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}
Pub Date : 2025-02-21DOI: 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.
{"title":"Comprehensive examination of the bright and dark sides of generative AI services: A mixed-methods approach","authors":"Sang-Hyeak Yoon , Sung-Byung Yang , So-Hyun Lee","doi":"10.1016/j.elerap.2025.101491","DOIUrl":"10.1016/j.elerap.2025.101491","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"70 ","pages":"Article 101491"},"PeriodicalIF":5.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480518","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 : 2025-02-20DOI: 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.
{"title":"Trust as the catalyst: Transforming perceived to created value in blockchain traceability","authors":"Liwei Pan , Xianpei Hong","doi":"10.1016/j.elerap.2025.101487","DOIUrl":"10.1016/j.elerap.2025.101487","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"70 ","pages":"Article 101487"},"PeriodicalIF":5.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488580","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 : 2025-02-20DOI: 10.1016/j.elerap.2025.101489
Ying Xie , Saleh Md Arman , Chang Su
The success of the circular economy transition depends on the involvement of all stakeholders. However, research on consumer participation in the circular economy is limited. This study identifies the micro-level dynamics of the circular economy within the post-consumption network, mainly focusing on re-commerce operations. Through exploring re-commerce networks in 20 countries and a statistical analysis of factors influencing sales performances on Facebook, this research aims to identify the primary elements influencing re-commerce activities on Facebook and their role in promoting circular economy. This study uses grounded theory and a mixed-methods approach, combining literature reviews and interviews, to explore the impact of re-commerce on the circular economy. Based on the research findings, this research develops four propositions to promote future research on post-consumption networks in the circular economy context.
{"title":"Circular economy in post consumption network: The role of re-commerce groups in social media platforms","authors":"Ying Xie , Saleh Md Arman , Chang Su","doi":"10.1016/j.elerap.2025.101489","DOIUrl":"10.1016/j.elerap.2025.101489","url":null,"abstract":"<div><div>The success of the circular economy transition depends on the involvement of all stakeholders. However, research on consumer participation in the circular economy is limited. This study identifies the micro-level dynamics of the circular economy within the post-consumption network, mainly focusing on re-commerce operations. Through exploring re-commerce networks in 20 countries and a statistical analysis of factors influencing sales performances on Facebook, this research aims to identify the primary elements influencing re-commerce activities on Facebook and their role in promoting circular economy. This study uses grounded theory and a mixed-methods approach, combining literature reviews and interviews, to explore the impact of re-commerce on the circular economy. Based on the research findings, this research develops four propositions to promote future research on post-consumption networks in the circular economy context.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"70 ","pages":"Article 101489"},"PeriodicalIF":5.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465251","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 : 2025-02-05DOI: 10.1016/j.elerap.2025.101485
Jingrui Hou , Zhihang Tan , Shitou Zhang , Qibiao Hu , Ping Wang
The proliferation of fake reviews on the internet has had significant repercussions for both consumers and businesses. However, existing research predominantly employs a binary classification approach to ascertain review authenticity, often neglecting the rich multimodal context information and nuanced intentions embedded within them. To bridge this gap, our study introduces a novel task, Fake Review Intention Detection in Review Context (FRIDRC), which aims to detect fake review intentions by leveraging both textual and visual information, and constructs a dataset comprising both manually and AI-generated fake reviews. Additionally, we develop a predictive framework encompassing modules for multimodal representation and modality fusion. These modules, while independent, are synergistic and effectively tackle the challenge of discerning fake review intentions. Our framework demonstrates outstanding performance, achieving an average F1 score exceeding 0.97 and a Macro F1 score surpassing 0.96 in this task and outperforming advanced pre-trained models. This research not only presents an effective methodology for accurately identifying and addressing fake review intentions but also underscores the efficacy of leveraging multimodal review context information in fake review detection. The dataset and code implementation are publicly available for further research.
