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This longitudinal research examines US symphony orchestra sector organizations to determine individual efficiencies in allocating resources (donations, governmental/private funding, etc.) for desirable outputs (concerts, educational programs, community outreach). It provides researchers and managers with a tool for identifying, assessing and mitigating organizational inefficiencies.

This study assesses relative efficiencies in performing arts organizations using Data Envelopment Analysis (DEA), a widely-used nonparametric data-intensive benchmarking technique that determines an optimal “production frontier” of best-practice organizations among their peers and assesses their abilities to turn multivariate inputs into multivariate desired outputs.

This analysis highlights efficiency differences in a wide range of orchestras in converting available resources into performance-related outputs. It provides individual arts organizations with useful results for developing practical benchmarks to achieve organizational efficiency improvement.

This study provides constructive benchmarking guidance for improving efficiencies of relatively-inefficient organizations. Future analysis can expand the scope to utilize a two-stage DEA model to provide more specific guidance to arts organizations.

This pragmatic analysis enables arts/culture institutions to assess their organizational efficiencies and identify opportunities to optimize resources in producing social outputs for their target markets.

Efficiency improvements enable performing arts organizations to provide additional artistic/social services, with fewer resources, to larger audiences.

This research demonstrates the abilities of DEA analysis to assess both a sector and its individual organizations to determine efficiencies, identify sources of inefficiencies and assess longitudinal efficiency trends.

The emerging disintegrative processes of transitional economies are influencing companies’ business models in terms of consumer behaviour, especially food markets, which offer usual, common and traditional consumer products. Beyond investigating potential consumer misbehaviour, a further aim of this study is the building of a theoretical-descriptive model for consumer misbehaviour in food markets, which could influence the contextual complexity in business relationships, as well as the management of raw materials, services acquisition and final product sales. The research applies the “input-output” model (Ferrero, 1968) to some specific marketing theories, adopting an interdisciplinary approach for understanding the relationships between consumer behaviour and a company’s business model.

The research is both qualitative and quantitative in nature. In the first phase, the research was conducted among representatives of grocery stores using an exploratory approach; thus, an in-depth interview method was used. In the second phase, direct research among consumers was conducted using an online survey. After the verification of correctness, validity and reliability, a final 1,200-questionnaire dataset was analysed

The most common consumer misbehaviour in food markets concerns the theft of foodstuff or the adoption of bad behaviour towards grocery stores employees. Market and store representatives have highlighted a large scale of pathological consumer misbehaviour, mostly due to psychological conditions at the individual (habits, lifestyle or personality) and collective (family or other social groups) levels. According to previous studies, consumer misbehaviour in food markets seems to be substantially affected by three factors: motivation, capacity and opportunity. These factors strongly impact the input-output model through which the company interacts with the context.

The three-factor model reveals advantages and applications, allowing for a simple explanation of consumer misbehaviour in food markets and stores. It can contribute to scientific theory development (especially theories related to consumer behaviour, customer relationship management, partnership marketing and supply chain management) and generate support for understanding complex relations among consumers, food producers, factories and food stores. In this direction, the management of knowledge about consumers and their behaviour is indispensable.

Artificial intelligence (AI) models are demonstrating day by day that they can find long-term solutions to improve wastewater treatment efficiency. Artificial neural networks (ANNs) are one of the most important of these models, and they are increasingly being used to forecast water resource variables. The goal of this study was to create an ANN model to estimate the removal efficiency of biological oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) at the effluent of various primary and secondary treatment methods in a wastewater treatment plant (WWTP).

The MATLAB App Designer model was used to generate the data set. Various combinations of wastewater quality data, such as temperature(T), TN, TP and hydraulic retention time (HRT) are used as inputs into the ANN to assess the degree of effect of each of these variables on BOD, TN, TP and TSS removal efficiency. Two of the models reflect two different types of primary treatment, while the other nine models represent different types of subsequent treatment. The ANN model’s findings are compared to the MATLAB App Designer model. For evaluating model performance, mean square error (MSE) and coefficient of determination statistics (R2) are utilized as comparative metrics.

