Examining relationship between service quality, user satisfaction and performance impact in the context of smart government in UAE

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 6, December 2020, pp. 6026~6033
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i6.pp6026-6033  6026
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Examining relationship between service quality,
user satisfaction and performance impact in the context of
smart government in UAE
Ali Ameen1
, Dawoud Al-Ali2
, Osama Isaac3
, Fathey Mohammed4
1,2,3
Faculty of Business and Accountancy, Lincoln University College, Malaysia
4
School of Computing, Universiti Utara Malaysia (UUM), Malaysia
Article Info ABSTRACT
Article history:
Received Mar 30, 2020
Revised May 6, 2020
Accepted May 18, 2020
Governments attempt to use all forms of information technologies including
Internet and mobile computing to be able to transform relationships with
citizens. However, there is a clear gap between the indicator of the impact of
technology innovation output and government’s vision in United Arab
Emirates (UAE). In this regard, investigating the relationship between
service quality, user satisfaction, and performance impact may help
the government to mark its current progress and milestone achievement.
This research proposed a model based on Delone & McLean IS success
model by considering the research context. The modeling of structural
equations via PLS (Partial least squares) regression was applied to evaluate
the model within the context of public sector in the UAE. The data was
collected from a sample of 147 employees in public organizations using
a questionnaire. Results demonstrated that the quality of service has
a significant effect on user satisfaction. In addition, quality of service and
user satisfaction positively influences the staff performance. The outcome of
this research helps to enhance the understanding of the impact of smart
government applications.
Keywords:
Performance impact
Service quality
Smart government
UAE
User satisfaction
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Ali Ameen,
Faculty of Business and Accountancy,
Lincoln University College,
Selangor, Malaysia
Email: ali.ameen@aol.com
1. INTRODUCTION
Internet has rapidly become vital to the majority of our routine activities and has considerably
affected every facet of organisational functioning [1]. It plays an important role in the management of
information which in turn is used to improve decision making process, quality of communication, efficiency
of operations and knowledge acquirement [2-5].
One of the United Arab Emirates (UAE) Vision 2021 pillars is transforming to competitive
knowledge economy (innovation, creativity, and financial services) using innovative technologies.
Organizational innovation and smart government initiative are drivers of achieving the goal. The current
smart and e-governance systems should continuously be evaluated, service delivery quality should be
improved, and transparency and accountability of the UAE Government should be enhanced.
The government of UAE emphasizes that it’s vision and long-term strategy is to always strive to be
number one in the world in all aspects including technology usage. According to Global Innovation Index
2017, UAE is ahead its neighbours in ICT access and innovation output; however, it has some kind of
inconsistency between the two indicators where innovation output is significantly low compared to the level
of ICT access. The new generation of ICT transforms public services into a Smart Government. The concept

Int J Elec & Comp Eng ISSN: 2088-8708 
Examining relationship between service quality, user satisfaction, and performance impact ... (Ali Ameen)
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of the 'SMART' stands for the main feature of new technology which is social, mobile, analytics,
radical-openness trust [6-12] .
Although innovativeness is being increasingly investigated in the academic literature as an important
variable in the context of technology adoption and its impact, there is a clear gap between the indicator of
the impact of technology innovation output and UAE government’s vision of the future in this regard.
More specifically, there is lack of empirical studies examining the relationship between service quality,
user satisfaction, and performance impact in the context of smart government in UAE. To be able to reduce
this discrepancy, this research addresses the relationship between the quality of service in smart government
initiative and users’ satisfaction, and the impact of these two factors on the performance of public sector
organizations in UAE. The paper is organized so that the proposed model is introduced by identifying
the constructs and related hypotheses. Then, the applied research method is described. Next, data analysis
and results are reported. Finally, results discussion and conclusion is presented.
