Electronic Commerce Research and Applications最新文献

Navigating innovation: the strategic dynamics of AI-driven live streaming 引领创新：人工智能驱动的直播的战略动态

IF 6.3 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2026-03-01 Epub Date: 2026-02-01 DOI: 10.1016/j.elerap.2026.101581

Haoyu Zhou , Chaocheng He , Jiang Wu , Liuxin Zou

The rise of AI streamers in the live-streaming industry has significantly reshaped stakeholder strategies and transformed the ecosystem. While these AI streamers offer operational advantages, their adoption also presents notable challenges. Existing research has primarily focused on the business-to-consumer (B2C) perspective, neglecting the broader dynamics of how AI streamers influence multi-stakeholder interactions within the live streaming ecosystem. This study addresses this gap by developing a tripartite evolutionary game model to explore the strategic interactions among brands, Multi-Channel Networks (MCNs), and platforms, thereby providing a comprehensive understanding of AI streamers’ impact on stakeholders’ strategic decisions. The findings indicate that brand adoption of AI streamers is influenced by factors beyond the balance of additional revenue and adoption costs. Even when additional revenue surpasses adoption costs, brands may hesitate due to concerns over long-term sustainability and technological risks. Sensitivity analysis reveals a non-linear relationship between costs and benefits, highlighting that as adoption costs rise, brands may revert to traditional live streaming despite high revenue. Regulatory strategies play a critical role in shaping brand adoption decisions. In particular, moderate supervision fosters both innovation and stability, whereas overly strict or lenient regulations can hinder AI adoption. Thus, effective calibration of regulations supports AI adoption without compromising market stability. This research contributes to the theoretical understanding of multi-stakeholder ecosystems and offers practical insights for integrating AI innovation with effective governance in live-streaming commerce.

人工智能主播在直播行业的崛起，极大地重塑了利益相关者的战略，并改变了生态系统。虽然这些人工智能流媒体提供了运营优势，但它们的采用也带来了显著的挑战。现有的研究主要集中在企业对消费者（B2C）的角度，而忽略了人工智能流媒体如何影响直播生态系统中多方利益相关者互动的更广泛动态。本研究通过开发三方进化博弈模型来探索品牌、多渠道网络（mcn）和平台之间的战略互动，从而全面了解人工智能流媒体对利益相关者战略决策的影响，从而解决了这一差距。研究结果表明，人工智能流媒体的品牌采用受到额外收入和采用成本平衡之外的因素的影响。即使额外收入超过采用成本，品牌也可能会因为对长期可持续性和技术风险的担忧而犹豫不决。敏感性分析揭示了成本和收益之间的非线性关系，强调随着采用成本的上升，品牌可能会回归传统的直播，尽管收入很高。监管策略在塑造品牌采用决策方面发挥着关键作用。特别是，适度的监管可以促进创新和稳定，而过于严格或宽松的监管可能会阻碍人工智能的采用。因此，监管的有效校准支持人工智能的采用，而不会损害市场稳定。本研究有助于对多利益相关者生态系统的理论理解，并为将人工智能创新与直播商业的有效治理相结合提供实践见解。

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引用次数: 0

What makes a review helpful? A multimodal prediction model in e-commerce 什么使评论有用？电子商务中的多模态预测模型

IF 6.3 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2026-03-01 Epub Date: 2026-02-21 DOI: 10.1016/j.elerap.2026.101586

