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

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.

This paper aims to investigate the determinants of performance for insurance companies in Tunisia from 2004 to 2017. Namely, we consider three dimensions of determinants; those related to firms’ microenvironment, macroenvironment and meso or industry environment. The performance of insurance companies is measured using three criteria: Return On Assets (ROA), Return On Equity (ROE), and Combined Ratio. The independent variables are categorized into three groups: microeconomic variables (Firm Size, Financial leverage, Capital management risk, Volume of capital, and Age of the firm), meso-economic variables (Concentration ratio and Insurance Sector Size), and macroeconomic variables (Inflation, Unemployment, and Population Growth). The General Least Squares (GLS) regression technique is employed for the analysis. The study reveals that the financial performance of Tunisian insurance companies is positively influenced by firm size, capital amount, and risk capital management. On the other hand, it is negatively influenced by leverage level, industry size, concentration index, inflation, and unemployment. In terms of technical performance, the capital amount of the firm, industry size, age of the firm, and population growth have a positive impact. However, firm size, leverage, concentration index, and risk capital management negatively affect technical performance. This paper contributes to the existing literature by examining the determinants of performance specifically for insurance companies in Tunisia. Besides the classical proxies of performance, this paper has the originality of using the technical performance which is the most suitable for the case of Insurance companies.

The research is focused on the evolution of the enterprises, in the field of specialized professional services, medium-period, enterprises that implemented projects financed within Regional Operational Program (ROP) during the 2007–2013 financial programming period. The analysis of the economic performance of the micro-enterprises corresponds to general objectives, but there can be outlined connections between these performances and other economic indicators that were not considered or followed through the financing program. The study case is focused on the development of micro-enterprises in the services area, in the Central Region, Romania (one of the eight development regions in Romania). The scientific approach for this article was based on a regressive statistical analysis. The analysis included the economic parameters for the enterprises selected, comparing the economic efficiency of these enterprises, during implementation with the economic efficiency after the implementation of the projects, during medium periods, including the sustainability period. The purpose of the research was to analyse the economic efficiency of the selected micro-enterprises, after finalizing the projects’ implementation. The authors intend to point out the need for a managerial instrument based on the economic efficiency of companies that are benefiting from non-reimbursable funds. This instrument should be taken into consideration in planning regional development at the national level, regarding the conditions and results expected. Although the authors used regressive statistical analysis the purpose was to prove that there is a need for additional managerial instruments when the financial allocations are being designed at the regional level. This study follows the interest of the authors in proving that the efficiency of non-reimbursable funds should be analysed distinctively on the activity sectors.