External Performance Measures Research Articles

A Comprehensive Evaluation of Rough Sets Clustering in Uncertainty Driven Contexts

This paper presents a comprehensive evaluation of the Agent BAsed Rough sets Clustering (ABARC) algorithm, an approach using rough sets theory for clustering in environments characterized by uncertainty. Several experiments utilizing standard datasets are performed in order to compare ABARC against a range of supervised and unsupervised learning algorithms. This comparison considers various internal and external performance measures to evaluate the quality of clustering. The results highlight the ABARC algorithm’s capability to effectively manage vague data and outliers, showcasing its advantage in handling uncertainty in data. Furthermore, they also emphasize the importance of choosing appropriate performance metrics, especially when evaluating clustering algorithms in scenarios with unclear or inconsistent data. Keywords: rough sets, clustering, metrics.

Predictive Modeling and Experimental Validation for Assessing the Mechanical Properties of Cementitious Composites Made with Silica Fume and Ground Granulated Blast Furnace Slag

Using sustainable cement-based alternatives, such as secondary cementitious raw materials (SCMs), could be a viable option to decrease CO2 emissions resulting from cement production. Previously conducted studies to determine the optimal mix designs of concrete primarily focused on either experimental approaches or empirical modeling techniques. However, in these experimental approaches, few tests could be performed for optimization due to time restrictions and lack of resources, and empirical modeling methods cannot be relied on without external validation. The machine learning-based approaches are further characterized by certain shortcomings, including a smaller number of data points, a less robust connection among the controlling factors, and a lack of comparative analyses among machine learning models. Furthermore, the literature on predicting the performance of concrete utilizing binary SCMs (silica fume (SF) and ground granulated blast furnace slag (GGBS)) is not available. Therefore, to address these drawbacks, this research aimed to integrate ML-based models with experimental validations for accurate predictions of the compressive strength (CS) and tensile strength (TS) of concrete that includes SF and GGBS as SCMs. Three soft computing techniques, namely the ANN, ANFIS, and GEP methods, were used for prediction purposes. Eight major input parameters, including the W/B ratio, cement, GGBS, SF, coarse aggregates, fine aggregates, superplasticizer, and the age of the specimens, were considered for modeling. The validity of the established models was assessed by using external experimental validation criteria, statistical metrics, and performance measures. In addition, sensitivity and parametric analyses were performed. Based on statistical measures, the ANFIS models outperformed other models with higher correlation and lower statistical error values. However, the GEP models exhibited superior performance compared to ANFIS and ANN with respect to the closeness of the RMSE, MAE, RSE, and R2 values between the training, validation, and testing sets for both the CS and TS models. Experimental validation showed strong evidence for the applicability of the proposed models with an R2 of 0.88 and error percentages of less than 10%. Sensitivity and parametric investigations demonstrated that the input variables exhibited the patterns described in the experimental dataset and the available literature. Hence, the proposed models are accurate, have better prediction performance, and can be used for design purposes.

Application of mobile sensors based on deep neural networks in sports psychological detection and prevention

This article proposes a low-power, low-cost, programmable neural network processor for IoT applications. Aiming at the human inertial motion capture system, a human motion sensor detection method based on long- and short-term memory network is proposed. It provides a practical method for studying human motion detection based on mobile sensors and portable devices. Based on the research of human movement sensor detection method based on long and short-term memory network, this paper analyzes the current research status of sports psychological fatigue detection through the production mechanism, external performance, evidence and entertainment measures of sports psychological fatigue, in order to obtain a better solution. Quickly eliminate the mental fatigue of athletes. At the same time, improve and improve the physical and psychological functions of athletes of different qualities, and further improve sports performance. So far, the theoretical definition of sports mental fatigue and the operational definition of sports mental fatigue are still the center of discussion and research by sports psychologists. This article will also elaborate on the development of the most important concepts describing sports fatigue at home and abroad, using depth Neural network and mobile sensor technology have carried out research and exploration on the mental recognition of athletes in sports.

