Conventional Performance Measures Research Articles

Labour market inequality: a comparative political economy perspective

Abstract Large and rising earnings and employment inequalities in countries such as the USA and the UK pose fundamental questions for social scientists and challenges for policymakers. Mainstream economists have explained these outcomes mainly in terms of competitive market forces, particularly the mismatch between technology-driven demands for skills and those supplied by workers. In contrast, political economy perspectives focus on employer–employee bargaining power, driven by the monopsony power of firms and declining institutional and policy protections for workers. We critically assess the evidence on the education wage premium and employment polarization used to support the competitive market explanation for US wage inequality. We then show that the incidence of decent pay for young less-educated workers varies sharply across similarly rich countries and that new indices of institutional bargaining power closely correspond to differences in pay distributions but are uncorrelated with conventional measures of employment performance. We conclude that well-designed institutional and policy changes can substantially promote shared growth and the well-being of working families.

Linking administrative data on children's social care to investigate the outcomes of provision

ObjectivesThis paper will present findings from a study of linked administrative data on children’s social care (CSC) services in England. Its objectives were to identify and profile the underlying categories of demand for CSC services in England and compare the outcomes of provision between these categories, using a range of measures.
 MethodsAn anonymised longitudinal dataset was assembled for children in need and children in care who received services in England between 2015-21. Latent Class Analysis (LCA) was used to identify mutually exclusive categories of demand for all children subject to a statutory social work assessment within that period, based on the factors identified at assessment. Descriptive profiles of these categories were constructed using demographic data as well as children’s intervention pathway following assessment. Survival analysis using adjusted Cox regression models was then undertaken to compare rates of re-referrals, repeat child protection plans, and re-entries to care, for children receiving services within each category.
 ResultsThe latent class analysis found twelve distinct categories of demand for children’s social care services, which were consistent across local authorities. Conditional probabilities were used to interpret the demand represented by each category, in consultation with stakeholders. The most prevalent category was domestic abuse and violence (19.7%), followed by complexities around parental mental health (18.4%). Other categories included disability, child mental health, risks outside the home, complex domestic abuse, and concerns about another person in the family or household. The profile of children varied across these categories, including age, gender, ethnicity, the level of risk identified by services, and the type of provision received. Significant differences were also found in the outcomes of provision across categories, especially in re-referrals and re-entries to care.
 ConclusionThe categories create an evidence-based typology of demand to supplement conventional performance measures. Local profiling of these categories can be support designing and planning services and aligning the textual information gathered in assessments with the statistical data used for audit and inspection.

COMPARING VALUE - BASED AND TRADITIONAL MEASURING TOOLS. EVIDENCE FROM PAKISTAN

This paper's primary goal is to evaluate the assertion that value-based measures are preferable to traditional accounting based financial-performance measures, such as profit after tax (PAT), earnings per share (EPS), return on assets (ROA), return on equity (ROE), and return on investment (ROI), in the non-financial sector of Pakistan, while also providing empirical evidence to support the claim. Additionally, it evaluates and tests the informational value of different performance measures and how they relate to stock return. The sample of 97 non-financial Pakistani companies included in this study was obtained from the Pakistan Stock Exchange (PSX) between 2016 and 2021. The information richness of value-based measurements and conventional performance measures in explaining shareholders' returns is investigated using a variety of regression models. Relative information content tests shows that traditional accounting-based measures such as EPS, ROE and ROA performed better than value based measures in explaining the returns of Pakistan non-financial companies. Incremental information content of value based measures doesn’t have any contribution to information content above traditional performance measures. The claim of superiority of value based measures over accounting-based measures in association with shareholder returns is proved invalid in Pakistan non-financial companies. According to the study's findings, value-based financial performance indicators are not any better at explaining stock returns of Pakistani non-financial enterprises than traditional accounting-based financial performance measures. Panel data are tested using the Hausman's test for fixed and random effects, the variance inflation factor (VIF) test for multicollinearity, and the Durbin-Watson test for autocorrelation in order to ensure substantial results.

