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This paper aims to investigate the relationship of female participation in labor force with the cybersecurity maturity of nations and the enabling role of e-government development in moderating the same.

The authors have conducted fixed-effects regression using archival data for 149 countries taken from secondary sources. Furthermore, the authors have grouped the sample countries into four levels of cybersecurity maturity (unprepared, reactive, anticipatory and innovative) using clustering techniques, and studied the influence of their interest variables for individual groups.

Results show that female participation in labor force positively influences national cybersecurity maturity, and e-government development positively moderates the said relationship, thereby enabling the empowerment of women.

Encouraging broader participation of women in the labor force and prioritizing investments in e-government development are essential steps that organizations and governments may take to enhance a country’s cybersecurity maturity level.

This study empirically demonstrates the impact of the nuanced interplay between female participation in labor force and the e-government development of a nation on its cybersecurity maturity.

Informed by the resource-based and resource-advantage theories, this study, a comparative study, aims to examine the core dimensions of nation brands – culture, tourism, exports, foreign direct investment, migration and governance – from the company-based brand equity perspective in a sample of 48 countries clustered into three groups (strong, moderate and weak nation brands) from 2011 to 2019 to identify the most critical predictors of nation brand strength in each cluster.

A clustering technique was applied to the modified Country Brand Index to cluster the included countries into strong, moderate and weak nation brands. The authors were then able to analyze each cluster in an effort to explore the relative importance of the predictor variables and determine if that importance varied across the clusters.

This approach revealed novel findings of great importance to policymakers and academics. The results indicate the resources that contribute the most to nation brand equity in each cluster. Such information can guide policymakers in effectively leveraging these strategic resources. First, the cultural dimension was a more critical predictor concerning countries with moderate and weak nation brands than countries with strong brands. Second, tourism exhibited the highest predictive importance concerning all the clusters. For academics, these findings help foster a better understanding of the determinants of nation brand strength, as aligned with the resource-based and resource-advantage theories.

The findings of this study contribute to the literature concerning nation brand management, particularly the stream related to nation brand equity monetization.

Bayesian cubature (BC) has emerged to be one of most competitive approach for estimating the multi-dimensional integral especially when the integrand is expensive to evaluate, and alternative acquisition functions, such as the Posterior Variance Contribution (PVC) function, have been developed for adaptive experiment design of the integration points. However, those sequential design strategies also prevent BC from being implemented in a parallel scheme. Therefore, this paper aims at developing a parallelized adaptive BC method to further improve the computational efficiency.

By theoretically examining the multimodal behavior of the PVC function, it is concluded that the multiple local maxima all have important contribution to the integration accuracy as can be selected as design points, providing a practical way for parallelization of the adaptive BC. Inspired by the above finding, four multimodal optimization algorithms, including one newly developed in this work, are then introduced for finding multiple local maxima of the PVC function in one run, and further for parallel implementation of the adaptive BC.

The superiority of the parallel schemes and the performance of the four multimodal optimization algorithms are then demonstrated and compared with the k-means clustering method by using two numerical benchmarks and two engineering examples.

Multimodal behavior of acquisition function for BC is comprehensively investigated. All the local maxima of the acquisition function contribute to adaptive BC accuracy. Parallelization of adaptive BC is realized with four multimodal optimization methods.

To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles.

The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method.

The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage.

This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.

The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.

A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.

The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.

This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.

Energy production and distribution is undergoing a revolutionary transition with the advent of disruptive technologies such as the Internet of Energy (IoE), 5G and artificial intelligence (AI). IoE essentially involves automating and enhancing the energy infrastructure: the power grid from grid operators to energy generators and distribution utilities. The IoE also relies on powerful connectivity networks such as 5G, big data analytics and AI to optimise its operation. By incorporating the technology that employs ubiquitous devices such as smartphones, tablets or smart electric vehicles, it will be possible to fully exploit the potential of IoE using 5G networks. 5G networks will provide high speed connections between devices such as drones, tractors and cloud networks, to transfer huge amounts of sensor data. Additionally, there are many sources of isolated data across the main energy production units (generation, transmission and distribution), and the data is increasing at phenomenal rates. By applying AI to these data, major improvements can be brought at each stage of the energy production chain. Tying renewable energy to the telecommunications sector and leveraging on the potential of data analytics is something which is gaining major attention among researchers and industry experts. This chapter therefore explores the combination of three of the most promising technologies i.e. IoE, 5G and AI for achieving affordable and clean energy, which is SDG 7 in the UN Sustainable Development Goals (SDGs).

