Background: In the context of organizational innovation frameworks, knowledge plays a crucial role in sparking new ideas and bolstering innovation capabilities. Insights gathered from various sources can act as a catalyst for generating fresh concepts and pushing boundaries. Moreover, the effectiveness of innovation within an organization can be influenced by factors like employee retention and strategies in human resource management, which can either enhance or hinder the correlation between knowledge accumulation and innovation outcomes. The employee innovation performance involves a series of tasks carried out by individuals who not only possess knowledge and skills but also demonstrate consistency, active involvement in decision-making, intrinsic motivation, and a flair for innovation. Objective: This study endeavors to provide valuable insights into how non-standard service relationships, psychological contracts, and knowledge sharing practices can collectively impact and drive innovation in the green manufacturing sector. Arrangement: In the investigation of employee innovation performance within the development of the green manufacturing industry, the focus will be on exploring non-standard service relationships, psychological contracts, and knowledge sharing. These three specific facets play a pivotal role in shaping the innovation landscape in organizations operating within the realm of sustainable manufacturing. The arrangement of this study will begin by examining the impact of non-standard service relationships on employee innovation performance. By dissecting unconventional service models and their correlation with innovation behaviors, we aim to uncover novel insights that can fuel sustainable innovation practices in the green manufacturing sector. Method: The study adopts a quantitative methodology to collect data, concentrating on a group of employees across eight distinct outsourcing firms. This selection results in a comprehensive sample of 299 participants. For the analysis and manipulation of the data, the research utilizes Sructural Equation Modeling (SEM) based on Partial Least Squares (PLS) software. This choice facilitates a meticulous and structured analysis of the data gathered, ensuring precision in the research findings. Results: The research findings reveal a significant and positive influence of psychological contracts on the propensity for knowledge sharing among employees. This suggests that organizations that emphasize establishing strong psychological contracts are likely to nurture a work environment conducive to the free exchange of knowledge and ideas, thus promoting a culture of collaboration and continuous improvement. Additionally, the data points to a noteworthy positive correlation between the act of knowledge sharing and the ability of an organization to offer unique, non-standard services. This underscores the role of knowledge sharing as a catalyst for innovation, indicating that organizations encouraging such exchanges are in a better position to innovate and provide services that adapt to the changing demands of customers and stakeholders. Conclusion: The research underscores the critical but nuanced role of knowledge sharing in driving employee innovation, especially when contrasted with its pronounced impact on developing non-standard services. It highlights the necessity for organizations to create environments conducive to the free exchange of ideas, fostering innovation. The findings also reveal the significant influence of innovative service offerings and strong psychological contracts on boosting employee creativity and service quality, respectively. For the green manufacturing sector, these insights stress the importance of robust psychological contracts and an innovation-centric culture. Emphasizing trust, open communi

This study investigates the impact of entrepreneurial orientation and green innovation on the performance of SMEs. This research explores the wood waste industry in Ngawi, an area that has never been studied before, thus providing a new perspective and unique local relevance. These findings underscore the critical role of entrepreneurial orientation and green innovation in driving sustainable business growth and improving SME performance. The results show that both entrepreneurial orientation and green innovation having a positive and significant link with SMEs performance. Further, the study reveals that the relationship between entrepreneurial orientation and green innovation having a positive and significant link with SMEs performance mediated by knowledge-sahring. The study also highlights the importance of larger sample sizes, and external factors to provide more comprehensive insights for practitioners and policymakers.

This research aimed to investigate the role of humanizing leadership in enhancing the effectiveness of change management strategies within organizations. Specifically, it focused on how humanizing leadership influences change outcomes and the extent to which organizational culture moderates this relationship. The study addressed critical questions regarding the impact of leadership behaviors, such as model vulnerability, emotional intelligence, open communication, and psychological safety on effective change management and employee performance. A quantitative approach was employed to provide a comprehensive analysis of the phenomena. Quantitative data were collected from a sample of 325 employees through surveys that measured perceptions of Humanizing leadership behaviors, organizational culture, and change outcomes. Data was analyzed by IBM SPSS 26.0. The findings revealed that humanizing leadership behaviors significantly enhances the success of change initiatives, primarily through improved employee engagement and reduced resistance. Organizational culture was found to play a moderating role, amplifying the positive effects of empathetic and inclusive leadership practices. The study provides actionable recommendations for organizational leaders and managers to foster a culture that supports humanizing leadership. By adopting leadership strategies that emphasize vulnerability, empathy, and inclusivity, organizations can enhance their adaptability and resilience against the backdrop of continuous change. These findings are particularly valuable for enhancing managerial practices and informing policy within corporate settings.

In recent years, the environment in the manufacturing industry has become strongly competitive, which is why companies have found it necessary to constantly adjust their strategies and take actions aimed at improving their performance and competitiveness in a sustainable way to grow and remain in the market. Therefore, this paper aims to present an analysis to explain the current situation in the manufacturing industry in Aguascalientes, Mexico, by means of a survey in which product eco-innovation (PEI), process eco-innovation (PrEI) and organizational eco-innovation (OEI) and its effect on environmental performance (EP) and sustainable competitive performance (SCP) were measured. The results show that (EP) is positively and significantly influenced by (PEI) and (PrEI), while no significant influence is found for (OE). Furthermore, it is confirmed that environmental performance positively and significantly influences (SCP). The findings obtained from this study point to the relevance of promoting eco-innovation activities in the manufacturing sector, as this will ensure sustainable competitiveness.

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.