• The Journal of Industrial Distribution & Business
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• v.9 no.4
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• pp.63-72
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• 2018

Purpose - The purpose of this paper is to investigate the effects of online game properties, social interaction, and player satisfaction on intention to online games in Chinese gamers. Research design, data, and methodology - This study is an empirical analysis assuming that properties of online games, social interaction and satisfaction will induce Chinese gamers' intention to play online games. We set the relationship between the six variables as follows. First, the properties of online games, exogenous variables, were identified by three factors: entertainment, security, participation and challenge. Second, we had input social interaction among gamers as another exogenous variables. Third, the gamer's satisfaction of online games was added to the research model as a mediating variable between exogenous variables and endogenous variables. Finally, gamer's intention to play influenced by satisfaction and social interaction was used as final endogenous variable. The data used for the empirical analysis were collected through questionnaires for Chinese under age 35 who enjoy the online games. The data used in the research were finally extracted from 195 questionnaires. The collected data were tested through the analysis of the measurement model (Step 1) and the analysis of the structural model (Step 2). The covariance structure equation model (SEM) was used for the analysis. The measurement model and structural model were evaluated by the maximum likelihood method. Results - The results of the empirical analysis are as follows. The satisfaction of online games were entertainment and security had a significant effect to satisfaction; but participation and challenge and social interaction had no significant effect on satisfaction. The social interaction among gamers and the satisfaction with online games have a significant influence on the intention to play online games. As a result, the attributes of the game were affecting the intention to play the game after satisfaction. Social interaction influenced the intention to play online games rather than satisfaction itself. Conclusions - This study provide some practical implications for the new companies who want to enter the online game industry and seek to competitiveness in China, and provide theoretical implications on the role of interaction among gamers in the study of online games.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.64-72
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• 2002

In the field of plant maintenance, data that are gathered by sensors on multiple machines are handled and analyzed. Online or pseudo online data handling is required on such fields. When the data occurrence speed exceeds the data handling speed, multiple data should be handled at a time (batch data handling or pseudo online data handling). If l amount of data are received at one time following N amount of data, how to estimate the new parameters effectively is a great concern. A new simplified calculation method, which calculates the N data's weights, is introduced. Numerical examples show that this new method has a fairly god estimation accuracy and the calculation time is less than 1/10 compared with the case when the whole data are re-calculated. Even under the restriction calculation ability in the apparatus is limited, this proposed method makes the failure detection of equipments possible in early stages with a few new coming data. This method would be applicable in many data handling fields.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.249-260
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• 2019

The study examines the impact of financial risk, convenience risk, non-delivery risk; return policy risk and product risk on online consumer behavior of Malaysian consumers. The research employed a self-administered survey to collect empirical data from 245 Malaysian online shoppers by using convenience sampling. Cronbach alpha was calculated to confirm the reliability of the data and then normality was assessed. Confirmatory Factor Analysis was then conducted to test the model using the goodness-of-fit tests. And finally, structural equation modeling is used to test the hypotheses and draw conclusions. IBM SPSS AMOS version 22.0 was utilized for data analysis. The research indicates that product risk, convenience risk, and return policy risk have a significant and positive impact on online shopping behavior. Financial risk is found to have insignificant and negative effects on consumer behavior. In addition, the non-delivery risk is found to have a significant and negative impact on online shopping behavior. The findings provide a useful model for measuring and managing perceived risk in online shopping which may result in an increase in participation of Malaysian consumers and reduce their cognitive deficiencies in the e-commerce environment. Several managerial implications are discussed along with the scope for future research.

• Journal of Distribution Science
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• v.20 no.8
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• pp.59-67
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• 2022

Purpose: This study identifies how twelve key success factors of distribution strategies for community enterprises in Thailand achieve higher performances. Research design, data, and methodology: The samples in this study were 400 entrepreneurs throughout the country. The instrument for data elicitation was a questionnaire. The descriptive and inferential statistics for data analysis were percentage, mean, standard deviation, T-Test, F-Test, multiple regression, and multiple correlations. Results: The results revealed that, overall, the samples showed high opinions on online distribution strategies in all aspects. In detail, the three highest factors were as follows: 1) electronic satisfaction, 2) product characteristics and electronic trust, and 3) the quality and success in online distribution. In detail, the three highest aspects of online distribution success were customer loyalty, financial performance, and work management, respectively. The online distribution strategies influencing community enterprises' success were electronic trust, electronic loyalty, social information, electronic satisfaction, and online distribution tools, which had a statistical significance of 71. Conclusions: This research has made an essential contribution to community enterprise entrepreneurs should focus on and adopt these 8P+4ODS concepts to increase sales, maintain brand loyalty of existing customers, get new customers, develop learning, and improve the working potentials of community enterprise entrepreneurs.

