The Impact of Sustainable Infrastructure Development and Green Innovation on the Tourism Sector of KSA: The Role of Vision 2030 As An Intervening Variable

3. Methodology

The research follows a quantitative approach to investigate how stakeholders from Saudi Arabia’s tourism industry perceive sustainable practices and how they view the influence of Vision 2030. Quantitative method was chosen for its effectiveness in systematically analysing and measuring perceptions and relationship among variables, allowing for statistical and generalisability of the findings (Duckett, 2021). The research instrument was a self-made questionnaire, which was created after studying many articles and connecting them to the main concepts in the study. The study ensured the questions were well-designed, and experts in academia and industry looked at the questionnaire. The survey questionnaire had two sections; the first asked demographic questions, and the second involved a set of 19 statements that assessed main variables where the 19^th statement was an open statement. The responses were noted on a 5-point Likert scale with different anchors like “low to high,” “poor to high,” and “very low to very high” based on the statement. This scale variation in anchors was intended to most appropriately fit each statement and maximize response precision. The survey successfully captured participants’ views on various dimensions pertinent to the study.

The statements were conceptually divided to create unique variables, so that every variable represents exactly one aspect of the study. This makes both the questionnaire more transparent and the construct validity of the measurement better. Table 1 shows the statement numbers; the items of the questionnaire were systematically categorised according to their conceptual significance to create unique variables. This ensured exact measurement of every construct in the study. Sustainable infrastructure development and green innovation were identified as independent variables due to their critical roles in promoting ecological transportation, sustainable accommodation, and renewable energy projects supporting tourism development. However, 3 of them were dropped due to low loadings in the preliminary testing.

Table 1. Variable statement mapping.

A total of 249 participants were recruited for the survey from two Saudi semi-governmental organisations, including Diriyah Development Company and Red Sea Global. Diriyah directs its efforts to cultural tourism developments while Red Sea Global focuses on reshaping the nation’s touristic and ecological scenarios. To achieve wide distribution, a survey distribution was made possible through WhatsApp and email. Around 70% of the participants participated in the survey, showing strong engagement from the targeted participants. Missing values were addressed through list-wise deletion to maintain data integrity, as they were minimal. Outliers were identified using standardised residuals and handled by examining their influence on the model, with extreme cases excluded where necessary. To reduce common method bias, the respondents’ anonymity was respected, and different styles of questions were used. Moreover, the Harman test revealed that common method bias did not greatly affect the study results. The test-factor showed common method bias accounted for less than 50% of the variance, indicating it did not significantly affect the study results.

Through purposive sampling, the researcher verified that participants met the experience and knowledge requirements of the Saudi Arabian tourism sector. Potential survey participants needed to demonstrate professional work in tourism development alongside organisational membership to Diriyah Development or Red Sea Global and at least three years of experience within the tourism industry. All the participants lived within Saudi Arabia while directly working on projects associated with Vision 2030’s tourism development.

The research analysis utilised Partial Least Squares Structural Equation Modelling (PLS-SEM) and Microsoft Excel for data processing. The researcher chose PLS-SEM because it is excellent in dealing with complicated associations and can process small datasets (Hair et al., 2019). The research started by examining the measurement model to establish construct validity and reliability before executing the structural model analysis to study variable relationships. Confirmatory Factor Analysis (CFA) parameters were assessed to ensure construct validity and reliability, including factor loadings, composite reliability, and Average Extracted Variance (AVE). Path testing assessed the importance and reliability of the relationships via path coefficients and t-values. The study required PLS-SEM because it matched its explanatory nature and predictive aims instead of hypothesis confirmation. Survey items were used to operationalise “Sustainable Infrastructure Development” and “Green Innovation” before PLS-SEM analysed their effects on sustainable tourism practices and Vision 2030’s impact on these dynamics.

