Electronic Commerce Research and Applications最新文献

{"title":"Stance detection for customer advocacy identification in online customer engagement: A deep learning approach","authors":"Bilal Abu-Salih ,&nbsp;Ruba Abu Khurma ,&nbsp;Ahmad K. Al Hwaitat ,&nbsp;Malak Al-Hassan ,&nbsp;Abeer F. Alkhwaldi ,&nbsp;Muder Almiani","doi":"10.1016/j.elerap.2026.101583","DOIUrl":"10.1016/j.elerap.2026.101583","url":null,"abstract":"<div><div>Customer advocacy is increasingly recognised as one of the most valuable outcomes of brand–consumer relationships. Unlike simple measures of satisfaction or sentiment, advocacy captures whether customers are willing to publicly endorse, recommend, or defend a brand. Yet measuring advocacy in online environments remains difficult: much of the existing work in sentiment analysis cannot distinguish between general positivity and brand-related support. In this paper, we propose a stance detection approach that is designed specifically to capture advocacy in brand–customer exchanges on social media. To model these interactions, we introduce a hybrid architecture that combines ELECTRA embeddings, a hierarchical attention bidirectional GRU to capture brand reply discourse, and capsule networks to preserve fine-grained stance cues. Our empirical evaluation shows that the proposed model achieves an overall Accuracy of 81.2%, Macro-F1 of 74.8%, and Cohen’s κ of 0.71, outperforming strong baselines such as BERT-base (Accuracy 74.6%, Macro-F1 67.5%) and ELECTRA-base (Accuracy 77.3%, Macro − F1 70.1%). The most notable gains are observed in the Antagonist class, where our model improves F1 from 58.1% (BERT) to 66.1%, effectively reducing misclassification of critical replies. Furthermore, stance proportions are strongly correlated with external measures of advocacy (Pearson’s r = 0.68 for Advocate share, r = -0.61 for Antagonist share), validating stance detection as a scalable proxy for monitoring advocacy. Our findings highlight how computational models of stance can advance both academic understanding and practical management of brand advocacy in Online Customer Engagement.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"76 ","pages":"Article 101583"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173994","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}

{"title":"Enhancing consumer engagement in virtual shopping: The role of social presence in shaping switching intentions","authors":"Xue Li ,&nbsp;Lanhui Cai ,&nbsp;Kum Fai Yuen","doi":"10.1016/j.elerap.2026.101580","DOIUrl":"10.1016/j.elerap.2026.101580","url":null,"abstract":"<div><div>Virtual shopping has emerged as a promising avenue for enhancing consumers’ retail experience, yet the understanding of presence in consumer switching behavior toward this emerging shopping channel remains limited. Drawing on a conceptual framework grounded in social presence theory, this study identifies the multidimensional structure of social presence and examines how its components shape consumers’ switching intentions, while also accounting for heterogeneity in demographic characteristics and shopping preferences. The study employs a self-administered questionnaire to collect data from 621 residents in Singapore. A second-order structural model is used to examine the the relationships among social presence, hedonic value, utilitarian value, perceived control, and switching intention. Moreover, a multi-group analysis is conducted to explore nuanced variations across certain demographic segments and product categories. Findings underscore the importance of social presence, hedonic value, and utilitarian value in shaping consumers’ switching behavior in virtual shopping environments. Moreover, the findings indicate that gender, product type, and product function can significantly moderate the relationship between social presence and switching intention. This study enriches the concept of social presence and consumer behavioral intention in virtual shopping. Managerially, the research offers insights for enhancing virtual shopping experiences via prioritizing social interactions.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"76 ","pages":"Article 101580"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173993","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}

