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The COVID-19 pandemic has generated a crisis that has hit the European economy and the currently existing systems. To cope with the crisis, Europe has started an investment aiming the energy transition and crisis. Portugal, Spain and Greece have received the approval of their National Recovery Plans from the European Commission, with a definition of spending up to 2026 through the European Union (EU) Next Generation Found. The study investigates whether the Green Deal policies are relaunched by the plans financed and whether the pandemic has changed and conditioned the priorities of the energy transaction. The study uses the lens of corporate social responsability (CSR) and relapse measurable across the Sustainable Development Goals (SDGs).

The Green Deal policies supported by new European investments in the three countries were analyzed through a content analysis (CA) technique to investigate the associated practical and theoretical elements.

The energy theme has a relevance compared to other issues in the investment plans envisaged in Greece, Portugal and Spain. The analysis highlights energy efficiency, sustainable energy and reduction of consumption among the main themes. Energetically, sustainable building plays a central role. The study highlights the relationship between Green Deal policies, CSR, SDGs and management tools adopted.

The study strengthens the relationship between the Green Deal, CSR and SDGs by identifying policies that have already been implemented and theoretical and practical gaps on which politicians and scholars will have to investigate and support in the process of development and continuity of the identified pillars.

Artificial intelligence (AI) can help in defining preventive strategies in taking decisions in complex situations. This paper aims to research how workers might deal with intervening AI tools, with the goal of improving their daily working decisions and movements. We contribute to deepening how workers might deal with intervening AI tools aiming at improving their daily working decisions and movements. We investigate these aspects within a field, which is growing in importance due to environmental sustainability issues, i.e. waste management (WM).

This manuscript intends to (1) investigate if AI allows better performance in WM by reducing social security costs and by guaranteeing a better continuity of service and (2) examine which structural change is required to operationalize this predictive risk model in the real working context. To achieve these goals, this study developed a qualitative inquiry based on face-to-face interviews with highly qualified experts.

There is a positive impact of AI schemes in helping to detect critical operating issues. Specifically, AI potentially represents a tool for an alignment of operational behaviours to business strategic goals. Properly elaborated information, obtained through wearable digital infrastructures, allows to take decisions to streamline the work organization, reducing potential loss due to waste of time and/or physical resources.

Being a qualitative study, and the limited extension of data, it is not possible to guarantee its replication and generalizability. Nevertheless, the prestige of the interviewees makes this research an interesting pilot, on such an emerging theme as AI, thus eliciting stimulating insights from a deepening of information coming from respondents’ knowledge, skills and experience for implementing valuable AI schemes able to an align operational behaviours to business strategic goals.

The most critical issue is represented by the “quality” of the feedback provided to employees within the business environment, specifically when there is a transfer of knowledge within the organization.

The study focuses on a less investigated context, the role of AI in internal decision-making, particularly, for what regards the interaction between managers and workers as well as the one among workers. Algorithmically managed workers can be seen as the players of summarized results of complex algorithmic analyses offered through simpleminded interfaces, which they can easily use to take good decisions.

Accountability and transparency represent two concepts that are gaining more and more importance in the higher education systems. Universities are increasingly called upon to provide both financial and non-financial information. This circumstance has attracted the interest of academics interested in examining the transparency levels of universities. However, limited attention has been paid to corporate governance disclosure. This study aims to bridge this important gap by analyzing the amount of corporate governance information disseminated by Italian universities through their website and the factors capable of influencing this level of disclosure.

This study uses manual content analysis on a sample of 92 Italian universities to measure the extent of corporate governance information dissemination. In addition, it uses various regression models to test the research hypotheses.

Empirical results demonstrate, first, an adequate commitment to online corporate governance disclosure and, second, a greater propensity toward the dissemination of corporate governance information by the largest and public universities.

The findings greatly enrich the academic literature and have important practical implications for universities, policymakers, and lawmakers.

This paper aims to investigate the literacy corpus regarding the potential of big data to improve public decision-making processes and direct these processes toward the creation of public value. This paper presents a map of current knowledge in a sample of selected articles and explores the intersecting points between data from the private sector and the public dimension in relation to benefits for society.

A bibliometric analysis was performed to provide a retrospective review of published content in the past decade in the field of big data for the public interest. This paper describes citation patterns, key topics and publication trends.

The findings indicate a propensity in the current literature to deal with the issue of data value creation in the private dimension (data as input to improve business performance or customer relations). Research on data for the public good has so far been underestimated. Evidence shows that big data value creation is closely associated with a collective process in which multiple levels of interaction and data sharing develop between both private and public actors in data ecosystems that pose new challenges for accountability and legitimation processes.

The bibliometric method focuses on academic papers. This paper does not include conference proceedings, books or book chapters. Consequently, a part of the existing literature was excluded from the investigation and further empirical research is required to validate some of the proposed theoretical assumptions.

Although this paper presents the main contents of previous studies, it highlights the need to systematize data-driven private practices for public purposes. This paper offers insights to better understand these processes from a public management perspective.

Scholarly interest in the business effects of organised crime (OC) has recently increased. This study aims to conduct a systematic literature review (SLR) on the conditions under which OC could pose a threat to or take control of firms within a particular context.

We use narrative synthesis and thematic analysis, with a sample of 46 theoretical and empirical studies published over the past 30 years on the relationship between OC and firms within the disciplines of Business, Management and Accounting (BMA).

