Efficiency Performance Measures Research Articles

Reduced left-without-being-seen rates and impact on disparities after guest services ambassadors implementation.

The rate of patients who leave without being seen (LWBS) from an emergency department (ED) is a common measurement of quality, operational efficiency, and patient satisfaction. We hypothesized that adding a nonclinical staff role, guest service ambassadors (GSA), to the ED waiting room would decrease LWBS rates and reduce existing differences by race, ethnicity, sex, and primary language for ED patients. We conducted an observational cohort study at a quaternary care academic ED in the Midwestern United States with approximately 60,000 annual visits between April and December 2022. GSAs were trained to guide patients and visitors through the check-in process and help manage the waiting room. LWBS rates were compared between pre- and postimplementation periods using logistic regression. Using two-sample proportion tests, subgroup analyses were performed to assess differences according to race, ethnicity, sex, and primary language. We analyzed 50,507 ED visits including 9798 during periods of GSA coverage. GSA presence was associated with a reduction in LWBS rate from 3.4% to 2.0% (absolute risk reduction [ARR] of 1.4%, χ2 = 17.357, p < 0.001) with an adjusted odds ratio (OR) of 0.65 (95% confidence interval [CI] 0.49-0.85). There was a greater reduction in LWBS for Black, Indigenous, and people of color (BIPOC) patients compared to White patients (BIPOC ARR 1.8%, 95% CI 0.39%-3.14%; White ARR 1.2%, 95% CI 0.48%-1.94%). There was a reduction in LBWS rates for both males and females (female ARR 1.7%, 95% CI 0.80%-2.63%; male ARR 1.0%, 95% CI 0.06%-1.90%). The reduction in LWBS for patients speaking a language other than English and requiring interpreter services did not meet statistical significance (ARR 1.4%, 95% CI -1.04% to 3.85%). Although some disparities remain, our study suggests that GSAs may provide an effective strategy to reduce the overall LWBS rate and reduce disparities across diverse demographic groups including BIPOC and female patients.

The Role of Internal Audit in Enhancing Performance: A Case Study of Mirwais Neeka Institute of Higher Education, Kandahar, Afghanistan

The Internal Audit is a systematic process of analyzing risk, its likelihood and impact and taking necessary steps to avoid and control risks by its various control measures particularly in educational institutions. The prime objective of this research is to study and explore the specific impact of the internal audit function on the performance of Mirwais Neeka Institute of Higher Education in Kandahar. This study investigates how the internal audit function at Mirwais Neeka Institute contributes to the institution’s overall performance by examining audit processes, stakeholder perceptions, and tangible outcomes. The population for this study consists of administrators, faculty, students, and relevant staff of Mirwais Neeka Institute of Higher Education involved in or affected by internal audit processes and purposive sampling technique has been used for selecting representative sample of 40 respondents from the population. The analysis employs descriptive statistics, correlations, and Ordinary Least Squares Regression to determine the relationships between internal audit functions and organizational performance. The study concludes that internal audit functions are crucial in enhancing risk mitigation, operational efficiency, and performance measurement, thus significantly impacting the performance of Mirwais Neeka Institute. The research provides valuable insights for administrators and policymakers, emphasizing the need for robust internal audit practices to improve institutional performance. Future research could explore additional factors influencing organizational performance and delve deeper into the specific components of internal audit functions driving performance improvements.

Regulatory Fragmentation and Operational Efficiency

ABSTRACTThis study employs a fragmentation index derived from Federal Register data to explore the relationship between regulatory fragmentation and operational efficiency. I find a positive association between regulatory fragmentation and operational efficiency, validated through various robustness checks, including entropy balancing, instrumental variable methods, fixed effects at different levels, change specification models, and alternative measures of operational efficiency. Additional analyses indicate that this positive association is more pronounced in firms exhibiting internal control weaknesses and facing higher operational uncertainty. Overall, my results suggest that although regulatory fragmentation poses challenges, it also creates opportunities for firms to enhance their operation efficiency. This research sheds light on the multifaceted effects of regulatory environments, providing valuable insights for policymakers and corporate strategists in managing regulatory complexities.

Operational efficiency, agency costs and the mediating role of payout policy

ABSTRACT The association between a firm’s operational efficiency and its payout policy has received little attention in literature despite their common linkage to agency costs. Our findings suggest a significant linkage between the three. First, operationally efficient firms are associated with significantly higher agency conflicts of free cash flows. Second, operationally efficient firms exhibit a significantly greater propensity to pay dividends, thereby substantially reducing the associated agency costs compared to their non-dividend paying counterparts. This mediating role of dividends remains robust when considering different measures of operational efficiency and in a matched data. Using Data Envelopment Analysis (DEA) to obtain operational efficiency and Nearest Neighbour Matching (NMM) to get a matched sample, the findings imply that a firm’s operational and managerial decisions are interconnected in managing agency problems stemming from free cash flows.

