Pub Date : 2025-04-11DOI: 10.1016/j.elerap.2025.101502
Huiran Li , Qiucheng Li , Baozhu Feng
In search engine advertising (SEA) market, where competition among retailers is intense and multifaceted, channel coordination between retailers and manufacturers emerges as a critical factor, which significantly influences the effectiveness of advertising strategies. This research attempts to provide managerial guidelines for cooperative advertising in the SEA context by modeling two cooperative advertising decision scenarios. Scenario I defines a simple cooperative channel consisting of one manufacturer and one retailer. In Scenario II, we consider a more general setting where there is an independent retailer who competes with the Manufacturer-Retailer alliance in Scenario I. We propose a novel cooperative advertising optimization model, wherein a manufacturer can advertise product directly through SEA campaigns and indirectly by subsidizing its retailer. To highlight the distinctive features of SEA, our model incorporates dynamic quality scores and focuses on a finite time horizon. In each scenario, we provide a feasible equilibrium solution of optimal policies for all members. Subsequently, we conduct numerical experiments to perform sensitivity analysis for both the quality score and gross margin. Additionally, we explore the impact of the initial market share of the competing retailer in Scenario II. Finally, we investigate how retail competition affects the cooperative alliance’s optimal strategy and channel performance. Our identified properties derived from the equilibrium and numerical analyses offer crucial insights for participants engaged in cooperative advertising within the SEA market.
{"title":"Dynamic cooperative strategies in search engine advertising market: With and without retail competition","authors":"Huiran Li , Qiucheng Li , Baozhu Feng","doi":"10.1016/j.elerap.2025.101502","DOIUrl":"10.1016/j.elerap.2025.101502","url":null,"abstract":"<div><div>In search engine advertising (SEA) market, where competition among retailers is intense and multifaceted, channel coordination between retailers and manufacturers emerges as a critical factor, which significantly influences the effectiveness of advertising strategies. This research attempts to provide managerial guidelines for cooperative advertising in the SEA context by modeling two cooperative advertising decision scenarios. Scenario I defines a simple cooperative channel consisting of one manufacturer and one retailer. In Scenario II, we consider a more general setting where there is an independent retailer who competes with the Manufacturer-Retailer alliance in Scenario I. We propose a novel cooperative advertising optimization model, wherein a manufacturer can advertise product directly through SEA campaigns and indirectly by subsidizing its retailer. To highlight the distinctive features of SEA, our model incorporates dynamic quality scores and focuses on a finite time horizon. In each scenario, we provide a feasible equilibrium solution of optimal policies for all members. Subsequently, we conduct numerical experiments to perform sensitivity analysis for both the quality score and gross margin. Additionally, we explore the impact of the initial market share of the competing retailer in Scenario II. Finally, we investigate how retail competition affects the cooperative alliance’s optimal strategy and channel performance. Our identified properties derived from the equilibrium and numerical analyses offer crucial insights for participants engaged in cooperative advertising within the SEA market.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"71 ","pages":"Article 101502"},"PeriodicalIF":5.9,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847318","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}
Pub Date : 2025-04-05DOI: 10.1016/j.elerap.2025.101499
Wang Zhao , Dongxiao Gu , Rui Mao , Xiaoyu Wang , Xuejie Yang , Kaixuan Zhu , Hao Hu , Haimiao Mo , Erik Cambria
The COVID-19 pandemic has markedly exacerbated the complexities surrounding the diagnosis and prognosis of diverse severe pneumonia types, posing extraordinary challenges to healthcare systems worldwide. While previous AI-based approaches primarily targeted COVID-19 severe pneumonia and sought to enhance machine learning accuracy, they often neglected critical aspects such as distinguishing diagnostic and prognostic features among COVID-19 infectious, non-COVID infectious, and non-infectious severe pneumonia, as well as the explainability and fairness of user-centric AI assist decisions. This study es the need for robust, fair, and reliable diagnosis and prognosis of severe pneumonia within the context of the COVID-19 pandemic. This paper introduces a user-centric framework that first employs a GaussianCopula-based data augmentation method to enhance fairness by addressing small imbalanced sample sets. Following this, the framework introduces