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The purpose of this paper is to understand failure to change by examining patterns of coherent structure and agency characteristics in changing organizations in specific sectors and to provide specific recommendations for intervention in these patterns.

A large survey in 367 organizations engaged in different change processes and from different sectors, among employees in different positions.

The paper finds that there are five patterns among changing organizations, each with their own specific problems, characteristics, and change approaches that require different interventions.

Parsimony in research models and the study of overall relations between variables does not help to understand failure to change. More integrative approaches are needed that take variety among changing organizations into account.

Change agents should not opt for a “one best strategy” for change but choose a contingent change approach that takes into consideration the specific characteristics of their organizations, change processes, and contexts in order to make change more successful.

This paper establishes that successful change cannot be explained by one or a few variables but is contingent on an interplay of agency, structure, and contextual characteristics. Together, these characteristics form constellations that characterize different sectors. The paper provides suggestions for more successful change.

The purpose of this paper is to test the applicability and the performance of an approach called logical analysis of data (LAD) on the detection of faults in rotating machinery using vibration signals.

LAD is a supervised learning data mining technique that relies on finding patterns in a binary database to generate decision functions. The hypothesis is that a LAD‐based decision model can be used as an effective tool for automatic detection of faults in rolling element bearings. A novel Multiple Integer Linear Programming approach is used to generate patterns for the LAD decision model. Frequency and time‐based features are extracted from rotor bearing vibration signals and are pre‐processed to be suitable for use with LAD.

The results show good classification accuracy with both time and frequency features.

The diagnostic tool implemented in the form of software in a production or operations maintenance environment can be very helpful to maintenance experts as it reveals the patterns that lead to the diagnosis in interpretable terms which facilitates efforts to understand the reasons behind the components' failure.

The proposed modifications to the LAD‐based decision model which is being tested for the first time in the field of fault detection in rotating machinery lead to improved accuracy results in addition to the added value of result interpretability due to this distinctive property of LAD.

This paper aims to present how iterative round‐trip modelling between two different business process modelling tools can be enabled on a conceptual level. Iterative round‐trip modelling addresses model transformations between high‐level business and executable process models, and how to maintain these transformations in change time. Currently, the development of these process models is supported by different tools. To the authors' best knowledge, no coherent collaborative tool environment exists that supports iterative round‐trip modelling.

This paper is primarily based on a literature review of state‐of‐the‐art business to IT transformations regarding business process modelling. The architecture of integrated information systems (ARIS) and Cordys tools are used as an example case in this research. ARIS is a business process analysis (BPA) tool suited for analyzing and designing business processes, while the execution and monitoring of these processes is allowed by Cordys, a business process management suite (BPMS). The theory is used for transforming between ARIS event‐driven process chains from the business perspective and business process modelling notation in Cordys from the IT perspective.

A conceptual framework is proposed to couple a BPA and BPMS tool for round‐trip business process modelling. The framework utilizes concepts from the model‐driven architecture for structurally addressing interoperability and model transformations. Ensuring iterative development with two tools requires traceability of model transformations.

In many organizations, BPA and BPMS tools are used for business process modelling. These are in practice often two different worlds, while they concern around the same business processes. Maintaining multiple versions of the same process models across two tools is a considerable task, as they often are subject to design changes. Interoperability between a BPA and BPMS tool will minimize redundant activities, and reduce business to IT deployment time.

This research provides a theoretical base for coupling a BPA and BPMS tool regarding iterative round‐trip modelling. It provides an overview of the current state‐of‐the‐art literature of business process modelling transformations, and what is necessary for maintaining interoperability between tools. The findings indicate what is expected in tool support for iterative development in business process modelling from analysis and design to execution.

This study aims to explore brand meaning from a consumer perspective, identifying tangible attributes and intangible associations and their arrangement in brand meaning frameworks. Previous literature has focused on brand meaning flowing from intangible associations, and new insights are offered into the tangible attributes’ contribution to brand meaning.

