Keywords

Knowledge Management, Effectiveness e-HRM, Electronic Polarization, Electronic Selection, Training and Electronic Development, Electronic Performance Evaluation, North Private Universities, Jordan.

Introduction

Everyone talks about knowledge management, but how can it be used and successfully applied? This query from a corporate representative stems from a practical issue that many businesses have when attempting to comprehend and implement knowledge management (KM) for their business. In recent years, there has been a great deal of interest in knowledge management ( Barley et al., 2018). The knowledge-based perspective of the company in the strategic management literature shifts the focus on resource knowledge and suggests that knowledge is the most significant resource in generating a sustainable competitive advantage (Kianto et al., 2017). However, not all knowledge management practices have been proved to improve corporate performance or provide a competitive advantage. For the effective implementation of knowledge management projects in a company, several elements and their relationships must be addressed (Valmohammadi & Ahmadi, 2015).

In general, due to the strong growth of methods of transmission via the internet, cyberspace, and other means of communication, as well as the proliferation of technology and global openness, the importance of knowledge management has grown in recent years. Knowledge management plays a critical part in the success of organizational activities (Al- Qudah et al., 2020). As a result, there have been a lot of studies concentrating on knowledge management and its role in the efficacy of human resource performance, particularly electronic ones that match the rhythm of this fast-paced period. One of the modern era's results is the efficacy of e-HRM. There is still much to understand about the mechanisms by which some organizational practices combine with e-HRM methods to create performance results that contribute to competitive advantage (Qudah, 2021).

The significance of the study arises from the significance of the issue under discussion. Organizations can acquire a competitive advantage through knowledge management. Based on the function of the research variables (knowledge management and e-HRM) in supporting high performance and meeting customer and customer expectations. In terms of scientific significance, consider the factors examined and represented by current administrative occurrences (Qudah et al, 2021). It is the last destination of the world's most prestigious companies. This study will attempt to contribute to the depth of the issue and the Arab library by engaging in an intellectual discussion of the most prominent academics and authors' perspectives on its aspects and application. In terms of practical significance, this entails providing a practical contribution to the method of developing knowledge management efforts and upgrading them to a level that is close to and parallel to the efficacy of e-HRM (Qudah et al., 2021).

The nature of the influence of knowledge management on the efficacy of e-HRM may be uploaded based on the components of the problem and directed by them. Furthermore, by following the theoretical routes of specialist literature, a philosophical conceptual framework for the themes (knowledge management and the efficacy of e-HRM) and associated factors is being built(Al-Qudah et al., 2020).The information it conveys must be rooted (Qudah et al., 2021). To provide a conceptual framework that describes the explanatory directions of these topics and connects those to the scientific field study of the Arab and Jordanian libraries, as well as to employ the accumulation of knowledge in knowledge science and e-HRM on the topics discussed thought and visualization in developing the dimensions of the study methodology. Finally, the results and recommendations will serve as a guide for departments in research institutions (Qudah, 2021).

Subsequently, the goal of this study was to determine the knowledge management in e- HRM effectiveness in northern Jordan's private universities. This document's structure includes the following elements: Section 2 establishes the theoretical foundation for the variable connection. Section 3 introduces the research design. Section 5 investigates data using a descriptive-analytical approach to validate hypotheses and generate conclusions. Section 6 delves into the findings, examines potential theoretical and practical consequences, and identifies weaknesses in the study.

Literature Review And Hyporgesis Developmemt

Knowledge management (Knowledge Generation, Knowledge Storage, Knowledge Dissemination, and Knowledge Application) and the Electronic Human Resource Management Effectiveness .

The extensive use of networks and information technology has expanded our ability to store, transfer, and produce knowledge, allowing and hastening the establishment of a knowledge-based, economic, organizational, and technical environment (Lupton & Beamish, 2016 ; Ndabari, 2021). This product incorporates the Knowledge Management dimensions found in existing systems, such as generating knowledge, storing knowledge, disseminating, and application of knowledge, which exist in many areas (Abubakar et al., 2019) and have been expanded to other areas by others (Obeidat et al., 2016; Abualoush et al., 2018; Mohajan, 2017).

