The development of e-commerce has enabled manufacturers to wholesale products to retail platforms, which then resell them to consumers. However, manufacturers may enter the end market by either establishing an independent official retail store (direct encroachment) or selling directly to consumers on the platform (agency encroachment). In response to the competitive pressure caused by such encroachment, the platform is motivated to invest in data-driven marketing (DDM) to enhance consumer purchasing utility. To this end, we analyse the interaction between the manufacturer’s encroachment strategy and the platform’s DDM decision using a game-theoretic model. The key results are as follows. First, the manufacturer prefers direct encroachment when the commission rate is high and selling cost is low, and agency encroachment when the commission rate is low. The decision depends on two opposing effects: the competition and expansion effects. Second, DDM is not always effective in deterring encroachment. Under certain conditions, DDM may instead induce the manufacturer to introduce a direct channel. Third, as the commission rate increases, the equilibrium outcome may evolve from “DDM + no encroachment” to “no DDM + agency encroachment”, and then back to “DDM + no encroachment”. Interestingly, during these transitions, the manufacturer’s (platform’s) profit increases (decreases) abruptly. Finally, we further explore six extensions and validate the robustness of our main conclusions.
{"title":"Direct or agency? Manufacturer encroachment under platform’s data-driven marketing","authors":"Renji Duan , Zhenzhong Guan , Xinlan Ye , Jianbiao Ren","doi":"10.1016/j.elerap.2025.101567","DOIUrl":"10.1016/j.elerap.2025.101567","url":null,"abstract":"<div><div>The development of e-commerce has enabled manufacturers to wholesale products to retail platforms, which then resell them to consumers. However, manufacturers may enter the end market by either establishing an independent official retail store (direct encroachment) or selling directly to consumers on the platform (agency encroachment). In response to the competitive pressure caused by such encroachment, the platform is motivated to invest in data-driven marketing (DDM) to enhance consumer purchasing utility. To this end, we analyse the interaction between the manufacturer’s encroachment strategy and the platform’s DDM decision using a game-theoretic model. The key results are as follows. First, the manufacturer prefers direct encroachment when the commission rate is high and selling cost is low, and agency encroachment when the commission rate is low. The decision depends on two opposing effects: the competition and expansion effects. Second, DDM is not always effective in deterring encroachment. Under certain conditions, DDM may instead induce the manufacturer to introduce a direct channel. Third, as the commission rate increases, the equilibrium outcome may evolve from “DDM + no encroachment” to “no DDM + agency encroachment”, and then back to “DDM + no encroachment”. Interestingly, during these transitions, the manufacturer’s (platform’s) profit increases (decreases) abruptly. Finally, we further explore six extensions and validate the robustness of our main conclusions.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"75 ","pages":"Article 101567"},"PeriodicalIF":6.3,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705615","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 : 2025-11-01DOI: 10.1016/j.elerap.2025.101557
Yongjie Yan , Hui Xie
In modern e-commerce, recommender systems are vital for personalization. However, many systems exhibit “contextual blindness,” failing to distinguish between fundamental user motivations like established brand affinity and exploratory category-seeking. This limitation leads to suboptimal recommendations and missed revenue opportunities. To address this gap, we propose the Heterogeneous Graph Context-Aware Recommender (HGCAR). The framework constructs a multi-relational graph of users, items, brands, and categories. It employs a hierarchical attention mechanism to not only predict user choices but also to diagnose the underlying drivers by quantifying the influence of each context (e.g., brand vs. category) for each user. The resulting user-specific attention weights () function as managerially interpretable diagnostics. This allows practitioners to segment users based on their primary purchasing drivers (e.g., “Brand Loyalists” vs. “Category Explorers”), enabling the deployment of highly targeted marketing campaigns. The proposed framework is evaluated on large-scale Amazon datasets. Results show that HGCAR achieves significant improvements in recommendation accuracy over state-of-the-art baselines. Furthermore, an illustrative simulation suggests that segmenting users with our diagnostic weights has the potential for substantial increases in marketing campaign Return on Investment (ROI). This work bridges the gap between predictive accuracy and managerial actionability, transforming recommendation engines from black-box predictors into strategic decision tools for personalized marketing and inventory optimization.