A phenomenological approach was adopted, and meanings were gathered from lived experiences with consumers of local food brands. Quasi-ethnographic methods were used, including accompanied shopping trips to food fairs and local farm shops, kitchen visits and in-depth interviews in and around the county of Dorset in the south-west of England.

The findings demonstrate that tangible attributes have sensorial and functional brand meanings and are mentally processed. Both hierarchical and flatter patterned approaches are present when connecting attributes and associations. The hierarchical approach reflects both short and long laddering approaches; the flatter alternative offers an interwoven, patterned presentation.

This is a small in-depth study of local food brands, and the findings cannot be generalised across other brand categories.

Local food brand practitioners can promote relevant sensorial (e.g. taste) and functional (e.g. animal welfare) attributes. These can be woven into appropriate intangible associations, creating producer stories to be communicated through their websites and social media campaigns.

A revised brand meaning theoretical framework updates previous approaches and develops brand meaning theory. The study demonstrates that tangible attributes have meaning and hierarchical connections across tangible attributes, and intangible associations should not always be assumed. An additional patterned approach is present that weaves attributes and associations in a holistic, non-hierarchical way.

Manufacturing firms are always faced with the problem of promoting operational performance and labor‐force management. The utilization of human resources is closely correlated with operations and production performance. This study investigates the correlation between human resource management (HRM) and business performance of large‐scale manufacturing firms in Taiwan. First, 16 subjects of HRM are designed to survey the importance level and achievement level of HRM by the sample firms. Productivity indices are also defined to measure business performance. Based on the survey, four critical HRM factors including 12 subjects are extracted by factor analysis. The difference between importance level and achievement level of subjects contained in each factor is examined. Furthermore, considering importance and achievement levels of HRM as features, fuzzy clustering analysis is employed to categorize the firms into four patterns. With various HRM characteristics, each pattern has different business performance in terms of productivity. Using a pattern approach, these findings can aid the firms in each pattern to improve their productivity by improving their HRM strategies.

The paper aims to investigate the possibilities to control friction in lubricated systems by surface patterning, making use of a multi-scale approach. Surface patterns inside the tribological contact zone tend to directly reduce friction, whereas surface patterns located in the close proximity of the contact area can improve the tribological performance by avoiding lubricant starvation and migration. Finally, optimized surface patterns were identified by preliminary laboratory tests and transferred to a journal bearing, thus testing them under more realistic conditions.

Surface patterns on a large scale (depth > 10 µm) were fabricated by micro- and roller-coining, whereas surface patterns on a small scale (depth < 2 µm) were produced by direct laser interference patterning. The combination of both techniques resulted in multi-scale surface patterns. Tribologically beneficial surface patterns (verified in ball-on-disk laboratory tests) were transferred onto a journal bearing’s shaft and tested on a special test-rig. To characterize the lubricant spreading behavior, a new test-rig was designed, which allowed for the study of the lubricant’s motion on patterned surfaces under the influence of a precisely controlled temperature gradient.

All tested patterns accounted for a pronounced friction reduction and/or an increase in oil film lifetime. The results from the preliminary laboratory tests matched well, with results from the journal bearing test-rig, both tests showing a maximum friction reduction by a factor of 3-4. Numerical investigations, as well as experiments, have shown the possibility to actively guide lubricant over patterned surfaces. Smaller periodicities, as well as greater structural depths and widths, led to a more pronounced anisotropic spreading and/or greater spreading velocities. Multi-scale surfaces demonstrated the strongest effects regarding the lubricant’s spreading behavior.

Friction, as well as lubricant migration, can be successfully controlled by using micro-coined, laser-patterned and/or multi-scale surfaces. To the best of the authors’ knowledge, the study demonstrates for the first time the unique possibility to transfer results obtained in laboratory tests to a real machine component.

The purpose of this paper is to address a classic problem – pattern formation identified by researchers in the area of swarm robotic systems – and is also motivated by the need for mathematical foundations in swarm systems.

