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.     

In-store consumer behavior: How mobile recommendation agents influence usage intentions,product purchases,and store preferences

Product information given in purchase situations influences purchase behavior. In online purchase situations, the use of recommendation agents increases the value of product information as information becomes adaptive and thus more relevant to consumers’ information needs. Correspondingly, mobile recommendation agents (MRAs) may also increase the value of product information in bricks-and-mortar stores. In this sense, product information is not only adaptive but can also be requested at any place such as in front of products consumers are interested in. Because unprecedented, we investigate the use of a MRA that is virtually bound to a physical product via an RFID-enabled mobile device and provides product information. Based on Theory of Planned Behavior, Innovation Diffusion Theory, and Technology Acceptance Model, we develop a model to better understand the impact of MRAs on usage intentions, product purchases and store preferences of consumers. This model is then tested in a lab experiment (n = 47). Among high usability scores, results indicate that perceived usefulness of a MRA influences product purchases, predicts usage intentions and store preferences of consumers. Thus, new business models for retail stores can be considered in which MRAs satisfy both the information needs of consumers and the communication needs of retailers.     

Key dimensions of business-to-consumer web sites

The rapid growth in the electronic commerce over the Internet has fuelled predictions and speculations about what makes a business-to-consumer (B2C) web site effective. Yet, there are very few empirical studies that examine this issue. We examined the key characteristics of a B2C web site as perceived by online consumers. Based on a questionnaire survey of 214 online shoppers, we empirically derived four key dimensions of B2C web sites: information content, design, security, and privacy. Though all these dimensions seem to have an impact on the online purchase intent of consumers, security and privacy were found to have greater effect on the purchase intent of consumers. The implications of the findings for online merchants are discussed.     

To Err is human? How typographical and orthographical errors affect perceptions of online reviewers

Consumers increasingly rely on online product reviews when making purchase decisions. However, assessing the credibility of online reviewers presents consumers with unique challenges. This paper examines how consumer perceptions of reviewer credibility are influenced by the presence and type of textual errors in the review itself. The results of an online experiment indicate that consumers’ reactions to textual errors are moderated by their general trust in others. Low-trust consumers are relatively insensitive to textual errors in judging reviewer credibility. However, high-trust consumers are less forgiving of typographical errors (which may signal carelessness) than orthographical errors (which may indicate cognitive challenges). Implications for future research are discussed.     

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.     

Perceived information transparency in B2C e-commerce: An empirical investigation

Consumers abandon their online purchases at an e-commerce website partly due to the lack of information transparency of the website. We identify the antecedents of consumers’ perceived information transparency of an e-commerce website and its effects on consumers’ online purchase intention. We collected data through a scenario-based survey conducted in a laboratory setting. We found that (1) product transparency, vendor transparency, and transaction transparency significantly influence perceived information transparency; (2) perceived information transparency significantly increases consumers’ online purchase intention; and (3) perceived risk partially mediates the effects of perceived information transparency on purchase intention.     

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.     

The influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption

Social media websites have created valuable opportunities for electronic word of mouth (eWOM) conversations. People are now able to discuss products and services of brands with their friends and acquaintances. The aim of this study is to examine the influence of these conversations in social media on consumers' purchase intentions. For this purpose, a conceptual model was developed based on the integration of Information Adoption Model (IAM) and related components of Theory of Reasoned Action (TRA). The new model, which is named as Information Acceptance Model (IACM), was validated through structural equation modelling (SEM) based on surveys of 384 university students who use social media websites. The results confirm that quality, credibility, usefulness and adoption of information, needs of information and attitude towards information are the key factors of eWOM in social media that influence consumers’ purchase intentions. Theoretical and practical implications are discussed as well as recommendations for further research.     

