Key performance measurement capabilities for managing distributed teams

Distributed teams are a reality for several companies nowadays, many authors covered their benefits and problems, and the rate of adoption of such team's structure by companies is growing fast. Since these teams are more present in companies, a performance measurement system must get adapted to fulfill the gap of not having a vast theory about the subject. To fill that gap, this paper brings results from previous steps in the research (Systematic Literature Review and Qualitative analysis of the data). It presents to a group of experts to reach a consensus on which capabilities are essential to managing/developing distributed teams' performance. The experts were exposed to the information following a Delphi Panel format and provided output that reached consensus and refined the list. The experts indicated that a group of six capabilities (engagement, development of a culture of performance measurement, organizational learning, alignment between planning and execution, accurate information and consistency) are essential to have their performance measurement system working correctly and reaching all functions. The work also identified the success factors for virtual teams, providing directions for the adoption and the monitoring of this kind of team that gained importance during the COVID-19 pandemic.

Next normal for business post pandemics: COVID-19

The world's supply chains are changing as both expected and unexpected environmental pandemics occur. Even though some may be expected, the full extent and ramifications a pandemic will have is an estimate at best. Thus, both flexibility and resiliency are becoming crucial to efficient supply chain systems. This study analyses the recent COVID-19 phenomenon and uses it to gauge reactions, best practices, resilience-based issues and operational performance metrics in order to assist with potential future pandemics. Education, as seen, plays a pivotal role in effectively offering options to combat uncertainty and fluid situations. Such dynamic environments have historically posed a serious problem to operations;however, with proper preparation and care taken options are available today that help marginalize harm of future pandemics.

Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications

COVID-19 is an infectious disease that causes acute respiratory distress syndrome due to the SARS-CoV-2 virus. Rapid and accurate screening and early diagnosis of patients play an essential role in controlling outbreaks and reducing the spread of this disease. This disease can be diagnosed by manually reading CXR images, but it is time-consuming and prone to errors. For this reason, this research proposes an automatic medical image segmentation system using a combination of U-Net architecture with Batch Normalization to obtain more accurate and fast results. The method used in this study consists of pre-processing using the CLAHE method and morphology opening, CXR image segmentation using a combination of U-Net-4 Convolution Block architecture with Batch Normalization, then evaluated using performance measures such as accuracy, sensitivity, specificity, F1-score, and IoU. The results showed that the U-Net architecture modified with Batch Normalization had successfully segmented CXR images, as seen from all performance measurement values above 94%.

Systematic Literature Review of Performance Measurement Systems in Health Sector of Pakistan

Building The research study is primarily focused on identifying the parameters of Performance Measurement System within the healthcare sectors of Pakistan. The main purpose is to identify the efficacy of different Performance Measurement Systems within Pakistan, and its impacts on performance of physicians. Considering the current performance and situation in healthcare sector of Pakistan, it has been analyzed that the country has come a long way towards progress, however there is still a major lacking of proper standards and guidelines which must be followed in all the healthcare institutions. The problem statement emphasizes over the need of PMS in the healthcare institutions, with the help of which the improvements and efficacy in performance of the healthcare professionals can be determined. The research objective designed for this study is identify the impact of Performance Measurement Systems on the improvisations in current practices, on patient satisfaction and recovery, changes in patterns of mortality rates and budgetary control within the country for healthcare sectors. In order to conduct this research study, the type of research method which has been mainly opted is qualitative analysis involving the write up of a Systematic Literature Review. This review has been designed on the basis of PRISMA method, and proper skimming of research articles have been performed accordingly. 22 articles have been taken for further investigation, published after the year of 2010. The indicators which have been focused on in this study include Patient Satisfaction, Mortality, Survival rates and Cost Allocation to healthcare sectors of the country. Based on the findings of number of research articles, it has been identified that Patient Satisfaction and Cost Allocation have not been improved via Performance Management System. However, during the COVID-19 pandemic, the mortality and survival rates in the public and private sectors of the country were controlled due to constant supervision by governmental agencies and the use of an effective and efficient Performance Measurement method for staff members in the healthcare industry.

