In real world practice, trade-in programs are offered by either a manufacturer or an e-commerce platform. Parties that offer a trade-in service are faced with a trade-off between trade-in rebates and the residual income. By adopting the game theory, this paper explored the selection of trade-in provider with respect to a manufacturer and an e-commerce platform. The results show that in some cases, all trade-in models generated higher manufacturing costs than models with no trade-in program. However, in other cases, not all trade-in models can cope with manufacturing costs that are higher than those associated with models that have no trade-in program. Furthermore, both above two firms will offer the trade-ins when profits which they have obtained satisfied a certain condition. We also identified an interesting phenomenon whereby the manufacturer decided whether it wanted to delegate the trade-ins to the e-commerce platform or provide it jointly. The e-commerce platform can decide whether it wants to accept the delegation or jointly offer it. This study also obtain that trade-in models makes customers get more surplus and can produce greater environmental benefits. Moreover, both the customer surplus and the environmental benefits in delegated trade-in model is the same that in jointly trade-in model.
{"title":"Who should provide a trade-in service under the online agency-selling mode?","authors":"Xigang Yuan , Zujun Ma , Xiaoqing Zhang , Dalin Zhang","doi":"10.1016/j.elerap.2024.101454","DOIUrl":"10.1016/j.elerap.2024.101454","url":null,"abstract":"<div><div>In real world practice, trade-in programs are offered by either a manufacturer or an e-commerce platform. Parties that offer a trade-in service are faced with a trade-off between trade-in rebates and the residual income. By adopting the game theory, this paper explored the selection of trade-in provider with respect to a manufacturer and an e-commerce platform. The results show that in some cases, all trade-in models generated higher manufacturing costs than models with no trade-in program. However, in other cases, not all trade-in models can cope with manufacturing costs that are higher than those associated with models that have no trade-in program. Furthermore, both above two firms will offer the trade-ins when profits which they have obtained satisfied a certain condition. We also identified an interesting phenomenon whereby the manufacturer decided whether it wanted to delegate the trade-ins to the e-commerce platform or provide it jointly. The e-commerce platform can decide whether it wants to accept the delegation or jointly offer it. This study also obtain that trade-in models makes customers get more surplus and can produce greater environmental benefits. Moreover, both the customer surplus and the environmental benefits in delegated trade-in model is the same that in jointly trade-in model.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"68 ","pages":"Article 101454"},"PeriodicalIF":5.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552724","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}
Pub Date : 2024-11-01DOI: 10.1016/j.elerap.2024.101456
Joaquin Rodriguez , Gabriele Piccoli
Although often credited with leveling the competitive playing field, platforms pose novel challenges for millions of complementors within their ecosystems. This study explores the tactics complementors use to maintain superior visibility on these platforms. Building on competitive repertoire theory, we conceptualize two categories of competitive actions that capture the dual environmental complexity faced by complementors: inside and outside competitive moves. We assemble a unique dataset from a leading food delivery platform in Europe, providing a comprehensive view of complementors’ competitive repertoires and visibility over ten months. We find that complementors’ inside competitive repertoires with high volume and complexity are associated with sustained superior visibility. However, we also find that complementors whose competitive repertoires diverge from those of their competitors are more likely to exit the superior visibility strata. Additionally, we identify outside action repertoires as a second pathway to differentiation, built on complementors’ idiosyncratic resources and less dependent on platform architecture and rules.
