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The purpose of this study is to describe latent classes explaining the innovation logic in the Finnish construction companies. Innovativeness is a driver of competitive performance and vital to the long-term success of any organisation and company.

Using finite mixture structural equation modelling (FMSEM), the authors have classified innovation logic into latent classes. The method analyses and recognises classes for companies that have similar logic in innovation activities based on the collected data.

Through FMSEM analysis, the authors have identified three latent classes that explain the innovation logic in the Finnish construction companies – LC1: the internal innovators; LC2: the non-innovation-oriented introverts; and LC3: the innovation-oriented extroverts. These three latent classes clearly capture the perceptions within the industry as well as the different characteristics and variables.

The presented latent classes explain innovation logic but is limited to analysing Finnish companies. Also, the research is quantitative by nature and does not increase the understanding in the same manner as qualitative research might capture on more specific aspects.

This paper presents starting points for construction industry companies to intensify innovation activities. It may also indicate more fundamental changes for the structure of construction industry organisations, especially by enabling innovation friendly culture.

This study describes innovation logic in Finnish construction companies through three models (LC1–LC3) by using quantitative data analysed with the FMSEM method. The fundamental innovation challenges in the Finnish construction companies are clarified via the identified latent classes.

The purpose of this study is to assess the barriers to the implementation of blockchain technology in construction supply chain management in Nigeria.

This study employed a quantitative research approach through a questionnaire survey that was conducted among professionals in the Nigerian construction industry using the snowball sampling method, which resulted in a selection of 155 respondents. The collected data were analysed using descriptive and exploratory factor analysis (EFA), while Cronbach's alpha was used to assess the reliability.

The analysis revealed that all barriers ranked above the average mean item score. It also revealed that all professionals have a convergent opinion on the barriers. EFA was used in clustering the identified barriers into two categories: technological and socio-political barrier.

This research was carried out in the Southwestern region which is one of the six geo-political zones in Nigeria using a cross-sectional survey method.

The findings provide valuable insights into the barriers to the implementation of blockchain in supply chain management for professionals and practitioners in the Nigerian construction industry.

The research categorised the barriers into technological and social-political barrier and identified that lack of digitalisation is the major barrier to the implementation of blockchain technology in construction supply chain.

This study aims to deepen the understanding of the challenges and implications entailed by deploying mobile clinics in conflict zones to reach populations affected by violence and cut off from health-care services.

This research combines an integrated literature review and an instrumental case study. The literature review comprises two targeted reviews to provide insights: one on conflict zones and one on mobile clinics. The case study describes the process and challenges faced throughout a mobile clinic deployment during and after the Iraq War. The data was gathered using mixed methods over a two-year period (2017–2018).

Armed conflicts directly impact the populations’ health and access to health care. Mobile clinic deployments are often used and recommended to provide health-care access to vulnerable populations cut off from health-care services. However, there is a dearth of peer-reviewed literature documenting decision support tools for mobile clinic deployments.

This study highlights the gaps in the literature and provides direction for future research to support the development of valuable insights and decision support tools for practitioners.