Heena Lim , Seonu Park , Qinglong Li , Xinzhe Li , Jaekyeong Kim

The rapid expansion of e-commerce has made online reviews critical to consumer decision-making. However, the overwhelming volume and inconsistent quality of these reviews significantly complicate the identification of valuable information. Consequently, machine learning and deep learning-based review helpfulness prediction (RHP) has emerged as a robust approach for addressing these challenges. While unimodal RHP methods primarily analyze textual content, multimodal approaches enhance predictive accuracy by integrating textual and visual content. Despite these advantages, existing models often overlook shallow features, such as review readability and image quality, which are critical to consumer evaluation. To provide a more comprehensive framework for RHP, an elaboration likelihood model–grounded multimodal cue-based helpfulness-prediction model (MCHPM) is proposed. MCHPM extracts central cues (semantic content from text and images) using bidirectional encoder representations from transformers (BERT) and VGG-16, whereas Python-based feature extraction captures peripheral cues (e.g., text readability and image quality). Finally, a co-attention mechanism identifies cue interdependencies, and a Gated Multimodal Unit effectively weighs each modality during prediction. Empirical evaluation on a real-world Amazon review dataset demonstrates that the proposed MCHPM outperforms benchmark models, thus validating the criticality of incorporating shallow features into multimodal RHP studies. Overall, these findings offer valuable insights into enhancing RHP and ensuring more informed consumer decision-making in e-commerce.

电子商务的迅速发展使得网上评论对消费者的决策至关重要。然而，这些评论的压倒性数量和不一致的质量显著地使有价值信息的识别复杂化。因此，机器学习和基于深度学习的审查帮助预测（RHP）已经成为解决这些挑战的有力方法。单模态RHP方法主要分析文本内容，而多模态方法通过整合文本和视觉内容来提高预测准确性。尽管有这些优势，但现有的模型往往忽略了一些肤浅的特征，比如评论的可读性和图像质量，而这些对消费者的评价至关重要。为了给RHP提供一个更全面的框架，提出了一个基于精细似然模型的多模态线索帮助预测模型（MCHPM）。MCHPM使用来自转换器（BERT）和VGG-16的双向编码器表示提取中心线索（来自文本和图像的语义内容），而基于python的特征提取捕获外围线索（例如，文本可读性和图像质量）。最后，共同注意机制识别线索的相互依赖性，门控多模态单元在预测过程中有效地权衡每个模态。在真实的亚马逊评论数据集上的实证评估表明，所提出的MCHPM优于基准模型，从而验证了将浅层特征纳入多模态RHP研究的重要性。总的来说，这些发现为提高RHP和确保电子商务中更明智的消费者决策提供了有价值的见解。

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引用次数: 0

Key decision criteria for integrating non-fungible tokens (NFTs) into e-commerce platforms 将不可替代代币（nft）集成到电子商务平台的关键决策标准

IF 6.3 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2026-03-01 Epub Date: 2026-01-15 DOI: 10.1016/j.elerap.2026.101575

Pei-Hsuan Tsai , Silvana Trimi , Jia-Wei Tang

This paper develops a comprehensive multi-criteria decision-making (MCDM) framework to evaluate the integration of non-fungible tokens (NFTs) into e-commerce platforms. Grounded in Saaty’s Benefits, Opportunities, Costs, and Risks (BOCR) model, the framework systematically captures both favorable and unfavorable decision dimensions. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method models causal relationships among the four BOCR dimensions and sixteen evaluation criteria, while the DEMATEL-based Analytic Network Process (DANP) derives their relative weights. Empirical data were obtained through an expert-validated questionnaire completed by 365 e-commerce professionals in Taiwan. DEMATEL results reveal that Cost, Risk, and Opportunity function as causal drivers, whereas Benefit operates as a reactive outcome. However, DANP analysis shows that Benefit holds the highest global weight among the four dimensions, underscoring its centrality in platform-level decision priorities. At the criteria level, Electronic Word of Mouth, System Quality, Data Protection, and Profit-Sharing Mechanisms emerge as most influential. By formalizing the evaluation of interdependent criteria across strategic dimensions, this dual-method framework advances NFT adoption research and offers a replicable, BOCR-based model anchored in platform decision-making. Theoretically, this study contributes by extending BOCR-based evaluation into the emerging context of NFT-enabled commerce and by demonstrating how DEMATEL and DANP can jointly capture causal influence and interdependence among platform-level decision criteria. Practically, the findings provide e-commerce operators with an evidence-based tool for prioritizing system reliability, data protection, incentive mechanisms, and electronic word-of-mouth strategies when designing NFT-augmented services. The proposed framework is also adaptable to broader Web3 innovations, offering firms a structured approach to assessing and comparing token-based business models under conditions of uncertainty.

