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We provide a novel panel model to decompose total factor productivity (TFP) growth in the Greek industry at the firm level while we tackle the contribution of R&D. We, therefore, opt for parametric methodology that provides statistical inference and would validate the results. Our modeling departs from prior strong assumptions such as error terms across firms being independent. In fact, we provide a novel limited information maximum likelihood (LIML) estimation method that adequately deals with the issue of endogeneity and model misspecification. We demonstrate that our model detects variability in terms of TFP growth components across industries and firms. Our results show that R&D would enhance TFP of Greek firms, albeit the crisis has had a detrimental impact. Financial ratios such as liquidity and solvency ratios also affect TFP as we demonstrate that both would enhance TFP. The solvency ratio is important as it provides an estimate of whether the firm can cope with debt. We also note variability across small versus medium and large firms and report that small firms are more productive and spend more of their revenues on R&D. In terms of policy, our evidence warrants higher R&D spending to enhance TFP growth, though R&D funding is a concern. [ABSTRACT FROM AUTHOR]

Purpose: to determine the most effective options for irrigation regimes, growth stimulation that meet the requirements of resource-saving technology for common beans cultivation under the conditions of Astrakhan region. Materials and methods. To study the effect of pre-sowing inoculation of planting material (seeds) of beans, as well as irrigation regimes, a field experiment was set up. The object of the study was the common bean variety Rubin, admitted to Nizhnevolzhsky region. Field studies and observations were carried out according to generally accepted methods. The statistical analysis of the obtained results was carried out according to the B. A. Dospekhov method. Results. In the course of the study, it was found that the primary criteria in the formation of common bean variety Rubin high yields are the water regime, mineral nutrition and the biological potential of leguminous plants. Pre-sowing inoculation with various biofertilizers had a positive effect on the formation of the yield of the studied crop and, accordingly, on the crop structure elements. This was especially evident in the variants with the application of Azotovit with irrigation regimes of 65-70-65 and 70-75-70 % of the minimum moisture capacity (MC). The crop yield was 4.2–5.4 t/ha, which differed significantly from the control variant. Conclusions. Based on the results of the study, it should be noted that the maximum yield options differed by the maximum number of beans per plant (from 19.0 to 25.6 pcs.), the number of seeds 45.2–63.0 pcs., and also the weight 1000 grains from 352.0 to 364.0 g. The increase relative to the control without treatments was from +2.2 to +3.1 t/ha, depending on the irrigation regime of the studied crop. The most significant profit, as well as the level of profitability, was obtained in two options with irrigation regimes of 65-70-65 and 70-75-70 % MC when inoculating bean seeds with Azotovit.

The deployment of Blockchain technology in the tourism industry is already becoming a reality with the gradual emergence of innovative business models. At its core is the promise of improving the efficiency of the tourism service value chain and enhancing the quality of the service provided to the end customer. This paper analyses research trends focused on using Blockchain technology in tourism. The aim is to determine how this technology impacts the tourism sector and its sustainability. A systematic review, descriptive bibliometric analysis, and network analysis based on co-authorship, co-citation, and keyword analysis criteria, among others, have been used. The results reveal that the subject matter analysed is generating a growing trend in academic research in the fields of sustainable management and supply chain efficiency. The activities in the tourism sector that are incorporating this technology to a greater extent are those related to the areas of marketing, logistics, and smart business models, according to the data extracted from the analysis. This technology already enables the application of solutions that predict and promote tourist behaviour based on sustainable behaviour and consumption habits, generating value for the different stakeholders.

Abstract Introduction Posttraumatic swelling causes a delay in surgery, a prolonged hospital stay and a higher risk of complications. Thus, soft tissue conditioning following complex ankle fractures is of central importance in their perioperative management. Since the clinical benefit of VIT usage on the clinical course has been shown, it should now be investigated whether it is also cost-efficient in doing so. Materials and methods Included are published clinical results of the prospective, randomised, controlled, monocentric VIT study that have proven the therapeutic benefit in complex ankle fractures. Participants were allocated in a 1:1 ratio into the intervention group (VIT) and the control group (elevation). In this study, the required economic parameters of these clinical cases were collected on the data of the financial accounting and an estimation of annual cases had been performed to extrapolate the cost-efficiency of this therapy. The primary endpoint was the mean savings (in €). Results Thirty-nine cases were studied in the period from 2016 to 2018. There was no difference in the generated revenue. However, due to lower incurred costs in the intervention group, there were potential savings of about €2000 (p ITT = 0.073) to 3000 (p AT = 0.008) per patient compared to the control group with therapy costs decreasing as the number of patients treated increases from €1400 in one case to below €200 per patient in 10 cases. There were 20% more revision surgeries in the control group or 50 min more OR time, respectively, and an increased attendance by staff and medical personnel of more than 7 h. Conclusions VIT therapy has been shown to be a beneficial therapeutic modality, but it is so not only in regard to soft-tissue conditioning but also cost efficiency.

