Electronic Commerce Research and Applications最新文献_第6页

Transparent prediction of financial analyst recommendation quality using generalized additive model 基于广义加性模型的金融分析师推荐质量透明预测

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-07-09 DOI: 10.1016/j.elerap.2025.101524

Shuai Jiang , Xiaoxin Pan , Yanhong Guo , Chuanren Liu , Hui Xiong

Financial analysts play a key role in financial decision-making, but the reliability of their recommendations can fluctuate dramatically depending on changes in analyst competence and contextual dynamics, posing a significant challenge to investors seeking guidance. This study unveils a novel explainable deep learning architecture, termed Quality Attribution Network (QuANet), which innovates by integrating a Generalized Additive Model framework, amplifying prediction accuracy and facilitating an in-depth understanding of how distinct variables contribute to the quality of analyst recommendations. Further, QuANet incorporates an attention mechanism to discern salient features, thereby ensuring that critical analyst, rating, and stock information receives appropriate weight. Empirical validation on extensive datasets corroborates QuANet’s superiority over existing benchmarks across diverse quality prediction metrics. Enhancing predictive capability translates into tangible gains for investment strategies, underscoring the model’s practical applicability. Additionally, QuANet’s attribution capabilities enable nuanced differentiation between analysts, pinpointing those endowed with genuine expertise within the financial advisory landscape. In sum, this research advances the analytical toolkit for assessing analyst recommendations by introducing a model that harmonizes predictive prowess with interpretative clarity. Investors stand to benefit from the transparent insights generated, facilitating the extraction of valuable knowledge from analyst recommendations to inform judicious investment decisions.

金融分析师在财务决策中发挥着关键作用，但他们建议的可靠性可能会因分析师能力和背景动态的变化而大幅波动，这对寻求指导的投资者构成了重大挑战。本研究揭示了一种新的可解释的深度学习架构，称为质量归因网络（QuANet），它通过集成广义可加模型框架进行创新，提高了预测准确性，并促进了对不同变量如何影响分析师建议质量的深入理解。此外，QuANet结合了一个注意机制来识别显著特征，从而确保重要的分析师、评级和股票信息得到适当的权重。对大量数据集的实证验证证实了QuANet在不同质量预测指标上优于现有基准的优势。增强预测能力转化为投资策略的有形收益，强调了模型的实际适用性。此外，QuANet的归因功能可以对分析师进行细微的区分，准确地指出那些在金融咨询领域具有真正专业知识的分析师。总而言之，本研究通过引入一个协调预测能力和解释清晰度的模型，推进了评估分析师建议的分析工具包。投资者将从产生的透明见解中受益，促进从分析师建议中提取有价值的知识，从而为明智的投资决策提供信息。

{"title":"Transparent prediction of financial analyst recommendation quality using generalized additive model","authors":"Shuai Jiang , Xiaoxin Pan , Yanhong Guo , Chuanren Liu , Hui Xiong","doi":"10.1016/j.elerap.2025.101524","DOIUrl":"10.1016/j.elerap.2025.101524","url":null,"abstract":"<div><div>Financial analysts play a key role in financial decision-making, but the reliability of their recommendations can fluctuate dramatically depending on changes in analyst competence and contextual dynamics, posing a significant challenge to investors seeking guidance. This study unveils a novel explainable deep learning architecture, termed Quality Attribution Network (QuANet), which innovates by integrating a Generalized Additive Model framework, amplifying prediction accuracy and facilitating an in-depth understanding of how distinct variables contribute to the quality of analyst recommendations. Further, QuANet incorporates an attention mechanism to discern salient features, thereby ensuring that critical analyst, rating, and stock information receives appropriate weight. Empirical validation on extensive datasets corroborates QuANet’s superiority over existing benchmarks across diverse quality prediction metrics. Enhancing predictive capability translates into tangible gains for investment strategies, underscoring the model’s practical applicability. Additionally, QuANet’s attribution capabilities enable nuanced differentiation between analysts, pinpointing those endowed with genuine expertise within the financial advisory landscape. In sum, this research advances the analytical toolkit for assessing analyst recommendations by introducing a model that harmonizes predictive prowess with interpretative clarity. Investors stand to benefit from the transparent insights generated, facilitating the extraction of valuable knowledge from analyst recommendations to inform judicious investment decisions.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"73 ","pages":"Article 101524"},"PeriodicalIF":5.9,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595731","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

Utilising podcast digital content marketing to influence consumer purchasing behaviour on e-commerce platform: A study on social presence and media richness theories 利用播客数字内容营销影响电子商务平台消费者购买行为：社交存在与媒体丰富度理论研究

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-07-04 DOI: 10.1016/j.elerap.2025.101529

Pei-Hsuan Tsai , Ming-Chia Hsieh , Jia-Wei Tang

Many e-commerce platforms have begun adapting their content management strategies to accommodate the growing popularity of podcasts and attract consumers’ attention. Despite its role in facilitating e-commerce brands to develop their identity and establish field expertise and attracting new consumers from diverse backgrounds, podcasting remains an underused area in the saturated landscape of digital marketing. Hence, this study aims to integrate social presence theory (SPT) and media richness theory (MRT) via grey multiple attribute decision-making (G-MADM) methods to examine the impact of incorporating podcast digital content marketing (DCM) into e-commerce platforms on consumer purchasing behaviour. The current work selected e-commerce platforms in Taiwan, with 656 and 657 valid responses gathered for Study 1 and Study 2, respectively. In examining podcast DCM strategies, causal relationships were determined using grey decision-making and trial evaluation laboratory (G-DEMATEL), while the influence weights of evaluation factors were established using grey DEMATEL-based on analytic network process (G-DANP). The study concludes with the research findings and recommendations. Based on Study 1 (SPT perspective), sense of identity (I) and perceived presence (P) and emotional presence (E) were the key factors requiring improvement. The variety of language (L), immediate feedback (S), and personalisation (H) were the key factors requiring improvement in Study 2 (MRT perspective). Apart from contributing to refining and enriching DCM and sales strategy planning for e-commerce platforms, these findings can facilitate e-commerce platforms to incorporate podcast experiences, improve their DCM strategies, and increase consumer repurchase intent.

