A Strategy to Specify Customizable Interoperability Maturity Models for Software Systems

The purpose of this study is to explore how knowledge can be managed across boundaries when implementing innovations in the healthcare sector is desired, in this specific case a healthcare quality register. The research is based on a qualitative, case study approach and comprises methodologies such as semi-structured interviews and document analysis. The findings of this study describe knowledge transferred across boundaries on a syntactic, semantic, and pragmatic level. On the syntactic level, knowledge of the innovation was transferred by training sessions for healthcare staff and through information to patients. On the semantic level, knowledge was transferred by knowledge brokering in the professional community of rheumatologists, and by creating collective stories and encouraging rheumatologists to “try” the innovation to find added value. On the pragmatic level, there were explicit conflicts of interest between physicians and healthcare authorities, as well as resistance from some rheumatologists to share knowledge of patients and treatment. The paper is concluded with implications for innovation practice in healthcare drawn from the study and ends with remarks about challenges ahead.

Software systems are increasingly composed of independently-developed components, which are often systems by their own. This composition is possible only if the components are interoperable, i.e., are able to work together in order to achieve some user task(s). However, interoperability is often hampered by the differences in the data types, communication protocols, and middleware technologies used by the components involved. In order to enable components to interoperate despite these differences, mediators that perform the necessary data translations and coordinate the components’ behaviours appropriately, have been introduced. Still, interoperability remains a critical challenge for today’s and even more tomorrow’s distributed systems that are highly heterogeneous and dynamic. This chapter introduces the fundamental principles and solutions underlaying interoperability in software systems with a special focus on protocols. First, we take a software architecture perspective and present the fundamentals for reasoning about interoperability and bring out mediators as a key solution to achieve protocol interoperability. Then, we review the solutions proposed for the implementation, synthesis, and dynamic deployment of mediators. We show how these solutions still fall short in automatically solving the interoperability problem in the context of systems of systems. This leads us to present the solution elaborated in the context of the European Connect project, which revolves around the notion of emergent middleware, whereby mediators are synthesised on the fly. We consider the GMES (Global Monitoring of Environment and Security) initiative and use it to illustrate the different solutions presented.KeywordsArchitectural mismatchesInteroperabilityMediator synthesisMiddleware

Maturity models have been used in several domains to evaluate system maturity according to specific aspects. Despite their popularity, maturity models have been criticized due to lack of empirical validation and effective methods to aid in their definition. This paper presents our efforts to systematize the development of maturity models towards a methodology. In this direction, tasks, artifacts, methods, and tools related to maturity model definition were proposed and organized as an initial methodology to support developers. In addition, a maturity model, named Amortisse was developed applying the proposed methodology. The results of this investigation show the Amortisse Maturity Model and the methodology are feasible. We hope this methodology can help the definition of maturity models in different domains contributing to maturity models standardization.

Systematic Literature Review of Industry 4.0 Maturity Model for Manufacturing and Logistics Sectors

Purpose: The aim of this conceptual study is bridging established theory on maturity models and business model innovation. The paper identifies boundary conditions and necessary steps for the design of an integrated maturity model for business model innovation. This contribution establishes a foundation that enables the assessment through selection of improvement measures and benchmarking. Design/methodology/approach: The paper systematically assesses the extant literature to establish ontological consistency in the bridging attempt and defines the boundary conditions and specific steps for subsequent model development. Findings: Prior published research only to a limited degree relates maturity model to business model innovation. Our assessment of extant literature reveals how innovation maturity models exhibit an extensive variety with regard to their application domain, number, and descriptors of dimensions, level of granularity, the design process, as well as empirical validation and the consideration of business model aspects. Based on these insights, the focus, scope, and steps towards a maturity model for business model innovation were defined. Originality/value: The results of the research provide an important foundation for further research and development steps towards a maturity model for business model innovation. Furthermore, the detailed analysis of innovation maturity models has potential to be used as a basis for the development of other maturity models in the innovation domain and as a blueprint for analyzing future maturity models in detail.

Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant to maturity models, especially in terms of the objectives, methods employed to develop and validate the models, and the scope and characteristics of maturity model development. Our analysis reveals that most works concentrate on developing maturity models with or without their empirical validation. It shows that the most significant proportion of models were designed for specific domains and purposes. Maturity model development typically uses a bottom-up design approach, and most of the models have a descriptive characteristic. Besides that, maturity grid and continuous representation with five levels are currently trending in maturity model development. Six out of 13 studies (46%) on AI maturity pertain to assess the technology aspect, even in specific domains. It confirms that organizations still require an improvement in their AI capability and in strengthening AI maturity. This review provides an essential contribution to the evolution of organizations using AI to explain the concepts, approaches, and elements of maturity models.

The exponential growth of data within organisations necessitates the implementation of effective data management practices, which in turn necessitates the establishment of data governance. The evaluation of the maturity of data governance can be carried out using maturity models. However, the existing data governance maturity models are limited in their consistency in terms of data governance capabilities used and lack empirical validation. To address this gap, this study aims to validate the set of data governance capabilities identified in prior research within large organisations. This study employs a case study research design, using semi-structured interviews with experts in data governance. As a basis for the semi-structured interviews, maturity models are designed as questionnaires to discuss the relevance of each data governance capability. The results of this study provide empirical validation of the set of data governance capabilities and contribute to the advancement of both data governance research and practice by providing a comprehensive, validated set of data governance capabilities for maturity model design to advance data governance within and between organisations.

• Figurative language styles are analyzed at lexical, syntactic, semantic, discourse-level, and pragmatic NLP levels • Discourse-level styles dominate due to sarcasm’s prevalence in social media • Arabic ranks second in dataset count, but lacks stylistic diversity coverage • Embedding-based features lead performance, especially in semantic tasks • Publicly available datasets remain scarce for pragmatic and syntactic styles Figurative language detection has emerged as a critical task in natural language processing (NLP), enabling machines to comprehend non-literal expressions such as metaphor, irony, and sarcasm. This study presents a systematic literature review with a multilevel analytical framework, examining figurative language across lexical, syntactic, semantic, discourse, and pragmatic levels. We investigate the interplay between feature engineering, model architectures, and annotation strategies across different languages, analyzing datasets, linguistic resources, and evaluation metrics. Special attention is given to morphologically rich and low-resource languages, where deep learning dominates but rule-based and hybrid approaches remain relevant. Our findings indicate that deep learning models–especially transformer-based architectures like BERT and RoBERTa–consistently outperform other approaches, particularly in semantic and discourse-level tasks, due to their ability to capture context-rich and abstract patterns. However, these models often lack interpretability, raising concerns about transparency. Additional challenges include inconsistencies in annotation practices, class imbalance between figurative and literal instances, and limited data coverage for under-resourced languages. The absence of standardized evaluation metrics further complicates cross-study comparison, especially when diverse figurative language styles are involved. By structuring our analysis through linguistic and computational dimensions, this review aims to facilitate the development of more robust, inclusive, and explainable figurative language detection systems.

BackgroundThe aim of this paper is to develop a maturity model (MM) for demand and capacity management (DCM) processes in healthcare settings, which yields opportunities for organisations to diagnose their planning and production processes, identify gaps in their operations and evaluate improvements.MethodsInformed by existing DCM maturity frameworks, qualitative research methods were used to develop the MM, including major adaptations and additions in the healthcare context. The development phases for maturity assessment models proposed by de Bruin et al. were used as a structure for the research procedure: (1) determination of scope, (2) design of a conceptual MM, (3) adjustments and population of the MM to the specific context and (4) test of construct and validity. An embedded single-case study was conducted for the latter two - four units divided into two hospitals with specialised outpatient care introducing a structured DCM work process. Data was collected through interviews, observations, field notes and document studies. Thematic analyses were carried out using a systematic combination of deductive and inductive analyses - an abductive approach - with the MM progressing with incremental modifications.ResultsWe propose a five-stage MM with six categories for assessing healthcare DCM determined in relation to patient flows (vertical alignment) and organisational levels (horizontal alignment). Our application of this model to our specific case indicates its usefulness in evaluating DCM maturity. Specifically, it reveals that transitioning from service activities to a holistic focus on patient flows during the planning process is necessary to progress to more advanced stages.ConclusionIn this paper, a model for assessing healthcare DCM and for creating roadmaps for improvements towards more mature levels has been developed and tested. To refine and finalise the model, we propose further evaluations of its usefulness and validity by including more contextual differences in patient demand and supply prerequisites.

