Electronic Commerce Research and Applications最新文献_第10页

Who should livestream first? Sequence of dual self-livestreaming rooms for manufacturers 谁应该先直播？制造商的双自直播室序列

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-04-01 DOI: 10.1016/j.elerap.2025.101498

Shoujie Cai , Sijie Li , Yiding Liu , Xiaohua Han

Livestreaming e-commerce has emerged as a highly effective online shopping format, capturing significant attention from manufacturers and retailers. A novel variant, self-livestreaming, is gaining traction. When manufacturers conduct multiple self-livestreaming events across different platforms, each livestreaming room or streamer resonates differently with consumers. In this context, two distinct consumer segments emerge: loyal consumers and regular consumers. This study examines the dual self-livestreaming strategy adopted by manufacturers, incorporating factors including room attractiveness and consumer types to determine the optimal pricing and sequencing for three distinct livestreaming strategies: S (simultaneous livestreaming in both rooms), L (the low-attractiveness room livestreams first), and H (the high-attractiveness room livestreams first). The results reveal that a lower proportion of loyal consumers or higher room attractiveness leads to greater profits for manufacturers. Moreover, the choice of livestreaming strategy for manufacturers varies based on room attractiveness and the proportions of the two consumer types. In the extended model, we analyze the impact of operational costs on the decision to use one or two rooms, particularly when the low-attractiveness room has no loyal consumers. Specifically, we explore how room attractiveness and the proportion of regular consumers influence room adoption decisions. These insights not only provide practical operational guidance but also enrich the existing literature on self-livestreaming operations.

直播电子商务已经成为一种高效的在线购物形式，引起了制造商和零售商的极大关注。一种新的变体——自直播——正在获得关注。当制造商在不同平台上举办多个自直播活动时，每个直播室或主播与消费者的共鸣都是不同的。在这种情况下，出现了两个不同的消费者群体：忠诚消费者和普通消费者。本研究考察了制造商采用的双重自直播策略，结合房间吸引力和消费者类型等因素，确定了三种不同的直播策略的最优定价和顺序：S（两个房间同时直播）、L（低吸引力房间首先直播）和H（高吸引力房间首先直播）。结果表明，忠诚消费者比例越低或房间吸引力越高，制造商的利润越高。此外，根据房间吸引力和两种消费者类型的比例，制造商对直播策略的选择也有所不同。在扩展模型中，我们分析了运营成本对使用一个或两个房间的决定的影响，特别是当低吸引力的房间没有忠实的消费者时。具体而言，我们探讨了房间吸引力和普通消费者的比例如何影响房间采用决策。这些见解不仅提供了实用的操作指导，而且丰富了现有的自直播运营文献。

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引用次数: 0

You are worth my tipping: Why do people voluntarily pay for User-Generated-Content on social media platforms? 你值得我给小费：为什么人们会自愿为社交媒体平台上的用户生成内容付费？

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-03-30 DOI: 10.1016/j.elerap.2025.101501

Yuejun Wang , Ding Wu , Xiangbin Yan

Social media platforms have begun to widely adopt the Pay-What-You-Want (PWYW) pricing model to sell User-Generated-Content (UGC). However, it is still under-explored why social media users voluntarily pay for UGC even if they can easily free-ride under PWYW conditions. In this paper, we theoretically derive and examine a model to understand users’ PWYW behaviors for UGC on social media. Drawing on social exchange theory, we treat perceived worth as the core antecedent and analyze the benefits and costs associated with users’ PWYW behaviors. In addition, we also propose that users’ PWYW experience and social endorsement are important contextual factors and examine their roles in shaping users’ PWYW decisions. To test the research model, we conducted an online survey study, and the results revealed two major findings. First, social media users mainly value the reciprocity for product and pleasure brought by PWYW behaviors but are also concerned about the perceived opportunity cost and inconvenience of e-payment process, based on which they form perceived worth that further determines their PWYW frequency. Second, social media users’ PWYW experience and social endorsement also influence their PWYW frequency, and the effects are partially and fully mediated by perceived worth, respectively. Our research reveals the crucial factors that motivate social media users’ PWYW engagement in UGC consumption and lays the foundation for future theoretical research and practical work.

