Introduction

Throughout history, the Fourth Industrial Revolution has represented an important and influential turning point in the future of countries. Since the emergence of the revolution, its first wave has brought about unprecedented changes in human history, as the practice of building smart cities has become an increasing standard in all parts of the world and one of the most important technological transformations the world is witnessing, (Laney & Jain, 2017).

This practice has led to many challenges that the world's governments must prepare for and prepare to benefit from them optimally, and one of the most important challenges is the creation of environmentally friendly civilized cities, technically integrated and carefully planned urban cities by strategic leaders capable of making investment decisions (Chen & Zhang, 2014).

Within the framework of the five-year plan of the United Arab Emirates developed for the year 2016 to 2021, Figure (1). In addition, its centenary 2071, the United Arab Emirates realized the importance of keeping pace with the technological revolution through the participation of the world in creating the future and transforming future challenges into opportunities and achievements. This will lead to the development of its smart cities as a global laboratory for the future industry .

The government of the United Arab Emirates continued to emphasize the adoption of the smart city strategy, through (UAE Strategy, 2016), noting that this strategy does not only mean that the country is on the right path to developing smart cities, but that there is a need for strategic leaders, (Gao et al., 2015), Leadership skills manage the city efficiently and effectively through strategic leaders by engaging citizens in development processes and innovations (Struis et al., 2014).

Likewise, the Global Competitiveness Report for the year 2019 issued by the World Economic Forum confirms the findings of the International Institute for Administrative Development. Within the classification, the UAE is the most competitive in the Arab region (25th in the general classification) (Figure 2).

Problem Statement

The main study problem is; In the role of strategic leadership, and how to choose the appropriate leadership styles, to transform cities into smart and digital cities, and to seek solutions and best practices, and to work on attracting a huge number of investments, and how to develop and develop the skills of administrative leaderships, and expand the horizons of strategic thinking, to face a data.

Moreover, perhaps most importantly, leaders should initiate the development of a culture of security and protection among smart city management employees and develop technical measures with their safe positions (Chen et al., 2012), and provide a platform for cooperation between many stakeholders in the field of smart cities (Ahmed et al., 2017).

Research Questiosns

1. Does the strategic leadership direction (planning and creative development policy, creative intelligence, and strategic visions), affect building cities smart sustainable?

2. What is the level of strategic directions of the leaderships in building smart sustainable cites?

3. What are the obstacles facing the development of the strategic leadership direction in building a sustainable smart city?

Research Objectives

The Following Study Objectives Aim to Answer the Above Study Questions

• Identifying the reality of strategic leadership and identifying leadership practices through (creative planning and development policy, creative intelligence, strategic visions) to build sustainable smart cities.

• Determining the level of strategic directions for leaderships for the purpose of building sustainable smart cities.

• highlighting on the obstacles facing the development of the strategic leadership direction, big in building a sustainable smart city.

Significance of Studying

The importance of the study is that it is the pioneering study in the context of the topic it will address, which are the open challenges and future direction in the increasingly complex work environment, (Aaltonen & Tempini, 2014), which is represented in the role of strategic leadership, of making investment decisions in smart cities (Tan et al., 2015).

Literature Review

Concept of Smart Cities

An integrated city operating in an innovative way, covering all areas of the economy, governance, society and health, (Moir et al., 2014), with a focus on support and effective participation, of the government sector and the role of strategic leaders and decision makers who make investment decisions to big data analysis (U4SSC, 2016).

A smart city provides a variety of applications and smart services (Van den Broek & Van, 2015) in these four regions, thus promoting coordinated developments in smart applications, (Tiefenbacher & Olbrich, 2015) that a smart city provides within the dimensions of reproduction, economic development and social interaction.

In addition to identifying the dynamic nature of representing the diversity of strategic elements to become the first in the world, (ubran Rainmaking Program) .Models of viable smart cities should be "multi-dimensional, encompassing different aspects of intelligence, (Ekbia et al., 2015) and emphasizing the importance of integration and interaction across multiple domains".(Figure 3)

The complexity of smart city deployment is twofold, on the one hand, the scarcity of resources to stimulate investments in infrastructure, (Stein et al.,2013) and on the other hand, upgrading the model and making it self-sustainable, so that citizens become the main users and consumers, sharing In developing business systems (Lycett, 2013), and utilizing data that improve work quality through a dynamic service delivery system, (Tamm et al., 2013) and for the purposes of smart city development, (Zegras et al., 2015).

Strategic Leadership Concept

The history of research on leadership goes back more than a thousand years, as the topic of leadership was a continuous discussion for several decades, especially on the topic of strategy, organization and development (Yunus, 2002).

