Opinion evolution of online consumer reviews in the e-commerce environment

Online consumer reviews play an important role in shaping potential customers’ purchase decisions in e-commerce. Previous studies have analyzed the influence of online consumer reviews on sales, mainly considering factors such as reviewers’ and viewers’ profiles, information provided, and product features. However, there are relatively few studies that discuss how online consumer reviews interact with each other and how consumers’ opinions evolve over time. This paper proposes an opinion evolution dynamics model that is applicable to online consumer reviews in the e-commerce environment by taking into account influencing factors such as viewer reading limits, review sorting and releasing strategies, convergence parameters, review posting possibilities, and confidence thresholds. Using multi-agent simulation based on the proposed opinion evolution dynamics model, the paper discusses how these factors affect viewers’ opinions, and the opinion evolution process itself. Finally, conclusions and managerial implications of the simulation results are discussed.     

Optimal pricing of online products based on customer anchoring-adjustment psychology

Online product pricing is an important factor that affects the purchase of customers and the earnings of e-commerce platform enterprises. It has an increasingly prominent influence on consumers’ purchasing decisions. To study the influence of customers’ anchoring psychology and product pricing on customers’ purchasing behaviors, this paper proposes a pricing model that considers online consumers to be anchored by the product price and sales. The cognitive bias of online consumers is described by a utility function based on an anchoring-adjustment heuristic, and consumer choice behavior and an online product pricing strategy are studied with a multinomial logit model. Then, it studies the influence of the anchoring point and anchoring degree on optimal pricing, sales and profit. In particular, when consumers are anchored only by sales of goods, the optimal solution is monotonic with respect to the anchoring degree. However, when consumers are anchored by both price and sales, both factors affect consumer behaviors and produce more complex results. This paper provides a flexible pricing mechanism for platform enterprises, and it can provide a theoretical basis and decision support for dynamic pricing of platform enterprises based on historical consumption data.     

Behavioral Model of Online Purchasers in E-Commerce Environment

This research studied the behavioral factors of Internet users when making an on-line purchase. A total of 424 responses were collected in a survey, comprising of 179 online responses and 245 mailed responses. The research shows that demographic and most psychological factors, as well as web-savvy features of a virtual storefront appear non influential in determining the probability of an Internet user making a purchase. This is contrary to beliefs held by most e-commerce businesses. The research findings indicate that e-commerce businesses should focus on employing logo assurance services, state-of-the-art security technology, provide an online customer-service center, establish warranties for products and services sold, maintain credit card payment facilities, and lastly, establish a policy for conflict resolution in the event of inaccurate billings. With the research results, a behavioral model of online purchasers is established. The behavioral model could assist e-commerce businesses to focus on the real concerns of consumers. The contribution of the model is to allow e-commerce businesses to streamline their online activities to cater to the major behavioral factors that influence consumers to purchase online. This could greatly increase the ability of e-commerce businesses to offer competitive virtue selling.     

Impact of online word-of-mouth on sales: the moderating role of product review quality

Facing with thousands of online product reviews, consumers usually pay close attention to those valuable ones which provide more specific and credible evaluations on products. Whether a close association exists between product review quality and sales is thus examined in this paper. By employing text mining techniques on multiple review features, a review is measured as one of the following two levels: high-quality or low-quality. In doing so, aggregate quality level of product’s whole reviews is also identified. Then, a two-level econometrical analysis is conducted on the real datasets from Amazon.cn. The results reveal that aggregate quality level of positive reviews and negative reviews interactively influence sales. In the situation the aggregate quality level of positive reviews is high meanwhile that of negative reviews’ is low, product sale is the highest, while in the opposite situation product sale is the lowest. The results also reveal that consumers understand product’s value from weighting positive and negative reviews of high-quality level, which then positively relates to product sales and exerts a dynamic effect on sales by the moderating role of product selling stage and popularity. The paper innovatively integrates the quantitative and qualitative characteristics of reviews to estimate their economic effect.     