社交媒体平台已开始广泛采用 "按需付费"（PWYW）的定价模式来销售用户生成内容（UGC）。然而，对于社交媒体用户为什么会自愿为 UGC 付费（即使在 PWYW 条件下他们可以轻松免费搭车），我们的研究还不够深入。在本文中，我们从理论上推导并研究了一个模型，以理解用户在社交媒体上为 UGC 付费的行为。借鉴社会交换理论，我们将感知价值作为核心前因，并分析了与用户 "PWYW "行为相关的收益和成本。此外，我们还提出用户的惠益行为体验和社会认可是重要的情境因素，并研究了它们在影响用户惠益行为决策中的作用。为了检验研究模型，我们进行了一项在线调查研究，结果显示了两大发现。首先，社交媒体用户主要看重 "想买就买 "行为带来的产品互惠和愉悦，但同时也关注电子支付过程中的感知机会成本和不便，在此基础上形成的感知价值进一步决定了他们的 "想买就买 "频率。其次，社交媒体用户的惠益行为体验和社会认可也会影响他们的惠益行为频率，而这两种效应分别部分和完全受到感知价值的中介作用。我们的研究揭示了促使社交媒体用户参与 UGC 消费的关键因素，为今后的理论研究和实践工作奠定了基础。

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引用次数: 0

Contrastive learning with adversarial masking for sequential recommendation 序列推荐的对抗掩蔽对比学习

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-03-24 DOI: 10.1016/j.elerap.2025.101493

Rongzheng Xiang , Jiajin Huang , Jian Yang

Sequential recommendation is of paramount importance for predicting user preferences based on their historical interactions. Recent studies have leveraged contrastive learning as an auxiliary task to enhance sequence representations, with the goal of improving recommendation accuracy. However, an important challenge arises: random item masking, a key component of contrastive learning, while promoting robust representations through intricate semantic inference, may inadvertently distort the original sequence semantics to some extent. In contrast, methods that prioritize the preservation of sequence semantics tend to neglect the essential masking mechanism for robust representation learning. To address this issue, we propose a model called Contrastive Learning with Adversarial Masking (CLAM) for sequential recommendation. CLAM consists of three core components: an inference module, an occlusion module, and a multi-task learning paradigm. During training, the occlusion module is optimized to perturb the inference module in both recommendation generation and contrastive learning tasks by adaptively generating item embedding masks. This adversarial training framework enables CLAM to balance sequential pattern preservation with the acquisition of robust representations in the inference module for recommendation tasks. Our extensive experiments on four benchmark datasets demonstrate the effectiveness of CLAM. It achieves significant improvements in sequential recommendation accuracy and robustness against noisy interactions.

顺序推荐对于根据用户的历史交互预测用户偏好至关重要。最近的研究利用对比学习作为辅助任务来增强序列表示，目的是提高推荐的准确性。然而，一个重要的挑战出现了：随机项掩蔽，对比学习的一个关键组成部分，虽然通过复杂的语义推理促进鲁棒表示，但可能在某种程度上无意中扭曲了原始序列语义。相比之下，优先考虑序列语义保存的方法往往忽略了鲁棒表示学习的基本屏蔽机制。为了解决这个问题，我们提出了一个序列推荐的对比学习与对抗掩蔽（CLAM）模型。CLAM由三个核心组件组成：推理模块、遮挡模块和多任务学习范式。在训练过程中，对遮挡模块进行优化，通过自适应地生成项目嵌入掩码，在推荐生成和对比学习任务中干扰推理模块。这种对抗性训练框架使CLAM能够在推荐任务的推理模块中平衡顺序模式保存和鲁棒表示的获取。我们在四个基准数据集上的大量实验证明了CLAM的有效性。它显著提高了序列推荐的准确性和抗噪声交互的鲁棒性。