{"title":"When emotions become (in-) appropriate: How social-contextual factors moderate the effect of emotional expressions on investment decisions in reward-based crowdfunding","authors":"Maximilian Raab,&nbsp;Sebastian Schlauderer ,&nbsp;Sven Overhage","doi":"10.1016/j.elerap.2026.101579","DOIUrl":"10.1016/j.elerap.2026.101579","url":null,"abstract":"<div><div>Investment decisions in reward-based crowdfunding are significantly shaped by the emotional appeal of campaign presentations. However, it remains still largely unclear how intensively emotional expressions should be used to most effectively persuade investors to fund a project. Building upon Emotions as Social Information theory, which suggests that effective emotional expressions must match social expectations and be perceived as appropriate by recipients, we studied the effect of emotional expressions in the text, pictures, and pitch videos of 16,967 Kickstarter campaigns. We found that the impact of emotional expressions on the investment decision is curvilinear across all modalities, meaning that it deteriorates or even reverses with higher intensities. We also found that this effect is moderated by the nature of the relationship between the entrepreneur and investors and by the perceived economic risk under which the investment decision is made. These factors seem to influence investors’ tolerance of more intensive emotional expressions.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"76 ","pages":"Article 101579"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173974","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}

{"title":"What to recommend is when to recommend: Modeling multi-granularity repurchase cycles","authors":"Yin Tang ,&nbsp;Zezheng Li ,&nbsp;Xiaotong Ye ,&nbsp;Xiaotong Luo ,&nbsp;Yonghui Chen ,&nbsp;Xing Qiu","doi":"10.1016/j.elerap.2026.101574","DOIUrl":"10.1016/j.elerap.2026.101574","url":null,"abstract":"<div><div>State-of-the-art sequential recommenders excel at predicting <em>what</em> a user will buy next, yet often fail to predict <em>when</em>. This is due to a flawed assumption: that repurchase intervals follow a single, simple pattern. We empirically demonstrate that the distribution of repeated-purchase intervals (DRPI) is, in fact, a complex mixture: typically a dominant power-law trend overlaid with multiple periodic spikes at weekly, monthly, and other granularities. We formalize this as the <strong>Principle of Multi-Granularity Repurchase Cycles</strong>. Ignoring this multi-modal reality introduces systematic timing bias, especially for frequently repurchased items. In view of this principle, we propose <em>MgRIA</em>, a novel recommendation paradigm that explicitly models these cycles. <em>MgRIA</em> uses a multi-granularity timestamp embedding to disentangle coexisting periodicities and a distribution-aware scoring mechanism to predict repurchase likelihood over time. Across three real-world datasets (Equity, Tafeng, and Taobao), <em>MgRIA</em> outperforms strong neural baselines such as BERT4Rec, TiSASRec, RepeatNet and DPGN by up to 0.9 absolute points in Recall@K and 0.14 points in Time-MRR@10, with average gains of 0.66 Recall@K and 0.14 Time-MRR@10 on the public Tafeng and Taobao benchmarks. The model also provides interpretability by revealing the specific repurchase cycles driving its predictions. By operationalizing our discovered principle, <em>MgRIA</em> bridges the gap between predicting <em>what</em> and <em>when</em>, while we also discuss practical limitations regarding dataset recency, domain transferability and computational overhead in real-time deployments.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"76 ","pages":"Article 101574"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080550","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}

{"title":"The effect of exploratory behaviour on online shopping stickiness: the role of enjoyment and satiation","authors":"Carmen Antón ,&nbsp;Carmen Camarero ,&nbsp;Itziar Oltra ,&nbsp;Elias Vega","doi":"10.1016/j.elerap.2026.101585","DOIUrl":"10.1016/j.elerap.2026.101585","url":null,"abstract":"<div><div>Users frequently spend longer than anticipated browsing online stores, resulting in online shopping stickiness, i.e., the intention to prolong navigation time within a browsing session, irrespective of the number of websites visited. This research investigates how exploration behaviours and browsing objectives impact stickiness during online shopping sessions. Specifically, the work analyses what influence exploration modes (specific vs. diversive) and goals (purchase-oriented vs. search-oriented) have on user stickiness, with perceived enjoyment and satiation acting as mediating factors. Using two experiments in the context of online clothing shopping and a field study, this work emphasizes the role of situational browsing elements in shaping stickiness behaviour. The findings enrich e-commerce literature by shifting the perspective from external factors (e.g., product or site attributes) to in-store exploration behaviours. This research offers valuable insights for e-commerce platforms that aim to optimize website interfaces to improve consumer experience and boost consumer retention, such as aligning navigation and content with user intentions and utilizing cross-category suggestions.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"76 ","pages":"Article 101585"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173995","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}

