Product review platforms in online marketplaces differ with respect to the granularity of product quality information they provide. While some platforms provide a single overall rating for product quality (also referred to as the single-dimensional rating scheme), others provide a separate rating for each individual quality attribute (also referred to as the multidimensional rating scheme). The multidimensional rating scheme is superior to the single-dimensional rating scheme, ceteris paribus, in reducing consumers’ uncertainty about product quality and value. However, we show that, when sellers respond to product ratings by adjusting their prices, compared to the single-dimensional rating scheme, the multidimensional rating scheme does not always benefit consumers, nor does it necessarily benefit sellers or society. The uncertainty associated with quality attribute rating and the extent of differentiation between competing products determines whether a finer-grained multidimensional rating scheme is superior to a coarser-grained single-dimensional rating scheme from the consumer, seller, and social planner perspectives. The main driver of the results is that more (less) granular and less (more) uncertain information exposes (hides) underlying differentiation, or a lack thereof, between competing products, which, in turn, alters upstream price competition in the presence of heterogeneous consumer preferences. The results demonstrate that focusing on the information transfer aspect of rating schemes provides only a partial understanding of the true impacts of rating schemes.
{"title":"When Paying for Reviews Pays Off: The Case of Performance-Contingent Monetary Rewards","authors":"Yinan Yu, Warut Khern-am-nuai, A. Pinsonneault","doi":"10.2139/ssrn.3161667","DOIUrl":"https://doi.org/10.2139/ssrn.3161667","url":null,"abstract":"Product review platforms in online marketplaces differ with respect to the granularity of product quality information they provide. While some platforms provide a single overall rating for product quality (also referred to as the single-dimensional rating scheme), others provide a separate rating for each individual quality attribute (also referred to as the multidimensional rating scheme). The multidimensional rating scheme is superior to the single-dimensional rating scheme, ceteris paribus, in reducing consumers’ uncertainty about product quality and value. However, we show that, when sellers respond to product ratings by adjusting their prices, compared to the single-dimensional rating scheme, the multidimensional rating scheme does not always benefit consumers, nor does it necessarily benefit sellers or society. The uncertainty associated with quality attribute rating and the extent of differentiation between competing products determines whether a finer-grained multidimensional rating scheme is superior to a coarser-grained single-dimensional rating scheme from the consumer, seller, and social planner perspectives. The main driver of the results is that more (less) granular and less (more) uncertain information exposes (hides) underlying differentiation, or a lack thereof, between competing products, which, in turn, alters upstream price competition in the presence of heterogeneous consumer preferences. The results demonstrate that focusing on the information transfer aspect of rating schemes provides only a partial understanding of the true impacts of rating schemes.","PeriodicalId":370988,"journal":{"name":"eBusiness & eCommerce eJournal","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116990648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the role of consumer herding and learning on the design of incentives for online customer reviews. Herding occurs when consumers are drawn to a product that appears to be popular because it has garnered a large number of reviews. Learning occurs when consumers infer product quality from reviews. We evaluate and compare three incentive policies. The first announces an incentive to all customers before purchase, the second offers an incentive after purchase, and the third rewards buyers only if they write positive, possibly fake, reviews. We use a generalized Polya urn process to model the evolution of reviews. The expected value of the resulting aggregate demand has the form of the Gompertz function. We obtain conditions under which each type of incentive is profitable, and preferred by a seller to the other incentives for reviews. The results imply that sellers should use different incentives policies depending on the quality and profit margin of a product. A pre-purchase incentive is the most profitable when product quality and profit margin are both high; an incentive offered to buyers after obtaining voluntary reviews is the most profitable when product quality is high and profit margin is low; and an incentive for only positive reviews is the most profitable when product quality and profit margin are both low. E-commerce platforms that limit their sellers to using post-purchase incentives might be more effective in curbing fake reviews if they also allow sellers to announce pre-purchase incentives to all customers.