本文开发了一个全面的多标准决策（MCDM）框架来评估不可替代代币（nft）与电子商务平台的整合。该框架以Saaty的利益、机会、成本和风险（BOCR）模型为基础，系统地捕获了有利和不利的决策维度。决策试验与评价实验室（DEMATEL）方法建立了四个BOCR维度和16个评价标准之间的因果关系模型，而基于DEMATEL的分析网络过程（DANP）则推导了它们的相对权重。本研究以365位台湾电商专业人士为对象，以专家验证问卷的方式取得实证资料。DEMATEL结果显示，成本、风险和机会是因果驱动因素，而收益是反应性结果。然而，DANP分析显示，在四个维度中，Benefit拥有最高的全局权重，强调了它在平台级决策优先级中的中心地位。在标准层面，电子口碑、系统质量、数据保护和利润分享机制是最具影响力的。通过形式化跨战略维度的相互依赖标准评估，这种双方法框架推进了NFT采用研究，并提供了一个可复制的、基于bocr的模型，该模型锚定在平台决策中。从理论上讲，本研究的贡献在于将基于bocr的评估扩展到新兴的nft支持的商业环境中，并展示了DEMATEL和DANP如何共同捕捉平台级决策标准之间的因果影响和相互依赖性。实际上，研究结果为电子商务运营商提供了一个基于证据的工具，用于在设计nft增强服务时优先考虑系统可靠性、数据保护、激励机制和电子口碑策略。提议的框架也适用于更广泛的Web3创新，为公司提供了一种结构化的方法来评估和比较不确定条件下基于代币的商业模式。

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引用次数: 0

One Size Does Not Fit All: Evaluating the Impact of Bank-FinTech Collaborations on Service Digitalization 一种模式不适合所有：评估银行-金融科技合作对服务数字化的影响

IF 6.3 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.elerap.2026.101582

Ting Cao , Murat Kristal , Xiaowen Huang

Digitalization is reshaping retail banking, yet its benefits are not uniform. Banks face the challenges of digitalizing existing services while creating new ones, often in collaboration with FinTech partners. Using survey data from Canadian retail banks and credit unions, this study empirically examines how digitalization efforts, operational agility, and FinTech collaboration jointly influence service performance in the digital age. Results show that digitalizing existing services enhance both standardized and customized services, whereas digitalizing new services benefit only the standardized ones. Moreover, results show that operational agility alone does not deliver value unless combined with FinTech collaboration. This synergy between internal agility and external collaboration is most effective in incremental digitalization of existing services, but not in those more radical development of novel ones. Theoretically, this study extends dynamic capabilities and combinative capabilities perspectives to the context of service digitalization by identifying boundary conditions of digitalization and highlighting the interplay among internal and external capabilities. Practically, this study cautions against the one-size-fits-all digitalization strategies, urging banks to tailor approaches to service types and collaboration choices in their digitalization journey.

数字化正在重塑零售银行业，但其带来的好处并不统一。银行面临着将现有服务数字化的挑战，同时创造新的服务，通常是与金融科技合作伙伴合作。本研究利用来自加拿大零售银行和信用合作社的调查数据，实证研究了数字化努力、运营敏捷性和金融科技协作如何共同影响数字时代的服务绩效。结果表明，现有业务数字化对标准化业务和定制业务都有促进作用，而新业务数字化只对标准化业务有促进作用。此外，结果表明，除非与金融科技合作相结合，否则单靠运营敏捷性无法带来价值。这种内部敏捷性和外部协作之间的协同作用在现有服务的增量数字化中最为有效，但在那些更激进的新服务开发中则不然。从理论上讲，本研究通过识别数字化的边界条件，突出内部和外部能力之间的相互作用，将动态能力和组合能力的视角扩展到服务数字化的背景下。实际上，本研究对一刀切的数字化战略提出了警告，敦促银行在数字化之旅中根据服务类型和合作选择量身定制方法。

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引用次数: 0

ROQuA: A RAG-based opinion question-answering framework for e-commerce reviews ROQuA：一个基于rag的电子商务评论意见问答框架

IF 6.3 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2026-03-01 Epub Date: 2026-01-19 DOI: 10.1016/j.elerap.2026.101576

