Higher education has been considered a focal point that guarantees the progression of society in the political, social, cultural, economic, and other fields ]1[. In most developed countries and developing countries, the solutions to problems and fulfillment of the needs of national development goals have been realized by universities and academics ]2[. The superiority of human resources and social capital in the production of science indicates the necessity of making changes in line with global changes in education and research in a country to achieve the above position]3[.
Today, faculty members work in environments where complex activities are required] 4[. However, today's situation has changed in such a way that based on the defined workload, many expectations of faculty members are met and their performance in this regard is often evaluated ]5[. Therefore, knowledge of the factors affecting job satisfaction can lead to a happier environment for individuals ]6[. Many factors affect the job satisfaction of faculty members, one of the most important of which is workload ]7[. Professors must perform more research than just teaching so that they can successfully fulfill their job commitments ]8[. The faculty members are the central sources around whom the process and results of higher education revolve ]9[.
Due to an increase in the need for higher education and units in the Islamic Azad University along with constraints in financial resources in this university, it is necessary to develop a workload program that is applicable, flexible, and compatible with academic and local conditions.
On the other hand, due to increasing changes in science and technology, it is necessary to periodically evaluate the workload of faculty members of universities to provide basic steps to improve the quality of education, provide opportunities for growth and development in faculty members, and establish justice.
Job satisfaction of the faculty members should be determined, followed by the identification of the real workload of the faculty members in higher education institutions, especially the Islamic Azad University. Accordingly, measures can be taken to allocate productive and fair time for faculty members.
When professors are acquainted with the workload, the university presidents can increase their efforts to strengthen their scientific, research, and cultural strength. Moreover, they will be able to participate in conferences, workshops, as well as scientific and entrepreneurial seminars.
It is of utmost importance to help the senior managers of the Azad Universities raise the awareness of the professors to create an active environment in the realm of teaching and enrichment of the students at these universities.
 
Objectives
This study aimed to provide a model for determining the faculty member workload at Hamadan Branch, Islamic Azad University, Hamadan, Iran.
 
Materials and Methods
This study was conducted based on an applied research method using quantitative and qualitative data. The data were collected through interviews and a workload questionnaire. This study can be considered a kind of exploratory research, and the researcher intends to present a model that can be presented under the research indicators in this way.
The present study includes three different phases. In order to determine the sample size of the qualitative part of the research, a purposeful non-random method was used which was continued until the theoretical saturation of the interview. A total of 15 interviews were performed, and the statistical population of the quantitative part included all full-time and part-time faculty members at Hamadan Branch, Azad Universities, Hamadan, Iran in the academic year 2018-19. During the study, the number of faculty members was determined at 388 cases who were selected using the stratified random method. Eventually, the sample size was estimated at 230 individuals to increase the accuracy of the results.
 
Closed Questionnaire
The first section of this scale seeks demographic characteristics that cover such information as gender, age, marital status, and education level. Furthermore, the components and sub-components obtained from the theoretical literature and interviews were classified into seven dimensions in the second section. The scores are rated on a 5-point Likert scale.
Lasheh method was utilized in order to determine the validity of this questionnaire. Moreover, quantitative content validity, two relative content validity coefficients (CVR), and content validity index (CVI) were used to evaluate the validity of the content.
Considering the Lavosheh method ]11[, it can be stated that in this method, according to the standard table of Lavosheh, the number of experts has been determined, and the minimum value of the narrative should be selected according to the number of experts.
According to the above explanations, the questionnaire was given to 10 professors and experts in the field of human resource management to determine validity [1]. The minimum validity value must be above 0.62 in order to obtain the reliability of the confidence questionnaire.
Where ne is the number of specialists who have answered the "necessary" option and N signifies the total number of specialists. If the calculated value is greater than the table value, the content validity of that item is accepted.
 

Equation 1. Content Validity Coefficient Index Formula
 
Furthermore, the minimum acceptable value for the CVI index is 0.79, and if the CVI index is less than 0.79, it should be reviewed in the questionnaire]12[, which requires a serious revision given the range of responses (unrelated).
The results showed that the calculated value was equal to 0.92, which was more than 0.79. Therefore, its narrative index was also confirmed. According to the research method applied in this study, the data were analyzed in SPSS and PLS software. In the qualitative analysis dimension, content analysis was used using semi-structured interviews to identify the most important criteria for modeling the workload of faculty members.
Krippendorf introduces content analysis as a research method used to duplicate and validate inferences from data about their text]13[. He considers the purpose of this analysis, the same as other research techniques, to provide cognition, new insight, image of reality, and a guide to action, which includes the three main steps of open coding, central coding, and code selection.
In the quantitative part of the research, the developed questionnaire, exploratory and confirmatory factor analysis, as well as structural equation modeling were used to evaluate the relationships in presenting the model for faculty member workload determination. The reliability of the questionnaire was assessed using Cronbach's alpha coefficient and compound reliability by Smart-PLS statistics. Moreover, the results were presented separately for each variable.
 
