scholarly journals Big Data Analysis of e-Commerce Efficiency and Its Influencing Factors of Agricultural Products in China

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yi Wen ◽  
Lingxi Kong ◽  
Gaoxiu Liu
Keyword(s):  
Influencing Factors ◽  
Tobit Model ◽  
Agricultural Products ◽  
Scale Efficiency ◽  
Agricultural Mechanization ◽  
Internet Users ◽  
Correlation Degree ◽  
Rural Revitalization ◽  
Dea Analysis ◽  
High Level

At present, the e-commerce industry of agricultural products plays a pivotal role in promoting income growth and helping rural revitalization. This paper collected relevant data in the recent 8 years (2012 to 2019) and used the DEA model and Tobit model to analyze the correlation degree between the efficiency and various influencing factors in China. DEA analysis results show that, in recent years, three efficiencies are quite different: the comprehensive efficiency and scale efficiency show an upward trend, while the pure technical efficiency remains at a high level. Tobit model results show that the number of urban Internet users, rural Internet users, logistics practitioners, the development of national economy are negatively correlated with e-commerce efficiency; the length of traffic construction has no significant correlation; the level of agricultural mechanization has a significant positive correlation. Hence, the paper puts forward four suggestions.

scholarly journals An analysis on service efficiency in nursing homes and spatio-temporal variation in China

2020 ◽  
Author(s):  
Liangwen Zhang ◽  
Ying Han ◽  
Ya Fang
Keyword(s):  
Nursing Homes ◽  
Temporal Variation ◽  
Social Workers ◽  
Technical Efficiency ◽  
Influencing Factors ◽  
Tobit Model ◽  
Scale Efficiency ◽  
Eastern Region ◽  
Service Efficiency ◽  
Spatio Temporal

Abstract Background: Pension services market in China is still at the early stage, problems like low service efficiency and low quality of nursing care already exist. So it is inevitable to analyze the efficiency and productivity and spatio-temporal variation, as well as its influencing factors in nursing homes all over the country. Methods: Data Envelopment Analysis (DEA) and Tobit model were applied to integrate several quality measures into a comprehensive benchmarking model. We present nationwide results and analyze the spatio-temporal distribution of the (technical efficiency) TE and productivity of nursing homes among Eastern, Central and Western of China. Furthermore, Tobit model was performed to explore factors associated with TE. Results The average TE, pure technical efficiency (PTE) and scale efficiency (SE) of nursing homes for the 5-year period were 0.909. 0.928 and 0.979, respectively. The TE and SE decreased from 2012 to 2014, but improved after 2014. TE is 0.98 in the Eastern region, 0.93 of that in the Central region and 0.91 of that in the Western region, with a decrease range of 2%, 7% and 9%, respectively. The average improvement range of the five input indexes of the non-DEA effective nursing homes was 27.26%, 20.62%, 19.77%, 22.04%, and 38.84%, respectively. The influencing factors of efficiency value of nursing homes indicated that if there are more social workers, more patients in the nursing homes, and more employees who are aged 56 and above, the TE and productivity of nursing homes will be higher. Conclusions There was considerable space for TE improvement in nursing homes due to the low proportion of effective TE. The TE and SE presented a decreasing trend from 2012 to 2014, which implicated that the large SE in nursing homes with less previous standardized management should be emphasized. (total factor productivity changes)TFPC experienced a decrease in productivity due to the adverse alteration in technological changes and pure technical efficiency. The service efficiency in nursing homes is facing with the problem of imbalance of regional development. The efficiency value of nursing homes was influenced by age of employees, the number of social workers and the number of older people. The measures and suggestions on improving efficiency of care in nursing homes were put forward.

scholarly journals Economic Growth, Policies and Influencing Factors: The Case of Albania

2016 ◽  
Vol 4 (1) ◽  
pp. 107
Author(s):  
Eleni Vangjeli ◽  
Anila Mancka
Keyword(s):  
Economic Growth ◽  
Monetary Policy ◽  
Fiscal Policy ◽  
Interest Rates ◽  
Empirical Analysis ◽  
Influencing Factors ◽  
Fiscal Policies ◽  
The Government ◽  
High Level ◽  
The Empirical Analysis

Monetary and fiscal policies are two policies that the government could use to keep a high level of growth, with a low inflancion. Fiscal policy has its initial impact on the stock market, while monetary policy in market assets. But, given that the goods and active markets are closely interrelated, both policies, monetary as well as fiscal have impact on the economy, increasing the level of product through the reduction of interest rates. In our paper we will show how functioning monetary and fiscal policies. But also in our paper we will analyze the different factors which have affected the economic growth of the country. The focus of our study is the graphical and empirical analysis of economic growth, policies and influencing factors. For the empirical analysis we have used data on the economic growth in Albania for 1996– 2014.

scholarly journals Research on the Influencing Factors of the Willingness of New Professional Farmers to Participate in Skills Training and Its Promotion during the Development of Urban ModernAgriculture