2. THE PROPOSED MODEL
The correlations between the proposed factors are conjectured in a conceptual model based on
Delone and McLean IS success model [13]. Figure 1 displays the proposed model which includes service
quality, user satisfaction and performance impact. Delone & Mclean developed the information system
success model, which known as DMISM, to comprehensively review IS success factors. Six categories of IS
success factors were concluded including system quality, information quality, IS use, user satisfaction,
individual impact, and organizational impact [6, 14-19]. Then in 2003, the model was updated by the same
authors (Delone & Mclean) by including service quality as a new dimension. Further, all impact
measurements were grouped into one category called “net benefit”, to avoid complicating the model with
more success measurements [20-22]. The proposed model examines the correlation between service quality,
user satisfaction and performance in the context of smart government services in UAE. Accordingly,
the proposed conceptual model has three hypotheses to be tested. The following sub sections define
the model constructs and related hypotheses.
Figure 1. The proposed conceptual model
2.1. Service quality (SERQ)
Service quality means the fulfilment of service provided to meet customer needs, satisfaction and
expectations [23]. With reference to the research of IS, in their restructured IS success model, Delone and
McLean [24] have defined service quality using these characteristics: reliability, tangibles, assurance,
responsiveness, and empathy. Delone & McLean [24] proposed that the service quality stands for user
support by the department of IS, and is often assessed by the reliability, responsiveness and empathy [24].
Oktal et al. [25] examined the impact of service quality on user satisfaction as well as the impact
of performance and stated its importance on both relationships [25]. As a result, the below hypotheses
are proposed:
H1: Service quality significantly influences user satisfaction.
H2: Service quality significantly influences performance impact.
2.2. User satisfaction (SAT)
User satisfaction is considered as one of the central aspects in IS discipline [26-30]. Thus, it has
been tested extensively in studying the use of systems and applications [25]. User satisfaction is defined as
the attitude of the user towards a computer system/application he interacts with [31]. It is the extent to which
the user thinks application/system fulfils his/her requirements. In addition, user satisfaction is considered as

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Int J Elec & Comp Eng, Vol. 10, No. 6, December 2020 : 6026 - 6033
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a measure of whether the user thinks that the system is useful and he wishes to use it again [25, 32, 33].
User satisfaction and performance are key constructs of the Delone and McLean IS success model, thus
the relationship between these two constructs has been confirmed widely in the literature. Alshamsi, et al., [7]
examined and confirmed the significance of the relationship between using smart government services and user
satisfaction. As a result, the following hypothesis is proposed:
H3: User satisfaction has a significant influence on performance impact.
2.3. Performance impact (PI)
The performance impact is the definitive measurement regarding the use of an IS, as being a key
aspect in the Delone and Mclean IS success model [2]. It has extensively been the focus of several studies in
this domain. Aparicio et al., [34] investigated factors that influence the performance impact of using
e-learning systems. Furthermore, a study by Tam and Oliveira [35] in Portugal examined the performance
impact of using mobile learning.
3. RESEARCH METHOD
The research method for this study involved creating a questionnaire of 13 items. The questionaire
items were developed by adapting the orginal measurments from the literature of IS in the context of
the study. The questionnaire used Likert scale suggested by [36], and applied in the previous studies in
the similar context [20, 32, 37, 38]. The data was collected using a self-managed questionnaire between
January 2017 and March 2018. Employees in governmental organisations in UAE were targeded. 200
questionnaires were distrbuted, and 160 were returned. After data screening process, 15 responses were
droped. According to Krejcie and Morgan [39] and Tabachnick and Fidell [40], the sample size (145) is
sufficient. Structural Equation Modelling-Variance Based (SEM-VB) was used to assess the proposed model
by utilising SmartPLS 3.0 [41]. Using PLS, measurement and structural model simultaneously can be
analysed which results in more accurate estimates [42]. Table 1 illustrates the demographic data of
the respondents.
