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Different scholars study organisational development through prismatic lenses of various determinants. Despite extensive analysis, it was found that there is little evidence to date on the measurement, analysis and prediction of organizational development using digital tools. The knowledge gap revealed the potential to define convergent and divergent determinants of organisational development. The study in the context of predicting convergent and divergent determinants of organisational development is divided into two parts – the definition of determinants for the surrogate model and the construction of the prediction model. In this publication, the first part is presented. Considering the different approaches to measuring organizational success, the determinants of processes and company competences emerge. Although organisational development represents one of the focal points, its determinants tend to be recorded and analyzed only over the medium or long term, precluding a short-term conditional parameter adjustment. This publication explores the convergent and divergent determinants of organisational development by conducting a quantitative and qualitative publication analysis and network analysis. The conceptualized organisational development model specifies the described determinants by extending them with further parameters, which can be applied for prediction using algorithms based on artificial intelligence. Based on the publication results, network analysis, and structural equation modelling, 13 determinants and 42 parameters were identified. These show a high degree of interconnectedness, highlighting the approach of divergent and convergent determinants in the overall construct of organisational development. These determinants and parameters form the framework for surrogate models and can serve as input or forecast data for different algorithms. Furthermore, a conceptual model for predicting organisational development, formulated based on defined parameters using machine learning, is presented. The second part of the study will be presented separately, a framework based on artificial intelligence was created for analyzing the current state of organisational development and predicting the next development scenarios based on the findings.
Different scholars study organisational development through prismatic lenses of various determinants. Despite extensive analysis, it was found that there is little evidence to date on the measurement, analysis and prediction of organizational development using digital tools. The knowledge gap revealed the potential to define convergent and divergent determinants of organisational development. The study in the context of predicting convergent and divergent determinants of organisational development is divided into two parts – the definition of determinants for the surrogate model and the construction of the prediction model. In this publication, the first part is presented. Considering the different approaches to measuring organizational success, the determinants of processes and company competences emerge. Although organisational development represents one of the focal points, its determinants tend to be recorded and analyzed only over the medium or long term, precluding a short-term conditional parameter adjustment. This publication explores the convergent and divergent determinants of organisational development by conducting a quantitative and qualitative publication analysis and network analysis. The conceptualized organisational development model specifies the described determinants by extending them with further parameters, which can be applied for prediction using algorithms based on artificial intelligence. Based on the publication results, network analysis, and structural equation modelling, 13 determinants and 42 parameters were identified. These show a high degree of interconnectedness, highlighting the approach of divergent and convergent determinants in the overall construct of organisational development. These determinants and parameters form the framework for surrogate models and can serve as input or forecast data for different algorithms. Furthermore, a conceptual model for predicting organisational development, formulated based on defined parameters using machine learning, is presented. The second part of the study will be presented separately, a framework based on artificial intelligence was created for analyzing the current state of organisational development and predicting the next development scenarios based on the findings.
Purpose: The purpose of this study to achieve territorial governance in the educational field, the practice of sustainable development and correct social responsibility are required. Method: For this reason, this study used a quantitative approach, a non-experimental design, and a causal correlation scope. The population included 261 directors of a local educational entity, while the sample considered 120 directors, after the intentional non-probabilistic test. Results and Conclusions: The instruments were validated with results in the Cronbach's Alpha test of 0.961 for sustainable development and 0.991 for social responsibility. It was possible to conclude that the variables significantly influence territorial educational governance. This could be demonstrated with the Nagelkerke R2 value of 0.981. Research implications: Through this study it was shown that training in sustainable development and university social responsibility affect the territorial educational governance of the directors of an Educational Management Unit, by 98.1%. Originality/value: The social justice dimension implies instructing citizens to combat gaps such as poverty, discrimination, and lack of attention to the most vulnerable sectors.
Purpose: This paper analyses challenges and opportunities posed by new non-standard forms of employment. Method: Such employment forms have emerged in response to changes in the labor market and the need for greater flexibility in the workforce. However, they also pose challenges to workers, employers and policymakers, such as the erosion of traditional employment protections and the need to adapt social welfare systems to meet the changing needs of workers. Results and conclusion: This paper argues that these challenges can be addressed through careful policy development that balances the need for flexibility with the need for worker protection. It explores the range of non-standard forms of employment, including part-time work, temporary work, agency work, self-employment and platform work, and the challenges each presents. It also examines the role of social partners, such as trade unions and employer organizations, in negotiating better terms and conditions for non-standard workers. Research implications: The paper identifies several opportunities associated with non-standard forms of employment, such as increased entrepreneurship and innovation, more excellent work-life balance, and the potential for new forms of social protection. Originality/value: This paper comprehensively analyses the challenges and opportunities associated with new non-standard forms of employment. It provides insights for policymakers, employers and workers seeking to navigate the rapidly changing labour market.
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