How administrative reforms influence the capacity for implementing the social investment state

For several decades, the social investment (SI) state has been heralded as the saviour of the welfare state, while at the same time being criticised for being just another instance of neoliberal downsizing of the welfare state. Recently, efforts have been made to provide clearer conceptualisations of how to assess the existence and impact of SI. However, these attempts have hitherto mainly focused on the policy functions and instruments of the SI state. This article contributes to existing research by offering a novel analytical framework on the capacity needed by street-level organisations (SLOs) to implement the central policy functions of the SI state, and by elucidating how administrative reforms influence this capacity. The article applies the framework to the implementation in Denmark of Active Labour Market Policies (ALMPs) in local job centres. This case is considered an SI ‘flagship’ in terms of formal policies, while also having undergone multiple administrative reforms, which makes it highly illustrative for the central argument of the article – that the success or failure of an SI approach is not only determined by politics and formal policies. The empirical analysis reveals how the capacity to implement SI policies has been enhanced by administrative reforms; this has been done by giving job centres more room for discretion and enhancing their ability to make long-term investments and to promote integrated service provision across different service areas. However, at the same time the local job centres remain closely monitored and controlled through an external accountability performance measurement system.

The Impact of Sustainability Practices on Share Performance with Mediation of Board Members Experience: A Study on Malaysian Listed Companies

The purpose of this paper is to explore the impact of ESG sustainability practices (i.e., Environmental, Social, Governance/economic) on share performance. Moreover, the objective of the study is to investigate the sustainability practices with mediation of board member experience, which might contribute in maintaining the share performance. The study is unique in such a way that instead of analysing the stated relationship with internal financial performance measures such as return on asset (ROA) or return on equity (ROE), this study will investigate the relationship using external performance measures such as firm share performance. In this research, data were collected from 100 Bursa Malaysia listed companies using purposive sampling during the sampling period from 2017 to 2020. The data were analysed using the Autoregressive Distributed Lag (ARDL) bound testing model instead of a traditional regression model to examine the causal relationship. The results of the study showed the long-run steady relationships through the error correction term (ECT) at the optimum lag. Further, the findings also revealed that there is no short run association between the sustainability practices and the stock performance with mediation of the board experience. The findings also showed that sustainability practices have a significant impact on share performance with mediation of board experience. It is found that sustainability practices, especially environmental and social, are essential to attract investors. The results have also demonstrated that a board of directors of different ages has different knowledge, competencies, and expertise which could prove beneficial in terms of board diversity that decides to adapt the best sustainability practices. These findings provide some inference for future research on the relationship of sustainability practices and share performance with other mediating factors of board attributes.

External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review.

To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis. In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based radiologic diagnosis that included external validation, published from January 1, 2015, through April 1, 2021. Studies using nonimaging features or incorporating non-DL methods for feature extraction or classification were excluded. Two reviewers independently evaluated studies for inclusion, and any discrepancies were resolved by consensus. Internal and external performance measures and pertinent study characteristics were extracted, and relationships among these data were examined using nonparametric statistics. Eighty-three studies reporting 86 algorithms were included. The vast majority (70 of 86, 81%) reported at least some decrease in external performance compared with internal performance, with nearly half (42 of 86, 49%) reporting at least a modest decrease (≥0.05 on the unit scale) and nearly a quarter (21 of 86, 24%) reporting a substantial decrease (≥0.10 on the unit scale). No study characteristics were found to be associated with the difference between internal and external performance. Among published external validation studies of DL algorithms for image-based radiologic diagnosis, the vast majority demonstrated diminished algorithm performance on the external dataset, with some reporting a substantial performance decrease.Keywords: Meta-Analysis, Computer Applications-Detection/Diagnosis, Neural Networks, Computer Applications-General (Informatics), Epidemiology, Technology Assessment, Diagnosis, Informatics Supplemental material is available for this article. © RSNA, 2022.

Analytical Comparison of Clustering Techniques for the Recognition of Communication Patterns

The systematic processing of unstructured communication data as well as the milestone of pattern recognition in order to determine communication groups in negotiations bears many challenges in Machine Learning. In particular, the so-called curse of dimensionality makes the pattern recognition process demanding and requires further research in the negotiation environment. In this paper, various selected renowned clustering approaches are evaluated with regard to their pattern recognition potential based on high-dimensional negotiation communication data. A research approach is presented to evaluate the application potential of selected methods via a holistic framework including three main evaluation milestones: the determination of optimal number of clusters, the main clustering application, and the performance evaluation. Hence, quantified Term Document Matrices are initially pre-processed and afterwards used as underlying databases to investigate the pattern recognition potential of clustering techniques by considering the information regarding the optimal number of clusters and by measuring the respective internal as well as external performances. The overall research results show that certain cluster separations are recommended by internal and external performance measures by means of a holistic evaluation approach, whereas three of the clustering separations are eliminated based on the evaluation results.

