• Journal of Digital Convergence
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• v.19 no.7
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• pp.129-138
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• 2021

Many organizations are applying robotic process automation (RPA) to automate repetitive and rule based tasks to enhance the accuracy and efficiency of works. Some members are willing to join the projects hoping to eliminate annoying and meaningless tasks, but others are resisting this innovation fearing that they may lose their jobs. In this study, both positive and negative antecedents are posited to influence the performance in adopting RPA. The effects of relative advantage, compatibility, change management effect, innovation resistance and satisfaction, conclusively to performance improvement were examined via a survey of 109 employees involved in the 11 RPA projects in a manufacturing company, and the structural equation model analysis. The research considering the consumer characteristics of the innovation resistance model can be followed for the development of individualized change management strategy.

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.143-163
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• 2019

The purpose of this study is to identify acceptance factors and influencing factors of respondents' adoption of smart IoT technology to companion animal health based on the integrated technology acceptance model. Based on the previous studies, we constructed the hypotheses by defining the technical factors, social factors, control variables, and mediating variables (UTAUT), and set the hypotheses between the independent variables of each factor and the dependent intention. A research model was designed to verify the relationship between variables. We developed questionnaires on the items and verified them through data collected from 494 people. As a result, product design, quality of service, product performance, and quality service of technological factors had a significant effects on performance expectancy and effort expectancy. However, product safety, product function awareness, and product price did not significantly affect performance expectancy and effort expectancy. Social influence had significant effects on cognitive effect, welfare system, and welfare facilities. In conclusion, the comparative analysis of technical factors and social factors showed that social factors have more significant effects on welfare systems and facilities.

• Information Systems Review
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• v.23 no.4
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• pp.23-43
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• 2021

Due to the recent transition to a non-face-to-face society, many organizations are quickly adapting to foster a smart work environment. The introduction of smart work does not simply end with incorporating ICT systems or solutions into business models since fundamental factors such as forms of employment and work styles need to be in line with the progression of technological advances. However, previous studies regarding smart work focus on improvements in productivity and efficiency from a technology acceptance perspective. Therefore, there is a lack of discussion on innovation resistance from employees and management control when ICT systems are introduced into the workplace. This study empirically analyzes the moderating effects of the organizational control method for employees and innovation resistance within a smart work environment. Additionally, this study aims to identify the structural characteristics that employees resist from an innovation resistance perspective when organizational innovation occurs. The empirical analysis of this study suggests that when smart work such as ICT technology is introduced into the workplace the level of innovation resistance decreases when there is a high level of relative advantage and self-efficacy, whereas the level of innovation resistance increases when there is a high level of use complexity. Moreover, this study revealed that the level of innovation resistance increases when the employees' behaviors were controlled. The results of this study intend to contribute to improving business management by suggesting factors worth considering when incorporating smart work into work places through a thorough case analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.337-339
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• 2011

M&S 기반의 가상환경 설계운영은 자원절약 및 위험성 감소 등 다양한 측면에서 효율성을 지니고 있다. 국방은 현실세계에서 실시하기 어려운 군사작전 시뮬레이션 운영을 위해 국방 M&S 개념을 도입하고, 이를 기반으로 무기체계의 소요도출부터 전력화 단계까지 프로세스 전반에 걸쳐 가상 환경에서의 설계 운영 및 검증을 수행하는 SBA 를 도입하였다. 그러나 모델링 및 시뮬레이션 과정에서 발생되는 고비용, 위험성 등의 문제점은 SBA 도입 효율성에 대한 논란의 요인이 된다. 따라서 SBA 도입 효과를 입증할 수 있는 지표 제시가 요구되며, 이 연구에서는 입증 지표로서 비용, 시간, 품질 및 위험도의 4 가지 요소를 분석한다. 또한 소프트웨어 개발비용 산정의 어려움 및 SBA의 고비용 가능성을 이유로 SBA 에 대한 개발비용 산정의 필요성을 제기한다. SBA 개발비용 산정을 위해 기존에 제시된 소프트웨어 개발비용 산정 모델 적용을 제안하고, COCOMO 모델, Putnam 모델, FP모델, 전문가의 감정 및 델파이 기법 등 소프트웨어 개발비용 산정모델을 기반으로 SBA 개발비용 산정에 적합한 모델을 분석한다.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.249-268
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• 2002