Tangible Rewards for More Than Just Productivity: Examining Canadian Public Accounting Firms' Rewards Programs*

Companies spend significant amounts of money on tangible rewards programs, even during the economic turmoil of the COVID-19 pandemic. The prevalence, growth, and significance of these expenditures highlight the importance of understanding the purpose and use of these programs by organizations. Research on public accounting (PA) firms' compensation plans has focused on the balance between professional and commercial incentives in partner profit-sharing schemes but has failed to examine the incentives for nonpartner audit professionals. However, it is exactly these professionals who do a substantial amount of work on audit engagements. This paper has three main purposes. First, we investigate the nature and composition of PA firms' tangible rewards programs and provide a detailed description. Second, we examine the use of firms' tangible rewards programs to provide evidence of what actions are being rewarded. We use Almer et al.'s (2005, Behavioral Research in Accounting 17: 1-22) framework, which presents dimensions of the auditors' professional contribution, and explores whether firms recognize these dimensions using tangible rewards. Third, we develop future research questions to help explore the use of tangible rewards in firms without structured output. We collect archival data on the use of tangible rewards from each of the Big 4 PA firms and three of the next four largest international accounting firms in Canada. We find that firms use their tangible rewards programs for "building a culture of recognition," for performance incentives, and for employee and firm development, thus rewarding a broad set of measures beyond the incentive measures for hours worked.

Conceptualising Circular economy performance with non-traditional valuation methods: Lessons for a post-Pandemic recovery

The circular economy (CE) has gained importance in the post-COVID-19 pandemic recovery. Businesses, while realising the CE benefits, have challenges in justifying and evaluating the CE benefits using available performance measurement tools, specifically when considering sustainability and other non-traditional benefits. Given the rising institutional pressures for environmental and social sustainability, we argue that organisations can evaluate their CE implementation performance using non-market-based environmental goods valuation methods. Further, the effectiveness of the CE performance measurement model can be enhanced to support supply chain sustainability and resilience through an ecosystem of multi-stakeholder digital technologies that include a range of emerging technologies such as blockchain technology, the internet-of-things (IoT), artificial intelligence, remote sensing, and tracking technologies. Accordingly, a CE performance measurement model (CEPMM) is conceptualised and exemplified using seven COVID-19 disruption scenarios to provide insights that can be addressed through CE practices. Analyses and implications are presented along with areas for future research.

Airline catering supply chain performance during pandemic disruption: a Bayesian network modelling approach

PurposeThe supply chain (SC) encompasses all actions related to meeting customer requests and transferring materials upstream to meet those demands. Organisations must operate towards increasing SC efficiency and effectiveness to meet SC objectives. Although most businesses expected the coronavirus disease 2019 (COVID-19) pandemic to severely negatively impact their SCs, they did not know how to model disruptions or their effects on performance in the event of a pandemic, leading to delayed responses, an incomplete understanding of the pandemic's effects and late deployment of recovery measures. Therefore, this study aims to consider the impact of implementing Bayesian network (BN) modelling to measure SC performance in the airline catering context.Design/methodology/approachThis study presents a method for modelling and quantifying SC performance assessment for airline catering. In the COVID-19 context, the researchers proposed a BN model to measure SC performance and risk events and quantify the consequences of pandemic disruptions.FindingsThe study simulates and measures the impact of different triggers on SC performance and business continuity using forward and backward propagation analysis, among other BN features, enabling us to combine various SC perspectives and explicitly account for pandemic scenarios.Originality/valueThis study's findings offer a fresh theoretical perspective on the use of BNs in pandemic SC disruption modelling. The findings can be used as a decision-making tool to predict and better understand how pandemics affect SC performance.