许多电子商务平台已经开始调整其内容管理策略，以适应播客的日益流行，并吸引消费者的注意力。尽管播客在促进电子商务品牌发展自己的身份和建立专业知识以及吸引来自不同背景的新消费者方面发挥了作用，但在饱和的数字营销领域，播客仍然是一个未被充分利用的领域。因此，本研究旨在通过灰色多属性决策（G-MADM）方法，整合社会存在理论（SPT）和媒体丰富度理论（MRT），研究将播客数字内容营销（DCM）纳入电子商务平台对消费者购买行为的影响。本研究选取台湾的电子商务平台，研究一和研究二分别收集了656和657个有效回复。在研究播客DCM策略时，使用灰色决策和试验评价实验室（G-DEMATEL）确定因果关系，使用基于分析网络过程的灰色dematel （G-DANP）建立评价因素的影响权重。本研究总结了研究结果和建议。基于研究1 （SPT视角），认同感(I)、感知在场感(P)和情绪在场感(E)是需要改善的关键因素。语言的多样性(L)，即时反馈(S)和个性化(H)是研究2中需要改进的关键因素（MRT观点）。这些发现除了有助于完善和丰富电子商务平台的DCM和销售策略规划外，还可以促进电子商务平台融入播客体验，改进DCM策略，提高消费者的再购买意愿。

{"title":"Utilising podcast digital content marketing to influence consumer purchasing behaviour on e-commerce platform: A study on social presence and media richness theories","authors":"Pei-Hsuan Tsai , Ming-Chia Hsieh , Jia-Wei Tang","doi":"10.1016/j.elerap.2025.101529","DOIUrl":"10.1016/j.elerap.2025.101529","url":null,"abstract":"<div><div>Many e-commerce platforms have begun adapting their content management strategies to accommodate the growing popularity of podcasts and attract consumers’ attention. Despite its role in facilitating e-commerce brands to develop their identity and establish field expertise and attracting new consumers from diverse backgrounds, podcasting remains an underused area in the saturated landscape of digital marketing. Hence, this study aims to integrate social presence theory (SPT) and media richness theory (MRT) via grey multiple attribute decision-making (G-MADM) methods to examine the impact of incorporating podcast digital content marketing (DCM) into e-commerce platforms on consumer purchasing behaviour. The current work selected e-commerce platforms in Taiwan, with 656 and 657 valid responses gathered for Study 1 and Study 2, respectively. In examining podcast DCM strategies, causal relationships were determined using grey decision-making and trial evaluation laboratory (G-DEMATEL), while the influence weights of evaluation factors were established using grey DEMATEL-based on analytic network process (G-DANP). The study concludes with the research findings and recommendations. Based on Study 1 (SPT perspective), sense of identity (I) and perceived presence (P) and emotional presence (E) were the key factors requiring improvement. The variety of language (L), immediate feedback (S), and personalisation (H) were the key factors requiring improvement in Study 2 (MRT perspective). Apart from contributing to refining and enriching DCM and sales strategy planning for e-commerce platforms, these findings can facilitate e-commerce platforms to incorporate podcast experiences, improve their DCM strategies, and increase consumer repurchase intent.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"73 ","pages":"Article 101529"},"PeriodicalIF":5.9,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570534","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

How signal intensity of altruistic and strategic motivation affects crowdfunding performance? Matching among funders and platform types 利他动机和战略动机的信号强度如何影响众筹绩效？资助者和平台类型的匹配

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-07-02 DOI: 10.1016/j.elerap.2025.101528

Hongke Zhao , Yaxian Wang , Hao Wei

Crowdfunding has gained significant scholarly attention, yet existing research primarily focuses on single-platform studies, limiting the generalizability of findings. We argue that investment motivations vary across platform types, influencing the effectiveness of altruistic and quality signals on crowdfunding performance. Using 114,095 projects from Indiegogo (reward-based) and 1,199,908 loan projects from Kiva (lending-based), we first conduct separate analyses within each platform to examine the impact of these signals. We then compare the marginal effects across platforms to assess how platform structure influences backer decision-making. Our results show that quality signals consistently enhance crowdfunding success but have a stronger influence in reward-based platforms, while the effect of altruistic signals varies, enhancing performance in lending-based platforms but diminishing it in reward-based platforms. Moreover, we identify a reciprocal inhibitory interaction between quality and altruistic signals, suggesting that emphasizing one type of signal may weaken the effectiveness of the other by diverting backers’ attention and influencing how they evaluate the project. These findings underscore the importance of platform differentiation in crowdfunding research and highlight the need to move beyond single-platform studies. Our study offers practical insights for crowdfunding initiators on how to tailor their campaigns based on platform-specific investor behavior.