Shengkai Zhou , Jiangtao Qiu , Ling Lin , Siyu Wang , Yun Xu

With the rapid development of large language models, retrieval-augmented generation (RAG) has become one of the most representative techniques to enhance the ability of LLMs on generating text contents based on retrieved information. However, applying RAG to opinion question–answering (QA) poses two key challenges: (1) retrieving reviews that are relevant to the question and (2) generating opinion summaries without hallucination. In this study, we propose ROQuA, an RAG-based framework designed to address these challenges in opinion QA. The framework incorporates several techniques, including review enrichment, question rewriting, and a three-stage prompting strategy, to improve the performance of ROQuA. We conduct experiments on three datasets—JD, Douyin, and Yelp—containing 182 k, 250 k, and 6 k reviews, respectively. Results demonstrate that ROQuA outperforms state-of-the-art models in opinion QA tasks. In particular, ROQuA achieves a 0.6-point improvement over the second-best model on LSBE, a metric introduced in this study for evaluating opinion QA. In addition, we provide an in-depth analysis of hallucination in RAG-based opinion QA and show that careful review selection and prompt engineering substantially reduce hallucinated content.

随着大型语言模型的快速发展，检索增强生成（retrieval-augmented generation， RAG）技术已成为增强法学硕士基于检索信息生成文本内容能力的最具代表性的技术之一。然而，将RAG应用于意见问答（QA）面临两个关键挑战：(1)检索与问题相关的评论；(2)无幻觉地生成意见摘要。在本研究中，我们提出了ROQuA，一个基于rag的框架，旨在解决意见QA中的这些挑战。该框架结合了多种技术，包括复习充实、问题重写和三阶段提示策略，以提高ROQuA的性能。我们在三个数据集上进行了实验——京东、抖音和yelp，分别包含182万条、250万条和6万条评论。结果表明，ROQuA在意见QA任务中优于最先进的模型。特别是，ROQuA在LSBE上比第二好的模型提高了0.6点，LSBE是本研究中用于评估意见QA的度量。此外，我们对基于rag的意见QA中的幻觉进行了深入分析，并表明仔细的审查选择和及时的工程设计大大减少了幻觉内容。

{"title":"ROQuA: A RAG-based opinion question-answering framework for e-commerce reviews","authors":"Shengkai Zhou , Jiangtao Qiu , Ling Lin , Siyu Wang , Yun Xu","doi":"10.1016/j.elerap.2026.101576","DOIUrl":"10.1016/j.elerap.2026.101576","url":null,"abstract":"<div><div>With the rapid development of large language models, retrieval-augmented generation (RAG) has become one of the most representative techniques to enhance the ability of LLMs on generating text contents based on retrieved information. However, applying RAG to opinion question–answering (QA) poses two key challenges: (1) retrieving reviews that are relevant to the question and (2) generating opinion summaries without hallucination. In this study, we propose ROQuA, an RAG-based framework designed to address these challenges in opinion QA. The framework incorporates several techniques, including review enrichment, question rewriting, and a three-stage prompting strategy, to improve the performance of ROQuA. We conduct experiments on three datasets—JD, Douyin, and Yelp—containing 182 k, 250 k, and 6 k reviews, respectively. Results demonstrate that ROQuA outperforms state-of-the-art models in opinion QA tasks. In particular, ROQuA achieves a 0.6-point improvement over the second-best model on LSBE, a metric introduced in this study for evaluating opinion QA. In addition, we provide an in-depth analysis of hallucination in RAG-based opinion QA and show that careful review selection and prompt engineering substantially reduce hallucinated content.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"76 ","pages":"Article 101576"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coping with e-commerce filter bubbles: proactive versus reactive strategies 应对电子商务过滤泡沫：主动与被动策略

IF 6.3 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2026-03-01 Epub Date: 2026-02-08 DOI: 10.1016/j.elerap.2026.101584