A comparison of the usage between financial and non-financial performance metrics in Small and Medium Enterprises (SMEs) in South Africa

The purpose of this study is to explore the performance measurement approach of small and medium enterprises (SMEs) in the South Durban region of South Africa. It has long been known that small and medium enterprises (SMEs) play an important role in economic growth and income distribution in the world's major economies. However, due to globalisation, the fourth industrial revolution and increased competition from multinationals, South African SMEs face new challenges in measuring their performance. To respond to these challenges, SMEs must adopt efficient and effective performance measurement models. However, currently, according to the researchers, there is no such performance measurement framework. This study aims, among others, to close this gap in the literature. This study uses a deductive, positivist research design to comprehensively explore performance measurement practices in small and medium enterprises (SMEs) in a given region. Based on Morgan's sample size table, size 217 was chosen. The use of financial and non-financial performance measures is preferred by SMEs as it shows better performance compared to only one measure. In addition, the study found a positive correlation between business size and age, indicating that larger businesses tend to be older.The results of this study help to understand the complex relationship between job dimensions, age, and performance, highlighting the importance of choosing appropriate performance measures. Future research is recommended to gain a deeper understanding of these relationships and how they vary across contexts and sectors.

Design optimization of groundwater circulation well based on numerical simulation and machine learning

The optimal design of groundwater circulation wells (GCWs) is challenging. The key to purifying groundwater using this technique is its proficiency and productivity. However, traditional numerical simulation methods are limited by long modeling times, random optimization schemes, and optimization results that are not comprehensive. To address these issues, this study introduced an innovative approach for the optimal design of a GCW using machine learning methods. The FloPy package was used to create and implement the MODFLOW and MODPATH models. Subsequently, the formulated models were employed to calculate the characteristic indicators of the effectiveness of the GCW operation, including the radius of influence (R) and the ratio of particle recovery (Pr). A detailed collection of 3000 datasets, including measures of operational efficiency and key elements in machine learning, was meticulously compiled into documents through model execution. The optimization models were trained and evaluated using multiple linear regression (MLR), artificial neural networks (ANN), and support vector machines (SVM). The models produced by the three approaches exhibited notable correlations between anticipated outcomes and datasets. For the optimal design of circulating well parameters, machine learning methods not only improve the optimization speed, but also expand the scope of parameter optimization. Consequently, these models were applied to optimize the configuration of the GCW at a site in Xi’an. The optimal scheme for R (Q = 293.17 m3/d, a = 6.09 m, L = 7.28 m) and optimal scheme for Pr (Q = 300 m3/d, a = 3.64 m, L = 1 m) were obtained. The combination of numerical simulations and machine learning is an effective tool for optimizing and predicting the GCW remediation effect.

Analysis of the Operational Efficiency Measurement of Domestic Logistics Enterprises Against the Background of a Low-Carbon Economy

As an indispensable link between low-carbon economic activities, the green development of logistics enterprises determines their market competitiveness and industry development ability. In this paper, 80 domestic logistics enterprises identified in 2022 are selected as objects, and the three-stage data envelope analysis (DEA) method is used to construct the enterprise operation efficiency evaluation index system with the input-output data of 2017–2021. The results show that the overall operating efficiency of domestic logistics enterprises is not high. In 2017, the comprehensive technical efficiency and pure technical efficiency of effective companies reached their peaks, accounting for 22.5% and 35%, respectively. The scale efficiency of effective companies only accounted for 35%. The company's technology and management caliber are negatively impacted by external environmental conditions. There are redundancies in operating input and unreasonable allocations of operating resources. According to the analysis results, this paper puts forward some improvement suggestions for the sustainable development of domestic logistics enterprises from both the government and enterprises.