최근 ERP 시스템이 기업의 전략적 도구로 인식되어지면서, 대기업뿐만 아니라 중소기업에서도 이에 대한 관심이 확산되고 있다. 이러한 경영환경의 변화로 ERP 시스템 도입에 대한 최고경영자 및 일반직원들의 인식이 이전에 비해 높아지고는 있지만, 사실상 인식만 있을 뿐 이에 대한 실제적인 실천전략이 미흡한 실정이다. 이는 중소기업에서 ERP 시스템 구축을 위해서는 수개월 또는 수년간에 걸쳐서 기업의 많은 자원이 투입돼야하기 때문으로 중소기업이 이를 극복하고 성공적인 ERP 시스템을 구축하기 위해서는 중소기업 환경에 적합한 ERP시스템 구축방안이 마련되어야 할 것이다. 따라서 본 논문에서는 기존에 선행 연구된 연구자료를 비교ㆍ분석하여 중소기업의 특성에 적합한 ERP 시스템 구축 성공요인과 구축전략을 도출하였다. 주요성공요인으로는 교육훈련, 명확한 도입목표 설정, 적합한 패키지 선정, 최고경영진의 참여, 그리고 현업사용자의 참여가 높은 빈도율을 나타냈다. ERP 시스템 구축방법론은 준비단계, 분석단계, 구축단계, 구현단계, 종료단계의 다섯 단계로 나눠볼 수 있으며 각각의 수행단계별로 Activity가 정의되고 이에 대한 산출물이 발생하게 된다. 또한 ERP 도입시 소프트웨어의 자체개발보다는 패키지 도입이, 그리고 이의 접근방법으로는 선정된 모듈을 일괄적으로 적용하는 Big-Bang방식이 중소기업 실정에 더 합리적이며, 구현시기에 있어서는 Big-Bang방식과 더불어 기존업무에 ERP 시스템을 구현함으로써 ERP 시스템이 제공하는 기능과 함께 BPR의 효과도 얻는 방법이 가장 적합할 것으로 사료된다. 끝으로 본 논문에서는 ERP 시스템 도입의사가 있어도 기업환경 여건이 따라주지 않아 실천에 옮기지 못하는 중소기업을 고려하여 도입지원방안으로써 산ㆍ학 연계모델을 제시하였다. 중소기업은 ERP 시스템 도입에 필요한 인적ㆍ물적 자원을 대학으로부터 얻음으로써 자체 내 보유능력의 한계를 극복하고, 대학은 현장실습을 통해 풍부한 경험을 쌓음으로써 우수 인력 양성에 기여할 수 있을 것이다.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.249-268
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• 2002

최근 ERP 시스템이 기업의 전략적 도구로 인식되어지면서, 대기업뿐만 아니라 중소기업에서도 이에 대한 관심이 확산되고 있다. 이러한 경영환경의 변화로 ERP 시스템 도입에 대한 최고경영자 및 일반직원들의 인식이 이전에 비해 높아지고는 있지만, 사실상 인식만 있을 뿐 이에 대한 실제적인 실천전략이 미흡한 실정이다. 이는 중소기업에서 ERP 시스템 구축을 위해서는 수개월 또는 수년간에 걸쳐서 기업의 많은 자원이 투입돼야하기 때문으로 중소기업이 이를 극복하고 성공적인 ERP 시스템을 구축하기 위해서는 중소기업 환경에 적합한 ERP시스템 구축방안이 마련되어야 할 것이다. 따라서 본 논문에서는 기존에 선행 연구된 연구자료를 비교ㆍ분석하여 중소기업의 특성에 적합한 ERP 시스템 구축 성공요인과 구축전략을 도출하였다 주요성공요인으로는 교육훈련, 명확한 도입목표 설정, 적합한 패키지 선정, 최고경영진의 참여, 그리고 현업사용자의 참여가 높은 빈도율을 나타냈다. ERP 시스템 구축방법론은 준비단계, 분석단계, 구축단계, 구현단계, 종료단계의 다섯 단계로 나눠볼 수 있으며 각각의 수행단계별로 Activity가 정의되고 이에 대한 산출물이 발생하게 된다. 또한 ERP 도입시 소프트웨어의 자체개발보다는 패키지 도입이, 그리고 이의 접근방법으로는 선정된 모듈을 일괄적으로 적용하는 Big-Bang방식이 중소기업 실정에 더 합리적이며, 구현시기에 있어서는 Big-Bang방식과 더불어 기존업무에 ERP 시스템을 구현함으로써 ERP 시스템이 제공하는 기능과 함께 BPR의 효과도 얻는 방법이 가장 적합할 것으로 사료된다. 끝으로 본 논문에서는 ERP 시스템 도입의사가 있어도 기업환경 여건이 따라주지 않아 실천에 옮기지 못하는 중소기업을 고려하여 도입지원방안으로써 산ㆍ학 연계모델을 제시하였다. 중소기업은 ERP 시스템 도입에 필요한 인적ㆍ물적 자원을 대학으로부터 얻음으로써 자체 내 보유능력의 한계를 극복하고, 대학은 현장실습을 통해 풍부한 경험을 쌓음으로써 우수 인력 양성에 기여할 수 있을 것이다.