A proposed circular-SCOR model for supply chain performance measurement in manufacturing industry during COVID-19

PurposeCOVID-19 is a global event affecting supply chain operations and human health. With COVID-19, many issues in business models, business processes and supply chains, especially in the manufacturing industry, have had to change. The ability to analyze supply chain performances and ensure circularity in supply chains has become one of the factors whose importance has increased rapidly with COVID-19. Therefore, it aims to determine which supply chain performance criteria come to the fore for the company under consideration to accelerate the transformation into high performance and circularity in supply chains.Design/methodology/approachIn this study, a new circular-SCOR model is proposed, and 17 supply chain performance measurement criteria are prioritized for a manufacturing company in the context of circular economy principles during COVID-19 by using stepwise weight assessment ratio analysis and analytical hierarchy process method, separately.FindingsAs a result, for both methods, in the case study discussed, the demand fulfillment rate is determined as the most prominent criterion in line with the circular economy principles in the COVID-19 period in manufacturing supply chains.Originality/valueIt is expected that this study will contribute to managers and policy makers as it addresses the "new normal” that started after COVID-19 and the criteria to be considered in supply chain performance measurement and emphasizes the need to adopt circular supply chains, especially in manufacturing industries.

Jaya Honey Badger optimization-based deep neuro-fuzzy network structure for detection of (SARS-CoV) Covid-19 disease by using respiratory sound signals

PurposeThe Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more difficult because of different sizes and resolutions of input image. Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approachThe major contribution of this research is to design an effectual Covid-19 detection model using devised JHBO-based DNFN. Here, the audio signal is considered as input for detecting Covid-19. The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel-frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm.FindingsThe performance of proposed hybrid optimization-based deep learning algorithm is estimated by means of two performance metrics, namely testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.Research limitations/implicationsThe JHBO-based DNFN approach is developed for Covid-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implicationsThe proposed Covid-19 detection method is useful in various applications, like medical and so on.Originality/valueDeveloped JHBO-enabled DNFN for Covid-19 detection: An effective Covid-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The DNFN is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non-Covid-19. Moreover, the DNFN is trained by devised JHBO approach, which is introduced by combining HBA and Jaya algorithm.

The role of Industry 4.0 technologies on performance measurement systems of supply chains during global pandemics: an interval-valued intuitionistic hesitant fuzzy approach

PurposeThis paper aims to investigate Supply Chain (SC) Performance Measurement Systems (PMSs) (SCPMSs) that are suitable and applicable to evaluate SC performance during unexpected events such as global pandemics. Furthermore, the contribution of Industry 4.0 Disruptive Technologies (IDTs) to implement SCPMSs during such Black Swan events is investigated in this study.Design/methodology/approachThe research methodology is based upon a novel qualitative and quantitative mixed-method. A Systematic Literature Review (SLR) was initially employed to identify two complete lists of SCPMSs and IDTs. Then, a novel Interval-Valued Intuitionistic Hesitant-Fuzzy (IVIHF)-Delphi method was firstly developed in this paper to screen the extracted SCPMSs. Afterward, the Propriety, Economic, Acceptable, Resource, Legal (PEARL) indicator of the Hanlon method was innovatively applied to prioritize the identified IDTs for each finalized SCPMS.FindingsTwo high-score SCPMSs including the SC operations reference (SCOR) model and sustainable SCPMS were recommended to improve measuring the performance of the pharmaceutical SC of emerging economies such as Iran in which the societal, biological and economic issues were undeniable, particularly during unexpected events. Employing nine IDTs such as simulation, big data analytics, cloud technologies, etc., would facilitate implementing sustainable SCPMS from distinct perspectives.Originality/valueThis is one of the first papers to provide in-depth insights into determining the priority of contribution of IDTs in applying different SCPMSs during global pandemics. Proposing a novel multi-layer mixed-methodology involving SLR, IVIHF-Delphi, and the PEARL indicator of the Hanlon method is another originality offered by this paper.

Guest editorial: Performance measurement in supply chains during disruptions: lessons from the COVID-19 pandemic

