Perceptions of Customer Service,Information Privacy,and Product Quality From Semiotic Design Features in an Online Web Store

The rise of the World Wide Web for electronic commerce has led to a proliferation of companies selling products online. The global nature of the Internet allows customers to browse the products of companies with which they are wholly unfamiliar. However, concerns about customer service, information privacy, and product quality discourage purchasing from unknown companies. In this article, the effects of semiotic Web design features on expectations of these performance criteria in a purchase situation are investigated. Specifically, the presence and prominence of links to customer service and a site privacy policy, and the existence of product ratings and customer testimonials, were tested to measure their effects on customer perceptions and expectations. Results indicate that some design features have a strong semiotic effect on customer expectations. Prominent links to customer service and a site privacy policy significantly increased expectations of customer service and privacy protection. The presence of product ratings increased perceptions of product quality. All 3 design features led to increased likelihood of purchase. Furthermore, participants were not aware of these effects and reported not considering product ratings in their decisions. Implications of these results on Web site design and consumer behavior are discussed.     

中文网络评论的IT产品特征挖掘及情感倾向分析

为探索中文客户评论中的IT产品特征及相关情感倾向的挖掘,帮助IT生产商和服务商提高改进产品和服务质量,提高竞争力。该文将采用情感分析技术,提出基于客户感知价值的产品特征挖掘算法,实现对于评论中IT产品特征及其情感倾向的语义分析、动态提取和综合信息挖掘;并根据用户的关注权重将产品特征和情感倾向进行排列。采用从互联网下载的真实IT产品评论语料中进行实验,初步验证了该方法的有效性。     

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。     

Investigating the intention to purchase digital items in social networking communities: A customer value perspective

Some social networking community service providers have earned revenue by selling digital items to their community members. We examined SNC member decisions to purchase digital items based on customer value theory. Six factors were extracted from three dimensions of customer value: functional, social, and emotional value. Our findings indicated that the effects of value on member purchase intentions were significant in terms of the emotional and social dimensions. Our results should help SNC providers by improving their sales of digital items.     

Perceived derived attributes of online customer reviews

The influence of online customer reviews (OCRs) on customers' purchase intention has recently gained considerable attention, in both academic and business communities. Technology allows customers to freely and easily post their comments and opinions online about any product or service; this type of customer review can have a significant effect on customers' purchase decisions. Previous studies, however, have mainly focused on the influence of the virtual attributes of OCRs such as volume and valence on consumers' intentions, while limited attention has been paid to understanding the effects of the derived attributes. This study, thus, aims to understand the impact of the perceived derived attributes of OCRs on customer trust and intention. This study develops a – Perceived Derived Attributes (PDA) - model, based on the inclusion of perceived control from the Theory of Planned Behaviour (TPB) with the Technology Acceptance Model (TAM), in order to investigate the effects of OCRs on customers’ purchasing intention. A total of 489 responses to a survey were collected from users of amazon.com. The findings from this study suggest that customer trust in an e-vendor and their intention to shop online are significantly affected by perceived usefulness, perceived ease of use and perceived enjoyment of OCRs. Furthermore, the sense of control derived from OCRs significantly affects customer intention and significantly affects customer trust in e-vendors, particularly for customers who frequently check OCRs before making a purchase. Clearly, those attributes of OCRs are linked to the development of the shopping environment, which consequently can affect sales.     

Predicting airline customers’ recommendations using qualitative and quantitative contents of online reviews

Customers generally give ratings and reviews for different services that they get online or offline. These reviews and ratings aspects are effectively helpful to both the company and customers to receive feedback and make the right decisions, respectively. However, the number of reviews and ratings can increase exponentially, bringing a new challenge for the company to manage and track. Under these circumstances, it will also be hard for the customer to make the right decision. In this work, we summarize text reviews and ratings given by passengers for different airlines. The objective of this research is to predict whether the recommendation made by the customer is positive or negative. Two types of features, namely, textual feature and explicit ratings, are extracted from the dataset and other attributes. We found the relationship between such sentiments and feelings expressed in online reviews and predictive consumer recommendation decisions. We have considered quantitative content with qualitative content of online reviews in predicting recommendation decisions, which shows the work’s novelty. Additionally, the obtained results yield an essential contribution to the existing literature in terms of service evaluation, making managerial policies, and predictive consumer recommendations, etc. Moreover, we hope that this work would be helpful for practitioners who wish to utilize the technique to make the quick and essential hidden information by combining textual reviews and various service aspects ratings.

