Peer-to-Peer Markets Einav, Liran; Farronato, Chiara; Levin, Jonathan
Annual review of economics,
01/2016, Volume:
8, Issue:
1
Journal Article
Peer reviewed
Open access
Peer-to-peer markets such as eBay, Uber, and Airbnb allow small suppliers to compete with traditional providers of goods or services. We view the primary function of these markets as making it easy ...for buyers to find sellers and engage in convenient, trustworthy transactions. We discuss elements of market design that make this possible, including search and matching algorithms, pricing, and reputation systems. We then develop a simple model of how these markets enable entry by small or flexible suppliers, and how they impact existing firms. Finally, we consider the regulation of peer-to-peer markets and the economic arguments for different approaches to licensing and certification, data, and employment regulation.
•We study consumers’ engagement online through websites and social networking sites and related peer recommendations.•Data has been collected from Generation Y consumer segments who shop ...online.•Online shopping via Facebook and website service quality has positive effect on trust.•Peer recommendations have a significantly stronger influence on attitudes of females than they do on attitudes of males.•Trust has a positive and significant effect on attitude toward e-tailer and loyalty intentions.
Consumers increasingly search for, evaluate, and buy items via social media and websites, but little is known about how these activities affect their level of trust, attitudes toward online retailing, and online shopping behaviors. Therefore, this study focuses on how online shopping via Facebook, peer recommendations, and website service quality affect consumer trust, attitudes and loyalty intentions in e-tailing. An online survey was conducted with Generation Y Italian consumers who used Facebook searches of various websites to shop for clothing online. Confirmatory factor analysis was used to validate the constructs, and structural equation modeling (SEM) was employed to test the hypotheses. Findings confirm that website service quality and consumers’ predispositions to use Facebook for online shopping directly and positively affect consumer trust toward an e-tailer. In contrast, peer recommendations affect attitude directly rather than indirectly via trust. The results further indicate that peer recommendations have a significantly stronger influence on attitudes of females than they do on attitudes of males.
In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews ...on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis.
This paper extends previous studies on the organizational impact of Internet technologies by analyzing factors affecting e-business use and its effect on organizational innovation in manufacturing ...Small and Medium-Size Enterprises (SMEs). In addition, the mediating effect of organizational innovation on the relationship between e-business and firm performance is analyzed. Grounded in the Technology-Organization-Environment (TOE) theory and the Knowledge-Based View (KBV), this paper develops an integrative research model which analyzes those relations using partial least squares (PLS) structural equation modeling on a dataset of 175 Spanish manufacturing SMEs. Results suggest that e-business use emerges from technological and internal organizational resources rather than from external pressure. In addition, results show that e-business use contributes positively to firm performance through organizational innovation.
Website localization plays an important role in guiding firms on how to customize websites across countries in which they have a local presence. However, few studies on website localization have ...systematically examined this topic from a theoretically grounded perspective. Drawing upon the theoretically driven consumer-company identification perspective, we propose that three website localization strategies (web similarity strategy, web distinctiveness strategy, and web prestige strategy) have positive effects on local users’ perceived website localization, which, in turn, is related to local users’ website loyalty. We further investigate the effects of these website localization strategies in a cross-cultural setting, focusing on the individualism-collectivism dimension of culture. Based on our online experiments in both the United States and China, we find that these website localization strategies have significant impacts on consumers’ perceived website localization, which is related to consumers’ website loyalty. We also find that web distinctiveness and web prestige strategies are more effective for people from collectivistic societies than for those from individualistic societies, whereas web similarity strategies do not differ across societal types. These findings highlight the importance of website localization strategies for customizing websites for global e-commerce.
Purpose
The purpose of this paper is to review augmented reality (AR) within retailing by identifying, outlining and discussing definitions of AR, applications of AR that are relevant for retailers, ...and the value AR provides for retailers and consumers.
Design/methodology/approach
The paper is based on a review of AR research within the business-oriented literature and an overview of current AR applications within retailing.
Findings
Based on previous literature, the paper presents a synthesised definition of AR, its main elements and how it differs from virtual reality. Furthermore, it reviews and provides examples of three major types of AR applications in retailing: online web based, in-store and mobile app based. Finally, the paper identifies the specific value that AR applications may provide for consumers and retailers.
Originality/value
The paper contributes an overview of a relatively recent but rapidly emerging theme that has not yet been sufficiently reviewed. It outlines areas for further research and thus provides value for both researchers and retail practitioners.
TikTok Shop is one of the features in TikTok application which facilitates users to buy and sell products. The integration of TikTok Shop with social media has provided new opportunities to reach ...customers and increase sales. However, the closure of TikTok Shop has caused controversy among the public. This study aims to analyze the views and responses of TikTok users in Indonesia to the closure of TikTok Shop. The dataset used was obtained from Twitter. The research methodology consists of labeling, oversampling, splitting, and machine learning, which includes SVM, Random Forest, Decision Tree, and Deep Learning (H2O). The contribution of this research enriches our understanding of the implementation of machine learning, especially in sentiment analysis of TikTok Shop closures. From the test results, it is known that Deep Learning (H2O) + SMOTE obtained AUC 0.900, without using SMOTE, AUC 0.867. SVM + SMOTE obtained AUC 0.885, without using SMOTE AUC 0.881. Random Forest + SMOTE obtained AUC 0.822, while without using SMOTE AUC 0.830. Decision Tree + SMOTE AUC 0.59; without SMOTE, AUC 0.646. Deep Learning (H2O) with SMOTE produces better performance compared to SVM, Random Forest, and Decision Tree. With an AUC of 0.900; it can be said that Deep Learning (H2O) has excellent performance for sentiment analysis of TikTok Shop closures. This research has significant implications for social electronic commerce due to its potential utilization by social media analysts.
While there is some evidence that review length, review score, and argument frame can impact consumers' perceptions regarding the helpfulness of online consumer reviews, studies have not yet ...identified the most appropriate levels of such factors in terms of maximizing perceived helpfulness of these reviews. Drawing on Negativity Bias and Cue-Summation theories, we propose a theoretical model that explains online reviews' helpfulness based on specific characteristics of these reviews (i.e., length, score, argument frame). The model is empirically validated using two datasets of online consumer reviews related to products and services from Amazon.com and Insureye.com respectively. We also employ ANOVA analyses to reveal the levels of each of these characteristics that result in maximizing perceived helpfulness of online consumer reviews. Further, we employ an artificial neural network approach to predict the helpfulness of a given review based on its characteristics. Our findings reveal that the most helpful online consumer reviews are those that are associated with medium length, lower review scores, and negative or neutral argument frame. Our results also reveal that there is no major difference between the characteristics of the most helpful online consumer reviews related to products or services. Finally, our findings reveal that the most helpful factor in predicting the helpfulness of an online consumer review is the review length. Theoretical and practical contributions are outlined.
•Multimethod have been used including sentiment analysis, PLS-SEM, ANOVA, and ANN.•Helpful reviews have medium length, lower scores, and negative argument frame.•There is no difference between the most helpful reviews for products or services.•Most helpful factor in predicting the helpfulness of a consumer review is length.
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