{"title":"Sustaining superior visibility within digital platforms through inside and outside competitive action repertoires","authors":"Joaquin Rodriguez , Gabriele Piccoli","doi":"10.1016/j.elerap.2024.101456","DOIUrl":"10.1016/j.elerap.2024.101456","url":null,"abstract":"<div><div>Although often credited with leveling the competitive playing field, platforms pose novel challenges for millions of complementors within their ecosystems. This study explores the tactics complementors use to maintain superior visibility on these platforms. Building on competitive repertoire theory, we conceptualize two categories of competitive actions that capture the dual environmental complexity faced by complementors: <em>inside</em> and <em>outside</em> competitive moves. We assemble a unique dataset from a leading food delivery platform in Europe, providing a comprehensive view of complementors’ competitive repertoires and visibility over ten months. We find that complementors’ inside competitive repertoires with high volume and complexity are associated with sustained superior visibility. However, we also find that complementors whose competitive repertoires diverge from those of their competitors are more likely to exit the superior visibility strata. Additionally, we identify outside action repertoires as a second pathway to differentiation, built on complementors’ idiosyncratic resources and less dependent on platform architecture and rules.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"68 ","pages":"Article 101456"},"PeriodicalIF":5.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572551","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 : 2024-11-01DOI: 10.1016/j.elerap.2024.101457
Chao Fang , Shuzhong Ma
Electronic word of mouth (eWOM) has attracted considerable research interest in the past two decades. This paper revisits the impact of eWOM in the context of international business. Using review data scraped from AliExpress, a cross-border e-commerce platform, we show that the impact of online reviews is related to the identity of reviewers. Consumers are most affected by reviews from their home country, followed by reviews from neighboring countries, while they are not affected by reviews from strangers. This confirms the existence of home bias in the consumption of review information. In addition, the bias is more profound among consumers from countries with higher levels of uncertainty avoidance and trust. Our study is among the first to investigate eWOM in digitized international business. By discovering and reporting how consumers react to reviews with different identities, we offer actionable implications for digital platforms to improve the effectiveness of online reviews.
{"title":"Home is best: Review source and cross-border online shopping","authors":"Chao Fang , Shuzhong Ma","doi":"10.1016/j.elerap.2024.101457","DOIUrl":"10.1016/j.elerap.2024.101457","url":null,"abstract":"<div><div>Electronic word of mouth (eWOM) has attracted considerable research interest in the past two decades. This paper revisits the impact of eWOM in the context of international business. Using review data scraped from AliExpress, a cross-border e-commerce platform, we show that the impact of online reviews is related to the identity of reviewers. Consumers are most affected by reviews from their home country, followed by reviews from neighboring countries, while they are not affected by reviews from strangers. This confirms the existence of home bias in the consumption of review information. In addition, the bias is more profound among consumers from countries with higher levels of uncertainty avoidance and trust. Our study is among the first to investigate eWOM in digitized international business. By discovering and reporting how consumers react to reviews with different identities, we offer actionable implications for digital platforms to improve the effectiveness of online reviews.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"68 ","pages":"Article 101457"},"PeriodicalIF":5.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552725","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 : 2024-11-01DOI: 10.1016/j.elerap.2024.101461
Zishuo Jin , Feng Ye , Nadia Nedjah , Xuejie Zhang
With the rapid development of the Internet and the concomitant exponential growth of information, we have entered an era characterized by information overload. The abundance of data has rendered it increasingly arduous for users to pinpoint specific information they require. However, various forms of recommendation algorithms proffer solutions to this challenge. These algorithms predict items or products that may pique users’ interest based on their historical behavior, preferences, and interests. As one of the current hot research fields, recommendation algorithms are extensively employed across E-commerce platforms, movie streaming services, and various other contexts to cater to the diverse needs of users. In this context, a multi-recommendation algorithms comparison platform is proposed, which includes a two-fold model: online evaluation and offline evaluation. Taking the data set of the Chinese Amazon online shopping mall as the experimental data, item-based collaborative filtering (Item-CF) algorithm, content-based (TF-IDF) algorithm, item2vec model, alternating least squares (ALS) algorithm and neural network algorithm are evaluated in the offline model. In the real-time recommendation part, model-based algorithm is used to achieve the users’ rating mechanism. And the metrics used for evaluation include: precision, recall, accuracy and performance. The experimental results show that the average performance of hybrid algorithms such as ALS algorithm and neural network algorithm is higher than that of other traditional algorithms, and the real-time recommendation system achieves the purpose of improving recommendation speed. By integrating various recommender algorithms into the multi-recommendation algorithms comparison platform, this platform automatically computes and presents various performance indicators based on the user-provided dataset. It aids E-commerce platforms in making informed decisions regarding algorithm selection.
