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This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.

This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.

This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.

This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.

This paper presents a method for adapting an Information Security Focus Area Maturity (ISFAM) model to the organizational characteristics (OCs) of a small- and medium-sized enterprise (SME) cluster. The purpose of this paper is to provide SMEs with a tailored maturity model enabling them to capture and improve their information security capabilities.

Design Science Research was followed to design and evaluate the method as a design artifact.

The method has successfully been used to adapt the ISFAM model to a group of SMEs within a regional cluster resulting in a model that is aligned with the OCs of the cluster. Areas for further investigation and improvements were identified.

The study is based on applying the proposed method for the SMEs active in the transport, logistics and packaging sector in the Port of Rotterdam. Future research can focus on different sectors and regions. The method can be used for adapting other focus area maturity models.

The resulting adapted maturity model can facilitate the creation and further development of a base of common or shared knowledge in the cluster. The adapted maturity model can cut the cost of over implementation of information security capabilities for the SMEs with scarce resources.

The resulting adapted maturity model can facilitate the creation and further development of a base of common or shared knowledge in the cluster. The adapted maturity model can cut the cost of over implementation of information security capabilities for the SMEs with scarce resources.

The objective of this study is to examine current business and management research on “Industry 4.0 base technologies” and “business models” to shed light on this vast literature and to point out future research agenda.

The authors conducted a bibliometric analysis of scientific publications based on 482 documents collected from the Scopus database and a co-citation analysis to provide an overview of business model studies related to Industry 4.0 base technologies. After that a qualitative analysis of the articles was also conducted to identify research trends and trajectories.

The results reveal the existence of five research themes: smart products (cluster 1); business model innovation (cluster 2); technological platforms (cluster 3); value creation and appropriation (cluster 4); and digital business models (cluster 5). A qualitative analysis of the articles was also conducted to identify research trends and trajectories.

First, the dataset was collected through Scopus. The authors are aware that other databases, such as Web of Science, can be used to deepen the focus of quantitative bibliometric analysis. Second, the authors based this analysis on the Industry 4.0 base technologies identified by Frank et al. (2019). The authors recognize that Industry 4.0 comprises other technologies beyond IoT, cloud computing, big data and analytics.

Drawing on these analyses, the authors submit a useful baseline for developing Industry 4.0 base technologies and considering their implications for business models.

In this paper, the authors focus their attention on the relationship between technologies underlying the fourth industrial revolution, identified by Frank et al. (2019), and the business model, with a particular focus on the developments that have occurred over the last decade and the authors performed a bibliometric analysis to consider all the burgeoning literature on the topic.

The authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.

The authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.

The authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.

Proposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.

Longitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.

Timely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.

The study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

The purpose of this paper is to analyze the business models that 3D printer manufacturers apply to commercialize their technologies. The authors investigate these business models and analyze whether there are business model patterns. The paper describes the gestalt of the business model patterns and discusses differences and similarities.

The authors review the literatures on business models and 3D printing technology. The authors apply a componential business model approach and carry out an in-depth analysis of the business models of 48 3D printer manufacturers in Europe and North America. The authors develop a framework focusing on value proposition, value creation and value capture components. Cluster analysis is used to identify business model patterns.

The results indicate that there are two distinct business model patterns in the industry. The authors termed these patterns the “low-cost online business model” and the “technology expert business model.” The results demonstrate that there is a relationship between business model and technology. The identified patterns are independent of age, company size and country of origin.

The empirical results complement and extend existing literature on business models. The authors contribute to the discussion on business models in the context of novel technology. The technology seems to influence the gestalt of the business model. The sample is limited to European and North American companies and the analysis is based on secondary data.

This is the first empirical study on the business models of 3D printer manufacturers. The authors apply an original mixed-methods approach and develop a framework that can function as a starting point for future research. 3D printer manufacturers can use the identified business model patterns as blueprints to reduce the risk of failure or as a starting point for business model innovation.

Turkey is a maritime country with its current merchant fleet and shipyards, geographical location, young population and growth potential. Clustering, being one of the important improvement methods of global competition power, is widely used in the maritime sector. Analysing the clustering level and potential of Istanbul, which is the major city of Turkey, in regard to economic and social aspects is a basic step for increasing global competitiveness in this sector. This study aims to measure the clustering level of Istanbul’s maritime sector and also define the effect of clustering level on firm performance.

The clustering levels of Istanbul’s maritime transportation and supporting firms, shipyards and maritime equipment manufacturers are measured by means of a survey based on Porter’s diamond theory in this paper. The relationship between clustering level and firm performance is defined by using simple linear regression and fuzzy linear regression methods. The weights of the criteria are calculated by means of entropy method.

It is concluded that despite its deficits, Istanbul’s maritime sector has significant potential to become a major maritime cluster not only in its region but also worldwide. The effect of clustering level on firm performance was observed to be statistically significant, but not high. The results of the simple linear regression and fuzzy linear regression methods are compared.