Towards effective online review systems in the Chinese context: A cross-cultural empirical study

Online reviews, as one kind of quality indicator of products or service, are becoming increasingly important in influencing purchase decisions of prospective consumers on electronic commerce websites. With the fast growth of the Chinese e-commerce industry, it is thus indispensable to design effective online review systems for e-commerce websites in the Chinese context, by taking into account cultural factors. In this paper, we conduct two empirical studies on online reviews. Firstly, we study how culture differences across countries (i.e., China and the USA) impact the way in which consumers provide online reviews. Secondly, we investigate the impact of online reviews on product sales in the Chinese context, and show that directly copying the ideas of successful online review systems in the USA will deteriorate the effectiveness of the systems in China. Finally, we propose several suggestions for the development of effective online review systems in the Chinese context based on the results of our two empirical studies and the findings in previous studies.     

An information gain-based approach for recommending useful product reviews

Recently, many e-commerce Web sites, such as Amazon.com, provide platforms for users to review products and share their opinions, in order to help consumers make their best purchase decisions. However, the quality and the level of helpfulness of different product reviews are not disclosed to consumers unless they carefully analyze an immense number of lengthy reviews. Considering the large amount of available online product reviews, this is an impossible task for any consumer. Therefore, it is of vital importance to develop recommender systems that can evaluate online product reviews effectively to recommend the most useful ones to consumers. This paper proposes an information gain-based model to predict the helpfulness of online product reviews, with the aim of suggesting the most suitable products and vendors to consumers. Reviews are analyzed and ranked by our scoring model and reviews that help consumers better than others will be found. In addition, we also compare our model with several machine learning algorithms. Our experimental results show that our approach is effective in ranking and classifying online product reviews.     

Time effect of disconfirmation on online shopping

To succeed in the rapidly growing and highly competitive e-commerce environment, it is important to understand the relationship between the cognition and continued usage behaviour of online shopping customers as they relate to enhancing customer conversion and retention. A longitudinal perspective is adopted in this study and expectation disconfirmation theory is applied to examine how disconfirmation influences the relationship between customer expectations and repurchase intentions when considering the effect of time. This work aims at (1) identifying the relationships between the pre-purchase expectations and post-purchase attitudes of online consumers; (2) elucidating how previous purchase experience influences subsequent purchase intention; and (3) examining the moderating role of disconfirmation in an online shopping context. The results provide useful implications for the management of customer expectations and future purchase behaviour in customers with different levels of disconfirmation.     

基于贝叶斯网络的在线商品评价质量评估

随着电子商务领域的迅速发展,在线商品评价规模日益庞大,评价质量参差不齐,用户难以筛选有用评价信息做出购买决策,因此如何有效识别高质量评价信息成为重要议题。以在线商品评价的有用性投票为基础定义评价质量,使用贝叶斯网络表示在线商品评价的相似性及不确定性,通过对在线商品评价信息进行多维度特征统计,构建在线商品评价质量评估模型,使用概率推理机制对在线商品评价质量进行分类预测,并给出评价质量分类置信度。在真实数据集上验证模型有效性及高效性。     

Community engagement and online word of mouth: An empirical investigation

How customer engagement in a firm’s social media marketing platform ? online brand community ? might affect word-of-mouth behavior is an open research issue as well as a vital business problem in e-commerce. To this end, we collected a set of longitudinal data that include community engagement behavior, e-commerce transactions, and customers’ post-purchase reviews. Using a control function approach, we find that consumer engagement in a brand community not only increases the likelihood of generating post-purchase reviews but also increases the likelihood of posting positive online reviews. Furthermore, we find that customer tenure has a positive moderating effect.     

“When Is Short,Sweet?” Selection Uncertainty and Online Review Presentations

Online customer reviews are an important part of e-commerce product selection. When used effectively, online reviews may reduce the uncertainty inherent in making product selection decisions online, but how best to deal with thousands of online customer reviews? Past research considers online review summarization, where reviews are reduced to numeric ratings, key phrases, keywords or product characteristics. However, in their original form, online reviews contain the carefully crafted narratives of past customers, elements of which may not be amenable to summarization. In this research, we present findings of a laboratory experiment which examines the impact of review summarization when evaluating different types of products online. Key findings include evidence that perceptions of product selection uncertainty depend on online review presentation format and the category of the product under consideration. Additionally, the study provides evidence that the e-commerce retailers may benefit from varying online review presentations across specific types of products.     