4. Results

4.1. Demographics

As per Table 2, the population analysis of the study’s 249 respondents shows a high percentage of male respondents (60.2%) over females (39.8%), a pattern mirroring the gender balance typical among the Saudi Arabian tourism industry workforce (Alharbi, 2023). In terms of age, most respondents are in the 30–39 years category (38.2%), followed by 20–29 years (28.1%), showing a fair to young and dynamic workforce actively working towards sustainable tourism development (Mir & Kulibi, 2023). The fact that 22.1% belongs to the 40–49 age group and 11.6% belongs to the 50+ age group indicates the presence of mature professionals working for the sector. This demographic spread indicates the presence of both new ideas and experienced knowledge, which is critical for driving innovation and sustainability in terms of Vision 2030 goals.

Table 2. Demographics.

4.2. Measurement Model Using Confirmatory Factor Analysis

As per Table 3, the reliability and convergent validity of the constructs within this research were thoroughly tested using multiple proven metrics. (Brown, 2015) explains that principal component analysis facilitates testing for component structure for discriminant, convergent validity, and reliability. In contrast, Cronbach’s alpha and composite reliability are key measures of internal consistency and reliability of latent constructs. According to (Kline, 2023), Cronbach’s alpha and composite reliability coefficients must be greater than 0.7 to confirm reliability. As per the above table, Cronbach’s alpha values for green innovation (0.768), sustainable infrastructure development (0.718), tourism in KSA (0.887), and Vision 2030 (0.766) demonstrate excellent internal consistency among the items on measurement. On the other hand, composite reliabilities for the above constructs range between 0.830 and 0.918, further verifying the stability and strength of the measurement instrument. These findings maintain that the constructs are accurately measured to ensure consistency in survey items.

Convergent validity was also established by the Average Variance Extracted (AVE), which evaluates the proportion of variance explained by a construct concerning the measurement error (Alqublan, 2021). Table 3 shows factor loadings which exceeded the threshold of 0.6, suggesting that the measurement items are acceptable. All of the constructs were sufficiently reliable and required no changes to their items, adding to the strength of the model used for measuring. (Hair et al., 2017; and Latan et al., 2017) proposed that values of AVE greater than 0.5 reflect good convergent validity. From Table 3, the AVE of green innovation (0.684), sustainable infrastructure development (0.620), tourism in KSA (0.691), and Vision 2030 (0.591) are all greater than 0.5, and this reflects clearly that the variables under observation reflect their underlying constructs well. The above indicators show the measurement model’s robustness, supporting the structural analysis’s validity and accuracy. The validity and reliability conclusions are consistent with general best practices in social sciences research and well establish a basis for interpreting path relationships in the model.

As per Table 4, the discriminant validity of the constructs is evaluated using the Heterotrait-Monotrait (HTMT) ratio, which effectively measures the differences between latent variables and prevents multicollinearity as asserted by (Wong, 2011). According to (Rönkkö & Cho, 2022), HTMT values should be less than 0.85 based on the threshold to ascertain sufficient discriminant validity. Table 4 shows interrelatedness between the study constructs, supporting that each construct is related more strongly to its indicators than to other constructs, hence supporting discriminant validity. These measure of distinctness all below the 0.85 threshold, indicate that although the constructs are connected, they are still empirically separate, affirming the model’s theoretical basis. (Hair et al., 2019; and Henseler et al., 2015). Therefore, the HTMT outcomes confirm the discriminant properties of the measurement model to maintain confidence in the following structural path analyses.

Table 3. Reliability and convergent validity testing.

Table 4. Discriminant validity.

Table 5. Path coefficients.

Note: *** indicates significance at 1%, ** indicates significance at 5%, * indicates significance at 10%

4.3. Path Analysis

The path coefficients in Table 5 indicate statistically significant and strong direct correlations between the focal constructs. Green Innovation exerts a significant direct influence on both Sustainability (β = 0.461, p = 0.000) and Vision 2030 (β = 0.431, p = 0.000), suggesting that environmental innovation plays a significant role in both ecological objectives and national development policies. Sustainability also plays an important role in Vision 2030 (β = 0.439, p = 0.000), and Vision 2030 has a strong and direct effect on the Tourism Sector in Saudi Arabia (β = 0.694, p = 0.000), which also points to its mediating role. The above findings indicate that Vision 2030 is a structural enabler via which sustainability and innovation propel tourism growth. As (Hair et al., 2021) explain, these strong direct relationships between variables provide a good basis for testing and justifying mediation analysis in PLS-SEM models.

