步先进的人工智能模型的见解。

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引用次数: 0

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