In one of the pillars that employs a mix of acquisition and knowledge development to improve the way business is done is knowledge management to effect on the e-HRM effectiveness.

Knowledge is frequently the foundation for efficient utilization of many vital areas (Nisar, Prabhakar & Strakova, 2019). As a result, the researcher reached the following conclusion:

H0: There is no statistically significant impact of knowledge management (Knowledge generation, knowledge storage, knowledge dissemination, and knowledge application) affecting electronic human resource management effectiveness in the in northern Jordan's private universities at level (α ≤ 0.05).

Knowledge management and the electronic human resource management effectiveness (E- polarization, E-selection and recruitment, E-training and development, and E-performance evaluation)

Previous study has found that there are several critical elements that influence organizations' decisions to implement e-HRM. In this part, we looked at these aspects through the lens of e-HRM and knowledge management (Bondarouk et al., 2017). These elements are divided into four categories: E-polarization, E-selection and recruiting, E-training and development, and E-performance evaluation Al Shobaki et al.(2018) discovered that E-polarization, E-selection and recruitment, E-training and development, and E- performance evaluation all influence knowledge management adoption. Similarly, previous study indicates that E-polarization and E-performance assessment in conjunction with knowledge management determine the success or failure of knowledge management and e-HRM deployment (Marler & Parry, 2016; Ahmed & Ogalo, 2019). Furthermore, it was discovered in Singaporean SMEs that e-HRM decisions (such as E-selection) had a significant influence on knowledge management adoption (Galanaki et al., 2019). Furthermore, personnel at universities and their familiarity with knowledge management enhance e-HRM deployments (Bilgic, 2020; Azhar Naima, 2019). As a result, the researcher reached the following conclusion:

H01: There is no statistically significant impact of knowledge management affecting E-polarization in the in northern Jordan's private universities at level (α ≤ 0.05).

H02: There is no statistically significant impact of knowledge management affecting E-selection and recruiting in the in northern Jordan's private universities at level (α ≤ 0.05).

H03: There is no statistically significant impact of knowledge management affecting E-training and development in the in northern Jordan's private universities at level (α ≤ 0.05).

H04: There is no statistically significant impact of knowledge management affecting E-performance evaluation in the in northern Jordan's private universities at level (α ≤ 0.05).

Research Design

The descriptive and analytical approach was used in order to obtain the necessary data and to analyze and describe the data of the study sample and present it in the form of tables and figures. A questionnaire was designed for this purpose. The statistical program (SPSS) was used to analyze the sample answers and then present the results and recommendations

demonstrating the reality of knowledge management at Jordanian private institutions and its impact on the efficacy of electronic human resource management The frequency distributions of the study sample answers, percentages, arithmetic mean, and standard deviation of these answers were adopted, and where the study relied on the five-card scale, the level of each variable will be limited between (5-1) four levels and includes two levels in the case of increase from the adult hypothetical Medium (3) it is good in the case of increase from the adult hypothetical.

Data Analysis and Research Findings

It was assured that the following conditions were met in order to test the study hypotheses:

H0: Knowledge management where the general computational medium of knowledge management axis in total (3.85) at a good level and standard deviation (0.88) and difference coefficient (20.21) and the study of knowledge management, namely: (knowledge generation, knowledge storage, knowledge dissemination, knowledge application) as follows:

First dimension: knowledge generation

Knowledge generation has been chosen as one dimension of knowledge management, according to the research model (after). It had a larger arithmetic mean (3.85) than the hypothetical Medium (3), as well as a decent level of standard deviation (0.88) and a coefficient of difference (20.21). This dimension landed in fourth place, and table 1 shows the computational circles and standard deviations of the knowledge creation dimension based on sample responses.