Clinical Prediction Models in Sports Medicine: A Guide for Clinicians and Researchers.

Participating in sport carries inherent risk of injury. Clinicians execute high-level clinical reasoning and decision making to support athletes to achieve the best outcomes. Accurately diagnosing a problem, estimating prognosis, or selecting the most suitable intervention for each athlete is challenging. Clinical prediction models are tools to assist clinicians in estimating the risk or probability of a health outcome for an individual by using data from multiple predictors. Although common in general medical literature, clinical prediction models are rare in sports medicine. The purpose of this article was to (1) describe the steps required to develop and validate (ie, evaluate) a clinical prediction model for clinical researchers, and (2) help sports medicine clinicians understand and interpret clinical prediction model studies. Using a case study to illustrate how to implement clinical prediction models in practice, we address the following issues in developing and validating a clinical prediction model: study design and data, sample size, missing data, selecting predictors, handling continuous predictors, model fitting, internal and external validation, performance measures, reporting, and model presentation. Our work builds on initiatives to improve diagnostic and prognostic clinical research, including the PROGnosis RESearch Strategy (PROGRESS) series of papers and textbook and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. J Orthop Sports Phys Ther 2021;51(10):517-525. doi:10.2519/jospt.2021.10697.

Comparison of quantitative internal and external measures of performance for trainees in cytopathology fellowships

Cytopathology fellowships need measures to assess performance of fellows. We sought to compare several internal quantitative assessment metrics in our fellowship with external metrics, such as performance on the American Society of Cytopathology (ASC) Progressive Evaluation of Competency (PEC) examination and United States Medical Licensing Examination (USMLE). Quantitative parameters generated from our laboratory information system (LIS) on cytopathology fellows were evaluated over 6 years, including case volume and diagnostic discrepancies, in addition to ASC PEC and USMLE scores. For discrepancy reports, interpretations made by the fellow were compared with that of the cytopathologist, and classified as none (concordant), minor (<2-levels) or major (≥2-levels). We evaluated internal and external metrics on 13 fellows over 6 years. The program average diagnostic concordance rate was 89.9%, with an average major discrepancy rate of 1.5%, and an average monthly case volume of 260 cases. More fellows with above-average ASC PEC performance showed above-average concordant diagnoses and lower case volume, while below-average PEC scores were seen more often with higher major discrepancy rates. More fellows with above-average USMLE scores had higher case volumes, while low USMLE scores showed a trend towards higher major discrepancy rates. Our fellowship program has used a variety of internal and external measures of performance for cytopathology fellows. Although the findings show no statistically significant finding correlating performance, these quantitative parameters generated from our LIS were helpful to identify areas of improvement, facilitate comparison to peers, and provide case volume documentation.

Evaluating Correlations of External Factors and Performance Measures of the Multimodal Transportation System in Florida

The objective of this study is to quantify the correlation between nearly 100 external factors and over 70 performance measures of the Florida multimodal transportation system, based on the empirical data covering a 10 year period (2009–2018). We first use time-lagged cross-correlation to quantify the correlation between each pair of external factors and performance measures. We then identify the highly correlated external factors with all performance measures or a subset of them. We find that Percentage of Population in Poverty (Florida), Number of Housing Units (Florida), House Price Index (National), Consumer Price Index–Rent Price Index (Florida), and Percentage of Population in Poverty (National) are the external factors highly correlated with the whole system, while the external factors highly correlated with different modes vary. This paper thus contributes to the transportation performance measurement literature by proposing a practical procedure based on time series analysis to help transportation agencies identify important external factors for tracking and monitoring.

