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The aim of this study is to empirically explore and analyze the concrete tasks of output measurement and the inherent challenges related to these tasks in a traditional and autonomous professional public work setting – the judicial system.

The analysis of the tasks is based on a categorization of general performance measurement motives (control-motivate-learn) and main stakeholder levels (society-organization-professionals). The analysis is exploratory and conducted as an empirical content analysis on materials and reports produced in two performance improvement projects conducted in European justice organizations.

The identified main tasks in the different categories are related to managing resources, controlling performance deviations, and encouraging improvement and development of performance. Based on the results, key improvement areas connected to output measurement in professional public organizations are connected to the improvement of objectivity and fairness in budgeting and work allocation practices, improvement of output measures' versatility and informativeness to highlight motivational and learning purposes, improvement of professional self-management in setting output targets and producing outputs, as well as improvement of organizational learning from the output measurement.

The paper presents empirically founded practical examples of challenges and improvement opportunities related to the tasks of output measurement in professional public organization.

This paper fulfils an identified need to study how general performance management motives realize as concrete tasks of output measurement in justice organizations.

Intellectual capital (IC) plays a crucial role in today’s volatile business landscape, yet its measurement remains complex. To better navigate these challenges, the authors propose the Integrated Intellectual Capital Measurement (IICM) model, an innovative, robust and comprehensive framework designed to capture IC amid business uncertainty. This study focuses on IC measurement models, typically reliant on secondary data, thus distinguishing it from conventional IC studies.

The authors conducted a systematic literature review (SLR) and bibliometric analysis across Web of Science, Scopus and EBSCO Business Source Ultimate in February 2023. This yielded 2,709 IC measurement studies, from which the authors selected 27 quantitative papers published from 1985 to 2023.

The analysis revealed no single, universally accepted approach for measuring IC, with company attributes such as size, industry and location significantly influencing IC measurement methods. A key finding is human capital’s critical yet underrepresented role in firm competitiveness, which the IICM model aims to elevate.

This is the first SLR focused on IC measurement amid business uncertainty, providing insights for better management and navigating turbulence. The authors envisage future research exploring the interplay between IC components, technology, innovation and network-building strategies for business resilience. Additionally, there is a need to understand better the IC’s impact on specific industries (automotive, transportation and hospitality), Social Development Goals and digital transformation performance.

This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.

In typical model-based calibration, linearization errors are derived inevitably, and non-negligible negative impact will be induced on the identification results if the rotational kinematic errors are not small enough or the lengths of links are too long, which is common in the industrial cases. Thus, an accurate two-step kinematic calibration method minimizing the linearization errors is presented for a six-DoF serial robot to improve the calibration accuracy.

The negative impact of linearization on identification accuracy is minimized by removing the responsible linearized kinematic errors from the complete kinematic error model. Accordingly, the identification results of the dimension-reduced new model are accurate but not complete, so the complete kinematic error model, which achieves high identification accuracy of the rest of the error parameters, is combined with this new model to create a two-step calibration procedure capable of highly accurate identification of all the kinematic errors.

The proportions of linearization errors in measured pose errors are quantified and found to be non-negligible with the increase of rotational kinematic errors. Thus, negative impacts of linearization errors are analyzed quantitatively in different cases, providing the basis for allowed kinematic errors in the new model. Much more accurate results were obtained by using the new two-step calibration method, according to a comparison with the typical methods.

This new method achieves high accuracy with no compromise on completeness, is easy to operate and is consistent with the typical method because the second step with the new model is conveniently combined without changing the sensors or measurement instrument setup.

This paper aims to explore the development of investment decision tools by understanding the rationality behind the disposition effect. We suspect that not all disposition decisions are irrational. The decisions should be evaluated based on the bounded rationality of the individuals’ target and tolerance level, which is not covered in previous literature. Adding the context of individual preference (target and tolerance) in their decision could improve the classic measurement of disposition effect.

The laboratory web experiment is prepared to collect the responses in holding and selling the stocks within 14 days. Two groups of Gen Z investors are observed. The control group makes a decision based on their judgment without any system recommendation. In contrast, the second group gets help inputting their target and tolerance. Furthermore, the framing effect is also applied as a reminder of their target and tolerance to induce more holding decisions on gain but selling on loss.

The framing effect is adequate to mitigate the disposition effect but only at the early day of observation. Bounded rationality explains the rationality of liquidating the gain because the participants have reached their goal. The framing effect is not moderated by days to affect the disposition effect; over time, the disposition effect tends to be higher. A new measurement of the disposition effect in the context of bounded rationality is better than the original disposition effect coefficient.

Gen Z investors need a system aid to help their investment decisions set their target and tolerance to mitigate the disposition effect. Investment firms can make a premium feature based on real-time market data for investors to manage their assets rationally in the long run. Bounded rationality theory offers more flexibility in understanding the gap between profit maximization and irrational decisions in behavioral finance. The government can use this finding to develop a suitable policy and ecosystem to help beginner investors understand investment risk and manage their assets based on subjective risk tolerance.