Table 1 Compurational and Standard Deviations of Knowledgy Gerneration Dimension
Dimension knowledge generation	No.	Terms	Mean	Standard	Coefficient
				deviation
	1	The university relies on internal	3.9	0.91	22.22
		expertise to generate knowledge
	2	The university does not need more	4.11	0.76	22.21
		knowledge generation
	3	Move the University with other	3.7	0.82	20.77
		universities in knowledge sharing
	4	Knowledge gained as a result of the	3.9	0,89	20.44
		experiment
	5	The university deliberately creates its	3,81	0.79	19.04
		own knowledge
		Generating knowledge needs
	6	expertise not available at the	3.61	0.98	18.87
		University
	Computational medium for knowledge		3.05	2.00	22.21
	generation

According to the preceding table, the sample's maximum response achieved the second paragraph (the university does not require further knowledge) with an arithmetic mean (4.11) and a very excellent standard deviation (0.76) and an imbalance coefficient (22.21). While the lowest arithmetic mean of the sixth paragraph, which reads (generating knowledge requires competence not accessible at the university), attained an arithmetic mean (3.61) and a reasonable standard deviation (0.88) as well as an imbalance coefficient (18.87) and placed sixth.

Second dimension: knowledge storage

The analysis yielded the following results: The general arithmetic mean (3.95) is higher than the arithmetic mean, as are the standard deviation (0.73) and the imbalance coefficient (18.80). It came in second place in terms of importance in comparison to other dimensions of knowledge management and achieved the highest answer of the sample examined in the tenth paragraph, which (its text stores knowledge according to great possibilities) with an arithmetic mean (4.15), a very good standard deviation (0.90), and a difference coefficient (18.02). According to Table 2, the eighth paragraph was the most important in terms of relative importance, stating that (the University aspires to improve its knowledge stock) came in last position with an arithmetic mean (3.91) good and standard deviation (0.70) and coefficient of difference (17.33).

Table 2 Compurational and Standard Deviations of Knowledgy Storage Dimension
Dimension knowledge storage	No.	Terms	Mean	Standard deviation	Coefficient
	7	It stores knowledge with great potential	3.90	0.91	22.22
	8	The university works on updating	4.11	0.76	22.21
		knowledge continuously
	9	Technologies and means of storing	3.70	0.82	20.77
		knowledge can be expanded
	10	The university works to save its	3.90	0,89	20.44
		cognitive data in centralized servers
	11	The university is working on the	3,81	0.79	19.04
		sustainability of its knowledge reservoir
	General medium of knowledge storage		3.61	0.98	18.87
	dimension

Third dimension: dissemination of knowledge

It followed the publishing of knowledge in an arithmetic medium (4.40), at a very high level, with a standard deviation (0.75) and a difference coefficient (16.80). The dimensions of other knowledge management took first place in terms of relative significance, reaching the top of the replies paragraph (seminars and lectures are a means of spreading knowledge) with an arithmetic mean (4.15), a standard deviation (0.90), and an imbalance coefficient (18.02). The best paragraphs (dissemination of knowledge to employees via the university website) had an arithmetic mean (4.150), a very excellent standard deviation (0.90), and a coefficient of difference of 0.90. (18.02). The university released pamphlets, bulletins, and Wall publications to spread knowledge in an arithmetic medium (3.05) and a good standard deviation (0.75) and difference coefficient (17.86) at the final rank of this dimension, as shown in Table 3.

Table 3 Compurational and Standard Deviations of Knowledgy Dissemination Dimension
Dimension knowledge dissemination	No.	Terms	Mean	Standard deviation	Coefficient
	12	Seminars and lectures are a means of spreading knowledge	4.15	2.92	10.22
	13	Universities issue brochures, brochures and Wall journals to spread knowledge	3.25	2.85	18.06
	14	Knowledge of the university staff is available through its website and local archive	12	84	8.04
	15	Disseminating knowledge through scientific conferences, seminars and lectures	14	83	8.02
	16	The method of disseminating knowledge collectively through the university administration and staff	13	82	6.62
	The general medium of knowledge dissemination dimension		142	85	6.02

Fourth dimension: application of knowledge

The next table 4 shows the computational circles and standard deviations of the dimension of knowledge application based on the sample replies, which came with a general arithmetic mean (3.99) and a decent standard deviation (0.75) and a difference coefficient (16.80). It ranked third in terms of the relative significance of the dimensions of knowledge management and had the highest replies to the paragraph (the University uses knowledge based on advice and guidance with a very new arithmetic mean (3.90) and standard deviation (0.67) and difference coefficient (16.02). The paragraph (employees are free to apply knowledge based on their talents with an arithmetic mean of 3.87, a standard deviation of 0.58, and an imbalance coefficient) had the lowest replies (15.87).

