Online retailers offer free shipping services, such as threshold free shipping (TFS) and membership free shipping (MFS), to promote sales and provide a better shopping experience to consumers in online retailing. Although MFS attracts more member-consumers, it encourages consumers to place more small orders than TFS, which significantly increases the operational costs of the online retailer. To address this issue, we propose two price discount policies under the MFS service, namely the limited-time discount and the threshold discount. Then, we build analytical models under these two policies to explore the impacts of offering price discounts on the retailer’s profit and consumers’ welfare. We find that no matter which discount policy is adopted, consumers are more likely to consolidate several small orders from different time periods into a big one to obtain the discount. The economies of scale generated by consumers consolidating their orders under these discount policies can help reduce online retailers’ operational costs. Therefore, regardless of any discount policy offered by the online retailer under the MFS service, consumers will place more big orders and more member-consumers are attracted, i.e., the online retailer can have its cake and eat it too. Our research findings provide decision-making insights for practitioners who offer free shipping services and price discounts to consumers in online retailing.
{"title":"Have Your Cake and Eat It? Price Discount Programs under the Membership Free Shipping Policy in Online Retailing","authors":"Zhipeng Tang, Guowei Hua, Tai Chiu Edwin Cheng, Xiaowei Li, Jingxin Dong","doi":"10.3390/jtaer19010012","DOIUrl":"https://doi.org/10.3390/jtaer19010012","url":null,"abstract":"Online retailers offer free shipping services, such as threshold free shipping (TFS) and membership free shipping (MFS), to promote sales and provide a better shopping experience to consumers in online retailing. Although MFS attracts more member-consumers, it encourages consumers to place more small orders than TFS, which significantly increases the operational costs of the online retailer. To address this issue, we propose two price discount policies under the MFS service, namely the limited-time discount and the threshold discount. Then, we build analytical models under these two policies to explore the impacts of offering price discounts on the retailer’s profit and consumers’ welfare. We find that no matter which discount policy is adopted, consumers are more likely to consolidate several small orders from different time periods into a big one to obtain the discount. The economies of scale generated by consumers consolidating their orders under these discount policies can help reduce online retailers’ operational costs. Therefore, regardless of any discount policy offered by the online retailer under the MFS service, consumers will place more big orders and more member-consumers are attracted, i.e., the online retailer can have its cake and eat it too. Our research findings provide decision-making insights for practitioners who offer free shipping services and price discounts to consumers in online retailing.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":"138 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139581257","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}
María Jesús Carrasco-Santos, Carmen Cristófol-Rodríguez, Ismael Begdouri-Rodríguez
This research study explores the representation of men in fashion advertising and investigates whether societal and fashion evolution has contributed to a departure from traditional stereotypes. The research methodology comprised three phases: content analysis, surveys, and in-depth interviews with an expert panel, examining how men’s clothing has been communicated in fashion over a span of 50 years, with a focus on three renowned brands: Lacoste, Burberry, and Hugo Boss. The findings reveal a notable shift in fashion advertising targeting men, characterized by increased racial diversity among models and a more diverse depiction of attitudes and poses. However, homosexual or bisexual couples remain largely unrepresented. The study highlights the influence of advertising on shaping the image of the “new man”, evident through the diminishing gender boundaries in clothing and accessories and the persistent struggle to break free from stereotypes. The study underscores the significance of ongoing efforts to promote diversity and inclusivity in fashion advertising.