{"title":"Herding, Learning and Incentives for Online Reviews","authors":"R. Kohli, Xiao Lei, Yeqing Zhou","doi":"10.2139/ssrn.3709486","DOIUrl":"https://doi.org/10.2139/ssrn.3709486","url":null,"abstract":"We investigate the role of consumer herding and learning on the design of incentives for online customer reviews. Herding occurs when consumers are drawn to a product that appears to be popular because it has garnered a large number of reviews. Learning occurs when consumers infer product quality from reviews. We evaluate and compare three incentive policies. The first announces an incentive to all customers before purchase, the second offers an incentive after purchase, and the third rewards buyers only if they write positive, possibly fake, reviews. We use a generalized Polya urn process to model the evolution of reviews. The expected value of the resulting aggregate demand has the form of the Gompertz function. We obtain conditions under which each type of incentive is profitable, and preferred by a seller to the other incentives for reviews. The results imply that sellers should use different incentives policies depending on the quality and profit margin of a product. A pre-purchase incentive is the most profitable when product quality and profit margin are both high; an incentive offered to buyers after obtaining voluntary reviews is the most profitable when product quality is high and profit margin is low; and an incentive for only positive reviews is the most profitable when product quality and profit margin are both low. E-commerce platforms that limit their sellers to using post-purchase incentives might be more effective in curbing fake reviews if they also allow sellers to announce pre-purchase incentives to all customers.","PeriodicalId":370988,"journal":{"name":"eBusiness & eCommerce eJournal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128032530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E-Commerce applications use reputation-based trust models based on the feedback comments and ratings gathered. The “all better Reputation” problem for the sellers has become very huge because a buyer facing problem to choose truthful sellers. This paper proposes a new model “CommTrust” to valuate trust by mining feedback comments that uses buyer comments to calculate reputation scores using multidimensional trust model. An algorithm is proposed to mine feedback comments for dimension weights, ratings, which combine methods of topic modeling, natural language processing and opinion mining. This model has been experimenting with the dataset which includes various user level feedback comments that are obtained on various products. It also finds various multi-dimensional features and their ratings using Gibbs-sampling that generates various categories for feedback and assigns trust score for each dimension under each product level.
{"title":"Commtrust: A Multi-Dimensional Trust Model for E-Commerce Applications","authors":"M. Divya","doi":"10.2139/ssrn.3703008","DOIUrl":"https://doi.org/10.2139/ssrn.3703008","url":null,"abstract":"E-Commerce applications use reputation-based trust models based on the feedback comments and ratings gathered. The “all better Reputation” problem for the sellers has become very huge because a buyer facing problem to choose truthful sellers. This paper proposes a new model “CommTrust” to valuate trust by mining feedback comments that uses buyer comments to calculate reputation scores using multidimensional trust model. An algorithm is proposed to mine feedback comments for dimension weights, ratings, which combine methods of topic modeling, natural language processing and opinion mining. This model has been experimenting with the dataset which includes various user level feedback comments that are obtained on various products. It also finds various multi-dimensional features and their ratings using Gibbs-sampling that generates various categories for feedback and assigns trust score for each dimension under each product level.","PeriodicalId":370988,"journal":{"name":"eBusiness & eCommerce eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130151706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Product fit uncertainty is cited as one of the top reasons for high online product return rates. Fit describes how well a product suits a consumer’s needs. The value of a product drops sharply when it deviates from a customer’s ideal fit. In this study, we focus on ordinal fit, a type of fit attribute that can be ordered on a scale, e.g. the size of apparel, and the difficulty level of courses. By leveraging a change in the product review system at an online retailer, we examine the impacts of two types of fit information – fit valence (an overall evaluation of a product’s ordinal-fit attribute) and fit reference (a reviewer’s ordinal-fit attribute and her choice of the product’s fit attribute) – on returns of apparel goods. Using the lens of advice-taking, we reveal the important role of the context of fit opinions (i.e. fit reference) in facilitating shoppers to better interpret fit valence by enabling effective ordinal-fit adjustment and, consequently, reducing product returns. We employ a predictive analytics framework for counterfactual prediction via the Generalized Synthetic Control method to address endogeneity issues and shed light on the dynamic treatment effect. Our findings indicate that fit valence alone can lower product returns only in a limited situation – when the majority of reviewers agree on the fit valence. In other cases – when either the fit valences are inconsistent or far and few between, it is the combination of fit valence and fit reference that lowers product returns. With the availability of both types of fit information, similar reviewers play an important role in helping improve the accuracy in ordinal-fit adjustments. Yet, albeit less effective, information from reviewers with dissimilar body sizes can also help make useful ordinal-fit adjustments. Besides, shoppers appear to benefit from both positive and negative fit valences, as long as they are aided by fit reference. Our empirical insights are relevant to many situations where ordinal-fit attributes dominate consumers’ product evaluation process. Accordingly, we provide useful implications for online sellers grappling with high product return rates.