Table 4 Compurational and Standard Deviations of Knowledgy Application Dimension
Dimension knowledge application	No.	Terms	Mean	Standard deviation	Coefficient
	17	The University applies knowledge based on guidance and guidance	3.92	2.68	16.22
	18	The university works to delegate powers to its employees	3.88	2.66	15.92
	19	There is a huge database that provides the benefit of knowledge	3.01	2.64	15.00
	20	University workers have difficulty applying knowledge	3.05	2.62	15.92
	21	Allowing employees to apply the knowledge according to their abilities	3808	2.50	15.08
	The general medium of knowledge application dimension		3.99	2.85	16.02

Effectiveness of e-HRM

The arithmetic center of the axis of effectiveness of e-HRM in total (4.05) and at a very good level and standard deviation (0.70) and difference lab (18.12) and achieved this dimension the fourth degree in terms of relative importance compared to the rest of the dimensions and the next detail these dimensions with the shortcut up and:

H01: electronic polarization

We can see from the preceding Table 5 data that the paragraph (the University utilizes electronic polarization when asking personnel) had the greatest arithmetic mean (4.01) and a very excellent level, as well as a standard deviation (0.76) and a coefficient of difference (16.80). The lowest replies were for the paragraph (electronic polarization eliminated conventional paper submission) with an arithmetic mean (3g17) and a decent level, as well as a standard deviation (0.75) and an imbalance factor (15.42).

Table 5 Compurational and Standard Deviations of the First Sub- Hypothesis Electronic Ploarization
Electronic polarization	No.	Terms	Mean	Standard	Coefficient
				deviation
		The University uses electronic	4.21	2.86	16.02
	22	polarization in the request of
		employees
	23	The methods of electronic	3.92	2.68	18.08
		polarization are constantly updated
	24	The University uses social media to	3.08	2.01	16.08
		attract employees
		The university is interested in setting	3.02	2.80	15.55
	25	standards and performance
		indicators for electronic polarization
	26	University permanent website to	3.18	2.06	15.42
		attract Electronic on the internet
	27	Electronic coordination between	3.22	2.45	16801
		departments to request staff and
		faculty members
	28	Electronic polarization eliminated	3.96	2.85	16.26
		traditional paper submission
	The general medium of electronic		4.25	2.82	10.12
	polarization

H02: electronic selection and appointment

Whereas the results in Table 6 show that the overall arithmetic mean of the dimension of selection and electronic appointment attained (4.32) a satisfactory level, the standard deviation (0.68) and coefficient of difference (16.06). This factor was principally determined by its relative relevance in comparison to the dimensions of e-HRM. The best solution to the paragraph (polarization software is known for its speed and accuracy) achieved an excellent arithmetic average (4.80), standard deviation (0.68), and coefficient of difference (16.72). The paragraph was reached by the lowest-scoring replies (some electronic human resources programs work on electronic screening of applicants).

Table 6 Compurational and Standard Deviations of the Second Sub-Hypothesis Electronic Selection and Appointment
electronic selection and appointment	No.	Terms	Mean	Standard	Coefficient
				deviation
	29	Employees are selected by	4.88	2.64	16.82
		electronic job submission
	30	The polarization program is fast and	4802	2.60	16.43
		accurate
		The appointment is based on the	4.44	2.56	16.68
	31	electronic screening of applicants '
		applications
		Recruitment applications are	4.42	2.13	16.45
	32	withdrawn under colorful and up-to-
		date devices
	33	Some programs work on electronic	22	2.22	16.54
		screening of job applicants
	The general medium of electronic selection and appointment		4.32	2.60	16.26

H03: training and electronic development

According to Table 7, the arithmetic mean of the dimension of training and electronic development of e-HRM (4.20) is at a satisfactory level, with a standard deviation (0.70) and the coefficient of difference (16.22). In terms of relative relevance, this dimension emerged top when compared to the aspects of electronic Human Resource Management efficacy. The best replies to the text (workers are taught on devices and equipment on a regular basis) have an arithmetic mean (4.30), a very excellent level, a standard deviation (0.70), and a coefficient of difference (16.22). The lowest replies to the text (employees are encouraged to develop new skills) had an arithmetic average of 4.06, a standard deviation of 0.62, and a difference coefficient of 0. (17.70).