Table 1. Respondents’ profiles
Demographic Item Categories Persentage
Do you use e-
government services
1. Yes
2. No
100%
0%
Gender 1. Male
2. Female
53.1%
46.9%
Age 1. 24-29 years
2. 30-39 years
3. 40 and more
15.9
24.2
59.9
Nationality 1. Local
2. International
62%
38%
Education level 1. Secondary School
2. Undergraduate
3. Postgraduate
18.4%
64.7%
16.9%
Occupation 1. Study or ICT
2. Work in Services
3. Work in Planning
4. Others
34.5%
28.0%
22.4%
15.1%
Online service Access
rate
1. Daily
2. Weekly
3. Monthly
4. Occasionally
39.8%
22.9%
24.2%
13.1%
Session time (in
minutes)
1. 0-10
2. 11-20
3. 21-30
4. > 30
36.3%
23.2%
15.9%
24.7%
4. RESULTS
Based on partial least squares (PLS), two-stage analytical technique; measurement model
assessment (verifying the reliability and validityof the instrument) and structural model assessment
(examining the hypothesized relationships) was applied after reporting the descriptive results [43]. According
to Hair et al., [43], the measurement model examines each construct’s scale, and the structural model
examines the correlation between the constructs in the model. PLS technique is capable of analysis
the structural and measurement model simultaneously with a significant degree of accuracy [14, 44-49].

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4.1. Descriptive analysis
Table 2 shows the results of standard deviation (SD) and mean of every parameter scale in
the instrument. The respondents were asked to reveal their opinion regarding service quality, user
satisfaction, and performance impact based on a 5-point Likert scale varying from 5 (strongly agree) to 1
(strongly disagree). The score of the impact of performance was the highest with 3.263 mean and
SD of 1.018.
Table 2. Descriptive results and reliability indicators
Construct Items
Loading
(> 0.5)
Mean SD
α
(> 0.7)
CR
(> 0.7)
AVE
(> 0.5)
Service
Quality
(SERQ)
SERQ_1
SERQ_2
SERQ_3
0.943
0.939
0.958
3.189 1.157 0.942 0.963 0.897
User
Satisfaction
(SAT)
SAT_1
SAT_2
SAT_3
0.933
0.926
0.930
3.109 1.107 0.922 0.950 0.865
Performance
Impact
(PI)
PI_1
PI_2
PI_3
PI_4
PI_5
PI_6
PI_7
0.872
0.877
0.909
0.916
0.910
Deleted
0.909
3.263 1.018 0.952 0.962 0.808
4.2. Assessment of measurement model
Reliability and validity (comprising discriminant and convergent validity) of the constructs’
measurments were assessed in this stage. Cronbach alpha (α) was tested to assess the reliability of each
construct in the model. The cronbach alpha scores of all constructs ranged from 0.922 to 0.952,
which exceeded the satisfaction value of 0.7 [50-52]. In addition, for inspecting the reliability, the composite
reliability (CR) values were reported. CR values ranged from 0.950 to 0.963, which also were greater than
0.7 [53-56] in Table 2. Thus, construct reliability has been fulfilled for all constructs. In addition, indicator
reliability assessment was conducted by examining factor loadings. As per Hair et al. [50], the values
exceeding 0.70 suggest substantial factor loadings. Table 1 shows that all items have factor loadings’ values
greater than the suggested value of 0.7 except PI_6 which was droped from PI scale because of low loading.
For the convergent validity assessment, AVE (average variance extracted) was examined. AVE is
refered as the degree to which an item is correlated positively with the items in the same construct.
All the AVE scores ranged from 0.808 to 0.897, which is beyond the minimum criteria of 0.50 [57].
Thus, all constructs have convergent validity as shown in Table 2.
The degree to which each construct is distinct and captures a phenomenan not represented by other
constructs is refered as discriminant validity. Cross-loadings and Fornell-Larcker criterian were used to
assess the discriminant validity. Cross-loadings measurement is applied as the initial step in examining
discriminant validity [43]. The results show that the items’ outer loadings on each construct exceed all
the outer loadings with other constructs. This indicates that the cross-loading assessment had met
the requirements as shown in Table 3.