The purpose of this paper is to identify different types of resource and capability configurations among Hungarian family and nonfamily firms and explore which compositions can be considered competitive. In a rivalrous, dynamic world, understanding which sets of resources and capabilities lead to a higher level of competitiveness is vital.

This paper is based on a quantitative competitiveness survey carried out between November 2018 and July 2019 in Hungary. The authors used the Firm Competitiveness Index (FCI) to measure competitiveness and the resource-based view (RBV) approach to understand which configurations of resources and capabilities are responsible for a higher level of competitiveness based on 32 variables. An exploratory factor and cluster analysis were conducted to analyze the ownership's effect on firm competitiveness. The final sample size contained 111 companies, of which 53 were identified as family and 58 as nonfamily firms.

Factor analysis reveals five factors determining resources and capabilities: “operational,” “leadership,” “knowledge management,” “transformation” and “networking.” Based on these factors, the cluster analysis identified five groups in terms of types of family and nonfamily firms: “Lagging capabilities,” “Knowledge-based leadership,” “Innovativeness and transformation-oriented management,” “Relationship-oriented management” and “Business operation-oriented management.” Results show that nonfamily businesses focus on operational and leadership capabilities, reaching a higher FCI than family businesses, which are likely to invest more in their networking, transformation and knowledge management capabilities.

By defining the different configurations family and nonfamily firms rely on to reach competitiveness, the paper applies an essential element to the Hungarian and Middle Eastern European contexts of family business research. The findings contribute to developing family business literature and point out specific resources and capabilities family firms should focus on to shift toward reaching a higher level of professionalization and competitiveness. The characterization of different types of competitiveness comparing family and nonfamily firms enables the firms to assess customized implications.

The purpose of this study/paper is conducting a Systematic mapping review, as a systematic literature review method for reviewing the literature of new product development by textmining and mapping the results of this review.

This research has been conducted with the aim of systematically reviewing the literature on the field of design and development of products based on textual data. This research wants to know, how text data and text mining methods, can use for the design and development of new products.

This review finds out what are the most popular algorithms in this field? What are the most popular areas in using these approaches? What types of data are used in this area? What software is used in this regard? And what are the research gaps in this area?

The contribution of this review is creating a macro and comprehensive map for research in this field of study from various aspects and identifying the pros and cons of this field of study by systematic mapping review.

The paper aims to clarify the relationship between problem-solving skills and socialization of first-year university students and propose talent management strategies for university management, course instructors and administrators. Thus, this paper identifies three student clusters among the first-year bachelor students. This paper aims to propose a talent management framework and makes recommendations for course instructors and administrators.

In this paper, a Simulated Work Experience is applied to collect data on problem-solving skills and demographics of first-year business students. Based on the anonymous competency and demographic data of 546 students, 3 clusters were identified with a hierarchical K-means clustering method and linked with talent management and curriculum design strategies.

The paper provides empirical insights about how the demographic background of the first-year students affects the students' problem-solving skills. This paper identifies three clusters – laggers, unpolished diamonds and drivers – and proposes a talent management framework to support the students' personal and professional development. The proposed talent management framework is based on the direction of upskilling and type of talent management incentives and outlines four distinct categories: extracurricular reward, tutoring and catching up, perform-or-punish and up-or-out systems. This paper makes suggestions to course administrators and instructors how to incorporate talent management and competency mapping aspects into the curriculum and syllabus design activities.

The research is limited to problem-solving skills and focused only on first-year business students.

The paper includes practical implications for business school management, course administrators and instructors about competency mapping, talent management strategies, curriculum and syllabus design.

The research is based on the competency mapping of 546 first-year students at Budapest Business School. The data were collected via a Simulated Work Experience, where the students were participating in a virtual business project.