an explainable AI system designed to classify three types of severe pneumonia using demographic and physiological indicators, offering transparent decision-making processes and an understandable analysis of prognosis risk factors. Our fair system utilizes transparent models exclusively, which enables healthcare practitioners to access intelligent and reliable medical services such as pre-diagnosis and prognosis analysis (the likelihood of death) of severe pneumonia. The results show the data augmentation method efficiently reduces data bias and enhances fairness, reaching 70.70% distribution similarity. Our transparent model-based severe pneumonia classification module achieves 98.88% F1-scores on a real-world dataset. The transparent mechanism reveals that the four most significant features for classifying severe pneumonia types are ‘Interleukin_6’, ‘Albumin’, ‘D_Dimer’, and ‘CD4_absolute_count’. Meanwhile, the explainable statistical analysis identifies critical mortality risk factors for each pneumonia category: ‘Blood platelet’ and ‘Creatinine’ for COVID-19 severe pneumonia, ‘Hemameba’, ‘Interleukin-6’, and ‘Uric Acid’ for non-COVID-19 infectious severe pneumonia, and ‘Hemameba’, ‘BNP’, ‘Cholesterol’, and ‘PT’ for non-infectious severe pneumonia. Our study highlights the potential of transparent machine learning algorithms for accurate diagnosis and Cox proportional regression for transparent risk trend prediction. These analytical tools and medical results can facilitate early and appropriate management of pneumonia patients for doctors, potentially revolutionizing diagnostic processes and patient care strategies to improve clinical outcomes.
{"title":"Explainable, robust and fair user-centric AI system for the diagnosis and prognosis of severe pneumonia","authors":"Wang Zhao , Dongxiao Gu , Rui Mao , Xiaoyu Wang , Xuejie Yang , Kaixuan Zhu , Hao Hu , Haimiao Mo , Erik Cambria","doi":"10.1016/j.elerap.2025.101499","DOIUrl":"10.1016/j.elerap.2025.101499","url":null,"abstract":"<div><div>The COVID-19 pandemic has markedly exacerbated the complexities surrounding the diagnosis and prognosis of diverse severe pneumonia types, posing extraordinary challenges to healthcare systems worldwide. While previous AI-based approaches primarily targeted COVID-19 severe pneumonia and sought to enhance machine learning accuracy, they often neglected critical aspects such as distinguishing diagnostic and prognostic features among COVID-19 infectious, non-COVID infectious, and non-infectious severe pneumonia, as well as the explainability and fairness of user-centric AI assist decisions. This study es the need for robust, fair, and reliable diagnosis and prognosis of severe pneumonia within the context of the COVID-19 pandemic. This paper introduces a user-centric framework that first employs a GaussianCopula-based data augmentation method to enhance fairness by addressing small imbalanced sample sets. Following this, the framework introduces an explainable AI system designed to classify three types of severe pneumonia using demographic and physiological indicators, offering transparent decision-making processes and an understandable analysis of prognosis risk factors. Our fair system utilizes transparent models exclusively, which enables healthcare practitioners to access intelligent and reliable medical services such as pre-diagnosis and prognosis analysis (the likelihood of death) of severe pneumonia. The results show the data augmentation method efficiently reduces data bias and enhances fairness, reaching 70.70% distribution similarity. Our transparent model-based severe pneumonia classification module achieves 98.88% F1-scores on a real-world dataset. The transparent mechanism reveals that the four most significant features for classifying severe pneumonia types are ‘Interleukin_6’, ‘Albumin’, ‘D_Dimer’, and ‘CD4_absolute_count’. Meanwhile, the explainable statistical analysis identifies critical mortality risk factors for each pneumonia category: ‘Blood platelet’ and ‘Creatinine’ for COVID-19 severe pneumonia, ‘Hemameba’, ‘Interleukin-6’, and ‘Uric Acid’ for non-COVID-19 infectious severe pneumonia, and ‘Hemameba’, ‘BNP’, ‘Cholesterol’, and ‘PT’ for non-infectious severe pneumonia. Our study highlights the potential of transparent machine learning algorithms for accurate diagnosis and Cox proportional regression for transparent risk trend prediction. These analytical tools and medical results can facilitate early and appropriate management of pneumonia patients for doctors, potentially revolutionizing diagnostic processes and patient care strategies to improve clinical outcomes.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"71 ","pages":"Article 101499"},"PeriodicalIF":5.9,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816268","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}
Pub Date : 2025-04-01DOI: 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.