{"title":"A scalable framework for ranking integration in large-scale online reviews: Integrating clustering and multi-attribute decision-making","authors":"Mengchun Ma ,&nbsp;Bin Yu ,&nbsp;Zeshui Xu","doi":"10.1016/j.elerap.2025.101570","DOIUrl":"10.1016/j.elerap.2025.101570","url":null,"abstract":"<div><div>The proliferation of online reviews has transformed consumer decision-making in the hospitality industry, yet information overload remains a critical challenge. This study proposes a novel hotel ranking framework that integrates cluster analysis with Multi-Attribute Decision-Making (MADM) methods to address the issue of preference differences in large-scale user rating data. By leveraging the X-means algorithm, large-scale user evaluations are partitioned into clusters with homogeneous preferences, enabling efficient intra-cluster ranking via the TOPSIS method. A hybrid weighting mechanism, combining group size and information entropy, ensures balanced aggregation of rankings across clusters. The proposed dominance-non-dominance degree metric synthesizes global preferences, offering a robust solution for ranking 16 five-star hotels in London. Comparative analyses with traditional MADM methods demonstrate superior consistency (Kendall’s <span><math><mrow><mi>τ</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>983</mn></mrow></math></span>, Spearman’s <span><math><mrow><mi>ρ</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>997</mn></mrow></math></span>) and stability across diverse datasets. This framework not only streamlines decision-making for travelers but also provides actionable insights for hoteliers to refine service quality and market positioning.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"75 ","pages":"Article 101570"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841472","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}

{"title":"A reinforcement learning-driven framework for the Q-commerce multi-product unit scheduling problem","authors":"Weijian Zhang ,&nbsp;Min Kong ,&nbsp;Weimin Tan ,&nbsp;Yingxin Song ,&nbsp;Amir M. Fathollahi-Fard","doi":"10.1016/j.elerap.2025.101571","DOIUrl":"10.1016/j.elerap.2025.101571","url":null,"abstract":"<div><div>With the rapid emergence of Quick commerce (Q-commerce), e-commerce fulfillment systems are shifting from hour-level to minute-level responsiveness. However, this ultra-fast delivery mode introduces complex operational challenges such as multi-warehouse coordination, multi-stage processing, and high-frequency, small-batch order scheduling. To address these challenges, this study formulates and formally defines the Q-commerce multi-product unit scheduling problem with transportation and setup times, which systematically characterizes the outbound scheduling process under multi-resource collaboration. Given the problem’s strong NP-hard nature, a double Q-Learning-based variable neighborhood search (DQL-VNS) algorithm is developed. This algorithm integrates reinforcement learning with metaheuristic optimization to adaptively select neighborhood operators and adjust perturbation intensity, thereby enabling intelligent self-learning within complex search spaces. Extensive computational experiments show that DQL-VNS effectively reduces makespan and total tardiness. In large-scale instances, it achieves over a 10% reduction in average order delay compared with benchmark algorithms. Moreover, the results reveal that the multi-product unit decomposition strategy significantly enhances outbound efficiency and reduces tardiness. In terms of system configuration, the multi-warehouse-few-station mode outperforms the few-warehouse-multi-station mode by achieving better workload balance and greater fulfillment responsiveness. Additionally, due-date flexibility has a substantial impact on scheduling performance, emphasizing its critical role in maintaining customer satisfaction and delivery reliability. Overall, this study presents a novel modeling perspective and an intelligent optimization framework for outbound scheduling and resource coordination in Q-commerce, providing both theoretical and practical insights for developing responsive and sustainable instant retail logistics systems.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"75 ","pages":"Article 101571"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884652","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}

{"title":"Direct or agency? Manufacturer encroachment under platform’s data-driven marketing","authors":"Renji Duan ,&nbsp;Zhenzhong Guan ,&nbsp;Xinlan Ye ,&nbsp;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":"2026-01-01","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}