Table 7 Compurational and Standard Deviations of the Third Sub- Hypothesis Training and Electronic Development
training and electronic development	No.	Terms	Mean	Standard deviation	Coefficient
	29	Personnel are periodically trained on devices and equipment	4.32	2.82	16.22
	30	Management helps employees adopt modern methods of work	4.12	2.66	18.43
	31	Modern training programs are available to keep pace with new variables	4.20	2.62	15.32
	32	Workers are encouraged to learn new skills	4.26	2.62	18.82
	33	The university offers courses abroad to develop and train its staff	4.16	2.63	16.92
	The general medium of training and electronic development		4.22	2.82	16.22

H04: electronic performance evaluatio

The electronic performance evaluation dimension's general arithmetic mean (4.40) and a satisfactory level, standard deviation (0.72), and coefficient of difference (16.60) in Table 8. When compared to the dimensions of the effectiveness of e-HRM, where the highest answers reached the paragraph (the performance of employees is evaluated according to electronic programs with an arithmetic mean (4.66) very good and standard deviation (0.66) and difference coefficient), this dimension came in fourth and last place (15.87). While the lowest replies obtained a paragraph (the university tries to develop the performance of employees and develop them), an arithmetic mean (4.20), a decent standard deviation (0.34), and a coefficient of difference (15.61).

Table 8 Compurational and Standard Deviations of the Third Sub- Hypothesis Electronic Performance Evaluation
electronic performance evaluation	No.	Terms	Mean	Standard deviation	Coefficient
	34	The performance of employees is evaluated according to electronic programs	4.66	2.66	15.08
	35	Electronic evaluation programs allow employees to object with specific electronic means	4.44	2.88	15.82
	36	The university adopts clear performance standards	4.33	2.00	16.14
	37	Employees are informed of their evaluation results electronically	4.22	2.84	16.94
	38	The university works to develop and develop the performance of employees	4.22	2.34	15.61
	The general medium of electronic performance evaluation		4.42	2.82	16.62

Discussion and Conclusion

The link between knowledge management and the efficacy of electronic human resource management. It was articulated according to the study model with the following premise: (there is a substantial association between knowledge management and the efficacy of e-HRM) and five sub-hypotheses to investigate the correlations shown in table 9.

Table 9 Correlation Between the Dimensions of Knowleghe Management and Effectiveness of Human Resources Management Eletronic
	Electronic polarization	Electronic selection and appointment	Training and electronic development	Electronic performance assessment	Effectiveness of e-HRM
Knowledge management	2.326	28352	2.348	2.352	2.488
Knowledge generation	2.266	2.162	)2.229	2.156	2.220
Storage of knowledge	2.296	2.266	2.31	2.411	2.424
Dissemination of knowledge	2.366	2.386	)2.440	2.388	2.49
Application of knowledge	)2.134	2.25	2.325	2.218	2.329

The correlation coefficient between knowledge management and e-HRM effectiveness. Indicating a proportionate link and a high level of efficacy between knowledge management and e-HRM effectiveness. The correlation coefficient between knowledge management and electronic polarization was (0.306). This indicates that knowledge management and post- electronic polarization have a direct relationship with an acceptable amount of moral relevance. The correlation coefficient between knowledge management and after the evaluation of electronic performance (0.352). Demonstrates an acceptable and significant relationship between knowledge management and post-electronic selection. The correlation coefficient between knowledge management and after the electronic selection was (0.347). This demonstrates a reasonable and substantial relationship between knowledge management, post-training, and E- development. At the semantic level, the correlation coefficient between knowledge management and after the electronic selection was (0.532). This suggests that there is a reasonable and significant relationship between knowledge management and E-performance evaluation.

H01 hypothesis posits a substantial relationship between knowledge generation and e- HRM efficacy. Knowledge generation and the efficacy of e-HRM have a direct and average association. The coefficient of association between knowledge creation and electronic polarization. There is no association between knowledge generation and electronic polarization of e-HRM. There is no link between after knowledge generation and after electronic selection for e-HRM. That there is a direct, acceptable, and meaningful relationship between knowledge generation, post-development, and e-training for e-HRM. This suggests that there is no relationship between after knowledge generation and after electronic e-HRM evaluation.

