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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.

The purpose of this paper is to survey the automobile insurance fraud detection literature in the past 31 years (1990–2021) and present a research agenda that addresses the challenges and opportunities artificial intelligence and machine learning bring to car insurance fraud detection.

Content analysis methodology is used to analyze 46 peer-reviewed academic papers from 31 journals plus eight conference proceedings to identify their research themes and detect trends and changes in the automobile insurance fraud detection literature according to content characteristics.

This study found that automobile insurance fraud detection is going through a transformation, where traditional statistics-based detection methods are replaced by data mining- and artificial intelligence-based approaches. In this study, it was also noticed that cost-sensitive and hybrid approaches are the up-and-coming avenues for further research.

This paper’s findings not only highlight the rise and benefits of data mining- and artificial intelligence-based automobile insurance fraud detection but also highlight the deficiencies observable in this field such as the lack of cost-sensitive approaches or the absence of reliable data sets.

This paper offers greater insight into how artificial intelligence and data mining challenges traditional automobile insurance fraud detection models and addresses the need to develop new cost-sensitive fraud detection methods that identify new real-world data sets.

This chapter is devoted to sustainable human resource management that leads to sustainable competitiveness. It features the ways human resources can be managed to carry out sustainable goals and the impact of sustainability on employees' attitudes and behaviours. The aim of this study is to explore the complex objectives of sustainability and human resource management and empirically investigate the dynamic relationship between human resources in science and technology and sustainable competitiveness in the case of 35 European countries. Our contribution emphasizes this interrelationship and its causality. For this research, we applied a vector auto-regression (VAR) model, and the Granger causality method to examine the relationship between human resources in science and technology and sustainable competitiveness. A panel data included 314 observations between 2012 and 2021. The panel VAR for analysing the impulse response function was enriched with the 5% and 95%, using Monte Carlo simulations. The research results revealed bidirectional causality in the European countries between human resources in science and technology and sustainable competitiveness. Human resources in science and technology trigger sustainable competitiveness and vice versa. As an element of originality, our study demonstrates that human resources in science and technology contribute to sustainable performance, and, on the other hand, a more competitive and sustainable environment contributes to the development of human resources in science and technology. Thus, the chapter outlines the role of human resources in science and technology with regard to sustainable human resource management (HRM), and how to navigate these objectives so that they can positively influence sustainable competitiveness.

The Hungarian Revolution of 1956 shook the Soviet Union to the core and provided the West with the iconic image of the freedom fighter willing to risk all for the cause of freedom. The pathos of the lost cause provided Hungarians with a new set of heroes akin to those of the failed 1848 Revolution, the best known being Prime Minister Imre Nagy who was executed for siding with the revolutionaries in their bid to establish a sovereign republic. His belated funeral on June 16, 1989 undermined the moral and political authority of the communist regime that had attempted to consign Nagy and his confederates to oblivion and seemed to mimic Emile Durkheim's analysis of piaculum and the conscience collective. But the spectacle of Nagy's funeral only temporarily shrouded significant differences between and within those factions demanding pluralist society, most recently revealed in the acrimonious celebration of the 50th anniversary of the Hungarian Revolution. These debates are rooted in Hungary's deeply troubled past that strongly questioned republican values in contrast to the authoritarian values of the Hapsburg monarchy, alliance with the Axis, genocide, and its relationship to communism in the wake of the disaster of World War II. Jacques Derrida tells us that it is not easy to exorcise our ghosts; instead, we are prompted to reconstruction. Memory studies, stimulated by studies of the Holocaust, transformed the sociological imagination (especially Friedlander, 1993; LaCapra, 1998a, 1998b, 1998c, 1998d). There has been what Michael Roth referred to as “a turning of oneself so as to be in relation to the past” as an act of witness. The traumatic memory of the 1956 Revolution provides yet another case in which a traumatic past is still salient to the political actors in the contemporary arena. This chapter immerses itself in the emergence of historical sociology and with it “memory studies,” that is: (1) the relationship between identity, memory, and embodiment; and (2) the relationship between historical circumstance and collective memory formation (described in diverse approaches such as Adorno, 1959; 1997; Nora, 1989; Postone, Martha, & Kobyashi 2009). In particular, there is in historical sociology an emergent interest in (1) commemorative practices, memorializing addresses, memento; and (2) the struggles over memory, remembering, and forgetting.