众筹已经获得了重要的学术关注，但现有的研究主要集中在单一平台的研究上，限制了研究结果的普遍性。我们认为，投资动机因平台类型而异，影响利他主义和质量信号对众筹绩效的有效性。我们使用Indiegogo（基于奖励）上的114095个项目和Kiva（基于贷款）上的1199908个贷款项目，首先在每个平台上进行单独分析，以检查这些信号的影响。然后，我们比较了不同平台的边际效应，以评估平台结构如何影响支持者的决策。我们的研究结果表明，质量信号持续提高众筹成功率，但在奖励型平台上具有更强的影响，而利他信号的影响则有所不同，在借贷型平台上提高了众筹成功率，但在奖励型平台上降低了众筹成功率。此外，我们确定了质量信号和利他信号之间的互惠抑制相互作用，表明强调一种信号可能会通过转移支持者的注意力并影响他们如何评估项目而削弱另一种信号的有效性。这些发现强调了平台差异化在众筹研究中的重要性，并强调了超越单一平台研究的必要性。我们的研究为众筹发起者提供了如何根据特定平台的投资者行为定制他们的活动的实用见解。

{"title":"How signal intensity of altruistic and strategic motivation affects crowdfunding performance? Matching among funders and platform types","authors":"Hongke Zhao , Yaxian Wang , Hao Wei","doi":"10.1016/j.elerap.2025.101528","DOIUrl":"10.1016/j.elerap.2025.101528","url":null,"abstract":"<div><div>Crowdfunding has gained significant scholarly attention, yet existing research primarily focuses on single-platform studies, limiting the generalizability of findings. We argue that investment motivations vary across platform types, influencing the effectiveness of altruistic and quality signals on crowdfunding performance. Using 114,095 projects from Indiegogo (reward-based) and 1,199,908 loan projects from Kiva (lending-based), we first conduct separate analyses within each platform to examine the impact of these signals. We then compare the marginal effects across platforms to assess how platform structure influences backer decision-making. Our results show that quality signals consistently enhance crowdfunding success but have a stronger influence in reward-based platforms, while the effect of altruistic signals varies, enhancing performance in lending-based platforms but diminishing it in reward-based platforms. Moreover, we identify a reciprocal inhibitory interaction between quality and altruistic signals, suggesting that emphasizing one type of signal may weaken the effectiveness of the other by diverting backers’ attention and influencing how they evaluate the project. These findings underscore the importance of platform differentiation in crowdfunding research and highlight the need to move beyond single-platform studies. Our study offers practical insights for crowdfunding initiators on how to tailor their campaigns based on platform-specific investor behavior.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"73 ","pages":"Article 101528"},"PeriodicalIF":5.9,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557391","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

From knowledge tracing to preference tracing: Capturing dynamic user preferences for personalized recommendation 从知识跟踪到偏好跟踪：捕捉动态用户偏好以进行个性化推荐

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-06-30 DOI: 10.1016/j.elerap.2025.101527

Jungmin Hwang , Hakyeon Lee

Individual preferences change over time. While sequential recommenders have gained attention for accommodating changing user preferences, they have struggled to identify users’ preferences at a granular, component-wise level. This paper introduces a novel approach called preference tracing, inspired by the concept of knowledge tracing, originally developed in the educational domain. Knowledge tracing dynamically estimates a student’s knowledge state through interactions with question–answer pairs and knowledge components, predicting the likelihood of correctly answering an exercise based on the estimated knowledge state. Similarly, preference tracing continuously estimates a user's preference state as they engage with content over time, predicting whether a user will enjoy a specific movie based on the estimated preference state. Our empirical evaluations demonstrate that Bayesian knowledge tracing (BKT)-based preference tracing not only delivers comparable predictive performance but also effectively captures users’ preference states at a component-wise level. Moreover, deep learning-based knowledge tracing (DLKT)-based preference tracing, which operates without predefined movie components, outperforms recent deep learning-based recommendation models, unveiling its potential to provide more accurate and nuanced recommendations.

个人偏好会随着时间而改变。虽然顺序推荐因适应不断变化的用户偏好而受到关注，但它们在细粒度、组件级别上难以识别用户偏好。本文介绍了一种新的方法，即偏好追踪，其灵感来自于最初在教育领域发展起来的知识追踪概念。知识跟踪通过与问答对和知识组件的交互，动态估计学生的知识状态，根据估计的知识状态预测正确回答练习的可能性。类似地，偏好跟踪在用户与内容互动的过程中持续估计用户的偏好状态，根据估计的偏好状态预测用户是否会喜欢特定的电影。我们的实证评估表明，基于贝叶斯知识跟踪（BKT）的偏好跟踪不仅提供了可比较的预测性能，而且在组件层面上有效地捕获了用户的偏好状态。此外，基于深度学习的基于知识跟踪（DLKT）的偏好跟踪在没有预定义电影组件的情况下运行，优于最近基于深度学习的推荐模型，揭示了其提供更准确和细致入微推荐的潜力。