[...]it becomes essential to understand the PM aspects in the face of emergency situations such as COVID-19. Since the seminal article by Benita Beamon proposing new performance measures for evaluating supply chain performance, the literature has evolved. [...]the guest editors would also like to thank the authors for their contributions and for choosing our special issue as a relevant platform to communicate their research works. The insights drawn from this SI will provide them with effective guidance to help them design, implement and improve performance measurement systems capable of effectively measuring different supply chain processes and issues during unexpected and disruptive events.Table 1 Articles published in this special issue Article Title Purpose 1 Airline catering supply chain performance during pandemic disruption: a Bayesian network modelling approach This study aims to consider the impact of implementing Bayesian network (BN) modelling to measure SC performance in the airline catering during the pandemic context 2 The role of Industry 4.0 technologies on performance measurement systems of supply chains during global pandemics: an interval-valued intuitionistic hesitant fuzzy approach This study aims to investigate supply chain performance measurement systems (SCPMSs) that are suitable and applicable to evaluate SC performance during unexpected events such as global pandemics. [...]it considers the contribution of Industry 4.0 Disruptive Technologies (IDTs) to implement SCPMSs during such black swan events 3 A systematic literature review on supply chain resilience in SMEs: learnings from COVID-19 pandemic This paper presents the state-of-art literature on supply chain resilience in SMEs in the context of the coronavirus (COVID-19) pandemic and provides a comprehensive view of insights gained and gaps identified and suggests potential areas of future research 4 A proposed circular-SCOR model for supply chain performance measurement in the manufacturing industry during COVID-19 This study aims to determine which supply chain performance criteria come to the fore for the company under consideration to accelerate the transformation into high performance and circularity in supply chains, considering that the ability to analyse supply chain performances and ensure circularity in supply chains has become one of the factors whose importance has increased rapidly with COVID-19 5 How do food supply chain performance measures contribute to sustainable corporate performance during disruptions from the COVID-19 pandemic emergency?

COVID-19 impact on the operational efficiency of a downtown hotel

This research article utilizes a bootstrap DEA approach for assessing the operational efficiency of a single southwestern downtown hotel during the COVID-19 pandemic. The hotel's performance is benchmarked throughout a 12-month period, which includes a two-month shutdown period due to COVID-19. The bias-corrected technical and scale efficiencies before and after the pandemic shutdown are estimated. The undesirable effect of the COVID-19 pandemic is significantly unquestionable. Results suggest that the hotel was not operating on the optimum scale before the shutdown;furthermore, the pandemic aggravated its performance even more. Interestingly, the hotel's technical efficiency was not affected by the pandemic, confirming the scale of operations as the highly significant improvement opportunity after the shutdown. Managerial recommendations are discussed as well. [ FROM AUTHOR] Copyright of Anatolia: An International Journal of Tourism & Hospitality Research is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Performance Evaluation of Lithuanian II Pillar Pension Funds Using Rolling Window Technique

This paper presents results of performance evaluation of Lithuanian II pillar pension funds using rolling window technique. The Lithuanian pension system has three pillars: mandatory (Ist, social insurance system), quasi-optional (IInd, life-cycle pension funds) and optional (IIIrd, any kind of pension funds or insurance). Investments in II pillar from standard funds were changed to life-cycle funds in 2019. To reveal different behavior of market risk and performance of funds, we used 120 days windows (rolled by 1 day). Risk-adjusted performance of funds was measured by employing mean return, average recovery and Sharpe-based ratios such Calmar ratio, Sortino ratio, adjusted Sharpe ratio, VaR Sharpe ratio. However, to describe market risk we only focused on 5 special time windows related to COVID-19.

THE IMPACT OF THE COVID-19 PANDEMIC ON THE ACTIVITIES OF HEALTH CARE PERSONNEL.

To study the impact of the pandemic on the activities of the staff of an organization Aim providing cardiac care. The object of the study is medical organization personnel. The subject of the study is the GBUZ "KKKD" personnel opinion. The study periods are 2012 and 2021. Methods Rating questions were coded with a five-point Likert scale. The mean value and standard deviation (M+/-SD), Pearson Chi-square, p (%) were calculated. The critical level of statistical significance was taken as p<=0.05. The study showed a significant increase in high estimates of the staff performance both at the unit and organization where the respondent worked. The high subjective ratings are confirmed by the objective data of GBUZ "KKKD" activity in 2021. However, the pandemic situation reflected on the personnel perception of their productivity: there is an increase of high productivity positive assessment both in a structural division and in the whole organization. 2021 survey revealed the conditions caused by the COVID-19 pandemic which affected the performance of the staff. Among the most significant ones are the following: the lack of necessary reagents, worsening of financial and living conditions, Results epidemiological restrictions, the lack of actions and orders coordination, heavy workload, fatigue and burnout, the reduction of salary, irresponsibility of patients and their removal to different departments, bureaucracy, the lack of information and staff interaction. The study also showed an increase in the proportion of respondents' answers regarding the inability to work more efficiently from 21.6% in 2012 to 29.4% in 2021, which may be caused by the work stress and the special working conditions during the COVID-19 pandemic. All the reasons why it was impossible to work efficiently during the pandemic were divided into three groups: managerial, personal and clinical-organizational. The impact of the COVID-19 pandemic on staff performance is evident judging by the evaluation dynamics in two sociological studies. The prior factors of performance improvement and staff satisfaction include: expansion of social benefits, favorable Conclusion moral-psychological climate and working conditions. The study shows that in order to achieve the main goals of medical organization in the conditions of insurmountable risk the personnel have a significant role in making organizational decisions.Copyright © 2022 Complex Issues of Cardiovascular Diseases. All rights reserved.

