Acqui-hiring or Acqui-quitting: Post-M&A Turnover Prediction via a Dual-fit Model

Gaining highly skilled human capital is one of the primary reasons for corporate mergers and acquisitions (M&A), especially for knowledge-intensive industries. However, the inevitable tensions brought by the divergent cultures and organizational misalignment during the M&A process result in high talent turnover rate and ultimately the integration failure. Hence, it is imperative to understand and prepare for the potential effects of M&A process on employee turnover. To this end, we propose a novel dual-fit model induced heterogeneous Graph Neural Network (GNN) model to predict the talent turnover trend in the post-M&A process, by taking into account the complex relationship among the acquirer firm, the acquiree firm, and the acquired employees. Specifically, we creatively design a dual-fit model comprised of both the firm-level compatibility and employee-firm fit. Extensive evaluations on large-scale real-world data clearly demonstrate the effectiveness of our approach

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure

Decision Making in Innovation and Entrepreneurship: a Collection of Conjoint-based Studies

Diese Arbeit mit dem Titel „Decision Making in Innovation and Entrepreneurship – A Collection of Conjoint-based Studies“ beschäftigt sich mit unterschiedlichen Themenbereichen der Kognition, Entscheidungsfindung und des Engagements im innovativen und unternehmerischen Umfeld. Die Conjoint-Analyse bildet dabei die Basis für diese Untersuchungen. Sie erlaubt, Rückschlüsse auf das Entscheidungsverhalten von Unternehmensgründern, deren Mitarbeiter, sowie von Projektmanagern zu ziehen. Forschung im Bereich der Kognition und Entscheidungsfindung von Akteuren im innovativen und unternehmerischen Umfeld ist von hoher Bedeutung. Forschung und Entwicklung in Universitäten und Forschungsinstituten sowie innovative Unternehmen tragen beachtlich zur Innovationstätigkeit und somit zum Wirtschaftswachstum einer Ökonomie bei. Jedoch weisen junge Unternehmen eine geringe Überlebenswahrscheinlichkeit auf. Oft ist das Scheitern junger Unternehmen auf Fehler in Managemententscheidungen und Beurteilungen, z.B. einem zu großen Optimismus, zurückzuführen. Auch Innovationsprojekte von Unternehmen und Forschungsinstituten scheitern deshalb häufig. Einsichten in die Entscheidungsfindung von Unternehmensgründern und Projektmanagern können unser Wissen über mögliche Ursachen für das Scheitern erweitern und Handlungsanweisungen aufzeigen. Weiterhin bilden Mitarbeiter in jungen innovativen Unternehmen eine wichtige Ressource. Ausgeprägtes Engagement von Mitarbeitern kann den Unternehmenserfolg positiv beeinflussen und somit die Überlebenswahrscheinlichkeit für junge Unternehmen erhöhen. Einflüsse auf das Engagement von Mitarbeitern, im jungen Unternehmen zu arbeiten, sollen in dieser Arbeit aufgezeigt werden, um somit praktische Implikationen für den Unternehmensgründer anzubieten

Prediction of stress levels in the workplace using surrounding stress

Occupational stress has a significant adverse effect on workers’ well-being, productivity, and performance and is becoming a major concern for both individual companies and the overall economy. To reduce negative consequences, early detection of stress is a key factor. In response several stress prediction methods have been proposed, whose primary aim is to analyse physiological and behavioural data. However, evidence suggests that solutions based on physiological and behavioural data alone might be challenging when implemented in real-world settings. These solutions are sensitive to data problems arising from losses in signal quality or alterations in body responses, which are common in everyday activities. The contagious nature of stress and its sensitivity to the surroundings can be used to improve these methods. In this study, we sought to investigate automatic stress prediction using both surrounding stress data, which we define as close colleagues’ stress levels and the stress level history of the individuals. We introduce a real-life, unconstrained study conducted with 30 workers monitored over 8 weeks. Furthermore, we propose a method to investigate the effect of stress levels of close colleagues on the prediction of an individual’s stress levels. Our method is also validated on an external, independent dataset. Our results show that surrounding stress can be used to improve stress prediction in the workplace, where we achieve 80% of F-score in predicting individuals’ stress levels from the surrounding stress data in a multiclass stress classification

Exploring turnover intentions of employees at a South African government education council

Orientation: Public sector organisations in South Africa, including educational institutions, experience high employee turnover. There is a general need for public sector organisations to retain valuable talent. Research purpose: This study aimed to explore the reasons behind turnover intentions at the government education council. Motivation for the study: Studies on employee turnover in the public sector focused on government departments and municipalities, with a scarcity of research studies on government education councils. Research approach/design and method: An exploratory qualitative research approach was followed which allowed for the usage of semi-structured interviews to collect data from employees (N = 11). Data were analysed using content analysis. The inductive coding method was used to get to themes and subthemes. Main findings: Employees may leave the government education council because of the micromanagement leadership styles, lack of trust by management, inadequate communication, poorly implemented performance management system, persistent workload, low pay and lack of work–life balance. However, they currently remain within the employment of the council because of its reputation, sense of belonging (teamwork), conducive work environment and career growth prospects. Practical/managerial implications: The government education council should promote autonomy and prioritise leadership skills, team building and other interventions to enhance trust, communication and work-life balance. The government education council should ensure the buy-in of the performance management system and its remuneration policy by employees. Contribution/value add: This study’s findings provide insights into the turnover intentions of employees at the government education council and then assist the organisation in strengthening its retention strategies

The role of causation, effectuation and bricolage in new service development processes

This thesis examines the role of different types of entrepreneurial cognitive logics—namely causation, effectuation, and bricolage (CEB)—in new service development (NSD) processes within a new venture. To understand how entrepreneurial cognitive logics are used in the NSD process, I adopt a process research approach to study how service comes to be within a new venture in the healthcare industry. My research employs a range of methods between 2013 and 2017, including observation, interviews, and document analysis. Within current NSD models, means are not considered as part of processes which lead to new services; instead, the NSD process is assumed to start with a conscious intent to create a new service. My research has identified that NSD processes are often means driven and that the service developers ask themselves means-driven questions considered to represent effectuation logic. Hence, I shift the attention of NSD research from stable and resource-rich environments to dynamic and resource-constrained ones. As a result, I suggest that effectuation and bricolage are key perspectives in understanding NSD in uncertain and resource-scarce environments. In doing so, I challenge the predominantly causation-based formal and linear NSD stage model typically proposed in existing research. The findings show how CEB logics interplay and shift in a complex manner over time. Situational triggers, resource position, and unanticipated consequences, along with actor-dependent responses to internal and external influences, add to the complexity of how CEB logics interplay and shift over time. Furthermore, researching CEB logics on individual, team, and organisational levels reveals that the different logics may also cause conflict, thus leading not only to positive outcomes but also to frustration and tensions within the new venture

A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

Stabilizing global foreign exchange markets in the time of COVID-19 : The role of vaccinations

Acknowledgements We are grateful to the managing editor, Professor Ali M. Fatemi, and two anonymous referees for valuable comments and suggestions. All remaining errors are our own.Peer reviewedPostprin