{"title":"From knowledge tracing to preference tracing: Capturing dynamic user preferences for personalized recommendation","authors":"Jungmin Hwang , Hakyeon Lee","doi":"10.1016/j.elerap.2025.101527","DOIUrl":"10.1016/j.elerap.2025.101527","url":null,"abstract":"<div><div>Individual preferences change over time. While sequential recommenders have gained attention for accommodating changing user preferences, they have struggled to identify users’ preferences at a granular, component-wise level. This paper introduces a novel approach called preference tracing, inspired by the concept of knowledge tracing, originally developed in the educational domain. Knowledge tracing dynamically estimates a student’s knowledge state through interactions with question–answer pairs and knowledge components, predicting the likelihood of correctly answering an exercise based on the estimated knowledge state. Similarly, preference tracing continuously estimates a user's preference state as they engage with content over time, predicting whether a user will enjoy a specific movie based on the estimated preference state. Our empirical evaluations demonstrate that Bayesian knowledge tracing (BKT)-based preference tracing not only delivers comparable predictive performance but also effectively captures users’ preference states at a component-wise level. Moreover, deep learning-based knowledge tracing (DLKT)-based preference tracing, which operates without predefined movie components, outperforms recent deep learning-based recommendation models, unveiling its potential to provide more accurate and nuanced recommendations.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"73 ","pages":"Article 101527"},"PeriodicalIF":5.9,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606170","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

IntentRec: Incorporating latent user intent via contrastive alignment for sequential recommendation IntentRec：通过顺序推荐的对比校准来结合潜在的用户意图

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-06-24 DOI: 10.1016/j.elerap.2025.101522

Seonjin Hwang , Younghoon Lee

Predicting the next item a user will interact with is a core task in sequential recommendation (SR). Traditional approaches predominantly focus on modeling patterns in item purchase sequences, yet often fall short in uncovering the underlying motivations behind user behavior. To overcome this limitation, we introduce IntentRec, a novel SR framework designed to incorporate latent user intent signals extracted from user-written reviews. Unlike conventional models that treat item sequences in isolation, IntentRec bridges the semantic gap between review content and behavioral data by aligning their representations in a shared embedding space through contrastive learning. Review sequences chronologically ordered text reflecting users’ thoughts serve as a rich source of intent, which is fused into the item sequence representation during training. To ensure practicality in real-time recommendation scenarios, our method excludes review inputs at inference time, acknowledging that reviews naturally occur after item interactions. IntentRec employs BERT, a pre-trained language model, to extract nuanced user intent from textual reviews, and introduces a cross-attention-enhanced contrastive loss to tightly couple review-derived signals with item-based preferences. Extensive experiments conducted on four widely-used SR benchmarks demonstrate that IntentRec consistently outperforms eight state-of-the-art baselines. Further ablation studies confirm the crucial role of review-based user intent in improving sequential recommendation accuracy.

预测用户将与之交互的下一个项目是顺序推荐（SR）的核心任务。传统的方法主要关注于道具购买序列的建模模式，但往往无法揭示用户行为背后的潜在动机。为了克服这一限制，我们引入了IntentRec，这是一种新的SR框架，旨在整合从用户撰写的评论中提取的潜在用户意图信号。与传统的孤立处理项目序列的模型不同，IntentRec通过对比学习在共享嵌入空间中对齐它们的表示，弥合了评论内容和行为数据之间的语义差距。回顾序列按时间顺序排列的文本反映了用户的想法，作为丰富的意图来源，在训练过程中融合到项目序列表示中。为了确保实时推荐场景的实用性，我们的方法在推理时排除了评论输入，承认评论自然发生在项目交互之后。IntentRec使用BERT（一种预先训练的语言模型）从文本评论中提取细微的用户意图，并引入交叉注意增强的对比损失，将评论衍生的信号与基于项目的偏好紧密耦合。在四个广泛使用的SR基准上进行的大量实验表明，IntentRec始终优于八个最先进的基准。进一步的消融研究证实了基于评论的用户意图在提高顺序推荐准确性方面的关键作用。

{"title":"IntentRec: Incorporating latent user intent via contrastive alignment for sequential recommendation","authors":"Seonjin Hwang , Younghoon Lee","doi":"10.1016/j.elerap.2025.101522","DOIUrl":"10.1016/j.elerap.2025.101522","url":null,"abstract":"<div><div>Predicting the next item a user will interact with is a core task in sequential recommendation (SR). Traditional approaches predominantly focus on modeling patterns in item purchase sequences, yet often fall short in uncovering the underlying motivations behind user behavior. To overcome this limitation, we introduce IntentRec, a novel SR framework designed to incorporate latent user intent signals extracted from user-written reviews. Unlike conventional models that treat item sequences in isolation, IntentRec bridges the semantic gap between review content and behavioral data by aligning their representations in a shared embedding space through contrastive learning. Review sequences chronologically ordered text reflecting users’ thoughts serve as a rich source of intent, which is fused into the item sequence representation during training. To ensure practicality in real-time recommendation scenarios, our method excludes review inputs at inference time, acknowledging that reviews naturally occur after item interactions. IntentRec employs BERT, a pre-trained language model, to extract nuanced user intent from textual reviews, and introduces a cross-attention-enhanced contrastive loss to tightly couple review-derived signals with item-based preferences. Extensive experiments conducted on four widely-used SR benchmarks demonstrate that IntentRec consistently outperforms eight state-of-the-art baselines. Further ablation studies confirm the crucial role of review-based user intent in improving sequential recommendation accuracy.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"73 ","pages":"Article 101522"},"PeriodicalIF":5.9,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480940","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