{"title":"Do Fit Opinions Matter? The Impact of Fit Context on Online Product Returns","authors":"Yang Wang, V. Ramachandran, O. Sheng","doi":"10.1287/ISRE.2020.0965","DOIUrl":"https://doi.org/10.1287/ISRE.2020.0965","url":null,"abstract":"Product fit uncertainty is cited as one of the top reasons for high online product return rates. Fit describes how well a product suits a consumer’s needs. The value of a product drops sharply when it deviates from a customer’s ideal fit. In this study, we focus on ordinal fit, a type of fit attribute that can be ordered on a scale, e.g. the size of apparel, and the difficulty level of courses. By leveraging a change in the product review system at an online retailer, we examine the impacts of two types of fit information – fit valence (an overall evaluation of a product’s ordinal-fit attribute) and fit reference (a reviewer’s ordinal-fit attribute and her choice of the product’s fit attribute) – on returns of apparel goods. Using the lens of advice-taking, we reveal the important role of the context of fit opinions (i.e. fit reference) in facilitating shoppers to better interpret fit valence by enabling effective ordinal-fit adjustment and, consequently, reducing product returns. \u0000 \u0000We employ a predictive analytics framework for counterfactual prediction via the Generalized Synthetic Control method to address endogeneity issues and shed light on the dynamic treatment effect. Our findings indicate that fit valence alone can lower product returns only in a limited situation – when the majority of reviewers agree on the fit valence. In other cases – when either the fit valences are inconsistent or far and few between, it is the combination of fit valence and fit reference that lowers product returns. With the availability of both types of fit information, similar reviewers play an important role in helping improve the accuracy in ordinal-fit adjustments. Yet, albeit less effective, information from reviewers with dissimilar body sizes can also help make useful ordinal-fit adjustments. Besides, shoppers appear to benefit from both positive and negative fit valences, as long as they are aided by fit reference. Our empirical insights are relevant to many situations where ordinal-fit attributes dominate consumers’ product evaluation process. Accordingly, we provide useful implications for online sellers grappling with high product return rates.","PeriodicalId":370988,"journal":{"name":"eBusiness & eCommerce eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126382155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider the product-ranking challenge that online retailers face when their customers typically behave as “window shoppers.” They form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue browsing. We design online learning algorithms for product ranking that maximize the number of customers who engage with the site. Customers’ product preferences and attention spans are correlated and unknown to the retailer; furthermore, the retailer cannot exploit similarities across products, owing to the fact that the products are not necessarily characterized by a set of attributes. We develop a class of online learning-then-earning algorithms that prescribe a ranking to offer each customer, learning from preceding customers’ clickstream data to offer better rankings to subsequent customers. Our algorithms balance product popularity with diversity, the notion of appealing to a large variety of heterogeneous customers. We prove that our learning algorithms converge to a ranking that matches the best-known approximation factors for the offline, complete information setting. Finally, we partner with Wayfair — a multibillion-dollar home goods online retailer — to estimate the impact of our algorithms in practice via simulations using actual clickstream data, and we find that our algorithms yield a significant increase (5–30%) in the number of customers that engage with the site. This paper was accepted by J. George Shanthikumar for the Management Science Special Issue on Data-Driven Prescriptive Analytics.