Abstract Biodiversity surveys may require the use of multiple types of sampling gear to maximize the efficiency of species detections, yet few studies have investigated how to optimally distribute effort among gear. In this study, we conducted eDNA metabarcoding and capture‐based sampling surveys (electrofishing, fyke netting, gillnetting, and seining) to sample fish species richness in a large northern temperate lake. We evaluated the success of the sampling methods individually and in combination to determine the allocation of effort and cost across sampling gear that provides the optimal approach for lake‐wide species inventories. We found that eDNA metabarcoding detected more species than any other sampling method, including 11 species that were not detected with any capture‐based approach. Optimal gear combination analyses revealed that detected species richness is maximized when most of the effort or budget is allocated to eDNA metabarcoding, with smaller allocations to seining and fyke netting. eDNA metabarcoding and capture sampling gear showed similar patterns of spatial heterogeneity in the fish community across habitat types, with pelagic samples forming a group that was distinct from nearshore samples. Our results indicate that eDNA metabarcoding is a rapid and cost‐efficient tool for biodiversity monitoring and that assessing the complementarity of multiple sampling types can inform the development of optimal approaches for measuring fish species richness.

This study examines the relationship between cost efficiency and privatization at 163 public research institutions in the United States between 2005 and 2015. We employ a spatial autoregressive (SAR) random-effects model and stochastic frontier analysis (SFA) to estimate the relationship between costs and four privatization variables: auxiliary enterprises as a percentage of total revenue, tuition and fees as a percentage of total revenue, private grants/contracts as a percentage of total revenue, and out-of-state first year enrollment. Results showed cost inefficiency at public research universities increased between 2005 and 2015, even as reliance on private sources of revenue increased. Public research universities exhibit 28.5% overall cost inefficiency over the time period studied, 85.6% of which is short-run cost inefficiency. This suggests that most of the cost inefficiency varies across years and may be the result of challenges that institutional leaders face adapting to short-term fluctuations in market-oriented sources of revenue. The results also show a nonlinear relationship between cost inefficiency and three of the privatization variables. Given the expectation of little to no increase in state support for public research universities, this study has implications for policy, institutional management, and future research. [ABSTRACT FROM AUTHOR]

Central government aims to stimulate the efficiency and technical change of public organizations. However, government primarily focuses on the institutions that deliver final public services, but not on the policy making institutions. This article analyses the productivity of central government departments (CGDs). From bureaucratic theory we hypothesize that productivity of these CGDs are low. In order to measure efficiency and technical change we estimate an average cost function based on data of Dutch individual CGDs during the period 2012-2019. The dataset consists of data on various services provided, resource usage and efficiency determinants. The cost function is estimated by a mixed-effect non-linear least squares method. The outcomes show that there are large efficiency differences among CGDs. It is also striking that technical change of the CGDs is nonexistent over time, probably due to a lack of innovative behaviour, unwieldy bureaucracies and increasingly complex paperwork. [ABSTRACT FROM AUTHOR]

For big data applications, it is important to allocate resources reasonably and schedule tasks effectively. As one of the popular big data processing frameworks, the default scheduling strategy of Spark still suffers from low resource utilization and high resource cost. In this paper, a low-cost task scheduling algorithm for Spark based on heterogeneous cloud environment is proposed to minimize cost while improving resource utilization. First of all, we construct a cost model for Spark based on the hierarchical relationship among applications, jobs, stages, and tasks. Then, based on the model, a low-cost task scheduling algorithm is proposed, which improves the utilization of computational resources by adjusting the task parallelism and achieves task scheduling with priority based on the distribution of data to be computed. We also propose a Reduce task load balancing partitioning algorithm (RTLBPA) based on prior information of data by dispersing and aggregating different keys with the same hash value in the data into different partitions. This algorithm can achieve load balancing and improve the execution time of the job by reducing the whole Reduce phase. Finally, we performed extensive experiments on the proposed algorithm using Hibench's workloads in the cloud environment. The result shows that the cost can be reduced by at least 22.71% compared with the existing algorithm under different workloads. As a result, the proposed algorithm can improve the cost efficiency of Spark cluster effectively while improving resource utilization. [ABSTRACT FROM AUTHOR]

