• Journal of the Korea Convergence Society
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• v.6 no.4
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• pp.205-212
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• 2015

This paper aims to investigate key factors affecting customer adoption of the online review from the three perspectives such as customer review characteristics, reviewer characteristics, and customer characteristics. We collected data on customers who have experience in purchasing products in Taobao. The major findings are as follows. First, the customer review amount and vividness are not directly related to customer adoption of the online review. Second, the trust of reviewer and perceived similarity have positive effects on customer adoption of the online review. Third, the prior knowledge and product involvement increase customer adoption of the online review. Finally, customers' purchase intention is greatly determined by customer adoption of the online review. This paper presents the importance of the management of customer reviews and management method for the stakeholders of shopping mall to advancing Chinese market.

• 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.

• Knowledge Management Research
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• v.24 no.4
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• pp.153-176
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• 2023

Generative AI, especially conversational AI like ChatGPT, has recently gained traction as a technological alternative for automating customer service. However, there is still a lack of research on whether current generative AI technologies can effectively replace traditional human managers in customer service automation, and whether they are advantageous in some situations and disadvantageous in others, depending on the conditions and environment. To answer the question, "Can generative AI replace human managers in customer service activities?", this study conducted experiments and surveys on customer online reviews of a food delivery platform. We applied the perspective of the elaboration likelihood model to generate hypotheses about whether there is a difference between positive and negative online reviews, and analyzed whether the hypotheses were supported. The analysis results indicate that for positive reviews, generative AI can effectively replace human managers. However, for negative reviews, complete replacement is challenging, and human managerial intervention is considered more desirable. The results of this study can provide valuable practical insights for organizations looking to automate customer service using generative AI.

• Journal of Information Technology Services
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• v.11 no.3
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• pp.103-127
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• 2012

With the advent of SNS (Social Network Services), the product reviews by friends in SNS are intensively utilized for online marketing. However, there is a lack of empirical evidence on the actual marketing effect of SNS reviews, although we need to identify who can be the target of SNS marketing in terms of customer attributes, preferences, or experiences. In this study, we investigate the moderating role of customer attributes in identifying the effect of SNS reviews on customer purchasing decision. As the moderating variables, we adopt 'information search experience' and 'perception of information overload'. Research results evidence that, in order to understand the effect of SNS reviews in a comprehensive manner, we need to examine it in the context of various related factors such as 'information search experience' and 'perception of information overload'. The results show that the persuading effect of SNS reviews for product purchasing is stronger for the customers with the lower information search experiences as well as the lower perception on the information overload on the web. This result delivers managerial implications on who can be the target customers of SNS marketing.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.443-451
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• 2021

The classification standards for hotels in Vietnam are different from many other hotel classification standards in the world. This study aims to analyze customer reviews on the TripAdvisor website to develop a new algorithm for hotel rating that is independent of Vietnam's hotel classification standards. This method can be applied to individual hotels, or hotels of a region or the whole country, while online booking sites only rate individual hotels. Data was crawled from TripAdvisor with 22,287 reviews of 5 cities in Vietnam. This study used a statistical model to analyze the review dataset and build an algorithm to rate hotels according to aspects or hotel overall. The results have less rating deviation when compared to the TripAdvisor system. This study also supports hotel managers to regularly update the status of their hotels using data from customer reviews, from which, managers can strategize long-term solutions to improve the quality of the hotel in all aspects and attract more travelers to Vietnam. Moreover, this method can be developed into an automatic system to rate hotels and update the status of service quality more quickly, thus, saving time and costs.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.76-85
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• 2012

Through opinion mining, we can analyze the degree of positive or negative sentiments that customers feel about important entities or attributes in online customer reviews. But, the limit of the opinion mining techniques is to provide only simple functions in analyzing the reviews. In this paper, we proposed novel techniques that can analyze the online customer reviews multi-dimensionally. The novel technique is to modify the existing OLAP techniques so that they can be applied to text data. The novel technique, that is, multi-dimensional analytic model consists of noun, adjective and document axes which are converted into four relational tables in relational database. The multi-dimensional analysis model would be new framework which can converge the existing opinion mining, information summarization and clustering algorithms. In this paper, we implemented the multi-dimensional analysis model and algorithms. we recognized that the system would enable us to analyze the online customer reviews more complexly.

• Asia pacific journal of information systems
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• v.31 no.4
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• pp.491-526
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• 2021

Airlines can employ a variety of quality monitoring procedures. In this study, we employ a content analysis of 8 years of online reviews for Korean airlines in contrast to other international airlines. Online airline reviews are infrequent, relative to the total number of passengers - the number of reviews is multiple orders of magnitude lower than passenger volumes - and online airline reviews are, therefore, not representative of passenger attitudes overall. Nevertheless, online reviews may be indicative of specific service issues, and draw attention to aspects that require further study by airline operators. Furthermore, significant words and phrases used in these airline reviews may help airline operators to rapidly automate filtering, partitioning, and analysis of incoming passenger comments via other channels, including email, social media posts, and call center transcripts. The current study provides insights into the contents of online reviews of Korean vs Other-International airlines, and opportunities for service enhancement. Further, we provide a set of marker words and phrases that may be helpful for management dashboards that require automated partitioning of passenger comments.

• Fashion & Textile Research Journal
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• v.16 no.1
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• pp.79-90
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• 2014

With the development of modern society, not only have the Internet and e-commerce been progressed but they also made 'consumption patten' diverse. Despite the internet clothing market growth, there is critical a disadvantage, which is consumers is not able to wear the products presented via online pictures. Thus, pictures on the internet are the only information customers can get, which has caused consciousness on the importance of dealing with 'customer review'. In spite of the fact that 'customer review' has undeniably evolved to be one of customers' essential requisites, the research on this subject is very limited. Until now, the studies on the internet shopping consumers' behavior mostly has to do with the features of 'customer review' such as 'a sense of exaggeration', 'usability', 'duality', 'purity', 'professionalism', 'reliability', and the 'similarity', etc.) Therefore, this study categorizes the characteristics of online shopping reviews to 'the number of reviews', 'the article-length', 'the existence of photos', 'the rewards for reviews', 'the contents of the reviews' and 'the freshness of the reviews' and reviews the impact of an features of 'customers' reviews' affecting the internet shopping sales promotion. Moreover, it is to contribute to the marketing strategies of a shopping mall by analyzing consumers' 'purchasing satisfaction', 'the intention of repurchasing', and 'the factors of viral marketing'.

• 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.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.21-36
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• 2022

Recently, as COVID-19 has made the e-commerce market expand widely, customers who have different consumption patterns appear in the market. Because companies can obtain opinions and information of customers from reviews, they increasingly face the requirements of managing customer reviews on online platform. In this study, we analyze customers and carry out market segmentation for classifying and defining type of customers in e-commerce. Specifically, K-means clustering was conducted on customer review data collected from Wemakeprice online shopping platform, which leads to the result that six clusters were derived. Finally, we define the characteristics of each cluster and propose a customer management plan. This paper is possible to be used as materials which identify types of customers and it can reduce the cost of customer management and make a profit for online platforms.