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The existence of mismatches between training and jobs is relatively common and is accentuated in times of crisis where unemployment is growing. The negative effects that this phenomenon can generate on both workers and the economy makes its study relevant. The objective of this research is to analyse whether graduates of the Catalan university system have jobs according to their educational level.

This paper sees how graduates’ own and acquired characteristics influence the probability of a mismatch from the analysis of the microdata of different waves of the employment insertion survey conducted by the Agència per a la Qualitat del Sistema Universitari de Catalunya (AQU).

The main conclusions focus on confirming that more humanities-oriented degrees tend to have a higher level of mismatch while technology or medicine approach a perfect fit. Therefore, bringing the education and business systems together is important to reduce this gap. Meanwhile, in terms of activities, services such as hospitality and retail have historically been sectors with a poor fit, and what has happened with Catalan graduates has not been an exception.

The main contribution of the research has been to highlight where there is a greater mismatch from the point of view of training, the type of work and its evolution over time, detecting the need to adjust labour supply and demand.

In Chile, there is clear progress in inclusion policies; however, during the last decade, there is a decrease in the participation of young people with special educational needs (SEN), especially in higher education. This chapter studies the factors that influence the educational trajectories of students with SEN and their entry into higher education using quantitative and qualitative methods. It shows both how trajectories of SEN students are shaped and the relationship of educational policies in such trajectories.

Creditor rights reduce or increase agency problems in corporations, affecting financial decisions. This study examines the impact of India’s Insolvency and Bankruptcy Code of 2016 (hereinafter referred to as the Code) on firms’ investment and investment sensitivity based on their health, considering this Code as more creditor-friendly than pre-existing bankruptcy laws.

This study uses a quasi-natural experiment that employs the difference-in-differences (DID) and propensity score matching difference-in-differences (PSM DiD) approach by considering the Code as treatment and categorizing the firms into distressed (treated group) and healthy (control group) firms. For the purpose of analysis, a fixed-effect regression model is used.

This study finds that distressed firms reduce their investment after the Code, but healthy firms do not observe any change. It shows that the reduction in investment of the distressed group is significantly greater than that of the healthy group due to agency conflict and the liquidation bias hypothesis. However, the reduction in investment is not followed by the change in investment sensitivity.

This study adds to the existing studies on the impact of the Insolvency and Bankruptcy Code, 2016 on investment. No study explores the relationship between this Code and investment based on the financial health of the firms. Also, none of the studies explores the impact of the Code on investment sensitivity. The results show that this Code has provided stronger protection to the creditors, which hurts the internal stakeholders’ interests. The study has implications for policymakers and academicians.

This study aims to investigate the interplay between artificial intelligence (AI) integration, organizational digital culture, human resource management (HRM) practices and employee sustainable performance in luxury hotels in Malaysia. It seeks to elucidate how AI adoption influences organizational dynamics, shapes HRM practices and impacts employee sustainable performance over time.

Using a quantitative approach, survey questionnaires derived from prior research were utilized. Analysis using G*Power software determined an appropriate sample size, with psychometric evaluation validating scale development. Statistical analyses using Statistical Package for Social Sciences (SPSS) 28.0 and SmartPLS 4 confirmed data reliability and validity.

Out of the five hypotheses, three were supported. A positive relationship was found between AI adoption and employee sustainable performance, highlighting AI’s potential to enhance productivity and job satisfaction. However, the relationship between AI adoption and organizational digital culture was not supported. On the other hand, HRM practices positively influenced employee sustainable performance. In addition, organizational digital culture was positively associated with employee sustainable performance, underscoring the role of digital fluency in driving workforce productivity. Conversely, AI failed to moderate the relationship between HRM practices and employee sustainable performance.

The study’s focus on luxury hotels in Malaysia and its reliance on cross-sectional data, suggesting the need for longitudinal designs and diverse organizational contexts in future research. Comparative studies across sectors and countries could offer insights into variations in AI adoption practices and their impact on organizational performance.

This study contributes to theoretical frameworks by empirically examining complex relationships between AI integration, HRM practices, organizational digital culture and employee performance, emphasizing the importance of considering organizational context and cultural factors in understanding the implications of AI adoption for sustainable performance enhancement.