Enhancing Najran’s sustainable smart city development in the face of urbanization challenges in Saudi- Arabia

Figures & data

Figure 1. Hub of the Smart sustainable cities. Source: Adapted from Bibri, and Krogstie (2020) ; Alamoudi et al. (2023).

Figure 2. ICT Inclusion in Urban Management. Source: Wlodarczak (2017).

Table 1. Essentials of the Smart Building System and Functionalities.

Key factors	Detailed domain	References
(i) Security and Safety	Smart buildings incorporate security and safety measures. Regular maintenance of building systems and functionalities can help ensure that the building is safe for occupants Resilience Planning	Nasiri et al. (2016); Olsthoorn et al., (2016); Oke et al., (2020).
(ii) Building systems and functionalities	Energy Efficiency Well-maintained building systems and functionalities operate more efficiently, reducing energy consumption and associated costs. This can include systems such as HVAC, lighting, and water supply	Olsthoorn et al., (2016), Ghaffarianhoseini et al. (2016).
(iii). Sustainability and durability	Smart buildings prioritize sustainability by incorporating green building practices, such as using recycled materials, reducing water consumption, and implementing renewable energy systems. Proper maintenance can prolong the lifespan of building systems and functionalities, reducing the need for costly repairs or replacements down the line	Rahman et al. (2018).Johnson and Samakovlis (2019).
(iv) Compliance	User-Centric Design Buildings are subject to various codes and regulations that require regular maintenance of building systems and functionalities to ensure compliance. Failure to comply can result in fines, legal liability, or even closure of the building	Ghaffarianhoseini et al. (2016) Miller (2024).
(v) Connectivity	Accessibility and Inclusivity Smart buildings are connected to the internet and can communicate with other devices, systems, and networks, enabling seamless data exchange and remote monitoring and control.	Jia et al. (2019), Aldakheel et al. (2023).
(vi). Indoor Environment Quality (IEQ)	Smart buildings focus on providing a high-quality indoor environment for occupants, including factors such as temperature, humidity, air quality, and lighting levels.	Ghaffarianhoseini et al. (2016); Aldakheel et al. (2023).

Figure 3. Study’s Hypothetical Framework.

Figure 4. Najran city districts. Satellite image. Source: AlQuhtani (2023).

Table 2. Measurement of variables.

Independent Variables/Predictor for Smart Cities	Features and Measurement Ratings	References
Section 1: Participants’ backgrounds
Demographic	Nominal & Ordinal	Author’s conceptualization
Section 2: Measuring the Smart Cities Variable
1. Urban Sustainability (US1-US5)	‘5’-point rated scale	Borsekova et al. (2016); Kaluarachchi (2021); Bibri (2021).
2. Citizen participation (CP1-CP3)	‘5’-point rated scale	Simonofski et al. (2019); Sánchez-Corcuera et al. (2019); Kaluarachchi (2021)
3. Smart mobility (SM1-SM3)	‘5’-point rated scale	Bamwesigye and Hlavackova (2019); Maldonado Silveira Alonso Munhoz et al. (2020); Porru et al. (2020)
4. Smart living (SL1-SL3)	‘5’-point rated scale	Ahvenniemi et al. (2017); Kaluarachchi (2021)
5. Smart Government (SG1-SG3)	‘5’-point rated scale	Castelnovo et al. (2015); Puron-Cid (2015)
6. Smart economy (SE1-SE3)	‘5’-point rated scale	Anthopoulos et al. (2015); Ahvenniemi et al. (2017).
Section 3: Smart Buildings and Environment Variables (SBE)
1. Energy efficiency (SBE1)	‘5’-point’rated scale	Nasiri et al. (2016); Olsthoorn et al., (2016); Ghaffarianhoseini et al. (2016); Frost and Sullivan (2023); Gonzalez et al., (2020); Kaluarachchi (2021).
2. Safety and security (SBE2)
3. User-centric design (SBE3)
4. Accessibility and inclusivity (SBE4)
5. Resilience planning (SBE5)

Table 3. Selected Case Studies Structures in Najran city.