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引用次数: 0

Online reviews generated by generative artificial intelligence versus human: A study of perceived differences and user adoption behavior 生成式人工智能与人类生成的在线评论：感知差异和用户采用行为的研究

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-03-19 DOI: 10.1016/j.elerap.2025.101497

Xusen Cheng, Ang Zeng, Bo Yang, Yu Liu, Xiaoping Zhang

Companies in various industries are attempting to integrate Generative Artificial Intelligence (GAI) into their existing businesses. In the e-commerce domain, GAI has shown tremendous potential in generating online reviews. However, existing literature has paid less attention to how consumers respond to GAI-generated reviews versus human-generated reviews. Moreover, little research has explored whether and why consumers are willing to use GAI to generate online reviews. By conducting two experiments, this study investigates how consumers respond differently to GAI-generated reviews versus human-generated reviews and identifies potential factors that influence consumers’ willingness to use GAI to generate reviews. Findings indicate that although there is no significant difference in consumers’ perceptions between human-generated and GAI-generated reviews in terms of review credibility, review richness, and review usefulness, only half of the participants are willing to use GAI to generate reviews. Further analysis results suggest that individuals who consider GAI unethical tend to avoid using GAI. Those with high personal innovativeness are more willing to use GAI to generate online reviews. Our findings deepen the understanding of consumer attitudes toward GAI-generated reviews and provide implications for the deployment of GAI in the online review system.

各行各业的公司都在尝试将生成式人工智能（GAI）整合到他们现有的业务中。在电子商务领域，GAI在产生在线评论方面显示出巨大的潜力。然而，现有文献很少关注消费者对人工智能生成的评论和人类生成的评论的反应。此外，很少有研究探讨消费者是否愿意以及为什么愿意使用GAI来生成在线评论。通过进行两个实验，本研究调查了消费者对人工生成评论和人工生成评论的不同反应，并确定了影响消费者使用人工生成评论意愿的潜在因素。研究结果表明，尽管消费者对人工生成的评论和人工智能生成的评论在评论可信度、评论丰富性和评论有用性方面的看法没有显著差异，但只有一半的参与者愿意使用人工智能生成评论。进一步的分析结果表明，认为GAI不道德的个体倾向于避免使用GAI。个人创新能力高的人更愿意使用GAI生成在线评论。我们的研究结果加深了对消费者对人工智能生成的评论的态度的理解，并为在在线评论系统中部署人工智能提供了启示。

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引用次数: 0

Expert or partner: The matching effect of AI chatbot roles in different service contexts 专家或合作伙伴：AI聊天机器人角色在不同服务环境中的匹配效果

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-03-17 DOI: 10.1016/j.elerap.2025.101496

Yimin Zhu, Jiaming Liang, Yujie Zhao

Anthropomorphizing AI chatbots has become a widely adopted strategy to enhance customer-chatbot interactions. However, prior research has largely overlooked the role of social anthropomorphism, particularly how assigning different social roles to AI chatbots influences customer acceptance. To address this gap, this research investigates the impact of specific social roles across various service contexts on customer acceptance and the mechanisms underlying this effect. Through four experimental studies conducted in both field and laboratory settings, the findings consistently reveal a significant matching effect between AI chatbot roles and service contexts on customer acceptance, as well as the mediating roles of perceived competence and perceived warmth. Specifically, in utilitarian-dominant services, customers preferred expert (vs. partner) chatbots because they were perceived as more competent. Conversely, in hedonic-dominant services, customers favored partner (vs. expert) chatbots because they were perceived as warmer. These findings contribute to the understanding of customer acceptance of AI chatbots by highlighting the influence of various AI roles in different service contexts, and offer practical implications for companies to enhance the effectiveness of AI chatbots through role-matching strategies.