The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities. [ABSTRACT FROM AUTHOR]

Background The evidence-base for mass tuberculosis screening among persons with diabetes (PWD) is poor. We evaluated the yield and costs of mass screening among PWD in eastern China. Methods We included individuals with type 2 diabetes from 38 townships in Jiangsu Province. Screening comprised of physical examinations, symptom screening, and chest X-rays; smear and culture testing were performed through clinical triage. We assessed the yield and number needed to screen (NNS) to detect 1 tuberculosis case among all PWD, those with symptoms, and with suggestive chest X-rays. Unit costing was collected to estimate screening costs and to calculate cost per case detected. We performed a systematic review of other mass tuberculosis screening programs concentrated on PWD. Results Of 89 549 screened PWD, 160 were diagnosed with tuberculosis (179 cases per 100 000 persons; 95% confidence interval [CI]: 153–205). The NNS was 560 (95% CI: 513–606), 248 (95% CI: 217–279), and 36 (95% CI: 24–48) among all participants, with abnormal chest X-rays, and symptoms. The cost per case was high overall (US$13 930) but lower with symptoms (US$1037) and high fasting blood glucose levels (US$6807). From systematic review, the pooled NNS to detect one case among all PWD (regardless of symptoms or chest X-ray results) in high- versus low-burden settings was 93 (95% CI: 70–141) versus 395 (95% CI: 283–649). Conclusions A mass tuberculosis screening program focused on PWD was feasible however, the overall yield was low and not cost-efficient. Risk-stratified approaches may be practical among PWD in low- and medium tuberculosis burden settings. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the mechanical properties and cost efficiency of direct ink writing (DIW) printing of two different zirconia inks compared to casting and subtractive manufacturing. Zirconia disks were manufactured by DIW printing and the casting process and divided into six subgroups (n = 20) according to sintering temperatures (1350 °C, 1450 °C and 1550 °C) and two different ink compositions (Ink 1, Ink 2). A CAD/CAM-milled high strength zirconia (3Y-TZP) was added as reference group. The biaxial flexural strength (BFS) was measured using the piston-on-three-balls test. X-ray-diffraction (XRD) was used for microstructural analysis. The cost efficiency was compared for DIW printing and subtractive manufacturing by calculation of the manufacturing costs of one dental crown. Using XRD, monoclinic and tetragonal phases were detected for Ink 1, for all other groups no monoclinic phase was detected. The CAD/CAM-milled ceramic showed a significantly higher BFS than all other groups. The BFS of Ink 2 was significantly higher than the BFS of Ink 1. At a sintering temperature of 1550 °C the mean BFS of the printed Ink 2 was 822 ± 174 MPa. The BFS of the cast materials did not show a significantly higher BFS than the corresponding printed group for any tested parameter-set. The manufacturing costs of DIW printed crowns are lower than the manufacturing costs of CAD/CAM-milled crowns. DIW has a high potential to replace subtractive processes for dental applications, as it shows promising mechanical properties for appropriate ink compositions and facilitates a highly cost effective production. [ABSTRACT FROM AUTHOR]

In recent years, competition in the banking sector has increased, and even banks today have competition from non-banking institutions. Given that banks are the main intermediaries for the channeling of savings and investments, this situation presents pressure on the banking sector. Due to competition in the banking sector, banks are trying to find ways to increase their efficiency and take advantage of economies of scale and scope. The purpose of this study was to examine the cost efficiency of the banking sector in Kosovo. Data from eight commercial banks during the period 2014-2020 (quarterly financial statements) are analyzed to find out which specific variables of the banking sector affect cost efficiency. Our first stage of methodology was to find cost efficiency using the traditional model of Data Envelopment Analysis. In a second stage, we use the multiple regression model to find the impact of determinants in the cost-effectiveness. We included several variables such as the return on assets, capital adequacy, size of the bank, bank liquidity, loan loss provision coverage ratio, return on equity, and loans to asset ratio. Our results show that cost effectiveness was mainly explained by profitability (ROA), size of the bank, loan loss provision coverage ratio, and Return on Equity (ROE). Meanwhile, capital over total assets, total loans to total assets, and loans to deposits do not have any impact on cost efficiency. [ABSTRACT FROM AUTHOR]