Manipulation of online reviews: An analysis of ratings, readability, and sentiments

As consumers become increasingly reliant on online reviews to make purchase decisions, the sales of the product becomes dependent on the word of mouth (WOM) that it generates. As a result, there can be attempts by firms to manipulate online reviews of products to increase their sales. Despite the suspicion on the existence of such manipulation, the amount of such manipulation is unknown, and deciding which reviews to believe in is largely based on the reader's discretion and intuition. Therefore, the success of the manipulation of reviews by firms in generating sales of products is unknown. In this paper, we propose a simple statistical method to detect online reviews manipulation, and assess how consumers respond to products with manipulated reviews. In particular, the writing style of reviewers is examined, and the effectiveness of manipulation through ratings, sentiments, and readability is investigated. Our analysis examines textual information available in online reviews by combining sentiment mining techniques with readability assessments. We discover that around 10.3% of the products are subject to online reviews manipulation. In spite of the deliberate use of sentiments and ratings in manipulated products, consumers are only able to detect manipulation taking place through ratings, but not through sentiments. The findings from this research ensue a note of caution for all consumers that rely on online reviews of books for making purchases, and encourage them to delve deep into the book reviews without being deceived by fraudulent manipulation.     

Trusting expert- versus user-generated ratings online: The role of information volume,valence, and consumer characteristics

The warranting principle, signaling theory, and theories of informational social influence suggest conditions when either user-generated information, or information originating from traditional experts, might be privileged online. A random sample of 1207 U.S.-based adults with Internet access completed an experiment that manipulated the source, volume, and valence of online movie ratings in order to test predictions derived from these perspectives. Results indicated that ratings volume is positively associated with trust of, reliance on, and confidence in user-generated content, as well as the congruence between one’s own and others’ opinions; that ratings source and volume interact to impact credibility perceptions, reliance on user-generated information, and opinion congruence, such that people tend to favor experts when there is low information volume, but favor user-generated information under conditions of high information volume; and that people’s opinions and behavioral intentions converge with the online ratings information to which they are exposed. In addition, these effects apply more strongly to people more conversant with user-generated content. Results indicate important theoretical extensions by demonstrating that social information online may be filtered through signals indicating its veracity, which may not apply equally to all social media users.     

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 role of affect and cognition on online consumers' decision to disclose personal information to unfamiliar online vendors

Based on the privacy calculus framework and the stimulus-organism-response (S-O-R) model, this study examines online information disclosure decision as a result of affective and cognitive reactions of online consumers over several stages, i.e. an initial stage where an overall impression is formed about an unfamiliar online vendor, and a subsequent information exchange stage where information necessary to complete the ecommerce transaction will be provided to the online vendor. We found that, initial emotions formed from an overall impression of a Web site act as initial hurdles to information disclosure. Once online consumers enter the information exchange stage, fairness-based levers further adjust privacy beliefs.     

Product type and consumers’ perception of online consumer reviews

Consumers hesitate to buy experience products online because it is hard to get enough information about experience products via the Internet. Online consumer reviews may change that, as they offer consumers indirect experiences about dominant attributes of experience products, transforming them into search products. When consumers are exposed to an online consumer review, it should be noted that there are different kinds of review sources. This study investigates the effects of review source and product type on consumers’ perception of a review. The result of the online experiment suggests that product type can moderate consumers’ perceived credibility of a review from different review sources, and the major findings are: (1) consumers are more influenced by a review for an experience product than for a search product when the review comes from consumer-developed review sites, and (2) a review from an online community is perceived to be the most credible for consumers seeking information about an experience product. The findings provide managerial implications for marketers as to how they can better manage online consumer reviews.     

How user relationships affect user perceived value propositions of enterprises on social commerce platforms

Social commerce is a popular topic issue in the academic field. Prior research mostly focused on the effectiveness of social commerce on consumers. However, the effect of social commerce on electronic commerce enterprises has rarely been involved. By combining social network theories, information theories, and the value theory of electronic commerce, with information quality and credibility as the path, this study investigates how inter-user relationship on a social commerce platform affect user’s perceptions of the value proposition of electronic commerce enterprises. The results show that the instrumental inter-user relationships can significantly improve a consumer’s perceptions of a product’s quality and information, and then improve the perception of efficiency value proposition and novelty value proposition of electronic commerce enterprises. The expressive relationships between users can effectively improve the expression of consumer awareness of product credibility, and thus enhance the consumer’s perception of the value proposition of electronic commerce enterprises.     

