• Fashion & Textile Research Journal
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• v.18 no.1
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• pp.38-47
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• 2016

This research analyzes the influence of mobile commerce characteristics on consumer's purchase intention using a theoretical Technology Acceptance Model (TAM) constructed on previous studies and a review of the literature to explain the effect of mobile fashion shopping characteristics on consumer's purchase intention. In constructing structural equation model, Mobile commerce characteristics variables such as 'security', 'enjoyment', and 'personalization' were selected as external variables affecting TAM. A questionnaire was distributed to consumers in their 20's-30's who had purchased fashion products using a mobile shopping channel. Statistical methods of confirmatory factor analysis, correlation, and covariance structural analysis using Amos 19.0 package were employed for the analysis of 453 effective data responses. The results were as follows. First, extended TAM was shown be the appropriate model to explain the influence of mobile commerce characteristics on consumer's purchase intention in mobile fashion shopping. Second, 'security' had a significant positive influence on perceived usefulness (PU), however it affected perceived ease of use (PEOU) negatively. Third, 'enjoyment' had a significant influence only on PEOU, while 'personalization' was found to affect both PEOU and PU significantly. Fourth, PEOU affected PU significantly. Finally, both PEOU and PU had a significant influence on consumer's purchase intention.

• Journal of Information Technology Applications and Management
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• v.27 no.1
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• pp.75-95
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• 2020

Smart Factory is the decisive factor of the Fourth Industrial Revolution and is a key field for national competitiveness. Until now, most smart factory research has focused on policy and technology. In order to spread more technology, it is necessary to study what factors influence the adoption of smart factory technology in the enterprise. Nevertheless, little research has been done. In this study, based on the UTAUT (Unified Theory of Acceptance and Use of Technology), which has been proved through many years of research, I have studied the factors that influence the acceptance of smart factory technology. As a result of research, performance expectancy, social influence, and facilitating conditions of UTAUT model had a positive(+) effect on behavior intention. Their relationship of influence was in the order of performance expectancy (β = .459)> facilitating conditions (β = .212)> social influence (β = .210). However, it was found that the effort expectancy did not affect the behavior intention, and the impact of the newly perceived risk on the behavior intention to use was not confirmed. The main reason is that the acceptance of smart factory technology is not a matter of personal interest but a matter of organizational choice. Trust, on the other hand, was found to be partially mediated between performance expectancy, facilitating conditions, social influence and behavior intention. For many years, many researchers have validated the UTAUT, which has been validated through various empirical studies. It is academically meaningful to begin the study of factors affecting the acceptance of smart factory technology in terms of the UTAUT. In practice, it is necessary to provide SME employees with more information related to the introduction of smart factories, to provide advanced services related to the establishment of smart factories, and to establish a standardized model for each industry.

• Asia-Pacific Journal of Business
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• v.12 no.3
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• pp.1-36
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• 2021

Purpose - This empirical study, aims to identify the determinants of adoption and acceptance of mobile payment as to understand why it is successful in some countries in Sub-Saharan Africa but failing in others. A comparative study of a successful mobile payment service and a purported failed one was done as to have some insights to the factors affecting acceptance of the technology. Design/methodology/approach - The strength of three notable theories: theory of diffusion of innovation (DOI), the extended unified theory of user acceptance of information technology (UTAUT2) and self-efficacy theory were use. The self-efficacy of government support inclusion as, a moderating variable in the form of infrastructure, securing transaction and price value revealed the relevance of government in the success of mobile payment service. By means of a field survey of 705 subjects in two separate regions of Africa (East and West), the data was collected and use to test the research model. Findings - The study result shows the importance of the moderating factor of government support to the success of mobile payment of any nation. The result also shows the importance of the perception of relative advantage, compatibility, complexity, social influence as already revealed by other studies. Research implications or Originality - Mobile payment success in some part of Sub-Saharan Africa is well known but also suggested to fail in some Sub-Saharan African countries. Buttressing the need for understanding of the factors affecting mobile payment acceptance. This article empirically examined the factors influencing the success of mobile payment, and we implicated that if the implementation of mobile payment is to be successful for mobile commerce in any nation, adoption, acceptance and use by its citizen is imperative.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.29
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• pp.173-196
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• 2006

Online game business has emerged as the most lucrative entertainment industry, with over 10 million players in South Korea and over 30 million in Japan in 2005. The popularity of online games can be attributed to the availability of broadband network, pushing online games into the mainstream entertainment culture. The age distribution of online game players is expanding and a variety of new games are under development to target certain age groups. While the interactive entertainment market continues to expand, with many new online game publishers entering the Japan, relatively little is known about which factors influence online game players' behavioral intentions to play continuously in this area. This study investigates major factors which influence the acceptance of online game services based on the theoretical backgrounds of the technology acceptance model(TAM) and the flow theory. This paper extended the Davis' TAM model by including the flow concept as another major factor toward the intention to play online game. Based on data collected from online questionnaire survey, we show that the proposed model provides an adequate fit to the data, and that the flow experience is another important factor influencing the intention to play online game, as well as the perceived ease of use.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.143-151
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• 2021