To evaluate the performance and estimate the efficiency of Decision-Making Units (DMUs) in Data Envelopment Analysis (DEA), the available data are used. These data are usually divided into two categories of inputs and outputs based on their natures. If the price data is also available for inputs, it is necessary to calculate another type of the efficiency called Cost Efficiency (CE). Since the efficiency of units in such a framework is depended on the both quantities of inputs and outputs and also the prices of inputs, it is important to find the sources of cost inefficiency related to each of the factors and plans to address them. In this paper, we intend to present a new decomposition of CE and observed cost versus optimal cost, which are raised from each of the factors involved in the cost inefficiency, in a non-competitive pricing environment which the input price vector for different DMUs can be different. Moreover, for the first time, in parallel with using the PPS based on input and output quantities and introducing some cost inefficiency factors related to this set, we will introduce new sets called price and cost production sets that the first is based on the prices of inputs and output factors, and the second is based on the optimal vectors of inputs and prices obtained from two previous PPS, and then we will introduce other factors of cost inefficiency in the sets. Accordingly, new decomposition for cost inefficiencies will be presented. Also, in the previous analyzes, congestion inefficiency has not been considered as one of the important factors in cost inefficiency. In this study, we also intend to consider the impact of this factor on CE. [ABSTRACT FROM AUTHOR]

Improving cost efficiency is one of the main ways to reduce the production cost of agricultural products and increase farmers' income. Human capital is an important way to improve cost efficiency. This article takes five major corn production areas as an example, uses data envelopment analysis to measure the cost efficiency and makes an empirical analysis of the impact of human capital on cost efficiency and its action path by using bidirectional fixed-effect model. It is found that the impact of educational human capital on cost efficiency presents a positive ‘U' shape, and there are differences among different regions through technical efficiency and allocation efficiency paths. Healthy human capital has a significant positive impact on cost efficiency, but with the increase of the number, its role will gradually weaken, except for the southwest region, and its impact path is allocation efficiency. The impact of technical human capital on cost efficiency shows a positive ‘U’ type change, and the change of each production area is roughly the same as the whole. Its impact on cost efficiency is mainly realised through allocation efficiency. This article provides a theoretical basis for different policy measures to better play the role of human capital in improving cost efficiency in China.

The purpose of this study was to examine the effect of corporate strategy on financial performance with capital structure as an intervening variable. The advantage of this research lies in taking the object of the company in the agroindustry sector in the Covid-19 pandemic situation which has not been the concern of researchers. The population is all agroindustry companies, listed in Indonesian Stock Exchange. Data collection technique using purposive sampling method and a total of 52 sample data were obtained with a time span of 2020-2021 which were processed using the multiple regression method. The results show that there is an effect of corporate strategy on capital structure, as well as capital structure that affects financial performance. However, the opposite result is shown by the non-influence of corporate strategy variables on financial performance. Thus, the mediating role of capital structure in the relationship between corporate strategy and financial performance has been clearly proven. The results of this study are able to provide future research recommendations in the form of using variations of company strategies that can have different implications, especially for companies in the agroindustry sector during the covid-19 pandemic. In addition, this study also recommends companies to manage their capital structure optimally to generate value to financial performance. This research will also contribute on the fast growth of literature related to corporate strategy, capital structure, and financial performance as well as the development of agency theory, signal theory and pecking order theory.

This research aims to examine the impact of implementing a Human Resources Information System (HRIS) on an organization’s performance. Using data collected from 14 interviewees who are managers, executives, and engineers at the Directorate of Personnel Management, Compensation, and HR systems in the Company of Sonelgaz. We find that the implementation of HRIS in an organization does have an impact and a positive one on the organization’s performance in terms of HR processes, time efficiency, information effectiveness, and decision-making effectiveness. However, we also find that HRIS implementation does not have any impact on the cost efficiency within the organization. Theoretical and practical implications were discussed.

The deployment of Blockchain technology in the tourism industry is already becoming a reality with the gradual emergence of innovative business models. At its core is the promise of improving the efficiency of the tourism service value chain and enhancing the quality of the service provided to the end customer. This paper analyses research trends focused on using Blockchain technology in tourism. The aim is to determine how this technology impacts the tourism sector and its sustainability. A systematic review, descriptive bibliometric analysis, and network analysis based on co-authorship, co-citation, and keyword analysis criteria, among others, have been used. The results reveal that the subject matter analysed is generating a growing trend in academic research in the fields of sustainable management and supply chain efficiency. The activities in the tourism sector that are incorporating this technology to a greater extent are those related to the areas of marketing, logistics, and smart business models, according to the data extracted from the analysis. This technology already enables the application of solutions that predict and promote tourist behaviour based on sustainable behaviour and consumption habits, generating value for the different stakeholders. [ABSTRACT FROM AUTHOR]

