Internal Performance Measures Research Articles

Internal Performance Measures and Earnings Management: Evidence from Segment Earnings

ABSTRACT We examine whether the design of a firm’s internal management accounting system is associated with GAAP earnings management. We exploit the fact that ASC 280 mandates a “management approach” requiring multisegment firms to disclose their segment earnings as defined internally by their management accounting system. We posit that the less these segment earnings are decoupled from GAAP earnings, the higher are the costs of earnings management because earnings management spills over to segment earnings and distorts information used for internal decision-making. Thus, we predict that firms with more decoupled segment earnings engage in more earnings management. Using a large sample of U.S. firms from 1998 to 2020, we find support for this prediction. We also find that the decision usefulness of segment earnings for segment investment purposes decreases as earnings management increases, with this association being more pronounced for firms with less decoupled segment earnings measures. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M41; G14.

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.

Developing a performance management toolkit for Botswana tennis clubs

Performance management facilitates the attainment of goals in sport organisations. tennis clubs should implement performance management practices to maximise their profit, improve their services delivery and stakeholder satisfaction. In Botswana, tennis does not have a large followership or high participation rate. Therefore, Botswana tennis Clubs could use performance management initiatives to improve their organisational practices, participation rates and profitability. Thus, the aim of this paper was to develop and propose a performance management toolkit for Botswana tennis clubs. The paper relied on a review of the Balanced Scorecard, the Performance Prism, and components of performance management. Based on the review, a performance management toolkit for Botswana tennis clubs was developed. The toolkit comprises of planning for the external environment, planning for the internal environment, implementation, and performance measurement. The toolkit recognises the cyclic nature of the performance management process therefore, the feedback and feedforward obtained from performance measurement are used to inform future performance management plans. This paper can benefit Botswana sport managers and help them to implement performance management practices. This paper can also contribute towards sport management research on performance management in sports clubs.

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.

Unveiling undergraduate production engineering students' comprehension of process flow measures

Paper aims This study analyzes the comprehension of production engineering students about the influence of some key variables on the process performance measures in a service process. Originality This paper points out the need for educators to re-evaluate their approaches to teaching the Operations Management (OM) principles related to process flow measures. Research method This study used scenario-based role-playing experiments with 2×2×2 between-subject factorial design with three independent variables (variability of activities, capacity utilization, and resource pooling) and four dependent variables related to key internal process performance measures (Flow Time, Overall Quality of service, Quality of service employees, and Queue Size). The sample was composed of 178 undergraduate production engineering students from a large university in Brazil from various institution units. Main findings These results show that students perceived the use of resource pooling as an impactful practice. However, the students did not correctly identify the effects of increasing resource utilization and the variability on flow time and queue size when activities are pooled. Implications for theory and practice The teaching of basic concepts of OM requires the support of computational tools. Undergraduate courses that contemplate subjects in the field of OM should work more intensely on simulation-based learning.

Pragmatic evaluation of data mining models based on quality assessment & metric analysis

Data mining is the hearth of most modern day cyber physical deployments. Due to such large-scale use cases, selection of these models is of primary importance while developing cyber physical systems. But mining models are highly variant in terms of their internal performance characteristics, which makes it difficult for researchers to identify optimum models for their application-specific & performance-specific deployments. Moreover, these data mining models also vary in terms of their internal qualitative & quantitative performance measures. These measures include, precision, accuracy, recall, sensitivity, deployment cost, computational complexity, scalability, etc. Due to such a wide variation in performance, it is ambiguous for researchers to identify optimum models for their application deployments. To reduce this ambiguity, a comparison of these models in terms of their performance-level nuances, function advantages, deployment-specific limitations, and context-specific future scopes is described in this text. Researchers will be able to identify optimum models for their functional-specific deployments. It was observed that Neural Network (NN) based models including Convolutional NNs, Region based NNs, Recurrent NNs, etc. showcased better functional characteristics when compared with linear mining models for large-scale use cases. To further simplify model selection, this text compared the underlying models in terms of their performance metrics including accuracy, complexity, scalability, etc. Depending on this performance-specific evaluation, it was observed that bioinspired models when combined with deep learning techniques can outperform existing models for multiple application scenarios. This will further allow readers to identify optimum models based on their performance-specific characteristics. This text also evaluates a novel Mining Scalability Metric (MSM), which combines primary & secondary performance measures, and assists in identification of mining techniques that have higher accuracy, with lower complexity, and faster response, thereby reducing the ambiguity of model selection process.

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.

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.