     

The evaluation for perceived quality of products based on text mining and fuzzy comprehensive evaluation

With the growth of the Internet and electronic commerce, more and more customers browse online reviews to understand products and service reputation. Online reviews can provide decision support for customers to purchase a product that is to their satisfaction. Manufacturers can also mine and analyze valuable information in favor of design and production from online reviews. Customer satisfaction is mainly determined by perceived quality of products. Hence, this study establishes a new method to evaluate the perceived quality by combining text mining with a fuzzy comprehensive evaluation method. The new evaluation method offers ideas and methods for future work to combine text mining technology with traditional evaluation methods. Customers can also make better purchase decisions and manufacturers design and manufacture better products by using this evaluation method.     

A growing or depreciating love? Linking time with customer satisfaction through online reviews

Through online customer reviews of hotels, this study examines the link between time and customer satisfaction. We categorize the factors into different types, indicating that customers’ online review behavior and their level of satisfaction can change according to their duration of time with the service providers. Independent hotels and hotels with a higher star level, compared with chain hotels and hotels with a lower star level, amplify the influence of various attributes on satisfaction over time. We provide implications for firms to increase customer satisfaction by enhancing their attribute-level performance in various lengths of time.     

中文网络评论的产品特征提取及情感倾向判定

随着互联网和电子商务的发展,用户在购买或使用商品之后会在网络站点上发表对产品的评论,大量的产品评论中所包含的丰富信息,可以为生产厂商和用户提供重要的决策依据。基于文本的语义和语言分析,提出了从产品评论中提取用户关注的产品特征的方法,并根据用户的关注程度对产品特征进行排序;同时,根据观点词的极性值判定用户对产品特征的情感倾向以及情感倾向强度。本研究采用从互联网上获得的针对笔记本电脑的产品评论作为实验对象,实验结果初步证明该方法具有良好的准确率和召回率。     

Factors affecting female user information adoption: an empirical investigation on fashion shopping guide websites

With the prosperity of online shopping platforms, similar or even the same products tend to have a large variety of sources to be purchased from. More and more consumers seek the product information from online review websites before making a purchase, as they are willing to provide reviews or share their purchase experience. These behaviors turn the online review websites into vertical and community-based sales channels. Based on the Information Adoption Model, this study conducted an empirical investigation to analyze female users’ information adoption process when using fashion shopping guide website. The results show that information quality and source credibility have significant impact on information usefulness, which in turn contributes to information adoption. In addition, users with different levels of purchasing motivation demonstrate different dependence on information quality and source credibility.     

A comparison of purchase decision calculus between potential and repeat customers of an online store

Potential and repeat customers of an online store possess different amount of information and use different criteria for making purchase decisions. Internet vendors should therefore adopt different sales strategies for creating initial sales and generating repeat sales. Yet little is known about the differences in online purchase decision making between the two customer groups. This study examines the differences between potential and repeat customers based on mental accounting theory and information processing theory. We found that value perception (of transactions made with the online vendor) as an overall judgment for decision making is more strongly influenced by the non-monetary (perceived risk) factor than by the monetary factor (perceived price) for potential customers, whereas it is more strongly influenced by the monetary factor than by the non-monetary factor for repeat customers. The findings of our study would help Internet vendors develop customized strategies for creating initial sales and repeat sales.     

“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.     

Which is more important in Internet shopping,perceived price or trust?

Price and trust are considered to be two important factors that influence customer purchasing decisions in Internet shopping. This paper examines the relative influence they have on online purchasing decisions for both potential and repeat customers. The knowledge of their relative impacts and changes in their relative roles over customer transaction experience is useful in developing customized sales strategies to target different groups of customers. The results of this study revealed that perceived trust exerted a stronger effect than perceived price on purchase intentions for both potential and repeat customers of an online store. The results also revealed that perceived price exerted a stronger influence on purchase decisions of repeat customers as compared to that of potential customers. Perceived trust exerted a stronger influence on purchase decisions of potential customers as compared to that of repeat customers.     