Is replacing standard investments with ESG substitutes a good choice?

We compare traditional portfolio selection strategies with traditional stock market indexes in their standard and ESG versions. In addition to the comparison of 12 indexes from different markets in their ESG and standard versions. To compare the portfolios we use conventional performance measures, and the results show that replacing the indexes with their ESG versions does not detract from performance in terms of the Shape ratio and other measures. In general, there is no statistically significant difference between the returns of the indexes and the portfolios built with these indexes compared to those using the standard versions. So that the investor concerned with environmental and social issues can replace their equity market investments with ESG assets without harming their financial performance.

Differences in Multiple-Choice Questions of Opposite Stem Orientations Based on a Novel Item Quality Measure

Objective. To determine whether there are differences in the performance and quality of multiple-choice items with opposite stem orientations (positive or negative), based on a novel item quality measure and conventional psychometric parameters.Methods. A retrospective study was conducted on multiple-choice assessment items used in years two and three of pharmacy school for pharmacotherapy and related courses administered between August 2018 and December 2019. Conventional psychometric parameters (difficulty and discrimination indices), average response time, nonfunctional distractor percentage, and a novel measure of item quality of negatively worded items were compared with those of control items, namely positively worded items (n=103 each). This novel measure uses difficulty and discrimination in tandem for the decision to reject, review, or retain items in an assessment. Statistical analyses were performed on continuous and categorical variables, on the relationship between difficulty and discrimination, and on differences in correlation coefficients between positively and negatively worded items.Results. Stem orientation was not significantly associated with the novel measure of item quality. Also, there were no significant differences between positively and negatively worded items in any of the psychometric parameters. There were significant, negative correlations between difficulty and discrimination indices in both groups, and the correlation coefficients were significantly stronger in positively versus negatively worded items.Conclusion. Items with opposite stem orientations show no differences in the novel item quality measure nor in conventional measures of performance and quality, except in difficulty-discrimination relationships. This suggests that negatively worded items should be used when necessary, but cautiously.

Curse or Blessing? The Use of Subjective Performance Evaluation in the Digital Era

Subjective performance evaluation (SPE) in executive compensation raises concerns among investors due to a potential lack of pay-for-performance. However, while these concerns are reasonable, subjective performance evaluation can also supplement the incompleteness of objective performance measures and thereby improve incentive contracting. Based on this reasoning, we believe that SPE is essential for the progress of digital innovation where conventional performance measures typically fail to provide accurate information. Our results empirically verify that the adoption of SPE indeed encourages executives to engage in digital innovation. Moreover, we find that this positive association depends on CEOs' perception of director's ability and willingness to deviate from objective results when making subjective evaluations. Thus, our results are valuable for investors, firms, and compensation consultants thinking about SPE and struggling with digital innovation.

Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers

Conventional binary classification performance metrics evaluate either general measures (accuracy, F score) or specific aspects (precision, recall) of a model's classifying ability. As such, these metrics, derived from the model's confusion matrix, provide crucial insight regarding classifier-data interactions. However, modern- day computational capabilities have allowed for the creation of increasingly complex models that share nearly identical classification performance. While traditional performance metrics remain as essential indicators of a classifier's individual capabilities, their ability to differentiate between models is limited. In this paper, we present the methodology for MARS (Method for Assessing Relative Sensitivity/ Specificity) ShineThrough and MARS Occlusion scores, two novel binary classification performance metrics, designed to quantify the distinctiveness of a classifier's predictive successes and failures, relative to alternative classifiers. Being able to quantitatively express classifier uniqueness adds a novel classifier-classifier layer to the process of model evaluation and could improve ensemble model-selection decision making. By calculating both conventional performance measures, and proposed MARS metrics for a simple classifier prediction dataset, we demonstrate that the proposed metrics' informational strengths synergize well with those of traditional metrics, delivering insight complementary to that of conventional metrics.

IS IT FAIR TO ASSESS THE PERFORMANCE OF ISLAMIC BANKS BASED ON THE CONVENTIONAL BANK PLATFORM?