PT Manunggal Jaya Makmur is one of the companies engaged in the transportation and contracting-based sector that provides integrated services from the start point to the end. In addition, it is also engaged in the repair of heavy equipment such as mixer trucks, dump trucks, colt diesel, and repair of other heavy equipment. In supporting the company's activities, PT. Manunggal Jaya Makmur implements a strategy for supplying raw materials and spare parts. In its operational activities, PT. Manunggal Jaya Makmur is still constrained by the overstock problem which ultimately causes a buildup of goods. In this study, material classification was carried out using ABC Theory analysis. Then the methods used in planning and controlling inventory management are the POQ and EOQ methods. After getting the results, the methods used during the research will be compared with the methods that have been applied so far in the company to find out the most effective methods to be applied in cost efficiency and work productivity and can maximize the profits received by the company. Based on the analysis carried out, the cost of calculating the total inventory of spare parts using the EOQ method was Rp. 205,513,000, the LFL method was Rp. 274,141,349 and the POQ method was Rp. 273,502,120. So, from these data, it can be concluded that the most optimal total cost of inventory is the EOQ method to produce greater profits and minimize overstock. [ABSTRACT FROM AUTHOR]

This paper uses accounting concepts—particularly the concept of Return on Investment (ROI)—to reveal the quantitative value of scientific research pertaining to a major US cyberinfrastructure project (XSEDE—the eXtreme Science and Engineering Discovery Environment). XSEDE provides operational and support services for advanced information technology systems, cloud systems, and supercomputers supporting non-classified US research, with an average budget for XSEDE of US$20M+ per year over the period studied (2014–2021). To assess the financial effectiveness of these services, we calculated a proxy for ROI, and converted quantitative measures of XSEDE service delivery into financial values using costs for service from the US marketplace. We calculated two estimates of ROI: a Conservative Estimate, functioning as a lower bound and using publicly available data for a lower valuation of XSEDE services; and a Best Available Estimate, functioning as a more accurate estimate, but using some unpublished valuation data. Using the largest dataset assembled for analysis of ROI for a cyberinfrastructure project, we found a Conservative Estimate of ROI of 1.87, and a Best Available Estimate of ROI of 3.24. Through accounting methods, we show that XSEDE services offer excellent value to the US government, that the services offered uniquely by XSEDE (that is, not otherwise available for purchase) were the most valuable to the facilitation of US research activities, and that accounting-based concepts hold great value for understanding the mechanisms of scientific research generally. [ABSTRACT FROM AUTHOR]

The econometric analysis of cost functions is based on the analysis of the conditional distribution of the cost Y given the level of the outputs X ∈ R + p and given a set of environmental variables Z ∈ R d . The model basically describes the conditional distribution of Y given X ≥ x and Z = z . In many applications, the dimension of Z is naturally large and a fully nonparametric specification of the model is limited by the curse of the dimensionality. Most of the approaches so far are based on two-stage estimations when the frontier level does not depend on the value of Z. But even in the case of separability of the frontier, the estimation procedure suffers from several problems, mainly due to the inherent bias of the estimated efficiency scores and the poor rates of convergence of the frontier estimates. In this paper we suggest an alternative semi-parametric model which avoids the drawbacks of the two-stage methods. It is based on a class of model called the Proportional Incremental Cost Functions (PICF), adapted to our setup from the Cox proportional hazard models extensively used in survival analysis for durations models. We define the PICF model, then we examine its properties and propose a semi-parametric estimation. By this way of modeling, we avoid the first stage nonparametric estimation of the frontier and avoid the curse of dimensionality keeping the parametric n rates of convergence for the parameters of interest. We are also able to derive n -consistent estimator of the conditional order-m robust frontiers (which, by contrast to the full frontier, may depend on Z) and we prove the Gaussian asymptotic properties of the resulting estimators. We illustrate the flexibility and the power of the procedure by some simulated examples and also with some real data sets. [ABSTRACT FROM AUTHOR]

There is a gap in the literature related to understanding the relationship between outsourcing of accounting functions and financial performance (productivity and cost efficiency) of SMEs. This study examines the relationship between accounting outsourcing and financial performance in terms of cost efficiency and profitability in SMEs operating in Greater Sydney, Australia. An online survey of employees and managers (N = 80) of Australian SMEs using snowball sampling assessed the demographics, accounting outsourcing, organisational profitability, and cost-effectiveness of these SMEs. Data were analysed using linear regression and correlation analyses. The empirical analysis revealed that the outsourcing of tax services was related to a better financial performance of SMEs in terms of both cost efficiency and profitability. The outsourcing of management accounting functions was beneficial to the productivity of SMEs, whereas the outsourcing of general accounting was not related to higher productivity. Collectively, the findings of this study will assist SMEs in identifying resource gaps and the reasons for turning to external accountancy services to close the gaps and improve their financial performance. [ABSTRACT FROM AUTHOR]