Alarm Prediction in Cellular Base Stations Using Data-Driven Methods

The importance of cellular networks continuously increases as we assume ubiquitous connectivity in our daily lives. As a result, the underlying core telecom systems have very high reliability and availability requirements, that are sometimes hard to meet. This study presents a proactive approach that could aid satisfying these high requirements on reliability and availability by predicting future base station alarms. A data set containing 231 internal performance measures from cellular (4G) base stations is correlated with a data set containing base station alarms. Next, two experiments are used to investigate (i) the alarm prediction performance of six machine learning models, and (ii) how different predict-ahead times (ranging from 10 min to 48 hours) affect the predictive performance. A 10-fold cross validation evaluation approach and statistical analysis suggested that the Random Forest models showed best performance. Further, the results indicate the feasibility of predicting severe alarms one hour in advance with a precision of 0.812 (±0.022, 95 % CI), recall of 0.619 (±0.027) and F1-score of 0.702 (±0.022). A model interpretation package, ELI5, was used to identify the most influential features in order to gain model insight. Overall, the results are promising and indicate the potential of an early-warning system that enables a proactive means for achieving high reliability and availability requirements.

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.

Gathering Validity Evidence on an Internal Medicine Clerkship Multistep Exam to Assess Medical Student Analytic Ability

Construct: The definition of clinical reasoning may vary among health profession educators. However, for the purpose of this paper, clinical reasoning is defined as the cognitive processes that are involved in the steps of information gathering, problem representation, generating a differential diagnosis, providing a diagnostic justification to arrive at a leading diagnosis, and formulating diagnostic and management plans. Background: Expert performance in clinical reasoning is essential for success as a physician, and has been difficult for clerkship directors to observe and quantify in a way that fosters the instruction and assessment of clinical reasoning. The purpose of this study was to gather validity evidence for the Multistep exam (MSX) format used by our medicine clerkship to assess analytical clinical reasoning abilities; we did this by examining the relationship between scores on the MSX and other external measures of clinical reasoning abilities. This analysis used dual process theory as the main theoretical framework of clinical reasoning, as well as aspects of Kane’s validity framework to guide the selection of validity evidence for the investigation. We hypothesized that there would be an association between the MSX (a three-step clinical reasoning tool developed locally), and the USMLE Step 2 CS, as they share similar concepts in assessing the clinical reasoning of students. We examined the relationship between overall scores on the MSX and the Step 2 CS Integrated Clinical Encounter (ICE) score, in which the student articulates their reasoning for simulated patient cases, while controlling for examinee’s internal medicine clerkship performance measures such as the NBME subject exam score and the Medicine clerkship OSCE score. Approach: A total 477 of 487 (97.9%) medical students, representing the graduating classes of 2015, 2016, 2017, who took the MSX at the end of each medicine clerkship (2012–2016), and Step 2 CS (2013–2017) were included in this study. Correlation analysis and multiple linear regression analysis were used to examine the impact of the primary explanatory variables of interest (MSX) onto the outcome variable (ICE score) when controlling for baseline variables (Medicine OSCE and NBME Medicine subject exam). Findings: The overall MSX score had a significant, positive correlation with the Step 2 CS ICE score (r = .26, P < .01). The overall MSX score was a significant predictor of Step 2 CS ICE score (ß = .19, P < .001), explaining an additional 4% of the variance of ICE beyond the NBME Medicine subject score and the Medicine OSCE score (Adjusted R2 = 13%). Conclusion: The stepwise format of the MSX provides a tool to observe clinical reasoning performance, which can be used in an assessment system to provide feedback to students on their analytical clinical reasoning. Future studies should focus on gaining additional validity evidence across different learners and multiple medical schools.

Impact of Corporate Governance on Firm's Financial Performance (AComparative Study of Developed and Non Developed Markets)

The study has been conducted to compare the impact of corporate governance on firm’s financial performance between USA (Developed Country) and Pakistan (Developing Country). The corporate governance is measured by Board’s Ownership, Effectiveness, Size and Structure, Its Independency, CEO Duality and Board’s education and Experience whereas the firm’s financial performance is measure by the Return on Asset and Return on Equity. A sample of 100 listed companies from Karachi Stock Exchange of Pakistan and New York Stock Exchange of USA has been investigated to analyze the relationship during the period 01 Jan 2010 to 31 Dec 2015 regarding their comparative firm’s financial performance in respect of Corporate Governance. The impact of corporate governance in the 100 listed firms that are taken in the sample from Pakistan and USA were analyzed through collection primary data by floating the questionnaire physically in Pakistan and online for USA. The internal and external performance of firm’s for both countries was measured by taking Return on Asset (ROA) and Return on Equity (ROE) as internal performance measure and Market to Book Value Ratio, of Tobin’s Q and Marris, for measuring the external performance. From the study it has been found that corporate governance codes are being followed in both countries but it is better in the developed countries. In Pakistan, due to most family owned business there exists some conflicts exists between the corporate governance codes drawn by SECP with their already drawn already set procedure whereas in USA strict Corporate Governance codes are being followed. The variables like Board ownership, Board Education and Experience, effectiveness and CEO Duality has positive relationship with the firm’s performance but Board Size has negative correlation. The study also found that there is no relationship between independency of director with firm’s performance.

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.

The Use of Learning and Study Strategies Inventory (LASSI) to Investigate Differences Between Low vs High Academically Performing Medical Students.

