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Purpose: This study uses TAM theory to better understand variables that are indicative of MOOCs’ courses’ completion rates; furthermore, this paper scrutinizes the quantitative relationship between MOOC platform usage and learning satisfaction. Methodology: This was an applied research in a virtual community called Coursera where people all over the world participate. The research design was quantitative and the questionnaire link was posted on the Coursera, with convenience sampling. Data collection process started from November 12, 2020 until February 23, 2021, and 234 users of the selected MOOC platform (Coursera) participated to evaluate the proposed model. A multivariable systematic technique (PLS) was applied to analyze the model. To conduct the reliability test, individual item loadings and internal consistency were consulted. Convergent validity of the model was measured via the Structural Equation Model (SEM). The examination of the SEM was incorporated an evaluation of the path coefficients and R2 values. Findings: The loadings of all measurement items are larger than 0.792, indicating sound internal reliability of the dataset. Moreover, the Cronbach's alpha values are all > 0.7 which proves the internal consistency of the research model. In this research, the range of CR is 0.838 to 0.947 and the range of AVEs 0.634–0.857, both exceeding the threshold values for desirable convergent validity. To obtain discriminant validity, the square root of AVE should be larger than the correlation among the constructs. The value of each AVE’s square root is greater than the off-diagonal components. The model explains 21.1% of the variance in perceived usefulness, 20.9% in perceived ease of use, 26.9% in attitude to use, 20.0% in MOOC platform actual usage and 24.2% of the variance in learning satisfaction. The path coefficient from quantity and quality of knowledge (β=0.261, p<0.01) and perceived feedback (β=0.215, p<0.01) to PU are positive. The results show that perceived feedback (β=0.275, p<0.01), perceived complexity (β=-0.367, p<0.01) significantly and meaningfully affect PEOU. The PU showed a positive and strong effect on attitude to use (β=0.271, p<0.01), MOOC platform actual usage (β=0.360, p<0.01), and learning satisfaction (β=0.277, p<0.01). Conclusion: The results indicated that the quantity and quality of the knowledge and perceived feedback have a positive and significant impact on the perceived usefulness of MOOCs. The perceived complexity as a negative construct was found to be an important indicator of MOOC platforms usage.

Purpose: The aim of the present study was to identify the Effective Components of HRM for Efficient Labor Capacity at Islamic Free Universities in the North East of Iran. Methodology: The scientific model of this research was based on a qualitative interview with the University of Azarbaijan Azad and a selective method of targeting and snowballs. Finally, by referring to the experts, 20 qualitative interviews were conducted and using the Grendard theory to identify the components of human resources management in order to attract the efficient force, the necessity of each of the extracted model components for human resources management for efficient power absorption was investigated. The research was analyzed using coding method in three stages: open, axial and selective. Findings: The results showed that the total number of nodes in non-repetitive codes was 110 in the two HRMs and 38 in the effective recruiting sector, based on the conceptual similarities of the combination of 77 nodes (common codes) in the HR department and 38 in the absorption Effective power has been extracted based on the intuition and understanding of the researchers from the subject and their share in the form of three main components related to human resources management, individual factors, organizational factors, environmental factors and 4 sub-components and efficient absorption in The template has three main components for providing employee information, delegation, payroll and 3 sub-components in the form of Tree nodes were categorized. Conclusion: The main components of human resources management include personal factors, organizational factors, environmental factors, and the three main components of employee awareness trust in employees, and to justify the findings of this research can cite theoretical foundations and previous findings.