{"title":"A comparative study of various recommendation algorithms based on E-commerce big data","authors":"Zishuo Jin , Feng Ye , Nadia Nedjah , Xuejie Zhang","doi":"10.1016/j.elerap.2024.101461","DOIUrl":"10.1016/j.elerap.2024.101461","url":null,"abstract":"<div><div>With the rapid development of the Internet and the concomitant exponential growth of information, we have entered an era characterized by information overload. The abundance of data has rendered it increasingly arduous for users to pinpoint specific information they require. However, various forms of recommendation algorithms proffer solutions to this challenge. These algorithms predict items or products that may pique users’ interest based on their historical behavior, preferences, and interests. As one of the current hot research fields, recommendation algorithms are extensively employed across E-commerce platforms, movie streaming services, and various other contexts to cater to the diverse needs of users. In this context, a multi-recommendation algorithms comparison platform is proposed, which includes a two-fold model: online evaluation and offline evaluation. Taking the data set of the Chinese Amazon online shopping mall as the experimental data, item-based collaborative filtering (Item-CF) algorithm, content-based (TF-IDF) algorithm, item2vec model, alternating least squares (ALS) algorithm and neural network algorithm are evaluated in the offline model. In the real-time recommendation part, model-based algorithm is used to achieve the users’ rating mechanism. And the metrics used for evaluation include: precision, recall, accuracy and performance. The experimental results show that the average performance of hybrid algorithms such as ALS algorithm and neural network algorithm is higher than that of other traditional algorithms, and the real-time recommendation system achieves the purpose of improving recommendation speed. By integrating various recommender algorithms into the multi-recommendation algorithms comparison platform, this platform automatically computes and presents various performance indicators based on the user-provided dataset. It aids E-commerce platforms in making informed decisions regarding algorithm selection.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"68 ","pages":"Article 101461"},"PeriodicalIF":5.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656672","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}
Pub Date : 2024-11-01DOI: 10.1016/j.elerap.2024.101464
Yanfen Zhang, Qi Xu
As live streaming selling continues to be an emerging channel with many uncertainties, brand manufacturers must adopt a deliberate approach to utilize this medium and develop their capabilities effectively. This study examines the adoption of live streaming selling by considering customers interacting with the streamer in real-time to acquire more product information and additional interaction value. Three sales models are constructed: the traditional selling channel (Model T), the live streaming selling channel (Model L), and the dual channel that combines traditional selling and live streaming selling (Model TL). The impact of live streaming selling on value creation is revealed through a comparative analysis. Finally, as an extension model, we consider the scenario where manufacturers pay streamers different commissions based on actual market demands. The results show that adopting live streaming selling depends on the commission and operating costs of live streaming. When the commission paid to the streamer is low, contemplating live streaming selling can create more value for all parties. When the commission is moderate, the manufacturer should adopt the dual channel of traditional selling and live streaming selling to create more value. Conversely, when the commission and operating costs are high, adopting live streaming selling is not recommended. Furthermore, when market demand is high, manufacturers achieve greater profits by paying streamers a fixed commission. Conversely, when market demand is low, manufacturers gain higher profits by paying streamers different commissions based on actual market demand.
{"title":"Whether and how to adopt live streaming Selling: A perspective on interaction value creation","authors":"Yanfen Zhang, Qi Xu","doi":"10.1016/j.elerap.2024.101464","DOIUrl":"10.1016/j.elerap.2024.101464","url":null,"abstract":"<div><div>As live streaming selling continues to be an emerging channel with many uncertainties, brand manufacturers must adopt a deliberate approach to utilize this medium and develop their capabilities effectively. This study examines the adoption of live streaming selling by considering customers interacting with the streamer in real-time to acquire more product information and additional interaction value. Three sales models are constructed: the traditional selling channel (Model T), the live streaming selling channel (Model L), and the dual channel that combines traditional selling and live streaming selling (Model TL). The impact of live streaming selling on value creation is revealed through a comparative analysis. Finally, as an extension model, we consider the scenario where manufacturers pay streamers different commissions based on actual market demands. The results show that adopting live streaming selling depends on the commission and operating costs of live streaming. When the commission paid to the streamer is low, contemplating live streaming selling can create more value for all parties. When the commission is moderate, the manufacturer should adopt the dual channel of traditional selling and live streaming selling to create more value. Conversely, when the commission and operating costs are high, adopting live streaming selling is not recommended. Furthermore, when market demand is high, manufacturers achieve greater profits by paying streamers a fixed commission. Conversely, when market demand is low, manufacturers gain higher profits by paying streamers different commissions based on actual market demand.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"68 ","pages":"Article 101464"},"PeriodicalIF":5.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656640","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 : 2024-11-01DOI: 10.1016/j.elerap.2024.101458
