Online User Reviews Research Papers (original) (raw)

We develop a two-stage conceptual consumer decision model from the risk perspective to understand the role of online user reviews in consumers' Willingness-To-Pay (WTP). In stage one, consumers assess product uncertainty with product... more

We develop a two-stage conceptual consumer decision model from the risk perspective to understand the role of online user reviews in consumers' Willingness-To-Pay (WTP). In stage one, consumers assess product uncertainty with product reviews. In stage two, they assess seller uncertainty with seller reviews, conditional on their assessment of product uncertainty in stage one. We further develop an operationalization of our conceptual model using the expected utility theory and derive hypotheses on the effects of online user reviews on consumers' WTP. We test our hypotheses using data from an experimental study and an empirical study.

Our study employs sentiment analysis to evaluate the compatibility of Amazon.com reviews with their corresponding ratings. Sentiment analysis is the task of identifying and classifying the sentiment expressed in a piece of text as being... more

Our study employs sentiment analysis to evaluate the compatibility of Amazon.com reviews with their corresponding ratings. Sentiment analysis is the task of identifying and classifying the sentiment expressed in a piece of text as being positive or negative. On e-commerce websites such as Amazon.com, consumers can submit their reviews along with a specific polarity rating. In some instances, there is a mismatch between the review and the rating. To identify the reviews with mismatched ratings we performed sentiment analysis using deep learning on Amazon.com product review data. Product reviews were converted to vectors using paragraph vector, which then was used to train a recurrent neural network with gated recurrent unit. Our model incorporated both semantic relationship of review text and product information. We also developed a web service application that predicts the rating score for a submitted review using the trained model and if there is a mismatch between predicted rating score and submitted rating score, it provides feedback to the reviewer.

With the aim of enhancing their online reputation, several hospitality businesses have started soliciting their guests to write online reviews. Available studies have not yet evaluated the effects of this strategy. To fill this knowledge... more

With the aim of enhancing their online reputation, several hospitality businesses have started soliciting their guests to write online reviews. Available studies have not yet evaluated the effects of this strategy. To fill this knowledge gap, this study draws on the Theory of Psychological Reactance and investigates guests' attitudinal and behavioral reactions to received solicitations. Evidence collected from a sample of Italian travelers indicates that soliciting reviews has both benefits and drawbacks: it increases the number of reviews for the business, but it also irritates a significant share of guests. Particularly high levels of irritation arise when a business explicitly asks its guests to write positive reviews. The implications of these findings for the reputation management strategy of hospitality businesses are discussed.

The objective of persuasive technology researches is to develop persuasive systems that are able to change or reshape human behavior. Persuasive technology has quickly found a wide range of applications in many fields of research and... more

The objective of persuasive technology researches is to develop persuasive systems that are able to change or reshape human behavior. Persuasive technology has quickly found a wide range of applications in many fields of research and development like marketing, health, safety and environment. The key element in designing successful persuasive systems is the improvement of the persuasion process. An important factor that should be included in the persuasion process is the user experience. This paper reviews the current trends of persuasive technology and shows some example of the available persuasive systems. The contribution of this paper is proposing a new and promising research direction for building persuasive systems that take the user feedback as a key element in the persuasion process. Some of the systems that follow this approach have been proposed and illustrated.

In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to... more

In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to exploit in order to identify the information associated with specific reviewed items. Opinion mining has been widely used to identify the significant features of items (e.g., cameras) based upon user reviews. Feature extraction is the most critical step to identify useful information from texts. Most existing approaches only find individual features about a product without revealing the structural relationships between the features which usually exist. In this paper, we propose an approach to extract features and feature relationships, represented as a tree structure called feature taxonomy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature taxonomy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that our proposed approach is able to capture the product features and relations effectively.

With the broad reach of the Internet, online users frequently resort to various word-of-mouth (WOM) sources, such as online user reviews and professional reviews, during online decision making. Although prior studies generally agree on... more

With the broad reach of the Internet, online users frequently resort to various word-of-mouth (WOM) sources, such as online user reviews and professional reviews, during online decision making. Although prior studies generally agree on the importance of online WOM, we have little knowledge of the interplay between online user reviews and professional reviews. This paper empirically investigates a mediation model in which online user reviews mediate the impact of professional reviews on online user decisions. Using software download data, we show that a higher professional rating not only directly promotes software download but also results in more active user-generated WOM interactions, which indirectly lead to more downloads. The indirect impact of professional reviews can be as large as 20% of the corresponding total impact. These findings deepen our understanding of online WOM effect, and provide managerial suggestions about WOM marketing and the prediction of online user choices.