Lei Hou , Yichen Huang , Xue Pan

Personalized recommendations on e-commerce platforms enhance user experience and drive sales but may also create filter bubbles, limiting consumer exposure to diverse products and potentially leading to poor purchase decisions. While previous studies have primarily focused on algorithmic solutions, they have overlooked the active role of consumers in coping with filter bubbles. Building upon Protection Motivation Theory, this study explores how consumers’ threat appraisal and coping appraisal jointly drive their motivation to adopt proactive and reactive coping strategies. Analysis of survey data (

N = 386

) reveals that higher perceived vulnerability and severity of filter bubbles increase both proactive and reactive coping motivations. The perceived benefits of personalized recommendations directly deter only reactive coping, while its impact on proactive coping is mediated by threat appraisal and coping cost. For coping appraisal, consumers are more motivated to adopt either proactive or reactive coping when they perceive higher efficacy or lower cost of the strategy. This study provides a novel perspective by integrating user-centric coping mechanisms into the discussion of filter bubbles, particularly highlighting the dual nature of personalized recommendations and how their benefits and threats interactively shape consumer behaviors.

电子商务平台上的个性化推荐增强了用户体验，推动了销售，但也可能产生过滤泡沫，限制了消费者接触不同产品的机会，并可能导致糟糕的购买决策。虽然以前的研究主要集中在算法解决方案上，但他们忽视了消费者在应对过滤气泡方面的积极作用。本研究以保护动机理论为基础，探讨消费者的威胁评价和应对评价如何共同驱动他们采取主动和被动应对策略的动机。调查数据分析（N=386）表明，更高的感知脆弱性和过滤气泡的严重性增加了主动和被动应对动机。个性化推荐的感知收益仅直接抑制被动应对，而其对主动应对的影响受威胁评价和应对成本的中介作用。在应对评价中，当消费者感知到策略的效能更高或成本更低时，他们更倾向于采取主动应对或被动应对。本研究通过将以用户为中心的应对机制整合到过滤气泡的讨论中，提供了一个新的视角，特别强调了个性化推荐的双重性质，以及它们的好处和威胁如何相互影响消费者行为。

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引用次数: 0

AI or manual customer service? Post-sale customer service strategy of EPSC considering consumers’ return behavior under different CRI modes 人工智能还是人工客服？考虑不同CRI模式下消费者退货行为的EPSC售后客服策略

IF 6.3 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2026-03-01 Epub Date: 2026-02-26 DOI: 10.1016/j.elerap.2026.101587

Yufei Wang , Fengmin Yao , FaXin Cheng , Yingluo Yan

Traditional customer service, once based on direct human interaction, is increasingly replaced by artificial intelligence (AI) interfaces, which are reshaping consumers’ post-sale experiences and merchants’ post-sale assurance mechanisms. This study examines how different service modes (manual vs. AI) interact with complimentary return-freight insurance (CRI) to determine post-sale outcomes within an e-commerce platform supply chain (EPSC). A Stackelberg game model is developed to analyze the strategic interactions between the platform and the merchant under four configurations (manual or AI customer service, with or without CRI), incorporating the emotional-void effect of AI customer service and the influence of manual compensation. The analytical results reveal that return convenience outweighs price sensitivity in stimulating demand, whereas excessive manual compensation reduces profits for all parties. Moreover, the merchant prefers manual customer service when CRI is available, but may switch to AI service once compensation becomes sufficiently high. Counterintuitively, the merchant is more likely to adopt CRI under the AI customer service mode. Unlike previous studies that exclusively focus on pre-sale AI applications, this research formalizes the post-sale emotional-void and compensation mechanisms, thereby extending technology-adoption theory and offering guidance for assurance policies in platform-governed supply chains.

传统的客户服务，曾经基于直接的人际互动，越来越多地被人工智能（AI）界面所取代，这正在重塑消费者的售后体验和商家的售后保障机制。本研究考察了不同的服务模式（人工与人工智能）如何与免费退货运费保险（CRI）相互作用，以确定电子商务平台供应链（EPSC）中的售后结果。建立Stackelberg博弈模型，分析平台与商家在人工客服、人工客服、有无CRI四种配置下的战略互动，同时考虑人工客服的情感空虚效应和人工补偿的影响。分析结果表明，在刺激需求方面，回报便利性优于价格敏感性，而过度的人工补偿会降低各方的利润。此外，当CRI可用时，商家更倾向于人工客服，但一旦补偿足够高，商家可能会转向人工智能服务。与直觉相反的是，在AI客服模式下，商家更有可能采用CRI。与以往专门关注售前AI应用的研究不同，本研究将售后情感缺失和补偿机制形式化，从而扩展了技术采用理论，并为平台治理供应链中的保证政策提供指导。