• Journal of Distribution Science
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• v.22 no.9
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• pp.39-48
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• 2024

Purpose: The purpose of this study is to collect and analyze a large amount of data from online ticket distribution platforms that offer multiple airlines and different routes so that they can improve their ticket distribution marketing strategies and provide services that are more suitable for consumer's needs. The results of this study will help airlines improve the quality of their online platform services to provide more benefits and convenience by providing access to multiple airlines and routes around the world on one platform. Research design, data and methodology: For the study, 200 people completed the survey between May 1 and June 15, 2024, of which 191 copies were used in the study. Results: The hypothesis testing results of this study showed that among the components of the recommendation algorithm, decision comport, novelty, and evoked interest recurrence had a positive effect on perceived recommendation quality, but curiosity did not have a positive effect on recommendation quality. The perceived recommendation quality of the online platform positively influenced recommendation satisfaction, and the higher the perceived recommendation quality, the higher the intention to continue the relationship. Finally, higher recommendation satisfaction was associated with higher relationship continuation intention. Conclusion: it's important to continue researching online ticketing platforms. Online platforms will also need to be systems that use technology and data analytics to provide a better user experience and more benefits.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.633-650
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• 2013

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

• Journal of Information Technology Applications and Management
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• v.13 no.3
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• pp.181-200
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• 2006

The purpose of this study is to examine factors influencing online product transformation focusing on benefits of online shopping and word of mouth. Generally, it has been known that buying search goods is more proper than experience goods in the online. However benefits of online shopping and word of mouth make product transformation from experience goods to search goods and the product transformation promote the purchase of experience goods online. We developed a conceptual model of online product transformation including benefits of online shopping(e.g. good price and convenience), online word of mouth (e.g. bulletin board and consumer reviews), and consumer characteristics (e.g. innovativeness and Internet usage). Also, we suggest several research propositions on online product transformation. The implications for marketing strategies of experience goods and furher research direction are suggested.

• Journal of Distribution Science
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• v.14 no.7
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• pp.15-19
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• 2016

Purpose - India is predicted to lead the world in online sales, but the behavioral range of online consumer has not been researched adequately. Moreover, the research on the role of psychological variables like consumer innovativeness in online purchase behavior has not been investigated. This paper aims to unravel the role of 'consumer innovativeness' in predicting online purchase intention. Further, this paper examines the effect of consumer innovativeness on 'online information search' and 'attitude' in online purchase areas. Research design, data, and methodology - This study uses factor analysis to confirm the convergent and discriminant validities from the adopted scales. Regression analysis was employed to test the various hypotheses in this study. Results - This study finds that consumer innovativeness affects positively 'information search', 'attitude' and 'purchase intentions' in online purchase circumstances. Conclusions - Consumer Innovativeness has emerged as a significant factor affecting online purchase intentions. It has also been confronted with an important variable affecting online information search and attitudes for online purchase.

• The Journal of Information Systems
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• v.29 no.4
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• pp.171-191
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• 2020

Purpose The study aims to compare the online review writing behavior of users in China and the United States through text mining on online reviews' text content. In particular, existing studies have verified that there are differences in online reviews between different cultures. Therefore, the purpose of this study is to compare the differences between reviews written by Chinese and American tourists by analyzing text contents of online reviews based on cultural theory. Design/methodology/approach This study collected and analyzed online review data for hotels, targeting Chinese and US tourists who visited Korea. Then, we analyzed review data through text mining like sentiment analysis and topic modeling analysis method based on previous research analysis. Findings The results showed that Chinese tourists gave higher ratings and relatively less negative ratings than American tourists. And American tourists have more negative sentiments and emotions in writing online reviews than Chinese tourists. Also, through the analysis results using topic modeling, it was confirmed that Chinese tourists mentioned more topics about the hotel location, room, and price, while American tourists mentioned more topics about hotel service. American tourists also mention more topics about hotels than Chinese tourists, indicating that American tourists tend to provide more information through online reviews.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1257-1266
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• 2021

The study examines factors that influence shopping intention of online consumers in Vietnam. Studied factors include consumers' attitude, subjective norms, perception of behavioral control, perception of usefulness, perceived risks and trust. The expansion of Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) are used as basic theories. We have surveyed people who have experiences on online shopping. There are 836 selected questionnaires that are qualified for data processing. The collected data are analyzed through a process which starts from scale reliability test to exploratory factor analysis (EFA), correlation analysis and regression analysis. The results show that shopping intention of online consumers are positively affected by their attitude, subjective norms, perception of behavioral control, perception of usefulness and trust. In contrast, online shopping intention is negatively affected by the perceived risks that online shopping could bring. Among those factors, the perception of risk is shown to have the strongest influence to online shopping intention. The findings of this study suggest that managers and retailers can apply cash-on-delivery method and design their website with user-friendly interface to enhance online shopping intention of consumers. The Government is also recommended to fulfill the law system to reduce customers' perception of financial risks.