For both training and testing, the R values for the ANN models were greater than 0.99. Based on the comparisons, it was discovered that the ANN model can be used to estimate the removal efficiency of BOD, TN, TP and TSS in WWTP and that the ANN model produces very similar and satisfying results to the APPDESIGNER model. The R-value (Correlation coefficient) of 0.9909 and the MSE of 5.962 indicate that the model is accurate. Because of the many benefits of the ANN models used in this study, it has a lot of potential as a general modeling tool for a range of other complicated process systems that are difficult to solve using conventional modeling techniques.

The objective of this study was to develop an ANN model that could be used to estimate the removal efficiency of pollutants such as BOD, TN, TP and TSS at the effluent of various primary and secondary treatment methods in a WWTP. In the future, the ANN could be used to design a new WWTP and forecast the removal efficiency of pollutants.

This paper aims to understand the implementation of a circular economic business where animal manure is used to produce biogas and alternative fertilizer in a regional network of manure suppliers and biogas producers and to reveal the impacts of five variables (manure quantity, transportation distance, manure dry content, manure price and manure discharge price) on the economic sustainability of manure-based biogas supply chains.

An enterprise input-output approach is used to model physical and monetary flows of the manure-based biogas supply chain. Computational experiments are performed on all variables to identify under which conditions the cooperation is beneficial for all actors.

The cooperation is profitable for a large-scale farm (>20,000 t/year) if biogas producer (b) pays farmer (f) to receive its manure (5 €/t) or if f sells manure for free and manure disposal costs are >10 €/t. Cooperation is always profitable for b if f pays b to supply its manure (5€/t). If b receives manure for free, benefits are always positive if b is a medium-large-scale plant (>20,000 t/year). For a small-scale plant, benefits are positive if manure dry content (MDC) is ≥12 per cent and transportation distance is ≤10 km.

The paper adds value to the biogas production research, as it makes holistic analysis of five variables which might change under different policy and geographical conditions. The investors in biogas production, suppliers and transportation companies can find correspondence to empirical findings for their own site-specific cases.

The aim of this study is to provide energy supply chain in Indonesia. Therefore, the best scenario has been created to fulfill energy scarcity by global supply chain partnership. All impact social-economy will be advantages. The input output analysis has been applied in this paper. The impact of output and impact of job creation are possible to describe, significantly. All barriers and opportunities that can be achieve from the lowest price, efficiency and also to reduce unemployment rate during a decade of building nuclear power plants. This research work can be applicable model for future nuclear power plants construction or other type of energy source construction that give significant impact to the economy and business sustainability. Therefore, by this project plan will cover energy needs for domestics and improve the local corporate productivity, especially for nuclear power plants equipment.

The purpose of this study is to analyze the environmental efficiency level and trend of the transportation sector in the upper–mid–downstream of the Yangtze River Economic Belt and the JingJinJi region in China and assess the effectiveness of policies for protecting the low-carbon environment.

This study uses the meta-frontier slack-based measure (SBM) approach to evaluate environmental efficiency, which targets and classifies specific regions into regional groups. First, this study employs the SBM with the undesirable outputs to construct the environmental efficiency measurement models of the four regions under the meta-frontier and group frontiers, respectively. Then, this study uses the technology gap ratio to evaluate the gap between the group frontier and the meta-frontier.

The analysis reveals several key findings: (1) the JingJinJi region and the downstream of the YEB had achieved the overall optimal production technology in transportation than the other two regions; (2) significant technology gaps in environmental efficiency were observed among these four regions in China; and (3) the downstream region of the YEB exhibited the lowest levels of energy consumption and excessive CO₂ emissions.

To evaluate the differences in environmental efficiency resulting from regions and technological gaps in transportation, this study employs the meta-frontier model, which overcomes the limitation of traditional environmental efficiency methods. Furthermore, in the practical, the study provides the advantage of observing the disparities in transportation efficiency performed by the Yangtze River Economic Belt and the Beijing–Tianjin–Hebei regions.

