• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.13 no.5
• /
• pp.29-39
• /
• 2018

Recently, In traditional markets, visitors are declining. The reason is the growth of large stores and Internet shopping malls. The government continues to support and policy to revitalize traditional markets. Government support has been focused on the selective attributes of traditional markets. However, the purchase intention of users in traditional markets is lowered. The reason is that it is in the hygiene of the traditional market. This study analyzed whether the optional attributes of traditional markets and service quality increase the intention of visit, In addition, the users of the traditional market analyzed the hygiene factor as an important factor in the intention of the visit. The results of the analysis is First, convenience, accessibility, transparency, attractiveness, and economic feasibility of selective attributes of traditional markets were analyzed to affect the intention to visit. Second, the merchant efficiency, the display efficiency, the product efficiency, and the transaction efficiency of the service quality of the traditional market influence on the visit intention. However, facility efficiency was not found to have any effect. Third, merchant hygiene factors, facility hygiene factors, and commodity hygiene factors were found to affect the intention to visit. These traditional market hygiene factors were analyzed to control the intention to visit. Therefore, it can be said that the hygiene factor of the traditional market plays a role in raising the intention of visiting the traditional market in activating the traditional market. The conclusion is that merchants and support groups should be prioritized in order to revitalize traditional markets. The importance of environmental hygiene is introduced and implications for research results are suggested.

• Korean Chemical Engineering Research
• /
• v.61 no.4
• /
• pp.523-541
• /
• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.