拟人化人工智能聊天机器人已经成为一种广泛采用的增强客户与聊天机器人互动的策略。然而，之前的研究在很大程度上忽视了社会拟人化的作用，特别是为人工智能聊天机器人分配不同的社会角色如何影响客户接受度。为了解决这一差距，本研究调查了不同服务背景下特定社会角色对客户接受度的影响以及这种影响的机制。通过在现场和实验室环境中进行的四项实验研究，研究结果一致揭示了人工智能聊天机器人角色和服务环境对客户接受程度的显著匹配效应，以及感知能力和感知温暖的中介作用。具体来说，在功利主义主导的服务中，客户更喜欢专家（而不是合作伙伴）聊天机器人，因为他们认为专家更有能力。相反，在以享乐为主导的服务中，客户更喜欢合作伙伴（而不是专家）聊天机器人，因为他们觉得他们更热情。这些发现有助于理解客户对人工智能聊天机器人的接受程度，强调了不同人工智能角色在不同服务环境中的影响，并为企业通过角色匹配策略提高人工智能聊天机器人的有效性提供了实际意义。

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引用次数: 0

Impact of viewer-streamer-content congruence on users’ behavioral intention in virtual streaming: The moderating effect of role-playing 观众-流媒体-内容一致性对虚拟流媒体用户行为意向的影响：角色扮演的调节作用

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-03-01 DOI: 10.1016/j.elerap.2025.101492

Yuangao Chen, Luonan Li, Wangyue Zhou

Virtual streaming, a novel and distinctive form of live streaming, has recently attracted considerable scholarly attention. However, few studies have focused on the elements that influence user behavioral intentions in virtual streaming. Based on consistency theory and dramaturgical theory, this study explores the impact of three dimensions of consistency, namely, streamer’s persona-live content congruence (PC), viewer’s interest-live content congruence (IC), and viewer’s value-streamer’s value congruence (VE), on immersion, attitude, and user behavioral intentions, as well as the moderating effect of role-playing ability. The research model is built combining literature analysis and semi-structured interviews, while empirical research is conducted based on the survey data of virtual streaming users. The results indicate that IC and VE exert a positive effect on users’ immersion, which in turn positively affects their attitude and behavioral intentions. Furthermore, the role-playing ability of virtual streamers positively moderates the relationship between IC and immersion, whereas it negatively moderates the relationship between PC and immersion. This study provides theoretical insights on virtual streaming and contributes managerial implications for practitioners.

虚拟流媒体是一种新颖而独特的直播形式，近年来引起了学术界的广泛关注。然而，很少有研究关注影响虚拟流媒体用户行为意图的因素。基于一致性理论和戏剧理论，本研究探讨了一致性的三个维度，即主播的角色-直播内容一致性（PC）、观众的兴趣-直播内容一致性（IC）和观众的价值-主播的价值一致性（VE）对沉浸感、态度和用户行为意图的影响，以及角色扮演能力的调节作用。研究模型采用文献分析和半结构化访谈相结合的方法建立，实证研究基于虚拟流媒体用户的调查数据。结果表明，IC和VE对用户沉浸感有正向影响，沉浸感又对用户态度和行为意向有正向影响。此外，虚拟主播的角色扮演能力正向调节IC与沉浸感的关系，而负向调节PC与沉浸感的关系。本研究提供了虚拟流媒体的理论见解，并对从业者提供管理启示。

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引用次数: 0

Sharing economy and quality competition among traditional service providers 共享经济与传统服务提供商的质量竞争

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-03-01 DOI: 10.1016/j.elerap.2025.101490

Tiziana D’Alfonso , Esther Gal-Or , Paolo Roma

We investigate the impact of the sharing economy on the quality of service offered by traditional businesses in the hospitality industry, on their profitability, and on societal welfare. We conduct the investigation in a market consisting of two different quality class hotels (high and low) prior to the entry of a peer-to-peer lodging platform and a population of consumers having different income levels. We find that for relatively poor economies, the sharing economy leads to higher prices, quality, and profits for both low and high class hotels. In contrast, the sharing economy may be detrimental to both hotels for relatively rich economies. In other cases, the sharing economy may introduce different effects on the behavior and fortunes of different classes of incumbent lodging suppliers. For instance, price and quality of low class accommodations may decline, whereas, interestingly, the price of high class accommodations may rise upon the emergence of a sharing platform, in spite of a decrease in quality. Moreover, while the sharing economy unambiguously increases aggregate consumer welfare, there are instances when consumers choosing high class accommodations are worse off after the entry of the sharing platform. Finally, we find that the total societal welfare does not always increase.