{"title":"Evolution of Men’s Image in Fashion Advertising: Breaking Stereotypes and Embracing Diversity","authors":"María Jesús Carrasco-Santos, Carmen Cristófol-Rodríguez, Ismael Begdouri-Rodríguez","doi":"10.3390/jtaer19010011","DOIUrl":"https://doi.org/10.3390/jtaer19010011","url":null,"abstract":"This research study explores the representation of men in fashion advertising and investigates whether societal and fashion evolution has contributed to a departure from traditional stereotypes. The research methodology comprised three phases: content analysis, surveys, and in-depth interviews with an expert panel, examining how men’s clothing has been communicated in fashion over a span of 50 years, with a focus on three renowned brands: Lacoste, Burberry, and Hugo Boss. The findings reveal a notable shift in fashion advertising targeting men, characterized by increased racial diversity among models and a more diverse depiction of attitudes and poses. However, homosexual or bisexual couples remain largely unrepresented. The study highlights the influence of advertising on shaping the image of the “new man”, evident through the diminishing gender boundaries in clothing and accessories and the persistent struggle to break free from stereotypes. The study underscores the significance of ongoing efforts to promote diversity and inclusivity in fashion advertising.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":"1 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139581256","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}
The Internet of Things (IoT) was born from the fusion of virtual and physical space and became the initiator of many scientific fields. Economic sustainability is the key to further development and progress. To keep up with the changes, it is necessary to adapt economic models and concepts to meet the requirements of future smart environments. Today, the need for electronic commerce (e-commerce) has become an economic priority during the transition between Industry 4.0 and Industry 5.0. Unlike mass production in Industry 4.0, customized production in Industry 5.0 should gain additional benefits in vertical management and decision-making concepts. The authors’ research is focused on e-commerce in a three-layer vertical IoT environment. The vertical IoT concept is composed of edge, fog, and cloud layers. Given the ubiquity of artificial intelligence in data processing, economic analysis, and predictions, this paper presents a few state-of-the-art machine learning (ML) algorithms facilitating the transition from a flat to a vertical e-commerce concept. The authors also propose hands-on ML algorithms for a few e-commerce types: consumer–consumer and consumer–company–consumer relationships. These algorithms are mainly composed of convolutional neural networks (CNNs), natural language understanding (NLU), sequential pattern mining (SPM), reinforcement learning (RL for agent training), algorithms for clicking on the item prediction, consumer behavior learning, etc. All presented concepts, algorithms, and models are described in detail.
{"title":"The Future of Electronic Commerce in the IoT Environment","authors":"Antonina Lazić, Saša Milić, Dragan Vukmirović","doi":"10.3390/jtaer19010010","DOIUrl":"https://doi.org/10.3390/jtaer19010010","url":null,"abstract":"The Internet of Things (IoT) was born from the fusion of virtual and physical space and became the initiator of many scientific fields. Economic sustainability is the key to further development and progress. To keep up with the changes, it is necessary to adapt economic models and concepts to meet the requirements of future smart environments. Today, the need for electronic commerce (e-commerce) has become an economic priority during the transition between Industry 4.0 and Industry 5.0. Unlike mass production in Industry 4.0, customized production in Industry 5.0 should gain additional benefits in vertical management and decision-making concepts. The authors’ research is focused on e-commerce in a three-layer vertical IoT environment. The vertical IoT concept is composed of edge, fog, and cloud layers. Given the ubiquity of artificial intelligence in data processing, economic analysis, and predictions, this paper presents a few state-of-the-art machine learning (ML) algorithms facilitating the transition from a flat to a vertical e-commerce concept. The authors also propose hands-on ML algorithms for a few e-commerce types: consumer–consumer and consumer–company–consumer relationships. These algorithms are mainly composed of convolutional neural networks (CNNs), natural language understanding (NLU), sequential pattern mining (SPM), reinforcement learning (RL for agent training), algorithms for clicking on the item prediction, consumer behavior learning, etc. All presented concepts, algorithms, and models are described in detail.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":"4 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139581185","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}
Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research and industry trends. In this paper, we propose a comprehensive framework to identify trends in consumer behavior using multiple layers of processing, including clustering, classification, and association rule learning. The aim is to help a major retailer in Saudi Arabia better understand customer behavior by utilizing the power of big data analysis. The proposed framework is presented as being generalized to gain insight into the generated big data and enable data-driven decision-making in other relevant domains. We developed this framework in collaboration with a large supermarket chain in Saudi Arabia, which provided us with over 1,000,000 sales transaction records belonging to around 30,000 of their loyal customers. In this study, we apply our proposed framework to those data as a case study and present our initial results of consumer clustering and association rules for each cluster. Moreover, we analyze our findings to figure out how we can further utilize intelligence to predict customer behavior in clustered groups.
