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The purpose of this study is to evaluate the profit efficiency of bioenergy industry and its determinants in EU28 region roadmaps for the transition towards energy efficiency which is increasingly perceived by stakeholders, researchers and the public as a pathway to bring dependency on fossil resources to a significant reduction. Many studies overlooked the importance of profit efficiency as a factor for bioenergy industry business improvement. More so, external environmental variables can play a key role in achieving profit efficiency in the industry.

This paper seeks to answer the questions on the following: (1) the profit efficiency level using the data envelopment analysis (DEA) approach in the EU28 region during the period between 1990 and 2018; and (2) to explore the impacts of external environmental variables on the profit efficiency level using panel regression model in the EU28 region during the period between 1990 and 2018.

Results revealed that gross domestic product, size of biomass and investment are essential for the development of the bioenergy industry and positively influence on profit efficiency level. The increase in temperature change decreased the profit efficiency level during 1990–2018.

For those profit-inefficient bioenergy industries in countries such as Cyprus and Ireland, participation in innovative programs, expanding a knowledge-based economic system and implementation of support policy for bioenergy technologies, by investing in biomass sources that are suitable for their respective renewable energy development will enhance specialization, resource efficiency and improved profitability can be expected in future.

Unlike other previous studies, this study investigated the profit efficiency by applying the DEA statistical method. Moreover, the authors have applied a second regression analysis to estimate the impacts of macroeconomic and microeconomic variables on the profit efficiency level. This study has focused on the EU28 region, including both developed and developing countries, to compare the level of profit efficiency levels in the selected sample. The authors have applied data panel analysis for the period from 1990 to 2018. No previous study has applied the methods, samples and periods as those used in this study. Therefore, this study contributes significantly to the bioenergy industry specifically and the renewable energy industry in general and to the associated extant research.

This paper aims to examine the relationships between socioeconomic development, renewable energy and the innovative process by providing: a descriptive analysis; a co-occurrence analysis of terms, thematic mapping and conceptual structure; and the typology of the textual corpus.

To analyze the relationship between “renewable energies, socioeconomic development and the innovative process,” it is necessary to build a theoretical foundation that contains the relevant scientific studies and reflects the current state of the art on the subject. For this, this study developed a systematic literature review (SLR) using the preferred reporting items for systematic reviews and meta-analyses research protocol to answer the relationship on the theme.

Research shows a global understanding of the need to invest in developing studies to reduce carbon dioxide emissions and improve economic growth. The main contributions lie in providing a typology of the state of the art, identifying the joint relationships between themes, insights into the key themes and indicating themes that must be developed. This study may also support future empirical studies as it provides a theoretical foundation for formulating hypotheses, which can be tested through qualitative and quantitative approaches.

The innovative character consists of addressing a shortage of SLRs on this theme. Thus, this paper fills this gap by providing a theoretical foundation for future scientific and academic knowledge generation. Furthermore, regarding the interdisciplinary aspects of this research as contributions, this paper presented different approaches and theoretical perspectives.

The growth of the Indian economy is accompanied by the rising trend of energy utilisation and its devastating effect on the environment. It is vital to understand the nexus between energy utilisation, climate and environment degradation and growth to devise a constructive policy framework for achieving the goal of sustainable growth. This study aims to analyse the long- and short-run association and direction of association between energy utilisation, carbon emission and growth of the Indian economy in the presence of structural break.

The study probes the association and direction of association between variables at both aggregate (total energy utilisation, total carbon emission and gross domestic product [GDP]) and disaggregates level (coal utilisation and coal emission, oil utilisation and oil emission, natural gas utilisation and natural gas emission along with GDP) over the time period of 50 years, i.e. 1971–2020. Autoregressive distributed lag model is used to examine the association between the variables and presence of structural break is confirmed with the help of Zivot–Andrews unit root test. To check the direction of association, vector error correction model Granger causality is performed.

Aggregate carbon emissions are affected positively by aggregate energy consumption and GDP in both short and long run. Bidirectional causality exists between total emissions and GDP, whereas a unidirectional causality runs from energy consumption towards carbon emission and GDP in the long run. At disaggregate level, consumption of coal energy impacts positively, whereas GDP influences coal emission negatively in the long run only. Furthermore, consumption of oil and GDP influences oil emissions positively in the long run. Lastly, natural gas is the energy source that has the fewest emissions in both short and long run.

There is a rapidly growing body of research on the connections and cause-and-effect relationships between energy use, economic growth and carbon emissions, but it has not conclusively proved how important the presence of structural breaks or changes within the economy is in shaping the outcomes of the aforementioned variables, especially when focusing on the Indian economy. By including the impact of structural break on the association between energy use, carbon emission and growth, where energy use and carbon emission are evaluated at both aggregate and disaggregate level, the current study aims to fill this gap in Indian literature.

This research explores the effect of bioenergy use on carbon dioxide releases in 28 European Union (EU-28) affiliated members starting from 1990 to 2018.