MCN portfolio: An efficient portfolio prediction and selection model using multiserial cascaded network with hybrid meta-heuristic optimization algorithm

ABSTRACT Generally, financial investments are necessary for portfolio management. However, the prediction of a portfolio becomes complicated in several processing techniques which may cause certain issues while predicting the portfolio. Moreover, the error analysis needs to be validated with efficient performance measures. To solve the problems of portfolio optimization, a new portfolio prediction framework is developed. Initially, a dataset is collected from the standard database which is accumulated with various companies’ portfolios. For forecasting the benefits of companies, a Multi-serial Cascaded Network (MCNet) is employed which constitutes of Autoencoder, 1D Convolutional Neural Network (1DCNN), and Recurrent Neural Network (RNN) is utilized. The prediction output for the different companies is stored using the developed MCNet model for further use. After predicting the benefits, the best company with the highest profit is selected by Integration of Artificial Rabbit and Hummingbird Algorithm (IARHA). The major contribution of our work is to increase the accuracy of prediction and to choose the optimal portfolio. The implementation is conducted in Python platform. The result analysis shows that the developed model achieves 0.89% and 0.56% regarding RMSE and MAE measures. Throughout the analysis, the experimentation of the developed model shows enriched performance.

Efficiency and Mergers and Acquisitions of Electric Utility Companies

Since the 1990s, market liberalization of the electricity industry has advanced all around the world. To survive in the drastically changing business environment, incumbent electric utility companies have conducted operational reforms, including Mergers and Acquisitions (M&amp;As), to enhance and/or complement existing business capabilities. The purpose of this study was to measure the operational efficiencies of 31 of the world’s largest electric utility companies using data from 2010 to 2020 and examine regional differences in and the impacts of M&amp;As on the efficiencies. For this purpose, we applied a new type of Data Envelopment Analysis (DEA) and Tobit model regression. We provide findings from the empirical analyses and discuss the business implications of M&amp;As for electric utility companies. The operational efficiency measures were different among regions, but did not show statistically significant changes over the study period from 2010 to 2020. Furthermore, the results of regression analyses indicate that the increasing number of M&amp;A buying transactions and M&amp;A total transactions has a negative marginal impact on the operational efficiency or leads to a lower operational efficiency for utility companies. Since electricity utility companies have not received gains in operational efficiency from increasing the number of M&amp;A transactions, they need to be more cautious about whether M&amp;A transactions can provide value to the operation and technology management.

Comparing Self-Report vs. Performance Measures of Attentional Control and Efficiency.

Background: The Attention Control Scale (ATTC) is a widely used self-report measure of attentional control capacities. However, research questions whether it accurately substitutes for objective attention control tasks. This study investigated ATTC's correlation with the Attention Network Test (ANT) across alerting, orienting, and executive control networks. We also used the Inverse Efficiency Score (IES) as an additional factor to check ATTC using ANT. Methods: We administered 143 participants who completed the ATTC questionnaire and ANT behavioral test assessing network efficiencies. Results: The results showed non-significant ATTC-ANT correlations across all networks. In an additional analysis, while the ATTC demonstrated factorial validity, subjective control was disconnected from actual attention regulation efficiency. A small male advantage emerged for executive control. Conclusions: Dissociations likely stem from attention complexity and method variances rather than overlap. The findings do not support the ATTC as a stand-alone proxy for performance-based measurement. Multifaceted assessments are essential for comprehensively capturing attentional control.

Asset Information Model Management-Based GIS/BIM Integration in Facility Management Contract

Achieving efficiency success status inside an organization’s built environment and obtaining a positive return on investments need robust and comprehensive asset management and maintenance processes based on the efficiency of contract information documents within the built asset lifecycle. This paper aims to highlight the appropriate interactive approach for construction projects to build the information flow scope of asset facility management contracts based on GIS (Geographical Information System) and BIM (Building Information Modeling) integration processes and sustainability standards, and project as-built contractual documents to support owners and stakeholders with the intent of improving asset management processes. Expert interviews and contract information flow types in several facility management processes conducted in both local and international facility management organizations were used to assist the information flow scope method. The study classified and built significant integrated information and data flow models for a case study to serve as contract guidelines, including efficiency performance measures and indicators for monitoring procedures, technical evaluation, and financial issues in order to provide high-performance service quality in facility management applications.

Efficient Cross-platform Multiplexing of Hardware Performance Counters via Adaptive Grouping

Collecting sufficient microarchitecture performance data is essential for performance evaluation and workload characterization. There are many events to be monitored in a modern processor while only a few hardware performance monitoring counters (PMCs) can be used, so multiplexing is commonly adopted. However, inefficiency commonly exists in state-of-the-art profiling tools when grouping events for multiplexing PMCs. It has the risk of inaccurate measurement and misleading analysis. Commercial tools can leverage PMCs, but they are closed source and only support their specified platforms. To this end, we propose an approach for efficient cross-platform microarchitecture performance measurement via adaptive grouping, aiming to improve the metrics’ sampling ratios. The approach generates event groups based on the number of available PMCs detected on arbitrary machines while avoiding the scheduling pitfall of Linux perf_event subsystem. We evaluate our approach with SPEC CPU 2017 on four mainstream x86-64 and AArch64 processors and conduct comparative analyses of efficiency with two other state-of-the-art tools, LIKWID and ARM Top-down Tool. The experimental results indicate that our approach gains around 50% improvement in the average sampling ratio of metrics without compromising the correctness and reliability.

