• Asia pacific journal of information systems
• /
• v.33 no.4
• /
• pp.1171-1187
• /
• 2023

This study explores how an organization has successfully implemented ML-supported HR services to resolve high employee turnover problems in the IT sector. The empirical setting of the research is where contradicting institutional logics exist among technical, HR, and business groups regarding the ML model development and use of the model predictions in HR services. Institutional framework is used to identify the roles of organizational actors and the legitimacy structures in the organizational environments that can shape or constrain the ML led organizational changes. In institutional theories, technology adoption and organizational change are not only constrained by organizational context, but also fostered through organizational actors' roles and efforts to increase the legitimacy for the change. This research found that when multiple contradicting institutional logics exist, legitimizing the establishment of an enabling environment for multiple logics to reconcile and for the project to move forward is critical. Industry-wide conditions, previous experiences with the pilot ML project, forming a TFT with clearly defined roles and responsibilities, and relevant KPIs are found to legitimize the HR team and the business division to collaborate with the technical personnel to launch ML-supported HR services.

• Journal of Information Technology Services
• /
• v.10 no.3
• /
• pp.77-94
• /
• 2011

Since the general quality of life has been improving, people have become interested in "well-being." The widespread acceptance of the importance of "well-being" to quality of life has encouraged people to take more interest in getting health information online when they need it. Expansive use of online health information suggests that individual characteristics (i.e., gender and other traits), Website features, and perceived trust are related to the primary concern for many online health information consumers. This study examines whether familiarity, perceived security, and reputation of health information on various Websites influence the relationship of trust and intention to use by gender. These research results will contribute to the adoption of online health information by gender and, moreover, will provide companies with an understanding of key characteristics of consumers who use emoticons and provide useful implications for marketing strategies to current and future consumers.

• 한국경영정보학회:학술대회논문집
• /
• 2007.06a
• /
• pp.545-550
• /
• 2007

The aim of this paper is to find out whether there is a difference in adopting and/or diffusing the information and communication technology (ICT) between countries. If there is, what are the primary factors that keep some countries from adopting and diffusing ICT while others do? To analyze the above problem, we adopted the Unified Theory of Acceptance and Use of Technology (UTAUT) suggested by Venkatesh et al. (2003), which consists of effort expectancy, performance expectancy and social influence. We also use the innovation diffusion functions, which are known to have the S-shape and are made up of the introduction, growth, maturity and decline phases. We do not, however, consider the decline phase, because the ICT that we are considering is not believed to be in that phase. Therefore, we researched how the three factors affect adoption in the three phases. We selected the cellular phone as the ICT, because it is considered to be the most popularly used ICT and its technology has been developing rapidly. We surveyed the cellular phone adopters in Korea, and the U.S. for 15 years from 1989 to 2003. Korea, and the U.S. represent newly developed and developed countries, respectively. For the data analysis, a survival analysis was used, as it could explain the characteristics of the potential adopters or non-adopters. We found that the ICT diffusion patterns, as well as the ICT diffusion factors, of the two countries were different. Therefore, we believe that the results of our research can be used in building strategies on reducing the digital divide gaps between countries.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.11
• /
• pp.253-264
• /
• 2021

The COVID-19 pandemic has imposed a number of restrictions on the lives of people and services, forcing them to adopt a "New Normal" way of living. Contactless technologies provide a mechanism to reduce the risk of infection, encouraging people to use touchless payment methods. The aim of this study is to develop an integrated framework based on the Health Belief Model and the Unified Theory of Acceptance and Use of Technology to justify the use of mobile payment during the COVID-19 pandemic in Vietnam. Based on a survey of 434 samples, the proposed conceptual model was empirically justified using structural equation modeling (SEM). This study found that performance expectancy, effort expectancy, enjoyment, perceived severity, and perceived susceptibility significantly and positively influenced behavioral intention of using contactless payment technologies. In addition, this study discovered that effort expectancy, perceived severity, and perceived susceptibility all have a positive impact on performance expectancy, while enjoyment triggered users' effort expectancy. By adding novel insights into the literature on the acceptance of technology during the pandemic, this study makes a major contribution to justifying how contactless payment technologies can reduce the risk of getting infected by COVID-19.

