• Asia pacific journal of information systems
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• v.25 no.4
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• pp.763-783
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• 2015

Online customer reviews (i.e., electronic word-of-mouth) has gained considerable interest over the past years. However, a knowledge gap exists in explaining the mechanisms among the factors that determine the product sales in online retailing environment. To fill the gap, this study adopts a principal-agent perspective to investigate the effect of customer reviews and customer incentives on product sales in online retail stores. Two customer review factors (i.e., average review ratings and the number of reviews) and two customer incentive factors (i.e., price discounts and special shipping offers) are used to predict product sales in regression analysis. The sales ranking data collected from the video game titles at Amazon.com are used to analyze the direct effects of the four factors and the interaction effects between customer review and customer incentive factors to product sales. Result reveals that most relationships exist as hypothesized. The findings support both the direct and interaction effects of customer reviews and incentive factors on product sales. Based on the findings, discussions are provided with regard to the academic and practical contributions.

• Knowledge Management Research
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• v.23 no.2
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• pp.253-276
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• 2022

Online reviews attract much attention because they play an essential role in consumer decision-making. Therefore, it is necessary to investigate the review attributes that affect the perceived helpfulness of consumers. However, most previous studies on the helpfulness of online reviews mainly focus on quantitative factors such as review volume and reviewer attributes. Recently, some studies have investigated the impact of review content and linguistic style matching on consumers' purchase decision-making. Those studies show that consumers consider additional review attributes when evaluating reviews in decision-making. To fill the research gap with existing literature, we investigated the impact of review content and linguistic style matching on review helpfulness. Moreover, this study investigated how the reviewers' expertise moderates the effect of the review content and linguistic style matching on the review helpfulness. The empirical results show that positive affective content has a negative effect on the review helpfulness. The negative affective content and linguistic style matching positively affect review helpfulness. Review expertise relieved the impact of negative affective content and linguistic style matching on review helpfulness. According to the mechanism confirmed in this study, online e-commerce companies can achieve corporate sales growth by identifying factors affecting review helpfulness and reflecting them in their marketing strategies.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.63-72
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• 2022

The interest in online education has increased due to the spread of COVID-19. Especially, in the online environment, the importance of scaffolding, which refers to the help and support provided by instructors or peer learners, is being emphasized. This study aimed to review the antecedents of online scaffolding at the university of Korea through the method of systematic review. For this purpose, we collected 578 studies conducted from 2010 to 2021 and selected 7 studies according to the criteria of selection and exclusion. As a result, we found general features, types, and effects of online scaffolding at the university of Korea. Based on these results, we discussed suggestions and implications for a follow-up study. We hope many empirical kinds of research will be conducted about online scaffolding at the university.

• International Journal of Contents
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• v.14 no.2
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• pp.16-23
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• 2018

Although there have been studies regarding the influence of customer reviews on consumer decision making at online shopping sites, research on factors affecting the perceived customer review quality for online shopping sites is limited. This study posits that sociability, which is one of the environmental factors of an online shopping site, can affect the quality of customer reviews. Sociability is a key factor in building a collaborative environment online, but studies have been limited to applying sociability to customer reviews that are the result of a collaborative environment. This study expects that sociability affects the performance of online shopping sites through the perceived information quality of customer reviews, and customers' efficacy. More specifically this study investigates the structural relationship between sociability, self-efficacy, collective efficacy, and the perceived information quality of the reviews in an online shopping context, regarding the patronage intention of customers. This study was conducted using a survey of 361 college students. The structural equation model results indicate that user perception of sociability increases self-efficacy and collective efficacy. The improved efficacy enhances the perceived information quality of reviews for online shopping sites, which increases patronage intention of customers. This study found that online shopping sites require a platform for customers to engage in social interaction to enhance their customers' loyalty and lifetime value.

• Journal of Distribution Science
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• v.20 no.11
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• pp.33-45
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• 2022

Purpose: The development of online shopping trends in Indonesia is increasing, and Tokopedia is becoming one of the most popular e-commerce websites. The purpose of this study is to obtain empirical evidence whether mobile shopping, customer review, perceived credibility, and Korean celebrity endorsement affect online purchase decision, whether online purchase decision affects customer satisfaction, and whether customer satisfaction affects brand loyalty of customers in Tokopedia e-commerce. Research Design, data and methodology: Quantitative survey with data was collected using an online questionnaire with sample characteristics were Tokopedia customers who lived in Jakarta by 385 samples using the purposive sampling method, and data analysis was conducted using the Smartpls application program version 3.0. Results: Mobile shopping, customer review, and perceived credibility had positive effects on online purchase decision at Tokopedia in Jakarta. However, Korean celebrity endorsement did not have a positive effect on online purchase decision at Tokopedia in Jakarta. Furthermore, online purchase decision had a positive effect on customer satisfaction at Tokopedia in Jakarta, and customer satisfaction had a positive effect on brand loyalty at Tokopedia in Jakarta. Conclusions: This study proposes significant implications for maintaining customer relationships to achieve purchasing decision, customer satisfaction, and brand loyalty in the e-commerce industry.

