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Insufficient productivity development in the global and Finnish infrastructure sectors indicates that there are challenges in genuinely achieving the goals of resource efficiency and digitalization. This study adapts the approach of capability maturity model integration (CMMI) for examining the capabilities for productivity development that reveal the enablers of improving productivity in the infrastructure sector.

Civil engineering in Finland was selected as the study area, and a qualitative research approach was adopted. A novel maturity model was constructed deductively through a three-step analytical process. Previous research literature was adapted to form a framework with maturity levels and key process areas (KPAs). KPA attributes and their maturity criteria were formed through a thematic analysis of interview data from 12 semi-structured group interviews. Finally, validation and refinement of the model were performed with an expert panel.

This paper provides a novel maturity model for examining and enhancing the infrastructure sector’s maturity in productivity development. The model brings into discussion the current business logics, relevance of lifecycle-thinking, binding targets and outcomes of limited activities in the surrounding infrastructure system.

This paper provides a new approach for pursuing productivity development in the infrastructure sector by constructing a maturity model that adapts the concepts of CMMI and change management. The model and findings benefit all actors in the sector and provide an understanding of the required elements and means to achieve a more sustainable built environment and effective operations.

Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.

The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.

The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.

By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.

The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.

This paper aims to present a summary of the performance measurement and evaluation plan of the Wyoming connected vehicle (CV) Pilot Deployment Program (WYDOT Pilot).

This paper identified 21 specific performance measures as well as approaches to measure the benefits of the WYDOT Pilot. An overview of the expected challenges that might introduce confounding factors to the evaluation effort was outlined in the performance management plan to guide the collection of system performance data.

This paper presented the data collection approaches and analytical methods that have been established for the real-life deployment of the WYDOT CV applications. Five methodologies for assessing 21 specific performance measures contained within eight performance categories for the operational and safety-related aspects. Analyses were conducted on data collected during the baseline period, and pre-deployment conditions were established for 1 performance measures. Additionally, microsimulation modeling was recommended to aid in evaluating the mobility and safety benefits of the WYDOT CV system, particularly when evaluating system performance under various CV penetration rates and/or CV strategies.

The proposed performance evaluation framework can guide other researchers and practitioners identifying the best performance measures and evaluation methodologies when conducting similar research activities.

To the best of the authors’ knowledge, this is the first research that develops performance measures and evaluation plan for low-volume rural freeway CV system under adverse weather conditions. This paper raised some early insights into how CV technology might achieve the goal of improving safety and mobility and has the potential to guide similar research activities conducted by other agencies.

Because BIM adoption is still afflicted by various types of hurdles, a complete BIM implementation model is required to provide the necessary methods for driving BIM adoption. As a result, this study looked into the parts of the BIM implementation model that had the most impact on increasing the percentage of BIM adoption in South Africa.

This study developed a four-wheel model of BIM implementation based on implementation process theory, which includes BIM inspiration, BIM capacity development, BIM use and BIM commitment. To assess BIM capacity development, two sub-constructs (BIM learning process and BIM learning methodologies) were used. Two sub-constructs were used to assess BIM utilisation (efficient BIM application and effective BIM application). The sub-constructs employed to quantify BIM motivation were organisational competitiveness, societal conformity and contractual obligations. Incentives, investments and obligations were used to assess BIM commitment. The model was validated using four assumptions and maximum likelihood estimation – structural equation modelling (MLE-SEM).

The MLE-SEM results demonstrated unequivocally that all of the constructions are critical components of the BIM deployment paradigm in the South African construction industry. BIM motivation, as characterised by organisational competitiveness and social compliance, has the greatest impact. The findings on BIM motivation also revealed that the desire for technological sophistication, competitiveness and social acceptance by clients are encouraging construction organisations and professionals to embrace BIM adoption.

This study's findings have contributed to the increasing body of literature on BIM deployment. The study has significant implications for achieving BIM implementation in underdeveloped nations where BIM deployment is either non-existent or in its early stages. The theoretical component of the study serves as the foundation for further analysis of BIM deployment.

This research is important for identifying BIM goals, developing a BIM implementation framework, allocating resources for BIM implementation and defining key performance indicators for BIM implementation. The BIM implementation aspects outlined in this study will be effective in lowering BIM adoption hurdles.

This study makes a unique contribution to BIM research by providing theoretical and empirical analysis into the elements of the BIM implementation model in a developing country. The study offers an excellent opportunity to further our understanding of BIM application in underdeveloped nations.

Major public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control.

To demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment.

Through the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries.

The authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.

Cloud Manufacturing (CM) is the manufacturing version of Cloud Computing and aims to increase flexibility in the provision of manufacturing services. On-demand manufacturing services can be requested by users to the cloud and this enables the concept of Manufacturing-as-a-Service (MaaS). Given the considerable number of prototypes and proofs of concept addressed in literature, this work seeks real CM platforms to study them from a business perspective, in order to discover what MaaS concretely means today and how these platforms are operating.

Since the number of real applications of this paradigm is very limited (if the authors exclude prototypes), the research approach is qualitative. The paper presents a multiple-case analysis of 6 different platforms operating in the manufacturing field today. It is based on empirical data and inductively researches differences among them (e.g. stakeholders, operational flows, capabilities offered and scalability level).