Table 3. Constructs’cross loadings
SERQ SAT PI
SERQ_1 .943 .552 .601
SERQ_2 .939 .534 .580
SERQ_3 .958 .541 .578
SAT_1 .547 .933 .625
SAT_2 .509 .926 .588
SAT_3 .541 .930 .590
PI_1 .573 .633 .872
PI_2 .558 .581 .877
PI_3 .566 .548 .909
PI_4 .550 .567 .916
PI_5 .569 .603 .910
PI_7 .517 .545 .909
Key: SERQ: service quality, SAT: user satisfaction, PI: performance impact

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Further, for discriminant validity assessment, Fornell-Larcker measurement is applied. The results
indicate that √AVE for each construct is greater than the inter-correlations with other constructs as shown in
Table 4, which demonstrates that each construct is distinct [58-59]. According to Hair et al., [43],
this indicates that all constructs had fulfilled discriminant validity.
Table 4. Fornell-Larcker criterion results
Factors
1 2 3
PI SERQ SAT
1 PI 0.899
2 SERQ 0.619 0.947
3 SAT 0.647 0.573 0.930
Note: Highlighted values represent √AVE while the others represent the correlations
4.3. Assessment of structural model
To test the proposed hypotheses, the significance of each path is estimated using a PLS
bootstrapping method with 5000 resamples. Verifying the hypotheses involves calculating path coefficients
(beta β), its corresponding t-values and R² [43]. The value of the β indicates the strengths of relationships
between the constructs. In addition, the R2
value represents the amount of variance explained by
independent variables.
Table 5 and Figure 2 illustrate the assessment of structural model representig the results of
the hypothesis testing, where all the 3 hypotheses are examined. Service quality considerably predicts user
satisfaction. Hence, H1 is supported with (t p < 0.001). Service quality and user
satisfaction significantly predict performance impact. Hence, H2 and H3 are supported with (
t p < 0.001) and (t p < 0.001), respectively.
The strength of the correlations between the constructs is evaluated by the path coefficients,
and in this case, the results demonstrate that the straightforward consequences of user satisfaction on
the performance impact are stronger than the effects of quality of service on the performance impact.
Furthermore, the R² values show adequate level of descriptive power, suggesting a substantial model [60-62].
Table 5. Structural model assessment results
Hypothesis Relationship Std Beta Std Error t-value p-value Decision R²
H1 SERQ→SAT 0.573 0.036 15.809 0.000 Supported 0.33
H2 SERQ→PI 0.370 0.044 8.432 0.000 Supported 0.51
H3 SAT→ PI 0.435 0.042 10.376 0.000 Supported
Key: SERQ: service quality, SAT: user satisfaction, PI: performance impact
Figure 2. PLS algorithm results

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5. DISCUSSION
Service quality was found to positively influences user satisfaction in using smart government
services among employees in public sector in the UAE, which is supported by previous studies
[6, 19, 20, 38, 63-66]. It is explained by the fact that the more the employee is able to use smart government
services at the time or place he/she wants, find platforms that are relevant to his tasks, and is able to
communicate interactively through it, the more the employees consider their use of smart government
services to be a wise decision, as it meets their expectations, and leaves them feeling generally satisfied.
It was also discovered that the quality of service significantly affected performance impact.
This effect, which is supported by past studies [32, 33, 37, 67-69], has been described by the idea that when
a staff member of a public sector organisation is able to utilise smart government applications at the place or
time he/she wishes, find platforms appropriate for his tasks, and is also able to be in touch interactively using
it, then this will bring about a performance enhancement in 3 perspectives: efficiency (complete tasks easily
and quickly), knowledge acquisition (acquire new skills and knowledge, generate innovative ideas) and
improve communication with clients and employees, besides decision quality and service delivery.
In the long run, user satisfaction was discovered to positively influence the impact of performance
of smart government applications among staff members within public sector organisations in the UAE.
This effect is supported by the past studies [70-72], and is justified by the fact that when staff members in
public sector organisations are already satisfied with the application, it will make a contribution to their
performance improvement in 3 perspectives: efficiency, knowledge acquirement and with regards to
communication with clients and employees, besides decision quality and service delivery.
