• The Research Journal of the Costume Culture
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• v.18 no.1
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• pp.118-132
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• 2010

The purpose of this study is to understand factors of risk perception and purchase obstruction by consumer characteristics and purchase experience of clothing in online. The collection of the research materials was progressed by online and offline. Out of 374 usable questionnaires used for examining this study, 278 questionnaires were collected from offline and 107 questionnaires were collected from online. Frequency analysis, factor analysis, reliability analysis, t-test, One-way ANOVA and multiple regression analysis using SPSS WIN 12.0 were conducted. Three factors of perceived risk were extracted: harmonic/image, quality/shopping process, payments. Based on these dimensions, ANOVA was conducted. The results indicated that the more purchasing experience people had, the less the extent of perceived risk they got, and quality/shopping process risk mostly among them. As the factors which obstruct purchasing decision, a security obstruction, a reliability obstruction, a convenient obstruction and an information insufficient obstruction are extracted. Also, the factors have got the result of same aspects as the perceived risk recognized by the Internet shopping experience. Meaningful differences between groups appear at security obstruction, reliability obstruction, and convenient obstruction. Perceived risk almost influenced on purchase obstruction when purchasing clothes in Internet shopping mall. When consumers perceiving harmony/image risk highly make decisions, they usually hesitate or abandon due to reliability obstruction, convenient obstruction. All the factors: including security obstruction, reliability obstruction, convenient obstruction and information insufficient obstruction made consumers perceiving quality/shopping process risk highly obstruct purchase decision.

• Journal of Digital Convergence
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• v.13 no.7
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• pp.79-88
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• 2015

This study focused on the idea that consumers who are dissatisfied with their body form tend to be more interested in weight control behavior. This research connects this relationship with consumers' risk perception on the internet and consequent decision hesitation behavior. Empirical results extracted three factors of weight control behavior: diet, physical treatment, and medication and exercise. Weight control behavior was different by gender but not by age. Consumers who were dissatisfied with their body form were likely to do exercise, but other types of dissatisfaction (weight dissatisfaction and height dissatisfaction) were not significantly related to weight control behavior. Weight dissatisfaction influenced perceived size risk significantly when shopping online. Diet, physical treatment, and medication had significant influence on perceived size risk when shopping online. Perceived size risk had significant influence on decision delay and offline switch behavior. This study took a convergence approach, which connects consumer characteristics with online shopping behavior.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.1-18
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• 2022

As digital technology converges into the e-commerce market across industries, online transactions have activated, and the use of online has increased. With the recent spread of infectious diseases such as COVID-19, this market flow is accelerating, and various product information can be provided to customers online. Providing a variety of information provides customers with various opportunities but causes difficulties in decision-making. The recommendation system can help customers to make a decision more effectively. However, the previous research on recommendation systems is limited to only quantitative data and does not reflect detailed factors of products and customers. In this study, we propose an intelligent recommendation system that quantifies the attributes of products and customers by applying text mining techniques to qualitative data based on online reviews and integrates the existing objective indicators of total star rating, sentiment, and emotion. The proposed integrated recommendation model showed superior performance to the overall rating-oriented recommendation model. It expects the new business value to be created through the recommendation result reflecting detailed factors of products and customers.

• Journal of Platform Technology
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• v.11 no.3
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• pp.45-55
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• 2023

This paper presents a model that supports decision-makers in the Korean film industry to maximize the success of online movies. To achieve this, we collected historical box office movies and clustered them into types to propose a model predicting each type's online box office performance. We considered various features to identify factors contributing to movie success and reduced feature dimensionality for computational efficiency. We systematically classified the movies into types and predicted each type's online box office performance while analyzing the contributing factors. We used automated machine learning (AutoML) techniques to automatically propose and select machine learning algorithms optimized for the problem, allowing for easy experimentation and selection of multiple algorithms. This approach is expected to provide a foundation for informed decision-making and contribute to better performance in the film industry.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.483-490
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• 2021

In Indonesia, coffee shops, commonly called warung or kedai shops, have begun to appear amid society from remote villages to urban centers. Therefore, the purpose of this study is to examine the effect of cafe atmosphere (i.e., exterior, interior, interior point-of-purchase displays and store layout) on the purchase decision of Generation Z. This study is conducted because of cafe competition is currently overgrowing. This study model consisted of five variables: exterior, interior, interior point-of-purchase displays, store layout, and purchase decision. Sampling in this study used non-probability, with a purposive sampling technique. According to predetermined criteria, the data collection technique employed a questionnaire distributed online to consumers had visited a cafe at least once in the last three months. This study's sample was 137 cafe visitors in Yogyakarta, representing one of the big cities in Indonesia. Therefore, the data was analyzed by using multiple regression. The results of the study indicated that the exterior and interior had a positive and significant effect on purchasing decision. Likewise, interior point-of-purchase displays and store layout positively and significantly affected purchase decision. In addition, this study's findings generally concluded that the cafe atmosphere had a positive and significant effect on purchase decision.

