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Working arrangements’ hybridity has become paramount, particularly after the coronavirus disease 2019 pandemic. A remote working environment has indubitable advantages (e.g. the ability to work from anywhere and at any time). However, such flexibility comes at the cost of being virtually always connected. This duality poses challenges for talent management (TM) in determining who can thrive under these specific conditions and how. This study explores how employees respond to this extended connectivity – namely, work connectivity behaviour after-hours (WCBA) – and its influence on proactive talent behaviour by constructing and testing a theoretical model that differentiates employees’ reactions to this condition.

This study collected data from 400 mainland Chinese employees using online and offline methods. Owing to the potentially varied effects of working in digital environments on employees, a dual mediation regression model was employed to test the hypotheses.

Remote and hybrid work and, specifically, the increased connectivity experienced by employees can be a “double-edged sword” in influencing their proactive behaviour (PB). While employees experience increased organisation-based self-esteem, which positively correlates with more intense PB, this prolonged exposure may also cause emotional exhaustion (EE), which has a negative correlation with PB. Jointly considering both mediation effects revealed that WCBA’s total effect on PB remained negative.

This study enriches the debate regarding the development of TM practices specifically designed for remote work. It recommends paying greater attention to how employees react to increased connectivity experienced in remote and hybrid working environments. Increased self-esteem or passive EE are possible elements for identifying employees’ talent potential. The separation between work and after-work is becoming blurred in the digital age, which reduces employees’ motivation and ability to exploit their inner talents. Therefore, organisations must find alternatives to preserve their talent pools. This study enriches theoretical research on WCBA, promoting an in-depth application of the theory of job-demand resources in the digital age.

While different attempts have been made to use artificial intelligence (AI) to codify communicative behaviors and analyze startups’ video presentations in relation to crowdfunding projects, less is known about other forms of access to entrepreneurial finance, such as video pitches for candidacies into startup accelerators and incubators. This research seeks to demonstrate how AI can enable the startup selection process for both entrepreneurs and investors in terms of video pitch evaluation.

An AI startup (Speechannel) was used to predict the outcomes of startup video presentations by analyzing text, audio, and video data from 294 video pitches sent to a leading European startup accelerator (LUISS EnLabs). 7 investors were also interviewed in Silicon Valley to establish the differences between humans and machines.

This research proves that AI has profound implications with regards to the decision-making process related to fundraising and, in particular, the video pitches of startup accelerators and incubators. Successful entrepreneurs are confident (but not overconfident), engaging in terms of speaking quickly (but also clearly), and emotional (but not overemotional).

This study not only fills the existing research gap but also provides a practical guide on AI-driven video pitch evaluation for entrepreneurs and investors, reshaping the landscape of entrepreneurial finance thanks to AI. On the one hand, entrepreneurs could use this knowledge to modify their behaviors, enabling them to increase their likelihood of being financially backed. On the other hand, investors could use these insights to better rationalize their funding decisions, enabling them to select the most promising startups.

This paper makes a significant contribution by bridging the gap between theoretical research and the practical application of AI in entrepreneurial finance, marking a notable advancement in this field. At a theoretical level, it contributes to research on managerial decision-making processes – particularly those related to the analysis of video presentations in a fundraising context. At a practical level, it offers a model that we called the “AI-enabled video pitch evaluation”, which is used to extract features from the video pitches of startup accelerators and incubators and predict an entrepreneurial project’s success.

This paper investigates the relationship between commitment to ESG practices and firm performance using a synthetic index based on ESG disclosure and ESG performance scores.

Using the Mazziotta-Pareto aggregation method, we develop a novel synthetic index of ESG engagement based on ESG rating and disclosure. This index is employed in a dynamic panel regression, implemented using the Arellano-Bond estimator, to explain profitability in a sample of 146 listed Canadian firms over the period spanning from 2014 to 2021.

ESG practices may either foster or hinder firm performance. In particular, a synergy emerges between the social and environmental dimensions of ESG practices, shedding light on the relevance of high standards in terms of environmental and social activities.

The study emphasizes the significance of acknowledging the various facets of ESG engagement and the necessity of transcending the current constraints of accessible ESG data and ratings. Synthetic indices combining different types of ESG information may contribute to mitigating the problems created by strategic disclosure on the part of firms, which typically results in undesirable practices such as greenwashing and social washing.

This is the first study that applies the Mazziotta-Pareto method to develop a synthetic index of ESG engagement, tackling each pillar separately. Moreover, when investigating the effect of ESG engagement on profitability, we allow for cross-pillar synergies and/or trade-offs.