Later on, the need for the concept of strategic leadership emerged instead of traditional leadership, especially with the rapid changes and surrounding situations facing the organization and the need to have a strategic vision for the future to increase motivation and encourage creativity and innovation (Al-Murabba, 2008).

Robert Levingson defined leadership as reaching the goal by the best means and at the lowest costs, i.e. within the limits of the available resources (Al-Wahaibi, 2005). In addition, (Crow, 1993) defined strategic leadership. Those actions focus on the strategic vision to achieve it.

Strategic leadership is characterized by clarity of the future vision and seeks to achieve effectiveness and efficiency among the goals, available opportunities and capabilities acquired through experiences (Woerner & Wixom, 2015), and the knowledge that distinguishes the leadership personality and how to develop it through specialized programs in leadership skills in order to create future Leaders to put the organization at the forefront (Yunus, 2002).(Figure 4)

The aforementioned discussion also concluded that the strategic leadership orientation is a strong indicator of building smart cities. Therefore, the first hypothesis of this study was formulated as follows:

H1: There is a positive impact relationship between the dimensions of the strategic leadership direction and building smart sustainable cities.

Artificial Intelligence Strategy

Artificial intelligence technology, as an important tributary to building a competitive, high-productivity knowledge economy based on innovation, scientific research and modern technology, by 2021 (Laney & Jain, 2017).

The aforementioned discussion also concluded that the policy of artificial intelligence is a strong indicator of building smart cities. Therefore, the following hypotheses for this study were formulated as follows:

H1a: Policy affects positively and significantly on the direction of strategic leadership in building smart cities.

Policy of Creative Competencies and Capabilities

Those human resources represent a capital resource that needs investment, while maintaining the effective organizational culture, and focusing on the essential and behavioral dimensions (Hill & Jones, 1998).

The aforementioned discussion also concluded that the policy of the policy of creative competencies and capabilities is a strong indication of the direction of the strategic leadership in building smart sustainable cities. Therefore, the following hypotheses for this study were formulated as follows:

H1b: Policy influences creative competencies and capabilities positively and significantly on the direction of strategic leadership in building smart sustainable cities.

Creative Planning and Development Policy

“Henry Fawel” defined the planning process (Awad, 2012), as predicting what the future will be like while preparing for this future.

The planning and development of creative policy, it is a positive plan to develop and improve the performance of the organization, to fit the changing environment (Hawari, 1992), a systematic process dedicated to achieving the priorities of (Hill & Jones, 1998).

According to previous literature, the policy of creative planning and development is considered a strong indicator of the direction of strategic leaders. In addition, many previous studies have reported that the policy of creative planning and development has a positive importance on the direction of strategic leaders in building smart cities.

The following hypothesis was reached:

H1c: Planning and development policy affects positively and significantly on the strategic leadership direction in building smart sustainable cities.

Strategic Visions

Future visions for building smart cities target infrastructure, for massive data sets, (Policy Exchange, 2016), to study large amounts of data, to uncover patterns (Bilal et al., 2016), and to obtain insights to extract valuable information in various Sectors.

The following hypothesis was reached:

H1d: Strategic visions positively and significantly affect the direction of the strategic leadership in building smart sustainable cities.

Creative Intelligence

According to the previous literature, Creative Intelligence is considered a strong indicator of the strategic leadership orientation. In addition, many previous studies have reported that Creative Intelligence has a positive importance on the strategic leadership orientation in building smart sustainable cities.

The following hypothesis was reached:

H1e: Creative Intelligence affects positively and significantly on the direction of strategic leaders in building smart sustainable cities.

Network Theory

In Figure (5), the network theory assumes that obtaining positive results through the influence of data on preparing a global strategy and setting global standards for smart cities.

Development of the Model of Situational Management Leadership Theory

Researchers have worked explicitly on the use of the theory of situational leadership in the technological literature. The figure (6) is a model that contains changes and developments, models based on the original model of the theory, that obtaining positive results through the influence of the Directs strategic leadership on preparing a global strategy and setting global standards for smart cities, and for the purposes of creating the relationship between directs strategic leadership and strategy investment decisions, the strategic leadership must be able to think strategically, to deal with rapid variables and high risk environments, (Sosik et al., 2005).

Methodology

Study Population

The framework of the study population was defined as it included 10 government departments that relate to the economy and trade in the United Arab Emirates. The study focuses on collecting data on the demographic characteristics of the respondents from statistical departments and information technology departments such as the Ministry of Interior, the Ministry of Economy, Federal Customs Authorities and the Ministry of Health throughout the UAE United Arab Emirates, so that the number of employees in this study includes (300) employees.

