Journal of Business Economics and Management

A new hybrid approach to the impact of renewable energy consumption on economic growth: sectoral differences in European Union countries

2024-09-27T18:28:10+03:00

The current energy crisis has shown all states that energy from renewable sources can be a determining factor in the states’ sustainable development. Several papers have studied the relationship between renewable energy consumption and economic development, finding various situations, but there is no consensus. Thus, this study aims to first investigate the causal relationship between economic growth and total and sectoral renewable energy consumption (European Union and each Member State, for 2004–2020) by testing various linear and non-linear regressions to choose the fit model. Second, the investigation extends to analysing the impact of renewable energy consumption by sector on economic development. A hybrid approach is used, namely structural equation modelling and artificial neural networks. The study findings indicate the effect and the meaning (directly or inversely) exerted by the three sectoral components on economic growth, with different intensities from one country to another. There is a significant influence on the consumption of renewable energy in the heating and cooling sectors and transport on gross domestic product at the European Union level and for most member states. Based on the obtained results, a series of theoretical, practical, and political implications are provided.

Enhancing environmental sustainability in Asian textile supply chains: insights from agile practices and mediating variables

2024-09-27T18:28:09+03:00

Improving prediction accuracy of open shop scheduling problems using hybrid artificial neural network and genetic algorithm

2024-09-29T16:33:17+03:00

Scheduling issues are typically classified as constrained optimization problems that examine the allocation of machines and the sequence in which tasks are processed. Regarding the existence of one machine, identification of works processing sequence forms a complete time schedule. Therefore, following a review of previous works, the goal of the present study is designing a mathematical model for open shop scheduling (OSS) problems using different machines aiming at minimizing the maximum time required to complete the works using an artificial neural network (ANN) and genetic algorithm (GA). The research data were driven from a Shoe company carried out between the years 2019 and 2020. The GA and ANN methodologies were employed to analyze and forecast the scheduling of activities within the shoe manufacturing sector. The findings indicated that the probability associated with the third population of the GA was 0.15. Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives.

The impact of executive team competency-sharing degree on firm innovation performance

2024-10-09T18:28:21+03:00

In China’s high-quality development context, the key role of executive teams in corporate innovation is increasingly prominent. However, effectively utilizing the synergistic effect of competencies within these teams to boost innovation performance remains a crucial research issue. A sample of 2,350 companies in Shanghai and Shenzhen A-shares from 2010 to 2022 was used to examine the impact of the executive team’s capability-sharing degree on firm innovation performance. The study indicates that the degree of managerial competence sharing, entrepreneurial competence sharing, and technological competence sharing within the executive team positively impacts firm innovation performance. These findings remain robust in the face of variations in dependent variable measures, lagged independent variables, and the propensity score matching method. Environmental dynamism is identified as playing a positive moderating role in the relationship between the influence of managerial, entrepreneurial, and technological capability-sharing degrees and firm innovation performance. Further analysis of heterogeneity reveals that the impact of the executive team’s capability-sharing degree on firms’ innovation performance varies based on the nature of property rights, firm size, and industry competitiveness. This study, grounded in top-level ladder theory, offers a framework for enhancing team capabilities and overcoming growth obstacles through executive team competence sharing.

The impact of the real interest rate on green investment: evidence from the United States

2024-10-14T18:28:25+03:00

Monetary policy has an impact on CO₂ emissions which is not entirely understood in the literature. Whereas there is a consensus that the impact is indirect through investments, the literature does not investigate the impact of monetary policy on (green) investments. Additionally, we argue in this paper that monetary policy can have a different impact on ‘green’ investments and ‘brown’ investments. This paper focuses, therefore, on the impact of monetary policy on investments. In particular, this paper empirically investigates whether the real interest rate has a different effect on green investment, compared to general investment, using quarterly data from the United States between 2004 and 2020. The results from the autoregressive distributed lag model show that the real interest rate is negatively related to the ratio of green investment relative to total investment. This result emphasizes the importance of the green investment effect channel and suggests that monetary policy has an unintentional role in climate policy which should be considered by policy makers.

Opportunities in clean energy equity markets: the compelling case for nuclear energy investments

2024-10-16T18:28:27+03:00

This study analyzes the post-pandemic dynamics and investment potential of diverse clean energy equities, including solar, wind, nuclear, and other renewable assets, highlighting nuanced differences and investment opportunities within this critical sector. The analysis reveals that nuclear energy portfolios (NLR) exhibit notable resilience, sustaining growth amidst significant market volatility. Within the mean-variance portfolio optimization (MVO) framework, this study identifies strategic investments that balance risk and return, underscoring NLR’s role as a stabilizing force and return enhancer, as evidenced by its predominant allocation in both Minimum Variance and Tangency Portfolios. Employing advanced stochastic modeling and simulation techniques, the research uses a uniform distribution to generate random portfolio weights, ensuring comprehensive and unbiased exploration of the feasible solution space, thereby enhancing the robustness of the portfolio optimization process. The findings also illustrate the diversification merits of integrating clean energy equities into broader portfolios comprising traditional stocks and bonds, with nuclear-focused equity significantly enhancing the efficient frontier. Results underscore the superiority of the nuclear energy exchange-traded fund (ETF) both as a standalone investment and as a crucial component of diversified portfolios, highlighting its contribution to investment performance and risk management. This approach offers insights for investors and policymakers navigating the intersection of finance, sustainability, and economic growth post-pandemic.

Investigating the impact of time allocation on family well-being in China

2024-10-18T18:28:30+03:00

This study aims to analyze the relationship between family time allocation patterns and subjective well-being of Chinese married women. Using the fixed-effect ordered logit model and data from CFPS 2014–2018, this study empirically explores the impact mechanism and heterogeneity of family time allocation patterns on married women’s well-being. The results indicate that the mode of working full-time and handling the majority of housework is an impact negatively for women, and this result is robust. Further heterogeneity analysis reveals that market work has a weaker impact on low-educated women’s well-being than housework, but this is reversed for high-educated women. In particular, the single-time poverty brought on by housework specifically reduces women’s well-being, more than that of dual-time poverty of work time and housework time. Also, the happiness efficiency resulting from women reducing housework time will increase with the rise in happiness levels. Accordingly, this paper highlights three policy implications: enhancing happiness effectiveness, optimizing family time allocation patterns, and raising women’s status. The conclusion clarifies the path to gender equality in family labor division and provides new recommendations for relevant nations on how to promote the equalized gender division of labor and enhance the standard of living for women.

A comprehensive review of behavioral biases in financial decision-making: from classical finance to behavioral finance perspectives

2024-10-18T18:28:29+03:00

This paper offers a detailed analysis of the evolution of financial decision-making theories, focusing on the shift from classical finance to behavioral finance. Classical finance theories, including the Efficient Market Hypothesis and Modern Portfolio Theory, assume that investors behave rationally and that the market is efficient. However, these theories have faced criticisms highlighting the importance of considering irrational behaviors in financial markets. Behavioral finance addresses this gap by integrating psychological insights into financial decision-making. This study systematically reviews the literature on behavioral biases that affect individual investors, identifying fundamental biases and their impact on investment decisions. The analysis emphasizes the role of cognitive limitations and psychological tendencies in shaping market dynamics, influencing asset pricing, investment strategies, and market returns. The research also notes a shift in focus from market-level outcomes to the behavior of individual investors, with an increase in publications. The paper concludes that understanding investors’ biases is crucial for developing effective risk management strategies and investment recommendations, ultimately leading to improved market performance. The findings underscore the growing importance of behavioral finance in explaining investor behavior and market anomalies, highlighting areas for future research in this evolving field.