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Research excellency has long been the dominant paradigm in assessing academic quality and hence a prime determinant of academic careers. Lately, this approach to academic performance has come under higher scrutiny for its narrow focus on the individual, promoted an exclusive, performance-oriented talent management and inhibiting collaboration, transparency and societal involvement.

As a response to the limitations of the excellency policy, this chapter examines the emergence of open science as a transformative force in the academic world. Open science represents a paradigm shift, emphasizing the importance of transparency, and increased societal engagement in the academic process. It opens up the possibility to include the context dimension, multiple stakeholders and a more diverse set of development and performance indicators.

This chapter stresses the urgent need to realign our system of recognition and rewards with the premise of open science and with talent management. By highlighting the disconnect between current recognition mechanisms and the values of universities, this chapter emphasizes the necessity of transformative changes at institutional and systemic levels.

To provide concrete insights into the implementation of these changes, this chapter explores a case study of Utrecht University. This specific example showcases how strategic decisions at an institute level allow navigation of the complexities of recognizing and rewarding open science practices. The Utrecht University case study serves as an inspiration for other institutions seeking to embrace open science and adapt their policies and practices accordingly.

Purpose: The business world has become more turbulent than ever. Organisations must be proactive to meet the challenges of the increasingly disruptive, dynamic, and unpredictable world. One technique that has supported leaders and organisations under challenging circumstances is ‘backcasting’, which works by envisioning a desirable future state and then working backwards to attain it. The current study aims to extend an understanding of the backcasting practices and strategic approaches that can be used by leadership teams in different industries in order to survive in turbulent times and can be adapted within and beyond any pandemic.

Methodology: The research employs a desktop research method to review and compare the most commonly used strategies that leaders from different sectors can use for their respective organisations to thrive in the VUCA world.

Findings: There needs to be more research on the applicability and relevance of backcasting that the leaders of different sectors can employ. The study would provide insights that would bridge the existing research gap and come up with suitable strategies that can be employed for dealing with VUCA challenges in the Indian context.

Significance: The outcome of the study will be helpful to the leaders in designing and aligning ‘out of the box’ backcasting strategies to survive in the highly disruptive world.

In academia, assessment is often narrow in its focus on research productivity, its application of a limited number of standardised metrics and its summative approach aimed at selection. This approach, corresponding to an exclusive, subject-oriented concept of talent management, can be thought of as at odds with a broader view of the role of academic institutions as accelerating and improving science and scholarship and its societal impact. In recent years, open science practices as well as research integrity issues have increased awareness of the need for a more inclusive approach to assessment and talent management in academia, broadening assessment to reward the full spectrum of academic activities and, within that spectrum, deepening assessment by critically reflecting on the processes and indicators involved (both qualitative and quantitative). In terms of talent management, this would mean a move from research-focused assessment to assessment including all academic activities (including education, professional performance and leadership), a shift from focus on the individual to a focus on collaboration in teams (recognising contributions of both academic and support staff), increased attention for formative assessment and greater agency for those being evaluated, as well as around the data, tools and platforms used in assessment. Together, this represents a more inclusive, subject-oriented approach to talent management. Implementation of such changes requires involvement from university management, human resource management and academic and support staff at all career levels, and universities would benefit from participation in mutual learning initiatives currently taking shape in various regions of the world.

This chapter examines three Italian cities that have experienced a transition from “Fordism to tourism”: Genoa, Turin, and Milan. After an industrial crisis, they have invested in culture and tourism as alternative ways of development. This transition is examined using the theoretical framework of urban regimes highlighting five development trends: the city as a growth machine, the Fordist city, the creative city, the city as entertainment machine, and the blue-green city. By adopting this theoretical framework, the evidence shows how academic institutions, tour operators, and public authorities may or may not work together for the tourism development of their cities.

Purpose: This chapter examines the impact of artificial intelligence (AI) on employability and dispels the misconception that AI negatively affects job opportunities. The study aims to shed light on the ways in which AI can enhance employability by complementing natural intelligence and enabling employees to demonstrate creativity in various aspects of their work.

Need for the study: In the 21st century, AI has become ubiquitous, and governments worldwide are actively promoting its integration into various industries and systems. However, concerns about the potential negative consequences of AI have emerged.

Methodology: It is reviewing commentary secondary sources of data viz. books, articles, journals, newspaper articles, reports which have been considered to bring forth the advent of AI being an important premise for the construct of employability

Findings: The findings of this study reveal that the perceived negative impact of AI on employability is a misconception. AI technology, such as Alexa, ChatGPT, and OpenAI, has made significant advancements in the market but is still unable to pass the Turing test. Consequently, it is recommended that AI companies take a pause to fully understand and address the consequences associated with AI implementation.

Practical implications: The practical implications of this study are twofold. First, it debunks the myth that AI jeopardises employability associated with natural intelligence, highlighting the importance of human skills in conjunction with AI technologies. Second, it calls for a strategic approach for organisations and governments to adapt to AI while ensuring the workforce remains adaptable and equipped with the necessary skills. This study provides insights for policymakers, employers, and individuals to embrace AI to augment human potential and improve global market productivity.