{"title":"Forgiveness catalyst or risk amplifier? The impact of AI customer service anthropomorphism on consumers’ forgiveness willingness","authors":"Huawei Liu ,&nbsp;Xiqing Han ,&nbsp;Sihong Li ,&nbsp;Xiyang Zhao ,&nbsp;Min Zhang","doi":"10.1016/j.elerap.2026.101572","DOIUrl":"10.1016/j.elerap.2026.101572","url":null,"abstract":"<div><div>In the business domain, AI customer service has become increasingly prevalent, yet service failures remain inevitable. A key challenge lies in mitigating the negative emotions that arise from such failures. Given that anthropomorphism significantly shapes consumer–AI interactions, it is essential to examine its influence on service failure outcomes and consumers’ willingness to forgive. This study investigates the degree of anthropomorphism in AI customer service as the independent variable, consumers’ forgiveness willingness toward companies as the dependent variable, and aversion to AI service failures as the mediating variable. Additionally, task criticality and relational norms are introduced as moderating variables. A research model is developed and tested through one pilot and three formal scenario-based experiments, followed by data analysis using SPSS. The results reveal four key findings: (1) In service failure contexts, a higher degree of anthropomorphism in AI customer service positively influences consumers’ willingness to forgive. (2) Aversion to AI mediates the relationship between anthropomorphism and forgiveness; specifically, higher anthropomorphism leads to lower aversion and, consequently, greater forgiveness. (3) Task criticality moderates this effect: under high task criticality, anthropomorphic AI increases forgiveness even in the face of failure, whereas under low task criticality, anthropomorphism has no significant effect on forgiveness. (4) Relational norms also moderate the effect. In public relationships (e.g., long-term or communal exchanges), higher anthropomorphism enhances forgiveness. However, in exchange-based relationships, anthropomorphism does not alleviate negative emotions or promote forgiveness. These findings offer theoretical insights and practical implications for the anthropomorphic design of AI systems and for managing customer relationships in AI-mediated service contexts.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"75 ","pages":"Article 101572"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977633","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}

{"title":"Integrating customer preferences into operational decision-making for prioritizing emerging technologies in last-mile delivery","authors":"Ahmet Çalık ,&nbsp;Esra Boz ,&nbsp;Sinan Çizmecioğlu ,&nbsp;Erfan Babaee Tirkolaee","doi":"10.1016/j.elerap.2026.101573","DOIUrl":"10.1016/j.elerap.2026.101573","url":null,"abstract":"<div><div>The rapid expansion of e-commerce has positioned last-mile delivery as the most critical and resource-intensive stage of modern supply chains. Firms must balance multiple and often conflicting objectives: reducing costs, minimizing environmental impacts, and meeting growing customer expectations for speed, reliability, and personalization. While previous research has focused on operational efficiency and routing optimization, limited attention has been given to frameworks that integrate customer preferences with technology-enabled decision-making. This study develops a hybrid decision support model by extending the classical Simple Weight Calculation (SIWEC) method with grey numbers (G-SIWEC), capable of handling uncertainty in subjective judgments to generate robust criterion weights. These weights are incorporated into a multi-objective optimization model, solved using the Weighted Sum Scalarization Method (WSSM), to minimize delivery costs and emissions while maximizing service quality. The model explicitly considers emerging delivery technologies, including drone, Autonomous Vehicle (AV), and bicycle (e-bike), to explore innovative, sustainable, and customer-centric delivery strategies. Findings highlight technology-specific patterns: drone and bicycle excel in lightweight, eco-friendly deliveries, AV dominates mid-range logistics, and conventional truck remains indispensable for heavy loads. By linking customer preferences to technology-driven operational decisions, this work provides practical insights for managers and policymakers seeking to design efficient, sustainable, and innovative last-mile delivery systems for e-commerce. These implications should be interpreted within the context of the numerical experiment conducted in this study.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"75 ","pages":"Article 101573"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977834","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}