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

• Journal of Smart Tourism
• /
• v.4 no.1
• /
• pp.31-38
• /
• 2024

This study investigates the adoption of technology in smart tourist destinations, with a focus on the Baby Boomer generation. The objective is to analyze the perceived utility, intention to use, and risk perception of this generation regarding Information and Communication Technologies (ICT) in the context of smart tourist destinations. Data were collected through questionnaires administered to elderly groups belonging to the Baby Boomer generation in two Brazilian cities. Data analysis was conducted using statistical tools such as SPSS and Microsoft Excel, with an emphasis on regression analysis with moderation. The results indicate that when Baby Boomer tourists feel insecure about technology, they tend to not perceive its utility, impacting its acceptance. On the other hand, lower risk perceptions lead to an increase in perceived utility and, consequently, a greater intention to use technology in travel planning. The moderating role of risk perception in the relationship between perceived utility and intention to use is emphasized. The findings highlight the need for managers of smart destinations to consider the risks perceived by tourists, focus on diverse age groups, and implement strategies that address digital exclusion.

• The Journal of Information Systems
• /
• v.30 no.1
• /
• pp.65-84
• /
• 2021

Purpose Based on the development of artificial intelligence and big data technologies, the RegTech has been emerged to reduce regulatory costs and to enable efficient supervision by regulatory bodies. The word RegTech is a combination of regulation and technology, which means using the technological methods to facilitate the implementation of regulations and to make efficient surveillance and supervision of regulations. The purpose of this study is to describe the recent adoption of RegTech and to provide basic examples of applying RegTech to capital market regulations. Design/methodology/approach English-based ontology and deep learning technologies are quite developed in practice, and it will not be difficult to expand it to European or Latin American languages that are grammatically similar to English. However, it is not easy to use it in most Asian languages such as Korean, which have different grammatical rules. In addition, in the early stages of adoption, companies, financial institutions and regulators will not be familiar with this machine-based reporting system. There is a need to establish an ecosystem which facilitates the adoption of RegTech by consulting and supporting the stakeholders. In this paper, we provide a simple example that shows a procedure of applying RegTech to recognize and interpret Korean language-based capital market regulations. Specifically, we present the process of converting sentences in regulations into a meta-language through the morpheme analyses. We next conduct deep learning analyses to determine whether a regulatory sentence exists in each regulatory paragraph. Findings This study illustrates the applicability of RegTech-based ontology and deep learning technologies in Korean-based capital market regulations.

• 한국디지털정책학회:학술대회논문집
• /
• 2005.06a
• /
• pp.401-411
• /
• 2005

The major market of Information Systems has been large companies. But with the rapid development of light weight Information Technology, the importance of SME (Small and Medium sized Enterprise) sector has increased dramatically. Many solutions have appeared in market for SMEs. Although there are many differences in the context of IT use between large companies and SMEs, not many studies has focused on this point. This study analyzes the preference for ERP, SCM, CRM, etc. by SMEs.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.27-65
• /
• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Korean Management Science Review
• /
• v.24 no.1
• /
• pp.127-145
• /
• 2007

This study attempts to examine the acceptance factors of mSFA systems based on the innovation diffusion and technology acceptance model, and to measure the performance effects of mSFA systems using BSC metrics. Results show that (1) the characteristics of mobility and interactivity have positive impacts on perceived usefulness, ease of use, and professional fit. But the characteristics of personal identity were not perceived as useful due to users' negative feelings about privacy infringement and surveillance. (2) Job fit has positive impacts on perceived usefulness and professional fit. (3) Perceived usefulness, ease of use, and professional fit positively influence the degree of users' dependence on mSFA systems, which have positive impacts on users' performance measured by the personal BSC metrics including perspectives of finance, customer, internal process, and learning and growth.