6. CONCLUSION
Even though the governmental organisations of UAE are ahead of their regional peers in terms of
performance, it is trying hard to enhance the services of its public sector institutions, the results of this
research could be regarded as one of the proposals that can be used in this direction. The aim of this research
was to test the impact of technology attributes quality of service on user satisfaction as well as impact of
performance of smart government applications on the employees in the public sector organisations in
the UAE. It has offered a case to authenticate parts of Delone & McLean IS success model and, despite
various restrictions faced by the research; it has been able to give an insight into a new context of smart
governments. The results revealed that all three hypotheses were significant, and the independent variables
significantly explain 51% of performance impact.
REFERENCES
[1] F. S. Al-Obaithani and A. A. Ameen, “Toward Proposing Smart-Government Maturity Model: Best Practices,
International Standards, and Six-Sigma Approach,” in 1st International Conference on Management and Human
Science (ICMHS 2017), pp. 124-137, 2017.
[2] W. H. DeLone and E. R. McLean, “Information Systems Success Measurement,” Foundations and Trends in
Information Systems, vol. 2, no. 1, pp. 1-116, 2016.
[3] R. Cheung and D. Vogel, “Predicting user acceptance of collaborative technologies: An extension of the technology
acceptance model for e-learning,” Computers and Education, vol. 63, pp. 160-175, 2013.
[4] W. Cheung, et al., “Prediction of Internet and World Wide Web usage at work: A test of an extended Triandis
model,” Decision Support Systems, vol. 30, no. 1, pp. 83-100, 2000.
[5] M. K. Chang, et al., “Literature derived reference models for the adoption of online shopping,” Information and
Management, vol. 42, no. 4, pp. 543-559, 2005.
[6] R. Alshamsi, et al., “Impact of Smart Government Usage and Smart Government Effectiveness on Employee
Happiness,” Test Engineering and Management, vol. 82, pp. 12086-12100, 2020.
[7] O. Alshamsi, et al., “Examining the Impact of Dubai Smart Government Characteristics on User Satisfaction,”
International Journal of Recent Technology and Engineering, vol. 8, no. 2S10, pp. 319-327, 2019.
[8] O. Alshamsi, et al., “Towards a Better Understanding of Relationship between Dubai Smart Government
Characteristics and Organizational Performance,” International Journal of Recent Technology and Engineering,
vol. 8, no. 2S10, pp. 310-318, 2019.
[9] K. Alghawi, et al., “The Role of Smart Government Characteristics for Enhancing UAE’s Public Service Quality,”
International Journal on Emerging Technologies, vol. 10, no. 1a, pp. 1-7, 2019.
[10] S. Albreiki, et al., “Impact of Internal Government Efficiency and Service Delivery Infrastructure on the Smart
Government Effectiveness in UAE,” Int. Journal on Emerging Technologies, vol. 10, no. 1a, pp. 124-131, 2019.
[11] S. Albreki, et al., “The Influence of Knowledge Management on the Smart Government Effectiveness:
An Empirical Study in UAE,” International Journal on Emerging Technologies, vol. 10, no. 1a, pp. 105-114, 2019.
[12] F. Al-Obthani and A. Ameen, “Association between Transformational Leadership and Smart Government among
Employees in UAE Public Organizations,” International Journal on Emerging Technologies, vol. 10, no. 1a,
pp. 98-104, 2019.

 ISSN: 2088-8708
6032
[13] S. Petter, et al., “Information systems success: The quest for the independent variables,” Journal of Managememnt
Information Systems, vol. 29, no. 4, pp. 7-62, 2013.
[14] Y. Alkatheeri, et al., “The Mediation Effect of Management Information Systems on the Relationship between
Big Data Quality and Decision making Quality,” Test Engineering and Management, vol. 82, pp. 12065-12074,
2020.
[15] M. Rahmah, et al., “The Moderation Effect of Technology Usage on the Relationship between Organizational
Innovation and Organizational Learning,” Test Engineering and Management, vol. 82, pp. 12114-12128, 2020.