This paper aims to study the inter-sectoral linkages in the Egyptian economy, to increase the efficiency of allocating L.E 100bn fiscal stimulus package (FSP) to tackle the economic fallout from COVID-19 based on the strength of the backward and forward linkages of various sectors, and the values of both employment and value-added multipliers. The paper also measures the impact of the new FSP on the capability of various sectors in creating job opportunities and increasing economic growth.

The paper studies the intersectoral linkages by calculating backward and forward linkages index based on the latest input and output tables available for the Egyptian economy published in 2018. It also depends on a bivariate optimization model to distribute new investments allocated through the FSP based on the values of both employment and value-added multiplier for those sectors. The paper calculated both employment and value-added coefficients to measure the impact of the FSP on creating job opportunities and increasing growth rates.

Based on the results of the empirical analysis, both key sectors (with strong backward and forward linkages) and sectors with strong backward linkages have the highest impact on creating job opportunities and increasing growth rates in the Egyptian economy, which means that allocating FSPs in a way which targets those sectors, especially during economic crisis, could help in increasing the positive impacts of those packages.

The paper is based on the unbalanced growth theory of Hirschman and uses the empirical analysis to study the intersectoral linkages and allocate new investments through FSP through different sectors. The main policy implication of the empirical results of this paper suggests targeting the key sectors and the sectors with strong backward linkages during tough economic times related to COVID-19, to increase the positive impact of the package on the whole economy.

We adopt a super slack-based measurement (SBM) data envelopment analysis (DEA) model to estimate the efficiency of five biggest freight rail operators in Russia, which are included in the top 30 freight rail operators in terms of two dimensions – financial and operational efficiency during 2013–2017. The result shows that the private companies characterized by high financial and operational efficiency, while the Rossiiskye Zheleznye Dorogi (RZD) subsidiaries characterized by sufficiently low financial and operational efficiency scores. And the result also presents that operational efficiency score of operators handling universal rolling stock is higher than financial efficiency scores. In contrast, financial efficiency scores of operators handling special rolling stock is higher than operational efficiency scores. rail freight operators in addition to a special rolling stock park should have a universal rolling stock park for higher profitability. State-owned companies and its subsidiary operate inefficiently in the midst of a market economy in Russia. Rail freight operators for a higher level of financial efficiency should be transferred to the private sector.

In the current literature, there is little systematic research on the relationship among adjustment of the income distribution, change in economic structure and improvement of macroeconomic efficiency.

This paper expands Marx's reproduction schema into the “Marx–Sraffa” three-department structure table comprising fixed capital, general means of production and means of consumption and employs China's input–output table from 1987 to 2015 to portray the relationship between income distribution and macroeconomic efficiency under investment-driven growth.

This paper calculates the wage–profit curve of China's economy and evaluates the space of macroeconomic efficiency improvement in China based on the deviation between actual and potential income distribution structure.

The results show that there is a downward trend of the profit rate, which meets Marx's theoretical prediction, and the decline in the profit rate is mainly attributed to an increase in the organic composition of capital arising from the rapid growth of fixed capital investment under extended growth. The analysis of macroeconomic efficiency shows that the space for improving macroeconomic efficiency is extremely limited under traditional growth pattern and that China must transform its economic development pattern and foster new economic growth drivers.

This paper applies the decomposition method proposed by Wang et al. (2013), together with the multi-national input-output tables from World Input-Output Database (WIOD) to estimate the value-chain transition in East Asian production network. Specifically, we calculate and examine the domestic value-added absorbed abroad, foreign value-added embodied in country’s gross exports, and vertical specialization measures to explore the relative positions of major East Asian countries in the global production chain over the period of 1995-2011. The analyses are at country-aggregate, country-sector, bilateral-aggregate and bilateral-sector levels. Based on our results, we answer the important question of whether Taiwan and South Korea have used China’s production chains as an intermediary to re-export their products to other countries in the world. Furthermore, we answer the question that over the 1995-2011 periods, have Taiwan and South Korea exploited cheap labor from China to add value to their products before re-exported them to the rest of the world?