The classic Proportion Gain Realized (PGR) and Proportion Loss Realized (PLR) measurements cannot accommodate several contexts of users’ targets and tolerance in their choices, which we argue need to be re-evaluated with bounded rationality. Therefore, this article proposed new measurements that account for the users’ target and tolerance level to evaluate the rationality of their decision.

The quest for improved facilities management (FM) delivery is receiving immense focus through the incorporation of innovative technologies such as cyber-physical systems (CPS). The system’s high computational capabilities can aid in the abatement of some of the challenges plaguing FM functions. However, the requisite ingredients for the uptake of the system for FM have still not gained scholarly attention. Because performance measurement is a vital index in determining the outcome of FM methods, this study aims to investigate the influence of performance measurement indicators that are influential to the uptake of CPS for delivering FM functions.

A qualitative technique was adopted using the Delphi technique. The panel of experts for the study was selected through a well-defined process based on stipulated criteria. The experts gave their opinions in two rounds before consensus was attained on the identified performance measurement indicators, whereas methods of data analysis were measures of central tendency, inter-quartile deviation and Mann–Whitney U test.

Results from this study showed that 11 of the performance indicators were of very high significance in the determination of the uptake of CPS for FM functions, whereas 5 of the indicators were proven to be of high significance. Furthermore, there was no statistical difference in the opinions of the experts based on their affiliation with academic institutions and professional practice.

The findings of this study contribute practically by aiding policymakers, facility managers and relevant stakeholders with the vital knowledge of delivery mandates for efficient FM services that can spur the uptake of digital technologies such as CPS.

This study contributes to the body of knowledge as it unveils a roadmap of the expected performance output and its accompanying evaluation that would drive the adoption of a promising technology such as CPS in the delivery of FM tasks.

Purpose: Different approaches have been applied to define and measure service quality as both a process and a performance-intensive presentation. The literature agreed that service quality assessments are essential in measuring performance, however they could only reach a partial consensus. Although the Servqual scale is the most widely known and used tool in measuring service quality, it has received various criticisms. This study presents the difficulties encountered in measuring service quality and an evaluation of the Servqual scale.

Methodology: The study provides a conceptual framework, examining the research on the subject in the literature and the criticisms of the Servqual scale. It is designed as a compilation/assessment to improve the perspectives of the interested parties by bringing together evaluations about the pros and cons of the most well-known service quality scale.

Findings: Measurement of service quality is a complex business. Although it has received various criticisms, the Servqual scale which enables the measurement of quality in the services sector is still the most frequently used measurement tool.

Practical Implications: The difficulty of defining the service and its quality, which arises from the characteristics of the services, does not eliminate the necessity of measuring the quality of the service. With this study, a conceptual framework has been provided on the subject, a collective evaluation has been made for the most widely used scale, and the ground has been prepared for improvement and development in the criticisms of the scale.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

Performing accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various manufacturing steps, e.g. heat treatment or cutting techniques, the magnetic material properties can strongly vary locally, and the assumption of homogenized global material parameters is no longer feasible. This paper aims to present the general methodology and two different solution strategies for determining the local magnetic material properties using reference and simulation data.

The general methodology combines methods based on measurement, numerical simulation and solving an inverse problem. Therefore, a sensor-actuator system is used to characterize electrical steel sheets locally. Based on the measurement data and results from the finite element simulation, the inverse problem is solved with two different solution strategies. The first one is a quasi Newton method (QNM) using Broyden's update formula to approximate the Jacobian and the second is an adjoint method. For comparison of both methods regarding convergence and efficiency, an artificial example with a linear material model is considered.

The QNM and the adjoint method show similar convergence behavior for two different cutting-edge effects. Furthermore, considering a priori information improved the convergence rate. However, no impact on the stability and the remaining error is observed.

The presented methodology enables a fast and simple determination of the local magnetic material properties of electrical steel sheets without the need for a large number of samples or special preparation procedures.

This study aims to conceptualize, rethink and systematize methods used for measurement and evaluation (M&E) corporate communication.

The reflection is based on 462 key English-language books and papers devoted to M&E in the fields of corporate communication and public relations from the 1970th to 2023. Keywords in the titles and abstracts found the necessary materials. A critical analysis of the central concepts, models and methods described in the literature was conducted. As a result, a new model that unifies and structures the M&E toolkit is proposed for discussion.

Despite the significant contribution to developing a wide range of M&E models, they are still not perfect and universal. In addition, this system of approaches is continuously self-evolving and changing under the influence of digital innovations, so it requires steady rethinking and updating. On the other hand, most previous studies focused on communication management processes, losing focus on communication aspects. This led to the need for an alternative view based on proven theories to fill this gap. The proposed model combines quantitative and qualitative M&E methods for the five main components of corporate communication (communicator, audience, content, channels and result), covering a wide range of tools, from statistical and sociological research to big data analysis and neuro research.

This work contributes to developing the M&E theory of corporate communication, systematizing existing methods and opening new research perspectives. From a practical point of view, companies can use the presented approach for a more accurate and objective internal evaluation of the main components of corporate communication.