This paper aims to explore the determinants of the level of minibonds issued by companies. In doing so, it discusses the importance of minibonds in providing a market-based funding source. In the empirical analysis, special attention is paid to the study of the recovery from the COVID-19 crisis.
The analysis is carried out through an econometric approach, on the basis of a high-quality empirical dataset related to the Italian small- and medium-sized enterprises (SMEs). The reference period covers the recent pandemic. From a theoretical point of view, a regression model is implemented, including a multicollinearity analysis and an outlier detection procedure.
The results of the study indicate that factors such as leverage, cash flow, firm collaterals and seniority can explain the amount of minibonds issued. These findings provide valuable insights into the drivers of minibond issuance and highlight the potential benefits of minibonds as a funding option for Italian SMEs.
Importantly, results highlight relevant managerial implications at two levels. On one side, we carry on a managerial discussion about the worthiness of accessing the minibonds market; on the other side, we give insights on the managerial implications related to the features of the companies issuing minibonds.
The paper investigates an innovative financial instrument that has been introduced recently and has not yet been studied in depth. To the best of our knowledge, this is the first contribution assessing the main drivers for minibonds issuance level, which is a timely and relevant managerial research topic. In addition, this study also takes into account the impact of the COVID-19 pandemic on minibond issuance, making the analysis appropriate for explaining the current economic context.
This is the application of the Entropy and TOPSIS model to assess the eco-efficiency of European financial institutions using environmental, social, and governance (ESG) strategies. The aim is to categorize financial institutions based on key factors such as environmental training and management and to examine the alignment between ideal ESG performance and eco-efficiency.
The study uses environmental, social, and governance (ESG) strategies to identify and categorize eco-entrepreneurs in European financial institutions. The study utilizes data to examine the structure between environmental training, effective management practices, and the green performance of financial institutions.
The study shows that European financial institutions exhibit varying degrees of eco-efficiency as assessed using the Entropy and TOPSIS model applied to ESG strategies. Surprisingly, the study found that institutions with a high ESG performance do not always match those with the highest eco-efficiency.
They emphasize the need for financial institutions to align their operations with sustainable practices. This research provides insights to increase eco-efficiency and improve the ESG performance of financial institutions. It also informs policy and decision-making in these institutions in relation to environmental training and management practices, contributing to the wider dialogue on sustainable finance.
This indicates a discrepancy between ESG ratings and actual eco-efficiency, emphasizing the need to reassess the ESG framework. The study findings are crucial for aligning financial institutions with sustainable practices and improving the effectiveness of the ESG framework, especially for institutions at the lower end of the eco-efficiency spectrum.
The present article aims to examine the development of the theoretical framework surrounding collaborative consumption (CC) standards in recent years regarding European short-stay accommodation booking platforms. The sharing economy has significantly impacted the tourist accommodation market in recent years. Starting with the use of experimental data on CCs published on Eurostat in 2019, this article analyzes the correlation between choices of CCs for short-stay accommodation, employment and the economic crisis.
A vector autoregressive panel approach was applied to investigate the correlation between CC short-stay accommodation choices using panel organization data from 561 EU regions between 2018 and 2021.
Analyzing the connection between the main data panel variables, a positive correlation was found, followed by an increasing trend in CC use. A self-multiplying effect is generated; that is, the more people use CC, the more electronic captures occur. Consequently, the improvement of CC use and knowledge-intensive activities in short-stay accommodation is strongly linked with employment and GDP.
The originality of the investigation is to examine with a cross-sectional panel data overview the reasons that can push stakeholders to adopt CC and to clearly define a new perimeter of research in terms of the endpoint of CC in short-stay accommodation. Furthermore, the study seeks to assess the end-point congruence to utilize CC as a new gamble to accelerate digital knowledge in the hospitality sector.
Despite talent management’s (TM) importance for improving organizations' competitiveness and resilience, the pandemic highlighted the weakness of organizational-level TM strategies. Therefore, the objective of this study is to investigate the moderating impact of HR analytics on the relationship between TM and its individual outcomes (talent motivation and quality of hires) and subsequently, their impact on organizational outcomes (talent retention).
The structural equation modeling (SEM) technique was used to analyze 219 online questionnaires administered to HR managers from European companies.
A positive relationship exists between TM activities and talent motivation as well as the quality of hires. Furthermore, HR analytics positively moderates these relationships. Finally, talent motivation and the quality of hires are positively related to talent retention.
This study offers several contributions to theory, as it analyzes TM from an individual perspective and provides further empirical confirmation of the potential benefits of HR analytics and additional grounding to the contingency theory.
Our results will allow practitioners to better orient their HR investments, with positive effects for their organizations and their employees.
This study demonstrates that HR analytics can help organizations adopt a human-centric approach to TM, thus increasing the chances for talents to fully express their potential.
This study takes a step forward toward considering TM outcomes from an individual perspective, responding to new generations' need to pay more attention to their individualities. HR analytics can be a suitable tool to do so, as it can provide insights and suggestions based on the actual organizational context, making TM a more data-driven process.
The aim of this paper is to explore how multi-national corporations (MNCs) can effectively adopt artificial intelligence (AI) into their talent acquisition (TA) practices. While the potential of AI to address emerging challenges, such as talent shortages and applicant surges in specific regions, has been anecdotally highlighted, there is limited empirical evidence regarding its effective deployment and adoption in TA. As a result, this paper endeavors to develop a theoretical model that delineates the motives, barriers, procedural steps and critical factors that can aid in the effective adoption of AI in TA within MNCs.
Given the scant empirical literature on our research objective, we utilized a qualitative methodology, encompassing a multiple-case study (consisting of 19 cases across seven industries) and a grounded theory approach.
Our proposed framework, termed the Framework on Effective Adoption of AI in TA, contextualizes the motives, barriers, procedural steps and critical success factors essential for the effective adoption of AI in TA.
This paper contributes to literature on effective adoption of AI in TA and adoption theory.
Additionally, it provides guidance to TA managers seeking effective AI implementation and adoption strategies, especially in the face of emerging challenges.
To the best of the authors' knowledge, this study is unparalleled, being both grounded in theory and based on an expansive dataset that spans firms from various regions and industries. The research delves deeply into corporations' underlying motives and processes concerning the effective adoption of AI in TA.
The purpose of this literature review is, first, to understand how employees with disabilities in the context of COVID-19 have been studied under the talent management (TM) approach; second, to explore what we know about the predictors and outcomes that have been linked to TM practices in that area; and third, to identify gaps in our understanding and provide insights for future research.
This paper presents a systematic literature review (SLR) based on 38 academic sources published in high-impact indexes from 2020 to 2023.
The existing research shows COVID-19 as a crucial context that led organizations to more precarious and segmented TM practices, which had negative consequences for employees with disabilities, both at the individual level (reduced satisfaction and income, and increased health issues) and the organizational level (increased unemployment, turnover and discrimination as well as declining performance and productivity).
This paper provides essential contributions to the field of TM in the relatively unexplored context of employees with disabilities since the emergence of COVID-19. Our literature review suggests there is significant room for developing and implementing adjusted TM strategies and practices to foster effective inclusiveness, accommodations and supportive work environments for employees with disabilities. From this evidence, a number of key avenues for future research and key implications for academics and practitioners are provided.
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