Hongzhen Lai , Yanju Zhou , Xiaohong Chen , Guiping Li
To meet consumers’ expectations for experiential shopping, an increasing number of online retailers are expanding offline channels, with physical showrooms and physical showrooms emerging as the two most popular offline channel modes. Both channels can match consumers’ demand for on-site experiences. However, the two modes differ in terms of service cost, demand promotion efficiency, and channel differentiation generation. The article considers three channel structures: single online channel, online channel with physical store, and online channel with physical showroom, as well as two scenarios: non-competitive and competitive. This study examines whether online retailers should integrate offline channels and which offline channel mode they should use by comparing the profit changes of themselves and rivals when three channel structures are adopted in two scenarios. It is found that: (i) If there is a significant difference between the online and physical store channels, providing physical stores help online retailer raise profits. If the physical showroom is highly effective at promoting demand, omnichannel retailers will dominate the entire online market, and expanding physical showrooms is always profitable; (ii) Whether in a competitive or non-competitive environment, online retailers should weigh channel differentiation and the efficiency of the offline channel modes in promoting demand to select the best channel mode; (iii) Due to channel differentiation, online retailers’ creation of physical stores, as well as increasing competition among physical stores, will have no impact on rival retailers. However, providing physical showrooms will result in lower sales volume and revenue for rival retailers.
{"title":"Physical stores versus physical showrooms: Channel structures of online retailers","authors":"Hongzhen Lai , Yanju Zhou , Xiaohong Chen , Guiping Li","doi":"10.1016/j.elerap.2024.101458","DOIUrl":"10.1016/j.elerap.2024.101458","url":null,"abstract":"<div><div>To meet consumers’ expectations for experiential shopping, an increasing number of online retailers are expanding offline channels, with physical showrooms and physical showrooms emerging as the two most popular offline channel modes. Both channels can match consumers’ demand for on-site experiences. However, the two modes differ in terms of service cost, demand promotion efficiency, and channel differentiation generation. The article considers three channel structures: single online channel, online channel with physical store, and online channel with physical showroom, as well as two scenarios: non-competitive and competitive. This study examines whether online retailers should integrate offline channels and which offline channel mode they should use by comparing the profit changes of themselves and rivals when three channel structures are adopted in two scenarios. It is found that: (i) If there is a significant difference between the online and physical store channels, providing physical stores help online retailer raise profits. If the physical showroom is highly effective at promoting demand, omnichannel retailers will dominate the entire online market, and expanding physical showrooms is always profitable; (ii) Whether in a competitive or non-competitive environment, online retailers should weigh channel differentiation and the efficiency of the offline channel modes in promoting demand to select the best channel mode; (iii) Due to channel differentiation, online retailers’ creation of physical stores, as well as increasing competition among physical stores, will have no impact on rival retailers. However, providing physical showrooms will result in lower sales volume and revenue for rival retailers.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"68 ","pages":"Article 101458"},"PeriodicalIF":5.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586844","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 : 2024-10-18DOI: 10.1016/j.elerap.2024.101455
Min-Yang Li, Fang-Yu Shih
This study explores the two-phase green reverse logistics problem with time windows and a focus on perishable items that pose a significant challenge in the management of returned goods in e-commerce. We proposed a mixed integer programming model that considers carbon emissions, fuel consumption costs, facility establishment and operating costs, among other factors.
We incorporated reinforcement learning concepts to adjust parameters in traditional genetic algorithms, which often have inflexible parameter settings, thereby enhancing both the efficiency and quality of the solutions. The Q-learning algorithm was adopted as the learning method, and various action combinations of reinforcement learning were explored and compared. We further evaluated the performance of different genetic algorithm variations. The results indicate that the proposed algorithm provides high-quality solutions, and that effective parameter configuration significantly impacts the algorithm’s overall performance.