The impact of the war on banking in Ukraine

The article is devoted to the study of the operation of the banking system of Ukraine during the war period. The key indicators of the banking sector have been analyzed, in particular, the number of banking institutions and the dynamics of assets and liabilities. The key financial results of the banking sector, namely net profit and loss, return on assets and capital have been analyzed. It is revealed that maintenance of operational efficiency and effective measures taken by the National Bank of Ukraine allowed the banking sector to adapt to the crisis conditions and make a profit under martial law.

Comparison of diagnosis-based risk adjustment methods for episode-based costs to apply in efficiency measurement

BackgroundThe recent rising health spending intrigued efficiency and cost-based performance measures. However, mortality risk adjustment methods are still under consideration in cost estimation, though methods specific to cost estimate have been developed. Therefore, we aimed to compare the performance of diagnosis-based risk adjustment methods based on the episode-based cost to utilize in efficiency measurement.MethodsWe used the Health Insurance Review and Assessment Service–National Patient Sample as the data source. A separate linear regression model was constructed within each Major Diagnostic Category (MDC). Individual models included explanatory (demographics, insurance type, institutional type, Adjacent Diagnosis Related Group [ADRG], diagnosis-based risk adjustment methods) and response variables (episode-based costs). The following risk adjustment methods were used: Refined Diagnosis Related Group (RDRG), Charlson Comorbidity Index (CCI), National Health Insurance Service Hierarchical Condition Categories (NHIS-HCC), and Department of Health and Human Service-HCC (HHS-HCC). The model accuracy was compared using R-squared (R2), mean absolute error, and predictive ratio. For external validity, we used the 2017 dataset.ResultsThe model including RDRG improved the mean adjusted R2 from 40.8% to 45.8% compared to the adjacent DRG. RDRG was inferior to both HCCs (RDRG adjusted R2 45.8%, NHIS-HCC adjusted R2 46.3%, HHS-HCC adjusted R2 45.9%) but superior to CCI (adjusted R2 42.7%). Model performance varied depending on the MDC groups. While both HCCs had the highest explanatory power in 12 MDCs, including MDC P (Newborns), RDRG showed the highest adjusted R2 in 6 MDCs, such as MDC O (pregnancy, childbirth, and puerperium). The overall mean absolute errors were the lowest in the model with RDRG ($1,099). The predictive ratios showed similar patterns among the models regardless of the subgroups according to age, sex, insurance type, institutional type, and the upper and lower 10th percentiles of actual costs. External validity also showed a similar pattern in the model performance.ConclusionsOur research showed that either NHIS-HCC or HHS-HCC can be useful in adjusting comorbidities for episode-based costs in the process of efficiency measurement.

Covenant violation and operational efficiency

AbstractWe examine the impact of covenant violation on corporate operational efficiency. Using an aggregate measure of operational efficiency developed by Demerjian et al. (Management Science, 58, 2012, 1229–1248), we provide strong empirical evidence that covenant violations hinder firms from achieving operational efficiency. Our finding is robust to alternative definitions of operational efficiency and various model specifications to address potential endogeneity issues. Further analyses show that lower operational efficiency is attributable to covenant‐violating firms' under‐investments in capital and labour. In addition, the negative effect of covenant violation on operational efficiency is not universally the same, and is less evident in violating firms with greater agency problems.

Investment preference for either technical or operational energy efficiency measures to achieve sustainable maritime shipping

The reduction in energy consumption in shipping is a crucial issue to achieve a more sustainable sector. Nevertheless, investments in energy efficiency are inhibited by barriers. Consequently, under a Principal-Agent approach, this study aims to analyze the factors affecting the investment preference for either technical or operational measures. To date, the research problem has barely been addressed from a similar approach. This work further integrates agency theory with the identification of barriers and drivers, as well as the cost–benefit ratio from both an environmental and a financial perspective. This makes it possible to consider shipping management from a more comprehensive perspective. The study sample is current and representative (658 individual bulk carriers). The research was carried out utilizing two binominal logistic models that provide similar results when testing the proposed hypotheses. The outcomes show that regulatory factors, such as the distance of a vessel’s technical emissions from EEDI requirements (standardized coefficients: −2.8352 and −2.5069), and Principal-Agent problems, such as split incentives (standardized coefficients: −1.0059 and −0.9828), have the greatest influence on investment preferences. As a consequence of Principal-Agent problems, vessels operating under Time Charter contracts are less likely to invest in technical measures than in operational ones. Verified information and activity promote technical measures. Maritime regulation promotes technical measures in younger vessels, especially those meeting only the minimum requirements. Better knowledge can help achieve a more environmentally responsible shipping sector. The role of shipowners and charterers should be highlighted, and transparency should be promoted to enable well-informed decisions to be made.