我们研究了共享经济对酒店业传统企业提供的服务质量、盈利能力和社会福利的影响。我们在一个p2p住宿平台进入之前，在一个由两种不同质量等级的酒店（高、低）和不同收入水平的消费者组成的市场中进行调查。我们发现，在相对贫穷的经济体中，共享经济为中低档酒店和高档酒店带来了更高的价格、质量和利润。相比之下，对于相对富裕的经济体来说，共享经济可能对两家酒店都是有害的。在其他情况下，共享经济可能会对不同类别的现有住宿供应商的行为和财富产生不同的影响。例如，低级住宿的价格和质量可能会下降，而有趣的是，高级住宿的价格可能会随着共享平台的出现而上涨，尽管质量有所下降。此外，虽然共享经济无疑增加了消费者的总福利，但也有一些情况是，在共享平台进入后，消费者选择高档住宿的情况更糟。最后，我们发现社会总福利并不总是增加的。

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引用次数: 0

Apologizing with a smile or crying face? Exploring the impact of emoji types on customer forgiveness within chatbots service recovery 笑着道歉还是哭着道歉？在聊天机器人服务恢复中探索表情符号类型对客户宽恕的影响

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-02-21 DOI: 10.1016/j.elerap.2025.101488

Chenze Xie, Junhong Zhu, Yuguang Xie, Changyong Liang

While advancements in AI have facilitated the uptake of chatbots across a range of sectors, incidents of service failures have been documented in numerous instances involving chatbot users. In this context, it is of paramount importance for chatbots to adopt appropriate service recovery strategies in order to mitigate and minimise the negative impact of chatbots failures. This research proposes that the use of emojis by chatbots when apologising represents an effective strategy for the recovery of customers following the occurrence of online service failures. The results of three scenario-based experiments indicated that the use of negative emojis by chatbots was more likely to result in customer forgiveness than the use of positive emojis, provided that the severity of the service failure was low. Moreover, the utilisation of negative emojis by chatbots fosters customer forgiveness by enhancing perceived empathy, whereas the deployment of positive emojis has the opposite impact by increasing perceived ambiguity. These findings provide crucial guidance for online retailers in the design of chatbot customer service strategies, emphasizing the pivotal role of subtle emoji differences in attaining customer forgiveness.

虽然人工智能的进步促进了聊天机器人在一系列领域的应用，但在涉及聊天机器人用户的许多实例中，都记录了服务故障事件。在这种情况下，对于聊天机器人来说，采用适当的服务恢复策略以减轻和最小化聊天机器人故障的负面影响至关重要。这项研究表明，聊天机器人在道歉时使用表情符号是一种有效的策略，可以在在线服务出现故障后恢复客户。三个基于场景的实验结果表明，在服务失败的严重程度较低的情况下，聊天机器人使用消极表情符号比使用积极表情符号更有可能获得客户的原谅。此外，聊天机器人使用消极表情符号会增强感知到的同理心，从而促进客户的宽恕，而使用积极表情符号会增加感知到的模糊性，从而产生相反的影响。这些发现为在线零售商设计聊天机器人客户服务策略提供了重要指导，强调了表情符号的微妙差异在获得客户原谅方面的关键作用。

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引用次数: 0

Comprehensive examination of the bright and dark sides of generative AI services: A mixed-methods approach 生成式人工智能服务的光明和黑暗的全面检查：混合方法的方法

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-02-21 DOI: 10.1016/j.elerap.2025.101491