The integration of remote sensing data in agricultural statistics is a research topic with a long history. The research focus is on using statistical models to link ground and remote sensing data such that the resulting estimators are design-consistent. A design-consistent estimator assisted by linear models is well established in the literature. However, it requires enough geographic information about the boundaries of agricultural parcels to develop a simple sample of areas. Many countries use complex samples based on non-georeferenced list frames of households or farms and reduce to point data the georeferenced information required for linking ground and remote sensing data. Data on crop acreage observed at a point are necessarily categorical because a point is dimensionless. Little work has been done on the integration of categorical ground data within complex list samples using remote sensing data. Our focus was on using multinomial logit models for this integration. Special attention was paid to evaluate the cost efficiency of remote sensing data. [ABSTRACT FROM AUTHOR]

Tyres are an expensive input into the cost of a bus business, yet very little is known about what impact an improvement in the performance of tyres, linked in part to improved maintenance practices, might have not only in reducing the costs of tyres but its impact on fuel consumption and overall maintenance costs. This paper uses data collected over a 12 month period from a bus operator in Sydney where we fitted sensors on all tyres of 35 buses monitored on a monthly data capture plan, which together with data obtained from a telematics system installed in each bus and other data describing the operating environment, enabled us to develop a system of equations to identify endogenous and exogenous influences on tread depth loss of each tyre and fuel consumption, and to map this into implications on maintenance costs. The estimated model system is built into a decision support system to investigate ways in which a bus business can improve its cost efficiency through a changed tyre-related maintenance and operating regime. The findings suggest significant opportunities to improve cost efficiency by a careful review of the decisions related to the practices associated with tyres. [ABSTRACT FROM AUTHOR]

An estimated 30% of the North American piping plover (Charadrius melodus) breeding population occurs in Canada, where it is Endangered. Predator exclosures are a common management tool across the species' range to increase nest success. Using data from 1998 to 2010 on 820 nests, collected as part of an ongoing management program in Alberta, we compare daily nest survival (DNS), numbers of chicks hatched and fledglings produced, and cost/chick among three treatments (large, medium, small exclosures) and natural nests. During the early period (1998–2001), when all three types of exclosures were applied, there were no significant differences in DNS between exclosed nests and natural nests. During the late period (2002–2010), when only small exclosures were applied, nests with small exclosures had a significantly higher DNS rate (x¯ = 0.994, n = 594) compared with natural nests (x¯ = 0.984, n = 88). Nests with small exclosures also hatched more chicks (x¯ = 3.21, n = 598) and produced more fledglings (x¯ = 1.17, n = 337) than natural nests (x¯ = 1.73 chicks, n = 31; x¯ = 0.59 fledglings, n = 21) during the late period. However, considering only successful nests, the differences were not significant for either period, indicating no added benefit of exclosures beyond protecting the nest. The cost/chick rate was lowest using small exclosures (cylindrical, 40 by 60 cm), and this portable design is well‐suited for widespread application in the field. We demonstrate increased DNS for nests with small exclosures and a mean fledging rate close to the goal of 1.25 chicks/nest/year, although this did not increase Alberta's piping plover population beyond the timeframe of systematic exclosure application. Long‐term, cost‐efficient management tools that increase nest success and fledging rates should continue. [ABSTRACT FROM AUTHOR]

The wood industry faces challenges due to rising prices and limited wood availability, putting pressure on material efficiency in wood processing. This justifies the analysis of the relationship between efficiency and economy in pine wood processing. The study aimed to measure the impact of variations in the thickness of logs, changes in the technology of their further processing, and changes in prices of raw materials and products on the material efficiency in the context of large-scale production of furniture elements made of pinewood. The raw material input consisted of three categories of log sizes, from which the specialized purpose lumber was produced. The lumber was then processed into semi-finished furniture elements with three technologies: without detecting natural wood defects, with human detection, and with automatic detection. The study was conducted in Poland from 2020 to 2022. The material efficiencies in every stage of the analyzed wood processing and the cost efficiencies were calculated and analyzed based on the results obtained under real industrial conditions. The main findings are as follows: (1) when comparing the logs in the three tested diameter ranges (14–23 cm, 23–30 cm, and more than 30 cm), it can be observed that the overall material efficiency of sawing is in the range of 70%–85% and increases with the thickness of the log; (2) the share of 38 mm specialized sawn timber in the total amount of sawn timber was 41%–58% and increased with increasing log diameter; (3) the economic efficiency of the technological process is 170%–290%, based on the log size and the technology of further processing employed. The determining factor affecting cost efficiencies is unexpected changes in raw material prices and product demand in 2022. The findings suggest that while improvements in processing technology can boost efficiency, they cannot fully offset the rise in raw wood material prices. [ABSTRACT FROM AUTHOR]