{"title":"From clicks to context: A heterogeneous graph framework for diagnosing consumer shopping goals and personalizing retail strategy","authors":"Yongjie Yan , Hui Xie","doi":"10.1016/j.elerap.2025.101557","DOIUrl":"10.1016/j.elerap.2025.101557","url":null,"abstract":"<div><div>In modern e-commerce, recommender systems are vital for personalization. However, many systems exhibit “contextual blindness,” failing to distinguish between fundamental user motivations like established brand affinity and exploratory category-seeking. This limitation leads to suboptimal recommendations and missed revenue opportunities. To address this gap, we propose the Heterogeneous Graph Context-Aware Recommender (HGCAR). The framework constructs a multi-relational graph of users, items, brands, and categories. It employs a hierarchical attention mechanism to not only predict user choices but also to diagnose the underlying drivers by quantifying the influence of each context (e.g., brand vs. category) for each user. The resulting user-specific attention weights (<span><math><mi>β</mi></math></span>) function as managerially interpretable diagnostics. This allows practitioners to segment users based on their primary purchasing drivers (e.g., “Brand Loyalists” vs. “Category Explorers”), enabling the deployment of highly targeted marketing campaigns. The proposed framework is evaluated on large-scale Amazon datasets. Results show that HGCAR achieves significant improvements in recommendation accuracy over state-of-the-art baselines. Furthermore, an illustrative simulation suggests that segmenting users with our diagnostic weights has the potential for substantial increases in marketing campaign Return on Investment (ROI). This work bridges the gap between predictive accuracy and managerial actionability, transforming recommendation engines from black-box predictors into strategic decision tools for personalized marketing and inventory optimization.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"74 ","pages":"Article 101557"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465768","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 : 2025-11-01DOI: 10.1016/j.elerap.2025.101560
Wei Zhang, Hao Ran, Yuan Gong, Xiaohui Zhou
Live-stream commerce—marked by real-time interactivity, immersive visuals, and embedded social cues—has become mainstream. While it expands exposure, its speed and reach can also amplify negative sentiment, posing acute risks for intangible cultural heritage (ICH) brands that bear commercial and cultural responsibilities. Using the Stimulus–Organism–Response framework, we examine how live-shopping experience and brand-crisis perception shape purchase intention via parallel mediators: brand trust (cognitive) and ICH-preservation awareness (affective). Focusing on Pien Tze Huang—an ICH-listed, time-honoured Chinese brand—we triangulate literature review, field investigation, and a survey (N = 432). Structural-equation modelling with multiple mediation yields three findings. (i) Engaging live-shopping elevates professional trust and deepens cultural identification, jointly increasing purchase intention. (ii) Crisis perception erodes trust yet heightens awareness of cultural scarcity; under certain conditions the latter dominates, paradoxically raising purchase intention. (iii) Trust and ICH-preservation awareness transmit these influences in parallel, underscoring intertwined cognition and emotion. Managerially, ICH brands should pair interactive, evidence-first livestreams with transparent, multi-platform communication; explain pricing while demonstrating craft and provenance; and ensure compliant, traceable supply chains that reconcile commercial logic with heritage stewardship. These conclusions are analytical generalisations from a high-salience ICH setting; portability depends on heritage depth and verifiable transparency. Future research should adopt longitudinal or cross-case designs across ICH categories and cultural contexts, incorporating behavioural traces to test boundary conditions and strengthen external validity.