“Domino effects on eWOM?” understanding consumers’ dynamic perceptions of online travel reviews and perceived travel risk: A three-stage longitudinal approach “对eom的多米诺效应？”了解消费者对在线旅游评论的动态看法和感知的旅游风险：一个三阶段纵向方法

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-06-21 DOI: 10.1016/j.elerap.2025.101526

Tao Sun , Junjiao Zhang , Han Zhou

Although the impact of the COVID-19 pandemic is gradually diminishing, its influence still persists through people’s experience of travel consumption, including travel risk perception and cautious information processing modes of online travel reviews (OTRs). Since the onset of COVID-19, literature has witnessed an upsurge in illuminating tourists’ intro-pandemic risk perceptions and information behaviors. However, from an evolutionary perspective, a whole spectrum to trace and compare the variations in tourist risk perception and OTR evaluation patterns over time remains unclear. Spanning three investigations pre-, during, and post-pandemic (in 2019, 2020, and 2023), results generally confirm that people’s perception of travel risk has undergone an inverted-U-shaped change, yet perceived equipment risk still maintains at a high level. Additionally, drawing upon the information adoption model (IAM), the results indicate that individuals increasingly consider the argument quality cues (informativeness, persuasiveness) and source credibility cues (expertise, trustworthiness, homophily) of online travel reviews as important over time. The dynamic relationships among different attributes of online travel reviews, perceived information usefulness, and perceived travel risk were also illuminated. Theoretically, findings of this study enriched our understanding of the dynamic role of IAM elements in predicting information usefulness and perceived travel risk in different phases of a public health crisis context. Practically, this study not only provides guidelines on post-pandemic risk management for tourism and hospitality managers, but also gives specific advice for travel websites to best optimize their marketing communication strategies through online reviews in alliance with different risk communication contexts.

尽管新冠肺炎疫情的影响正在逐渐减弱，但其影响仍然存在于人们的旅游消费体验中，包括旅行风险感知和在线旅游评论（OTRs）的谨慎信息处理模式。自新冠肺炎疫情发生以来，有关阐释游客疫情引入风险认知和信息行为的文献激增。然而，从进化的角度来看，尚不清楚如何追踪和比较游客风险感知和OTR评估模式随时间的变化。在2019年、2020年和2023年的三次调查中，结果普遍证实，人们对旅行风险的感知经历了倒u型变化，但对装备风险的感知仍保持在较高水平。此外，根据信息采纳模型（IAM），结果表明，随着时间的推移，人们越来越重视在线旅游评论的论点质量线索（信息量、说服力）和来源可信度线索（专业知识、可信度、同质性）。分析了在线旅游评论不同属性、感知信息有用性和感知旅游风险之间的动态关系。从理论上讲，本研究的发现丰富了我们对IAM要素在公共卫生危机背景下不同阶段预测信息有用性和感知旅行风险中的动态作用的理解。实际上，本研究不仅为旅游和酒店管理人员提供了大流行后风险管理指南，而且还为旅游网站提供了具体建议，以便通过在线评论与不同的风险沟通环境相结合，最佳地优化其营销传播策略。

{"title":"“Domino effects on eWOM?” understanding consumers’ dynamic perceptions of online travel reviews and perceived travel risk: A three-stage longitudinal approach","authors":"Tao Sun , Junjiao Zhang , Han Zhou","doi":"10.1016/j.elerap.2025.101526","DOIUrl":"10.1016/j.elerap.2025.101526","url":null,"abstract":"<div><div>Although the impact of the COVID-19 pandemic is gradually diminishing, its influence still persists through people’s experience of travel consumption, including travel risk perception and cautious information processing modes of online travel reviews (OTRs). Since the onset of COVID-19, literature has witnessed an upsurge in illuminating tourists’ intro-pandemic risk perceptions and information behaviors. However, from an evolutionary perspective, a whole spectrum to trace and compare the variations in tourist risk perception and OTR evaluation patterns over time remains unclear. Spanning three investigations pre-, during, and post-pandemic (in 2019, 2020, and 2023), results generally confirm that people’s perception of travel risk has undergone an inverted-U-shaped change, yet perceived equipment risk still maintains at a high level. Additionally, drawing upon the information adoption model (IAM), the results indicate that individuals increasingly consider the argument quality cues (informativeness, persuasiveness) and source credibility cues (expertise, trustworthiness, homophily) of online travel reviews as important over time. The dynamic relationships among different attributes of online travel reviews, perceived information usefulness, and perceived travel risk were also illuminated. Theoretically, findings of this study enriched our understanding of the dynamic role of IAM elements in predicting information usefulness and perceived travel risk in different phases of a public health crisis context. Practically, this study not only provides guidelines on post-pandemic risk management for tourism and hospitality managers, but also gives specific advice for travel websites to best optimize their marketing communication strategies through online reviews in alliance with different risk communication contexts.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"73 ","pages":"Article 101526"},"PeriodicalIF":5.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365374","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

Personalize it, no return: Nudging online consumers towards product personalization that makes the product non-returnable with herd instinct and regret nudges 个性化，无回报：通过从众本能和后悔推动，将在线消费者推向产品个性化，使产品不可退货

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-06-13 DOI: 10.1016/j.elerap.2025.101525

Changyuan Feng , Francisco J. Martínez-López , Yangchun Li , Jordi Campo-Fernandez