CONVERTER ◽  
2021 ◽  
pp. 199-210
Author(s):  
Yixi Liu, Pingyan Guo, Zhiyao Ma, Chun Hu
Keyword(s):  
Influencing Factors ◽  
Skill Training ◽  
Skills Training ◽  
Training Effect ◽  
Training Methods ◽  
Technical Problems ◽  
Agricultural Industry ◽  
Rural Revitalization ◽  
Key Factor ◽  
The Times

Objective: Talent is the key factor in the implementation of the Rural Revitalization Strategy. Based on the willingness and influencing factors of new professional farmers to participate in skill training in the development of modern urban agriculture, this study seeks to study the education of professional farmers from the perspective of demand. Methods: Based on the questionnaire survey data of new vocational farmers in Wenzhou, this study systematically analyzed the current situation, training willingness, training methods, and training effect of new vocational training, and made quantitative statistical analysis of the original basic data. Combined with the characteristics of agricultural industry and post, this study empirically analyzed their perception of participating in skill training and the influencing factors. Results: The number of new vocational farmers willing to participate in training was significantly higher than that of farmers unwilling to participate in training. The frequency of technical problems encountered in agricultural production, the times of training, the evaluation of training effect, effect, cost, teacher level, hardware level, certificate, and other factors have a significant impact on the willingness of new vocational farmers to participate in skills training. Conclusion: This study proposes to build a vocational occupation education system to enhance the training intention of new occupation farmers. During the COVID-19, the innovative form of webcast sales realized the unification of technology and service.

scholarly journals Analisis Pengaruh Penggunaan Facebook Bagi Mahasiswa dan Hubungannya dengan Gangguan Kecanduan Facebook

2019 ◽  
Vol 3 (3) ◽  
pp. 167
Author(s):  
Ronal Watrianthos ◽  
Ibnu Rasyid Munthe ◽  
Rahma Muti’ah
Keyword(s):  
Social Networking Sites ◽  
Leisure Time ◽  
Online Survey ◽  
Rapid Development ◽  
Empirical Studies ◽  
Internet Users ◽  
Spss Software ◽  
Facebook Addiction ◽  
High Level ◽  
Almost All

Along with the rapid development of Social Networking Sites (SNS), social media, recently, has become a lifestyle for many people around the world, including in Indonesia. The data in January 2018 showed that in Indonesia out of 132.7 million internet users, almost all (131 million), or up 23% from the data in 2017, were Facebook users with the dominance of 18-24 years old, 35% of whom were the highest active users. The rapid growth of Facebook users annually in Indonesia, especially in the age of students and college students, encourages researchers to conduct many empirical studies of Facebook use among students. There is a tendency for using Facebook continuously to create FAD effects (Facebook Addiction Disorder) among students and can affect the spirit of learning. This study also discusses what is the motivation for using Facebook and seeing the potential for FAD to occur. In this study, an online survey over 375 respondents from several students in Labuhanbatu District was conducted. To explore respondents' motives in using Facebook, respondents were given questions that were divided into the following five motives: social interaction, leisure time, entertainment, friends, and communication. While to look for potential addiction, respondents were given questions using the Bergen Facebook Addiction (BFAD) scale. In getting a connection between the motives for using Facebook and Facebook Addiction, the data was tested by analysis of variants (ANOVA) and partial tests using SPSS software. The results obtained were 65.8% of participants were at a moderate level, while 20.3% were at a low level, and only 13.9% of participants were at a high level. While the most significant motive affecting respondents in using Facebook is the motive to fill the time and motives for communicating.

Calculation of regional agricultural production efficiency and empirical analysis of its influencing factors-based on DEA-CCR model and Tobit model

2021 ◽  
pp. 1-14
Author(s):  
Pengfei Zhou ◽  
Siyuan Yang ◽  
Xiaohang Wu ◽  
Yang Shen
Keyword(s):  
Empirical Analysis ◽  
Influencing Factors ◽  
Agricultural Production ◽  
Production Efficiency ◽  
Malmquist Index ◽  
Tobit Model ◽  
Annual Growth Rate ◽  
Productivity Level ◽  
Agricultural Modernization ◽  
Ccr Model

The improvement of agricultural production efficiency and the transformation of production mode are the core of promoting agricultural modernization. Taking Chongqing as a sample case, this paper uses DEA-CCR Model, Malmquist Index and Tobit Model to calculate and analyze its agricultural production efficiency and its influencing factors, and accurately identifies the problems existing in its agricultural transformation and development. It has an important policy reference value for improving agricultural innovation and competitiveness, promoting the steady development of rural revitalization, and realizing agricultural modernization, which also provides some reference and enlightenment for countries or regions with similar characteristics of mountain agriculture development in the world to enhance regional agricultural production efficiency. Through empirical analysis and investigation, it is found that the overall agricultural production efficiency of Chongqing remains at the productivity level of 0.8 from 2009 to 2018, with an average annual growth rate of 12.8%, but there is a large gap in the level of regional development. Through Malmquist Index decomposition, it is found that agricultural technology progress has the greatest contribution to the improvement of production efficiency. Financial support for agriculture, urbanization level, regional economic development level and highway mileage have a significant positive impact on production efficiency, while the level of farmers’ disposable income has a negative impact on the increase of production efficiency, and the income gap between urban and rural residents fails to pass the significance test.