SLR and thematic analysis show that scholarly interest has focused on four key domains: OC as a firm, the impact of OC on firms, firms’ efforts to counter OC’s influence and governmental interventions. Using medical metaphors, we also develop a diagram depicting the interplay between OC and firms within the BMA literature.

This study contributes to the literature shaping an agenda to steer future research towards these four key themes. The effectiveness of anti-OC tools and measures depends on a thorough understanding of local norms, behaviours and business practices. In addition to measurement and methodological challenges, several grey areas remain, including the distinction between criminal enterprises and legitimate businesses. Ambiguities also surround the circumstances under which the OC preys upon firms or employs them to establish dominance over a territory.

This book chapter briefly discusses the historical and cultural context of ethnography and evaluation as positioned in the United States and the opportunities made possible by merging techniques and methodology. By merging, methodological pluralism and increased responsiveness to cultural contexts are leveraged, and the critical relevance of engaging communities in evaluation processes is underlined. However, engaging with communities with marginalized backgrounds can present complicated power dynamics in practice that require the evaluator to rethink their role. Equity-centered research practices from the Equity-Centered Research Framework are linked to expound on the need for transforming the evaluator's role to shift and share power with communities throughout engagement. We expand on how transformative work also requires ongoing examinations of positionality in the evaluator role through the lens of relationships. Borrowing from ethnographic concepts, relational dimensions of positionality are conceptualized as alignment, temporality, and place. Each component is illustrated with details on how power dynamics may occur throughout community engagement as well as ways to manage and mitigate power differentials between the roles of the evaluator and community partners. We then offer three broad evaluator practices to support transformation: (a) identify positionality, (b) embed into everyday practice, and (c) negotiate in collaborations with communities. With these practices, we reflect on critical questions and position communities as critical learning partners to promote actions to mitigate and manage ongoing power dynamics. We close this chapter with our experiences interrogating positionality to illustrate distinct burdens and insights as evaluators of color.

This study aims to examine the effects of Internet of Things (IoT) technology on efficiency and patient care in Italian and Israeli intensive care units (ICUs). The goal is to study how IoT might improve care settings by controlling health dynamics and responding to life-threatening circumstances.

This survey-based research explores IoT use, challenges and adaptability in ICUs in both countries. Interviews and surveys of ICU health-care workers are used to get both quantitative and qualitative data on integrating experiences and perspectives.

The research found significant variations between Italy and Israel due to technology infrastructures and health-care practices. Israel shows a more concentrated deployment in a major medical centre with advanced but limited uptake, whereas Italy shows application throughout ICUs highlighting regional health-care system disparities. Interoperability, data security and IoT training are common difficulties.

This research has limitations. One drawback is the geographical dispersion of study sites, with a bigger sample size in Italy than in Israel. This discrepancy may affect findings applicability. However, these preliminary findings provide a foundation for further research into the complexities of deploying IoT in various health-care settings.

This study compares IoT integration in two national health-care systems, adding to health-care technology literature. Regional variations affect technology adoption, but IoT may enhance ICU operations and patient care, according to one research. This study helps health-care practitioners, academics and policymakers understand the pros and cons of IoT in health care.

The purpose of this study is to examine the relationship between knowledge-oriented leadership (KOL) and business performance (BP) in a knowledge-intensive industry and further investigate, heretofore neglected, a mediating effect of intellectual capital and sustainable competitive advantage in the relationship between KOL and BP.

A quantitative approach was adopted to conduct the present study. Data were collected from the full-service branches of private banks in Tehran, Iran. A confirmatory factor analysis was conducted to ascertain the validity and reliability of the observed items, and a structural equation model was employed for testing the proposed hypotheses.

The findings revealed that both intellectual capital and sustainable competitive advantage fully mediate the influence of KOL on branch performance.

The study was conducted in the banking sector in Iran. Therefore, our conclusions may not be applicable to other countries. Future studies should be carried out with samples from other contexts. Moreover, as the study was cross-sectional, the causal relationships could not be inferred directly.

In this study, we found that knowledge-oriented leaders should increase their investment in intellectual capital to gain competitive advantage and improve branch performance.

This study contributes to advanced research on KOL by trying to explain how intellectual capital and sustainable competitive advantage can influence the relationship between KOL and BP in a knowledge-intensive service industry.

This study aims to determine the applicability of the placement-layering-integration model of money laundering (ML) in the South Asian context with emphasis on Pakistan by analysing different ML typologies.

This study applied content analysis in the first step. It explored three primary documents concerning ML typologies: Asia Pacific Group’s yearly reports on ML typologies from 2010 to 2021, the mutual evaluation reports and the National Risk Assessment of Pakistan. In the second step, expert interviews were recorded, and NVivo was used for data management and analysis.

This study found primary predicate offences: corruption, tax crimes, smuggling and drug and human trafficking. Pakistani launderers often use traditional typologies, including cash smuggling, round-tripping, multiple bank accounts, investment in real estate (in Pakistan and Dubai) and hawala. However, cybercrimes, cyber laundering and trade-based ML are rising. The politically exposed persons are involved in most of the laundering cases.

Rare studies specifically address the south Asian typologies and the limitations of the placement, layering and integration model. Therefore, there is a need to understand the current typologies used in developing, less regulated and undocumented jurisdictions like Pakistan.