• Knowledge Management Research
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• v.22 no.1
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• pp.243-267
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• 2021

Sentiment represents one's own state through the process of change to stimulus, and emotion represents a simple psychological state felt for a certain phenomenon. These two terms tend to be used interchangeably, but their meaning and usage are different. In this study, we try to find out how it affects the helpfulness of reviews by classifying sentiment and emotion through online reviews written by online consumers after purchasing and using various products and services. Recently, online reviews have become a very important factor for businesses and consumers. Helpful reviews play a key role in the decision-making process of potential customers and can be assessed through review helpfulness. The helpfulness of reviews is becoming increasingly important in practice as it is utilized in marketing strategies in business as well as in purchasing decision-making issues of consumers. And academically, the importance of research to find the factors influencing the helpfulness of reviews is growing. In this study, Yelp.com secured reviews on restaurants and conducted a study on how the sentiment and emotion of online reviews affect the helpfulness of reviews. Based on the prior research, a research model including sentiment and emotions for online reviews was built, and text mining analyzes how the sentiment and emotion of online reviews affect the helpfulness of online reviews, and the difference in the effects on emotions It was verified. The results showed that negative sentiment and emotion had a greater effect on review helpfulness, which was consistent with the negative bias theory.

• Journal of Distribution Science
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• v.20 no.8
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• pp.105-114
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• 2022

Purpose: The technology acceptance model (TAM) refers to a theory that maps the possibility or extent to which users can accept an innovative technology. The purpose of the current research is to investigate the mediating effect of TAM between online review and behavior intention for promoting golf app's distribution. Research design, data and methodology: In order to examine the relationship between app usage reviews, TAM, and behavioral intentions of golf app participants, the present author collected total 170 responses from South Korean participants based on web-based survey system. The main methodology which was selected by this study is mediation causality analysis that Baron and Kenny suggested. Results: The statistical findings definitely indicated that TAM mediating role exists between the positive emotion of golf app users regarding online reviews and positive behavior intention of golf app, which means that all three steps of mediation causality analysis were statistically significant. Conclusions: The present research concludes that the correct utilization of innovation in the design and implementation of the technology features translates into performance excellence. The model can be used to increase the online presence through innovation as a primary drive toward providing more convenience and accessibility to the users through mobile golf apps.

• The Journal of Information Systems
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• v.28 no.1
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• pp.1-16
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• 2019

Purpose The purpose of this study is to analyze causal relationship between quantity and quality for deriving ranking model of Online reviews. Thus, we propose implications for deriving the ranking model for retrieving Online reviews more effectively. Design/methodology/approach We collected Online review from Tripadvisor web sites which might be a kind of world-famous tourism web sites. We transformed the natural text reviews to quantified data which consists of quantified positive opinions, quantified negative opinions, quantified modification opinions, reviews lengths and grade scores by using opinion mining technologies in R package. We executed corelation and regression analysis about the data. Findings According to the empirical analysis result, this study confirmed that the review length influenced positive opinion, negative opinion and modification opinion. We also confirmed that negative opinion and modification opinion influenced the grade score.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Knowledge Management Research
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• v.23 no.1
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• pp.187-201
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• 2022

Online reviews written by tourists provide important information for the management and operation of the tourism industry. The star rating of online reviews is a simple quantitative evaluation of a product or service, but it is difficult to reflect the sincere attitude of tourists. There is also an issue; the star rating and review content are not matched. In this study, a star rating prediction model based on online review content was proposed to solve the discrepancy problem. We compared the differences in star ratings and sentiment by continent through sentiment analysis on tourist attractions and hotels written by foreign tourists who visited Korea. Variables were selected through TF-IDF vectorization and sentiment analysis results. Logit, artificial neural network, and SVM(Support Vector Machine) were used for the classification model, and artificial neural network and SVR(Support Vector regression) were applied for the rating prediction model. The online review rating prediction model proposed in this study could solve inconsistency problems and also could be applied even if when there is no star rating.