教材在线评论的情感倾向性分析

为了充分挖掘和应用电子商务网站中的教材评论信息,运用细粒度的情感分类算法对用户的在线评论进行分析,基于教材特征级的情感分析结果,辅助潜在客户和商家做出合理有效的决策.本文首先使用爬虫采集教材的在线评论文本,对其进行去噪、分词和词性标注等预处理;然后分析产品特征,在通用情感词典的基础上扩建领域情感词典;最后基于句法分析结果,结合教材评论的语言特性,设计适合教材评论的情感倾向性分析算法,并通过实验验证了算法的有效性.     

Design of review systems – A strategic instrument to shape online reviewing behavior and economic outcomes

As online reviews play a decisive role in consumers’ purchase decisions, e-commerce platforms are using review systems strategically to obtain a competitive advantage. However, the strategic potential can only be leveraged if the review system is designed appropriately. Research on the design of review systems and the effects of design choices has not yet been summarized or synthesized in a review article. We aim to close this gap by providing a scoping review. In our synthesis we posit that the design of review systems moderates the impact of online reviews on economic outcomes and the factors that drive the formation of reviews. After reviewing current research findings, we identify gaps and provide a research agenda covering three key themes: Design features, environments, and devices.     

Cloud Computing: An Examination of Factors Impacting Users’ Adoption

With a rapid growth in technology, cloud computing has become increasingly popular among individual users and businesses around the world. This computing technology continues to attract attention in both academic and business settings. The popularity of cloud computing is due to its ability to provide faster on-demand infrastructure, self-service, and independent ability to contribute to, and access, resources. Despite many potential benefits of cloud computing usage, there are certain aspects that have prohibited some consumers to accept this technology and remain reserved toward its adoption. There are some crucial factors to consider before cloud computing can be accepted entirely by the individual, larger business, or academic groups. This study thus aims at investigating how cloud computing is perceived by potential users and which factors have a tendency to encourage or discourage them to adopt cloud computing.     

Complements and substitutes in online product recommendations: The differential effects on consumers’ willingness to pay

Online product recommendations have been shown to influence consumers’ preferences and purchasing behaviors for recommended products. However, it remains an open empirical question whether and how recommendations of other products affect a consumer’s economic behavior for a focal product. In many e-commerce websites, a product is presented with co-purchase and co-view recommendations, which potentially contain complementary and substitutable products, respectively. Very little research has explored differential effects of recommending complementary and substitutable products. In this study, we explore how types of other recommended products impact consumers’ willingness to pay for a focal product through interactions with prices of recommended products and consumers’ decision stages. We conducted a 2³ randomized factorial experiment as well as two 2 × 2 experiments to examine these issues. Experimental results provide evidence that there is a significant interaction effect between the recommendation type and decision stage, which highlights the importance of recommendation timing on e-commerce platforms. Furthermore, it also illustrates that the effect of recommending complementary vs. substitutable products is subject to contextual factors such as consumers’ decision stages. Results of our study have significant implications for the design and application of online recommender systems.     

System on chip design service e-business value maximization through a novel MCDM framework

As the IC (integrated circuit) industry migrates to the System-on-Chip (SOC) era, the SOC design service industry is emerging. Meanwhile, in the past decade, the emergence of Internet has changed the high technology marketing approaches while e-commerce has already become one of the most efficient marketing channels. Thus, most leading SOC design service firms tried to leverage novel e-commerce business models to provide better services including online silicon intellectual property (SIP) sourcing, transactions, integration, etc. to assist customers in enhancing their innovation competences to shorten their time to market and thus, time to money. However, defining appropriate e-business models for commercializing new SIPs or SOC design services is not easy for both aspects of technology as well as business development. On one hand, from the aspect of technology, the technical site R&D or production managers are familiar with SOC technologies while do not really understand the needs of customers’ over the Internet. On the other hand, from the aspect of business development, the sales or marketing managers may be familiar with online customers’ needs, wants as well as demands while are unfamiliar to SOC technology developments. To overcome the above mentioned problems, an appropriate e-business model definition framework can overcome this cognitive gap and maximize the value-added of online SOC design services. In this paper, a novel analytic framework based on the concept of design service customers’ competence set expansions by leveraging high technology service firms’ capabilities and resources as well as novel multiple criteria decision making (MCDM) techniques, will be proposed. The framework being proposed can be leveraged by the design service firms to define an appropriate e-business model for possible SIP or design service businesses. Based on the proposed MCDM framework, an empirical study of an SIP commercialization e-business model definition inside an SIP Mall, an SIP e-commerce mechanism being operated by a SOC design service firm, will be provided for verifying the effectiveness of this novel analytic framework. The feasibility of the proposed framework in the real world can be verified by the empirical study. In the future, the novel MCDM framework can be applied to novel e-business model definitions in the SOC design service or other high technology industries.     