• Korean Journal of Childcare and Education
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• v.10 no.1
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• pp.81-94
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• 2014

This study is intended to identify the motivations for seeking child care credentials through online education and factors influencing the career decision-making. The study was conducted among students taking child care courses online, and data was analyzed through t-test, F-test, Scheffe posteriori test, and hierarchical regression analysis. The findings are as follows. 1) Motivation for seeking the credentials varied depending on background variable. Livelihood was a stronger motivation for low-income group than higher come group. Self-realization desire was a stronger motivation for the group with low education level than the group with higher education level. 2) Age affected the participants' career decision-making. The younger they were, the more dependent they were on other people's advice. 3) Different variables influenced the students' rational, intuitional, and dependent career-decision making. Factors influencing their rational career decision-making were age, motivation to acquire the credentials for making a living, and motivation for building a better future. For intuitional career decision-making, age affected the decision. For dependent career decision-making, motivation to seek credentials for making a living was an influencing variable.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.147-163
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• 2004

For the purchase of a cheaper product on the Internet, many customers have been trying to search online shopping mall sites and visit comparison-pricing shops that compare prices and other criteria of the product. Others have been participating into online auction markets or group-buying markets. However, a lot of online shopping malls, auction markets, and group-buying markets provide the same product with different prices. Since these marketplaces have different price settlement mechanism, it is very difficult for the customers to determine marketplace to purchase, considering different kinds of marketplaces at the same time. To overcome such limitations, decision rules and solution procedures for purchase decision making are necessary, which can cover multiple marketplaces simultaneously. For this purpose, purchase decision making in each market must be conducted to maximize customer's utility, and conflicts with other marketplaces must be resolved. Therefore, we have developed the rules and methods that can negotiate cooperatively the purchase decision making in several marketplaces, and designed an architecture of Intelligent Buyer Agent and a message structure to support the idea.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.377-393
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• 2020

Freight management systems require a new business model for rapid decision making to improve their business processes by dynamically analyzing the previous experience data. Moreover, the amount of data generated by daily business activities to be analyzed for making better decisions is enormous. Online-to-offline or offline-to-online (O2O) is an electronic commerce (e-commerce) model used to combine the online and physical services. Data analysis is usually performed offline. In the present paper, to extend its benefits to online and to efficiently apply the big data analysis to the freight management system, we suggested a system architecture based on O2O services. We analyzed and extracted the useful knowledge from the real-time freight data for the period 2014-2017 aiming at further business development. The proposed system was deemed useful for truck management companies as it allowed dynamically obtaining the big data analysis results based on O2O services, which were used to optimize logistic freight, improve customer services, predict customer expectation, reduce costs and overhead by improving profit margins, and perform load balancing.

• Management & Information Systems Review
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• v.27
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• pp.1-30
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• 2008

The objective of this paper is to gain a better understanding of factors influencing digital piracy and the use of paid services on the web. A model identifying and describing various factors which affect decision$\sim$makers' attitude toward sharing digital products and online behavioral intentions is constructed based on established theories of human behavior. The relationship between the continuous intention of sharing the products and the intention to use online services is also discussed. Findings show that a independent relationship exists between illegal and legal behaviors, which means stopping piracy by individuals can not lead to their buying of the products. Also while the attitude toward piracy was significantly related to the continuous intention of piracy behaviors, did not necessarily lead to the use intention of online services. The data also show that beliefs concerning consequences of digital piracy were significant enough to alter one's behavioral attitude. There was a significant relationship between perceived consequences and the intention to pirate digital products or to use paid services. The results also show that while the effect of economical and ethical factors were ascertained, that of social and legal factors have not been found. Self$\sim$efficacy has a moderating effect on the relationship between the attitude toward piracy and both intentions of online behavior. The implications of the findings to research and practice are discussed.

• Journal of Information Technology Services
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• v.6 no.1
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• pp.83-99
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• 2007

The purpose of this research is to identify the determinants of loyalty in online games. This study developed a research model to analyze the factors explaining the loyalty level from gamers, employing social identification, flow, and positive anticipated emotion as major research variables, and collected 1308 survey responses from gamers. Within the context of arguing that the exploration of age range issues with respect to online games is important, this research also examines the age differences in path coefficients. To this end, the structural model was tested with the data from entire data sample (i.e., the age of 10s, 20s, and 30s pooled together) and each of the subsamples (i.e., teens taken separately, twenties taken separately, and thirties taken separately). Properties of the causal paths, including standardized path coefficients, the significance of difference, in the hypothesized model, are also presented, so that we can investigate the relative influences of different dominants, demonstrating how teens, twenties, and thirties differ in their decision-making processes regarding the flow, social identification and loyalty from online games.