{"title":"Consumer purchase intention during brand crisis: A study on intangible cultural heritage brands in the context of e-commerce live streaming","authors":"Wei Zhang, Hao Ran, Yuan Gong, Xiaohui Zhou","doi":"10.1016/j.elerap.2025.101560","DOIUrl":"10.1016/j.elerap.2025.101560","url":null,"abstract":"<div><div>Live-stream commerce—marked by real-time interactivity, immersive visuals, and embedded social cues—has become mainstream. While it expands exposure, its speed and reach can also amplify negative sentiment, posing acute risks for intangible cultural heritage (ICH) brands that bear commercial and cultural responsibilities. Using the Stimulus–Organism–Response framework, we examine how live-shopping experience and brand-crisis perception shape purchase intention via parallel mediators: brand trust (cognitive) and ICH-preservation awareness (affective). Focusing on Pien Tze Huang—an ICH-listed, time-honoured Chinese brand—we triangulate literature review, field investigation, and a survey (N = 432). Structural-equation modelling with multiple mediation yields three findings. (i) Engaging live-shopping elevates professional trust and deepens cultural identification, jointly increasing purchase intention. (ii) Crisis perception erodes trust yet heightens awareness of cultural scarcity; under certain conditions the latter dominates, paradoxically raising purchase intention. (iii) Trust and ICH-preservation awareness transmit these influences in parallel, underscoring intertwined cognition and emotion. Managerially, ICH brands should pair interactive, evidence-first livestreams with transparent, multi-platform communication; explain pricing while demonstrating craft and provenance; and ensure compliant, traceable supply chains that reconcile commercial logic with heritage stewardship. These conclusions are analytical generalisations from a high-salience ICH setting; portability depends on heritage depth and verifiable transparency. Future research should adopt longitudinal or cross-case designs across ICH categories and cultural contexts, incorporating behavioural traces to test boundary conditions and strengthen external validity.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"74 ","pages":"Article 101560"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571235","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 : 2025-11-01DOI: 10.1016/j.elerap.2025.101565
Zhiyuan Nong, Jing Wu
This research investigates how social media affordances (SMA) influence social commerce customers’ knowledge sharing through their engagement in coactive vicarious learning (CVL). We extend the theory of SMA by incorporating the privacy-controlling ability afforded by social media. Five complementary forms of SMA then drive CVL, with knowledge sharing as the learning outcome. Specifically, this study examines the dual-stage moderating role of the need for cognitive closure (NCC), revealing how the extended SMA affects users with different psychological states. A quantitative survey was conducted on several leading Chinese social commerce platforms to test the research model. Data from 867 respondents were analyzed using the PLS-SEM method. The results confirm that: (1) SMA has a positive effect on CVL, and CVL positively affects knowledge sharing; (2) CVL partially mediates the relationship between the extended SMA and knowledge sharing; (3) NCC acts as a dual-stage positive moderator in the indirect effect of SMA on knowledge sharing through CVL. This research contributes to the literature on knowledge sharing and social commerce, deepens the understanding of consumer learning, and advances the application of cognitive psychology in the study of digital learning and consumer behavior.
{"title":"Understanding the knowledge sharing behaviors in social Commerce: Affordances, coactive vicarious Learning, and need for cognitive closure","authors":"Zhiyuan Nong, Jing Wu","doi":"10.1016/j.elerap.2025.101565","DOIUrl":"10.1016/j.elerap.2025.101565","url":null,"abstract":"<div><div>This research investigates how social media affordances (SMA) influence social commerce customers’ knowledge sharing through their engagement in coactive vicarious learning (CVL). We extend the theory of SMA by incorporating the privacy-controlling ability afforded by social media. Five complementary forms of SMA then drive CVL, with knowledge sharing as the learning outcome. Specifically, this study examines the dual-stage moderating role of the need for cognitive closure (NCC), revealing how the extended SMA affects users with different psychological states. A quantitative survey was conducted on several leading Chinese social commerce platforms to test the research model. Data from 867 respondents were analyzed using the PLS-SEM method. The results confirm that: (1) SMA has a positive effect on CVL, and CVL positively affects knowledge sharing; (2) CVL partially mediates the relationship between the extended SMA and knowledge sharing; (3) NCC acts as a dual-stage positive moderator in the indirect effect of SMA on knowledge sharing through CVL. This research contributes to the literature on knowledge sharing and social commerce, deepens the understanding of consumer learning, and advances the application of cognitive psychology in the study of digital learning and consumer behavior.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"74 ","pages":"Article 101565"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684779","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 : 2025-11-01DOI: 10.1016/j.elerap.2025.101563
JianHua Wang, Bin Zhao
As data privacy issues become increasingly prominent in the digital economy, differences in privacy protection investment and risk preferences among supply chain members frequently lead to decision-making conflicts. This study considers a two-tier supply chain consisting of a supplier and an e-commerce platform, where the platform may unintentionally cause consumer privacy breaches and faces a certain probability of being held accountable and compensating consumers. The objective is to explore the decision-making mechanisms of supply chain members under different risk preferences and privacy protection investment modes, and to analyze the impact of privacy protection investment and risk preferences on supply chain members by considering the compensation cost associated with privacy breaches. We derive the following main conclusions: regardless of whether the platform invests in privacy protection, an increase in the probability of privacy breaches suppresses market demand, lowers the wholesale price, and reduces the profits of both the supplier and the platform. Given a certain level of protection capability and technological efficiency, the e-commerce platform should actively invest in privacy protection. The platform’s risk attitude affects its pricing strategy and profit distribution structure; although a risk-averse platform tends to set a lower price to expand market demand, its subjective utility is generally lower than that of a risk-neutral platform.
