The proposed scientific article aims to analyze the application of Lean Six Sigma in the food industry. To this end, a detailed methodology has been designed that ranges from the selection of the works to the synthesis and presentation of the results obtained. The methodology is based on rigorous inclusion criteria to ensure the relevance and quality of the selected sources, including books, academic articles, theses, and other relevant documents. Through extensive searches of academic databases and other reliable sources, key works were identified that specifically address the implementation of Lean Six Sigma in the context of food production. Once the relevant papers were collected, a critical analysis was conducted to identify common themes, trends, and key findings. The works were classified according to their main focus, such as process improvement, waste reduction, supply chain optimization and food safety assurance. This categorization allowed the information to be organized in a coherent way and to facilitate the synthesis of the results. The results obtained were presented in a table that included details about each selected work, such as title, author, year of publication, abstract and links to the original source. This structured and rigorous approach provides a clear and comprehensive view of the topic, contributing to the advancement of knowledge in this area and offering practical guidance for practitioners and researchers interested in the application of Lean Six Sigma in the food industry. The literature on Lean Six Sigma in the food industry highlights its importance in improving efficiency, quality, and safety. Key recommendations include gradual implementation, appropriate training, focus on quality, and continuous improvement.

 This research aims to empirically examine the role of learning organization practices in enhancing sustainable organizational performance, utilizing knowledge management and innovation capability as mediating variables. The study was conducted in public IT companies across China, which is a vital sector for driving innovation and economic growth. A mixed-methods approach was employed, with quantitative methods accounting for 70% and qualitative methods for 30% of the research. Purposive sampling was utilized to distribute questionnaires to 546 employees from 10 public IT companies. Statistical analysis was conducted using Structural Equation Modeling (SEM). The findings indicate that learning organization practices significantly influence knowledge management practices (β = 0.785, p < 0.001) and innovation capability (β = 0.405, p < 0.001). Furthermore, knowledge management practices positively contribute to sustainable organizational performance (β = 0.541, p < 0.001), while innovation capability also has a positive effect (β = 0.143, p < 0.001). Moreover, knowledge management practices partially mediate the relationship between learning organization practices and sustainable performance, with a total effect of 0.788 (p < 0.001). The mediating role of innovation capability is also significant, with a total effect of 0.422 (p = 0.045). The study further includes qualitative in-depth interviews with 20 managers from 10 IT companies across five regions in China: East, South, West, North, and Central. Senior managers were selected through a stratified sampling method to ensure comprehensive representation by including both the largest and smallest companies in each region. These findings underscore the critical role of learning organizations in promoting sustainability through effective knowledge management and innovation capabilities within the IT sector.

This study explores the relationship between GDP growth, unemployment rate, and labor force participation rate in the Gulf Cooperation Council (GCC) countries from 1990 to 2018. Furthermore, the study incorporates control factors such as government spending, trade openness, and energy use into the regression equation. We used panel dynamic ordinary least squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS) estimators to investigate the relationships between variables in this investigation. The econometric technique accounts for nonstationary, endogeneity bias and cross-sectional dependencies between country-year observations. Cointegration was found among GDP growth, unemployment rate, and labor force participation. Long-term, the unemployment rate has a statistically significant negative effect on economic growth in the GCC nations. Meanwhile, the labor force participation rate significantly influences economic expansion in the long term. The expansion of government expenditures and international trade reduces economic growth. Alternatively, it is discovered that energy consumption has a substantial and positive effect on economic expansion. Okun’s rule and the unidirectional causality from economic growth to unemployment indicate that the primary cause of unemployment in GCC nations is a failure to adequately expand their economies. When developing economic strategies to reduce unemployment, policymakers are particularly interested in determining whether or not economic development and the unemployment rate are cointegrated.

Developing countries have witnessed a rise in infrastructure spending over the past decades; however, infrastructure spending in most developed countries, particularly the US, continues to decline. As a result, in 2021, the US Congress passed a Bipartisan Infrastructure Bill, which invests $1 trillion in the country’s infrastructure every year. Using the principal component analysis and VAR estimation, we analyzed the impact of infrastructure (transportation and water, railway networks, aviation, energy, and fixed telephone lines) on economic growth in the US. Our findings show that infrastructure spending positively and significantly impacted economic growth. Additionally, the impulse response analysis shows that shocks to infrastructure spending had positive and persistent effects on economic growth. Our results suggest that infrastructure investment spurs economic growth. Based on our findings, sustained public spending on transport and water, railway networks, aviation, energy, and fixed telephone lines infrastructure by the US government will positively impact economic growth in the country. The study also suggests that policies that promote infrastructure spending, such as the Bipartisan Infrastructure Law (Infrastructure Investment and Jobs Act) passed by the US Congress, should be enhanced to boost economic growth in the US. 

Sketching on stimulus-organism-response theory, this study aims to investigate the mediating effect of environmental passion on the relationship of the environmentally specific servant leadership with employees’ green behavior. Using purposive sampling approach, the authors adopted one month time-lagged approach to collected data from 232 academic employees in higher education institutions of China. Response rate in this study is 46.40%. The partial least-structural equation modeling (PLS-SEM) analysis was conducted in the smartpls 4.0 software to test the proposed hypotheses. The current empirical findings confirm that environmentally specific servant leadership significantly positively influence employee’s environmental passion and environmental passion significantly positively affects the employee’s workplace green behaviors. This current finding offered support in favor of mediating impact of environmental passion on the “environmentally specific servant leadership-employees workplace green behaviors” relationship. To the best of authors, this study is among pioneers’ studies to investigate the integrated relationship of environmentally specific servant leadership, environmental passion and green behavior in higher education institutions context of China. Limitations and implication have been elaborated at the end.

In this research, we explore the psychological factors that SMB owners who are micro-entrepreneurs and use SNS for entrepreneurial purposes rely on to make their self-employment decisions. Research-based on a merger of the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB) deals with how perceived ease of use (PEU), perceived usefulness (PU), attitude, subjective norms (SN), perceived behavioral control (PBC), openness to experience (OTE), and dominance contribute to people’s behavioural intention (BI) to use SNS for Data was collected from 342 SMB micro-entrepreneurs in the Delhi/NCR region of India by the means of a standardized questionnaire. Employing PLS-SEM, a partial least squares structural equation modeling was used to analyze the data. The results point out an impact of PU, attitude, and behavioral intention, and unappealing presentations, unacceptance of an explanation, unclear mechanisms, and domination do not make any difference. The research emphasizes how technophobe’s attitude, and the perception of effectiveness would impact micro-entrepreneurs desire to avail SNS for entrepreneurship efforts. Moreover, research shows the psychological understanding based on the SNS adoption by the small business owners, micro-entrepreneurs as well as for the practitioners and policymakers who are working to enhance the capability of the SMB. More investigations should be conducted on the other personality traits and cover more nations as demographic dividends in comparison to acquire more inclusive data.