This study examines differences in the Learning and Study Strategies Inventory (LASSI) 10-subscale scores between low and high academically performing medical students relative to internal and external examinations. The LASSI instrument was administered to 180 medical students from three classes (2016, 2017, and 2018). To measure the strength of association between LASSI 10-subscale scores and performance on overall biomedical sciences and the United States Medical Licensing Examination (USMLE) Step 1, Pearson product-moment correlation analyses were performed. One-way ANOVA and Bonferroni post-hoc analyses were performed to identify statistically significant differences on LASSI scores between students grouped by quartiles according to their performance on USMLE Step 1 examination (external performance measure) and by their average letter grade in the overall biomedical sciences (internal performance measure). Significant associations were observed between Anxiety, Motivation, and Test Strategies and students' performance on both overall biomedical sciences and USMLE Step 1 examinations. Anxiety, Motivation, and Test Strategies were different between "A" and "C" students in the overall biomedical sciences. Anxiety, Information Processing, Motivation, Selecting Main Idea, and Test Strategies were significantly different between the upper and lower quartiles in USMLE Step 1 student performance. Anxiety, Motivation and Test Strategies are the main LASSI subscales that were significantly different between high-performing and low-performing students for internal and external examinations. Interestingly, the same LASSI subscales Anxiety, Motivation, and Test Strategies were correlated with students' performance in internal and external examinations.

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.

EFFECTS OF AUDIT INTERNAL PERFORMANCE MEASURES, PROFESSIONAL EXPERTISE, AND INTERNAL AUDITOR WORK EXPERIENCE ON UNIVERSITY PERFORMANCE

The purpose this research is to research influenc of internal audit performance measures , professional expertise a nd auditor work experience towards state universities in Banten. The population in this study were auditors of the Internal Audit Unit (SPI) at State Universities in Indonesia. The research sample is the SPI auditor and the former State University SPI auditor in Banten. Data collection is done by coming directly to the SPI auditor and former auditor of State University SPI in Banten. This research was conducted by survey method through a questionnaire to SPI auditors and former SPI auditor of State University in Banten. Total of State Universities in Serang is 4 universities. From the survey result, the number of SPI auditors at State University in Banten is 117, consisting of 73 former SPI auditors and 44 SPI auditors. So, the number of distributed questionnaires same as proportion of total samples or respondents is 117 questionnaires. Data processing until hypothesis testing in this study uses SPSS. The results of this study are (1) size of internal audit performance has a significant positive effect on university performance, (2) professional expertise has no significant positive effect on university performance and (3) work experience of internal auditors has a significant effect on university performance.

Use of Internal Performance Measurement to Guide Improvement Within Medical Groups

Public reporting of provider performance currently encompasses a range of measures of quality, cost, and patient experience of care. However, little is known about how medical groups use measures for performance improvement. This information could help medical groups undertake internal measurement while helping payers, policy makers, and measurement experts develop more useful publicly reported measures and quality improvement strategies. An exploratory, qualitative study was conducted of ambulatory care medical groups across the United States that currently gather their own performance data. Eighty-three interviews were conducted with 91 individuals representing 37 medical groups. Findings were distilled into three major themes: (1) measures used internally, (2) strategies for using internal measurement for performance improvement, and (3) other uses of internal measurement. Medical groups used both clinical and business process measures, including measures from external measure sets and internally derived measures. Strategies for using internal measurement for quality improvement included taking a gradual, iterative approach and setting clear goals with high priority, finding workable approaches to data sharing, and fostering engagement by focusing on actionable measures. Measurement was also used to check accuracy of external performance reports, clarify and manage conflicting external measurement requirements, and prepare for anticipated external measurement requirements. Respondents in most groups did not report a need to assess costs of internal measurement or the capacity to do so. Despite challenges and barriers, respondents found great value in conducting internal measurement. Their experiences may provide valuable lessons and knowledge for medical group leaders in earlier stages of establishing internal measurement programs.

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.

The impact of accreditation agencies and other powerful stakeholders on the performance measurement in Polish universities

PurposeThe purpose of this paper is to extend the understanding of how internal organisational processes change in response to external demands, by investigating the changes undertaken by two Polish business schools (b-schools) in anticipation of and in response to the demands of accreditation agencies (AAs) and other powerful stakeholders. Specifically, it examines the internal research-related performance measurement (PM) system and changes in the use of performance information (PI).Design/methodology/approachThe case study method is adopted, using data from publicly available documents and interviews with the faculty and management at the two schools. The data are interpreted and analysed using the neo-institutional theory.FindingsPowerful stakeholders are the primary reason for changes in PM systems and the manner in which PI is used. Specifically, AAs reflect an additional layer in the PM system, allowing for a downward cascading PI effect. This also leads to a wider use of PI across different organisational levels.Research limitations/implicationsThis study focusses on two case studies in a region still undergoing transition. Thus, this analysis could be reinforced through additional cases, different data collection methods and cross-country and between-country comparative analyses.Originality/valueThe changes in PM systems and particularly the use of PI are discussed in the context of Polish higher education (HE) and, more broadly, the entire Central and Eastern Europe (CEE) region. Moreover, the consideration of two b-school cases facilitates a comparative analysis of the differences in PM systems and the use of PI in the context of stakeholders’ PI needs.