• Journal of the Ergonomics Society of Korea
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• v.36 no.2
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• pp.87-108
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• 2017

Objective: The purpose of this study was to precede the acceptance study based on automation steps and user experience that was lacked in the past study on the core technology of autonomous vehicle, ADAS. The first objective was to construct the acceptance model of ADAS technology that is the core technology, and draw factors that affect behavioral intention through user experience-based evaluation by applying driving simulator. The second one was to see the change of factors on automation step of autonomous vehicle through the UX/UA score. Background: The number of vehicles with the introduction of ADAS is increasing, and it caused change of interaction between vehicle and driver as automation is being developed on the particular drive factor. For this reason, it is becoming important to study the technology acceptance on how driver can actively accept giving up some parts of automated drive operation and handing over the authority to vehicle. Method: We organized the study model and items through literature investigation and the scenario according to the 4 stages of automation of autonomous vehicle, and preceded acceptance assessment using driving simulator. Total 68 men and woman were participated in this experiment. Results: We drew results of Performance Expectancy (PE), Social Influence (SI), Perceived Safety (PS), Anxiety (AX), Trust (T) and Affective Satisfaction (AS) as the factors that affect Behavioral Intention (BI). Also the drawn factors shows that UX/UA score has a significant difference statistically according to the automation steps of autonomous vehicle, and UX/UA tends to move up until the stage 2 of automation, and at stage 3 it goes down to the lowest level, and it increases a little or stays steady at stage 4. Conclusion and Application: First, we presented the acceptance model of ADAS that is the core technology of autonomous vehicle, and it could be the basis of the future acceptance study of the ADAS technology as it verifies through user experience-based assessment using driving simulator. Second, it could be helpful to the appropriate ADAS development in the future as drawing the change of factors and predicting the acceptance level according to the automation stages of autonomous vehicle through UX/UA score, and it could also grasp and avoid the problem that affect the acceptance level. It is possible to use these study results as tools to test validity of function before ADAS offering company launches the products. Also it will help to prevent the problems that could be caused when applying the autonomous vehicle technology, and to establish technology that is easily acceptable for drivers, so it will improve safety and convenience of drivers.

• Information Systems Review
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• v.21 no.1
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• pp.1-22
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• 2019

Mindfulness is the process of actively noticing new things. Today, companies have introduced and run mindfulness programs because the mindfulness has possible applications of productivity and innovation in corporation. However, role of mindfulness has not been clearly investigated in behavior research of Information System. The purpose of this study is to confirm the effects of mindfulness on technology acceptance process. Based on UTAUT Model, we examined how mindfulness in technology acceptance process moderate antecedent factors of acceptance intentions and use behavior. For empirical research, we conducted a survey on acceptance of smart watch of internet of things for employees of companies applying the mindfulness programs. then, we analyzed survey sample in empirical methodologies. Based on the empirical analysis, cognizance of alternative technologies in mindfulness factors increased the impact of performance expectancy on acceptance intention. Novelty seeking in mindfulness factors increased the impact of effort expectancy on acceptance intention. Awareness of local context in mindfulness factors decreased the impact of social influence on acceptance intention. engagement with technology in mindfulness factors increased the impact of facilitating conditions on use behavior. This study suggests academic implications and practical implications based on the results of the research. The implications will help to support and extend the theory of technology acceptance model while providing practical insights for IT acceptance by suggesting ways to utilize mindfulness in corporation.

• CRM연구
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• v.1 no.1
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• pp.49-69
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• 2006

Previous studies, Technology Acceptance Model (TAM) and Post Acceptance Model (PAM) have a little limitation in time series analysis. To solve this limitation, we used system dynamics as research methodology and designed simulation model based on TAM and PAM. Moreover, we designed new simulation model which can analyize time series data in customers' demand change from initial acceptance to post acceptance. This study targeted domestic mobile phone market. The simulation results showed that diffusion graph was similar to real data. That means we validated our simulation model. Since the simulation model offers the graph of customer's demand change by time, so it can be useful as a leaning tool. Therefore, we think this study helps IT companies use the model for forecasting of market demand.

• Journal of Navigation and Port Research
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• v.34 no.10
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• pp.807-815
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• 2010

This study analyzed the correlation between RFID acceptance intention and application, and as a result, extracted the technical stability, system quality and security of RFID as factors that affect the intention of receiving RFID in container terminal gate through preliminary research. This analysis was done on individuals engaged in container terminals which are in operation by adopting RFID in container terminal gate presently, and by distributing totally 255 copies of questionnaire survey, 248 copies were collected. As a result of statistical analysis of this study, the following conclusions were made: First, the technical stability of RFID acceptance in container terminal gate was not statistically significantly high. Second, the system quality and the security of RFID acceptance in container terminal gate were statistically significantly high. Finally, container terminal gate RFID technology acceptance intention was statistically significantly high in application performance.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.73-94
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• 2009