This study was a cross-sectional descriptive research to analyze the effects of sub-factors of cognitive emotional control on happiness levels. The participants of the study were 201 men and women in their 20s, and data were collected online from January 1 to 15 collected data were, 2001 using structured cognitive control and happiness level questionnaires. The collected data were conducted Independent t-test, Pearson correlation analysis, simple regression analysis, multiple regression Analysis, hierarchical regression analysis using SPSS 18.0 statistic program. As a result, the study appeared that the level of happiness by gender does not differ, and cognitive emotional control affected 58.5%. The average of cognitive emotional control was higher for all men, but women were higher than men in criticized others. Also, acceptance was the sub-factor of emotional control that most affected the level of happiness (β=-.587, p<0.01). Based on the results of this study, it is suggested that a systematic program on subject of acceptance, a sub-factor of cognitive emotional control, should be developed to improve the level of happiness.

• Journal of Korea Trade
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• v.26 no.1
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• pp.57-80
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• 2022

Purpose - This study aims to examine the relationships between the acceptance of the 4th industrial revolution logistics technology, logistics safety behavior, and logistics safety performance, as well as the moderated mediating effects of logistics safety behavior through safety culture in Korea. Design/methodology - Research models and hypotheses were established based on prior research related to the 4th industrial revolution logistics technology, logistics safety, and logistics performance. The survey was conducted on the employees of logistics companies, and reliability analysis, confirmatory factor analysis, discriminant validity analysis, structural equation model analysis, and mediating effect analysis were performed. In addition, the moderated mediating effect analysis applying SPSS Process Model No. 7 was conducted. Findings - Usefulness and sociality of the acceptance of the 4th industrial revolution logistics technology had a significant effect on logistics safety behavior. Ease of use, sociality, and efficiency had meaningful effect on logistics safety performance. And in the relationships between the acceptance of logistics technology and logistics safety performance, logistics safety behavior had a significant mediating effect. But the moderated mediating effect of safety behavior through safety culture was not significant. Logistics companies can improve logistics safety performance through the utilization of new logistics technologies such as intelligent logistics robots, autonomous driving technology, and artificial intelligence, etc. Originality/value - This is the first study to analyze the relationships between the acceptance of logistics technology in the 4th industrial revolution and logistics safety. In addition, previous studies analyzed mediating effects or moderating effects, but this is the first study to identify the moderated mediating effects of safety behavior through safety culture. In other words, it has originality in terms of research methodology.

• Proceedings of the Korea Database Society Conference
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• 2002.10a
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• pp.3-21
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• 2002

The purpose of this study is to demonstrate individual's mobile Internet acceptance by extending the technology acceptance model (TAM) to the field of the mobile Internet. This research utilized the structural equation model to examine the research model on the mobile Internet use. System quality and information quality as external variables were examined, and playfulness as an intrinsic factor was tested. The research model was successfully validated in the environment of extension model for the mobile internet.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.262-270
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• 2022

In the past two decades, there has been an increasing interest in project knowledge management, as knowledge is a crucial resource for project management success. Knowledge capture and sharing are two effective project management practices. Capturing and sharing project knowledge has become more efficient due to technological advances. Nevertheless, present technologies face several technical, functional, and usage obstacles and constraints. Thus, Blockchain technology might provide promising answers, yet, there is still a dearth of understanding regarding the technology's proper and practical application. Consequently, the goal of this study was to fill the gap in the literature about the adoption of Blockchain technology and to investigate the project stakeholders' acceptance and willingness to utilize the technology for capturing and sharing project knowledge. Due to this inquiry's exploratory and inductive characteristics, qualitative research methodology was used, namely the Grounded Theory research approach. Accordingly, eighteen in-depth, semi-structured interviews were conducted to collect the data. Concurrent data collection and analysis were undertaken, with findings emerging after three coding steps. Four influencing factors and one moderating factor were identified as affecting users' acceptance of Blockchain technology for capturing and sharing project knowledge. Consequently, the results of the study aimed to fill a gap in the existing literature by undertaking a comprehensive analysis of the unrealized potential of Blockchain technology to improve knowledge capture and sharing in the project management environment.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.41-76
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• 2020

This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.

• Journal of the Korea Safety Management & Science
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• v.8 no.4
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• pp.63-89
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• 2006

Recently, the technology is getting to be the most important factor for companies, as the industry is changing fast. The uncertainty and complexity of technology valuation arc higher so that the technology concentrated companies need more developed and high performance technology. This paper reviews the methods of technology valuation for five categories that have been developed by valuation researchers, (1) research of technology diffusion and acceptance model, (2) research of technology valuation, (3) research of technology import and export factor, (4) research of technology valuation model, (5) research of technology transfer and market. And we propose a new technology valuation model using need(market), seed(technology) and deeds(management) factor by cross impact matrix. This model gives us the reference negotiation range for deciding the amount of royalty. I hope this paper induces more research on this field of technology valuation.