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Using a dynamic capabilities approach, the present study aims to identify and assess the effects of organizational determinants on capabilities underlying Industry 4.0 design principles, such as integration, virtualization, real-time, automation and servitization.

PLS-SEM enables a two-stage hierarchical latent variable reflective-formative model which was used for assessing the effect of organizational determinants on Industry 4.0 design principles. Five hundred six manufacturing companies constitute the effective sample, representing a population of manufacturing companies in an industrialized country.

The findings reveal that Industry 4.0 design principles extensively depend on digitalization resource availability. At the same time, companies that possess digitalization and change management capabilities tend to devote more resources to digitalization. Finally, the paper reveals that networking and partnership capability is the critical enabler for change management and digitalization capabilities.

The paper provides empirical evidence that the successful development of Industry 4.0 design principles and their underlying integration, virtualization, real-time, automation and servitization capabilities are resource dependent, requiring significant upfront investment and continuous resource allocation. Further, the study implies that companies with networking and partnership, change management and digitalization capabilities tend to allocate more resources for Industry 4.0 transformation.

Exclusively focusing on empirical research that reported applied insights into determinants of Industry 4.0 design principles, the study offers unique implications for promoting Industry 4.0 digital transformation among manufacturing companies.

Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.

VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.

The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.

User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

This paper aims to explore the challenges and opportunities faced by buying centers in small and medium enterprises (SME) manufacturing companies in view of recent technological changes and the virtualization of communication.

This study uses a qualitative approach with multiple case studies to portray complex realities within the buying centers in the manufacturing SME context. The authors selected five Italian companies to portray the diverse characteristics, practices and policies of relevant stakeholders before reaching saturation with the issues explored.

The authors find that interactions among buying center members are more effective with greater collaboration and exchange (as opposed to competition and a struggle for power). Virtual/hybrid relations require greater intra-group cooperation, whereas diverse backgrounds and collaborative interactions help the flexibility and performance of the buying center. Greater use of technology produces certainty and automation, but it may also cause overload and biases that can be solved with the ability to analyze and clear responsibility for decisions.

The authors studied only five Italian companies in this study. Future research in other countries with diverse cultural and socio-economic conditions and methods would help extend this research.

The findings would improve the understanding of the challenges of adopting new purchase process technologies that would help automate routine tasks, produce useful data and support decision-making.

Unlike prior studies, this study uses an exploratory design to study the evolution of buying centers in SMEs to seek deeper insights into the challenges and opportunities faced by SMEs because of the growing use of emerging technologies.

Policymakers are developing national strategic plans to encourage organizations to adopt Construction 4.0 technologies. However, organizations often adopt the recommended technologies without aligning with organizational vision. Furthermore, there is no prioritization on which Construction 4.0 technology should be adopted, including the impact of the technologies on different criteria such as safety and health. Therefore, this study aims to evaluate Construction 4.0 technologies listed in a national strategic plan that targets the enhancement of safety and health.

A list of Construction 4.0 technologies from a national strategic plan is evaluated using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Then, the data are analyzed using reliability, fuzzy TOPSIS, normalization, Pareto, sensitivity, ranking and correlation analyses.

The analyses identified six Construction 4.0 technologies that are critical in enhancing safety and health: Internet of Things, autonomous construction, big data and predictive analytics, artificial Intelligence, building information modeling and augmented reality and virtualization. In addition, six pairs of Construction 4.0 technologies illustrate strong relationships.

This study contributes to the existing body of knowledge by ranking a list of Construction 4.0 technologies in a national strategic plan that targets the enhancement of safety and health. Decision-makers can use the study findings to prioritize the technologies during the adoption process. Also, to the best of the authors’ knowledge, this study is the first to evaluate the impact of Construction 4.0 technologies listed in a national strategic plan on a specific criterion.

This paper aims to consider the relationship between urban events and urban public space, asking whether cities have enough space for events and whether events have enough space in cities.

Policy analysis surrounding events and festivals in the Netherlands is used to understand the dynamics of urban events, supported by content analysis of policy documents. A vignette of event space struggles in Amsterdam illustrates the contradictions of the event/space relationship.

The research identifies a policy shift in the Netherlands towards urban events from expansive, festivalisation strategies to defensive, NIMBYist policies. It exposes contradictions between protecting space as a living resource and the exploitation of space for regenerative purposes. Three future scenarios for urban events are outlined: conflict and competition, growth and harmony and digitalisation and virtualisation.

Develops scenarios for the future relationship between events and urban space.

Provides an analysis of the recursive spatial implications of the growth of the events sector for cities and the growth of cities for events.

Cloud computing promises dependable services offered through next-generation data centres based on virtualization technologies for computation, network and storage. Big Data Applications have been made viable by cloud computing technologies due to the tremendous expansion of data. Disaster management is one of the areas where big data applications are rapidly being deployed. This study looks at how big data is being used in conjunction with cloud computing to increase disaster risk reduction (DRR). This paper aims to explore and review the existing framework for big data used in disaster management and to provide an insightful view of how cloud-based big data platform toward DRR is applied.

A systematic mapping study is conducted to answer four research questions with papers related to Big Data Analytics, cloud computing and disaster management ranging from the year 2013 to 2019. A total of 26 papers were finalised after going through five steps of systematic mapping.

Findings are based on each research question.

A specific study on big data platforms on the application of disaster management, in general is still limited. The lack of study in this field is opened for further research sources.

In terms of technology, research in DRR leverage on existing big data platform is still lacking. In terms of data, many disaster data are available, but scientists still struggle to learn and listen to the data and take more proactive disaster preparedness.

This study shows that a very famous platform selected by researchers is central processing unit based processing, namely, Apache Hadoop. Apache Spark which uses memory processing requires a big capacity of memory, therefore this is less preferred in the world of research.

The evolving visitors' expectations and the unfolding digital transformation compel rethinking on the service offering of museums and cultural institutions. Although digitalization and people-centeredness are widely exploited to enhance the visiting experience, there is limited evidence of their implications on organizational attractiveness. The article investigates this issue, examining the service attributes that entice visitors.

The study collected secondary data from the latest census study by the Italian Institute of Statistics on museums and cultural institutions. Two hierarchical regression models have been run on a sample of large publicly owned organizations (n = 312) to identify the service factors that were most effective in attracting Italian and foreign visitors.

Museums and cultural institutions undergoing a digital transformation were more effective in attracting visitors. The delivery of virtual tours and online events captivated the Italian audience. Foreigners appreciated the opportunity to use applications augmenting the on-site visit.

Digitalization and people-centeredness improve the attractiveness of museums and cultural institutions. Using digital channels to engage visitors fosters their desire to interact with cultural heritage. Furthermore, digitalization enriches the on-site visit, expanding conventional services with virtuality. However, the adverse effects on cultural heritage should be carefully handled.

This study highlights the service attributes that add to the attractiveness of museums and cultural institutions, enabling them to engage visitors and improve the visiting experience.

Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment.

In this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results.

The acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study.

The outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.