我们考虑了当他们的顾客通常表现为“橱窗购物者”时，在线零售商所面临的产品排名挑战。他们在浏览了商品的初始位置后，形成了对商品分类的印象，然后决定是否继续浏览。我们为产品排名设计了在线学习算法，以最大限度地提高与网站互动的客户数量。消费者的产品偏好和注意力持续时间是相互关联的，零售商不知道;此外，零售商不能利用产品之间的相似性，因为产品不一定具有一组属性。我们开发了一类在线学习-然后学习算法，该算法规定为每个客户提供排名，从之前客户的点击流数据中学习，为后续客户提供更好的排名。我们的算法平衡了产品的受欢迎程度和多样性，即吸引各种各样的异质客户的概念。我们证明了我们的学习算法收敛到一个与离线完整信息设置中最著名的近似因子相匹配的排名。最后，我们与Wayfair(一家价值数十亿美元的家居用品在线零售商)合作，通过使用实际点击流数据的模拟来估计我们的算法在实践中的影响，我们发现我们的算法使与网站互动的客户数量显著增加(5-30%)。这篇论文被J. George Shanthikumar接受，发表在《数据驱动的规范分析》管理科学特刊上。
{"title":"Learning to Rank an Assortment of Products","authors":"K. Ferreira, Sunanda Parthasarathy, S. Sekar","doi":"10.2139/ssrn.3395992","DOIUrl":"https://doi.org/10.2139/ssrn.3395992","url":null,"abstract":"We consider the product-ranking challenge that online retailers face when their customers typically behave as “window shoppers.” They form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue browsing. We design online learning algorithms for product ranking that maximize the number of customers who engage with the site. Customers’ product preferences and attention spans are correlated and unknown to the retailer; furthermore, the retailer cannot exploit similarities across products, owing to the fact that the products are not necessarily characterized by a set of attributes. We develop a class of online learning-then-earning algorithms that prescribe a ranking to offer each customer, learning from preceding customers’ clickstream data to offer better rankings to subsequent customers. Our algorithms balance product popularity with diversity, the notion of appealing to a large variety of heterogeneous customers. We prove that our learning algorithms converge to a ranking that matches the best-known approximation factors for the offline, complete information setting. Finally, we partner with Wayfair — a multibillion-dollar home goods online retailer — to estimate the impact of our algorithms in practice via simulations using actual clickstream data, and we find that our algorithms yield a significant increase (5–30%) in the number of customers that engage with the site. This paper was accepted by J. George Shanthikumar for the Management Science Special Issue on Data-Driven Prescriptive Analytics.","PeriodicalId":370988,"journal":{"name":"eBusiness & eCommerce eJournal","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134151002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background music adds a multi-sensory element to marketing and e-commerce. Applying interactive sensory-enabling technologies (SETs) to online shopping websites is an area of interest of sensory marketing. This research examines interactive background music in ecommerce and investigates how online consumer involvement moderates the effects of interactive music. Single-factor experiments with three conditions (interactive music, static background music, and control) were conducted to investigate its impact on experiential value, cognitive value, and purchase intention of high- and low-involvement consumers among both students (Study 1, N = 251) and non-student samples (Study 2, N = 218). Different music genres were applied to stimuli of the two studies to demonstrate generalizability of the findings. Results find that interactive music enhances the experiential value of e-commerce for low-involvement consumers. By contrast, high-involvement consumers show greater purchase intention under the interactive music condition due to a heightened level of perceived cognitive value. Involvement is an effective predictor of elaboration and purchase intention under the interactive music condition, but not under the other two conditions. The contribution is twofold:
1) it shows the impact of music as an interactive SET and,
2) demonstrates the moderating role of consumer involvement in the context of multi-sensory integration in e-commerce.
Theoretical and practical implications are discussed along with limitations and directions for future research.
{"title":"Interactive Music for Multisensory E-Commerce: The Moderating Role of Online Consumer Involvement in Experiential Value, Cognitive Value, and Purchase Intention","authors":"A. Hwang, Jeeyun Oh, A. Scheinbaum","doi":"10.2139/ssrn.3601256","DOIUrl":"https://doi.org/10.2139/ssrn.3601256","url":null,"abstract":"Background music adds a multi-sensory element to marketing and e-commerce. Applying interactive sensory-enabling technologies (SETs) to online shopping websites is an area of interest of sensory marketing. This research examines interactive background music in ecommerce and investigates how online consumer involvement moderates the effects of interactive music. Single-factor experiments with three conditions (interactive music, static background music, and control) were conducted to investigate its impact on experiential value, cognitive value, and purchase intention of high- and low-involvement consumers among both students (Study 1, N = 251) and non-student samples (Study 2, N = 218). Different music genres were applied to stimuli of the two studies to demonstrate generalizability of the findings. Results find that interactive music enhances the experiential value of e-commerce for low-involvement consumers. By contrast, high-involvement consumers show greater purchase intention under the interactive music condition due to a heightened level of perceived cognitive value. Involvement is an effective predictor of elaboration and purchase intention under the interactive music condition, but not under the other two conditions. The contribution is twofold: <br><br>1) it shows the impact of music as an interactive SET and, <br><br>2) demonstrates the moderating role of consumer involvement in the context of multi-sensory integration in e-commerce. <br><br>Theoretical and practical implications are discussed along with limitations and directions for future research.","PeriodicalId":370988,"journal":{"name":"eBusiness & eCommerce eJournal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126821942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper address the importance of Organizational learning as imperative for the use of Information technology in e-commerce. Based on the information presented from a well-known company in e-commerce Amazon just for their staff until 2025 is planning to invest 700 million dollars to train 100,000 employees (Scott, 2019). Technology is evolving every day and the need for advancing employee skills is a must for being in line with development trends of market and customer needs for efficient services.