{"title":"Privacy protection investment decisions of e-commerce platforms under mean-variance preferences","authors":"JianHua Wang, Bin Zhao","doi":"10.1016/j.elerap.2025.101563","DOIUrl":"10.1016/j.elerap.2025.101563","url":null,"abstract":"<div><div>As data privacy issues become increasingly prominent in the digital economy, differences in privacy protection investment and risk preferences among supply chain members frequently lead to decision-making conflicts. This study considers a two-tier supply chain consisting of a supplier and an e-commerce platform, where the platform may unintentionally cause consumer privacy breaches and faces a certain probability of being held accountable and compensating consumers. The objective is to explore the decision-making mechanisms of supply chain members under different risk preferences and privacy protection investment modes, and to analyze the impact of privacy protection investment and risk preferences on supply chain members by considering the compensation cost associated with privacy breaches. We derive the following main conclusions: regardless of whether the platform invests in privacy protection, an increase in the probability of privacy breaches <span><math><msub><mi>f</mi><mn>1</mn></msub></math></span> suppresses market demand, lowers the wholesale price, and reduces the profits of both the supplier and the platform. Given a certain level of protection capability and technological efficiency, the e-commerce platform should actively invest in privacy protection. The platform’s risk attitude affects its pricing strategy and profit distribution structure; although a risk-averse platform tends to set a lower price to expand market demand, its subjective utility is generally lower than that of a risk-neutral platform.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"74 ","pages":"Article 101563"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684777","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 : 2025-11-01DOI: 10.1016/j.elerap.2025.101564
Matthew O. Ayemowa , Roliana Ibrahim , Noor Hidayah Zakaria
Recommender systems often face challenges of cold start, specifically when expanding into new domains. The current research has shown the successful impact of Generative Adversarial Networks (GANs) on domain adaptation problems. However, the underutilization of GAN-based model needs more attention for more personalized recommendations. Domain adaptation helps to mitigate these issues by transferring knowledge from a source domain to a target domain. This paper proposes a novel approach that leverages Generative Adversarial Networks (GANs) to enhance domain adaptation in Cross-domain recommender systems (CDRS). The proposed model, Domain Adaptation Cross-domain Generative Adversarial Networks (DAC-GAN) with the incorporation of auxiliary information utilized the generator to produce synthetic user-item interactions in the target domain and a discriminator to distinguish between real and generated interactions, thereby improving the performance of the recommendation. By integrating auxiliary information into GANs model, the framework bridges the domain gap, and this enables accurate predictions in the target domain. Comprehensive experiments on benchmark datasets: Amazon, Movielens, Yelp and Book-crossing demonstrate the effectiveness of the proposed technique, by achieving significant improvements in the evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) compared to existing techniques. Experiments conducted on benchmark datasets demonstrate that DAC-GAN outperforms the existing methods in terms of recommendation accuracy and adaptability.