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Considering the real dilemma of deteriorating items, we explore how to find a balance between the conflicting achievements of enhancing flexibility and reducing costs. We propose a general dynamic model to penetrate customer orders by decoupling point with objective to minimize the cost. The closed forms of optimal solutions are obtained for companies to deal with the integrated problem of customer order decoupling point (CODP) decision and production–inventory plan simultaneously. Applications and numerical experiments are performed to illustrate practical insights for managers. The results show that the truth of zero-inventory policy is to avoid unnecessary inventory instead of absolutely no inventory. The CODP will shift forward with the increasing of customer demand rate and shift backward with the increasing of deterioration rate under the production smoothing policy. However, although it encounters the same changes of demand, the CODP moves with a smaller extent in high deterioration rate than in low. In addition, we find that during the growth phase of product life cycle, the increase of time-sensitive degree of demand is coupled with forward shifting of CODP; while during the decline phase, it is coupled with CODP backward shifting. [ABSTRACT FROM AUTHOR]

The paper has examined the efficiency of growing rice under different cultivation systems in Mandya district of Karnataka, using the data collected from 30 farmers under each of the cultivation systems namely, conventional, SRI (System of Rice Intensification), aerobic and DSR (Drum Seeded Rice) method. In total, the sample size was 120 rice growing farmers. The data was analysed using stochastic frontier cost function to estimate the cost efficiency of the farmers and to examine the factors influencing cost inefficiency. The results revealed that cost of seeds, fertilizers, human labour and machine labour had significant influence on cost of production. The variables such as age (0.239), landholding (0.022) and experience (0.215) in farming influenced cost inefficiency in conventional rice cultivation. Membership in organization (-0.075) and access to extension services (-0.289) were significant in explaining cost efficiency in SRI method. Education (-0.310) was the major factor which significantly contributed to the cost efficiency of aerobic farmers. Whereas, in DSR method age (-0.200) and access to extension services (0.083) were the significant factors. The mean cost or allocative efficiency score in aerobic was 0.94, which was highest among all the systems followed by SRI, DSR and conventional method with scores of 0.92, 0.88 and 0.55, respectively. [ABSTRACT FROM AUTHOR]

China's banking system has a relatively high level of state control, while an important task in regulating the banking system is to manage the profitability of banks. Using the stochastic frontier approach to assess the profitability of commercial banks not only allows for the bank's ability to generate profits relative to the leading banks in the industry to be assessed but also takes into account the specifics of the management technologies used and the influence of the market environment. This article analyzes the profitability of the Chinese banking system for the period 2012–2020 using the stochastic frontier approach from the position of the central bank. The specifics of the analysis from the bank's perspective imply a focus on the position of most banks regarding the level of best practices and trends in changing the overall level of profitability. Analysis may be of interest to banking regulators and researchers. In general, the Chinese banking system demonstrates a high level of profit efficiency and cost efficiency, although the dynamics of these indicators are negative. The reason for the negative dynamics is a decrease in the economic growth rate of the economy, the instability of the financial market and ongoing reforms. State-owned commercial banks are becoming highly profitable, while national joint-stock commercial banks are facing increasing competition and reducing efficiency of profitability. City and rural commercial banks maintain a high level of profitability due to state support. [ABSTRACT FROM AUTHOR]

Microfinance is a banking market in which operating costs are high, while non‐performing loans (NPLs) rates are low. While the existing literature tends to explain that the high operating costs arise from the provision of small loans, we argue that excessive efforts to control loan losses can also be a contributing factor. Therefore, this article investigates the relationship between NPLs and the cost efficiency of microfinance institutions (MFIs). Using a unique global sample of rated MFIs and applying stochastic frontier analysis together with Granger‐causality test and generalized method of moments (GMM), we find, in contrast to positive linear relationship evidence in commercial banking studies, a nonlinear (U‐shape) relationship between operating costs and NPLs. This implies that MFIs need to balance their cost efficiency with asset quality. [ABSTRACT FROM AUTHOR]

Higher education institutions train professional and scientific personnel. Therefore, the quality of higher education and its funding are vital for training highly qualified specialists. This study analyzes the annual volume of expenses (investments) per student in groups of countries, divided according to their socio-economic development, and competitiveness of higher education. The division of countries into groups is based on simultaneous compliance with the criteria for the quality of higher education and the level of social and economic development. The Ward’s clustering method was applied. The analysis was conducted based on data from 32 OECD countries and partner countries. The paper found a significant direct correlation between the level of competitiveness of higher education and the amount of its funding per student (R = 0.895). At the same time, a significant direct correlation was revealed between the level of competitiveness of higher education and the human development index (R = 0.787) and the global competitiveness index (R = 0.888). Finally, a significant direct correlation between the amount of expenditures and the level of competitiveness of higher education was found only in the cluster with the highest indicators of socio-economic development (Rs = 0.707). In other clusters, the correlation is weak or weakly inverse.