AN OVERVIEW OF CONSTRUCTION PERFORMANCE MEASUREMENT TOWARD PRODUCTIVITY IMPROVEMENT

The awareness of construction performance measurement which involves setting new operating targets and standards has been raised among majority of construction organizations. This is so because of the ever-increasing requirements and expectations of the clients which necessitated continuous improvement in the cost, timing and quality of construction output. Productivity as an important issue in any industry can only be improved when the effect of changes applied on method, effort and system that can be measured. This paper reviewed internal performance measurement which makes a construction organization focuses on identifying improvement areas within its structure through comparing its business operations with others who do it better, through setting new targets to meet. The paper identified key performance indicators of internal performance measurement which include; time, cost, quality, client satisfaction, effective communication, safety and health and risk management. Lastly, the paper reviewed external performance measurement which makes the construction industry as a whole attempt to improve its productivity through designing tools and techniques applicable to construction. The paper concluded that, in order to improve construction performance, it is essential to have accurate measurements reflecting current trends and practice toward effective productivity. The construction industries should effectively take up internal performance measurements and be more open to external performance measurements that have been successful in other industries and assess if adaptable to construction projects.

THE EFFECT OF PERFORMANCE MEASUREMENT SYSTEMIMPLEMENTATION ON THE JAMBI LOCAL GOVERNMENT PERFORMANCE

The purpose of this study is to investigate the effect of the implementation factors of Performance Measurement System (PMS) on government institution to the organizational performance of the Regional Device Work Unit (RDWU). Implementation, use and accountability are the stages of activity undertaken in the design of performance measurement systems. While the performance of the organization is the achievement of management as measured from the level of achievement of the program plan determined by the realization of the program that has been determined. Performance is measured by using a financial and non-financial approach whose data comes from the financial bureau and the regional organization bureau of Jambi province. The respondents of this study are 52 civil servants who are mostly part of the program staff. The type of data used is primary and secondary data from over 50 RDWU Jambi. This study is based on Quantitative Method of Structural Equation Modeling method using Partial Least Square (WarpPLS 6.00) analysis to test the hypothesis built. There is evidence that management commitment and leadership style have a significant positive effect on the use of PMS information. Legislative Council Authority and Supervision of Supreme Audit Board (SAB) also have a significant effect on performance accountability. This research has theoretical implication that institutional isomorphism occurs in everyday practice in the field and it plays an important role in the implementation process of performance measurement and accountability system in the public sector, especially in government of Jambi province. From the policy-making perspective, this research can be considered by the central government, legislative council and SAB that internal and external performance measurement system has a positive effect on the use of PMS and performance accountability in the provincial government of Jambi.

Analyzing the impact of environmental collaboration among supply chain stakeholders on a firm’s sustainable performance

In the era of industrialization, environmentalists are more concerned with the environment and so are continuously interested in investigating organizational factors that can facilitate the transition towards sustainability. This research systematically investigates the impact of the supply chain partner’s collaborative approach towards green practices on a firm’s sustainability performance. Stakeholder and coordination theories are used to underpin the study. A structural equation modeling technique is adopted to analyze data collected from 126 green supply chain professionals working at various manufacturing firms operating in Pakistan using a survey questionnaire. The results indicate significant and positive impacts of institution pressure and customer monitoring on the adoption of green supply chain management (GSCM) practices by organizations. This study also explains that organizational GSCM practices, external GSCM practices and performance measures have positive and significant relationships. These findings reveal that it is important for managers to address external GSCM pressures by adopting green practices and being a focal firm should undertake GSCM initiatives in collaboration with their suppliers and customers to achieve a holistic impact which ultimately leads to betterment in overall sustainability performance.

Evaluating the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF) over-reporting scales in a military neuropsychology clinic

ABSTRACTIntroduction: This study examines the utility of the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF) validity scales to detect invalid responding within a sample of active duty United States Army soldiers referred for neuropsychological evaluations.Method: This study examines the relationship between performance validity testing and performance on the MMPI-2-RF over-reporting scales. Specifically, mean differences between those who passed (n = 152; 75.6%) or failed (n = 49; 24.4%) performance validity testing were compared. Receiver operator characteristic analyzes were also conducted to expand available information on the MMPI-2-RF over-reporting sensitivity and specificity in an Army sample.Results: This study has two distinct findings. First, effect size differences between those passing and failing performance validity testing are classified as small to medium in magnitude (ranging from d = . 30/g = .32 on F-r to d = .66/g = .73 on RBS). Second, over-reporting scales have higher specificity and poorer sensitivity. Likewise, performance of the over-reporting scales suggests that those who exceeding recommended cut scores are likely to have failed extra-test performance validity measures.Conclusion: These findings suggest that many who fail external performance measures may be undetected on the MMPI-2-RF over-reporting scales and that those exceeding recommended cut scores are likely to have failed extra-test performance validity testing. Implications for research on, and practice with, the MMPI-2-RF in military populations are discussed.

