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This conceptual paper presents a proposal for improving a performance measurement (PM) system implementation process based on enterprise engineering (EE) guidelines, which gives the process a sense of completeness.

This paper analyzes a well-known process for PM systems implementation organized in two phases: identifying, designing and implementing the top-level performance measures; and cascading the top-level measures and identify appropriate lower-level performance measures. The proposed improvements to the studied process derive from the EE guidelines, which establish a basis for the structure of an organizational management system, the formalization and synchronization of processes, performance expectations, exception handling and change management.

The study reveals that not all EE guidelines are covered by the analyzed process, with four of them having no evidence of being adopted: involvement of people in process design and implementation; ensuring interoperability between different systems in the information structure; addressing of all possible exceptions; coherence and consistency of semantics across all processes.

By the lens of EE guidelines, this paper advances a how-to-guide. This paper can support managers and researchers on PM system design and implementation, given the importance and relevance of EE recommendations having a consistent and well-structured procedure.

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.

Constant changes in the environment seem to have become the biggest challenge of a modern enterprise, which emphasizes the constant need to integrate the enterprise into its changing environment. Aiming at eliminating the integration challenges, EA is proposed as a solution. The purpose of this paper is to survey and analyse the available literature on determining the role of EA in EI and also to identify gaps and state-of-the-art in research.

This paper presents a systematic mapping study that found 50 papers in the intersection of EA and EI, these papers were surveyed, analysed, and classified with respect to research focus, research method, and paper type.

Based on the analyses of the final 50 articles, the authors realized that “EA framework” is the dominating research focus of these studies. “Evaluation research” is recognized as the most common paper type in this area. However, “Experience paper” was a rare paper type in this research domain. “Constructive research” and “Case study/multiple case studies” are widely applied as the research method. “Survey”, “Delphi study” and “Grounded theory” are the least employed research methods. The conclusion was that there is a need for empirical research in this area. After analysing the articles based on their publication year, the authors also noticed a significant growth between 2004 and 2010. After 2010 the number of publications had a downward trend.

To the knowledge of the authors, this study is the first systematic literature study regarding the role of EA in EI. There are several systematic literature reviews about the EA or EI separately but none of them has addressed the specific realm of the research. Hence, the goal of this study is to provide a map of existing literature to enable improvement of the practice with the known research results and to identify gaps for future research.

This work focuses on the interoperability of the blockchain from the viewpoint of its technological evolution in the wider context of supply chain systems. Interoperability concerns the most since it is among the most persevering problems in the process of blockchain adaptation. The study aims to trace and pinpoint all the different methods that affect the interoperability of blockchains, gather all the obtainable evidence and recognize gaps in between the applicable approaches mentioned across the contemporary academic literature.

An analysis is done of seven interoperability approaches based on their relevance, practical usefulness and adoption processes concerning the blockchain. Mainly, this work examines interoperability from several different perspectives. An interoperability modular decomposition via a layered model has been defined through the level of conceptual interoperability (LCIM) and level of information system interoperability (LISI), which represent the most popular metrics with regard to assessing the level of interoperability from a qualitative level.

Generally, the findings of this work are directed in two major aspects: Firstly, it expands the research around blockchain technology interoperability by providing crucial background information and pinpointing important connotations for both industrial and academic environments. It also defines and analyzes several different approaches toward the blockchain platform's interoperability. Secondly, it also identifies and proposes several scenarios that take advantage of multiple blockchain application approaches, highlights various issues and challenges to the evolution of interoperability standards and solutions and indicates the desired areas of further research.

An essential aspect of the originality of this paper is that, contrary to other work contributions, this work summarized the different interoperability approaches for blockchain in supply chain systems. Mainly, seven approaches were discussed for practitioners and researchers, and the open issues and future research direction were considered.

This paper aims to offer a comprehensive examination of the various solutions currently accessible for addressing the challenge of semantic interoperability in cyber physical systems (CPS). CPS is a new generation of systems composed of physical assets with computation capabilities, connected with software systems in a network, exchanging data collected from the physical asset, models (physics-based, data-driven, . . .) and services (reconfiguration, monitoring, . . .). The physical asset and its software system are connected, and they exchange data to be interpreted in a certain context. The heterogeneous nature of the collected data together with different types of models rise interoperability problems. Modeling the digital space of the CPS and integrating information models that support cyber physical interoperability together are required.

This paper aims to identify the most relevant points in the development of semantic models and machine learning solutions to the interoperability problem, and how these solutions are implemented in CPS. The research analyzes recent papers related to the topic of semantic interoperability in Industry 4.0 (I4.0) systems.

Semantic models are key enabler technologies that provide a common understanding of data, and they can be used to solve interoperability problems in Industry by using a common vocabulary when defining these models.

This paper provides an overview of the different available solutions to the semantic interoperability problem in CPS.

The purpose of this research is to examine the impact of organizational factors on the adoption of interoperability technology for Indian portals.

This exploratory study was conducted through a survey questionnaire from 300 portals of government departments and public sector undertakings (PSUs) in India. Data were also collected from portals of Indian companies.

The study finds that adoption of interoperability for Indian portals is highly associated with certain organizational factors. In addition, multiple regression analysis reveals that the functionalities of government portals in India are significantly related to four organizational factors.

The research provides insights for government officials and practitioners to understand and improve the level of interoperability in government portals. The study also provides a 2 by 2 matrix framework that helps the government officials to focus on the relevant organizational factors.

