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The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.

This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration

The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.

The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.

The purpose of this paper is to develop a coherent and robust methodology for social impact measurement of social enterprises (SEs) that would provide the conceptual and practical bases for training and embedding.

The paper presents a holistic impact measurement model for SEs, called social impact for local economies (SIMPLEs). The SIMPLE impact model and methodology have been tried and tested on over 40 SEs through a series of three day training courses, and a smaller number of test cases for embedding. The impact model offers a five‐step approach to impact measurement called SCOPE IT; MAP IT; TRACK IT; TELL IT and EMBED IT. These steps help SE managers to conceptualise the impact problem; identify and prioritise impacts for measurement; develop appropriate impact measures; report impacts and embed the results in management decision making.

Preliminary qualitative feedback from participants reveals that while the SIMPLE impact training delivers positive learning experiences on impact measurement and prompts, in the majority of cases, the intensions to implement impact systems, the majority feels the need for follow up embedding support. Paricipant's see value in adopting the SIMPLE approach to support business planning processes. Feedback from two SEs which has receives in‐house facilitates embedding support clearly demonstrates the benefits of working closely with an organisation's staff team to enable effective implementation.

Some key future research challenges are identified as follows: systematically research progress in implementation after training for those participants that do not have facilitated embedding; to further test and develop embedding processes and models (using SIMPLE and other methods) with more SE organisations to identify best practices.

The SIMPLE fills a gap as a tool for holistic impact thinking that offers try and test accessible steps, with robust measures. The innovative steps take SEs through all key impact thought processes from conceptualisation to embedding guidance, feeding into business planning and strategic decision‐making processes. The comparison between the limitations of stand alone impact training and the benefits of facilitated embedding processes is instructive.

Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. The purpose of this paper is to present a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as partial least squares (PLS) path modeling.

A simulation study allows us to assess the suitability of the MICOM procedure to analyze the measurement invariance in PLS applications.

The MICOM procedure appropriately identifies no, partial, and full measurement invariance.

The statistical power of the proposed tests requires further research, and researchers using the MICOM procedure should take potential type-II errors into account.

The research presents a novel procedure to assess the measurement invariance in the context of composite models. Researchers in international marketing and other disciplines need to conduct this kind of assessment before undertaking multigroup analyses. They can use MICOM procedure as a standard means to assess the measurement invariance.

In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset.

In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments.

Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model.

For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.

Measurement uncertainty is present in all measurement processes in the field of production engineering. However, this uncertainty should be minimized to avoid erroneous decisions. Present methods to determine the measurement uncertainty are either only applicable to certain processes and do not lead to valid results in general or require a high effort in their application. To optimize the costs and benefits of the measurement uncertainty determination, a method has to be developed which is valid in general and easy to apply. The paper aims to discuss these issues.

This paper presents a new technique for determining the measurement uncertainty of complex measurement processes. The approximation capability of artificial neural networks with one hidden layer is proven for continuous functions and represents the basis for a method for determining a measurement model for continuous measurement values.

As this method does not require any previous knowledge or expertise, it is easy to apply to any measurement process with a continuous output. Using the model equation for the measurement values obtained by the neural network, the measurement uncertainty can be derived using common methods, like the Guide to the expression of uncertainty in measurement. Moreover, a method for evaluating the model performance is presented. By comparing measured values with the output of the neural network, a range in which the model is valid can be established. Combining the evaluation process with the modelling itself, the model can be improved with no further effort.

The developed method simplifies the design of neural networks in general and the modelling for the determination of measurement uncertainty in particular.

This paper aims to propose a four-point measurement model for directly measuring the pose (i.e. position and orientation) of industrial robot and reducing its calculating error and measurement uncertainty.

A four-point measurement model is proposed for directly measuring poses of industrial robots. First, this model consists of a position measurement model and an orientation model gotten by the position of spherically mounted reflector (SMR). Second, an influence factor analysis, simulated by Monte Carlo simulation, is performed to investigate the influence of certain factors on the accuracy and uncertainty. Third, comparisons with the common method are carried out to verify the advantage of this model. Finally, a test is carried out for evaluating the repeatability of five poses of an industrial robot.

In this paper, results show that the proposed model is better than the three-SMRs model in measurement accuracy, measurement uncertainty and computational efficiency. Moreover, both measurement accuracy and measurement uncertainty can be improved by using the proposed influence laws of its key parameters on the proposed model.

The proposed model can measure poses of industrial robots directly, accurately and effectively. Additionally, influence laws of key factors on the accuracy and uncertainty of the proposed model are given to provide some guidelines for improving the performance of the proposed model.

The purpose of this paper is to develop a comprehensive continuous innovation capability (CIC) measurement model in manufacturing sectors.

The development of this CIC model was conducted through three stages of research, i.e. identification of manufacturing continuous innovation measures (MCIMs), development of measurement model, followed by model evaluation and validation. MCIMs were identified using systematic literature review and focus group discussion. Selection process for MCIMs employed the fuzzy Delphi method. To develop measurement model, contextual relationships between MCIMs were assessed using total interpretive structural modeling, followed by measurements of MCIMs weight with the analytical network process method. Then, assessment indicators for each MCIM and criteria were determined as well as mathematical model to measure CIC scores. Model evaluation and validation were performed in two case studies: in an automotive company and an electronics company.

This research produced 50 criteria and 103 assessment indicators, as well as mathematical model to measure CIC scores. The validation process showed that currently developed model was deemed valid.

The results of this research are expected to provide a practical input for manufacturing company managers in managing their innovation activities systematically and comprehensively.

The CIC model is a new comprehensive measurement model; it integrates three fundamental elements of CI capability measurement, considering all important dimensions in a company and also able to explain contextual relationships between measured factors.

To clarify the nature of the error term in formative measurement models, as it had been misinterpreted in prior research.

The error term in formative measurement models is analytically contrasted with the measurement errors typically found in reflective measurement models.

It is demonstrated that, unlike in reflective measurement, the error term in formative models is not measurement error but rather a disturbance representing non‐modeled causes. It is also shown that, under certain circumstances, the inclusion of an error term is not necessary/appropriate.

Focus is only on first‐order measurement models; higher‐order specifications are not considered.

The paper helps researchers in their initial specification of formative measurement models as well as their evaluation of the subsequent model estimates, leading to better specifications for formative constructs.

This paper verifies the adequacy of perceived quality measurement instruments by comparing the SERVPERF and EP methods. After a discussion of the differences between the two methods, a quality perception measurement instrument for the company is developed and applied to a sample of ceramic company clients. The methods are compared by analyzing the multitrait‐multimethod matrix using the structural equation model methodology. Results indicate that SERVPERF has greater reliability, greater convergent and discriminant validity, explains variance more completely, and consequently introduces less bias.

This paper revisits the recent work of Chan and Qi which proposed an innovative performance measurement method for supply chain management. While the measurement method has many advantages, it can be unwieldy in practice. This paper aims to address these limitations and to propose a more user‐friendly alternative performance measurement model.

The performance measurement model described in this paper is a combination of two existing methods: Chan and Qi's model and the supply chain operations reference (SCOR) model. To demonstrate the applicability of the combined approach, actual SCOR level 1 performance data and the measurement information from a case supply chain (SC) are collected and processed by Chan and Qi's measurement algorithm.

These two methods complement each other when measuring SC performance.

This paper develops a practical and efficient measurement model that can resolve SC performance problems by incorporating the strengths of two different measurement models to create a synergistic new model.