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引用次数: 0

Stance detection for customer advocacy identification in online customer engagement: A deep learning approach 在线客户参与中客户支持识别的立场检测：一种深度学习方法

IF 6.3 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.elerap.2026.101583

Bilal Abu-Salih , Ruba Abu Khurma , Ahmad K. Al Hwaitat , Malak Al-Hassan , Abeer F. Alkhwaldi , Muder Almiani

Customer advocacy is increasingly recognised as one of the most valuable outcomes of brand–consumer relationships. Unlike simple measures of satisfaction or sentiment, advocacy captures whether customers are willing to publicly endorse, recommend, or defend a brand. Yet measuring advocacy in online environments remains difficult: much of the existing work in sentiment analysis cannot distinguish between general positivity and brand-related support. In this paper, we propose a stance detection approach that is designed specifically to capture advocacy in brand–customer exchanges on social media. To model these interactions, we introduce a hybrid architecture that combines ELECTRA embeddings, a hierarchical attention bidirectional GRU to capture brand reply discourse, and capsule networks to preserve fine-grained stance cues. Our empirical evaluation shows that the proposed model achieves an overall Accuracy of 81.2%, Macro-F1 of 74.8%, and Cohen’s κ of 0.71, outperforming strong baselines such as BERT-base (Accuracy 74.6%, Macro-F1 67.5%) and ELECTRA-base (Accuracy 77.3%, Macro − F1 70.1%). The most notable gains are observed in the Antagonist class, where our model improves F1 from 58.1% (BERT) to 66.1%, effectively reducing misclassification of critical replies. Furthermore, stance proportions are strongly correlated with external measures of advocacy (Pearson’s r = 0.68 for Advocate share, r = -0.61 for Antagonist share), validating stance detection as a scalable proxy for monitoring advocacy. Our findings highlight how computational models of stance can advance both academic understanding and practical management of brand advocacy in Online Customer Engagement.

顾客支持越来越被认为是品牌与消费者关系中最有价值的成果之一。与简单的满意度或情感衡量不同，拥护度衡量的是顾客是否愿意公开支持、推荐或捍卫一个品牌。然而，衡量在线环境中的宣传仍然很困难：许多现有的情感分析工作无法区分一般的积极性和与品牌相关的支持。在本文中，我们提出了一种立场检测方法，专门用于在社交媒体上的品牌-客户交流中捕捉倡导。为了对这些互动进行建模，我们引入了一个混合架构，该架构结合了ELECTRA嵌入、一个分层注意力双向GRU来捕获品牌回复话语，以及胶囊网络来保存细粒度的立场线索。我们的实证评估表明，该模型的总体准确率为81.2%，Macro-F1为74.8%,Cohen’s κ为0.71，优于BERT-base（准确率为74.6%，Macro-F1为67.5%）和ELECTRA-base（准确率77.3%，Macro-F1为70.1%）等强基线。在拮抗类中观察到最显著的增益，我们的模型将F1从58.1% （BERT）提高到66.1%，有效地减少了关键回复的错误分类。此外，立场比例与倡导的外部测量密切相关（倡导者份额的皮尔逊r = 0.68，拮抗剂份额的r = -0.61），验证了立场检测是监测倡导的可扩展代理。我们的研究结果强调了立场的计算模型如何促进在线客户参与中品牌宣传的学术理解和实际管理。

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引用次数: 0

Enhancing consumer engagement in virtual shopping: The role of social presence in shaping switching intentions 增强消费者在虚拟购物中的参与：社会存在在塑造转换意图中的作用

IF 6.3 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2026-03-01 Epub Date: 2026-01-30 DOI: 10.1016/j.elerap.2026.101580