	Case studies Smart Buildings	Information
Case study One		Park Inn by Radisson Najran, located at Land Plot B, Alfahad District, Najran, Saudi Arabia. Source: https://www.agoda.com/park-inn-by-radisson-najran/hotel/najran-sa.html?cid=1844104
Case study Two		Florida Inn Hotel King Abdulaziz Road, Najran, Najran, Saudi Arabia 66,262. Source: https://www.agoda.com/hyatt-najran-hotel/hotel/najran-sa.html?cid=1844104
Case study Three		Najran University is the Kingdom of Saudi Arabia’s largest university complex, occupying an area of 18 million square meters. The complex has two campuses, 15 colleges for male students and 10 colleges for female students, with a total capacity of 45,000 students. A medical city, a research center, and a sports and entertainment arena are also part of the university. Source: https://www.rakceramics.com/india/en-in/tiles/projects/najran-university-2016/

Table 4. Respondents-demographics.

Factors	Categorization	Frequency (N)	Percentages (%)
Gender	Male	268	54.80
	Female	221	45.19
Age	18–30	49	10.01
	31–40	156	31.90
	41–50	187	38.20
	51–and above	97	19.80
Professional background	Town planners	48	9.81
	Contract managers	88	17.99
	Civil engineers	62	12.67
	Architects	51	10.42
	Project managers	68	13.90
	Quantity surveyors	63	12.88
	Telecommunication and electrical engineers	53	10.83
	Data and information technicians	56	11.45
Education	Higher Diploma or certificate	102	20.85
	Bachelor Degree	189	31.46
	Master Degree	110	22.49
	Doctoral Degree	88	17.99
Occupations	Principal contractors	114	23.31
	Consulting firms	102	20.85
	Information Technology Company	88	17.99
	Government agencies	112	22.90
	Academic institutions	73	14.92
Years of experience	1-5years	125	25.56
	6–10 years	106	21.67
	11-15years	119	24.33
	16 years and above	139	28.42

Total Sample (N=489).

Table 5. Descriptive Statistics of Variables of Smart City and Environmental Sustainability.

Variables	Items	Mean Values (M)	Standard Deviation (SD)	Interpretations
1. Smart living	3	5.5134	1.45514	Strong
2. Urban Sustainability	5	5.7412	1.72376	Strong
3. Smart Governance	3	4.6805	1.76112	Moderate
4. Smart Mobility	3	4.1357	1.54680	Moderate
5. Smart Building and environment	5	5.6342	1.79410	Strong
6. Smart Economy	3	4.6719	1.17849	Moderate
7. Citizen participation	3	5.6834	1.16811	Strong

1–2.99: Low; 3.00–5.00: Moderate; 5.01–7.00: High

Table 6. Assessment of Loading, Full Collinearity, Reliability and Convergent Validity.

Constructs	Items	Factors Loading	Full collinearity	Cronbach’s Alpha	rho_A	Average variance Extracted (AVE)	Composite Reliability (CR)
1. Smart living	SL1	0.809	1.51	0.872	0.822	0.73	0.81
	SL2	0.812
	SL3	0.826
2. Urban Sustainability	US 1	0.959	2.21	0.932	0.902	0.88	0.94
	US 2	0.906
	US3	0.980
	US4	0.960
	US5	0.923
3. Smart Governance	SG1	0.977	1.89	0.979	0.869	0.66	0.97
	SG2	0.909
	SG3	0.988
4. Smart Mobility	SM1	0.808	2.01	0.942	0.931	0.65	0.89
	SM2	0.819
	SM3	0.877
5. Smart Building and environment	SBE1	0.889	1.36	0.965	0.911	0.93	0.84
	SBE2	0.844
	SBE3	0.855
	SBE4	0.811
	SBE5	0.822
6. Smart Economy	SE1	0.928	2.61	0.889	0.869	0.91	0.92
	SE2	0.944
	SE3	0.911
7. Citizen participation	CP1	0.955	2.51	0.945	0.953	0.98	0.96
	CP2	0.922
	CP3	0.933

Factor loadings > 0.7; CR > 0.7; rho_A > 0.7 and AVE > 0.5.