Massive ecommerce returns incur considerable return costs for online sellers, erode their competitiveness, burden their returns systems, and damage the natural environment. Reducing ecommerce returns can mitigate these negative consequences. Since most online sellers adopt a no-return policy for personalized products, inducing consumers to personalize more products should be an effective way for these sellers to reduce ecommerce returns. This article focuses on how online sellers use a herd instinct nudge and a regret nudge to induce consumers to use a product personalization service to reduce ecommerce returns. We also studied the effects of the nudges on several pivotal consumer perceptions and affects. A two-factor (a herd instinct nudge vs. no herd instinct nudge; a regret nudge vs. no regret nudge), between-subject experiment was conducted. This research revealed that both using a herd instinct nudge and using a regret nudge can lead to more consumer product personalization behaviors. Both nudges can make consumers perceive the service more valuable. Compared to a regret nudge, a herd instinct nudge should be a more superior method to induce consumer to use the service because it can increase consumer satisfaction with the seller but did not have a significant influence on consumer perceived threat to decision-making freedom. No interaction effect was found between the two nudges.

大量的电子商务退货给在线卖家带来了可观的退货成本，削弱了他们的竞争力，给他们的退货系统带来了负担，并破坏了自然环境。减少电子商务的回报可以减轻这些负面影响。由于大多数在线卖家对个性化产品采取不退货政策，诱导消费者个性化更多的产品应该是这些卖家减少电商退货的有效途径。这篇文章的重点是在线卖家如何使用群体本能和后悔推动来诱导消费者使用产品个性化服务来减少电子商务的回报。我们还研究了轻推对几个关键消费者认知和影响的影响。双重因素(群体本能推动vs.没有群体本能推动；进行了后悔轻推与不后悔轻推的受试者间实验。这项研究表明，使用群体本能推动和使用后悔推动都可以导致更多的消费者产品个性化行为。这两种推动都能让消费者觉得服务更有价值。与后悔推动相比，群体本能推动应该是一种更优越的诱导消费者使用服务的方法，因为它可以提高消费者对卖家的满意度，但对消费者感知到的对决策自由的威胁没有显著影响。两种推力之间没有相互作用。

{"title":"Personalize it, no return: Nudging online consumers towards product personalization that makes the product non-returnable with herd instinct and regret nudges","authors":"Changyuan Feng , Francisco J. Martínez-López , Yangchun Li , Jordi Campo-Fernandez","doi":"10.1016/j.elerap.2025.101525","DOIUrl":"10.1016/j.elerap.2025.101525","url":null,"abstract":"<div><div>Massive ecommerce returns incur considerable return costs for online sellers, erode their competitiveness, burden their returns systems, and damage the natural environment. Reducing ecommerce returns can mitigate these negative consequences. Since most online sellers adopt a no-return policy for personalized products, inducing consumers to personalize more products should be an effective way for these sellers to reduce ecommerce returns. This article focuses on how online sellers use a herd instinct nudge and a regret nudge to induce consumers to use a product personalization service to reduce ecommerce returns. We also studied the effects of the nudges on several pivotal consumer perceptions and affects. A two-factor (a herd instinct nudge vs. no herd instinct nudge; a regret nudge vs. no regret nudge), between-subject experiment was conducted. This research revealed that both using a herd instinct nudge and using a regret nudge can lead to more consumer product personalization behaviors. Both nudges can make consumers perceive the service more valuable. Compared to a regret nudge, a herd instinct nudge should be a more superior method to induce consumer to use the service because it can increase consumer satisfaction with the seller but did not have a significant influence on consumer perceived threat to decision-making freedom. No interaction effect was found between the two nudges.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"73 ","pages":"Article 101525"},"PeriodicalIF":5.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579681","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

The coherent two-phased process from sold online to redemption offline on an online daily-deal platform 在线团购平台从线上销售到线下赎回的连贯两阶段过程

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-06-13 DOI: 10.1016/j.elerap.2025.101523

Yingxin Song , Yezheng Liu , Xiayu Chen , Muhammet Deveci , Carol Xiaojuan Ou , Lingfei Li , Weizhong Wang

Daily-deal platforms closely cooperate with local retailers when issuing daily-deal coupons to profit from selling coupons online and redeeming them offline. However, most research on daily-deal business has only focused on online sales or the offline redemption process. We investigate the coherent two-phased process from selling coupons online to redeeming them offline, grounded in the lens of social judgment theory, to capture the full picture of the daily-deal business. By tracking the sales and redemption of 11,290 deals over a 13-month period on an online daily-deal platform and conducting various data analyses, we find that reputation and price curvilinearly affect the sold online of daily-deal coupons, which consequently positively affects coupon redemption offline. More specifically, the U test empirically indicates that the extreme point of the inverted U-shaped effect of reputation score is 86.0035 within the range [49.7353, 92.7551]. And the extreme point to price demonstrates a U-shaped effect is 399.6082 within the range [4.7060, 829.3651]. We further classify retailers’ daily deals into consumption on a group or individual level. Empirical data demonstrate that the inverted U-shaped effects of reputation and the U-shaped effects of price are weakened by group consumption. Furthermore, we investigate the moderating role of agglomeration on the relationship between daily-deal coupons sold online and redemption offline of daily-deal coupons. We also discussed the theoretical and practical implications.