{"title":"Who should livestream first? Sequence of dual self-livestreaming rooms for manufacturers","authors":"Shoujie Cai , Sijie Li , Yiding Liu , Xiaohua Han","doi":"10.1016/j.elerap.2025.101498","DOIUrl":"10.1016/j.elerap.2025.101498","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"71 ","pages":"Article 101498"},"PeriodicalIF":5.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760012","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}
Pub Date : 2025-03-30DOI: 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。
{"title":"You are worth my tipping: Why do people voluntarily pay for User-Generated-Content on social media platforms?","authors":"Yuejun Wang , Ding Wu , Xiangbin Yan","doi":"10.1016/j.elerap.2025.101501","DOIUrl":"10.1016/j.elerap.2025.101501","url":null,"abstract":"<div><div>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. <em>First</em>, 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. <em>Second,</em> 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.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"71 ","pages":"Article 101501"},"PeriodicalIF":5.9,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786301","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}
Pub Date : 2025-03-24DOI: 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.
{"title":"Contrastive learning with adversarial masking for sequential recommendation","authors":"Rongzheng Xiang , Jiajin Huang , Jian Yang","doi":"10.1016/j.elerap.2025.101493","DOIUrl":"10.1016/j.elerap.2025.101493","url":null,"abstract":"<div><div>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 <strong>C</strong>ontrastive <strong>L</strong>earning with <strong>A</strong>dversarial <strong>M</strong>asking (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.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"71 ","pages":"Article 101493"},"PeriodicalIF":5.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715546","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}
Pub Date : 2025-03-19DOI: 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.
{"title":"Online reviews generated by generative artificial intelligence versus human: A study of perceived differences and user adoption behavior","authors":"Xusen Cheng, Ang Zeng, Bo Yang, Yu Liu, Xiaoping Zhang","doi":"10.1016/j.elerap.2025.101497","DOIUrl":"10.1016/j.elerap.2025.101497","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"71 ","pages":"Article 101497"},"PeriodicalIF":5.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684431","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}
Pub Date : 2025-03-17DOI: 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.
{"title":"Expert or partner: The matching effect of AI chatbot roles in different service contexts","authors":"Yimin Zhu, Jiaming Liang, Yujie Zhao","doi":"10.1016/j.elerap.2025.101496","DOIUrl":"10.1016/j.elerap.2025.101496","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"71 ","pages":"Article 101496"},"PeriodicalIF":5.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684432","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}
Pub Date : 2025-03-01DOI: 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.
{"title":"Impact of viewer-streamer-content congruence on users’ behavioral intention in virtual streaming: The moderating effect of role-playing","authors":"Yuangao Chen, Luonan Li, Wangyue Zhou","doi":"10.1016/j.elerap.2025.101492","DOIUrl":"10.1016/j.elerap.2025.101492","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"70 ","pages":"Article 101492"},"PeriodicalIF":5.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577779","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}
Pub Date : 2025-03-01DOI: 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.
{"title":"Sharing economy and quality competition among traditional service providers","authors":"Tiziana D’Alfonso , Esther Gal-Or , Paolo Roma","doi":"10.1016/j.elerap.2025.101490","DOIUrl":"10.1016/j.elerap.2025.101490","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"70 ","pages":"Article 101490"},"PeriodicalIF":5.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Apologizing with a smile or crying face? Exploring the impact of emoji types on customer forgiveness within chatbots service recovery","authors":"Chenze Xie, Junhong Zhu, Yuguang Xie, Changyong Liang","doi":"10.1016/j.elerap.2025.101488","DOIUrl":"10.1016/j.elerap.2025.101488","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"70 ","pages":"Article 101488"},"PeriodicalIF":5.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474064","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}
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