Table 7. Correlation of constructs and the square root of AVEs Confirming the discriminant validity of reflective constructs.

Variables	1	2	3	4	5	6	7
1. Smart living	0.930^a
2. Urban Sustainability	0.350	0.831^a
3. Smart Governance	0.311	0.379	0.891 ^a
4. Smart Mobility	0.443	0.105	0.067	0.885 ^a
5. Smart Building	0.385	0.043	0.049	0.305	0.891 ^a
6. Smart Economy	0.333	0.151	0.274	0.220	0.390	0.991 ^a
7. Citizens participation	0.317	0.309	0.306	0.323	0.363	0.455	0.904 ^a

AVE stands for average variance extracted. a The square root of the AVE (variance shared across reflective constructs and their measures) is shown by diagonal elements (bold figures). Correlations between all variables in the research model are examples of off-diagonal elements.

Figure 5. Final Achieved Structured Model.

Table 8. Factor loadings and cross-loadings for the measurement model.

Constructs	SL (1)	SUS (2)	SG (3)	SM (4)	SBE (5)	SE (6)	CP (7)
SL1	0.809	0.256	0.117	0.326	0.206	0.256	0.011
SL2	0.812	0.289	0.227	0.224	0.269	0.289	0.112
SL3	0.826	0.233	0.307	0.132	0.273	0.233	0.011
SUS 1	0.119	0.959	0.221	0.223	0.326	0.396	0.234
SUS 2	0.109	0.906	0.214	0.128	0.329	0.309	0.109
SUS3	0.133	0.980	0.162	0.225	0.323	0.333	0.026
SUS4	0.209	0.960	0.133	0.173	0.103	0.109	0.138
SUS5	0.233	0.923	0.127	0.252	0.163	0.133	0.225
SG1	0.037	0.156	0.977	0.250	0.140	0.110	0.024
SG2	0.118	0.189	0.909	0.311	0.011	0.041	0.177
SG3	0.151	0.156	0.988	0.570	0.101	0.121	0.138
SM1	0.224	0.189	0.107	0.808	0.120	0.110	0.315
SM2	0.248	0.132	0.207	0.819	0.105	0.195	0.340
SM3	0.269	0.140	0.317	0.877	0.212	0.232	0.110
SBE1	0.128	0.107	0.256	0.286	0.889	0.219	0.232
SBE2	0.244	0.117	0.289	0.259	0.844	0.244	0.303
SBE3	0.211	0.311	0.233	0.223	0.855	0.305	0.463
SBE4	0.155	0.124	0.396	0.386	0.811	0.311	0.240
SBE5	0.222	0.122	0.309	0.329	0.822	0.122	0.211
SE1	0.233	0.103	0.333	0.303	0.003	0.928	0.103
SE2	0.128	0.147	0.109	0.159	0.359	0.944	0.159
SE3	0.244	0.391	0.133	0.183	0.283	0.911	0.223
CP1	0.211	0.134	0.110	0.190	0.290	0.190	0.955
CP2	0.155	0.132	0.041	0.021	0.021	0.221	0.922
CP3	0.222	0.113	0.121	0.191	0.091	0.051	0.933
Seven (7) factors extracted. Rotation Method: Promax with Kaiser Normalization.

Bold values indicate item loadings on the assigned constructs.

Table 9. Hypothesis Path coefficients.

Hypothesis and Relationships Constructs	Original sample (O)	Samples’ Mean (M)	Std Beta (β)	VIF values	T-value	Sig. values (p)	Hypothesis test decisions
H1. Smart living➔ Urban Sustainability	0.344	0.344	0.523	1.186	4.427**	0.001	Supported
H2. Citizenship participation➔ Urban Sustainability	0.468	0.566	0.405	1.622	4.034*	0.002	Supported
H3. Smart Building ➔ Urban Sustainability	0.365	0.366	0.512	1.199	7.753*	0.001	Supported
H4. Smart Economy➔ Urban Sustainability	0.704	0.705	0.415	2.753	11.406 **	0.050	Supported
H5. Smart Mobility➔ Urban Sustainability	0.347	0.348	0.345	2.700	6.918**	0.030	Supported
H6. Smart Governance ➔ Urban Sustainability	0.655	0.655	0.307	1.190	5.735**	0.010	Supported
H7. Smart living➔ Smart Economy	0.524	0.523	0.465	2.723	7.221**	0.002	Supported
H8. Citizens’ participation➔ Smart living	0.624	0.623	0.605	2.673	6.391**	0.003	Supported
H9. Citizens’ participation ➔ Smart building	0.534	0.535	0.406	2.173	5.241*	0.003	Supported

p < 0.05; **p < 0.00.