{"title":"Detecting fake review intentions in the review context: A multimodal deep learning approach","authors":"Jingrui Hou , Zhihang Tan , Shitou Zhang , Qibiao Hu , Ping Wang","doi":"10.1016/j.elerap.2025.101485","DOIUrl":"10.1016/j.elerap.2025.101485","url":null,"abstract":"<div><div>The proliferation of fake reviews on the internet has had significant repercussions for both consumers and businesses. However, existing research predominantly employs a binary classification approach to ascertain review authenticity, often neglecting the rich multimodal context information and nuanced intentions embedded within them. To bridge this gap, our study introduces a novel task, Fake Review Intention Detection in Review Context (FRIDRC), which aims to detect fake review intentions by leveraging both textual and visual information, and constructs a dataset comprising both manually and AI-generated fake reviews. Additionally, we develop a predictive framework encompassing modules for multimodal representation and modality fusion. These modules, while independent, are synergistic and effectively tackle the challenge of discerning fake review intentions. Our framework demonstrates outstanding performance, achieving an average F1 score exceeding 0.97 and a Macro F1 score surpassing 0.96 in this task and outperforming advanced pre-trained models. This research not only presents an effective methodology for accurately identifying and addressing fake review intentions but also underscores the efficacy of leveraging multimodal review context information in fake review detection. The dataset and code implementation are publicly available for further research.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"70 ","pages":"Article 101485"},"PeriodicalIF":5.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376710","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 : 2025-02-04DOI: 10.1016/j.elerap.2025.101476
Sai Venkatesh Chilukoti , Md Imran Hossen , Liqun Shan , Vijay Srinivas Tida , Mahathir Mohammad Bappy , Wenmeng Tian , Xiali Hei
Differentially Private Stochastic Gradient Descent (DP-SGD) has become a widely used technique for safeguarding sensitive information in deep learning applications. Unfortunately, DP-SGD’s per-sample gradient clipping and uniform noise addition during training can significantly degrade model utility and fairness. We observe that the latest DP-SGD-Global-Adapt’s average gradient norm is the same throughout the training. Even when it is integrated with the existing linear decay noise multiplier, it has little or no advantage. Moreover, we notice that its upper clipping threshold increases exponentially towards the end of training, potentially impacting the model’s convergence. Other algorithms, DP-PSAC, Auto-S, DP-SGD-Global, and DP-F, have utility and fairness that are similar to or worse than DP-SGD, as demonstrated in experiments. To overcome these problems and improve utility and fairness, we developed the DP-SGD-Global-Adapt-V2-S. It has a step-decay noise multiplier and an upper clipping threshold that is also decayed step-wise. DP-SGD-Global-Adapt-V2-S with a privacy budget () of 1 improves accuracy by 0.9795%, 0.6786%, and 4.0130% in MNIST, CIFAR10, and CIFAR100, respectively. It also reduces the privacy cost gap () by 89.8332% and 60.5541% in unbalanced MNIST and Thinwall datasets, respectively. Finally, we develop mathematical expressions to compute the privacy budget using truncated concentrated differential privacy (tCDP) for DP-SGD-Global-Adapt-V2-T and DP-SGD-Global-Adapt-V2-S.
{"title":"DP-SGD-global-adapt-V2-S: Triad improvements of privacy, accuracy and fairness via step decay noise multiplier and step decay upper clipping threshold","authors":"Sai Venkatesh Chilukoti , Md Imran Hossen , Liqun Shan , Vijay Srinivas Tida , Mahathir Mohammad Bappy , Wenmeng Tian , Xiali Hei","doi":"10.1016/j.elerap.2025.101476","DOIUrl":"10.1016/j.elerap.2025.101476","url":null,"abstract":"<div><div>Differentially Private Stochastic Gradient Descent (DP-SGD) has become a widely used technique for safeguarding sensitive information in deep learning applications. Unfortunately, DP-SGD’s per-sample gradient clipping and uniform noise addition during training can significantly degrade model utility and fairness. We observe that the latest DP-SGD-Global-Adapt’s average gradient norm is the same throughout the training. Even when it is integrated with the existing linear decay noise multiplier, it has little or no advantage. Moreover, we notice that its upper clipping threshold increases exponentially towards the end of training, potentially impacting the model’s convergence. Other algorithms, DP-PSAC, Auto-S, DP-SGD-Global, and DP-F, have utility and fairness that are similar to or worse than DP-SGD, as demonstrated in experiments. To overcome these problems and improve utility and fairness, we developed the DP-SGD-Global-Adapt-V2-S. It has a step-decay noise multiplier and an upper clipping threshold that is also decayed step-wise. DP-SGD-Global-Adapt-V2-S with a privacy budget (<span><math><mi>ϵ</mi></math></span>) of 1 improves accuracy by 0.9795%, 0.6786%, and 4.0130% in MNIST, CIFAR10, and CIFAR100, respectively. It also reduces the privacy cost gap (<span><math><mi>π</mi></math></span>) by 89.8332% and 60.5541% in unbalanced MNIST and Thinwall datasets, respectively. Finally, we develop mathematical expressions to compute the privacy budget using truncated concentrated differential privacy (tCDP) for DP-SGD-Global-Adapt-V2-T and DP-SGD-Global-Adapt-V2-S.