• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.83-90
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• 2023

This study aims to determine the direct effect of positive online reviews, negative online reviews, the usefulness of online reviews, reviewers' expertise, timeliness of online reviews, the volume of online reviews, and comprehensiveness of online reviews on accommodation booking intentions and also the indirect effect of positive online reviews on the intention of booking accommodations through trust as mediation. Research respondents are users of the accommodation booking application in Yogyakarta. Hypothesis testing was carried out using SEM (Partial Least Square). Data was collected by distributing questionnaires to 135 respondents. The results of this study indicate that the Usefulness of Online Reviews, Volume of Online Reviews, and Comprehensiveness of Online Reviews have a direct positive and significant influence on the accommodation booking Intention of booking application users in Yogyakarta. The variables of Negative Online Reviews and Timeliness of Online Reviews have negative and significant influences on the accommodation booking Intention of booking application users in Yogyakarta. Positive Online Reviews and Reviewer Expertise variables are not significant in this study. At the same time, the Trust variable has a full mediation relationship in an indirect relationship between the Positive Online Reviews variables and the accommodation booking Intention of booking application users in Yogyakarta.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.281-289
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• 2024

Online consumer reviews have become the most important basis for online shopping and product sales. Fake reviews are generated to boost sales because online consumer reviews play a vital role in consumers' decision making. The prevalence of fake reviews violates the regulations of the online business environment and misleads consumers in decision making. Thus, the present research investigates the effects of reviews' linguistic characteristics (i.e., analytical thinking, authenticity) on review fakeness. Specifically, this research examines whether (1) the level of analytical thinking is lower for fake (vs. genuine) reviews (hypothesis 1) and (2) the level of authenticity is lower for fake (vs. genuine) reviews (hypothesis 2). This research analyzed user-generated hotel reviews (genuine reviews, fake reviews) collected from MTurk. Linguistic Inquiry and Word Count (LIWC) 2022 was adopted to code review contents, and the hypotheses were tested using logistic regression. Consistent with the hypotheses 1 and 2, the results indicate that (1) analyticial thinking is negatively associated with review fakeness; and (2) authenticity is negatively associated with review fakeness. The findings provide important implications to identify fake reviews based on linguistic characteristics.

• Journal of Fashion Business
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• v.22 no.3
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• pp.89-108
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• 2018

This paper aims to explore the analysis of the meanings and processes of reading online consumer reviews and to construct a substantive theory that explains the process involved with the phenomenon of reading consumer reviews. In order to explore the phenomenon, this study employs a qualitative methodology. Following the grounded theory perspective, the researcher conducted interviews with 17 participants, who have subsequently shopped online and utilized online consumer reviews for shopping, and decidedly employed in-depth interviews with those participants. Through coding and making constant comparison, several themes emerged: improving confidence, trusting reviews, getting a sense of who reviewers are, seeking balance, processing and handling negative reviews, experiencing vicariously, increasing searchability, getting a sense of who they are in terms of similarity, and seeking benefits and the usage situations from consumer based reviews. Among the emerging themes, improving confidence can be considered a core category, which is influenced by the analysis of trusting reviews and the consumer vicarious experiences with a product. Moreover, this study discusses the relationships among the themes. This study concludes with a discussion of the results, implications, and limitations.

• The Research Journal of the Costume Culture
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• v.21 no.3
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• pp.440-451
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• 2013

Because of the e-shopping market consumers now have diverse options to choose when placing their orders, and find it easy to obtain the required information through the Internet. Especially, for consumers, product reviews posted on an e-tailer's website have become more important criteria than such information available elsewhere. Hence, this study investigated the influence of the direction and evaluative contents of online reviews on consumer attitudes toward clothing products. Four types of online reviews based on direction (positive/negative) and evaluative content in review information (objective/subjective) were used in the experimental design. Further, stimulus reviews were developed. Credibility, usefulness of reviews, product preference, and purchase intention were the measured dependent variables in each of the four situations of online review presentations. The results indicated that, overall, positive and objective online reviews resulted in a higher level of consumer attitude. The content in these reviews had a relatively stronger influence than the direction on attitudes toward online reviews. Overall, objective reviews generated a higher level of credibility and usefulness of information than subjective reviews. Regarding subjective reviews, negative information was more related to credibility, whereas positive information was more related to usefulness. Further, positive information had a higher influence than negative information on consumer attitudes.

• Fashion, Industry and Education
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• v.16 no.1
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• pp.19-35
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• 2018

This study explores the effect of information direction and order of cosmetics power bloggers on consumer responses. A total of 488 undergraduate students participated in experiments with mock-up stimuli of sunscreen product reviews by power bloggers. The study was conducted with four stimuli of product review posts (i.e., positive reviews only, positive-negative reviews in order, negative-positive reviews in order, negative reviews only) of the power bloggers. The results showed a significant difference in consumer responses according to information direction and order of product reviews of the power bloggers. Specifically, negative reviews were considered more objective and more useful than positive reviews were. However, positivity of reviews is crucial in generating more positive attitudes toward products, greater purchase intention, and greater word-of-mouth intention. In regard to information order, the negative-positive reviews generated more positive attitudes toward the product and greater purchase intention than did the positive-negative reviews, emphasizing the importance of ending product reviews with positive information so as to create positive responses. Referring to the findings, power bloggers and marketers using bloggers as a promotional tool would benefit by carefully designing information content in consideration of an appropriate direction and order of information to better fit their purpose.