{"title":"A Consumer Behavior Analysis Framework toward Improving Market Performance Indicators: Saudi’s Retail Sector as a Case Study","authors":"Monerah Alawadh, Ahmed Barnawi","doi":"10.3390/jtaer19010009","DOIUrl":"https://doi.org/10.3390/jtaer19010009","url":null,"abstract":"Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research and industry trends. In this paper, we propose a comprehensive framework to identify trends in consumer behavior using multiple layers of processing, including clustering, classification, and association rule learning. The aim is to help a major retailer in Saudi Arabia better understand customer behavior by utilizing the power of big data analysis. The proposed framework is presented as being generalized to gain insight into the generated big data and enable data-driven decision-making in other relevant domains. We developed this framework in collaboration with a large supermarket chain in Saudi Arabia, which provided us with over 1,000,000 sales transaction records belonging to around 30,000 of their loyal customers. In this study, we apply our proposed framework to those data as a case study and present our initial results of consumer clustering and association rules for each cluster. Moreover, we analyze our findings to figure out how we can further utilize intelligence to predict customer behavior in clustered groups.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":"73 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139501224","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}
Bassant A. Abdelfattah, Saad M. Darwish, Saleh M. Elkaffas
Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.
社交媒体平台让许多人得以公开表达和传播自己的观点。研究人员相当感兴趣的一个话题是社交媒体对股市预测的影响。关于公司或服务的正面或负面反馈都有可能影响其股票价格。然而，使用情感分析（SA）预测股市走势会遇到一些障碍，这些障碍源于先前研究中观察到的 SA 技术的不精确性，这些不精确性忽略了数据固有的不确定性，从而直接损害了股市指标的可信度。在本文中，我们利用中性逻辑（NL）改进了 SA 的过程，提出了一种新的模型，通过处理不确定和不确定的数据对推文进行精确分类，从而利用 SA 增强对股市走势的预测。在预测模型中，我们将情感分析结果和历史股市数据作为深度学习算法（称为长短期记忆（LSTM））的输入，以预测特定天数后的股票走势。本研究的结果表明，在使用相同数据集预测股价波动时，预测准确率超过了以往研究的准确率。
{"title":"Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis","authors":"Bassant A. Abdelfattah, Saad M. Darwish, Saleh M. Elkaffas","doi":"10.3390/jtaer19010007","DOIUrl":"https://doi.org/10.3390/jtaer19010007","url":null,"abstract":"Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":"7 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139463263","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}
Miguel Alves Gomes, Richard Meyes, Philipp Meisen, Tobias Meisen
Alongside natural language processing and computer vision, large learning models have found their way into e-commerce. Especially, for recommender systems and click-through rate prediction, these models have shown great predictive power. In this work, we aim to predict the probability that a customer will click on a given recommendation, given only its current session. Therefore, we propose a two-stage approach consisting of a customer behavior-embedding representation and a recurrent neural network. In the first stage, we train a self-supervised skip-gram embedding on customer activity data. The resulting embedding representation is used in the second stage to encode the customer sequences which are then used as input to the learning model. Our proposed approach diverges from the prevailing trend of utilizing extensive end-to-end models for click-through rate prediction. The experiments, which incorporate a real-world industrial use case and a widely used as well as openly available benchmark dataset, demonstrate that our approach outperforms the current state-of-the-art models. Our approach predicts customers’ click intention with an average F1 accuracy of 94% for the industrial use case which is one percentage point higher than the state-of-the-art baseline and an average F1 accuracy of 79% for the benchmark dataset, which outperforms the best tested state-of-the-art baseline by more than seven percentage points. The results show that, contrary to current trends in that field, large end-to-end models are not always needed. The analysis of our experiments suggests that the reason for the performance of our approach is the self-supervised pre-trained embedding of customer behavior that we use as the customer representation.