[16] P. B. Giang and A. A. Ali, “Big event and its values to build trust in destination image – a perspective in big cities
in vietnam,” International Journal of Recent Trends in Business and Tourism, vol. 4, no. 1, pp. 1-6, 2020.
[17] S. Salama, et al., “Impact of Availability of Knowledge Management Infrastructure on Improving the Performance
of The Education Sector Staff in Libya: Organizational Loyalty as a Mediating Variable,” International Journal of
Management and Human Science, vol. 4, no. 1, pp. 29-39, 2020.
[18] P. Sudhana, et al., “A multi-theoretical framework to better understand the college major choice in arts and design,”
Journal of Applied Research in Higher Education, 2020.
[19] R. Alshamsi, et al., “Investigating the Mediating Effect of Organizational Innovation on the relationship
between Smart Government Usage and Employee Happiness,” Test Engineering and Management, vol. 83,
pp. 12233-12244, 2020.
[20] H. Almarri, et al., “The Mediating Effect of Facilitating Conditions on The Relationship Between Actual Usage of
Online Social Networks (OSN) and User Satisfaction,” International Journal of Psychosocial Rehabilitaion,
vol. 24, no. 06, pp. 6389-6400, 2020.
[21] Y. Alkatheeri, et al., “The Effect of Big Data on the Quality of Decision-Making in Abu Dhabi Government
Organisations,” in Proceedings of ICDMAI 2019, Data Management, Analytics and Innovation, vol. 2, pp. 231-248,
2019.
[22] A. Haddad, et al., “The Impact of Technology Readiness on the Big Data Adoption Among UAE Organisations,”
in Data Management, Analytics and Innovation, pp. 249-264, 2020.
[23] A. Parasuraman, et al., “A Conceptual Model of Service Quality and It’s Implications for Future Research,”
Journal of Marketing., vol. 49, no. 4, pp. 41-50, 1985.
[24] S. Petter, et al., “Measuring information systems success: models, dimensions, measures, and interrelationships,”
European Journal of Information Systems, vol. 17, no. 3, pp. 236-263, 2008.
[25] O. Oktal, et al., “Measurement of internal user satisfaction and acceptance of the e-justice systems in Turkey,”
Aslib Journal of Information Management, vol. 68, no. 6, pp. 716-735, 2016.
[26] R. Al-Dhamari, et al., “Are audit committee characteristics important to the internal audit budget in Malaysian
firms?” Contaduria y Administracion, Accounting and Management, vol. 63, no. 2, pp. 21-22, 2018.
[27] M. S. Afshar, et al., “Audit management, need for closure and detection of misstatements,” Journal of Accounting
in Emerging Economies, vol. 9, no. 2, pp. 237-250, 2019.
[28] B. A. Al-Gamrh and R. A. Al-Dhamari, “Firm characteristics and corporate social responsibility disclosure,”
International Business Management, vol. 10, no. 18, pp. 4283-4291, 2016.
[29] R. A. Al-Dhamari, et al., “Board diversity and corporate payout policy: Do free cash flow and ownership
concentration matter?” Corporate Ownership and Control, vol. 14, no. 1-2, pp. 373-383, 2016.
[30] B. Al-Gamrh, et al., “The role of corporate governance strength in crisis and non-crisis times,” Applied Economics,
vol. 50, no. 58, pp. 6263-6284, 2018.
[31] I. Alrajawy, et al., “Examine Factors Influencing the Intention to use Mobile Learning in Yemen Public
Universities,” Asian Journal of Information Technology, vol. 16, no. 2, pp. 287-297, 2017.
[32] A. Ameen, et al., “Determining Underlying Factors that Influence Online Social Network Usage Among Public
Sector Employees in the UAE,” in International Conference of Reliable Information and Communication
Technology, pp. 945-954, 2018.
[33] A. Ameen, et al., “The Impact of Knowledge Sharing on Managing Organizational Change within Abu Dhabi
National Oil Organizations,” International Journal of Management and Human Science, vol. 2, no. 3, pp. 27-36,
2018.
[34] M. Aparicio, et al., “Grit in the path to e-learning success,” Computers in Human Behavior, vol. 66, pp. 388-399,
2017.