Mapping research in logistics and supply chain management during COVID-19 pandemic

Supply chains constantly face new problems that go way beyond the traditional issues of supply and demand uncertainty, particularly due to the impact of worldwide disruptive events. This paper aims at mapping academic literature examining so far issues related to the impact on supply chains of the COVID-19 outbreak based on a thorough literature review of peer-reviewed papers published in 2020. Following criteria established by the systematic approach, we build a framework based on the most recent papers of the discipline to address major disruptions that challenge SCM operations. We consider features from individual organisations, characteristics of the whole supply chain, performance metrics for long-term sustainability, and attributes from external disruptions. We derive a list of topics that deserve further investigation such as collaboration, technology adoption and knowledge creation together with their diffusion, strategies to shape and promote awareness along supply chain stakeholders.

Performance Measurement and Management Systems in Local Government Networks: Stimulating Resilience Through Dynamic Capabilities

Local government networks often develop in unpredictable environments and, as a consequence, their abilities and resources have to be prepared for flexible responses, the so-called "dynamic capabilities”. One of the most desirable capacities they might reach is resilience, understood as the skill to cope with unpredicted dangers after they become real. This paper reviews literature and conceptual outcomes resulting from the analysis and contextualization of the Dynamic Capabilities (DCs) Theory, providing a contribution to an effective improvement of resilient governance for performance measurement and management systems (PMMS) within local government networks. The fusion of the concepts of resilience, governance, and DCs applied to PMMS offers both theoretical and practical implications. Regarding the theoretical implications, the presence of DCs in resilient inter-municipal governance might help sense, shape and seize opportunities, as well as enhance, combine and reconfigure assets, not only for the single local government but also for the whole community. Concerning the practical implications, the work suggests that DCs applied to resilient governance al-low and facilitate the overcoming of bureaucratic resistances typical of public sector organizations through the networking of local governments that pursue compatible objectives. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Person Identification and Gender Classification Based on Vision Transformers for Periocular Images

Simple SummaryThe idea of identifying persons using the fewest traits from the face, particularly the area surrounding the eye, was carried out in light of the present COVID-19 scenario. This may also be applied to doctors working in hospitals, the military, and even in certain faiths where the face is mostly covered, except the eyes. The most recent advancement in computer vision, called vision transformers, has been tested for the UBIPr dataset for different architectures. The proposed model is pretrained on an openly available ImageNet dataset with 1 K classes and 1.3 M pictures before using it on the real dataset of interest, and accordingly the input images are scaled to 224 × 224. The PyTorch framework, which is particularly helpful for creating complicated neural networks, has been utilized to create our models. To avoid overfitting, the stratified K-Fold technique is used to make the model less prone to overfitting. The accuracy results have proven that these techniques are highly effective for both person identification and gender classification.AbstractMany biometrics advancements have been widely used for security applications. This field's evolution began with fingerprints and continued with periocular imaging, which has gained popularity due to the pandemic scenario. CNN (convolutional neural networks) has revolutionized the computer vision domain by demonstrating various state-of-the-art results (performance metrics) with the help of deep-learning-based architectures. The latest transformation has happened with the invention of transformers, which are used in NLP (natural language processing) and are presently being adapted for computer vision. In this work, we have implemented five different ViT- (vision transformer) based architectures for person identification and gender classification. The experiment was performed on the ViT architectures and their modified counterparts. In general, the samples selected for train:val:test splits are random, and the trained model may get affected by overfitting. To overcome this, we have performed 5-fold cross-validation-based analysis. The experiment's performance matrix indicates that the proposed method achieved better results for gender classification as well as person identification. We also experimented with train-val-test partitions for benchmarking with existing architectures and observed significant improvements. We utilized the publicly available UBIPr dataset for performing this experimentation.