{"title":"Solving the green reverse logistics problem in e-commerce using a reinforcement learning based genetic algorithm","authors":"Min-Yang Li, Fang-Yu Shih","doi":"10.1016/j.elerap.2024.101455","DOIUrl":"10.1016/j.elerap.2024.101455","url":null,"abstract":"<div><div>This study explores the two-phase green reverse logistics problem with time windows and a focus on perishable items that pose a significant challenge in the management of returned goods in e-commerce. We proposed a mixed integer programming model that considers carbon emissions, fuel consumption costs, facility establishment and operating costs, among other factors.</div><div>We incorporated reinforcement learning concepts to adjust parameters in traditional genetic algorithms, which often have inflexible parameter settings, thereby enhancing both the efficiency and quality of the solutions. The Q-learning algorithm was adopted as the learning method, and various action combinations of reinforcement learning were explored and compared. We further evaluated the performance of different genetic algorithm variations. The results indicate that the proposed algorithm provides high-quality solutions, and that effective parameter configuration significantly impacts the algorithm’s overall performance.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"68 ","pages":"Article 101455"},"PeriodicalIF":5.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529815","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 : 2024-09-25DOI: 10.1016/j.elerap.2024.101453
Qing Zhang , Tiaojun Xiao
This paper mainly considers an e-tailer-Stackelberg supply chain and characterizes the formative processes and distinct impacts of positive reviews and negative reviews on consumer purchases. The e-tailer can adopt either a pre-sales strategy (i.e., service strategy) or an after-sales strategy (i.e., rebate strategy) to encourage consumer purchases and stimulate positive reviews. By studying the above two strategies, we show that: (1) positive reviews and negative ones will exert a synergistic adverse effect on price decisions. (2) Given the exogenous rebate and service levels, the service (rebate) strategy becomes optimal for the e-tailer if consumers are more (less) willing to leave positive reviews. Notably, the e-tailer should refrain from adopting any incentive strategy when consumers exhibit a moderate willingness to provide positive reviews. (3) When the e-tailer can determine rebate and service levels, it should adopt the service (rebate) strategy if the threshold for positive reviews is intermediate (very low or high).
{"title":"Incentive strategies of an e-tailer considering online reviews: Rebates or services","authors":"Qing Zhang , Tiaojun Xiao","doi":"10.1016/j.elerap.2024.101453","DOIUrl":"10.1016/j.elerap.2024.101453","url":null,"abstract":"<div><div>This paper mainly considers an e-tailer-Stackelberg supply chain and characterizes the formative processes and distinct impacts of positive reviews and negative reviews on consumer purchases. The e-tailer can adopt either a pre-sales strategy (i.e., service strategy) or an after-sales strategy (i.e., rebate strategy) to encourage consumer purchases and stimulate positive reviews. By studying the above two strategies, we show that: (1) positive reviews and negative ones will exert a synergistic adverse effect on price decisions. (2) Given the exogenous rebate and service levels, the service (rebate) strategy becomes optimal for the e-tailer if consumers are more (less) willing to leave positive reviews. Notably, the e-tailer should refrain from adopting any incentive strategy when consumers exhibit a moderate willingness to provide positive reviews. (3) When the e-tailer can determine rebate and service levels, it should adopt the service (rebate) strategy if the threshold for positive reviews is intermediate (very low or high).</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"68 ","pages":"Article 101453"},"PeriodicalIF":5.9,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357684","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 : 2024-09-24DOI: 10.1016/j.elerap.2024.101452
Jakob J. Korbel , Marc Riar , Thorsten Pröhl , Rüdiger Zarnekow
Virtual 3D assets, i.e., 3D models that form the basis of virtual environments, products, and goods, are essential for the creation of a future metaverse. However, we have limited knowledge about the market dynamics in which virtual 3D assets are traded and the indicators that influence their value and pricing − and thus the purchasing mechanisms. The present study draws on multi-attribute utility and value- and competition-informed pricing theory to determine what drives the purchase of virtual 3D assets using secondary data from the marketplace Sketchfab. The empirical analysis indicates that the sellers’ value perception of virtual 3D assets contradicts the users’ interest and that organizational sellers outperform individual sellers by relying to a higher degree on competition- and value-informed pricing. Based on our findings, we identify implications for both organizational and individual sellers to refine their pricing strategies in accordance with the unique dynamics of 3D virtual asset marketplaces.
