• The Journal of Asian Finance, Economics and Business
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• v.5 no.4
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• pp.107-116
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• 2018

The aim of this study is to delve deeper into the online brand community study. This study tests (a) the effects of online brand community on its outcomes, (b) the impact of value creation practice construct as a whole, (c) the effects of value creating practice construct on the two types of loyalty, loyalty toward the brand and the community. Participants of this study (N=353) are members of four types of online brand communities (e.g., business-to-consumer virtual product support community, firm-hosted online community, user-generated online community, peer-to-peer problem-solving community, and social media based brand community). Data were collected online using Amazon Mechanical Turk from April 10, 2016 to May 10, 2016. The data were analyzed through structural equations modeling using AMOS 20. The three community markers (e.g., consciousness of kind, rituals and traditions, and moral responsibility) and the four value creation practices (e.g., social networking, impression management, community engagement, and brand use) are proved to be significant indicators of online brand community and value creation practice constructs, respectively. Test results showed that strong and effective online brand communities generate value creation practices, and value creation practices enhance brand loyalty. The mediating effects of community loyalty between value creation practices and brand loyalty were revealed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.85-97
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• 2021

Online consumer activities have increased considerably since the COVID-19 outbreak. For the products and services which have an impact on everyday life, online reviews and recommendations can play a significant role in consumer decision-making processes. Thus, to better serve their customers, online firms are required to build online-centric marketing strategies. Especially, it is essential to define core value of customers based on the online customer reviews and to propose these values to their customers. This study discovers specific perceived values of customers in regard to a certain product and service, using online customer reviews and proposes a customer value proposition methodology which enables online firms to develop more effective marketing strategies. In order to discover customers value, the methodology employs a text-mining technology, which combines a sentiment analysis and topic modeling. By the methodology, customer emotions and value factors can be more clearly defined. It is expected that online firms can better identify value elements of their respective customers, provide appropriate value propositions, and thus gain sustainable competitive advantage.

• Journal of Information Technology Applications and Management
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• v.21 no.3
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• pp.135-156
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• 2014

The Online business model for purchasing agent service is getting more popular. However, consumers perceive more risk when buying products from foreign online purchasing agents (FOPA) than from common online sellers (COS). This study focuses on finding out how consumers manage risk when they perceive risk and what different risk-reliever strategies they use when buying from high-risk FOPA and low-risk COS. This study has proved the following two. First, when consumers perceive risk at online purchasing, they tend to select risk-reliever strategies, such as the use of communication media, online assurance mark, seller's record, and secure payment to mitigate risk. With the application of those risk-reliever strategies, they built trust with the seller. Second, risk-perception of FOPA influences usage of communication media and check of online assurance mark more strongly than that of COS. On the contrary, risk-perception of COS influences the check of seller record more strongly than that of FOPA. This study helps to explain why FOPA is proliferating, despite its inherent high risk due to the fact that buyers and sellers are separated in time and space and that buyers and sellers have different social and cultural backgrounds. This study also helps managers of E-commerce to relieve consumer's risk-perception and to build trust.

• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.24-39
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• 2022

Online learning is becoming ubiquitous worldwide because of its accessibility anytime and from anywhere. However, it cannot be successfully implemented without understanding constructs that may affect its adoption. Unlike previous literature, this research extends the Unified Theory of Acceptance and Use of Technology with three well-known theories, namely compatibility, online self-efficacy, and knowledge sharing and acquisition to examine online learning adoption. A total of 264 higher education students took part in this research. Partial Least Squares-Structural Equation Modeling was used to evaluate the proposed theoretical model. The findings suggested that performance expectancy and compatibility were significant predictors of behavioral intention, whereas behavioral intention, facilitating conditions, and compatibility had a significant and direct effect on online learning's actual use. The results also showed that knowledge acquisition, knowledge sharing, and online self-efficacy were determinates of performance expectancy. Finally, online self-efficacy was a predictor of effort expectancy. The proposed model achieved a high fit and explained 47.7%, 75.1%, 76.1%, and 71.8% of the variance of effort expectancy, performance expectancy, behavioral intention, and online learning actual use, respectively. This study has many theoretical and practical implications that have been discussed for further research.

