• Journal of the Korean Home Economics Association
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• v.47 no.4
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• pp.99-110
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• 2009

The Technology Acceptance Model(TAM) proposed by Davis(1989) has been applied as a reliable and robust model in the study of user’s adoption of different technologies. However it is necessary to incorporate additional constructs to the original model in the quest for increased predictive power. This study investigate consumer’s acceptance of smart clothing applied by the extended TAM. Besides perceived ease of use and perceived usefulness, clothing involvement is included in the extended TAM. Data were collected from the adults over 20 years old living in Seoul and other metropolitan areas from March 2 to March 12, 2009. 815 copies of data were analyzed to examine a structural model and test research hypotheses using AMOS package. The study showed the extended TAM for smart clothing was validated empirically in predicting the individual’s acceptance of smart clothing and 5 hypotheses among 7 hypotheses were supported. Specifically, perceived ease of use, perceived usefulness, and clothing involvement were the factors affecting attitude toward smart clothing. Attitude toward smart clothing was influenced directly by perceived usefulness and clothing involvement but influenced indirectly by perceived ease of use. Acceptance intention toward smart clothing was influenced strongly by attitude. From a theoretical point of view, the study extended the TAM for smart clothing. Moreover, this study would be beneficial for those who would develop smart clothing by providing information about attitude and acceptance intention of smart clothing consumers.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.1007-1013
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• 2020

This study aims to understand the determinant of the employee's intention to use information system within the framework of the Technology Acceptance Model (TAM) and Information System Success Model (ISSM). This paper also aims to examine the influence of ISSM dimension on the perceived usefulness and the perceived ease of use. The research respondents were 248 employees of Universitas Negeri Semarang (UNNES) who are users of the Financial Information System (SIKEU). Data was obtained using a questionnaire that was distributed online via Google form. The data analysis method used is Structural Equation Model (SEM) analysis using the Warp-PLS software. The results showed that the dimensions of TAM (perceived ease of use and perceived usefulness) had a positive and significant influence on the employee's intention to use SIKEU. The ISSM dimension (system quality and information quality) also had a significant influence, although other ISSM dimensions (service quality) had not been proven to have a significant influence on the employee's intention to use SIKEU. Moreover, the results showed that the employee's intention to use is a determinant of SIKEU's actual usage. Perceived ease of use was significantly determined by system quality, information quality, and service quality. In addition, the perceived usefulness was significantly determined by system quality and information quality.

• Journal of Digital Convergence
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• v.12 no.9
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• pp.177-192
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• 2014

Telematics is a compound word derived from telecommunication and informatics, which provides wireless data service such as traffic information, living information, remote control, maintenance and etc. in a vehicle using GPS(Global Positioning System) and telecommunication. Telematics acceptance was investigated to analyze characteristics of telematics service and its market by using a theoretical technology acceptance model based on literature review. In this paper, valid factors, influencing telematics service acceptance, have been drawn in terms of user ease-of-use, usefulness and service characteristics. Modified measuring variables were used by referring to previous researches.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.87-96
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• 2002

This study is design ed to assess Information Technology (IT) acceptance models among Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM) and the revised TAM using structural equation modeling. Research results show that the revised TAM with perceived ease of use, perceived usefulness and IT usage variables is slightly outperformed TPB and TAM. From a managerial standpoint, the findings of this study reveal that in order to foster IT usage, it is important to encourage a positive perceived ease of use and usefulness.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.32-35
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• 1998

Technology Acceptance Model (TAM) has been widely used for predicting behavioral processes in which information technologies were accepted, but several previous studies point out that TAM may not explain the adoption process of complex information technologies effectively. This study empirically tests the limitation of TAM, and suggests a proposed research model by incorporating the concept of the perceived behavioral control into TAM. Study findings indicate the proposed model can predict the adoption process better than TAM does.

• Journal of Advanced Information Technology and Convergence
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• v.8 no.2
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• pp.111-118
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• 2018

Information communication authority module software contains various functions and services. A software test is the only acceptance test step. Recently, however, all development steps have become important. In particular, the software acceptance step is very high, and a study of the effective use of limited time and resources on acceptance step is needed. This study examined the efficiency of the Test Design Technique for Software Test. The proposed Test Design process was applied to the domain systems of info-communication and the degree of improvement was measured. This paper establishes Software Test design process Infrastructure based on the developed software.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Korean Library and Information Science Society
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• v.46 no.4
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• pp.529-549
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• 2015

For the spread of the open access institutional repository in Korea, this study aims to analyze the factors affecting the acceptance of the repository. To gather the research data, a questionnaire survey was conducted for acceptance and non-acceptance institutions of the open access repositories. The questionnaire consisted of questions based on the revised R-TDM. Integrated Technology Diffusion Model is a theory that seeks to explain how, why, and at what rate new technology and innovations spread through specific factors and cultures. According to the data analyzing, technology acceptance level of social factors was turned out the most effective factors to make a decision to accept the repository to their institutions. And institution factors, innovation resistance factors, and system factors were arranged in order of strong influence. This study will provides the basis for developing policies for dissemination and spread of repositories in Korea.

• Journal of Information Technology Applications and Management
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• v.31 no.3
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• pp.119-147
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• 2024

This study aims to analyze older adults's intention to use digital financial services. To verify the purpose, the '2022 Korean Senior Technology Acceptance Panel Survey' data were used. And a shortened Senior Technology Acceptance Model(STAM) reflecting the characteristics of older adults was applied. The results of Structural Equation Model analysis are as follows. First, the lower gerontechnology anxiety, the higher control beliefs reflecting perceived ease of use, self-efficacy and facilitating conditions and the intention to use digital financial services. Second, the health factor had a positive effect on the control beliefs. Third, the higher the control beliefs, the higher the attitudinal beliefs reflecting perceived usefulness and attitude and the intention to use digital financial services. Lastly, the higher attitudinal beliefs, the higher the intention to use digital financial services. The results suggest the need for interventions that can relieve gerontechnology anxiety and strengthen positive perceptions about control beliefs and attitudinal beliefs in order to increase older adults's intention to use digital financial services.

• 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.