The work is separated out as inspirations, applications, definitions, challenges and classifications of pattern formation in swarm systems based on recent literature. Further, the work proposes a mathematical model for swarm pattern formation and transformation.

A swarm pattern formation model based on mathematical foundations and macroscopic primitives is proposed. A formal definition for swarm pattern transformation and four special cases of transformation are introduced. Two general methods for transforming patterns are investigated and a comparison of the two methods is presented. The validity of the proposed models, and the feasibility of the methods investigated are confirmed on the Traer Physics and Processing environment.

This paper helps in understanding the limitations of existing research in pattern formation and the lack of mathematical foundations for swarm systems. The mathematical model and transformation methods introduce two key concepts, namely macroscopic primitives and a mathematical model. The exercise of implementing the proposed models on physics simulator is novel.

Organizations of today are becoming ever more focused on their business processes. This has resulted in an increasing interest in using best practices for business process re‐engineering. Two problems arise in connection to using best practices: how to find a best practice that suits particular purposes, and how to ensure that the process from the best practice has the same nature as the process under re‐engineering. The purpose of this paper is to address these issues.

The paper suggests using business process patterns, i.e. relatively high level business process models, for making near formal comparison of business processes. The paper analyzes widespread modeling techniques to find out which of them suits the task of building patterns for comparison. Based on this analysis, the state‐flow modeling technique is chosen and first steps towards formal definition of business process patterns based on this technique are suggested.

A pattern is defined based on the notions of state space, goal, as a surface in the state space, and valid movements towards the goal. A thinkable procedure of constructing patterns is demonstrated on two real‐life examples. A hypothetical procedure for comparing process is suggested but it still needs to be verified in practice.

The originality of the paper is the way the patterns are formulated and the underlying model, the state‐flow view of processes, upon which the patterns are founded.

Quality control charts are statistical process control tools aimed at monitoring a (manufacturing) process to detect any deviations from normal operation and to aid in process diagnosis and correction. The information presented on the chart is a key to the successful implementation of a quality process correction system. Pattern recognition methodology has been pursued to identify unnatural behaviour on quality control charts. This approach provides the ability to utilize patterning information of the chart and to track back the root causes of process deviation, thus facilitating process diagnosis and maintenance. Presents analysis and development of a statistical pattern recognition system for the explicit identification of unnatural patterns on control charts. Develops a set of statistical pattern recognizers based on the likelihood ratio approach and on correlation analysis. Designs and implements a training algorithm to maximize the probability of identifying unnatural patterns, and presents a classification procedure for real‐time operation. Demonstrates the system performance using a set of newly defined measures, and obtained results based on extensive experiments illustrate the power and usefulness of the statistical approach for automating unnatural pattern detection on control charts.

The purpose of this paper is to classify Chinese word semantic relations, which are synonyms, antonyms, hyponyms and meronymys.

Basically, four simple methods are applied, ontology-based, dictionary-based, pattern-based and morpho-syntactic method. The authors make good use of search engine to build lexical and semantic resources for dictionary-based and pattern-based methods. To improve classification performance with more external resources, they also classify the given word pairs in Chinese and in English at the same time by using machine translation.

Experimental results show that the approach achieved an average F1 score of 50.87 per cent, an average accuracy of 70.36 per cent and an average recall of 40.05 per cent over all classification tasks. Synonym and antonym classification achieved high accuracy, i.e. above 90 per cent. Moreover, dictionary-based and pattern-based approaches work effectively on final data set.

For many natural language processing (NLP) tasks, the step of distinguishing word semantic relation can help to improve system performance, such as information extraction and knowledge graph generation. Currently, common methods for this task rely on large corpora for training or dictionaries and thesauri for inference, where limitation lies in freely data access and keeping built lexical resources up-date. This paper builds a primary system for classifying Chinese word semantic relations by seeking new ways to obtain the external resources efficiently.