• Korean Journal of Community Nutrition
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• v.29 no.3
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• pp.199-211
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• 2024

Objectives: Online grocery shopping has gained traction with the digital transformation of retail. This study constructs a behavioral model combining values, attitudes, and reasons for behavior-specifically, facilitators and resistance-to provide a more novel discussion and further understand the relative influences of the various factors affecting continuance intention in online grocery shopping. Methods: Data were collected through an online questionnaire from consumers who had engaged in online grocery shopping during the past month in Seoul, Korea. All collected data were analyzed using descriptive analysis, and model validation was performed using partial least squares structural equation modeling. Results: Continuance intention is primarily driven by facilitative factors (compatibility, relative advantage, and ubiquity). Attitude can also positively influence continuance intention. Although resistance factors (price, tradition, and risk) do not significantly affect continuance intention, they negatively affect attitude. Values significantly influence consumers' reasoning processes but not their attitude. Conclusions: These findings explain the key influences on consumers' online grocery shopping behavior in Seoul and provide additional discussion and literature on consumer behavior and market management. To expand the online grocery market, consumers should be made aware of the potential benefits of the online channel; the barriers they encounter should be reduced. This will help sustain online grocery shopping behavior. Furthermore, its positive impact on attitude will further strengthen consumers' continuance intention.

• Journal of Agricultural Extension & Community Development
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• v.15 no.3
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• pp.433-460
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• 2008

The objectives of this study were to figure out the relationships of factors, e-quality, interactivity, trust and switching barrier, to affect the online customers loyalty. Moreover, e-quality was considered because of the problem of ssability originated from the environment that a lot of information and products are given at online business. This study employs a survey method to empirically test the proposed research model. A survey questionnaire was developed and 100 responses were collected. The data were analyzed using PLS method, a structural equation modeling method. The results of the study indicate that e-quality, interactivity, and trust influence switching barrier and that the switching barrier affects virtual relationship, which has an positive effect on loyalty. This study provides valuable theoretical and practical perspectives that e-quality is the most influential factor on virtual relationship and that, of the three factors of switching barrier, the virtual relationship is the most effective one to prevent customers online from changing their main online shop for products.

• The Journal of Information Systems
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• v.26 no.4
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• pp.327-353
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• 2017

Purpose Social media such as social network services, online forums, and customer reviews have produced a plethora amount of information online. Yet, the information deluge has created both opportunities and challenges at the same time. This research particularly focuses on the challenges in order to discover and track the service defects over time derived by mining publicly available online customer reviews. Design/methodology/approach Synthesizing the streams of research from text analytics, we apply two stages of methods of sentiment analysis and structural topic model incorporating meta-information buried in review texts into the topics. Findings As a result, our study reveals that the research framework effectively leverages textual information to detect, prioritize, and categorize service defects by considering the moving trend over time. Our approach also highlights several implications theoretically and practically of how methods in computational linguistics can offer enriched insights by leveraging the online medium.

• International Journal of Contents
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• v.8 no.1
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• pp.61-68
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• 2012

Previous research on online health (e-health) information was primarily focused on consumer information search behavior and information quality. Although studies addressing online information quality have flourished, they have yet to thoroughly examine whether consumers actually use the information they search. The purspose of this study suggests a conceptual framework that examines the potential antecedents of a consumers' willingness to depend on e-health information as an indicator of the consumer's trust of the provided e-health information. The proposed antecedents include healthcare product involvement, online skill level, perceived quality, and credibility of the source. Using structural equation modeling on online survey data, seven hypotheses, which describe the relationships between the variables of the model, were tested. The results indicate that consumer willingness to depend on provided e-health information was significantly influenced by the consumers' perceived quality and credibility of specific e-health information. Consumer involvement in healthcare and their online skill-level, respectively, also positively related to perceived quality and credibility regarding e-health information.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.12
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• pp.1297-1309
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• 2012

This study examined the effects of "me-model" body-size discrepancy on consumer's shopping mood, store satisfaction, and intention to revisit two types of online apparel stores (one featuring thin models and one featuring average-sized models). A convenience sample of women (n = 528) participated. Structural equation modeling was used to analyze the data. Participants who were thinner or similar to the models indicated positive shopping moods, a high level of online store satisfaction, and intended to revisit the stores when compared to participants who were larger than the models. Participants preferred the 'average-sized' model. This preference was attributed to the familiarity of the model and ability to effectively evaluate merchandise. The results revealed how models can influence apparel consumers in an online context.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.113-125
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• 2017

Customers have been affected by others' opinions when they make a purchase. Thanks to the development of technologies, people are sharing their experiences such as reviews or ratings through online or social network services, However, although ratings are intuitive information for others, many reviews include only texts without ratings. Also, because of huge amount of reviews, customers and companies can't read all of them so they are hard to evaluate to a product without ratings. Therefore, in this study, we propose a methodology to predict ratings based on reviews for a product. In a methodology, we first estimate the topic-review matrix using the Latent Dirichlet Allocation technic which is widely used in topic modeling. Next, we predict ratings based on the topic-review matrix using the artificial neural network model which is based on the backpropagation algorithm. Through experiments with actual reviews, we find that our methodology can predict ratings based on customers' reviews. And our methodology performs better with reviews which include certain opinions. As a result, our study can be used for customers and companies that want to know exactly a product with ratings. Moreover, we hope that our study leads to the implementation of future studies that combine machine learning and topic modeling.