Applying panels' fixed effect (FE) estimator and random effect (RE) estimator, the regressed findings are highly validated as they were robust by panel least square dummy variable corrected (LSDVC) and pooled ordinary least square (Pooled OLS) estimators.

The findings claimed that carbon dioxide releases decrease with an incline in bioenergy use and trade openness. On the other hand, fossil-fuel and economic growth indicators mounting carbon dioxide releases. The result implies that carbon dioxide releases in EU-28 region members can be mitigated significantly by mounting the quantity bioenergy use in generation channel. This will mostly participate in combating environmental pollution.

The study suggests for EU28 region members to enhance the portion of bioenergy in their fuel access to decrease emitted carbon dioxide. Governors in EU28 members should mainly encourage bioenergy expansion to raise its security and availability. The politicians of the EU28 members must assert on efficacy and productivity of bioenergy production to achieve energy accessibility and decrease dependency on conventional energy.

This research applies the recently improved model, the panel data analysis approach, which considered for the first-class impacts of estimators on the dependent variable and deals with the several problems of the common Pooled OLS estimator's manner and performance. Finally, this research contributes to the previous studies on ecological sustainability by examining the presence correlation among carbon dioxide emissions, bioenergy sustainability, trade openness, fossil fuel and gross domestic product in the EU28 region. Hence, it proves our research novelty, originality and contribution to the body of knowledge.

This study aims to examine the impact of environmental sustainability, social sustainability and corporate reputation on the business performance of energy companies operating in an emerging market.

A self-administered questionnaire was distributed to 400 managers in top and middle-level positions in energy companies located in Kuala Lumpur, Malaysia were collected through an online survey. These managers had a strong understanding of the operational aspects of the companies and possessed good knowledge of the company’s performance. The collected data were analyzed using multiple regression analysis to assess the hypothesized relationships.

The findings reveal significant influences of corporate reputation, environmental sustainability and social sustainability on the business performance of energy companies operating in an emerging market. Notably, corporate reputation emerges as the primary predictor, underscoring the significance of emphasizing the fundamental aspects of companies such as superior products or services, effective management practices and investment quality. A strong reputation is essential for attracting investors, customers and other stakeholders by meeting their expectations for high-quality products or services. It serves as a crucial factor in establishing trust and credibility, which are vital for sustained success in the market.

Energy companies should proactively integrate corporate reputation into their operational strategies to enhance business performance. Furthermore, they should develop and execute comprehensive environmental and social sustainability initiatives within their organizations. By doing so, they can effectively enhance both financial and non-financial performance while fostering a culture of employee engagement aimed at further enhancing productivity.

This study stands out as a unique and significant contribution to theory by using the triple bottom line framework as the underlying theory and integrating corporate reputation into the proposed framework. It represents a novel approach, particularly within the context of energy companies operating in an emerging market. This research serves as a valuable complement to prior studies primarily conducted in developed (Western) economies, expanding the knowledge base in this field.

The purpose of this paper is to determine how CEO overconfidence influences firm’s future risk in a sample of Chinese listed firms. It further examines the moderating effect of institutional investors on the association between CEO overconfidence and future firm risk.

The initial sample consists of Chinese A-share issuing firms listed on Shanghai and Shenzhen Stock Exchanges during the period starting from 2000 to 2017. This study classifies a CEO as overconfident if the forecasted profits of the firm are greater than the actual profits for majority of the time during the tenure of the CEO. Ordinary least squares regression is used as the primary estimation method for generating the results, however, firm fixed effects and two-stage least squares regressions have also been used for verifying the robustness of the results.

The results demonstrate that CEO overconfidence leads to an escalation in firm’s risk level over the subsequent years. However, the intensity of this positive association is weaker in state-owned firms. Analysis of the moderating effect of institutional investors reveals that only active institutional investors, specifically mutual funds and foreign institutional investors, play their governance role in reducing the effect of CEO overconfidence on firm’s risk level. Furthermore, the moderating effect of active institutional investors is weaker in state-owned firms.

The empirical evidence obtained by this study suggests that CEOs should exercise extreme diligence in decision-making. They must analyze a situation based on realistic facts and figures, rather than having misperception about their excessive abilities in controlling the outcomes of a situation. The findings also imply that regulators and policymakers should formulate strategies for motivating mutual funds and foreign investors to increase their shareholding in Chinese firms.

To the best of the authors’ knowledge, this is the first study that examines the impact of CEO overconfidence on future firm risk, not the current firm risk. Besides, literature regarding the role of external governance mechanisms in the context of behavioral biases is extremely scant. This study contributes to the literature by analyzing how the association between CEO overconfidence and firm’s future risk is influenced by the institutional investors’ ownership.

The purpose of this paper is to examine whether there is significant evidence that hedge fund managers engage in deceptive manipulation of their reported performance results.

A model of hedge fund performance has been developed using standard regression analysis incorporating dependent lagged variables and an autoregressive process. In addition, the extreme bounds analysis technique has been used to examine the robustness and sensitivity of the explanatory variables. Finally, the conditional influence of the global stock market’s returns on hedge fund performance and the conditional return behavior of the Hedge Fund Index’s performance have been explored.