This study is the first to examine the effect of organizational factors on interoperability adoption in Indian portals. The results lead to a number of recommendations for achieving interoperability for government portals in India.

The application of category theory to interoperability to increase understanding of the problems and to facilitate the development of practical tools for their solution.

Category theory is naturally suited to handling interoperability. The use of first order predicate logic in many information systems may be justified through its completeness. However, the work of Gödel shows that such systems are undecidable if they rely on formal systems of number and/or sets. For interoperability dyadic higher order logic is required, which is neither complete nor decidable if based on sets. However, pure category theory is still axiomatic so is also neither complete nor decidable. Applied category theory based on cartesian closed categories for process is natural and is both complete and decidable. Gödel's theorems therefore do not apply.

The paper finds that composed adjunctions appear particularly well‐suited for modelling interoperability, with composition of distinct functors for mapping across a number of levels and of endofunctors for business process interoperability. The monad/comonad category provides a powerful abstraction of the business process. The development of a tool based on categorial principles written in Haskell may be a way forward but only as an initial set model approach.

This paper applies categorial constructions which permit a natural formal approach to interoperability.

The purpose of this paper is to survey, systemize and analyse the e‐readiness of small and medium‐sized enterprises (SMEs) and determine which new technologies can be applied to build a digital business ecosystem (DBE) for SMEs. Based on the results of a survey, the authors' objectives are to propose an adequate solution system that uses open source solutions for the SME through the development of a prototype based on a DBE concept.

On the resulting principal components, the authors applied the variance analysis and built two LISREL (a linear structural equation system involving multiple indicators of unmeasured variables) models. LISREL can handle a wide array of problems and models. Based on a survey, an open source prototype solution was developed based on DBE philosophy.

The authors defined different clusters. An SME can then find within the clusters further methods that could be important for their business. The authors developed two LISREL models, aiming to examine the factors that impact the use of the community applications and how they impact them, as well as the factors that impact the increase of the on‐line sales. Based on the survey, a prototype system was designed and created that can be used for the set‐up of digital business networks.

The paper proposes an adequate solution system that uses open source solutions for the SMEs through the development of a prototype based on a DBE concept.

Building DBEs helps to create and operate value chains that help enterprises to extend their markets. Through their help, the inquirers and buyers get to know the elements of the value chain, and can therefore make decisions much easier. The paper shows how to define e‐attributes of SMEs and clusters to build and use a DBE system for them.

Because of the use of digital technologies in smart cities, municipalities are increasingly facing issues related to urban data management and are seeking ways to exploit these huge amounts of data for the actualization of data driven services. However, only few studies discuss challenges related to data driven strategies in smart cities. Accordingly, the purpose of this study is to present data driven approaches (architecture and model), for urban data management needed to improve smart city planning and design. The developed approaches depict how data can underpin sustainable urban development.

Design science research is adopted following a qualitative method to evaluate the architecture developed based on top-level design using a case data from workshops and interviews with experts involved in a smart city project.

The findings of this study from the evaluations indicate that the identified enablers are useful to support data driven services in smart cities and the developed architecture can be used to promote urban data management. More importantly, findings from this study provide guidelines to municipalities to improve data driven services for smart city planning and design.

Feedback as qualitative data from practitioners provided evidence on how data driven strategies can be achieved in smart cities. However, the model is not validated. Hence, quantitative data is needed to further validate the enablers that influence data driven services in smart city planning and design.

Findings from this study offer practical insights and real-life evidence to define data driven enablers in smart cities and suggest research propositions for future studies. Additionally, this study develops a real conceptualization of data driven method for municipalities to foster open data and digital service innovation for smart city development.

The main findings of this study suggest that data governance, interoperability, data security and risk assessment influence data driven services in smart cities. This study derives propositions based on the developed model that identifies enablers for actualization of data driven services for smart cities planning and design.

This study explores the enablers of data driven strategies in smart city and further developed an architecture and model that can be adopted by municipalities to structure their urban data initiatives for improving data driven services to make cities smarter. The developed model supports municipalities to manage data used from different sources to support the design of data driven services provided by different enterprises that collaborate in urban environment.

Enterprise systems integration (ESI) is necessary for today’s business environment to access real-time data and quickly respond to fluctuating market demand. business process change (BPC) as a significant prerequisite of ESI encompasses various challenges that must be tackled by employing success factors, techniques and approaches. The purpose of this paper is to focus on BPC challenges and develop a conceptual framework for addressing BPC challenges in ESI.

BPC challenges and their success factors were first identified through a literature analysis. Then, the findings from the literature were thematically analysed and qualitatively validated through 35 unstructured interviews for developing the conceptual framework.

The findings from the literature suggested 17 BPC challenge along with their success factors. During the validation process, 15 BPC challenges were accepted by all interviewees, while most of the respondents disagreed with the two challenges of “consolidation of information system re-engineering with BPR”, and “customization”. Moreover, “risk” was suggested as a BPC challenge by several interviewees. Thus, the study offered a modified list of BPC challenges, which was empirically validated.

The study proposes a conceptual framework for addressing BPC challenges in ESI that enables enterprises to design their systems integration roadmap, based on an understanding of BPC challenges and their success factors, as well as supporting solution providers to develop solutions for effective and efficient BPC. Furthermore, the framework will act as a basis for BPC and developing a similar framework for other related contexts, such as smart cities.