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"70 ","pages":"Article 101476"},"PeriodicalIF":5.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143332282","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 : 2025-02-01DOI: 10.1016/j.elerap.2025.101486
Yu Bai , Xiuwu Liao , Yang Liu
The provision of pre-owned physical version products by traditional brick-in-store platforms (such as GameStop) was once highly profitable but has rapidly declined with the rise of digital content. As publisher firms sell digital versions directly, they now compete with platforms that sell both new and pre-owned physical versions. This paper conducts an economic analysis on a traditional brick-in-store platform’s pre-owned strategies and a publishing firm’s competition strategies. We examine two key factors that significantly influence competition between pre-owned, digital, and physical versions: distribution contracts (wholesale and agency) and countermeasures (such as exclusive digital content). Generally, pre-owned strategies hurt both the firm and the platform. However, our analysis reveals that pre-owned products benefit platforms under certain condition. From the platform’s perspective, when beneficial, wholesale contract is preferable to agency contracts. Our findings propose an “enticement yet limitation” principle for platform managers when developing pre-owned strategies. From the firm’s perspective, offering extra content as countermeasure is beneficial, helping to mitigate the harmful effects of pre-owned versions and enhancing competitive effectiveness. Our results reveal the operational mechanism behind such content monetization strategy and emphasize the strategic importance of countermeasures when responding to platforms’ pre-owned strategies. Finally, we check the robustness in extensions with considering differences in costs, reservation values and various pre-owned versions. While the pre-owned product has sparked controversy and is generally believed to be detrimental to the industry, our results suggest that content monetization strategy in countermeasure may sometimes be economically beneficial to both firms and platforms. Our findings shed light on the key factors publisher firms and platforms should consider when deairing with pre-owned strategies in the digital content industries.
{"title":"The merits of the pre-owned strategy: Price competition in multichannel digital goods distribution","authors":"Yu Bai , Xiuwu Liao , Yang Liu","doi":"10.1016/j.elerap.2025.101486","DOIUrl":"10.1016/j.elerap.2025.101486","url":null,"abstract":"<div><div>The provision of pre-owned physical version products by traditional brick-in-store platforms (such as GameStop) was once highly profitable but has rapidly declined with the rise of digital content. As publisher firms sell digital versions directly, they now compete with platforms that sell both new and pre-owned physical versions. This paper conducts an economic analysis on a traditional brick-in-store platform’s pre-owned strategies and a publishing firm’s competition strategies. We examine two key factors that significantly influence competition between pre-owned, digital, and physical versions: distribution contracts (wholesale and agency) and countermeasures (such as exclusive digital content). Generally, pre-owned strategies hurt both the firm and the platform. However, our analysis reveals that pre-owned products benefit platforms under certain condition. From the platform’s perspective, when beneficial, wholesale contract is preferable to agency contracts. Our findings propose an “enticement yet limitation” principle for platform managers when developing pre-owned strategies. From the firm’s perspective, offering extra content as countermeasure is beneficial, helping to mitigate the harmful effects of pre-owned versions and enhancing competitive effectiveness. Our results reveal the operational mechanism behind such content monetization strategy and emphasize the strategic importance of countermeasures when responding to platforms’ pre-owned strategies. Finally, we check the robustness in extensions with considering differences in costs, reservation values and various pre-owned versions. While the pre-owned product has sparked controversy and is generally believed to be detrimental to the industry, our results suggest that content monetization strategy in countermeasure may sometimes be economically beneficial to both firms and platforms. Our findings shed light on the key factors publisher firms and platforms should consider when deairing with pre-owned strategies in the digital content industries.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"70 ","pages":"Article 101486"},"PeriodicalIF":5.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387862","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 : 2025-01-28DOI: 10.1016/j.elerap.2025.101484