{"title":"The Importance of Organizational Learning As Imperative for the Use of Information Technology in E-Commerce","authors":"Besim Kamberaj","doi":"10.2139/ssrn.3594083","DOIUrl":"https://doi.org/10.2139/ssrn.3594083","url":null,"abstract":"This paper address the importance of Organizational learning as imperative for the use of Information technology in e-commerce. Based on the information presented from a well-known company in e-commerce Amazon just for their staff until 2025 is planning to invest 700 million dollars to train 100,000 employees (Scott, 2019). Technology is evolving every day and the need for advancing employee skills is a must for being in line with development trends of market and customer needs for efficient services.","PeriodicalId":370988,"journal":{"name":"eBusiness & eCommerce eJournal","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121557987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online marketplace designers frequently run A/B tests to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect estimates obtained through Bernoulli randomized experiments are often biased due to violations of the stable unit treatment value assumption. This can be particularly problematic for experiments that impact sellers' strategic choices, affect buyers' preferences over items in their consideration set, or change buyers' consideration sets altogether. In this work, we measure and reduce bias due to interference in online marketplace experiments by using observational data to creating clusters of similar listings, and then using those clusters to conduct cluster-randomized field experiments. We provide a lower bound on the magnitude of bias due to interference by conducting a meta-experiment that randomizes over two experiment designs: one Bernoulli randomized, one cluster randomized. In both meta-experiment arms, treatment sellers are subject to a different platform fee policy than control sellers, resulting in different prices for buyers. By conducting a joint analysis of the two meta-experiment arms, we find a large and statistically significant difference between the total average treatment effect estimates obtained with the two designs, and estimate that 32.60% of the Bernoulli-randomized treatment effect estimate is due to interference bias. We also find weak evidence that the magnitude and/or direction of interference bias depends on extent to which a marketplace is supply- or demand-constrained, and analyze a second meta-experiment to highlight the difficulty of detecting interference bias when treatment interventions require intention-to-treat analysis.
{"title":"Reducing Interference Bias in Online Marketplace Pricing Experiments","authors":"David Holtz, R. Lobel, I. Liskovich, Sinan Aral","doi":"10.2139/ssrn.3583836","DOIUrl":"https://doi.org/10.2139/ssrn.3583836","url":null,"abstract":"Online marketplace designers frequently run A/B tests to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect estimates obtained through Bernoulli randomized experiments are often biased due to violations of the stable unit treatment value assumption. This can be particularly problematic for experiments that impact sellers' strategic choices, affect buyers' preferences over items in their consideration set, or change buyers' consideration sets altogether. In this work, we measure and reduce bias due to interference in online marketplace experiments by using observational data to creating clusters of similar listings, and then using those clusters to conduct cluster-randomized field experiments. We provide a lower bound on the magnitude of bias due to interference by conducting a meta-experiment that randomizes over two experiment designs: one Bernoulli randomized, one cluster randomized. In both meta-experiment arms, treatment sellers are subject to a different platform fee policy than control sellers, resulting in different prices for buyers. By conducting a joint analysis of the two meta-experiment arms, we find a large and statistically significant difference between the total average treatment effect estimates obtained with the two designs, and estimate that 32.60% of the Bernoulli-randomized treatment effect estimate is due to interference bias. We also find weak evidence that the magnitude and/or direction of interference bias depends on extent to which a marketplace is supply- or demand-constrained, and analyze a second meta-experiment to highlight the difficulty of detecting interference bias when treatment interventions require intention-to-treat analysis.","PeriodicalId":370988,"journal":{"name":"eBusiness & eCommerce eJournal","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132830946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we estimate the inefficiency in online auctions. Online auctions can be inefficient due to the Internet fraud. A typical example of Internet fraud is when sellers do not send goods to winning bidders even though they have received payment. Therefore, bidders always bear a risk of fraud, and this risk may lead to transaction failure. In our empirical example, we use eBay PlayStation 3 auctions held in 2009. We find that the efficiency loss is over $30 for more than 5% of online auctions. Furthermore, we also find the probability of the inefficient online auction is more than 0.25.