Clustering Algorithms in an Educational Context: An Automatic Comparative Approach

Despite an increasing consensus regarding the significance of properly identifying the most suitable clustering method for a given problem, a surprising amount of educational research, including both educational data mining (EDM) and learning analytics (LA), neglects this critical task. This shortcoming could in many cases have a negative impact on the prediction power of both the EDM and LA based approaches. To address such issues, this work proposes an evaluation approach that automatically compares several clustering methods using multiple internal and external performance measures on 9 real-world educational datasets of different sizes, created from the University of Tartu's Moodle system, to produce two-way clustering. Moreover, to investigate the possible effect of normalization on the performance of the clustering algorithms, this work performs the same experiment on a normalized version of the datasets. Since such an exhaustive evaluation includes multiple criteria, the proposed approach employs a multiple criteria decision-making method (i.e., TOPSIS) to rank the most suitable methods for each dataset. Our results reveal that the proposed approach can automatically compare the performance of the clustering methods and accordingly recommend the most suitable method for each dataset. Furthermore, our results show that in both normalized and nonnormalized datasets of different sizes with 10 features, DBSCAN and k-medoids are the best clustering methods, whereas agglomerative and spectral methods appear to be among the most stable and highly performing clustering methods for such datasets with 15 features. Regarding datasets with more than 15 features, OPTICS is among the top-ranked algorithms among the nonnormalized datasets, and k-medoids is the best among the normalized datasets. Interestingly, our findings reveal that normalization may have a negative effect on the performance of certain methods, e.g., spectral clustering and OPTICS; however, it appears to mostly have a positive impact on all of the other clustering methods.

Concept of Inclusive Optimal Performance (IOP): Theoretical and Conceptual understanding on the inclusive relationship between Internal Simultaneous Performance (ISP) and External Simultaneous Performance (ESP)

Small and Medium Enterprises play a significant role in economic well-being of a society, but unfortunately there is lack of proper structure and system for application of performance measurement in most of the SMEs. Hence, an inconsistency amongst the performance measures can be observed. The following paper focuses upon distinctive nature of small and medium business organizations (SMEs) and proposes a framework for effective performance measurement with respect to (SMEs). This research aims to provide such a framework for performance measurement that can ensure a balance between internal and external performance measures by introducing the concept of inclusive optimal performance (IOP) as a resultant of inclusive working between internal simultaneous performance (ISP) and external simultaneous performance (ESP). The need for inclusive strategic approach to internal and external performance factors has arrived primarily because of one sided competitiveness is produced due to exclusive strategic approach to inclusive and external performance factors of organization. In order to explore the legitimacy of the depicted argument the following research is based on concept review methodology of literature review through which this research assess the extensive literature with respect to performance measurement systems within SMEs. Through the integration of the psychology-oriented constructs of the internal and external locus of control theory, this research stem the inclusive nature and working of internal and external factors of performance analogy as a basis of the proposed framework that can be applied to ensure effective performance measurement at SMEs.

Is there an association between Vice‐Chancellors’ compensation and external performance measures?

AbstractWe provide evidence on the pay for performance relation between Australian university Vice Chancellors’ compensation and independent measures of university teaching, research and other performance indicators provided by external ranking bodies. Our results show limited association between university rankings and Vice Chancellors’ compensation, but confirm that Vice Chancellors’ compensation is predominantly driven by size measures based on the different components of revenue. Further, we find that few universities offer performance‐based bonus payments. Our results are robust with respect to a number of sensitivity tests.

Is Apollo Tyres Creating or Destroying Shareholders’ Wealth?

There has been a growing concern about the performance measures based on traditional accounting information such as return on equity (ROE), return on capital employed (ROCE), return on net worth (RONW), earning per shares (EPS), net operating profit after taxes (NOPAT) and return on investment (ROI). These measures although widely used fail to capture the shareholders’ value creation/destruction as a result of management actions. The concept of economic value added (EVA) and market value added (MVA) have gained popularity all over the world particularly in the USA, the UK and European countries. EVA and MVA are finding acceptance as internal and external performance measures because these two measures are consistent with the organizational objective of shareholders’ value creation. Due to its popularity, a lot of research work has been conducted in the late 1990s covering diverse issues on EVA and MVA. In the light of the above, the present study made an attempt to calculate EVA as well as MVA and to analyze whether Apollo Tyres is creating or destroying shareholders’ wealth during the study period.