Feifei Wang , Zeyue Zhang , Jie Song , Yixia Yang , Xiaoling Lu
The prevalence of multimodal data has become commonplace in e-commerce platforms. Both seller showcases (i.e., the seller’s show) and user-generated content (i.e., the buyer’s show) now incorporate diverse modalities, combining both textual and visual elements. In this work, we aim to unraveling the impact of seller’s show on buyer’s show through the anchoring effect. We narrow our research on the specific problem of review helpfulness prediction and further explore whether the anchoring effect can improve the prediction accuracy of review helpfulness. In pursuit of this goal, we develop the Multi-granularity Attention Network Model based on Anchoring Effect (MAN-AE). This model first extracts the multi-granularity features in both seller’s show and buyer’s show and then accounts for the anchoring effect through a cross-source transformer. Through extensive experiments on an Amazon dataset, we demonstrate the anchoring effect of seller’s show on buyer’s show in enhancing the review helpfulness prediction performance. In comparison with other state-of-the-art models, our model demonstrates significantly superior prediction performance.
{"title":"Unraveling the anchoring effect of seller’s show on buyer’s show to enhance review helpfulness prediction: A multi-granularity attention network model with multimodal information","authors":"Feifei Wang , Zeyue Zhang , Jie Song , Yixia Yang , Xiaoling Lu","doi":"10.1016/j.elerap.2025.101484","DOIUrl":"10.1016/j.elerap.2025.101484","url":null,"abstract":"<div><div>The prevalence of multimodal data has become commonplace in e-commerce platforms. Both seller showcases (i.e., the seller’s show) and user-generated content (i.e., the buyer’s show) now incorporate diverse modalities, combining both textual and visual elements. In this work, we aim to unraveling the impact of seller’s show on buyer’s show through the anchoring effect. We narrow our research on the specific problem of review helpfulness prediction and further explore whether the anchoring effect can improve the prediction accuracy of review helpfulness. In pursuit of this goal, we develop the <em>Multi-granularity Attention Network Model based on Anchoring Effect</em> (MAN-AE). This model first extracts the multi-granularity features in both seller’s show and buyer’s show and then accounts for the anchoring effect through a cross-source transformer. Through extensive experiments on an Amazon dataset, we demonstrate the anchoring effect of seller’s show on buyer’s show in enhancing the review helpfulness prediction performance. In comparison with other state-of-the-art models, our model demonstrates significantly superior prediction performance.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"70 ","pages":"Article 101484"},"PeriodicalIF":5.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143332281","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}
This research examines the mechanisms that foster or deter consumers’ adoption of digital storefronts that traditional brick-and-mortar retailers integrate for omnichannel operations in emerging markets, through the lens of Behavioural Reasoning Theory. Using a mixed-methods approach, the study first identifies specific reasons for and against shopping on the digital platforms of brick-and-mortar retailers through qualitative interviews with retail experts. These findings are then tested quantitatively with a survey of 1392 Indian omnichannel consumers, analysed using partial least squares structural equation modelling and importance-performance matrix analysis.
The results reveal that perceived product quality and shopping flexibility are the main drivers for adoption, while the attractiveness of alternatives and concerns over delivery timeliness are key deterrents. Additionally, the analysis considers the influence of perceived compatibility and the moderating effect of product type, with electronics and clothing as representative of search and experience goods, respectively. The analysis finds that perceived compatibility exerts a moderate to low effect on consumers’ reasoning and their attitudes, and that deterrents have a stronger negative impact for experience goods in shaping consumers’ attitude towards adopting the digital storefronts of brick-and-mortar retailers. The findings advance Behavioural Reasoning Theory in retail contexts, providing actionable insights for brick-and-mortar retailers to enhance their omnichannel strategies by addressing consumer-specific motivations and barriers.
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