[35] C. Tam and T. Oliveira, “Understanding the impact of m-banking on individual performance: DeLone & McLean
and TTF perspective,” Computer in Human Behavior, vol. 61, pp. 233-244, 2016.
[36] B. C. Lee, et al., “Learners’ acceptance of e-learning in South Korea: Theories and results,” Computer and
Education, vol. 53, no. 4, pp. 1320-1329, 2009.
[37] A. Ameen, et al., “Investigating the Key Factors Influencing the Use of Online Social Networks in Public Sector
Context in the UAE,” International Journal of Innovation, vol. 7, no. 3, pp. 392-411, 2019.
[38] H. Almarri, et al., “Antecedents And Outcomes of Online Social Networks (OSN) Usage among Public Sector
Employees,” International Journal of Psychosocial Rehabilitation, vol. 24, no. 06, pp. 6373-6388, 2020.
[39] R. V Krejcie and D. W. Morgan, “Determining sample size for research activities,” Educational and Psychological
Measurement, vol. 30, pp. 607-610, 1970.
[40] B. G. Tabachnick and L. S. Fidell, “Using Multivariate Statistics,” 6th ed. Pearson, 2012.
[41] C. M. Ringle, et al., “SmartPLS 3. Boenningstedt: SmartPLS,” 2015. [Online], Available: http://www.smartpls.com
[42] D. W. Barclay, et al., “The partial least square (PLS) approach to causal modeling: Personal computer adoption and
use as an illustration,” Technology Studies, vol. 2, no. 2, pp. 285-309, 1995.

6033
[43] J. F. Hair, et al., “A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM),” 2nd ed, London,
Thousand Oaks, SAGE, 2017.
[44] O. Isaac, et al., “Perceived Usefulness, Perceived Ease of Use, Perceived Compatibility, and Net Benefits: an
empirical study of internet usage among employees in Yemen,” in The 7th International Conference Postgraduate
Education (ICPE7), pp. 899-919, 2016.
[45] A. Al-Mulla, et al., “The Effect of Organizational Tensions, Merge Policy and Knowledge Sharing on Managing
Organizational Change: The Context of Abu Dhabi National Oil Organizations,” Journal of Engineering and
Applied Science, vol. 14, no. 8, pp. 2517-2531, 2019.
[46] P. Bastola, et al., “The Effect of E-Learning Actual Use on the Net Benefit among Public Universities Students in
Nepal,” in 22nd International Conference on IT Applications and Management (ITAM), pp. 158-164, 2019.
[47] O. Isaac, et al., “Examining the Relationship Between Overall Quality, User Satisfaction and Internet Usage: An
Integrated Individual, Technological, Organizaional and Social Perspective,” Asian Journal of Information
Technology, vol. 16, no. 1, pp. 100-124, 2017.
[48] O. Isaac, et al., “Towards a Better Understanding of Internet Technology Usage by Yemeni Employees in
the Public Sector: An Extension of the Task-Technology Fit (TTF) Model,” Research Journal of Applied Sciences,
vol. 12, no. 2, pp. 205-223, 2017.
[49] I. Alrajawy, et al., “Mobile Learning in Yemen Public Universities: Factors Influence Student’s Intention to Use,”
in The 7th International Conference Postgraduate Education (ICPE7), pp. 1050-1064, 2016.
[50] V. R. Kannana and K. C. Tan, “Just in time, total quality management, and supply chain management:
understanding their linkages and impact on business performance,” Omega, vol. 33, no. 2, pp. 153-162, 2005.
[51] F. Al-Obthani and A. Ameen, “Influence of Overall Quality and Innovativeness on Actual Usage of Smart
Government: An Empirical Study on the UAE Public Sector,” International Journal of Emerging Technologies,
vol. 10, no. 1a, pp. 141-146, 2019.
[52] B. A. Alneyadi, et al., “Moderating Effect of Organizational Culture on Relationship between Transformational
Leadership and Human Capital: An Empirical Study on Public Sector of UAE,” International Journal of Emerging
and Technologies, vol. 10, no. 1a, pp. 23-31, 2019.
