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The aim of this study is to provide administrators in government institutions a roadmap to achieve benefits of open government data (OGD) by reviewing and classifying studies with assessment or maturity models (MMs) in the OGD domain with a Multivocal Literature Review (MLR).

To achieve this goal, the authors conducted an MLR that includes data from not only the formal literature but also the grey literature (e.g. white papers and online documents).

Out of 3,569 sources, 81 studies were selected by following the elimination scheme and assessing sources by relevance and methodology. As a result of the quality assessment of the identified MMs based on predefined criteria, unambiguity, comparability, repeatability, completeness, clearness and objectivity, it was observed that there are a limited number of MMs in this domain and none of them fully satisfies the requirements.

This study is likely the first MLR on OGD domain. This MLR serves as a first step for future research on OGD assessment and MMs by presenting the need to establish a holistic approach covering all OGD dimensions, creation of an objective assessment method, prescriptive properties, and empirical evaluation demonstrating the applicability and usefulness at different scope levels.

The purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way.

This paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed.

It was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management.

This paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations.

Legibility, intelligibility, mental images and cognitive and syntactical mapping are significant issues that help expose the spatial knowledge necessary for effective urban design. They also help us understand how a city’s new image is forming. This paper aims to present a new holistic approach to define urban design strategies that improve a city’s imageability through cognitive and syntactic concepts.

The study establishes a coherent framework by including residents’ mental images and space syntax theory’s descriptors to understand how residents perceive their physical environment. Using a mixed-methods research design, the authors studied the Iskenderun city center’s image and spatial design. First, the authors used descriptive analysis techniques (questionnaires, verbal interviews and cognitive mapping) and consulted 110 Iskenderun residents. Second, the authors used analytical analysis techniques to investigate the structural relations among city elements with the help of space syntax descriptors.

The results demonstrated the importance of applying combined descriptive and analytic techniques to provide an understanding of the city’s image. The authors also offered a proposal including the appropriate urban design strategies to promote Iskenderun city center’s imageability.

Applying this new coherent framework can support design decision-making for redesigning cities at the micro level and for planning new cityscapes at the macro level.

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.