Cohesion and Performance in Military Occupation Specialty Training.

Cohesion within military teams is not only vital to their performance but also modulates the adverse impact of work stressors on mental health, including depression, distress, and morale. This study stems from previous findings concerning cohesion during recruit training in the Australian Army. In that study, ratings of cohesion clustered on three dimensions, namely horizontal bonding among team members, vertical bonding with leaders, and organizational bonding with the wider army. Ratings on all three dimensions increased during recruit training, similar to what has been during U.S. Army basic training. The present study takes the next step, which is to determine the relationship between team cohesion and external measures of group performance during training in three types of military occupational specialty, specifically, infantry, quartermaster, and administrative clerk. The final sample of respondents consisted of 261 infantry trainees, 22 quartermaster trainees, and 39 administrative clerk trainees. These sample sizes, their gender distribution (9% female), and age distribution are proportional to their representation in the Australian Army. The questionnaires given to trainees and their instructors were adapted from Siebold and Kelly's Platoon Cohesion Index used for measuring the types of bonding within a team. The questionnaire for trainees was administered three times during their respective courses. The cohesion questionnaire for instructors was administered at the completion of training. This study was conducted under defence ethics approval DPR-LREP 069-15. The trainees' ratings of horizontal, vertical, and organizational bonding generally started at a high value and further increased throughout each of the three courses. Vertical bonding tended to be higher than the horizontal bonding, which in turn was consistently higher than organizational bonding. At the end of each course, the trainees' ratings of horizontal bonding had a large significant correlation with their instructors' ratings of the trainees' horizontal bonding (r=0.70), while the ratings of vertical bonding by the trainees versus their instructors had a smaller correlation (r=0.21). In relation to the trainees' individual grades on their course, the trainees' grades were not significantly correlated with their section's horizontal bonding (r=0.29), while their section's mean grade was correlated with their instructors' ratings of horizontal bonding (r=0.44). The present results during military occupational specialty training paralleled previous findings that Australian Army recruits quickly developed solid team cohesion early in their training, which generally continued to rise in all three courses. Furthermore, as seen previously with recruits, vertical bonding between section members in all three courses and their instructor leaders tended to be higher than horizontal bonding among team members, which in turn was higher than vertical bonding of the trainees with the wider Army. These findings have useful implications for health professionals. When discussing feelings of depression, distress, and low morale, health professionals might explore a military member's sense of bonding with their team members, their leaders, and their wider organization as possible contributors to their concerns. By the same token, advice aimed at promoting cohesion may help evoke their protective effects.

The key factors of total quality management in the service sector: a cross-cultural study

PurposeLiterature refers to the key factors of total quality management (TQM) based on studies carried out in individual countries. However, few studies focus on studying the TQM factors in service companies based on multinational data. The purpose of this paper is to empirically identify the key TQM factors and their impact on internal and external customer performance measures across different countries.Design/methodology/approachThe research questions regarding the TQM factors and their effects were examined using a sample of service organisations from three countries (131 from Greece, 70 from Mexico and 151 from Spain). TQM factors and their impact on employee and customer satisfaction were analysed separately for each country. Exploratory factor analyses, coupled with multiple linear regression analyses, were conducted.FindingsThe key TQM factors identified are common among the three participating countries and can be summarised as follows: quality practices of top management, process management, employee quality management, customer focus, and employee knowledge and education. The adoption level of these five key factors of TQM varies across service organisations in different countries. The results also confirmed that some of the TQM elements are antecedents of customer- and employee-focused performance.Practical implicationsMultinational service organisations may use such an instrument to evaluate TQM implementation among worldwide operations and then benchmark their performance. In addition, an understanding of similarities and differences among countries would help managers around the world to address difficulties of TQM implementation related to the country culture.Originality/valuePrevious studies have compared key TQM factors across different countries in manufacturing, but overall, there has been a little attempt in the literature to analyse the adoption of TQM factors among service firms, as well the relationships between quality improvement and performance across different geographical regions.