• Title/Summary/Keyword: regression-based modelling

Search Result 53, Processing Time 0.019 seconds

Relation of Market Orientation and Organizational Performance in Nonprofit Human Service Organization toward Social Service Marketization (사회서비스 시장화에 따른 비영리 사회복지조직의 시장지향성과 조직성과 관계 연구)

  • Kwon, Sun-Ae;Kim, Gyo-Jung
    • The Journal of the Korea Contents Association
    • /
    • v.12 no.5
    • /
    • pp.227-242
    • /
    • 2012
  • This study is to experientially verify that market orientation affect organizational performance toward social service marketization. social service marketization put pressure upon financial environment in nonprofit human service organizations and exacerbate competition between nonprofit human service organizations and profit organizations each other. For survival, nonprofit human service organizations in social service market where various supply organization participate needs sensitivity to the environment. For this purpose the need of introduction of market orientation is being raised. At this point this study is useful that measure the level of market orientation and analysis impact of market orientation toward organizational performance in nonprofit human service organizations. Data was collected from busan nonprofit human service organizations in participate voucher program, number of total organization is 92. The level of market orientation was 3.63. Analysis of the impact of market orientation to organizational performance used regression analysis through structural equation modelling. As a result of the analysis, market orientation was affected by organizational performance. Based on the results, this study suggests that nonprofit human service organizations should need market orientation centered organization management and sensitive response toward external environment.

Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model (기계 학습 모델을 통해 XGBoost 기법을 활용한 부산 컨테이너 물동량 예측)

  • Nguyen Thi Phuong Thanh;Gyu Sung Cho
    • Journal of Internet of Things and Convergence
    • /
    • v.10 no.1
    • /
    • pp.39-45
    • /
    • 2024
  • Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

Performance of Drip Irrigation System in Banana Cultuivation - Data Envelopment Analysis Approach

  • Kumar, K. Nirmal Ravi;Kumar, M. Suresh
    • Agribusiness and Information Management
    • /
    • v.8 no.1
    • /
    • pp.17-26
    • /
    • 2016
  • India is largest producer of banana in the world producing 29.72 million tonnes from an area of 0.803 million ha with a productivity of 35.7 MT ha-1 and accounted for 15.48 and 27.01 per cent of the world's area and production respectively (www.nhb.gov.in). In India, Tamil Nadu leads other states both in terms of area and production followed by Maharashtra, Gujarat and Andhra Pradesh. In Rayalaseema region of Andhra Pradesh, Kurnool district had special reputation in the cultivation of banana in an area of 5765 hectares with an annual production of 2.01 lakh tonnes in the year 2012-13 and hence, it was purposively chosen for the study. On $23^{rd}$ November 2003, the Government of Andhra Pradesh has commenced a comprehensive project called 'Andhra Pradesh Micro Irrigation Project (APMIP)', first of its kind in the world so as to promote water use efficiency. APMIP is offering 100 per cent of subsidy in case of SC, ST and 90 per cent in case of other categories of farmers up to 5.0 acres of land. In case of acreage between 5-10 acres, 70 per cent subsidy and acreage above 10, 50 per cent of subsidy is given to the farmer beneficiaries. The sampling frame consists of Kurnool district, two mandals, four villages and 180 sample farmers comprising of 60 farmers each from Marginal (<1ha), Small (1-2ha) and Other (>2ha) categories. A well structured pre-tested schedule was employed to collect the requisite information pertaining to the performance of drip irrigation among the sample farmers and Data Envelopment Analysis (DEA) model was employed to analyze the performance of drip irrigation in banana farms. The performance of drip irrigation was assessed based on the parameters like: Land Development Works (LDW), Fertigation costs (FC), Volume of water supplied (VWS), Annual maintenance costs of drip irrigation (AMC), Economic Status of the farmer (ES), Crop Productivity (CP) etc. The first four parameters are considered as inputs and last two as outputs for DEA modelling purposes. The findings revealed that, the number of farms operating at CRS are more in number in other farms (46.66%) followed by marginal (45%) and small farms (28.33%). Similarly, regarding the number of farmers operating at VRS, the other farms are again more in number with 61.66 per cent followed by marginal (53.33%) and small farms (35%). With reference to scale efficiency, marginal farms dominate the scenario with 57 per cent followed by others (55%) and small farms (50%). At pooled level, 26.11 per cent of the farms are being operated at CRS with an average technical efficiency score of 0.6138 i.e., 47 out of 180 farms. Nearly 40 per cent of the farmers at pooled level are being operated at VRS with an average technical efficiency score of 0.7241. As regards to scale efficiency, nearly 52 per cent of the farmers (94 out of 180 farmers) at pooled level, either performed at the optimum scale or were close to the optimum scale (farms having scale efficiency values equal to or more than 0.90). Majority of the farms (39.44%) are operating at IRS and only 29 per cent of the farmers are operating at DRS. This signifies that, more resources should be provided to these farms operating at IRS and the same should be decreased towards the farms operating at DRS. Nearly 32 per cent of the farms are operating at CRS indicating efficient utilization of resources. Log linear regression model was used to analyze the major determinants of input use efficiency in banana farms. The input variables considered under DEA model were again considered as influential factors for the CRS obtained for the three categories of farmers. Volume of water supplied ($X_1$) and fertigation cost ($X_2$) are the major determinants of banana farms across all the farmer categories and even at pooled level. In view of their positive influence on the CRS, it is essential to strengthen modern irrigation infrastructure like drip irrigation and offer more fertilizer subsidies to the farmer to enhance the crop production on cost-effective basis in Kurnool district of Andhra Pradesh, India. This study further suggests that, the present era of Information Technology will help the irrigation management in the context of generating new techniques, extension, adoption and information. It will also guide the farmers in irrigation scheduling and quantifying the irrigation water requirements in accordance with the water availability in a particular season. So, it is high time for the Government of India to pay adequate attention towards the applications of 'Information and Communication Technology (ICT) and its applications in irrigation water management' for facilitating the deployment of Decision Supports Systems (DSSs) at various levels of planning and management of water resources in the country.