• Journal of Information Technology Services
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• v.21 no.4
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• pp.75-90
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• 2022

From the past to the present, reviews have had much influence on consumers' purchasing decisions. Companies are making various efforts, such as introducing a review incentive system to increase the number of reviews. Recently, as various types of reviews can be left, reviews have begun to be recognized as interesting new content. This way, reviews have become essential in creating loyal customers. Therefore, research and utilization of reviews are being actively conducted. Some studies analyze reviews to discover customers' needs, studies that upgrade recommendation systems using reviews, and studies that analyze consumers' emotions and attitudes through reviews. However, research that predicts the future using reviews is insufficient. This study used a dataset consisting of two reviews written in pairs with differences in usage periods. In this study, the direction of consumer product evaluation is predicted using KoBERT, which shows excellent performance in Text Deep Learning. We used 7,233 reviews collected to demonstrate the excellence of the proposed model. As a result, the proposed model using the review text and the star rating showed excellent performance compared to the baseline that follows the majority voting.

• Journal of Information Technology Services
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• v.16 no.2
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• pp.21-32
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• 2017

This study analyzes the variable consumer characteristics that influence the intention to use online product reviews. In online e-commerce, where purchases take place without consumers seeing the products in person, the product reviews left by other consumers who have already purchased the product are believed to be valuable information. However, when different consumers read the same product review, their responses to it may vary. This study analyzes the characteristics of consumers who utilize product reviews for their purchases. Consumer characteristics are categorized into personal information, personality, purchasing tendency, and experience related to product reviews. These factors are examined to see if they have direct or indirect effects on a consumer's intention to use product reviews when making online purchases. We surveyed a total of 240 consumers who had experience using e-commerce and knew about online product reviews. Once the data was collected, path analysis was conducted using the statistics tool AMOS. The study results reveal that consumers who are female, extroverted, and have higher price sensitivity think that product reviews left by others are useful, and that this "perceived usefulness" has a positive effect on the intention to use product reviews for making online purchasing decisions. In addition, consumers who are agreeable to others, have high brand sensitivity, and who have left numerous reviews themselves demonstrated the tendency to trust reviews left by others more. Thus, we conclude that this "perceived reliability" makes it more likely that a consumer will use product reviews when making online purchasing decisions. Future research can be done to develop this study further by analyzing whether providing online product reviews corresponding to the personal characteristics of consumers enhances the effect of product reviews on online purchasing decisions.

• Journal of Information Technology Services
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• v.14 no.4
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• pp.159-169
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• 2015

Customer reviews are one of the important sources for purchase decision makings in online stores. Online stores have tried to provide useful reviews in product pages to customers. To assess the usefulness of customer reviews before other users have voted enough on the reviews, diverse aspects of reviews were utilized in prevous studies. Style and semantic information were utilized in many studies. This study aims to test diverse alogrithms and datasets for identifying a proper classification method and threshold to classify useful reviews. In particular, most researches utilized ratio type helpfulness index as Amazon.com used. However, there is another type of usefulness index utilized in TripAdviser.com or Yelp.com, count type helpfulness index. There was no proper threshold to classify useful reviews yet for count type helpfulness index. This study used reivews and their usefulness votes on restaurnats from Yelp.com to devise diverse datasets and applied text mining approaches to classify useful reviews. Random Forest, SVM, and GLMNET showed the greater values of accuracy than other approaches.

• Asia pacific journal of information systems
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• v.31 no.4
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• pp.472-490
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• 2021

The detection of products with manipulated reviews has received widespread research attention, given that a truthful, informative, and useful review helps to significantly lower the search effort and cost for potential customers. This study proposes a method to recognize products with manipulated online customer reviews by examining the sequence of each review's sentiment, readability, and rating scores by product on randomness, considering the example of a Russian online retail site. Additionally, this study aims to examine the association between brand awareness and existing manipulation with products' reviews. Therefore, we investigated the difference between well-known and unknown brands' products online reviews with and without manipulated reviews based on the average star rating and the extremely positive sentiment scores. Consequently, machine learning techniques for predicting products are tested with manipulated reviews to determine a more useful one. It was found that about 20% of all product reviews are manipulated. Among the products with manipulated reviews, 44% are products of well-known brands, and 56% from unknown brands, with the highest prediction performance on deep neural network.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.347-364
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• 2022

Fraudulent companies or sellers strategically manipulate reviews to influence customers' purchase decisions; therefore, the reliability of reviews has become crucial for customer decision-making. Since customers increasingly rely on online reviews to search for more detailed information about products or services before purchasing, many researchers focus on detecting manipulated reviews. However, the main problem in detecting manipulated reviews is the difficulties with obtaining data with manipulated reviews to utilize machine learning techniques with sufficient data. Also, the number of manipulated reviews is insufficient compared with the number of non-manipulated reviews, so the class imbalance problem occurs. The class with fewer examples is under-represented and can hamper a model's accuracy, so machine learning methods suffer from the class imbalance problem and solving the class imbalance problem is important to build an accurate model for detecting manipulated reviews. Thus, we propose an OpenAI-based reviews generation model to solve the manipulated reviews imbalance problem, thereby enhancing the accuracy of manipulated reviews detection. In this research, we applied the novel autoregressive language model - GPT-3 to generate reviews based on manipulated reviews. Moreover, we found that applying GPT-3 model for oversampling manipulated reviews can recover a satisfactory portion of performance losses and shows better performance in classification (logit, decision tree, neural networks) than traditional oversampling models such as random oversampling and SMOTE.