The influence of information cascades on online purchase behaviors of search and experience products

Online users usually observe or refer to others’ behaviors and discount their own information when purchasing products online. This research employed a fixed-effect regression model to elucidate how information cascades could influence online purchase behaviors and how they moderated the influence of online word-of-mouth and product prices. To uncover the underlying mechanisms behind informational cascades, we compare search products and experience products. In particular, we utilize publicly available data from a B2C e-commerce site in China, i.e., Tmall.com. Our results indicate that online users’ choice of products was heavily driven by changes in product rankings after controlling for cumulative sales, online user reviews and product price, as predicted by informational cascades theory. Due to the information cascades effect, review volume had no impact on online users’ choice of products with high rankings, whereas it did exert a significant positive impact on consumer purchase decisions of products with low rankings. User rating had no impact on online users’ purchase decisions. Product price had a significant and negative impact for products with high rankings, but had a significant and positive influence on users’ choice for products with low rankings. Moreover, information cascades were more prominent for experience goods than for search goods.     

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.     

Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services

Online consumer reviews offer an unprecedented amount of information for consumers to evaluate services before purchase. We use the dual process theory to investigate consumer perceptions about information helpfulness (IH) in electronic word-of-mouth (eWOM) contexts. Results highlight that popularity signals, two-sided reviews, and expert sources (but not source trustworthiness) are perceived as helpful by consumers to assess service quality and performance. Although two-sided reviews exercise a significant influence on perceived IH, their influence on purchase intention was indirectly mediated by IH. IH predicts purchase intention and partially mediates the relationship between popularity signals, source homophily, source expertise, and purchase intention.     

The relationship between retailer-hosted and third-party hosted WOM sources and their influence on retailer sales

This study analyzes the value of retailer-and third-party hosted WOM by investigating how WOM valences and volumes of multiple sources interact with one other to influence retailer sales. Consumer opinions, experiences, and product recommendations posted on online WOM sites have become a major information source for consumer purchase decisions. Previous literature shows that WOM information can influence retailer sales in two ways – volume and valence, but most researchers investigate these two WOM effects separately. In reality, consumers evaluate volumes and valences jointly from multiple WOM sources for their purchase decisions. That is, there would be an interaction effect between them. Therefore, this study investigates how WOM valences and volumes at both retailer and third-party review web sites interact with one other to influence retailer sales. We collect sales rank data for 145 camera products from Amazon for a period of four months, and the corresponding online review data from Amazon and CNet for the same period. Our analysis shows that WOM valence is positively interacted with its own volume at both sources. We also find that retailer-hosted WOM valence is negatively interacted with third party-hosted WOM volume. Our findings indicate the importance of considering interaction effect between WOM sources.     

A decision-making algorithm for online shopping using deep-learning–based opinion pairs mining and q-rung orthopair fuzzy interaction Heronian mean operators

In the process of online shopping, consumers usually compare the review information of the same product in different e-commerce platforms. The sentiment orientation of online reviews from different platforms interactively influences on consumers’ purchase decision. However, due to the limitation of the ability to process information manually, it is difficult for a consumer to accurately identify the sentiment orientation of all reviews one by one and describe the process of their interactive influence. To this end, we proposed an online shopping support model using deep-learning–based opinion mining and q-rung orthopair fuzzy interaction weighted Heronian mean (q-ROFIWHM) operators. First, in the proposed method, the deep-learning model is used to automatically extract different product attribute words and opinion words from online reviews, and match the corresponding attribute-opinion pairs; meanwhile, the sentiment dictionary is used to calculate sentiment orientation, including positive, negative, and neutral sentiments. Second, the proportions of the three kinds of sentiments about each attribute of the same product are calculated. According to the proportion value of attribute sentiment from different platforms, the sentiment information is converted into multiple cross-decision matrices, which are represented by the q-rung orthopair fuzzy set. Third, considering the interactive characteristics of decision matrix, the q-ROFIWHM operators are proposed to aggregate this cross-decision information, and then the ranking result was determined by score function to support consumers' purchase decisions. Finally, an actual example of mobile phone purchase is given to verify the rationality of the proposed method, and the sensitivity and the comparison analysis are used to show its effectiveness and superiority.     

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized for investigating the customer review. The collected information is analyzed and processed by batch normalized capsule networks (NCN). The network explores the user reviews according to product details, time, price purchasing factors, etc., ensuring product quality and ratings. Then effective recommendation system is developed using a butterfly optimized matrix factorization filtering approach. Then the system’s efficiency is evaluated using the Rand Index, Dunn index, accuracy, and error rate.