除了自然语言处理和计算机视觉，大型学习模型也已进入电子商务领域。特别是在推荐系统和点击率预测方面，这些模型显示出了强大的预测能力。在这项工作中，我们的目标是根据客户的当前会话，预测客户点击给定推荐的概率。因此，我们提出了一种由客户行为嵌入表征和递归神经网络组成的两阶段方法。在第一阶段，我们在客户活动数据上训练自监督跳序嵌入。由此产生的嵌入表示法在第二阶段用于对客户序列进行编码，然后将其作为学习模型的输入。我们提出的方法不同于利用广泛的端到端模型进行点击率预测的主流趋势。实验结合了真实世界的工业用例和广泛使用的公开基准数据集，证明我们的方法优于当前最先进的模型。在工业用例中，我们的方法预测客户点击意向的平均 F1 准确率为 94%，比最先进的基准高出一个百分点；在基准数据集中，我们的方法预测客户点击意向的平均 F1 准确率为 79%，比经过最佳测试的最先进基准高出七个百分点以上。结果表明，与该领域目前的趋势相反，并不总是需要大型端到端模型。我们的实验分析表明，我们的方法之所以能取得如此优异的成绩，是因为我们使用了客户行为的自监督预训练嵌入作为客户表示。
{"title":"It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation","authors":"Miguel Alves Gomes, Richard Meyes, Philipp Meisen, Tobias Meisen","doi":"10.3390/jtaer19010008","DOIUrl":"https://doi.org/10.3390/jtaer19010008","url":null,"abstract":"Alongside natural language processing and computer vision, large learning models have found their way into e-commerce. Especially, for recommender systems and click-through rate prediction, these models have shown great predictive power. In this work, we aim to predict the probability that a customer will click on a given recommendation, given only its current session. Therefore, we propose a two-stage approach consisting of a customer behavior-embedding representation and a recurrent neural network. In the first stage, we train a self-supervised skip-gram embedding on customer activity data. The resulting embedding representation is used in the second stage to encode the customer sequences which are then used as input to the learning model. Our proposed approach diverges from the prevailing trend of utilizing extensive end-to-end models for click-through rate prediction. The experiments, which incorporate a real-world industrial use case and a widely used as well as openly available benchmark dataset, demonstrate that our approach outperforms the current state-of-the-art models. Our approach predicts customers’ click intention with an average F1 accuracy of 94% for the industrial use case which is one percentage point higher than the state-of-the-art baseline and an average F1 accuracy of 79% for the benchmark dataset, which outperforms the best tested state-of-the-art baseline by more than seven percentage points. The results show that, contrary to current trends in that field, large end-to-end models are not always needed. The analysis of our experiments suggests that the reason for the performance of our approach is the self-supervised pre-trained embedding of customer behavior that we use as the customer representation.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":"20 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139463210","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}
While numerous people use social mobile applications, ads within these apps are often avoided. Although the significance of prior negative experience and personality traits in impacting consumers’ perceptions and behaviors has been acknowledged, limited research has explored their influence on ad perceptions and avoidance. This study aims to examine the effects of prior negative experience and personality traits on ad perceptions and ad avoidance of Generation Y (Gen Y) and Generation Z (Gen Z) within two prominent mobile social apps: WeChat and TikTok. An online survey was used to gather data from 353 Chinese Gen Y and Gen Zers who were active users of WeChat and TikTok. Findings from several regression analyses show that prior negative experience is an essential determinant of ad avoidance, influencing not just directly but indirectly by diminishing perceived ad personalization and intensifying perceived goal impediment and ad clutter. Personality traits also significantly affect ad avoidance, with conscientiousness exerting a positive effect, whereas agreeableness has a negative impact. Notably, agreeableness, emotional stability, and openness to experience moderate the associations between ad perceptions and avoidance. Intriguingly, the effects of these factors are platform-specific, with WeChat’s main factor for ad avoidance being erceived goal impediment and TikTok’s main factor being ad clutter. Based on these findings, the theoretical and practical implications are discussed.
虽然很多人都在使用社交移动应用，但这些应用中的广告却常常被人们回避。虽然人们已经认识到先前的负面经验和个性特征对消费者的感知和行为有重要影响，但探讨它们对广告感知和回避的影响的研究却很有限。本研究旨在探讨在微信和嘀嗒这两款著名的移动社交应用中，以往的负面经验和个性特征对 Y 代（Y 世代）和 Z 代（Z 世代）的广告感知和广告回避的影响。本研究通过在线调查收集了 353 名中国 Y 世代和 Z 世代的数据，他们都是微信和嘀嗒的活跃用户。几项回归分析的结果表明，之前的负面经历是广告回避的一个重要决定因素，不仅直接影响广告回避，还通过降低感知到的广告个性化程度、加强感知到的目标障碍和广告杂乱程度间接产生影响。人格特质也会对广告回避产生重大影响，其中自觉性会产生积极影响，而宜人性则会产生消极影响。值得注意的是，宜人性、情绪稳定性和经验开放性缓和了广告感知与回避之间的关联。耐人寻味的是，这些因素的影响具有平台特异性，微信对广告回避的主要影响因素是 "目标受阻"，而嘀嗒的主要影响因素是 "广告杂乱"。基于这些发现，我们讨论了其理论和实践意义。
{"title":"Effects of Prior Negative Experience and Personality Traits on WeChat and TikTok Ad Avoidance among Chinese Gen Y and Gen Z","authors":"Ningyan Cao, N. Isa, Selvan Perumal","doi":"10.3390/jtaer19010006","DOIUrl":"https://doi.org/10.3390/jtaer19010006","url":null,"abstract":"While numerous people use social mobile applications, ads within these apps are often avoided. Although the significance of prior negative experience and personality traits in impacting consumers’ perceptions and behaviors has been acknowledged, limited research has explored their influence on ad perceptions and avoidance. This study aims to examine the effects of prior negative experience and personality traits on ad perceptions and ad avoidance of Generation Y (Gen Y) and Generation Z (Gen Z) within two prominent mobile social apps: WeChat and TikTok. An online survey was used to gather data from 353 Chinese Gen Y and Gen Zers who were active users of WeChat and TikTok. Findings from several regression analyses show that prior negative experience is an essential determinant of ad avoidance, influencing not just directly but indirectly by diminishing perceived ad personalization and intensifying perceived goal impediment and ad clutter. Personality traits also significantly affect ad avoidance, with conscientiousness exerting a positive effect, whereas agreeableness has a negative impact. Notably, agreeableness, emotional stability, and openness to experience moderate the associations between ad perceptions and avoidance. Intriguingly, the effects of these factors are platform-specific, with WeChat’s main factor for ad avoidance being erceived goal impediment and TikTok’s main factor being ad clutter. Based on these findings, the theoretical and practical implications are discussed.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":"14 7","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139438639","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}