团购平台在发行团购券时与当地零售商紧密合作，通过线上销售、线下兑换的方式获利。然而，大多数关于团购业务的研究只关注在线销售或线下兑换过程。我们以社会判断理论为基础，研究了从在线销售优惠券到线下兑换优惠券的连贯两阶段过程，以捕捉日常交易业务的全貌。通过对某线上团购平台13个月11290笔交易的销售和赎回情况进行跟踪，并进行各种数据分析，我们发现口碑和价格曲线对线上团购优惠券的销售有早期影响，进而对线下优惠券赎回有正向影响。更具体地说，U检验实证表明，声誉得分倒U形效应的极值点在[49.7353,92.7551]的范围内为86.0035。价格的极值点在[4.7060,829.3651]区间内为399.6082，呈现u型效应。我们进一步将零售商的日常交易分为群体消费和个人消费。实证数据表明，群体消费弱化了声誉和价格的倒u型效应。此外，我们还考察了集聚对团购券线上销售与团购券线下兑换关系的调节作用。我们还讨论了理论和实践意义。

{"title":"The coherent two-phased process from sold online to redemption offline on an online daily-deal platform","authors":"Yingxin Song , Yezheng Liu , Xiayu Chen , Muhammet Deveci , Carol Xiaojuan Ou , Lingfei Li , Weizhong Wang","doi":"10.1016/j.elerap.2025.101523","DOIUrl":"10.1016/j.elerap.2025.101523","url":null,"abstract":"<div><div>Daily-deal platforms closely cooperate with local retailers when issuing daily-deal coupons to profit from selling coupons online and redeeming them offline. However, most research on daily-deal business has only focused on online sales or the offline redemption process. We investigate the coherent two-phased process from selling coupons online to redeeming them offline, grounded in the lens of social judgment theory, to capture the full picture of the daily-deal business. By tracking the sales and redemption of 11,290 deals over a 13-month period on an online daily-deal platform and conducting various data analyses, we find that reputation and price curvilinearly affect the sold online of daily-deal coupons, which consequently positively affects coupon redemption offline. More specifically, the <em>U</em> test empirically indicates that the extreme point of the inverted U-shaped effect of reputation score is 86.0035 within the range [49.7353, 92.7551]. And the extreme point to price demonstrates a U-shaped effect is 399.6082 within the range [4.7060, 829.3651]. We further classify retailers’ daily deals into consumption on a group or individual level. Empirical data demonstrate that the inverted U-shaped effects of reputation and the U-shaped effects of price are weakened by group consumption. Furthermore, we investigate the moderating role of agglomeration on the relationship between daily-deal coupons sold online and redemption offline of daily-deal coupons. We also discussed the theoretical and practical implications.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"73 ","pages":"Article 101523"},"PeriodicalIF":5.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306573","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

The influence of virtual streamer on purchase intention: The moderated mediating effect of message strategy and live-streaming environment 虚拟主播对购买意愿的影响：消息策略和直播环境的调节中介作用

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-06-12 DOI: 10.1016/j.elerap.2025.101520

Xueying Wang, Yuexian Zhang

In the realm of live-streaming, virtual streamers represent a significant area of industry expansion. Despite the widespread adoption of both human-backed and AI-backed virtual streamers in marketing campaigns, the differential effects of these two types remain underexplored. Through three experimental studies, this research systematically examines how virtual streamer types (AI-backed vs. human-backed) influence purchase intention, while elucidating the underlying mechanisms and boundary conditions. The findings demonstrate three key insights: First, human-backed virtual streamers exert significantly stronger impacts on purchase intention compared to their AI counterparts, with perceived usefulness serving as the critical mediator. Second, a two-sided message strategy outperforms positive unilateral messaging in amplifying virtual streamers’ effectiveness via enhanced perceived usefulness. Third, the live-streaming environment moderates this mechanism differentially: human-backed streamers prove more effective in real-life environments, whereas AI-backed streamers show superior performance in virtual environments. Both effects operate through the pathway of perceived usefulness. This study advances theoretical understanding of virtual streamer efficacy while providing actionable guidelines for businesses to optimize streamer selection strategies.

在直播领域，虚拟流媒体代表了行业扩张的一个重要领域。尽管在营销活动中广泛采用了人工支持和人工智能支持的虚拟流媒体，但这两种类型的不同效果仍未得到充分探索。通过三项实验研究，本研究系统地考察了虚拟流媒体类型（人工智能支持与人类支持）如何影响购买意愿，同时阐明了潜在的机制和边界条件。研究结果显示了三个关键的见解：首先，与人工智能相比，人类支持的虚拟主播对购买意愿的影响要大得多，感知有用性是关键的中介。其次，双边消息策略优于积极的单边消息，通过增强感知有用性来放大虚拟流媒体的有效性。第三，直播环境对这种机制的调节是不同的：人类支持的流媒体在现实环境中更有效，而人工智能支持的流媒体在虚拟环境中表现出更好的性能。这两种效应都是通过感知有用性的途径起作用的。本研究推进了对虚拟拖缆效果的理论理解，同时为企业优化拖缆选择策略提供了可操作的指导方针。