Results
The present study was conducted based on a qualitative-quantitative research method. In this study, the collected data were analyzed using descriptive and inferential statistics. Descriptive statistical indicators, such as frequency tables of demographic characteristics of the subjects were examined at the descriptive level. Regarding the inferential statistics, related statistical tests, including exploratory factor analysis and sample t-test were used to determine the components of the model and examine the current situation, respectively. In addition, structural equation modeling was used to validate and fit the model. Moreover, the gap between the current and the desired situation was examined using the paired t-test in order to provide a solution.
If the findings of the table show the descriptive

indicators of the extracted dimensions, in the current situation, the lowest mean is related to "entrepreneurship" (2.16), and the highest mean related to "design" (3.51). In the optimal situation, the lowest mean is related to "culture" (4.21), and the highest mean is related to "individual development" (4.74) since the questions are rated on a 5-point Likert scale from (very low=1) to (very high=5). In fact, the middle number in the above range is three. This finding indicates that according to the respondents, the dimension of the faculty member workload at the Islamic Azad Universities of Hamadan province, Iran, in the desired situation is higher than the average.
Furthermore, regarding the skewness degree and elongation of the dimensions, the dimension and elongation of the existing and desired conditions of the skeleton are in the range from +2 to -2; accordingly, there is no violation of the normality in the data. Therefore, parametric tests can be used to analyze existing research data.
In this section, the research questions are examined using related statistical tests, including a sample t-test to determine the status of the variables, a dependent t-test to compare the status quo, desirable and exploratory factor analysis (analysis of the main component), and confirmatory factor analysis. Structural equation modeling has been used through LISREL software.
As shown in Table 1, there is a significant difference between the observed "design" and the theoretical average of the scale (expected average) (P<0.05 and t=7.903) since its significance level is less than 0.05. Therefore, the observed average "design" (3.52) is higher than the expected average (score 3). This means that the current state of "design" is above average.
As can be seen in Table 2, there is a significant difference between the observed "design" in the current situation and the desired situation (P<0.05, t=16.251) since its significance level is less than

 

Variables	Expected average=3
	X	SD	MD	T	df	sig
Design	3.52	0.99	0.51	7.903	229	0.000
Run	3.46	0.77	0.46	9.105	229	0.000
Evaluation	3.13	0.88	0.13	2.236	229	0.026
Training and teaching	3.37	0.84	0.37	6.678	229	0.000
Research at university	2.55	0.94	0.45 -	-7.256	229	0.000
External research out of university	2.42	0.87	-0.58	- 10.147	229	0.000
Research	2.49	0.87	0.51 -	8.927 -	229	0.000
Consultation	2.99	0.88	0.009 -	0.153 -	229	0.879
Executive and managerial activities	2.83	0.77	-0.17	-3.297	229	0.001
Individual development	3.19	0.89	0.19	3.208	229	0.002
Extracurricular activities	2.65	0.86	0.35 -	6.128 -	229	0.000
Entrepreneurship	2.16	0.86	0.84 -	14.905 -	229	0.000
Culture	2.45	1.03	0.55 -	8.049 -	229	0.000
Services	2.71	0.73	0.29-	6.017 -	229	0.000

 

0.05. Accordingly, the average desired "design" situation (4.57) is higher than the current average (3.52). This means that the current "design" situation is unfavorable.
In order to present, test, and confirm the workload model for faculty members, the structural equation modeling technique has been used for the second-order factor analysis model using the LISREL software package (Figure 1).
Given that all t-statistical values of the paths are greater than 1.96 (Figure 2), there is a significant
relationship between each dimension and the faculty member workload. The table 3 tabulates the fit indicators of the second-factor analysis model for the faculty member workload.
The obtained results reveal a good fitness of the model. This marking simply indicates whether they have received a powerful, free, and intermediate model of presence. The Chi-square test result shows an established consensus between the sample quadrilateral matrix and the covariance matrix (community).

 

Sig.	df	t	differences in averages	Average optimal situation	Average current situation	Dimensions
0.000	229	-16.251	-1.05	4.57	3.52	Design
0.000	308	-12.261	-0.80	4.27	3.46	Run
0.000	308	-26.00	-1.50	4.63	3.13	Evaluation
0.000	308	-20.058	-1.12	4.49	3.37	Training and teaching
0.000	308	-32.118	-2.08	4.63	2.55	Research at university
0.000	229	-32.941	-2.05	4.47	2.42	External research out of university
0.000	229	-34.470	-2.06	4.56	2.49	Research
0.000	229	-26.008	-1.59	4.58	2.99	Consultation
0.000	229	-25.819	-1.47	4.31	2.83	Executive and managerial activities
0.000	229	-27.192	-1.55	4.74	3.19	Individual development
0.000	229	-29.606	-1.81	4.46	2.65	Extracurricular activities
0.000	229	-34.468	-2.14	4.29	2.16	Entrepreneurship
0.000	229	-25.146	-1.76	4.22	2.45	Culture
0.000	229	-35.419	-1.72	4.43	2.71	Services

 

Limit	Estimates	Component name
P>0.05	= 73.67
	40=df
>0.05	1.84
>0.08	0.061	Average square root of RMSEA estimation error
<0.09	0.94	Good fit GFI
<0.09	0.91	AGFI modified fitness goodness
<0.09	0.95	Softened NFI shedding
<0.09	0.97	Non-softened NNFI
>0.05	0.045	The root of the mean squares remaining the standard SRMR
<0.09	0.98	Comparative CFI Failure

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