Biao Xu, Jinting Huang, Xiaodan Zhang, Thomas Brashear Alejandro
To bolster their competitiveness and profitability, prominent e-commerce platforms have embraced dual retailing channels: self-operating channels and online marketplaces. However, a discernible trend is emerging wherein e-commerce platforms are expanding their marketplaces to encompass competitive third-party suppliers. Motivated by this trend, this study sought to examine the strategic integration of a third-party product amidst the competition between a self-operating channel and a marketplace. This investigation involved the development of a game-theoretic model involving a platform and two representative suppliers—an incumbent supplier and a new entrant. Specifically, we delved into establishing an equilibrium partnership between the platform and the new entrant supplier while also evaluating the self-operating strategy of the established supplier. Our analysis uncovered a counterintuitive outcome: an escalation in the commission rate resulted in diminished profits for the established supplier. Furthermore, we ascertained that the economic implications of a competitive product entry pivot significantly on product quality. Lastly, we demonstrated that the revenue-sharing rate plays a pivotal role in influencing the self-operating strategy of the established supplier, and the market equilibrium hinges on the interplay among product quality, the commission rate, and the revenue-sharing rate. These insights provide invaluable guidance for marketers and e-commerce platforms in their strategic decision-making processes.
{"title":"Strategic Third-Party Product Entry and Mode Choice under Self-Operating Channels and Marketplace Competition: A Game-Theoretical Analysis","authors":"Biao Xu, Jinting Huang, Xiaodan Zhang, Thomas Brashear Alejandro","doi":"10.3390/jtaer19010005","DOIUrl":"https://doi.org/10.3390/jtaer19010005","url":null,"abstract":"To bolster their competitiveness and profitability, prominent e-commerce platforms have embraced dual retailing channels: self-operating channels and online marketplaces. However, a discernible trend is emerging wherein e-commerce platforms are expanding their marketplaces to encompass competitive third-party suppliers. Motivated by this trend, this study sought to examine the strategic integration of a third-party product amidst the competition between a self-operating channel and a marketplace. This investigation involved the development of a game-theoretic model involving a platform and two representative suppliers—an incumbent supplier and a new entrant. Specifically, we delved into establishing an equilibrium partnership between the platform and the new entrant supplier while also evaluating the self-operating strategy of the established supplier. Our analysis uncovered a counterintuitive outcome: an escalation in the commission rate resulted in diminished profits for the established supplier. Furthermore, we ascertained that the economic implications of a competitive product entry pivot significantly on product quality. Lastly, we demonstrated that the revenue-sharing rate plays a pivotal role in influencing the self-operating strategy of the established supplier, and the market equilibrium hinges on the interplay among product quality, the commission rate, and the revenue-sharing rate. These insights provide invaluable guidance for marketers and e-commerce platforms in their strategic decision-making processes.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":"1 7","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381274","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}
The digital economy and the sharing economy have changed the role citizens may acquire in society. Citizens can perform at least two roles from the open government perspective: on the one hand, they can be passive users/demanders of information and, on the other hand, they can provide or produce the information in an active manner. The objective of this paper is to offer a theoretical model to explain citizens’ incentives to participate in open government projects. Which is the opportunity cost of participation for the citizen? Which are the drivers of the preferences for the social good? This model is based on the utility function and consumption theory. We complement the theoretical framework with an exploratory–descriptive analysis based on a case study’s primary data about citizen participation. In democracy projects where citizens actively collaborate and could earn monetary gains or become entrepreneurs, the opportunity cost of participation is lower than in a passive type and the amount of the social good depends on the preferences. Preferences for social goods are related to community experiences and e-government and they also affect the decision to participate. Very few studies in the field of open government have pretended to explain citizens’ participation by using microeconomic foundations.