The general aspects for the future warfare shows that the concept of firepower and maneuver centric warfare has been replacing with that of information and knowledge centric warfare. Thus, some developed countries are now trying to establish the information systems to perform intelligent warfare and innovate defense operations. The C4I(Command, Control, Communication, Computers and Intelligence for the Warrior) systems make it possible to do modern and systematic war operations. The basic idea of this study is to investigate how TAM(Technology Acceptance Model) can explain the acceptance behavior in military organizations. Because TAM is inadequate in explaining the acceptance processes forcomplex technologies and strict organizations, a revised research model based upon TAM was developed in order to assess the usage of the C4I system. The purpose of this study is to investigate factors affecting the usage of C4I in the Korean Army. The research model, based upon TAM, was extended through a belief construct such as self-efficacy as one of mediating variables. The self-efficacy has been used as a mediating variable for technology acceptance, and the variable was included in the research model. The external variables were selected on the basis of previous research. The external variables can be classified into following: 1) technological, 2) organizational, and 3) environmental factors on the basis of TOE(Technology-Organization-Environment) framework. The technological factor includes the information quality and the task-technology fitness. The organizational factor includes the influence of senior colleagues. The environmental factor includes the education/train data. The external variables are considered very important for explaining the behavior patterns of information technology or systems. A structured questionnaire was developed and administrated to those who were using the C4I system. Total 329 data were used for statistical data analyses. A confirmatory factor analysis and structured equation model were used as main statistical methods. Model fitness Indexes for measurement and structured models were verified before all 18 hypotheses were tested. This study shows that the perceived usefulness and the self-efficacy played their roles more than the perceived ease of use did in TAM. In military organizations, the perceived usefulness showed its mediating effects between external variables and dependent variable, but the perceived ease of use did not. These results imply that the perceived usefulness can explain the acceptance processes better than the perceived ease of use in the army. The self-efficacy was also used as one of the three mediating variables, and showed its mediating effects in explaining the acceptance processes. Such results also show that the self-efficacy can be selected as one possible belief construct in TAM. The perceived usefulness was influenced by such factors as senior colleagues, the information quality, and the task-technology fitness. The self-efficacy was affected by education/train and task-technology fitness. The actual usage of C4I was influenced not by the perceived ease of use but by the perceived usefulness and selfefficacy. This study suggests the followings: (1) An extended TAM can be applied to such strict organizations as the army; (2) Three mediation variables are included in the research model and tested at real situations; and (3) Several other implications are discussed.

• 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.

• The Journal of Information Systems
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• v.13 no.2
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• pp.22-48
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• 2004

Information systems that are not used cannot be useful. In order to increase user acceptance, it is necessary to understand why people accept or reject information systems. Technology Acceptance Model(TAM) is one of the most influential research models for studying determinants how users accept information systems. Recently, Knowledge Management Systems(KMS) have become important components of corporate systems as the foundation of industrialized economics has shifted from natural resources to knowledge assets. This paper applies TAM to investigate users' acceptance of KMS in public administration institutions. It sampled 182 users who had experience in using KMS. Many empirical researches have suggested that TAM can be integrated with other organizational theories to improve its predictive and explanatory ower. We extended the basic TAM by the integration of appraisal and reward satisfaction theory. There are many external variables that influence the perception and the belief of system users. We introduced two external variables(job characteristics, IT self-efficacy) and one additional perception variable, perceived appraisal and reward(PAR) in the basic TAM model. The LISREL model analysis is used for finding out the causality among variables and testing the model fitness. As result, The IT self-efficacy influences to the perceived ease of use(PEOU) and the PAR, and the PEOU influences directly to the perceived usefulness(PU), the PAR, and the attitude toward KMS. The KMS participation intention(PI) was influenced by the PAR and the attitude directly,andbythePEOUindirectly. Finally, this paper suggests some guidelines for the adoption of KMS in public sectors on the basis of the study results.

• Management & Information Systems Review
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• v.6
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• pp.21-43
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• 2001

Contract requires, on principle, a meeting of minds. The basic elements of that contact are an offer by one party and an acceptance of the terms of the offer by the other party. In the electronic commerce an offer and the acceptance of an offer may be expressed by dispach or receipt of data messages. The contract which a data messages is used in the formation may be called electronic contract. The formation of the electronic contract is regulated by the Civil Code or the Commerce Code. The time of the contract formation is regulated by the Civil Code and the Commerce Code. But the time of dispach and receipt of the data message is stated in Article 9 of the Fundamental Law on Electronic Commerce. In this article the dispatch of a data message occurs when it enters an information system outside the control of the originator or of the person who sent the date message on behalf of the originator. The time of receipt of a data message, if the adressee has designated an information system for the purpose of receiving data message, is at the time when the data message enters the designated information system; or if the data message is sent to an information system of the adressee that is not the designated information system, at the time when the data message is retrieved by the addressee; if the addressee has not designated an information system, receipt occurs when the data message enters an information system of the addressee. The time of the electronic contract formation depends on the Civil Code or the Commerce Code and this Article 9 of the Fundamental Law on Electronic Commerce. An electronic contract may be formed by an offer and the acceptance of an offer by dispach or receipt of data message. An offer can not be withdrawn after it is accepted. But in mail-order sales and installment dealing an offer can be revoked after the goods were delivered. In this case the contract is lapsed.

• 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.