This paper begins by identifying a model of hedge fund performance using passive index funds that is well specified and robust. Next, the lag structure associated with hedge fund returns has been examined and it has been determined that it seems to take the hedge fund managers two months to integrate the global stock market’s returns into their reported performance; however, the lagged variables were reduced from the final model. The paper continues to explore the smoothing behavior by conditioning the dependent lagged variables on positive and negative returns and find that managers are conservative in their estimates of positive performance events, but, when experiencing a negative result, they seem to attempt to rapidly integrate that effect into the return series. The strength of their integration increases as the magnitude of the negative performance increases. Finally, the performance of returns for both the Hedge Fund Index and the passive indices were examined and no significant differences between the conditional returns were found.

The results of this analysis illustrate that hedge fund performance is not all that different from the performance of passive indices included in this paper, although it does offer investors access to a unique return distribution. From a management perspective, we are reminded that we need to be cautious about hastily arriving at conclusions about something that looks different or feels different from everything else, because, at times, our preconceived notions will cause us to avoid participating in something that may add value to our organizations. From an investment perspective, sometimes having something that looks and behaves differently from everything else, improves our investment experience.

This paper provides a well-specified and robust model of hedge fund performance and uses extreme bounds analysis to test the robustness of this model. This paper also investigates the smoothing behavior of hedge fund performance by segmenting the returns into two cohorts, and it finds that the smoothing behavior is only significant after the hedge funds produce positive performance results, the strength of the relationship between the global stock market and hedge fund performance is more economically significant if the market has generated a negative performance result in the previous period, and that as the previous period’s performance becomes increasingly negative, the strength of the relationship between the Hedge Fund Index and the global stock market increases.

The purpose of this study is to identify the impact of legal institutions and property rights protection on corporate innovation among developing countries.

To testify these hypotheses, we use firm-level data from the World Bank Enterprise Survey, and country-level information from Worldwide Governance Indicators, World Development Indicators and Global Competitiveness Reports. The final data set consists of 24,166 firm observations, from 41 developing countries.

By using a wide range of control variables, the results propose that well-organized legal institutions stimulate corporate innovation . More precisely, a strong rule of law, effective government and protected property rights encourage firm-level innovation. Countries’ rule of law guarantees to solve disputes between parties and provide legitimate rights in case of innovation replication. Rule of law also directs that rules made by policymakers to secure the rights of innovators are well enforced. Moreover, strong property rights ensure innovators that the innovations are protected, and in case of any infringement, the guilty party will be punished and fined.

This study aims to investigate the role of all effective aspects legal institutions and property rights protection on corporate innovation among developing countries. Such security to prevent unlawful duplication will ultimately increase innovation.

This study aimed to assess the potential of the Clinical Dementia Rating (CDR) Scale in the prognosis of dementia in elderly subjects.

Dementia staging severity is clinically an essential task, so the authors used machine learning (ML) on the magnetic resonance imaging (MRI) features to locate and study the impact of various MR readings onto the classification of demented and nondemented patients. The authors used cross-sectional MRI data in this study. The designed ML approach established the role of CDR in the prognosis of inflicted and normal patients. Moreover, the pattern analysis indicated CDR as a strong cohort amongst the various attributes, with CDR to have a significant value of p < 0.01. The authors employed 20 ML classifiers.

The mean prediction accuracy varied with the various ML classifier used, with the bagging classifier (random forest as a base estimator) achieving the highest (93.67%). A series of ML analyses demonstrated that the model including the CDR score had better prediction accuracy and other related performance metrics.

The results suggest that the CDR score, a simple clinical measure, can be used in real community settings. It can be used to predict dementia progression with ML modeling.

The purpose of this paper is to empirically validate the positive effect of learning on organizational resilience and, within this relationship, understand the role of unlearning in the COVID-19 crisis context and progress the current knowledge about these concepts.

This paper uses online survey data from German and Austrian organizations’ employees to test hypotheses derived from frameworks by Duchek (2019), Stephenson (2010) and Fiol and O’Connor (2017). The used questionnaire is built out of three pre-tested questionnaires to increase reliability. Conceptually, this paper takes a capability approach and a process perspective.

The results support the positive effect of organizational learning on resilience, while rejecting the hypothesized moderating effect of unlearning on this relationship. Organizational learning showed to have a particularly strong positive effect on the adaptive capacity of resilience, compared to organizational resilience overall.

To build a learning capability for organizational resilience, managers should foster an open system culture in their organization, which aims to be generally open to learn and adapt to be able to withstand adversity. During an organizational crisis, managers have the chance to rebuild organizational structures for better information flow, e.g. implementing formal knowledge management structures.

To the best of the authors’ knowledge, this paper is the first to empirically test the causal connection between organizational learning and resilience in the Central European context during the COVID-19 crisis. The inclusion of unlearning enriches the discourse about its conceptualizations and fosters future research.