Literature Study on Digital Marketing Trends and Their Impact on the Global Trade Industry

This article is a literature study that discusses digital marketing trends in the context of the global trade industry. The purpose of this study is to identify and analyze key trends in digital marketing as well as their impact on the global trade industry. Through a rigorous literature search and in-depth analysis, this article reveals that digital marketing has changed the global business landscape by influencing the way companies interact with customers, market products, and operate as a whole. Key trends discussed include the use of social media, content personalization, e-commerce, and the use of artificial intelligence technology. The impact includes global expansion, increased conversions, cost efficiency, and better performance measurement. However, there are also challenges to be overcome, including data privacy concerns and growing competition. The study provides an in-depth look at how digital marketing affects the global trade industry and provides a basis for further understanding of the topic.

LINKING FINANCIAL PERFORMANCE AND EFFICIENCY TO SUSTAINABILITY IN BANKING SECTOR: A LITERATURE SYNTHESIS

Efficiency generally translates to better financial performance and profitability and, thus, is often taken into account when analyzing activity of the banking sector. The sustainability approach adds social and environmental effects to the economic ones. Even though there have been studies on the different facets of the sustainable banking and its dimensions, it is important to discuss the integration of the sustainability concept with banking efficiency and financial performance measures. The objective of this research is to identify the prevailing indicators of sustainable banking in the presence of considerations on bank financial performance and efficiency. Such indicators can be used in multiple frameworks directed towards specific goals. Therefore, this paper also discusses the notions of the sustainable banking alongside the used methods used to handle the sustainability indicators. Thus, this paper presents the comprehensive review of sustainable banking linked to financial performance and efficiency, where indicators, and methods are analyzed in an integrated manner.

Enhanced subgraph matching for large graphs using candidate region-based decomposition and ordering

The subgraph matching problem associated with large graphs is an emerging research challenge in graph search due to the growing size of the web, social, and metabolic graphs, and the wide availability of graph databases. Such problems involve finding all instances (aka embedding) of the small-sized query graph in the associated large-sized reference graph. Many state-of-the-art algorithms, including VF3, RI, CFL-Match, and Glasgow, exist to solve subgraph matching problem. RI is one of the fastest subgraph matching algorithms focusing mainly on time efficiency performance measures. However, other performance measures, such as the number of found instances of the query graph (embedding count), the method of ordering the query graph’s vertices, and the number of recursive calls, are crucial for the efficiency and effectiveness of the subgraph matching. In this paper, the RI+ algorithm is proposed as an enhanced version of RI, which has been designed using candidate region-based decomposition and ordering. Three novel candidate region orderings have been introduced, namely vertex-count, density, and average-path-length, based on the structural properties of the candidate regions. On empirical analysis of RI+ on real-world data sets, it was observed that RI+ shows significant improvement in efficiency and effectiveness over RI on both performance evaluation measures, namely, embedding count and search time. The influence of the proposed candidate region orderings on the search time of RI+ was also analyzed, revealing that a suitable candidate region ordering has the potential to improve the search time of the proposed algorithm.

Electrical design for efficiency: technical and operational measures for optimizing the use of electrical power on ships

There is currently a growing concern in the shipping industry about energy consumption and environmental impacts. According to the International Maritime Organization's (IMO) energy efficiency guidelines, today's ships must have an energy efficiency management plan to reduce the CO2 emission and other pollutants. In this article, a bibliographic review of methodologies for the optimization of energy consumption on ships is carried out, starting from the identification of sources of energy losses, to the implementation of technical and operational measures that contribute to their improvement, making a qualitative evaluation of the identified methodologies. Sources of energy losses associated with equipment and auxiliary systems are analyzed, as well as opportunities for improvement in the use of electrical energy through the implementation of intelligent energy management systems, high efficiency motors, and lighting. The technical and operational energy efficiency measures described above demonstrate the importance of their implementation from the early stages of the ship's electrical design, as well as monitoring energy consumption during its life cycle, to improve energy efficiency on board.