虚拟三维资产，即构成虚拟环境、产品和商品基础的三维模型，对于创建未来的元宇宙至关重要。然而，我们对虚拟三维资产交易的市场动态、影响其价值和定价的指标--以及购买机制--了解有限。本研究借鉴了多属性效用以及价值和竞争定价理论，利用市场 Sketchfab 的二手数据来确定虚拟 3D 资产购买的驱动因素。实证分析表明，卖家对虚拟三维资产的价值认知与用户的兴趣相悖，而组织卖家通过更大程度地依赖竞争和价值导向定价，表现优于个人卖家。根据我们的研究结果，我们确定了组织和个人卖家根据三维虚拟资产市场的独特动态完善其定价策略的意义。
{"title":"What drives user interest and purchase of virtual 3D assets? An empirical investigation of 3D model attributes and pricing dynamics","authors":"Jakob J. Korbel , Marc Riar , Thorsten Pröhl , Rüdiger Zarnekow","doi":"10.1016/j.elerap.2024.101452","DOIUrl":"10.1016/j.elerap.2024.101452","url":null,"abstract":"<div><div>Virtual 3D assets, i.e., 3D models that form the basis of virtual environments, products, and goods, are essential for the creation of a future metaverse. However, we have limited knowledge about the market dynamics in which virtual 3D assets are traded and the indicators that influence their value and pricing − and thus the purchasing mechanisms. The present study draws on multi-attribute utility and value- and competition-informed pricing theory to determine what drives the purchase of virtual 3D assets using secondary data from the marketplace Sketchfab. The empirical analysis indicates that the sellers’ value perception of virtual 3D assets contradicts the users’ interest and that organizational sellers outperform individual sellers by relying to a higher degree on competition- and value-informed pricing. Based on our findings, we identify implications for both organizational and individual sellers to refine their pricing strategies in accordance with the unique dynamics of 3D virtual asset marketplaces.</div></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"68 ","pages":"Article 101452"},"PeriodicalIF":5.9,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357685","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}
Pub Date : 2024-09-14DOI: 10.1016/j.elerap.2024.101451
Yi Li , Suyang Yu , Yulin Chen , Yuanchun Jiang , Kun Yuan
Grasping complementary relationships between fashion product pairings is gaining increasing attention in the e-commerce field. Current methods primarily utilize visual cues to assess compatibility, which, despite their efficacy, often lack sufficient explainability. Meanwhile, the rich semantic details embedded in product attributes remain largely unexplored. To tackle this, we propose a novel framework called Explainable Attribute-augmented Neural framework (EAN), which integrates comprehensive attribute and visual data, enabling explainability in fashion product compatibility modeling. We conduct quantitative and qualitative experiments to demonstrate the effectiveness and explainability of our proposed framework. The practical significance of our research is twofold. Firstly, it helps consumers understand the underlying reasons for fashion item pairings, thereby assisting them in refining their dressing combinations. Secondly, it provides novel perspectives for product design and assists e-commerce platforms in creating more effective product marketing combinations.
{"title":"Explainable fashion compatibility Prediction: An Attribute-Augmented neural framework","authors":"Yi Li , Suyang Yu , Yulin Chen , Yuanchun Jiang , Kun Yuan","doi":"10.1016/j.elerap.2024.101451","DOIUrl":"10.1016/j.elerap.2024.101451","url":null,"abstract":"<div><p>Grasping complementary relationships between fashion product pairings is gaining increasing attention in the e-commerce field. Current methods primarily utilize visual cues to assess compatibility, which, despite their efficacy, often lack sufficient explainability. Meanwhile, the rich semantic details embedded in product attributes remain largely unexplored. To tackle this, we propose a novel framework called Explainable Attribute-augmented Neural framework (EAN), which integrates comprehensive attribute and visual data, enabling explainability in fashion product compatibility modeling. We conduct quantitative and qualitative experiments to demonstrate the effectiveness and explainability of our proposed framework. The practical significance of our research is twofold. Firstly, it helps consumers understand the underlying reasons for fashion item pairings, thereby assisting them in refining their dressing combinations. Secondly, it provides novel perspectives for product design and assists e-commerce platforms in creating more effective product marketing combinations.</p></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":"68 ","pages":"Article 101451"},"PeriodicalIF":5.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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