{"title":"The influence of virtual streamer on purchase intention: The moderated mediating effect of message strategy and live-streaming environment","authors":"Xueying Wang, Yuexian Zhang","doi":"10.1016/j.elerap.2025.101520","DOIUrl":"10.1016/j.elerap.2025.101520","url":null,"abstract":"<div><div>In the realm of live-streaming, virtual streamers represent a significant area of industry expansion. Despite the widespread adoption of both human-backed and AI-backed virtual streamers in marketing campaigns, the differential effects of these two types remain underexplored. Through three experimental studies, this research systematically examines how virtual streamer types (AI-backed vs. human-backed) influence purchase intention, while elucidating the underlying mechanisms and boundary conditions. The findings demonstrate three key insights: First, human-backed virtual streamers exert significantly stronger impacts on purchase intention compared to their AI counterparts, with perceived usefulness serving as the critical mediator. Second, a two-sided message strategy outperforms positive unilateral messaging in amplifying virtual streamers’ effectiveness via enhanced perceived usefulness. Third, the live-streaming environment moderates this mechanism differentially: human-backed streamers prove more effective in real-life environments, whereas AI-backed streamers show superior performance in virtual environments. Both effects operate through the pathway of perceived usefulness. This study advances theoretical understanding of virtual streamer efficacy while providing actionable guidelines for businesses to optimize streamer selection strategies.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"73 ","pages":"Article 101520"},"PeriodicalIF":5.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306605","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

Large language model meets chaos: A new deep learning model for fake review detection 大语言模型遇上混沌：一种新的虚假评论检测深度学习模型

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-06-10 DOI: 10.1016/j.elerap.2025.101521

Yu Fan , Haizhou Fan , Fuqian Zhang , Zhenhua Wang

Detecting fake online reviews is crucial for the e-commerce ecosystem. However, existing studies often fail to mine the intrinsic attributes of reviews, which limits detection performance. In this paper, we introduce a novel fake review detection model, LLMChaos, which investigates reviews from the perspectives of large language models (LLMs) and chaos theory. Specifically, we first propose a method that blends energy selection with LLMs to generate review time series. Second, we construct a space mapping mechanism with multiple chaotic attributes, embracing the intrinsic attributes of reviews. Finally, we design a hierarchical learning network that trains in deep contrastive learning across LLM layers, chaotic attribute layers, and Transformer layers. Extensive experiments demonstrate that LLMChaos is robust and state-of-the-art. For instance, on the Hotel dataset, LLMChaos achieves 94.78% F1, outperforming recent models by 1.42%-19.78%; on the Amazon dataset, LLMChaos achieves 93.15% F1, surpassing recent models by 1.45%-18.39%. Moreover, we contribute novel discoveries, for example, chaotic behaviors of reviews generally exhibit bounded ranges: Lyapunov exponent (0–0.0125), Correlation dimension (0.25–0.5), Kolmogorov entropy (0.75–0.85), Fractal spectrum (0–1.5), and Recurrence rate (0.005–0.015); real and fake reviews display distinct chaotic distributions.

检测虚假在线评论对电子商务生态系统至关重要。然而，现有的研究往往不能挖掘评论的内在属性，这限制了检测的性能。本文介绍了一种新的假评论检测模型LLMChaos，该模型从大语言模型（llm）和混沌理论的角度来研究评论。具体而言，我们首先提出了一种将能量选择与llm相结合的方法来生成复习时间序列。其次，我们构建了包含评论内在属性的多混沌属性空间映射机制。最后，我们设计了一个分层学习网络，在LLM层、混沌属性层和Transformer层之间进行深度对比学习训练。大量的实验表明，LLMChaos是鲁棒性和最先进的。例如，在酒店数据集上，LLMChaos达到了94.78%的F1，比最近的模型高出1.42%-19.78%；在Amazon数据集上，LLMChaos达到了93.15%的F1，比最近的模型高出1.45%-18.39%。此外，我们还做出了一些新的发现，例如评论的混沌行为通常表现出有限的范围：Lyapunov指数（0-0.0125）、相关维数（0.25-0.5）、Kolmogorov熵（0.75-0.85）、分形谱（0-1.5）和复发率（0.005-0.015）；真实评论和虚假评论呈现出明显的混乱分布。

{"title":"Large language model meets chaos: A new deep learning model for fake review detection","authors":"Yu Fan , Haizhou Fan , Fuqian Zhang , Zhenhua Wang","doi":"10.1016/j.elerap.2025.101521","DOIUrl":"10.1016/j.elerap.2025.101521","url":null,"abstract":"<div><div>Detecting fake online reviews is crucial for the e-commerce ecosystem. However, existing studies often fail to mine the intrinsic attributes of reviews, which limits detection performance. In this paper, we introduce a novel fake review detection model, LLMChaos, which investigates reviews from the perspectives of large language models (LLMs) and chaos theory. Specifically, we first propose a method that blends energy selection with LLMs to generate review time series. Second, we construct a space mapping mechanism with multiple chaotic attributes, embracing the intrinsic attributes of reviews. Finally, we design a hierarchical learning network that trains in deep contrastive learning across LLM layers, chaotic attribute layers, and Transformer layers. Extensive experiments demonstrate that LLMChaos is robust and state-of-the-art. For instance, on the Hotel dataset, LLMChaos achieves 94.78% F1, outperforming recent models by 1.42%-19.78%; on the Amazon dataset, LLMChaos achieves 93.15% F1, surpassing recent models by 1.45%-18.39%. Moreover, we contribute novel discoveries, for example, chaotic behaviors of reviews generally exhibit bounded ranges: Lyapunov exponent (0–0.0125), Correlation dimension (0.25–0.5), Kolmogorov entropy (0.75–0.85), Fractal spectrum (0–1.5), and Recurrence rate (0.005–0.015); real and fake reviews display distinct chaotic distributions.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"73 ","pages":"Article 101521"},"PeriodicalIF":5.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270313","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