MaaS has come true in some contexts, and today it is following two different deployment models: open or closed to the provider side. The open architecture is inspired by a truly open platform which allows any company to be part of the pool of service providers, while the closed architecture is limited to a single service provider of the manufacturing services, as it happens in most cloud computing services.

The research shoots a picture of what MaaS offers today in term of capabilities, what are the deployment models and finally suggests a framework to assess different levels of development of MaaS platforms.

Crisis services personnel are frequently deployed around the globe under highly demanding conditions. This raises the need to better understand the deployment process and more especially, sustainable reintegration after deployment. Despite recent research efforts, the study of the post-deployment stage, more specifically the reintegration process, remains fragmented and limited. To address these limitations, this review aims at (1) describing how reintegration is conceptualised and measured in the existing literature, (2) identifying what dimensions are associated with the reintegration process and (3) identifying what we know about the process of reintegration in terms of timing and phases.

Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, the authors identified 5,859 documents across several scientific databases published between 1995 and 2021. Based on predefined eligibility criteria, 104 documents were yielded.

Research has primarily focused on descriptive studies of negative individual and interpersonal outcomes after deployment. However, this review indicates that reintegration is dynamic, multi-sector, multidimensional and dual. Each of its phases and dimensions is associated with distinct challenges.

To the authors’ knowledge, this is the first research that investigates reintegration among different crisis services and provides an integrative social-ecological framework that identifies the different dimensions and challenges of this process.

The purpose of this study is to discover the learning mechanisms and temporal dynamics of implementing systems (Six Sigma) as it unfolds over time.

The data come from a European engineering company that was implementing a Six Sigma-based quality management system (QMS) over a seven-year period. The analysis is based on an event-sequence reconstruction of the implementation process as it unfolded over time and discovers four different learning mechanisms that emerged: programmatic, persistent, adaptive and dialectical learning mechanisms. The research follows a process design study, where the authors study how the process unfolds over time.

Much of the literature on implementing management systems suggests that implementation follows a prescribed sequence of “turn-key” steps. However, the findings show that only 40% of all events were driven by prescribed “turn-key” generic practices, while 56% of events required constructing new practices via adaptive and dialectical learning. Moreover, the implementation process did not proceed in a linear programmatic fashion, but instead followed a punctuated equilibrium pattern, which alternated between periods of incremental change and major organizational change. The study also found that implementation required changing many complementary organizational structures and practices that were interdependent with the management system (i.e. Six Sigma). By understanding the implementation process, managers can better assess the time and effort involved, better adapt the system to their situated context and predict critical junctures where implementation could break down.

This research complements the few studies that have examined the process of implementing management systems. Most studies examine factors or conditions that result in implementation success (the what of implementing systems), but few examine the process of implementation and the learning that takes place during implementation (the how of implementing systems), which is a complex nonlinear process that involves different modes of learning.

The purpose of this paper is to build possible future scenarios for indoor connectivity in a venue such as a university campus and build alternative value network configurations (VNCs) defining different local network deployment options, focused on the Finnish telecom market.

In this paper, Schoemaker’s scenario planning method (Schoemaker, 1995) is used to construct future scenarios and the VNC method of Casey et al. (2010) is used to build alternative VNCs. The paper studies the Aalto University campus network for current end-user data usage demand and the existing technology used in meeting the end-user needs and forecasts the demand for the next five years to understand the need for 5G.

This research concludes that with the provision of local spectrum licenses, there is an opportunity for venue owners to take the role of 5G local operator on the venue premises. Furthermore, it enables venue owners to collaborate with the incumbent mobile network operators (MNOs) in a neutral host model and provide venue-specific connectivity services.

A detailed economic assessment for the network deployment in the campus is considered for future study.

Considering the provision for local spectrum licenses, this paper has taken a unique attempt in identifying the future scenarios for local 5G network operations. It provides a strategic direction for the venue owners in adopting 5G technology and whether to make 5G or buy 5G from MNOs.

The purpose of this paper is to highlight the opportunity for the energy policy in Brazil to tackle the very high cost-effectiveness potencial of solar energy to the power system. Three mechanisms to achieve ambitious reductions in the greenhouse gas emissions from the power sector by 2030 and 2040 are assessed wherein treated as solar targets under ambitious reductions in the greenhouse gas emissions from the power sector. Then, three mechanisms to achieve these selected solar targets are suggested.

This paper reviews current and future incentive mechanisms to promote solar energy. An integrated energy system optimization model shows the most cost-efficient deployment level. Incentive mechanisms can promote renewable sources, aiming to tackle climate change and ensuring energy security, while taking advantage of endogenous energy resources potential. Based on a literature review, as well as on the specific characteristics of the Brazilian power system, under restrictions for the expansion of hydroelectricity and ambitious limitation in the emissions of greenhouse gases from the power sector.

The potential unexploited of solar energy is huge but it needs the appropriate incentive mechanism to be deployed. These mechanisms would be more effective if they have a specific technological and temporal focus. The solar energy deployment in large scale is important to the mitigation of climate change.

The value of the research is twofold: estimations of the cost-effective potential of solar technologies, generated from an integrated optimization energy model, fully calibrated for the Brazilian power system, while tacking the increasing electricity demand, the expected reduction of greenhouse gas emissions and the need to increase the access to clean and affordable energy, up to 2040; proposals of three mechanisms to deploy centralized PV, distributed PV and solar thermal power, taking the best experiences in several countries and the recent Brazilian cases.