{"title":"Mitigating domain adaptation and cold start challenges in cross-domain recommender systems using generative adversarial network model","authors":"Matthew O. Ayemowa , Roliana Ibrahim , Noor Hidayah Zakaria","doi":"10.1016/j.elerap.2025.101564","DOIUrl":"10.1016/j.elerap.2025.101564","url":null,"abstract":"<div><div>Recommender systems often face challenges of cold start, specifically when expanding into new domains. The current research has shown the successful impact of Generative Adversarial Networks (GANs) on domain adaptation problems. However, the underutilization of GAN-based model needs more attention for more personalized recommendations. Domain adaptation helps to mitigate these issues by transferring knowledge from a source domain to a target domain. This paper proposes a novel approach that leverages Generative Adversarial Networks (GANs) to enhance domain adaptation in Cross-domain recommender systems (CDRS). The proposed model, Domain Adaptation Cross-domain Generative Adversarial Networks (DAC-GAN) with the incorporation of auxiliary information utilized the generator to produce synthetic user-item interactions in the target domain and a discriminator to distinguish between real and generated interactions, thereby improving the performance of the recommendation. By integrating auxiliary information into GANs model, the framework bridges the domain gap, and this enables accurate predictions in the target domain. Comprehensive experiments on benchmark datasets: Amazon, Movielens, Yelp and Book-crossing demonstrate the effectiveness of the proposed technique, by achieving significant improvements in the evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) compared to existing techniques. Experiments conducted on benchmark datasets demonstrate that DAC-GAN outperforms the existing methods in terms of recommendation accuracy and adaptability.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"74 ","pages":"Article 101564"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618095","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 : 2025-11-01DOI: 10.1016/j.elerap.2025.101561
Jing Liu , Anqi Li , Pengwen Hou , Shuhua Chang
An increasing number of merchants sell products through dual channels on a platform, whose services (such as marketing, operations, warehousing, and payment) significantly enhance market demand. Sellers (merchants and platforms) can ride the service efforts of another channel for free, which is unexplained by existing theories. This paper develops a game to study the online channel structure on retail price and service effort. We systematically identify four strategic scenarios: single channel (resale and agency), independent dual channels, dual channels with unidirectional free-riding, and dual channels with bidirectional free-riding. Opening dual channels from a single channel can offer valuable flexibility to startups and merchants, but it can also intensify competition between channels. We emphasize the flexibility of dual channels, as a low commission allows companies to transfer sales between channels, alleviating the inefficiency of double marginalization and increasing the motivation for the platform to strive to provide diversified services. Conversely, under a high commission rate, it is not feasible to open up dual channels, whether to prevent wholesale price hikes or to suffer from the failure of service effort incentives, especially when the platform engages in free-riding. The free-riding of online dual channels has dual characteristics in terms of direction and effect. When the merchant free-rides, the level of service effort and sales improve, but the service decentralization effect is disrupted by the strategic increase in wholesale prices as the free-riding intensifies. We confirm that the direction and strength of free-riding behavior should be maintained in a non-linear relationship, benefiting both parties conditionally. Intuitively speaking, one who takes the initiative to free-ride is always better off with the service effort from another channel. The result is not so, as the loss of increased competition caused by merchants through the agency channel is much higher than the increased profits of encroaching on dual channels. The study provides insights for the platform and merchants to guide the services and pricing under the complex free-riding, further adjust channel structure for encroachment and competition.
{"title":"The online retail channel structure with bidirectional free-riding effect","authors":"Jing Liu , Anqi Li , Pengwen Hou , Shuhua Chang","doi":"10.1016/j.elerap.2025.101561","DOIUrl":"10.1016/j.elerap.2025.101561","url":null,"abstract":"<div><div>An increasing number of merchants sell products through dual channels on a platform, whose services (such as marketing, operations, warehousing, and payment) significantly enhance market demand. Sellers (merchants and platforms) can ride the service efforts of another channel for free, which is unexplained by existing theories. This paper develops a game to study the online channel structure on retail price and service effort. We systematically identify four strategic scenarios: single channel (resale and agency), independent dual channels, dual channels with unidirectional free-riding, and dual channels with bidirectional free-riding. Opening dual channels from a single channel can offer valuable flexibility to startups and merchants, but it can also intensify competition between channels. We emphasize the flexibility of dual channels, as a low commission allows companies to transfer sales between channels, alleviating the inefficiency of double marginalization and increasing the motivation for the platform to strive to provide diversified services. Conversely, under a high commission rate, it is not feasible to open up dual channels, whether to prevent wholesale price hikes or to suffer from the failure of service effort incentives, especially when the platform engages in free-riding. The free-riding of online dual channels has dual characteristics in terms of direction and effect. When the merchant free-rides, the level of service effort and sales improve, but the service decentralization effect is disrupted by the strategic increase in wholesale prices as the free-riding intensifies. We confirm that the direction and strength of free-riding behavior should be maintained in a non-linear relationship, benefiting both parties conditionally. Intuitively speaking, one who takes the initiative to free-ride is always better off with the service effort from another channel. The result is not so, as the loss of increased competition caused by merchants through the agency channel is much higher than the increased profits of encroaching on dual channels. The study provides insights for the platform and merchants to guide the services and pricing under the complex free-riding, further adjust channel structure for encroachment and competition.