Several studies found the weakness of using conventional performance measurement tools such as CAMELS and RGEC in Islamic banking and the lack of performance measurement tools following the characteristics of Islamic banks. The findings showed the need for a performance measurement system that integrates the objectives of sharia and the business objectives of sharia banking. This study explored the application of Maqashid shariah values in Islamic banking and initiated a Sharia Integrated Performance Measurement (SIPM) structure. The researchers used Grounded Theory and library research. Data were collected through in-depth interviews with practitioners, experts, and academics of Islamic banking, while secondary data was taken from the literature and previous research that discussed the measurement of Islamic banking performance. The data were analyzed through several stages: data reduction, data display, and concluding with the help of the Atlas.ti application version 8. This study would like to contribute to the idea of building a Sharia Integrated Performance Measurement (SIPM) platform through three components: Islamic commercial performance, Islamic social performance, and Islamic macroeconomics performance in Islamic banking.

Mapping Studies on Sustainability in the Performance Measurement of Public-Private Partnership Projects: A Systematic Review

The integration of sustainable practices into infrastructure projects under the auspices of public-private partnerships (PPPs) is vital in the attainment of United Nation’s Sustainable Development Goals (SDGs). Since the inception of the SDGs in 2015, the attention of world has been shifting towards more sustainable practices and it is essential that the conventional performance measurement models on PPP projects also adapt to the trend of sustainable practices. Therefore, This study aims at reviewing and operationalising sustainable performance measures for the PPP infrastructure projects. A systematic literature review (SLR) methodology was utilised in this study. The research process began with the search, retrieval and selection of thirty-three (33) journal articles. Thoroughly, the selected articles were contently analysed to form key themes that form the basis of this research’s findings. The outcomes of this review demonstrate twenty-seven (27) most critical sustainable performance criteria of PPP projects such as the lowest project costs, green index, disability-friendly inclusion rate and carbon emission per project among others. Although, the study is limited to few journal articles, it provides theoretical and practical understanding of integration of sustainability in PPPs. Further, it gives a list of relevant research gaps for further studies. This study contributes to the benchmarking and management of sustainable performance assessment of PPP projects.

Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers

Conventional binary classification performance metrics evaluate either general measures (accuracy, F score) or specific aspects (precision, recall) of a model’s classifying ability. As such, these metrics, derived from the model’s confusion matrix, provide crucial insight regarding classifier-data interactions. However, modern- day computational capabilities have allowed for the creation of increasingly complex models that share nearly identical classification performance. While traditional performance metrics remain as essential indicators of a classifier’s individual capabilities, their ability to differentiate between models is limited. In this paper, we present the methodology for MARS (Method for Assessing Relative Sensitivity/ Specificity) ShineThrough and MARS Occlusion scores, two novel binary classification performance metrics, designed to quantify the distinctiveness of a classifier’s predictive successes and failures, relative to alternative classifiers. Being able to quantitatively express classifier uniqueness adds a novel classifier-classifier layer to the process of model evaluation and could improve ensemble model-selection decision making. By calculating both conventional performance measures, and proposed MARS metrics for a simple classifier prediction dataset, we demonstrate that the proposed metrics’ informational strengths synergize well with those of traditional metrics, delivering insight complementary to that of conventional metrics.

Social stress in human-machine systems: opportunities and challenges of an experimental research approach

This article presents some deliberations on methodological approaches to researching the effects of work-related social stress on performance, with particular consideration being given to machine-induced social stress. The article proposes a broad methodological approach to examine such effects. A particular focus is placed on performance after-effects (e.g. unscheduled probe tasks), extra-role behaviour, and task management behaviour because of conventional performance measures (i.e. scheduled tasks) often being unimpaired by social stressors. The role of the ‘performance protection mode’ as an important concept is discussed. A distinction is made between three facets of after-effects: performance-related, behavioural, and emotional. Unscheduled probe tasks and voluntary tasks are proposed to measure performance-related and behavioural after-effects. Propositions for specific experimental scenarios are made, allowing for sufficiently realistic simulations of social stress at work. The availability of such lab-based simulations of work environments offers good opportunities for this line of experimental research, which is expected to gain in importance since highly automated systems may modify the impact of human-induced social stress or may even represent a social stressor themselves. Finally, the considerations presented in this article are not only of relevance to the domain of social stress but to experimental stress research in general.