[53] C. E. Werts, et al., “Intraclass reliability estimates: Testing structural assumptions,” Educational and Psychological
Measurement, vol. 34, no. 1, pp. 25-33, 1974.
[54] R.B. Kline, “Principles and practice of structural equation modeling,” 3rd ed. New York, The Guilford Press, 2010.
[55] D. Gefen, et al., “Structural equation modeling and regression: Guidelines for research practice,” Communications
of the Association for Information Systems, vol. 4, no. 1, pp. 1-79, 2000.
[56] D. Gefen and D. Straub, “The Relative Importance of Perceived Ease of Use in IS Adoption : A Study of
E-Commerce Adoption,” Journal of the Association for Information Systems, vol. 1 no. 8, pp. 1-28, 2000.
[57] J. F. Hair, et al., “Multivariate Data Analysis,” Pearson New International Edition, 2010.
[58] C. S. Chen, “Perceived risk, usage frequency of mobile banking services,” Managing Service Quality:
An International Journal, vol. 23, no. 5, pp. 410-436, 2013.
[59] G. Chen, et al., “A multilevel study of leadership, empowerment, and performance in teams,” Journal of Applied
Psychology, vol. 92, no. 2, pp. 331-346, 2007.
[60] L. Cohen, et al., “Research Methods in Education,” Routledge, New York, 2007.
[61] S. Cohen and N. Kaimenakis, “Intellectual Capital and Corporate Performance in Knowledge-Intensive SMEs,”
Learning Organization, vol. 14, no. 3, pp. 241-262, 2007.
[62] G. Chen, et al., “Conflict Management for Effective Top Management Teams and Innovation in China,” Journal of
Management Studies, vol. 42, no. 2, pp. 277-300, 2005.
[63] M. Rahmah, et al., “Effect of Organizational Innovation (Product Innovation, Process Innovation, and
Administrative Innovation) On Organizational Learning,” Test Engineering and Management, vol. 82,
pp. 12101-12113, 2020.
[64] G. S. A. K. S. Alshamsi, et al., “Impact of Transformational Leadership on Organizational Innovation:
Telecommunication Sector of Pakistan,” Test Engineering and Management, vol. 82, pp. 14783-14794, 2020.
[65] Y. Alkatheeri, et al., “The Effect of Knowledge Management Quality and Management Information Systems on
Decision Making Quality,” Test Engineering and Management, vol. 82, pp. 12075-12085, 2020.
[66] A. Althunibat, “Determining the factors influencing students’ intention to use m-learning in Jordan higher
education,” Computers in Human Behavior, vol. 52, no. 1, pp. 65-71, 2015.
[67] A. Ameen and K. Ahmad, “Towards Harnessing Financial Information Systems in Reducing Corruption :
A Review of Strategies,” Australian Journal of Basic and Applied Sciences, vol. 6, no. 8, pp. 500-509, 2012.
[68] A. Ameen and K. Ahmad, “A Conceptual Framework of Financial Information Systems to reduce corruption,”
Journal of Theorical and Applied Information Technology, vol. 54, no. 1, pp. 59-72, 2013.
[69] A. Ameen and K. Ahmad, “The Role of Finance Information Systems in anti financial corruptions: A theoretical
review,” in 11 International Conference on Research and Innovation in Information Systems (ICRIIS’11),
pp. 267-272, 2011.
[70] M. Heffernan, et al., “Exploring the HRM-performance relationship: the role of creativity climate and strategy,”
Employee Relations, vol. 38, no. 3, pp. 438-462, 2016.
[71] L. Para-González, et al., “Exploring the mediating effects between transformational leadership and organizational
performance,” Employee Relations, vol. 40, no. 2, pp. 412-432, 2018.
[72] K. Dartey-Baah, “Resilient leadership: a transformational-transactional leadership mix,” Journal of Global
Responsibility, vol. 6, no. 1, pp. 99-112, 2015.

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