H02 posits that there is a substantial association between stored knowledge and the efficacy of e-HRM. This suggests a direct association and an average level between knowledge storage and e-HRM efficacy. This demonstrates a direct relationship and an adequate amount of moral relevance between after knowledge storage and after electronic polarization. This demonstrates a link and an appropriate amount of moral relevance between knowledge storage and electronic selection. The results show a direct connection link and an adequate level of moral relevance between knowledge storage after training and electronic development. This reveals a direct, moderate, and substantial association between knowledge storage and electronic performance evaluation.

H03 implies a substantial association between knowledge dissemination and e-HRM efficacy. This demonstrates a direct, moderate, and substantial relationship between knowledge dissemination and electronic performance evaluation. This demonstrates a direct relationship and an adequate level of moral relevance between after knowledge dissemination and after electronic polarization. This demonstrates a reasonable and significant relationship between post- knowledge dissemination and post-electronic selection This shows a direct, moderate, and substantial relationship between post-knowledge dissemination and post-training and electronic development. This demonstrates a direct association and an adequate degree of moral relevance between after knowledge dissemination and after electronic performance evaluation.

H04 implies a considerable relationship between knowledge application and e-HRM effectiveness. This demonstrates a direct association and an adequate degree of moral relevance between after knowledge dissemination and after electronic performance evaluation. This demonstrates that there is no relationship between after knowledge application and after electronic polarization. This demonstrates an adequate and strong association between after knowledge application and after electronic selection. This demonstrates a direct relationship and an appropriate level of moral relevance between knowledge application and training and Electronic Development. This demonstrates a clear relationship between knowledge application and electronic performance evaluation.

The impact hypotheses were chosen from among the search variables using a simple linear regression model to examine the effect for each of the interpretive variables by comparing(Q) calculated by the value (Q) Tabular below the same level of moral for accepting or rejecting the hypothesis, as shown in table 10.

Table 10 The Value of R,B ,F for the Impact of Knowledge Management on the Effectiveness of Electronic Human Resources Manangement
Knowledge Manageme nt	Electronic polarization		Electronic selection and appointment		Training and electronic development		Electronic performance assessment		effectiveness of e-HRM		Moral models
											No.	%
Knowledge generation	0.09	0.0 08	0.16	0.0 2	0.23	0.0 3	0.13	0.0 1	0.1 6	0.0 1
	2.99	D	2.01	D	4.82	D	1.03	D	2.3 2	D	8	%122
Storage of knowledge	2.23	2.2 0	2.24	2.2 0	2.28	2.2 0	2.34	2.1 5	2.2 1	2.2 5
	12.8 3	D	11.2 5	D	11.0 5	D	21.0 8	D	8.1 8	D	7	%122
Disseminati on of knowledge	2.28	2.2 9	2.3	2.1 2	2.42	2.1 9	2.84	2.1 5	2.4 2	2.2 6	8	%122
	13.2 4	D	16.4 5	D	29.1 5	D	2.35	D	0.5 9	D
Application of knowledge	2.25	2.2 3	2.26	2.2 8	2.42	2.1 6	2.3	2.2 9	2.2	2.2 4	8	%122
	2.36	D	9.94	D	42.2 1	D	12.8 1	D	5	D
Total knowledge manageme nt	2.33	2.2 6	2.45	2.1 3	2.61	2.2	2.54	2.1 8	,2 38	2.2 0	8	%122
	0.04	D	10.3 2	D	31.2 5	D	25.6 3	D	36. 41	D
Moral models											35

At the semantic level, the computed value (Q) of the estimated model (36.41) is larger than the tabular value (Q) of (6.90). (0.01). This signifies that knowledge management has a statistically significant influence on the efficacy of e-HRM at the level of indication (1 percent), i.e. a degree of confidence (99 percent). The adult (0.232) coefficient of determination suggested that knowledge management may explain what rate (23%) of the changes in the variable response (efficacy of human resources management electronic) or remainder (77%) are other factors not included in the Model Search. The marginal slope factor (Q) value of (0.47) implies that enhancing knowledge management by one unit increases the efficacy of human resource management by one unit (47 percent). The preceding Table 10 demonstrates this at the level of sub-hypotheses.