{"title":"Application of a Microeconomic Approach for Explanation of Citizen Participation in Open Government","authors":"María Verónica Alderete","doi":"10.3390/jtaer19010003","DOIUrl":"https://doi.org/10.3390/jtaer19010003","url":null,"abstract":"The digital economy and the sharing economy have changed the role citizens may acquire in society. Citizens can perform at least two roles from the open government perspective: on the one hand, they can be passive users/demanders of information and, on the other hand, they can provide or produce the information in an active manner. The objective of this paper is to offer a theoretical model to explain citizens’ incentives to participate in open government projects. Which is the opportunity cost of participation for the citizen? Which are the drivers of the preferences for the social good? This model is based on the utility function and consumption theory. We complement the theoretical framework with an exploratory–descriptive analysis based on a case study’s primary data about citizen participation. In democracy projects where citizens actively collaborate and could earn monetary gains or become entrepreneurs, the opportunity cost of participation is lower than in a passive type and the amount of the social good depends on the preferences. Preferences for social goods are related to community experiences and e-government and they also affect the decision to participate. Very few studies in the field of open government have pretended to explain citizens’ participation by using microeconomic foundations.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":"25 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139067577","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}
Online reviews are an important part of product information and have important effects on consumers’ purchasing decisions. Some sellers try to manipulate the market by inducing online reviews. In this study, a signal game model based on Bayesian conditional probability is constructed to analyze the preconditions, decision-making process, and effect on market demand and profit of this behavior. The results show that first, when consumer sensitivity to rebates reaches a certain threshold, low-quality sellers will adopt a conditional rebate strategy to induce consumers to give positive reviews. Second, the optimal rebate cost (β*) is obtained, where β* increases with the product price (p), but it is not necessarily monotonic in consumers’ sensitivity to rebates (ρ) or the proportion of high-quality products (α). Third, the conditional rebate strategy can only work in a market dominated by low-quality goods. Using the conditional rebate strategy in a market dominated by high-quality goods will not bring benefits to low-quality sellers but will harm their profits. This study proposes that some developing online markets have collusive behaviors owing to a lack of regulations and laws, as well as consumers’ concern for small interests. Ensuring the orderly development of online markets will require joint efforts by platform enterprises, government agencies, and consumers.
{"title":"Can the Conditional Rebate Strategy Work? Signaling Quality via Induced Online Reviews","authors":"Lu Xiao, Chen Qian, Chaojie Wang, Jun Wang","doi":"10.3390/jtaer19010004","DOIUrl":"https://doi.org/10.3390/jtaer19010004","url":null,"abstract":"Online reviews are an important part of product information and have important effects on consumers’ purchasing decisions. Some sellers try to manipulate the market by inducing online reviews. In this study, a signal game model based on Bayesian conditional probability is constructed to analyze the preconditions, decision-making process, and effect on market demand and profit of this behavior. The results show that first, when consumer sensitivity to rebates reaches a certain threshold, low-quality sellers will adopt a conditional rebate strategy to induce consumers to give positive reviews. Second, the optimal rebate cost (β*) is obtained, where β* increases with the product price (p), but it is not necessarily monotonic in consumers’ sensitivity to rebates (ρ) or the proportion of high-quality products (α). Third, the conditional rebate strategy can only work in a market dominated by low-quality goods. Using the conditional rebate strategy in a market dominated by high-quality goods will not bring benefits to low-quality sellers but will harm their profits. This study proposes that some developing online markets have collusive behaviors owing to a lack of regulations and laws, as well as consumers’ concern for small interests. Ensuring the orderly development of online markets will require joint efforts by platform enterprises, government agencies, and consumers.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":"44 6 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139067770","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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