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images.

The area of medical diagnosis has been transformed by computer-aided diagnosis (CAD). With the advancement of technology and the widespread availability of medical data, CAD has gotten a lot of attention, and numerous methods for predicting different pathological diseases have been created. Ultrasound (US) is the safest clinical imaging method; therefore, it is widely utilized in medical and healthcare settings with computer-aided systems. However, owing to patient movement and equipment constraints, certain artefacts make identification of these US pictures challenging. To enhance the quality of pictures for classification and segmentation, certain preprocessing techniques are required. Hence, we proposed a three-stage image segmentation method using U-Net and Iterative Random Forest Classifier (IRFC) to detect orthopedic diseases in ultrasound images efficiently. Initially, the input dataset is preprocessed using Enhanced Wiener Filter for image denoising and image enhancement. Then, the proposed segmentation method is applied. Feature extraction is performed by transform-based analysis. Finally, obtained features are reduced to optimal subset using Principal Component Analysis (PCA). The classification is done using the proposed Iterative Random Forest Classifier. The proposed method is compared with the conventional performance measures like accuracy, specificity, sensitivity, and dice score. The proposed method is proved to be efficient for detecting orthopedic diseases in ultrasound images than the conventional methods.

Transcranial direct current stimulation (tDCS) alters the pattern of information processing in children with ADHD: Evidence from drift diffusion modeling

ObjectivePerformance accuracy and reaction time in cognitive tasks are routinely used to evaluate the efficacy of tDCS to affect cognitive task performance. tDCS alters the excitability of targeted brain areas and thereby alters performance of cognitive tasks. The drift diffusion model (DDM) provides some additional measures to explore information processing style, such as (non)decision time, bias for decision, and speed of information processing. DDM parameters are informative for the study of cognitive impairments in children with ADHD. In the present study, we aimed to evaluate the impact of tDCS on cognitive performance via DDM measures. MethodsThis study conducted DDM modeling and reanalysis of two exploratory, single-blinded, within-subject design experiments, which were published earlier. In the first experiment, twenty- four children with ADHD performed a Go/ No- Go task during anodal or sham tDCS over the right dlPFC. In the second experiment, twenty- five children with ADHD performed the 1- back working memory task during anodal or sham tDCS over the left dlPFC. We reanalyzed the data after DDM modeling. ResultsThe conventional performance measures revealed no significant effect of tDCS on No- Go accuracy in the first experiment and 1-back accuracy in the second experiment. The 1-back reaction time and speed-accuracy tradeoff were however improved under the real stimulation condition. The DDM measures identified increased No-Go- bias and decision time with respect to inhibitory control, and an increased threshold and amount of information required for response in the 1- back test. ConclusionIn children with ADHD, anodal tDCS over the right dlPFC induces more conservative and less impulsive decisions. Furthermore, anodal tDCS over the left dlPFC enhanced efficacy of working memory performance with respect to agility and capacity. The experimental results show that drift diffusion modeling is useful for evaluation of the impact of tDCS on the style of information processing.

The Impact of Quarantines, Lockdowns, and ‘Reopenings’ on the Commercialization of Science: Micro and Macro Issues

In 2020, almost all research labs in industry, academia, and the government were shut down for long periods of time by political leaders to control the spread of the coronavirus. We consider the “micro” and “macro” implications of ongoing coronavirus disruptions in scientific research and the dissemination and commercialization of that research. We have identified three key unanswered research questions regarding these unprecedented disruptions: (1) How is the pandemic affecting conventional measures of scientific output (the quantity and quality of basic research) and performance, social networks, and the strategic management of innovation? (2) How is the pandemic affecting technology transfer offices, incubators, accelerators, science and technology parks, and other aspects of the innovation ecosystem? (3) How do pandemic disruptions affect micro‐level factors, such as role conflict, identity, work‐life balance, equity, diversity, inclusion, “championing,” leadership, and organizational justice?

