data-driven

Intelligent human resource management is a new approach of combining two key elements namely human resource management and data analysis to improve processes and decisions, which helps organizations to transform their data in an intelligent way. The purpose of this research is to review the intelligent data-oriented human resources management system in open and public universities.

This research was based on the fundamental goal and the meta-analysis method was used. The investigated community included the research conducted on the subject in reliable scientific databases, which identified a total of 33 sources related to the desired characteristics. Open, central and selective coding methods were used to design the model.

The components of the final model included 15 themes, including: intelligent leadership, provision of strategic resources and targeted investment, suitable organizational architecture for training and recruitment and retention systems.

In order to realize intelligent data-driven human resource management, focusing on the identified components and operationalizing them in universities is of great importance.

Today, innovation in the economies of countries recognize as a competitive advantage. In today's dynamic environment, the development and survival of businesses require the adoption of strategies and attention to interdisciplinary sciences to achieve a deep understanding of the interactions between them. Thus, concepts in the field of innovation networks emerged to emphasize the importance of inter-organizational cooperation. Based on this, the research designs the startup innovation network model.

This study is descriptive in terms of applied purpose, qualitative in terms of method, and in terms of data collection, it is descriptive. The study applied the Data-Driven Approach to Internet of Things (IoT) Startups. The statistical population of the study was the members of the IoT startups. Fifteen startup managers entered the interview process using purposeful sampling and observing the principle of theoretical saturation as a sample. The results of in-depth semi-structured interviews were analyzed using three stages of open, axial, and selective coding.

The innovation network includes five categories: causal conditions, strategies, intervention conditions, contextual conditions, and consequences.

This model can be used to meet the challenge of startup managers in converting entrepreneurial ideas into innovative products with a competitive advantage and economic development.