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"74 ","pages":"Article 101561"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684639","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 : 2025-11-01DOI: 10.1016/j.elerap.2025.101558
Xin Liu , Xiaoyan Zhu , Jiaqi Wu
Over the past decade, a plethora of academic research has investigated the diffusion of retail central bank digital currency (RCBDC). However, many of these studies have concentrated on RCBDC diffusion among consumers, leaving a significant gap in understanding RCBDC diffusion from the merchant perspective. This oversight highlights the necessity to explore the determinants of merchants’ RCBDC adoption intentions. Taking the E-CNY (Electronic Chinese Yuan) as an illustration, this study attempts to construct a holistic model that incorporates the innovation diffusion theory (IDT), two-sided market theory (TMT), and institutional theory (IIT) to fathom the determinants of merchants’ RCBDC adoption intentions. Survey data on 474 merchants from China were analyzed using a tri-level method integrating the partial least squares structural equation modeling (PLS-SEM), artificial neural networks (ANN) and necessary condition analysis (NCA) techniques. The results demonstrate that critical mass, perceived complementarity, normative pressures, mimetic pressures, relative advantage, and compatibility are determinants regarding merchants’ E-CNY adoption intentions. Of these determinants, their relative importance toward merchants’ E-CNY adoption intentions follows a decreasing order with the determinant presented before, and with the first four being the necessary conditions for merchants’ E-CNY adoption intentions. This study advances the burgeoning knowledge on RCBDC adoption and deliberates insightful references for policymakers to foster RCBDC implementation.
{"title":"Payment innovation diffusion among merchants: An integrated view using a tri-level hybrid method","authors":"Xin Liu , Xiaoyan Zhu , Jiaqi Wu","doi":"10.1016/j.elerap.2025.101558","DOIUrl":"10.1016/j.elerap.2025.101558","url":null,"abstract":"<div><div>Over the past decade, a plethora of academic research has investigated the diffusion of retail central bank digital currency (RCBDC). However, many of these studies have concentrated on RCBDC diffusion among consumers, leaving a significant gap in understanding RCBDC diffusion from the merchant perspective. This oversight highlights the necessity to explore the determinants of merchants’ RCBDC adoption intentions. Taking the E-CNY (Electronic Chinese Yuan) as an illustration, this study attempts to construct a holistic model that incorporates the innovation diffusion theory (IDT), two-sided market theory (TMT), and institutional theory (IIT) to fathom the determinants of merchants’ RCBDC adoption intentions. Survey data on 474 merchants from China were analyzed using a tri-level method integrating the partial least squares structural equation modeling (PLS-SEM), artificial neural networks (ANN) and necessary condition analysis (NCA) techniques. The results demonstrate that critical mass, perceived complementarity, normative pressures, mimetic pressures, relative advantage, and compatibility are determinants regarding merchants’ E-CNY adoption intentions. Of these determinants, their relative importance toward merchants’ E-CNY adoption intentions follows a decreasing order with the determinant presented before, and with the first four being the necessary conditions for merchants’ E-CNY adoption intentions. This study advances the burgeoning knowledge on RCBDC adoption and deliberates insightful references for policymakers to foster RCBDC implementation.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"74 ","pages":"Article 101558"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519762","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 : 2025-11-01DOI: 10.1016/j.elerap.2025.101562
Wenyan Zhuo , Qiang Yan
This study examines an e-supply chain composed of a financially distressed supplier, a platform, and a bank operating within an online marketplace. The supplier, with an imperfect credit rating, may secure financing either from the bank or the platform. The platform offers two financing schemes: direct credit and assisted loans, the latter of which involves providing transaction information to banks to facilitate supplier borrowing. We discuss how the supplier’s credit rating influences the financing preferences of all members. Equilibrium analysis reveals that the supplier consistently favors bank credit financing (BCF), whereas the platform’s preference hinges on demand variability, the supplier’s credit rating, and the production cost. Specifically, the platform favors assisted loans if either of the following conditions holds: (i) when demand variability is high, and both credit ratings and production costs are low; (ii) when both demand variability and production costs are high; (iii) when moderate demand variability coincides with high production costs and low credit ratings. In scenarios where the financing strategies of the platform and supplier are inconsistent, we derive the Pareto zone of the platform’s interest rate in which both members prefer platform credit financing (PCF). Furthermore, we extend our model to incorporate an endogenous information service fee. Interestingly, we find that given a relatively high commission rate, the platform may opt to impose a negative information service fee. This effectively acts as a financing subsidy for the supplier.
