• Journal of Information Technology Applications and Management
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• v.30 no.5
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• pp.41-57
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• 2023

This paper aims to analyse the factors influencing the implementation of smart factories and their performance after implementation, using the grounded theory analysis method based on interview data. The research subjects were 21 companies that were selected by the Smart Manufacturing Innovation Promotion Group under the SME Technology Information Promotion Agency in 2020-2021 as the best case smart factory implementation companies, and introduced the intermediate stage 1 or above. A total of 87 concepts were generated as a result of the analysis. We were able to classify them into 16 detailed categories, and finally derived six broad categories. These six categories are "motivation for adoption", "adoption context", "adoption level", "technology adoption", "usage effect" and "management effect". As a result of the overall structure analysis, it was found that the adoption level of smart factory is determined by the adoption motivation, the IT technology experience affects the adoption level, the adoption level determines the usage and usage satisfaction, internal and external training affects the usage and usage satisfaction, and the performance or results obtained by the usage and usage are reduced defect rate, improved delivery rate and improved productivity. This study was able to derive detailed variables of environmental factors and technical characteristics that affect the adoption of smart factories, and explore the effects on the usage effects and management effects according to the level of adoption. Through this study, it is possible to suggest the direction of adoption according to the characteristics of SMEs that want to adopt smart factories.

• Journal of Information Technology Applications and Management
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• v.28 no.4
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• pp.45-57
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• 2021

This research explores how the success of smart factory adoption is influenced by firm's dynamic capability. This research describes the underlying processes on how organizations manipulate or adapt organizational elements harmoniously to implement smart factory successfully. Although understanding of these processes is essential to many researchers and practitioners in the field, the information system research literature contains very few examples of this type. The research is conducted in the following sequence: first, the concept of dynamic capability is presented followed by research methodology; and then the analyses of case data are presented followed by discussions and future directions. The results of this research show that the firms with higher dynamic capability adopted smart factory more easily through alignment of various organizational elements.

• Journal of Korea Technology Innovation Society
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• v.19 no.3
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• pp.545-571
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• 2016

The development of ICT brings a big change in manufacturing industries, and new information technology such as IoT, AR, and big data was applied on manufacturing process. As a result, the concept of smart factory has been introduced as a new manufacturing paradigm. In fact advanced countries like USA, Germany, and Japan have actively introduced smart factory in their manufacturing industries such as electronic, automobile, machinery, to improve production efficiency and quality. The manufacturing environment has been changed into flexible system, so that smart factory will be leading future manufacturing industries. Thes changes have more severe influence on Korean manufacturing industries. Mny industrial companies, have a strong interest in smart factory and they, particularly big enterprises, have been adopting smart factory to increase their manufacturing efficiencies. However, Korean small and medium-sized enterprises (SMEs) have many financial and technological difficulties so that the diffusion of smart factory in Korean SMEs has not been satisfiable up to present. However, smart factory is very important for enhancing their competitiveness in global market. Therefore, this study aims at identifying the standardization strategy of smart factory in so-called Korean 'roots industry' by presuming that the standardization will activate the diffusion of smart factory among Korean SMEs. For this purpose, first, this study examines the competitiveness of SMEs, especially in 'roots industry' and identifies the necessity of diffusion of smart factory among those SMEs. Second, based on the active review on the existing literature, this study identifies four factor groups that would influence the adoption or diffusion of standardized smart factory. They are technological, organizational, industrial and policy factors. Third, using those four factors, this study made two comprehensive case analyses on the adoption and diffusion of smart factory. These two companies belong to molding sector which is one of the important six sectors in 'root industry'. Finally, based on the theoretical and empirical analyse, this study suggests four strategies for activating the standardization of smart factory; international standardization, government-leading standardization, firm-leading standardization, and non-standardization.

• 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.

• 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.

• Journal of the Korea Safety Management & Science
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• v.24 no.2
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• pp.97-104
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• 2022

This study examines the effects of participation purpose, corporate readiness, and acceptance of changes that may occur in the course of expert guidance on the performance of smart factory. For this study, 129 questionnaires obtained from SMEs participating in the Smart Meister support project were used, and SPSS 18.0 and the AMOS 18.0 program were used for statistical processing for empirical analysis of the hypotheses test. It was found that the company's business participation motivation and readiness status had a significant effect on the acceptance and cooperation of changes that occurred during the consulting process. In addition, the acceptance and cooperation of changes within the company had a significant effect on the satisfaction with the Meister support project and the financial performance. Companies participating in the Meister support project need to clarify their motives for participating in the project and make stable corporate readiness in advance. In addition, based on the CEO's support, it is necessary to have a motivational program and to build an organizational culture that can actively accept innovation.

• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.79-94
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• 2022

Purpose - The purpose of this study is to examine firm-level attributes related to Korean manufacturing small and medium-sized enterprises' (SMEs') decisions to implement smart factories. Design/methodology/approach - This study uses the provided by the Ministry of SMEs and Startups of Korea and the Korea Federation of SMEs. Manufacturing SMEs' decisions to implement smart factories in 2018-2019 were analyzed using multinomial logit and ordered logit models. Findings - The findings of this study suggest that firms' decisions to implement smart factories were positively related to firm size, R&D intensity, international market scope, and transactional relationships with customers. However, smart factory implementation decisions were not related to firm age and CEO gender. Research implications or Originality - This study illuminates firm-level attributes that may drive organizational innovation in the era of Industry 4.0 and thus contributes to the innovation adoption literature. This study also contributes to growing research on smart factories by analyzing the actual, progressive decisions to implement smart factories, as opposed to perceived intentions to implement them.

• Korean small business review
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• v.42 no.4
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• pp.251-283
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• 2020

The purpose of this study is to provide assistance to the establishment of related policies to improve the level of acceptance and use of smart factories for SMEs in Korea. To this end, the Unified Technology Acceptance Model (UTAUT) was extended to select additional factors that could affect the intention to accept technology, and to demonstrate this. To achieve the research objective, a questionnaire composed of 7-point Likert scales was prepared, and a survey was conducted for manufacturing-related companies. A total of 136 questionnaires were used for statistical processing. As a result of the hypothesis test, performance expectation and social influence had a positive (+) positive effect on voluntary use, but effort expectation and promotion conditions did not have a significant effect. As an extension factor, the network effect and organizational characteristics had a positive (+) effect, and the innovation resistance had a negative effect (-), but the perceived risk had no significant effect. When the size of the company is large, the perceived risk and innovation resistance are low, and the level of influencing factors for veterinary intentions, veterinary intentions, and veterinary behaviors are excluded. Through this study, factors that could have a positive and negative effect on the adoption (reduction) of smart factory-related technologies were identified and factors to be improved and factors to be reduced were suggested. As a result, this study suggests that smart factory-related technologies should be accepted.