• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.371-390
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• 2008

• Korean Business Review
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• v.10
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• pp.85-104
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• 1997

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.317-317
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• Journal of Korean Society for Quality Management
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• 1977

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.503-503
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• 1989

• 한국산학경영학회:학술대회논문집
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• 2006

• Proceedings of the Technology Innovation Conference
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• 2002.02a
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• pp.35-50
• /
• 2002

• Journal of Korea Society of Industrial Information Systems
• /
• v.25 no.6
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• pp.143-158
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• 2020

This study examines the effect of VMD (Visual merchandising) of unmanned stores and the affordance of unmanned systems on a customer's value and intention of revisit. The proposed research model and developed hypotheses were tested using structural equations modeling based on data collected from 297 customers with experience in unmanned stores. The results of the study confirm the positive effects of the unmanned system's affordance and unmanned stores' VMD on customer value. In addition, there is a positive relationship between a customer's value and intention of revisit. The study provides valuable insights into the sustainability of unmanned stores after post-COVID 19 and the importance of developing unmanned stores' VMD and affordance.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.12
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• pp.329-342
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• 2021

The pharmaceutical industry is experiencing a productivity crisis with a low probability of success despite a long period of time and enormous cost. As a strategy to solve the productivity crisis, the use cases of Artificial Intelligence(AI) and Bigdata are increasing worldwide and tangible results are coming out. However, domestic pharmaceutical companies are taking a wait-and-see attitude to adopt AI platform for drug research. This study proposed a research model that combines the Value-based Adoption Model and the Innovation Resistance Model to empirically study the effect of value perception and resistance factors on adopting AI Platform. As a result of empirical verification, usefulness, knowledge richness, complexity, and algorithmic opacity were found to have a significant effect on perceived values. And, usefulness, knowledge richness, algorithmic opacity, trialability, technology support infrastructure were found to have a significant effect on the innovation resistance.