{"title":"Optimal financing strategies in a financially distressed online supply chain with an imperfect credit rating supplier","authors":"Wenyan Zhuo , Qiang Yan","doi":"10.1016/j.elerap.2025.101562","DOIUrl":"10.1016/j.elerap.2025.101562","url":null,"abstract":"<div><div>This study examines an e-supply chain composed of a financially distressed supplier, a platform, and a bank operating within an online marketplace. The supplier, with an imperfect credit rating, may secure financing either from the bank or the platform. The platform offers two financing schemes: direct credit and assisted loans, the latter of which involves providing transaction information to banks to facilitate supplier borrowing. We discuss how the supplier’s credit rating influences the financing preferences of all members. Equilibrium analysis reveals that the supplier consistently favors bank credit financing (BCF), whereas the platform’s preference hinges on demand variability, the supplier’s credit rating, and the production cost. Specifically, the platform favors assisted loans if either of the following conditions holds: (i) when demand variability is high, and both credit ratings and production costs are low; (ii) when both demand variability and production costs are high; (iii) when moderate demand variability coincides with high production costs and low credit ratings. In scenarios where the financing strategies of the platform and supplier are inconsistent, we derive the Pareto zone of the platform’s interest rate in which both members prefer platform credit financing (PCF). Furthermore, we extend our model to incorporate an endogenous information service fee. Interestingly, we find that given a relatively high commission rate, the platform may opt to impose a negative information service fee. This effectively acts as a financing subsidy for the supplier.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"74 ","pages":"Article 101562"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684778","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 : 2025-11-01DOI: 10.1016/j.elerap.2025.101556
He Xu , Dan Gao , Pengyu Chen , Yucui Liu
Paid content strategies in the digital content industry typically employ fixed price or pay-as-you-wish models. The pay-as-you-wish model allows consumers to choose their price and accommodates low-paying users but risks reduced revenue, while fixed pricing may lead to dissatisfaction due to consumers’ fairness concerns. This paper develops a stylized model with a monopoly provider and fair-minded consumers to identify the optimal paid content strategy, both with and without advertising revenue. Without advertising revenue, the pay-as-you-wish strategy is optimal when consumers are generous and the quality costs are high, offering higher quality only when costs are sufficiently high and consumers are generous enough. In contrast, fixed pricing consistently delivers higher quality when optimal. With advertising revenue, pay-as-you-wish becomes preferable when the quality costs are high, regardless of consumers’ generosity. However, advertising does not always encourage quality improvements compared to the no-advertising scenario. These findings offer practical insights for content pricing strategies.
{"title":"Monetizing digital content: fixed price or pay-as-you-wish?","authors":"He Xu , Dan Gao , Pengyu Chen , Yucui Liu","doi":"10.1016/j.elerap.2025.101556","DOIUrl":"10.1016/j.elerap.2025.101556","url":null,"abstract":"<div><div>Paid content strategies in the digital content industry typically employ fixed price or pay-as-you-wish models. The pay-as-you-wish model allows consumers to choose their price and accommodates low-paying users but risks reduced revenue, while fixed pricing may lead to dissatisfaction due to consumers’ fairness concerns. This paper develops a stylized model with a monopoly provider and fair-minded consumers to identify the optimal paid content strategy, both with and without advertising revenue. Without advertising revenue, the pay-as-you-wish strategy is optimal when consumers are generous and the quality costs are high, offering higher quality only when costs are sufficiently high and consumers are generous enough. In contrast, fixed pricing consistently delivers higher quality when optimal. With advertising revenue, pay-as-you-wish becomes preferable when the quality costs are high, regardless of consumers’ generosity. However, advertising does not always encourage quality improvements compared to the no-advertising scenario. These findings offer practical insights for content pricing strategies.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"74 ","pages":"Article 101556"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571233","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号