The concerned managers were counted in the Dubai Customs Department, and the total number of directors and experts in the economic field was (87) leading employees, while the remaining departments, which numbered (9), approximately (223) employees, to cover the different axes of the questionnaire, highlighting the main improvements and assessing weaknesses. The quantitative study method was used to conduct the questionnaire, where a database was provided containing the names of employees in the departments to be contacted for the purposes of the study. Table 1

Table 1 The Sample Size of The Study
No	Departments	Total number of target population	The minimum of study	Number of questionnaires retrieved
1	customs	50	40	6
2	Ministry of Interior	40	25	1
3	General Command of Dubai Police	40	25	15
4	Ministry of Health and Prevention	40	20	10
5	Dubai municipality	20	10	2
6	Dubai Electricity and Water Authority	20	5	1
7	the National Council	10	3	1
8	Ministry of Foreign Affairs	20	10	2
9	The Ministry of Economy	40	40	16
10	Identity Authority	20	10	4

Study Sample

The population of the United Arab Emirates is (9,771) million, there are basically (7) million employees in all sectors of the state, as the number of employees in the private sector has reached 5 million, while the number of employees in the government sector has reached 2 million.

The number of national employees working in the federal government within the leadership category reached 2,421 employees, of which 27.1% were in Abu Dhabi, 51.5% in Dubai, and 21.4% in the northern regions of the country.

Firstly, the number of employees cannot be studied in full, whether in government departments or the private sector. Second that the current line of study is to target government departments to build a smart city in the United Arab Emirates to make the results more generalizable. The technique of random probability sampling was used, where a link was sent by phone and then the data collection process was conducted through quantitative methods via the link, case studies and focus groups.

A random sample consisted of (300) people was selected, where (232) questionnaires were retrieved, and (68) questionnaires were excluded due to lack of answers, and therefore the response rate is (77%). From the questionnaires completed two months after the survey was sent, some numbers were included for communication in case the employees need further clarification and information related to the study and use the data for academic purposes only and in full confidentiality.

Spatial Boundaries: United Arab Emirates

Findings

Composite Reliability Values

In this predominant study, convergent validity was verified by values of mean contrast extracted (AVE) above the threshold of 0.50 as recommended by previous researchers.(Table 2 and Figure 7)

Table 2 Composite Reliability Values
AVE	CR	Loadings	Items	Construct
0.61	0.851	0.785	CPDP1	Creative planning and development policy
		0.785	CPDP2	(CPDP)
		0.783	CPDP3
		0.78	CPDP4
		0.779	CPDP5
		0.775	CPDP6
0.588	0.77	0.769	C I1	Creative intelligence
		0.769	C I2	(C I)
		0.765	C I3
		0.764	C I4
0.574	0.794	0.761	SV1	Strategic Visions
		0.759	SV2	(SV)
		0.758	SV3
		0.755	SV4
		0.754	SV5
0.559	0.78	0.752	ROP1	Regulatory oversight policy
		0.75	ROP2	(ROP)
		0.747	ROP3
		0.746	ROP4
		0.742	ROP5
0.527	0.701	0.733	GS1	Governance skill,
		0.727	GS2	(GS)
		0.722	GS3
		0.721	GS4
0.506	0.757	0.719	RP1	Risk Policy
		0.718	RP2	(RP)
		0.713	RP3
		0.711	RP4
		0.709	RP5
		0.699	RP6
0.501	0.739	0.69	SPP1	Security and Privacy Policy
		0.686	SPP2	(SPP)
		0.674	SPP3
		0.67	SPP4
		0.659	SPP5
		0.621	SPP6
		0.593	SPP7
0.501	0.739	0.729	DQ1	Data quality
		0.599	DQ2	(DQ)
		0.518	DQ3
0.679	0.878	0.847	AIP1	Artificial Intelligence Policy
		0.837	AIP2	(AIP)
		0.825	AIP3
		0.805	AIP4
		0.804	AIP5
0.631	0.866	0.801	PCCC1	policy of creative competencies and capabilities
		0.8	PCCC2	(PCCC)
		0.792	PCCC3
		0.791	PCCC4
		0.791	PCCC5
		0.79	PCCC6

Evaluating the Good Fit of the Structural Model

Finally, the effect size (f2) determines the effect of the external variable on the Q2 predictive relevance of the endogenous variable.(Table 3)

Table 3 Evaluating The Good Fit of The Structural Model
Subject of Evaluation	Actions	Threshold Values
Specified coefficient	R2	0.19 (weak), 0.33(modrernate ), 0.67 (substantial)
Path parameter	t-value	1.28 (P>0.10), 0.65 (P>0.05), 2.33 (P>0.01)
Effect size	F2	0.02(small), 0.15 (medium), 0.35(large)
Predictive relevancy	Q2	0.02(small), 0.15 (medium), 0.35(large)

Conclusively, (Table) represents the cut-off values of all parameters' such as coefficient of determination (R2), path parameter estimation (t-VALUE), effect sizes (f2) and predictive relationship (Q2)

According to the endorsement of (Hair et al., 2014) these threshold values are also applied in this study to test the inside model PLS-SEM, the collinearity test is imperative in evaluating the internal model of the study.