在本文中，我们估计了网上拍卖的低效率。由于网络欺诈，网上拍卖效率低下。网络欺诈的一个典型例子是，卖家即使收到了付款，也不向中标者发送货物。因此，投标人始终承担欺诈风险，这种风险可能导致交易失败。在我们的实证例子中，我们使用2009年举行的eBay PlayStation 3拍卖。我们发现，超过5%的在线拍卖的效率损失超过30美元。此外，我们还发现，无效的在线拍卖的概率大于0.25。
{"title":"Estimating Inefficiency in Online Auctions","authors":"Yohsuke Hirose","doi":"10.2139/ssrn.3557170","DOIUrl":"https://doi.org/10.2139/ssrn.3557170","url":null,"abstract":"In this paper, we estimate the inefficiency in online auctions. Online auctions can be inefficient due to the Internet fraud. A typical example of Internet fraud is when sellers do not send goods to winning bidders even though they have received payment. Therefore, bidders always bear a risk of fraud, and this risk may lead to transaction failure. In our empirical example, we use eBay PlayStation 3 auctions held in 2009. We find that the efficiency loss is over $30 for more than 5% of online auctions. Furthermore, we also find the probability of the inefficient online auction is more than 0.25.","PeriodicalId":370988,"journal":{"name":"eBusiness & eCommerce eJournal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116186240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abeer S. Alkhalfan, Zainab W. Altheeb, Noor A. Alshamsi, Heba W. Alothman, Ibrahim Almarashdeh, Muneerah Alshabanah, Daniah Alrajhi, M. Alsmadi
According to the fast-changing business environment nowadays, we have to be more effective and faster in responding to customers' needs to make them able to access products instantly. This can be done by designing an E-commerce web website for unused goods, which sells various fashions and goods to the customers. To implement an online shopping website, a virtual store on the Internet is needed which allows customers to seek products and select them from a catalog. The customer needs to fill some fields to order a specific product. The purpose of this paper is designing and implementation of the website of unused goods, which sells various fashions and goods to the customers, the good that will be for sale on the website are new unused goods which the customer couldn’t return to the store they buy from to any reason. The proposed system was developed using the Unified Modeling Language (UML), ASP.NET and Access.
{"title":"Designing and Developing of E-Commerce Website for Unused New Goods Shopping","authors":"Abeer S. Alkhalfan, Zainab W. Altheeb, Noor A. Alshamsi, Heba W. Alothman, Ibrahim Almarashdeh, Muneerah Alshabanah, Daniah Alrajhi, M. Alsmadi","doi":"10.32628/ijsrst207233","DOIUrl":"https://doi.org/10.32628/ijsrst207233","url":null,"abstract":"According to the fast-changing business environment nowadays, we have to be more effective and faster in responding to customers' needs to make them able to access products instantly. This can be done by designing an E-commerce web website for unused goods, which sells various fashions and goods to the customers. To implement an online shopping website, a virtual store on the Internet is needed which allows customers to seek products and select them from a catalog. The customer needs to fill some fields to order a specific product. The purpose of this paper is designing and implementation of the website of unused goods, which sells various fashions and goods to the customers, the good that will be for sale on the website are new unused goods which the customer couldn’t return to the store they buy from to any reason. The proposed system was developed using the Unified Modeling Language (UML), ASP.NET and Access.","PeriodicalId":370988,"journal":{"name":"eBusiness & eCommerce eJournal","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115656435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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