scholarly journals Spatial-Temporal Pattern and Its Influencing Factors on Urban Tourism Competitiveness in City Agglomerations Across the Guanzhong Plain

2019 ◽  
Vol 11 (23) ◽  
pp. 6743
Author(s):  
Jia Wan ◽  
Junping Yan ◽  
Xiaomeng Wang ◽  
Ziqiang Liu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Influencing Factors ◽  
Temporal Pattern ◽  
Explanatory Power ◽  
Urban Tourism ◽  
Ecological Environment ◽  
Single Factor ◽  
Tourism Resources ◽  
High Level ◽  
Environment Condition

Strengthening research on urban tourism competitiveness is vital in evaluating the current situation and potential of urban tourism, maintaining the sustainable development of the tourism economy and assisting in the regional macro decision making. In this study, an index system evaluation of urban tourism competitiveness in city agglomerations across the Guanzhong Plain is established by collecting cross-section data from the years 2017 and 2010. The entropy value method is adopted to determine the index weight. Cluster analysis is performed and the spatial-temporal pattern and evolution laws of urban tourism competitiveness among city agglomerations in the Guanzhong Plain are analyzed and the geographic detector utilized to discuss the influencing factors. Results show that the spatial gradient difference of urban tourism competitiveness of agglomerations in the Guanzhong Plain is significant. In 2010, it presented the characteristic of ‘the high and middle levels having a zonal distribution from east to west, and the low level was distributed along the north and south wings’. In 2017, the characteristic of ‘polarization’ became highly prominent, that is, the scope of high-level and low-level cities expanded and the scope of medium-level cities decreased. Urban tourism competitiveness in city agglomerations across the Guanzhong Plain exhibited a trend of ‘strengthening in the east, weakening in the west’. The competitiveness of resources and management shifted aggressively and supporting factors competitiveness underwent a slight change. The urban tourism competitiveness of city agglomerations in the Guanzhong Plain is generally low, while the urban tourism competitiveness of Xi’an had an absolute advantage in city agglomerations of the Guanzhong Plain. According to the cluster analysis results, resources and management competitiveness, supporting factors competitiveness, demand conditions competitiveness, situational conditions competitiveness and urban tourism competitiveness of Xi’an in 2010 and 2017 were all at an extremely high level, which was relatively higher than the index values of other cities in the city agglomerations of the Guanzhong Plain. Tourism resources, service support capacity, infrastructure support capacity, tourism income scale, tourism reception scale and economic development power are the core influencing factors of urban tourism competitiveness among city agglomerations in the Guanzhong Plain. The single factor explanatory power of destination management indicates a downward trend while the single factor explanatory power of the ecological environment condition shows an upward trend. Tourism resources are the leading interactive factor of urban tourism competitiveness, and destination management and ecological environment condition are the most significant indicators for the collaborative effect.

scholarly journals Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3081 ◽  
Author(s):  
Zeng ◽  
Lu ◽  
Liu ◽  
Zhou ◽  
Hu
Keyword(s):  
Influencing Factors ◽  
Regional Differences ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Tobit Model ◽  
Policy Implications ◽  
Dea Model ◽  
Emission Efficiency ◽  
Unit Root Testing ◽  
Technology Level

With the challenge to reach targets of carbon emission reduction at the regional level, it is necessary to analyze the regional differences and influencing factors on China’s carbon emission efficiency. Based on statistics from 2005 to 2015, carbon emission efficiency and the differences in 30 provinces of China were rated by the Modified Undesirable Epsilon-based measure (EBM) Data Envelopment Analysis (DEA) Model. Additionally, we further analyzed the influencing factors of carbon emission efficiency’s differences in the Tobit model. We found that the overall carbon emission efficiency was relatively low in China. The level of carbon emission efficiency is the highest in the East region, followed by the Central and West regions. As for the influencing factors, industrial structure, external development, and science and technology level had a significant positive relationship with carbon emission efficiency, whereas government intervention and energy intensity demonstrated a negative correlation with carbon emission efficiency. The contributions of this paper include two aspects. First, we used the Modified Undesirable EBM DEA Model, which is more accurate than traditional methods. Secondly, based on the data’s unit root testing and cointegration, the paper verified the influencing factors of carbon emission efficiency by the Tobit model, which avoids the spurious regression. Based on the results, we also provide several policy implications for policymakers to improve carbon emission efficiency in different regions.

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