An expert system for extracting knowledge from customers’ reviews: The case of Amazon.com,Inc.

E-commerce has proliferated in the daily activities of end-consumers and firms alike. For firms, consumer satisfaction is an important indicator of e-commerce success. Today, consumers’ reviews and feedback are increasingly shaping consumer intentions regarding new purchases and repeated purchases, while helping to attract new customers. In our work, we use an expert system to predict the sentiment of a product considering a subset of available customers’ reviews.     

Search product and experience product online reviews: An eye-tracking study on consumers' review search behavior

This study uses eye-tracking method to investigate consumers' online review search behavior by suggesting that it needs to consider the type of product reviewed. A review-product congruity proposition was testified through a self-report survey and an eye-tracking experiment. The proposition states that consumers of search products expect to seek attribute based reviews, while consumers shopping for experience products tend to seek experience based reviews. Two experiments were conducted in the human factors & ergonomics laboratory of Beihang University, China and all subjects are college students. The results of our first empirical experiment support our hypotheses by showing consumers' more active and positive responses to attribute based reviews when shopping for search products and to experience based reviews when purchasing experience products. The second experiment was conducted with eye tracking method to gain further insights. We found that consumers of search products are attracted and engaged more deeply by attribute based reviews. However, when they browse experience products, the difference of their fixations on experience based reviews and attribute based reviews is not significant, and thus the proposition is partially supported. This study extends our current understanding of consumers' online review search behavior by subsuming product type, which is necessary and helpful, and provides references on the classification and presentation of reviews to facilitate consumers' product judgement and decision making. Moreover, comparison of traditional empirical method and eye-tracking method can help deepen our understanding of complex consumer online shopping behavior.     

产品垃圾评论检测研究综述

互联网上的产品垃圾评论混淆视听,误导了潜在消费者。产品垃圾评论检测的目的就是将垃圾评论从评论文本中找到并去除,保留真实的产品评论供用户参考。首先将产品垃圾评论和互联网上其它常见的垃圾信息进行了对比,并把产品垃圾评论的检测和产品评论的质量判断、产品评论的情感分析等相关的工作进行了比较分析。然后从产品垃圾评论检测的数据集、检测方法两个角度对相关工作做了概述和分析。最后,在上述工作的基础上提出了一些产品垃圾评论检测研究中值得进一步关注的问题。     

The power of a thumbs-up: Will e-commerce switch to social commerce?

By taking advantage of social networking capabilities, social commerce provides features that encourage customers to share their personal experiences. The popularity of online social networks has driven the purchase decisions of buyers on social commerce sites, but few studies have explored why consumers switch between e-commerce (product-centered) and social (social-centered) commerce sites. In applying the push–pull–mooring model, the objective of this study was to gain an understanding of specifically how push, pull, and mooring factors shape their switching intentions. The findings revealed that push effect, in terms of low transaction efficiency, drives customers away from e-commerce sites, whereas the pull effects, including social presence, social support, social benefit, and self-presentation, attract customers to social commerce sites. Moreover, mooring effects, including conformity and personal experience, strengthened consumers’ behavior in switching between e-commerce and social commerce sites. Besides, conformity was also found to moderate the influences of social presence, social support, social benefit, and efficiency on switching intention, whereas personal experience moderated the effects of social benefit, self-presentation, and efficiency on switching intention. Such an understanding assists online retailers in understanding online shoppers’ switching behaviors, and thus turning social interactions into profits and sales.