Lean performance measurement system for an Indian automotive supply chain

Purpose: This paper aims to present a simple and innovative fuzzy methodology-based lean performance measurement system (L-PMS) for an Indian automotive supply chain. The paper also enlightens the influence of coronavirus disease 2019 (COVID-19) on supply chains and the practical implications of the unprecedented disruptions on the performance measurement systems. Design/methodology/approach: The L-PMS is divided into three phases. In the first phase, the key performance indicator (KPI) list, as deemed fit by the organization, is prepared using literature and suggestions from the case organization. The list contains 61 KPIs measuring 24 performance dimensions in seven functional areas of the supply chain. In the second phase, the KPI performance data (actual, best and worst) are collected using the enterprise resource planning (ERP) system. In the last phase, the leanness score of the case organization is calculated at four levels – KPI, dimension, functional area and overall organization. Findings: The overall leanness score of the case organization is 60%. The case organization uses KPIs from all seven functional areas, but it needs to improve the number of KPIs in administration and supplier management functional areas. The case organization uses only quantitative KPIs. However, the performance dimensions at the middle level are adequate. The leanness level of the case organization in different areas is highly variable (ranges from 45% to 91%). Research limitations/implications: The major limitation of the study is that the case study is done at a single organization. Practical implications: The managers at the different levels of the hierarchy can use the lean performance measurement score to leverage the better performing areas/dimensions/KPIs and improve poor performing areas/dimensions/KPIs. The lean performance measurement at functional area level can help leadership to give responsibility to different people for the improvement of leanness with respect to different dimensions/functional areas. The disruptive impact of COVID-19 should clearly be understood by the managers to make appropriate decisions based on the severity as measured at different levels. Originality/value: According to the authors' best knowledge, this is the first lean performance measurement application at the four hierarchical levels (KPI, performance dimension, functional area and overall organization). © 2023, Emerald Publishing Limited.

Efficacy Determination of Various Base Networks in Single Shot Detector for Automatic Mask Localisation in a Post COVID Setup

The COVID-19 pandemic is one of the rarest events of global crises where a viral pathogen infiltrates every part of the world, leaving every country face an inevitable threat of having to lock down major cities and economic hubs and put firm restrictions on citizens thus slowing down the economy. The risk of removal of lockdowns is the emergence of new waves of a pandemic causing a surge in new cases. These facts necessitate the containment of the virus when the lockdowns end. Wearing masks in crowded places can help restrict the spread of the virus through minuscule droplets in the air. Through the automatic detection, enumeration, and localisation of masks from closed-circuit television footage, it is possible to keep violations of post-COVID regulations in check. In this paper, we leverage the Single-Shot Detection (SSD) framework through different base convolutional neural networks (CNNs) namely VGG16, VGG19, ResNet50, DenseNet121, MobileNetV2, and Xception to compare performance metrics attained by the different variations of the SSD and determine the efficacies for the best base network model for automatic mask detection in a post COVID world. We find that Xception performs best among all the other models in terms of mean average precision.

COVID-19 Impacts on Border Community Organizations

The COVID-19 pandemic massively affected the nonprofit sector. This article explores how the crisis has impacted nonprofit organizations at a U.S.-Mexico border community with a large population of minorities and migrants. Guided by resource dependency theory and the nonprofit capacity building framework, surveys reveal that nonprofits with less financial support from the government sector, low leadership, and weak operational capacities receive critical impacts from the pandemic. The findings also show that local nonprofits are bonded closely to the community during the pandemic, which reflects the collectivistic culture in Hispanic/Latino communities. This study provides important insights on how local nonprofits with limited resources and an increase in demand from vulnerable populations struggled with the pandemic.