Objective: To evaluate the achievement degree of an organization to its goals, its productivity must be measured. Since an organization is composed of different groups, the productivity of its groups should be examined. This paper aims to introduce a suitable index to evaluate group cost productivity changes and identify factors affecting it. To explain the applicability of the proposed index, a study on the productivity evaluation of a bank's branches in different regions is presented. Methods: A suitable distance function is created to measure the cost efficiency of a group, and based on that, the group cost Malmquist index is introduced. Then, the influencing factors on the productivity growth of the groups are examined by focusing on intra-group and extra-group sections. Results: An indicator was presented to evaluate the cost productivity changes of a group of decision-making units and the factors that affect the cost productivity changes of the groups were identified. The proposed group Malmquist index breaks down into four components: Pure efficiency changes, scale efficiency changes, allocative efficiency changes, and cost technological changes. The pure efficiency change measures the optimal use of inputs to produce output indicators. The scale efficiency changes reflect the effect of changes in the size of branches of a region on its productivity growth. The allocative efficiency changes indicate the achieved changes in the optimal combinations of inputs considering the prices of each period. The cost technological changes component reflects changes in cost technology frontiers during two periods. All of these components were examined from two perspectives: intra-group and extra-group. In the intra-group perspective, the internal group frontier is considered, and in the extra-group perspective, the common frontier of all groups is focused. To explain the applicability of the proposed index and calculate the impact of its components on productivity growth, a real case study was presented. This case study evaluated the cost productivity changes of a bank's branches in eight different regions. The results showed that three regions have cost productivity growth. One area has almost no changes in cost productivity, and the other regions have productivity regressions. The results of measuring the group cost Malmquist productivity index on bank data provide meaningful and useful information about bank productivity changes in different areas. Conclusion: Most organizations consist of different groups and departments. Sometimes, it is necessary to examine a group of decision-making units instead of evaluating the performance of several decision-making units to analyze the role of environmental conditions on the performance assessment of the decision-making units. The results are shown that the proposed group cost Malmquist productivity index provides a suitable tool for evaluating the cost productivity changes of groups. Also, it prepares a clear perspective for managers of organizations for future policies by identifying the intra-group and extra-group factors affecting group cost productivity changes.

Many banks are competing to achieve bank cost efficiency for business continuity amidst the competition in the banking sector. So banks need to review cost-efficiency regularly. The purpose of this study was to analyze determinants of bank cost efficiency. Variable use bank size, capital adequacy, return on asset, group of the bank, credit risk, economic growth, and inflation on bank cost efficiency in Indonesia. Determination of bank input and output variables using a bank intermediation approach. This study used panel data on 38 banks in Indonesia for the period 2012-2018. This paper used the Stochastic Frontier Analysis (SFA) analysis method. The results of this study reveal four things. First, bank size has a negative effect bank cost efficiency. Second, credit risk has a positive effect on bank cost efficiency. Third, capital adequacy has a positive effect on bank efficiency. Fourth, the small core capital bank group has a negative effect on bank cost efficiency. Bank external variables do not affect bank cost efficiency in Indonesia.

• We use text mining and entropy method to measure bank digital transformation index. • Digital transformation significantly improves the cost efficiency of banks. • Digital transformation improves banks' cost efficiency through four mediating channels. • Digital transformation has heterogeneous impacts on different types of banks. This study explores the impact of digital transformation on the cost efficiency of commercial banks. Using panel data from 131 banks in China from 2013 to 2022, and employing text mining and entropy method to measure bank digital transformation index, we find that digital transformation significantly improves banks' cost efficiency. Digital transformation exerts its influence on banks' cost efficiency through four intermediary channels: facilitating business diversification, diminishing information asymmetry, enhancing fund utilization efficiency, and reducing risk. Moreover, heterogeneity analysis reveals that the impact of digital transformation on cost efficiency varies by bank size, capital status, asset structure, and ownership structure. [ABSTRACT FROM AUTHOR]