• Korean small business review
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• v.42 no.2
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• pp.117-137
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• 2020

We examine the current status of smart factory deployment and diffusion programs in Korea, and seek to promote manufacturing innovation from the perspective of SMEs. The main conclusions of this paper are as follows. First, without additional market creation and supply chain improvement, smart factories are unlikely to raise profitability leading to overinvestment. Second, new business models need to connect "manufacturing process efficiency" with "R&D" and "marketing" in value chain in smart factories. Third, when introducing smart factories, we need to focus on the areas where process-embedded technology is directly linked to corporate competitiveness. Based on the modularity-maturity matrix (Pisano and Shih, 2012) and the examples of U.S. Manufacturing Innovation Institute (MII), we establish the new smart factory deployment policy measures as follows. First, we shift our smart factory strategy from quantitative expansion to qualitative upgrading. Second, we promote by each sector the formation of industrial commons that help SMEs to jointly develop R&D, exchange standardized data and practices, and facilitate supplier-led procurement system. Third, to implement new technology and business models, we encourage partnerships, collaborations, and M&As between conventional SMEs and start-ups and business ventures. Fourth, the whole deployment process of smart factories is indexed in detail to identify the problems and provide appropriate solutions.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.327-351
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• 2023

This paper aims to derive success factors that successfully lead an artificial intelligence (AI) project and prioritize importance. To this end, we first reviewed prior related studies to select success factors and finally derived 17 factors through expert interviews. Then, we developed a hierarchical model based on the TOE framework. With a hierarchical model, a survey was conducted on experts from AI-using companies and experts from supplier companies that support AI advice and technologies, platforms, and applications and analyzed using AHP methods. As a result of the analysis, organizational and technical factors are more important than environmental factors, but organizational factors are a little more critical. Among the organizational factors, strategic/clear business needs, AI implementation/utilization capabilities, and collaboration/communication between departments were the most important. Among the technical factors, sufficient amount and quality of data for AI learning were derived as the most important factors, followed by IT infrastructure/compatibility. Regarding environmental factors, customer preparation and support for the direct use of AI were essential. Looking at the importance of each 17 individual factors, data availability and quality (0.2245) were the most important, followed by strategy/clear business needs (0.1076) and customer readiness/support (0.0763). These results can guide successful implementation and development for companies considering or implementing AI adoption, service providers supporting AI adoption, and government policymakers seeking to foster the AI industry. In addition, they are expected to contribute to researchers who aim to study AI success models.

• Journal of the Korea Institute of Building Construction
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• v.24 no.2
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• pp.273-284
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• 2024

This study investigates the impact of international oil price fluctuations on overseas construction orders secured by domestic and foreign companies. The analysis employs statistical data spanning the past 20 years, encompassing international oil prices, overseas construction orders from domestic firms, and new overseas construction orders from the top 250 global construction companies. The correlation between these variables is assessed using correlation coefficients(R), determination coefficients(R²), and p-values. The results indicate a strong positive correlation between international oil prices and overseas construction orders. The correlation coefficient between domestic overseas construction orders and oil prices is found to be 0.8 or higher, signifying a significant influence. Similarly, a high correlation coefficient of 0.76 is observed between oil prices and new orders from leading global construction companies. Further analysis reveals a particularly strong correlation between oil prices and overseas construction orders in Asia and the Middle East, potentially due to the prevalence of oil-related projects in these regions. Additionally, a high correlation is observed between oil prices and orders for industrial facilities compared to architectural projects. This suggests an increase in plant construction volumes driven by fluctuations in oil prices. Based on these findings, the study proposes an entry strategy for navigating oil price volatility and maintaining competitiveness in the overseas construction market. Key recommendations include diversifying project locations and supplier bases; utilizing hedging techniques for exchange rate risk management, adapting to local infrastructure and market conditions, establishing local partnerships and securing skilled local labor, implementing technological innovations and digitization at construction sites to enhance productivity and cost reduction The insights gained from this study, coupled with the proposed overseas expansion strategies, offer valuable guidance for mitigating risks in the global construction market and fostering resilience in response to international oil price fluctuations. This approach is expected to strengthen the competitiveness of domestic and foreign construction firms seeking success in the international arena.

• Journal of Distribution Research
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• v.13 no.2
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• pp.29-57
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• 2008

Empirical model was developed to test the building mechanism of trust dimensions and loyalty with the suppliers' bonding tactics to service firms. And interactive effects between suppliers' bonding tactics and service firms' store age was hypothesized in the trust-loyalty building processes. The model was tested in the context of animal clinics which belong to Korean Animal Hospital Association (KAHA). The data was analysed using structural equation model (SEM). The findings are as follows. First, two different relational bonding tactics play different roles in their effects on building trust dimensions toward suppliers. While supplier's social bonding tactic significantly influences on both the affective and cognitive trust of service firms, suppliers' structural bonding tactic only influences significantly on affective trust of service firms. Second, while suppliers' social bonding tactic influences on building service firms' loyalty significantly, suppliers' structural bonding tactic doesn't influence on building their loyalty. Suppliers' structural bonding tactic influences on building their loyalty indirectly through affective trust. Third, while service firms' affective trust influences on building loyalty significantly, cognitive trust doesn't. Their cognitive trust influences on building loyalty indirectly through affective trust. Fourth, the higher the firms' store age is, the more suppliers use social bonding tactics to build trust and loyalty directly. While the shorter the firms' store age is, the more suppliers partially use both the social and structural bonding tactics to build trust and loyalty. In conclusion, in the context of animal clinics' distribution channels, suppliers' relational bonding tactics with service firms differently influence to build trust sub-dimensions and loyalty. And, suppliers should take note of the interactive role of service firms' store age in the utilization of the different bonding tactics to build service firms' trust and loyalty toward suppliers. At the end of the paper, managerial implications, limitations, and future research directions are suggested.