Narrow Results

This chapter focuses on measuring the impact of the efficiency and productivity of the research units of the member universities of the CNU. The multi-stage DEA methodology was used with the BCC model of variable returns to scale (VRS). The results showed that in competitive conditions, the efficiency indicators of the DMUs for the universities are defined by business borders; however, when efficiency is analyzed in experimental conditions, the border is marked by the Quality Control Laboratory, Entomophagous Laboratory, the Biochemistry and Physiology Laboratory of UNAN León, and the Tissue Culture and Molecular Biology Laboratory. The efficiency indicators were 19% under constant yields, both in competition and experimental conditions, but varied from 27% to 38% under variable yields, surpassing the competition conditions. Finally, in terms of scale production, the efficiency indicators under experimental conditions decreased from 79% to 65%.

This chapter focuses on evaluating the efficiency of applying Biosolid Xolotlan (BX) on farms in western Nicaragua as a bio-input business. It measures efficiency and productivity for each farm engaged in bioinput operations. The data collected for the measurement process were gathered from each farm. The evaluation employed the DEA approach alongside the Malmquist index methodology to assess impact, while stochastic frontier methods were utilized to estimate econometric frontiers. The results indicate that the contribution of wage laborers (Li) and investment cost (Ki) positively influences farms that implemented BX. Additionally, the findings reveal an average technical efficiency of 48% in BX utilization, which had a positive impact on implementing farms. Farms that achieved higher production levels (frontiers) stood out: farm 9 at 88%, farm 5 at 86%, and farm 3 at 79%. Ultimately, it was discovered that the investment made by producers to enhance soil nutrient levels using BX for bioremediation of degraded soils significantly improved production efficiency.

With the development of big data technology, the number of Internet users in China has exceeded 1 billion. With the help of a mature Internet community, the commercial value of public opinion dissemination is gaining more and more attention. It has attracted many scholars in the fields of journalism, social psychology, law, computer, and programming. How to identify favorable or harmful information, how to guide the correct public opinion, and how to turn public opinion into commercial value have become research hot topics in recent years. This paper sets the research object as the hot topic data on the Internet community Zhihu and calculates the relative public opinion dissemination efficiency level in each DMU through the DEA model. Then, these topics would be grouped into eight categories so that we could analyze the relationship between the number of hot topics and the percentage of high-efficiency topics. Finally, based on the statistical results, we obtain three main modes of public opinion communication and we infer the principle of how the mode forms.

In this paper a multiple objectives programming method is applied to improve the discriminating power of classical Data Envelopment Analysis (DEA) method. Unlike the classical DEA model often producing many relatively efficient decision making units (DMUs), this new approach enjoys more discriminating power, which results in less DMUs with efficiency ratio as 1. In this approach, every DMU's efficiency evaluation is viewed as one objective function to be maximized. A set of common multipliers, input and output weights, can be located not difficultly by using the fuzzy multiple objectives programming approach. In comparison to the number of programming works being same as the number of the DMUs in traditional DEA model, the new approach just needs to solving multiple objectives programming problem once no matter how many DMUs are. Apparently, the new approach is comparatively suitable to solve a problem with a large number of DMUs.

The Virtual Organization (VO) concept has emerged as one of the most promising forms of collaboration among companies by providing a way of sharing their costs, benefits and risks, in order to attend particular demands. Although these advantages, VOs face several risks that need to be identified, measured, and mitigated through a well defined process. In this way, this paper proposes a hybrid DEA-Fuzzy method for analyzing risk in VO formation. This method assesses the level of risk present in a set of previously selected Service Providers (SPs) using Key Performance Indicators (KPIs), providing a way to helping decide on the VO formation.

The sustainability of 151 countries is assessed by applying Data Envelopment Analysis (DEA) and Cluster Analysis. To measure the level of sustainability, three aspects (social, environmental and economic) are taken into consideration in which 10 indicators are involved. To achieve the premise of the DEA process, Cluster Analysis is utilized to classify all the countries and regions into three clusters. For each cluster, DEA is applied to assess the sustainability of the countries, marking the sustainable ones at 100% efficiency or lower efficiency (relatively). Noticeably, in one particular cluster, countries are underdeveloped and mostly located in Africa and South America. This study contributes to the decision making of governments by providing evidence to sustainable development. It can also serve to inform the World Bank and Asian Infrastructure Investment Bank on those countries that need the most support and intervention. This study can be extended to any interested countries and regions.