Table 10. Coefficients of determination (R²), Predictive Power (Q²), SRMR, and NFI.

Major constructs	Coefficients of determination (R^2)	Predictive Power (Q^2)
Urban Sustainability	0.729	0.642
Smart living	0.415	0.287
Citizenship participation	0.648	0.825

Standardized Root Mean Square Residual (SRMR) = 0.051; Normed of Fit Index (NFI) = 0.812

Table

Constructs		Items
1.Smart living	SL1	Smart living aims to create more efficient, sustainable, and user-friendly living environments
	SL2	Smart living involves the adoption of smart home technologies and IoT devices contribute to the overall well-being
	SL3	Smart living interconnected with the technologies and data-driven solutions impact residents’ experiences, lifestyles, and perceptions of urban living,
2. Urban Sustainability	US 1	Urban Sustainability involves integrated smart technologies, such as IoT (Internet of Things) and AI (Artificial Intelligence), contribute to enhancing resource efficiency, reducing environmental impact, and promoting overall sustainability in urban environments
	US 2	Urban Sustainability involves implementing social, economic, and environmental solutions
	US3	Urban Sustainability involves participation in smart urban initiatives that influence the success and sustainability of smart city projects
	US4	Utilizing data in urban sustainability initiatives involves safeguarding privacy, security, and ensuring equitable access to the benefits of these technologies across diverse urban populations
	US5	Urban sustainability involves potential privacy implications associated with the widespread deployment of smart technologies in urban sustainability initiatives
3. Smart Governance	SG1	Smart Governance involves the integration of smart technologies and data analytics impact the efficiency, transparency, and responsiveness of governance systems,
	SG2	Smart Governance involves ethical and legal implications of deploying artificial intelligence and machine learning algorithms in smart governance,
	SG3	Smart Governance involves the challenges and opportunities associated with fostering a more inclusive and responsive smart governance framework
4. Smart Mobility	SM1	Smart mobility involves expansion of public transportation systems such as buses, subways, trams etc.
	SM2	Smart mobility involves reduction in the traffic congestion and air pollution.
	SM3	Smart mobility involves the integration of advanced technologies and innovative solutions to enhance transportation and urban mobility.
5. Smart Building and environment (Park Inn Radisson Najran; the Florida Inn Hotel; and the Najran University)	SBE1	Smart buildings and environment incorporates energy efficiency mechanism systems
	SBE2	Smart building and environment enables maximized safety and security.
	SBE3	Smart building and environment adopts user-centric design formalities
	SBE4	Smart building and environment enables resilience planning.
	SBE5	Smart building involves resilience planning.
6. Smart Economy	SE1	Smart economy includes the implementation of smart technologies and data-driven strategies contribute to the development and resilience of a smart economy
	SE2	Smart economy involves digital platforms, blockchain technology, and artificial intelligence shape the dynamics of a smart economy,
	SE3	Smart economy involves the inclusivity of smart economic initiatives in fostering sustainable development, reducing socio-economic disparities
7. Citizens participation	CP1	Citizens participation involves digital platforms and emerging technologies influence the nature and extent of citizen participation in democratic processes,
	CP2	Citizens participation involves technology be leveraged to address disparities and foster a more equitable and widespread involvement in civic activities
	CP3	Citizens participation fostering deliberative engagement among citizens in shaping public policies and decision-making processes

Note: A response relied on the “5-point” Likert scale of “5”- strongly agree, “4”-agree, “3”-neutral, “2”-disagree, and “1”-strongly disagree.

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