Therefore, to ensure that there is no collinearity problem, the VIF values for both combinations were also reported in (Table 4), the values confirmed the absence of a collinearity problem in the current study.

Collinearity Assessment

Table 4 Collinearity Assessment
	AIP	PCCC	CPDP	CI	CV	ROP	GS	RP	SPP1	DQ1
AIP										1.000
PCCC		1
CPDP				1
CI									1.000
CV						1
ROP								1
GS	1							1
RP	1	1.49	1.07		1.44	1	1	1
SPP	1.16	1	1.85	1.18	1.98	1.76	1	1.86	1.970	1.000
DQ		1.37	1.36	1.18	1	1	1	1.39	1.99

Evaluate the Coefficient of Determination (R2) Value

The main goal of PLS-SEM is to explain the internal latent variable and to assess R2 as the most vital criteria in the structural model. (Table 5)

Table 5 Coefficient of Determination R2
Variable Underlying Internal	Square Value of R _
Strategic leadership	0.644
Building smart sustainable cities.	0.519

Test Hypotheses of the Study: Structural Model Results (SEM Result and Hypothesis Testing): (Table 6)

Table 6 Structural Model Results
Hypothesis	Relationship	Path Coefficient	Std.Error	t -value	p -value	Supported
H1	BSSC< -SL	0.161	0.088	3.907	0.009	yes
H1a	SL< -AIP	0.224	0.066	4.082	0.007	yes
H1b	SL< -PCCC	0.308	0.062	3.734	0	yes
H1c	SL< -CPDP	0.211	0.082	6.926	0	yes
H1d	SL< -C I	0.232	0.052	3.439	0	yes
H1e	SL<- SV	0.326	0.023	2.655	0.004	yes

Effect size (f2):

Under the suggestions of Heineller & Vassot (2010), the effect size (f2) of the exogenous variables is determined for further analysis in addition to the influence of the particular external factors.

In addition, the small size of the effect (Chen et al., 2003), which can be meaningful, should not be neglected. (Table 7)

Table 7 Effect Size (f2)
Self-Constructed	Outdoor  Installations	Impact Size	Notes
	Creative Planning And Development Policy (CPDP)	0.14	Medium effect value
Strategic leadership	Creative Intelligence (CI)	0.267	Large effect value
SL	Strategic Visions (SV)	0.088	Small effect value
Making investment decisions	Regulatory Oversight policy (ROP)	0.071	small effect value
MID	Governance Skill,(GS)	0.232	Medium effect value
Big data strategy	Risk Policy (RP)	0.074	small effect value
BDS	Security and Privacy Policy (SPP)	0.413	Large effect value
	Data quality(DQ)	0.187	Large effect value
Building smart sustainable cities.	Artificial Intelligence Policy (AIP)	0.057	small effect value
BSSC	policy of creative competencies and capabilities (PCCC)	0.161	Medium effect value

Mediation Effect Test

In the PLS model, the mediation effect is determined by a bootstrap analysis (with 100 re-samples) structured with a detailed hypothesis, the significant result shown during a primer analysis of the indirect effect (β=0.773) along with a t-test of 3.696.

Thus, the study results revealed the mediating effect of the position between the relationship between big data and smart cities and statistical significance (β=0.733, t=3.696, p<0.01). Results of Path Coefficient (Mediation Results).(Table 8)

Table 7 Mediation Effect Test
Confidence Interval
No.	Relationship	Beta	t- value	decision
H	Strategic leadership< Making investment decisions< Big data analysis<< Building smart sustainable cities.	0.733	3.696	Supported

Note: p<0.05 (t>1.645); ** p<0.01 (t>2.33)

Discussion, Conclusion and Recommendations

Limitations and Future Recommendations

In the future, a comprehensive study, observations, proposals and in-depth interviews are proposed in order to gain an understanding of the smart ecosystem's operating environment and its sustainability. In the future, research may be looking at other variables that affect the building of smart cities and cooperating with regional and international bodies to attract experts in the field of smart city in order to obtain their knowledge ideas, and prepare a smart integrated project that analyzes all data so that it is linked to all systems in the relevant Dubai government departments. And global security systems and depends on the concept of risk analysis and does its own in real-time and direct analysis to help reduce smuggling operations and increase the security efficiency of the state community.

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