• Korean Business Review
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• v.19 no.2
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• pp.231-259
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• 2006

Performance-based budgeting(hereafter PB2) is supposed to improve efficiency and effectiveness of public organizations. Governmental PB2 is based on output flow rather than input flow. Since 1990s, many OECD nations including New Zealand and the United States have been changing their budget system to PB2. The Korean Government has been using input-based budgeting focusing on the size of expenditures. In 1999, The Ministry of Planning and Budget(hereafter MPB) conducted pilot projects for divisions or agencies of governmental departments for the purpose of improving efficiency in expenditure, rationalizing budget allotments, and inducing budget savings. However, performance budgeting may also have problems in terms of measurement and application. Due to the fact that public goals are abstract and may vary, it is difficult to induce performance indicators from public goals. There also seem not to be any performance indicators which correspond with the conditions of desirable indicators, such as validity, reliability, clarity, practicality, controllability and so on. In terms of applying an original budgeting plan, the difficulty of linking performance and budgeting, the appearance of unintended negative effect and externalities, and the repulsion of subordinated agencies are expected. Now days, local government is going to introduce PB2. Same problem may happen in the local government. And Therefore, the objectives of this paper are to introduce PB2 onlocal government and review a alternative strategy with the purpose of smoothly settling performance budgeting on local government. That is the linkage of Balanced Scorecard(BSC) and PB2.

• Journal of Venture Innovation
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• v.5 no.2
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• pp.111-127
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• 2022

With the progress of the 4th industrial revolution, interest in smart factories is increasing. The government is implementing a smart factory support project for small and medium-sized manufacturing companies. Therefore, in this study, factors influencing small and medium-sized enterprises(SME's) intention of smart factory acceptance were analyzed. In particular, it focused on how the perception of government support affects intention of smart factory acceptance. For the empirical analysis, a research model was established by reflecting the characteristics of SMEs and the technical factors of the smart factory centering on the technology acceptance theory. Based on the model set in this way, a questionnaire survey was conducted for employees of SMEs. In this study, a total of 231 samples of valid data were used for analysis. The empirical analysis results are as follows. It was analyzed that performance expectancy, social influence, technology utilization capability, CEO will, and employee resistance to innovation, all introduced as research variables, had a significant effect on the use intention of smart factory acceptance. In particular, it was found that employees' resistance to innovation had a negative (-) effect on their use intention. Meanwhile, to analyze the moderating effect of government support, it was divided into a group with high expectations for government support and a group with low expectations. As a result, it was found that there was a difference in the effect of CEO's will, employees' resistance to innovation, and social influence on the use intention. On the other hand, no significant difference was found in the relationship between performance expectancy, technology utilization capability on the use intention. Based on the empirical analysis results, the academic and practical implications of this study were presented.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.564-570
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• 2020

The development of cloud computing service technologies plays a particularly important role in the enterprise. The introduction of cloud computing services by companies is also contributing to the improvement of market value and changes in corporate earnings. In this study, we demonstrated the importance of priorities by selecting sub-items that can be evaluated by separating the expected characteristics of efficiency, economics, and dependencies when introducing cloud computing services. The main priority items were selected based on prior research and the survey was conducted by IT system experts. Using AHP analysis method, the results of performing a double contrast bridge resulted in the final priority. The findings will later be used as important data to determine an enterprise's decision to accept cloud computing services.

• 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.