H01 stated that (there is a significant effect between knowledge generation in the effectiveness of e-HRM and the models that appear in Table 10 which shows the moral and non- moral models between the search variables where it turns out: the calculated value (Q) of the model amounted to (5.101), which is greater than the tabular value (Q) of (3.94). This means that there is a statistically significant effect of knowledge generation dimension in the effectiveness of e-HRM at the level of significance (5%) i.e. a degree of confidence (95%). From the identification factor (2R) of (0.040), it is clear that knowledge generation is able to explain (4%) of the changes that occur in the responsive variable (effectiveness of e-HRM), while the remaining percentage (96%) follows other variables not included in the research model. It is evidenced by the value of the coefficient of marginal inclination (p) of the adult (0.173). That increased after generating the parameter by one unit will increase the effectiveness of human resources by (17%).

H02 stated that (there is a significant effect between the dimension of the storage of knowledge in the effectiveness of e-HRM and models that bloom in Table 10 Which shows the moral and non-moral models between the search variables, which shows: the value (Q) calculated for the model amounted to (21.129) which is the largest Tabular value of (6.90) at (1%) i.e. a degree of confidence (99%). By the value of the laboratory identification of ( 2R)adult (0.149)shows that after the storage is able to explain the rate of (15%) of the changes in the variable response (effectiveness of human resources management electronic)or rest (85%)proceed to other variables, not in the model study. Evidenced by the value of the coefficient of the marginal propensity (P) adult (0.252) that increased after storage parameter by one unit will increase the effectiveness of human resources by (25%).

H03 stated that (there is a significant effect between the dissemination of knowledge in the effectiveness of e-HRM) and from the models that appear in the table that shows the moral and non-moral models between the search variables shows the following: the calculated value (Q)of the estimated model amounted to (35.505) which is greater than the tabular value of (6.90)at Trust (99%). By the value of the coefficient of determination (2R) adult (version 0.228) includes now after the dissemination of knowledgeable to explain what rate (23%) of the changes in the variable response(effectiveness of human resources management electronic)or other women (77%) proceed to other variables not included in the Model Search. It is clear from the value of the marginal slope coefficient (Q) of (0.330) that an increase after the dissemination of knowledge in the amount of one unit will increase the effectiveness of e-HRM by (31%).

H04 stated that (there is an effect and a moral significance between after the application of knowledge in the effectiveness of e-HRM) and from the models that appear in Table 10 Which shows the moral and non-moral models between the search variables shows us the following: the value of (9 calculated for the estimated model amounted to (16.139), which is greater at the level of significance (1%) i.e. confidence (99%). Through the value of the determination factor (2R) of (0.118), it was found that after the application of knowledge is able to explain (12%) of the changes in the responsive variable (effectiveness of e-HRM). It is clear from the value of the marginal slope coefficient (Q) of (0.258) that an increase after the application of knowledge by one unit will increase the effectiveness of e-HRM by (26%).

Private universities seek to generate modern knowledge by establishing partnerships and alliances with other universities and exchanging experiences to raise the efficiency of e-HRM and face the rapid changes in functions and activities. Universities have information stored on their computers that facilitates the process of trade-off between job applicants according to certain criteria. Universities are keen to spread the knowledge they have among their employees and hold meetings between experts and their employees to facilitate the provision of knowledge. Prevent university employees have appropriate opportunities to apply the knowledge and keep abreast of changes in the business environment and providing them with opportunities and innovation and motivate them to raise their level of performance. In generating knowledge, universities should rely on establishing strategic alliances with their peers to contribute to raising their capabilities in providing trained human cadres. Universities should store the knowledge they possess in central computers by making it available to all in order to facilitate the process of trade-off between applicants for positions according to scientific standards and the selection of competencies that meet their job standards. Universities should spread the knowledge they possess by making it available to all and providing employees with new ideas. Universities should give their employees permission to apply the knowledge they possess and enable them to keep pace with the rapid changes in the business environment and give them the opportunity to innovate and innovate.

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