Sang-Hyeak Yoon , Sung-Byung Yang , So-Hyun Lee

Recent advancements in artificial intelligence (AI), particularly in generative AI (GAI), have significantly influenced society, prompting extensive discussions about their societal impact. While previous research has acknowledged both the benefits and challenges of AI, the rapid development of GAI has often proceeded without sufficient focus on actionable strategies to address potential risks and unintended consequences. Understanding both the positive and negative aspects of GAI is essential to ensure that technological progress is balanced and responsibly managed to mitigate potential risks and societal harm. This study identifies the positive and negative aspects of GAI from both public and expert viewpoints by applying a valence framework. Using a mixed-methods approach that integrates joint sentiment topic (JST) modeling with the combined use of ChatGPT and expert interviews, we investigated the key positive and negative factors associated with GAI. By integrating the insights gained from these different perspectives, the study proposes strategies for the effective and responsible use of GAI. The study contributes to the existing body of knowledge on GAI by offering a comprehensive understanding of its implications and providing guidance for its ethical and appropriate applications.

人工智能（AI）的最新进展，特别是生成式人工智能（GAI），对社会产生了重大影响，引发了关于其社会影响的广泛讨论。虽然之前的研究已经承认了人工智能的好处和挑战，但人工智能的快速发展往往没有充分关注可操作的策略，以解决潜在的风险和意想不到的后果。了解GAI的积极和消极方面对于确保技术进步的平衡和负责任地管理以减轻潜在风险和社会危害至关重要。本研究通过应用效价框架，从公众和专家的角度确定GAI的积极和消极方面。采用一种混合方法，将联合情感主题（JST）建模与ChatGPT和专家访谈相结合，我们调查了与GAI相关的关键积极因素和消极因素。通过整合从这些不同角度获得的见解，本研究提出了有效和负责任地使用GAI的策略。该研究对GAI的现有知识体系做出了贡献，提供了对其含义的全面理解，并为其伦理和适当的应用提供了指导。

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引用次数: 0

Trust as the catalyst: Transforming perceived to created value in blockchain traceability 作为催化剂的信任：将感知到的价值转化为区块链可追溯性中的创造价值

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-02-20 DOI: 10.1016/j.elerap.2025.101487

Liwei Pan , Xianpei Hong

Configuring and maintaining blockchain traceability systems incurs significant costs. Thus, understanding how firms can derive value from consumers through blockchain traceability is essential. We present a model that demonstrates how consumer-perceived technology value transforms into created value for firms, with trust acting as a catalyst. Our evaluation of the perceived value of blockchain traceability focuses on the sacrifices and benefits recognized by consumers. Analysis of 501 survey responses reveals that perceived price and the risk of data falsification are sacrifices that negatively impact perceived technology value. In contrast, the perceived quality of agri-food serves as a perceived benefit that enhances this perception. Furthermore, perceived value influences consumers’ intentions to repurchase and recommend, technology trust and brand trust are key enablers, highlighting a trust transfer effect from technology to brand. This study contributes to understanding technology adoption and customer relationship management in agricultural enterprises. By emphasizing trust as the catalyst, it provides valuable insights into leveraging blockchain traceability to create sustainable value for businesses.

配置和维护区块链跟踪系统会产生巨大的成本。因此，了解公司如何通过区块链可追溯性从消费者那里获得价值是至关重要的。我们提出了一个模型，展示了消费者感知的技术价值如何转化为企业创造的价值，信任作为催化剂。我们对区块链可追溯性感知价值的评估侧重于消费者认可的牺牲和利益。对501份调查回复的分析显示，感知价格和数据伪造风险是对感知技术价值产生负面影响的牺牲。相比之下，农业食品的感知质量作为一种感知利益，增强了这种感知。此外，感知价值影响消费者的再购买和推荐意愿，技术信任和品牌信任是关键的促成因素，突出了信任从技术到品牌的转移效应。本研究有助于理解农业企业的技术采用与客户关系管理。通过强调信任是催化剂，它为利用区块链可追溯性为企业创造可持续价值提供了有价值的见解。

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引用次数: 0