Ambiguity of objective image quality metrics: A new methodology for performance evaluation

Objective image quality metrics try to estimate the perceptual quality of the given image by considering the characteristics of the human visual system. However, it is possible that the metrics produce different quality scores even for two images that are perceptually indistinguishable by human viewers, which have not been considered in the existing studies related to objective quality assessment. In this paper, we address the issue of ambiguity of objective image quality assessment. We propose an approach to obtain an ambiguity interval of an objective metric, within which the quality score difference is not perceptually significant. In particular, we use the visual difference predictor, which can consider viewing conditions that are important for visual quality perception. In order to demonstrate the usefulness of the proposed approach, we conduct experiments with 33 state-of-the-art image quality metrics in the viewpoint of their accuracy and ambiguity for three image quality databases. The results show that the ambiguity intervals can be applied as an additional figure of merit when conventional performance measurement does not determine superiority between the metrics. The effect of the viewing distance on the ambiguity interval is also shown.

The effect of green marketing, green supply chain and green human resources on business performance: Balanced scorecard approach

Green management and its implementation and practices attracted the attention of both practitioners and academicians alike, which in part is due to its positive impact on the environment and sustainability, however, little is known about what really measures the effect of green management practices on business performance. Conventional measures of financial performance cannot reflect the proposed effect since green management is long-term oriented and not limited to financial performance only. This research explores the potentials of using balanced scorecard to measure the effect of green marketing, green supply chain and green human resources on the performance of the firms. Simple random sampling technique was followed in this research, and data were collected from 113 managers from different companies in service and manufacturing sectors in UAE. The findings indicate that green marketing was the major determinant of customers’ performance, green human resources was the major factor affecting learning and growth, and that green supply chain was the main influencer on financial performance and internal processes.

U-turn speed is a valid and reliable smartphone-based measure of multiple sclerosis-related gait and balance impairment

BackgroundPeople living with multiple sclerosis (MS) experience impairments in gait and mobility, that are not fully captured with manually timed walking tests or rating scales administered during periodic clinical visits. We have developed a smartphone-based assessment of ambulation performance, the 5 U-Turn Test (5UTT), a quantitative self-administered test of U-turn ability while walking, for people with MS (PwMS). Research questionWhat is the test-retest reliability and concurrent validity of U-turn speed, an unsupervised self-assessment of gait and balance impairment, measured using a body-worn smartphone during the 5UTT? Methods76 PwMS and 25 healthy controls (HCs) participated in a cross-sectional non-randomised interventional feasibility study. The 5UTT was self-administered daily and the median U-turn speed, measured during a 14-day session, was compared against existing validated in-clinic measures of MS-related disability. ResultsU-turn speed, measured during a 14-day session from the 5UTT, demonstrated good-to-excellent test-retest reliability in PwMS alone and combined with HCs (intraclass correlation coefficient [ICC] = 0.87 [95 % CI: 0.80–0.92]) and moderate-to-excellent reliability in HCs alone (ICC = 0.88 [95 % CI: 0.69–0.96]). U-turn speed was significantly correlated with in-clinic measures of walking speed, physical fatigue, ambulation impairment, overall MS-related disability and patients’ self-perception of quality of life, at baseline, Week 12 and Week 24. The minimal detectable change of the U-turn speed from the 5UTT was low (19.42 %) in PwMS and indicates a good precision of this measurement tool when compared with conventional in-clinic measures of walking performance. SignificanceThe frequent self-assessment of turn speed, as an outcome measure from a smartphone-based U-turn test, may represent an ecologically valid digital solution to remotely and reliably monitor gait and balance impairment in a home environment during MS clinical trials and practice.

