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Factors that Drive the Adoption of Smart Factory Solutions by SMEs

원문정보

Namjae Cho, Soo Mi Moon

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초록

영어

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.

목차

Abstract
1. Introduction
2. Research Background
2.1 Definition of Smart Factory
2.2 Deploying Smart Factories for SMEs
2.3 Review of Existing Smart Factory Research
3. Case Studies and Grounded Theory
3.1 Qualitative Case Study
3.2 Grounded Theory
4. Methods
4.1 Study Design
4.2 Collecting Data
5. Analysis
5.1 Open Coding and Categorization
5.2 Securing Category Validity
5.3 Episode Classification Results by Category
5.4 Axis Coding
5.5 Selective Coding
6. Conclusion
6.1 Summary and Meaning of Study Results
6.2 Implications of the Study
References

저자정보

  • Namjae Cho Professor, School of Business, Hanyang University
  • Soo Mi Moon Ph.D Scholar, School of Business, Hanyang University

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