Are mature firms more sustainable? An analysis of industry 4.0 maturity

Context: One of the leading challenges in Distributed Software Development (DSD) is to communicate correctly and promptly, as factors such as physical distance and lack of face-to-face contact can hinder this process. In this context, the Communication Maturity Model (C2M) was proposed as an option to support the improvement of communication in DSD. But this maturity model could not be effectively used in organizations, due to the absence a specific C2M based assessment method. Objective: This work aims to present the Standard C2M Based Assessment Method (SCBAM) in its basic dimension, the Basic Standard C2M Based Assessment Method (SCBAM-B). An assessment method to determine the maturity level of communication in DSD organizations, based on the C2M model. Method: The SCBAM-B was designed according to a methodology that included a review of the DSD literature, maturity and capacity models, evaluation methods, the development of a software tool, and evaluation with experts. Results: The SCBAM-B was perceived by experts as a relevant approach for assessing the communication level in organizations and propose a path for improvements. Conclusions: For being lightweight and capable of automation, the SCBAM-B has the potential to help the communication improvement in DSD organizations, in the light of the C2M model.

Interoperability of software systems is a critical, ever-increasing requirement in software industry. Conformance testing is needed to assure conformance of software and interfaces to standards and other specifications. In this chapter we shortly refer to what has been done in conformance testing around the world and in Finland. Also, testability requirements for the specifications utilized in conformance testing are proposed and test-case derivation from different kinds of specifications is examined. Furthermore, we present a conformance-testing environment for the healthcare domain, developed in an OpenTE project, consisting of different service-specific and shared testing services. In our testing environment testing is performed against open interfaces, and test cases can, for example, be in XML (extensible markup language) or CDA R2 (clinical document architecture, Release 2) form.Request access from your librarian to read this chapter's full text.

Advocates of application frameworks claim that this technology is one of the most promising, supporting large-scale reuse, increased productivity and quality, and reduced cost of software development. A number of its advocates suggest that the next decade will be a major challenge for the development and deployment of this technology. This study investigates the theory and practice of application frameworks technology to evaluate what works and what does not in systems development. The evaluation is based on quality criteria developed by the authors. The result of the study suggests that application frameworks technology does support large-scale reuse by incorporating other existing reuse techniques such as design patterns, class libraries and components. It also shows that the methodological support pertaining to building and implementing application frameworks is inadequate. Furthermore, it indicates that application frameworks technology may increase the quality of software in terms of correctness and reusability with some penalty factors but there is no guarantee of increasing the extendability and interoperability of software systems. There are still obstacles that restrict the potential benefits claimed by the proponents of application frameworks.

Context: Several empirical studies investigated the benefits and drawbacks of acquiring a Software Reference Architecture (SRA) to construct a family of software systems with similar architectural needs. However, these empirical results have not been synthesized by any study yet. Such synthesized evidence is essential to make informed decisions whether or not to adopt an SRA in an organization. Goal: To aggregate existing empirically- grounded evidence about the benefits and drawbacks of SRAs, aiming at supporting organizations' decision making on their adoption. Method: To identify primary studies in the technical literature through a systematic literature review, and then, use the Structured Synthesis Method (SSM) to aggregate qualitative and quantitative evidence through the use of diagrammatic models. Results: From the five identified primary studies, five SRA benefits have considerably increased their belief value after aggregation: interoperability of software systems, reduced development costs, improved communication among stakeholders, reduced risk, and reduced time- to-market. Also, one drawback of SRAs has increased its belief value: the required learning curve for developers. Conclusions: The aggregated results consolidate knowledge and confidence on some of the studied SRA effects. The commonly reported effects showed a clear increment of their belief and pointed out to broader generalization. The effects that did not show any belief increment are important to detect areas requiring further evidence to reach a higher degree of consolidation. Practitioners might benefit from these results to support the decision of adopting an SRA in practice.

Information Security Management Maturity Models