Search results

This final chapter of this book highlights and critically discusses some specific issues concerning talent management in the context of higher education raised in the chapters of this book. It recapitulates the transition higher education is going through. This transition started decades ago but was boosted by the movements of Open Science and Recognition and Rewards. It leads to a reorientation on the conceptualization of academic performance and subsequently also on the meaning of talent and talent management in academia. It points to a shift from an exclusive and performance orientation on talent, to an inclusive, developmental approach to talent management or a hybrid form. Yet, Thunnissen and Boselie state that there is a talent crisis in academia, and this crisis urges the need for more innovative ways of developing and implementing talent management practices. This chapter ends with some recommendations for further talent management research and practice.

In today's advanced economy, there is a broader presence of information revolution, such as artificial intelligence (AI). AI primarily drives modern banking, leading to innovative banking channels, services and solutions disruptions. Thus, this chapter intends to determine AI's place in contemporary banking and stock market trading.

Stock market forecasting is hampered by the inherently noisy environments and significant volatility surrounding market trends. There needs to be more research on the mantle of AI in revolutionising banking and stock market trading. Attempting to bridge this gap, the present research study looks at the function of AI in banking and stock market trading.

The researchers have synthesised the literature pool. They undertook a systematic review and meta-synthesis method by identifying the major themes and a systematic literature review aided in the critical analysis, synthesis and mapping of the body of existing material.

The study's conclusions demonstrated the efficacy of AI, which has played a robust role in banking and finance by reducing risk and operational costs, enabling better customer experience, improving regulatory complaints and fraud detection and improving credit and loan decisions. AI has revolutionised stock market trading by forecasting future prices or trends in financial assets, optimising financial portfolios and analysing news or social media comments on the assets or firms.

AI's debut in banking and finance has brought sea changes in banking and stock market trading. AI in the banking industry and capital market can provide timely and apt information to its customers and customise the products as per their requirements.

Early studies projected potential societal, economic and environmental benefits by the widespread deployment of Autonomous and Connected Transport (ACT) promising a significant reduction of transport costs and improvement in road safety. An effective way of assessing ACT impact is via simulations, where results are largely affected by the scenarios defining the ACT development. However, modelled scenarios are very diverse due to the huge uncertainty in ACT development and deployment. This chapter aims to shed light on the different ACT simulation scenarios and sustainability aspects that should be considered while developing or reporting the simulation results. To this end, this chapter discusses the various simulation approaches, what the required (or the typically utilised) pipelines are, and how some components are more important or less important than in ‘classic’ modelling and simulation approaches. Special focus is dedicated to the uncertainty related to ACT operational parameters and how these will impact transport modelling. To address said uncertainty, an analysis of current approaches to scenario building is provided, as the chapter guides the reader through different methodologies and clusters them in relation to the desired indicators. Finally, the chapter identifies and proposes Key Performance Indicators (KPIs) that are useful when applying simulation tools to assess ACT scenarios. These KPIs can be used for simulation scenario development to test particular sustainability aspects of ACT deployment and relevant policies.

High turnover rates, delay and dissatisfaction among PhD students about the high efforts and low rewards are common problems in doctoral education. Research shows that many different factors are associated with the mental health crisis in graduate education, but these diverse aspects have not often been studied in relation to talent management and human resource management (HRM) strategies. Based on questionnaires and in-depth interviews, this chapter critically assesses the factors that influence doctoral students’ well-being, using as theoretical framework the self-determination theory, concerned with the social and other conditions that facilitate or hinder human well-being and flourishing, and the job demands–resources model, an occupational stress model that suggests strain is a response to imbalance between demands on the individual and the resources he or she has to deal with those demands. These theoretical frameworks help to explore the perceived job demands and resources, and motivations of a sample of 25 PhD students in the Netherlands, in order to recommend adequate talent management strategies to improve PhD work conditions at universities and reduce the increasing levels of ill-being. The study proposes a collegial model, focussing on the enjoyment of work, instead of the current managerial model, which focusses on strengthening knowledge and skills, and stimulating performance-oriented behaviour. A differentiated approach is needed, offering customized talent development for each PhD student in order to respond to his or her specific qualities, improving general well-being. This radical shift in talent management is needed to counter the mental health crisis in doctoral studies.

Rapid technological change in the transport sector is leading to a growing range of potential and actual ‘business models’ deployable for the movement of goods and people. Two key uncertainties arise from this proliferation: first, concerning which ones can be economically viable, and, second, whether they can be both simultaneously economically viable and contribute to the imperatives of more sustainable mobility. The present chapter reviews and appraises the emergence of these new business models, drawing on both literature review and empirical research with entrepreneurs involved in the new mobility sector. Specifically, the potential of the UN Sustainable Development Goals (SDGs) (UN, n.d.) as a device to structure and frame the debate about what constitutes a valuable contribution to sustainable mobility is considered. A framework is developed which captures how mobility and transport have dependencies with the SDGs. From this analysis, key sustainability concepts are derived which have either a subsistence function (maintaining the basics of human life) or an enhancement function (enabling citizens to realise their potential whilst reducing impacts on the planet). Five different innovations involving mobility sector business entrepreneurship are then characterised using this framework to exemplify its ability to deconstruct and test claims that ‘smart mobility’ is also good for sustainability as well as good for business. It is concluded that the framework could contribute to a wider architecture of sustainability interrogation. It could promote discourse around a wide range of actors, posing questions and surfacing tensions and contingencies effectively, whilst providing a holistic, strategic assessment to inform more targeted, scientific evaluations of sustainability metrics.

Purpose: The prominence of quality education for building sustainable development is undeniable and is distinctly pointed out in 1 of the 14 sustainable development goals (SDGs). In the same context, this study intends to investigate the role of university commitment, perceived organisational prestige, student satisfaction, and perceived employability in enhancing sustainability in higher education.

Need of the Study: To evaluate how student satisfaction mediates the relationship between university commitment, perceived organisational prestige, and perceived employability with sustainable university institutes.

Methodology: An adapted questionnaire was used in this study to capture the perception of 458 management graduates selected through the purposive sampling method. Partial least squares structural equation modelling (PLS-SEM) technique was used to analyse the data with the help of Smart PLS software.

Findings: The results of this study show that student satisfaction is the strongest predictor of sustainable university institutes. University commitment was found to lead to student satisfaction significantly. Furthermore, student satisfaction wasn’t found to play the role of mediator in the proposed model.

Practical Implications: This study aims to fulfil theoretical, research, and management implications for students, higher education institutes (HEIs), and policymakers. HEIs are recommended to instil university commitment, perceived organisational prestige and student satisfaction via various practices and amendments in their curriculum. Students are recommended to enhance their perceived employability to achieve career sustainability.