Xue Li , Lanhui Cai , Kum Fai Yuen

Virtual shopping has emerged as a promising avenue for enhancing consumers’ retail experience, yet the understanding of presence in consumer switching behavior toward this emerging shopping channel remains limited. Drawing on a conceptual framework grounded in social presence theory, this study identifies the multidimensional structure of social presence and examines how its components shape consumers’ switching intentions, while also accounting for heterogeneity in demographic characteristics and shopping preferences. The study employs a self-administered questionnaire to collect data from 621 residents in Singapore. A second-order structural model is used to examine the the relationships among social presence, hedonic value, utilitarian value, perceived control, and switching intention. Moreover, a multi-group analysis is conducted to explore nuanced variations across certain demographic segments and product categories. Findings underscore the importance of social presence, hedonic value, and utilitarian value in shaping consumers’ switching behavior in virtual shopping environments. Moreover, the findings indicate that gender, product type, and product function can significantly moderate the relationship between social presence and switching intention. This study enriches the concept of social presence and consumer behavioral intention in virtual shopping. Managerially, the research offers insights for enhancing virtual shopping experiences via prioritizing social interactions.

虚拟购物已成为增强消费者零售体验的一种有前景的途径，但对消费者转向这一新兴购物渠道行为的存在性理解仍然有限。基于社会在场理论的概念框架，本研究确定了社会在场的多维结构，并考察了其组成部分如何影响消费者的转换意图，同时也考虑了人口特征和购物偏好的异质性。这项研究采用了一份自我管理的问卷，收集了621名新加坡居民的数据。采用二阶结构模型考察了社会存在、享乐价值、功利价值、感知控制和转换意向之间的关系。此外，还进行了多组分析，以探索某些人口细分和产品类别之间的细微差异。研究结果强调了社交存在、享乐价值和功利价值在塑造消费者在虚拟购物环境中的转换行为中的重要性。此外，性别、产品类型和产品功能对社交在场与转换意愿的关系有显著调节作用。本研究丰富了虚拟购物中社会在场与消费者行为意向的概念。在管理方面，该研究提供了通过优先考虑社交互动来增强虚拟购物体验的见解。

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引用次数: 0

When emotions become (in-) appropriate: How social-contextual factors moderate the effect of emotional expressions on investment decisions in reward-based crowdfunding 当情绪变得合适时：社会背景因素如何调节情绪表达对基于奖励的众筹投资决策的影响

IF 6.3 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2026-03-01 Epub Date: 2026-01-30 DOI: 10.1016/j.elerap.2026.101579

Maximilian Raab, Sebastian Schlauderer , Sven Overhage

Investment decisions in reward-based crowdfunding are significantly shaped by the emotional appeal of campaign presentations. However, it remains still largely unclear how intensively emotional expressions should be used to most effectively persuade investors to fund a project. Building upon Emotions as Social Information theory, which suggests that effective emotional expressions must match social expectations and be perceived as appropriate by recipients, we studied the effect of emotional expressions in the text, pictures, and pitch videos of 16,967 Kickstarter campaigns. We found that the impact of emotional expressions on the investment decision is curvilinear across all modalities, meaning that it deteriorates or even reverses with higher intensities. We also found that this effect is moderated by the nature of the relationship between the entrepreneur and investors and by the perceived economic risk under which the investment decision is made. These factors seem to influence investors’ tolerance of more intensive emotional expressions.

在基于奖励的众筹中，投资决策在很大程度上受到活动演示的情感吸引力的影响。然而，要想最有效地说服投资者为一个项目提供资金，应该使用多强烈的情感表达，这在很大程度上仍不清楚。基于情感作为社会信息理论（游戏邦注：该理论认为有效的情感表达必须符合社会期望，并被接受者视为适当的），我们研究了16,967个Kickstarter活动的文本、图片和宣传视频中情感表达的效果。我们发现，情绪表达对投资决策的影响在所有模式下都是曲线的，这意味着它会随着强度的增加而恶化甚至逆转。我们还发现，这种影响受到企业家和投资者之间关系的性质以及投资决策所处的感知经济风险的调节。这些因素似乎影响了投资者对更强烈的情绪表达的容忍度。

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引用次数: 0