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.     

Effects of consumer visit to online community and product channel on local sales of large consumer goods: Evidence from real estate industry

Online intermediaries have become important information sources for consumers’ purchase decision making. However, the economic values of consumer visits to two online intermediaries, namely, online community and product channel, have not been well studied. By collecting a dataset on consumer visits from a large real estate website to match with local offline housing sales data, we empirically explore the respective, relative, and interaction effects of consumer visits to online communities and product channels on sales of large consumer goods. We control for relevant factors, account for potential endogeneity issues, and perform various robustness checks to validate the consistency of our findings. Our results show that consumer visits to online communities have a more significant effect than those to product channels in driving sales. However, the interaction effect of consumer visits to these two online intermediaries on sales is negative. We also find that consumer website-related experience has a significant moderating effect on the relationship between consumer visits to product channels and sales. Our findings provide important theoretical contributions and managerial implications.     

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.     

Do online reviews reflect a product’s true perceived quality? An investigation of online movie reviews across cultures

This paper investigates when the reported average of online ratings matches the perceived average assessment of the population as a whole, including the average assessments of both raters and non-raters. We apply behavioral theory to capture intentions in rating online movie reviews in two dissimilar countries – China and the United States. We argue that consumers’ rating behaviors are affected by cultural influences and that they are influenced in predictable ways. Based on data collected from IMDB.com and Douban.com, we found significant differences across raters from these two different cultures. Additionally, we examined how cultural elements influence rating behavior for a hybrid culture – Singapore. To study whether online consumer reviews are subjected to under-reporting bias, which is, consumers with extreme opinions are more likely to report their opinions than consumers with moderate reviews causing online reviews to be a biased estimator of a product’s true quality, we compare the consumer reviews posted online with those from an experimental study. Our results shows that under-reporting is more prevalent among US online network, thus online reviews are a better movie perceived quality proxy in China and Singapore than in the US.     

Examining the Signaling Effect of E-tailers’ Return Policies

E-tailers normally have more accurate information regarding their products and services than consumers due to the nature of online transactions. To reduce consumers’ concerns of hidden information, e-tailers can use lenient return policies to attempt to signal high product and service quality. This research proposes a model to investigate the signaling effect of two components of return policy, return window—short return window vs. long return window and return depth—full refund vs. partial refund, on product quality and service quality. Data was collected using an online survey and analyzed using structural equation modeling. The results show that both a long return window and a full refund signal high service quality, which then positively affects purchase intention. When it comes to product quality, return window does not have a signaling effect, while the signaling effect of return depth is moderated by service quality.     

Price-aware matrix factorization model for personalized recommendations

Recommendation methods aim to assist consumers in their decision-making process to find products that they are quite likely interested in. Current recommendation methods generally use online consumer reviews or ratings to predict consumers’ preferences for products. In e-commerce transactions, price plays a significant role in consumers’ purchase decisions. And each consumer's preference for product prices is specific. In this paper, we propose a novel price-aware recommendation method based on the matrix factorization model which fully considers the effect of price. We distill the price preferences of consumers from ratings and discover consumers’ real preferences for products. We further calculate the price sensitivities of consumers, not only considering the difference in price preference between a consumer and others but also focusing on the consumers’ price preferences in a specific price range. The final predicted ratings are calculated by adding the term of price effect into the matrix factorization framework. The results show that the proposed method has achieved high accuracy and performed better than several existing methods in both rating prediction and Top-N recommendation. And the products we recommend for consumers are more in line with their price preferences. Moreover, we find that, to some extent, the proposed method can solve the long-tail product recommendation problem with the consideration of the price effect.