Corporate Performance Evaluation through Economic Value Added (EVA) and Other Conventional Measures: An Empirical Study

A successful performance measure evaluates how well an organisation performs in relation to its objectives. Since the primary objective of commercial organizations is normally assumed to be the maximisation of the wealth of its shareholders, it follows that performance measures should evaluate this. Besides the traditional measures like Return on Net worth (RONW), Return on Capital Employed (ROCE) and Earnings per Share (EPS), EVA is a new measure available to the corporate managers. The EVA framework developed by Stern Stewart & Company is gradually replacing the traditional measures of financial performance on account of its robustness and its immunity from creative accounting. The present paper measures corporate performance of selected Indian companies by using EVA and other conventional measures. An attempt has been made to analyze the effectiveness of Economic Value Added over conventional measures of corporate performance. The relationship between different measures of performance is studied by using correlation and regression analysis. After detailed analysis it was found that HUL is the best performing company as per three measures of performance namely, EVACE, ROCE and RONW. It was also found that RONW and EVACE are highly correlated and RONW and ROCE significantly affect EVACE.

Auditory Measures for the Next Billion Users.

A range of new technologies have the potential to help people, whether traditionally considered hearing impaired or not. These technologies include more sophisticated personal sound amplification products, as well as real-time speech enhancement and speech recognition. They can improve user's communication abilities, but these new approaches require new ways to describe their success and allow engineers to optimize their properties. Speech recognition systems are often optimized using the word-error rate, but when the results are presented in real time, user interface issues become a lot more important than conventional measures of auditory performance. For example, there is a tradeoff between minimizing recognition time (latency) by quickly displaying results versus disturbing the user's cognitive flow by rewriting the results on the screen when the recognizer later needs to change its decisions. This article describes current, new, and future directions for helping billions of people with their hearing. These new technologies bring auditory assistance to new users, especially to those in areas of the world without access to professional medical expertise. In the short term, audio enhancement technologies in inexpensive mobile forms, devices that are quickly becoming necessary to navigate all aspects of our lives, can bring better audio signals to many people. Alternatively, current speech recognition technology may obviate the need for audio amplification or enhancement at all and could be useful for listeners with normal hearing or with hearing loss. With new and dramatically better technology based on deep neural networks, speech enhancement improves the signal to noise ratio, and audio classifiers can recognize sounds in the user's environment. Both use deep neural networks to improve a user's experiences. Longer term, auditory attention decoding is expected to allow our devices to understand where a user is directing their attention and thus allow our devices to respond better to their needs. In all these cases, the technologies turn the hearing assistance problem on its head, and thus require new ways to measure their performance.

Mitochondria and the thermal limits of ectotherms.

Temperature is a critical abiotic factor shaping the distribution and abundance of species, but the mechanisms that underpin organismal thermal limits remain poorly understood. One possible mechanism underlying these limits is the failure of mitochondrial processes, as mitochondria play a crucial role in animals as the primary site of ATP production. Conventional measures of mitochondrial performance suggest that these organelles can function at temperatures much higher than those that limit whole-organism function, suggesting that they are unlikely to set organismal thermal limits. However, this conclusion is challenged by recent data connecting sequence variation in mitochondrial genes to whole-organism thermal tolerance. Here, we review the current state of knowledge of mitochondrial responses to thermal extremes and ask whether they are consistent with a role for mitochondrial function in shaping whole-organism thermal limits. The available data are fragmentary, but it is possible to draw some conclusions. There is little evidence that failure of maximal mitochondrial oxidative capacity as assessed in vitro sets thermal limits, but there is some evidence to suggest that temperature effects on ATP synthetic capacity may be important. Several studies suggest that loss of mitochondrial coupling is associated with the thermal limits for organismal growth, although this needs to be rigorously tested. Most studies have utilized isolated mitochondrial preparations to assess the effects of temperature on these organelles, and there remain many untapped opportunities to address these questions using preparations that retain more of their biological context to better connect these subcellular processes with whole-organism thermal limits.