According to the author’s knowledge, this paper is the first study using fuzzy linear regression and entropy methods to analyse maritime clusters. It evaluates the effect of clustering level on firm performance in the case of Istanbul maritime sector.

This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how federated learning techniques can be applied to the medical field are presented. About 80 reference studies described in the field were reviewed, and the federated learning framework currently being developed by the research team is provided. This paper will help researchers to build an actual medical federated learning environment.

Since machine learning techniques emerged, more efficient analysis was possible with a large amount of data. However, data regulations have been tightened worldwide, and the usage of centralized machine learning methods has become almost infeasible. Federated learning techniques have been introduced as a solution. Even with its powerful structural advantages, there still exist unsolved challenges in federated learning in a real medical data environment. This paper aims to summarize those by category and presents possible solutions.

This paper provides four critical categorized issues to be aware of when applying the federated learning technique to the actual medical data environment, then provides general guidelines for building a federated learning environment as a solution.

Existing studies have dealt with issues such as heterogeneity problems in the federated learning environment itself, but those were lacking on how these issues incur problems in actual working tasks. Therefore, this paper helps researchers understand the federated learning issues through examples of actual medical machine learning environments.

This study aims to find a dynamic model in an effort to optimize tourism performance in ecotourism destinations. The model structure is built based on competitive performance in geographic areas and the application of ecotourism elements that are integrated with big data innovation through artificial intelligence technology.

Data analysis is performed through dynamic system modeling. Simulations are carried out in three models: First, existing simulation models. Second, Scenario 1 is carried out by utilizing a causal loop through innovation of big data-based artificial intelligence technology to ecotourism elements. Third, Scenario 2 is carried out by utilizing a causal loop through big data-based artificial intelligence technology on aspects of ecotourism elements and destination competitiveness.

This study provides empirical insight into the competitiveness performance of destinations and the performance of implementing ecotourism elements if integrated with big data innovations that will be able to massively demonstrate the growth of sustainable tourism performance.

This study does not use a primary database, but uses secondary data from official sources that can be accessed by the public.

The paper includes implications for the development of intelligent technology based on big data and also requires policy innovation.

Sustainable tourism development.

This study finds the expansion of new theory competitiveness of ecotourism destinations.

The paper aims to illuminate the platform created by a cluster organization to facilitate its internationalization and thereby enhance its regional innovation system partners' competitiveness by providing access to global value chains and boosting innovativeness.

The study draws upon the interaction approach, focusing on the interaction process, interaction partners, relationship atmosphere, and relationship environment. A qualitative study was conducted at Future Position X, a Swedish cluster organization. A total of 58 interviews were conducted, including 48 face-to-face in-depth interviews between 2017 and 2019 with six key informants at FPX, representatives from 28 SMEs, ten members of regional innovation systems to which FPX belongs, and four process leaders of regional and local networks, in addition to online interviews with ten members of the regional innovation systems conducted via Microsoft Teams in March 2021. The time span of the study provides a longitudinal perspective.

The FPX cluster collaborates with actors in the quadruple helix, maintaining a mindset that has led to a number of new partner agreements in the global arena to secure the resources and expertise necessary for cluster activities, and thereby ensuring firms in FPX networks access to platforms for international expansion. Internationalization thus expands the cluster's knowledge base beyond the traditional environment of its member firms.

Very few innovations arise from the isolated work of a lone genius. Instead, most innovation is achieved through complex, interactive, iterative and cumulative learning processes in which a variety of actors are involved. The FPX cluster organization's internationalization platform is therefore vital to the internationalization of its partners since cluster actors lack the time, resources, knowledge, experience, and networks required to break into international markets singlehandedly.

This study suggests that, for practitioners and researchers alike, the growing importance and relevance of the regional innovation system cannot be overemphasized. It also holds policy and societal implications in that FPX's global network helps regional SMEs to internationalize, in addition to inspiring international firms to establish operations in the Gävleborg region, thereby helping to strengthen the overall GIS environment. Internationalization also expands the FPX cluster's knowledge base beyond the traditional environment of its firms, an example of this being the construction start of a Microsoft data centre in the region in 2020.

FPX is financed through taxation and grant funding. By initiating projects, creating relationships and building collaborations, FPX thus contributes to collaboration between business, academia and the public sector. FPX also contributes to knowledge development of new technology by creating meeting places and networks around digital issues, such as GIS, AI, the IoT and blockchain technology.

While earlier research has concentrated on endogenous gaps critical to cluster dynamics, comparatively little attention has been paid to exogenous gaps, i.e. linkages between regional clusters and innovation partners elsewhere in the world. This study showcases the richness of interactions in the cluster against the background of wider, global innovation interactions. Future research should examine other vital questions that remain unanswered, e.g. by measuring and exploring the extent to which regional innovation systems can contribute to long-term economic growth for society.