Table 6 shows the direct indirect effects, which demonstrate that Vision 2030 acts as a mediator of the relationship between Green Innovation and Tourism. The Green Innovation with Sustainability and Vision 2030 path is significant (β = 0.202, p = 0.000), showing that innovation promotes sustainability, which supports Vision 2030 outcomes. In addition, the indirect impact of Green Innovation with Vision 2030 and Tourism (β = 0.299, p = 0.000), and of Sustainability and Vision 2030 and Tourism (β = 0.340, p = 0.000), all indicate partial mediation because the direct relationship between Green Innovation and Tourism is still significant. This is in agreement with (Hair et al., 2019), who clarify that mediation is partial where both the direct and indirect effects are statistically significant. This fact attests that Vision 2030 is a major mechanism by which green innovation and sustainability lead to tangible growth in the tourism industry.

The Table 7 summarise the combined effect of the independent variables on the dependent variable. Green Innovation has a significant impact on Tourism (β = 0.439, p = 0.000), Vision 2030 (β = 0.633, p = 0.000), and Sustainability (β = 0.461, p = 0.000). Sustainability also has a significant impact on Tourism (β = 0.304, p = 0.000) and Vision 2030 (β = 0.439, p = 0.000). These results indicate that despite Vision 2030 playing a mediating role, the existence of large and significant direct effects affirms partial mediation. In this case, (Hair et al., 2021), state that in case both direct and indirect routes are significant, it is an exhibition of partial mediation, noting that despite Vision 2030 being an influential channel of change, Green Innovation and Sustainability also have direct influences on Saudi Arabian tourism outcomes.

Table 6. Specific indirect effects.

Note: *** indicates significance at 1%, ** indicates significance at 5%, * indicates significance at 10%

Table 7. Total effects.

Note: *** indicates significance at 1%, ** indicates significance at 5%, * indicates significance at 10%

4.4. Model Explanatory Power

As per Table 8, the model’s explanatory power analysis is focused on the R-squared and adjusted R-squared values. As seen in Table 8, the R-squared measures for the Tourism Sector in KSA and Vision 2030 stand at 0.481 and 0.552, respectively, while the adjusted R-squared measures are very similar to these at 0.479 and 0.549. These measures give valuable information regarding the model’s predictive capability and fit. The KSA Tourism variable has a much greater R-squared value of 0.481, which means that the model’s independent variables explain nearly 48.1% of the variance in tourism performance in Saudi Arabia. A robust explanatory power is found in the Vision 2030 construct, with R-squared = 0.552. This shows that more than 55% of the variance of Vision 2030-related results is explained by the model, representing a high model fit and prescribing Vision 2030 as a key and pivotal component of the conceptual framework.

Table 8. Explanatory power.

5. Discussion

The investigation presents significant findings about the connections between green innovations and sustainability elements related to Vision 2030 in Saudi Arabian tourism. The results showed that Vision 2030 was the main regulating factor that successfully connected sustainable tourism initiatives with green innovation activities. The research supports previous literature studies where national strategic plans create favourable conditions for sustainable development in tourism industries (Alsweilem et al., 2024; and Rahman & Al-Borie, 2021).

The study confirms the first hypothesis since the results verify a significant positive link positive relationship between green innovation and sustainable infrastructure development. Green technologies enhance energy efficiency and better environmental design in the tourism industry. Such innovations allow tourism companies to subscribe to sustainable operating models. This aligns with (Sarwar et al., 2021), who highlighted the contribution of eco-innovation towards infrastructure modernisation.

As per second hypothesis, research demonstrates that Vision 2030 guides national policy development and infrastructure modernisation and supports the tourism industry in implementing environment-friendly technologies to foster green innovation which corresponds to the study of (Ben Hassen, 2022). The research demonstrates a strong direct connection between Vision 2030 and sustainable practices because Vision 2030 effectively controls how sustainability integrates with tourism. As reported in this study, research by (Alasgah & Rizk, 2023) demonstrates that sustainable development promotion in the tourism sector strongly depends on well-defined national policies and frameworks.

The results of this study strongly affirm that Saudi Arabia’s Vision 2030 is an important and transformational catalyst in advocating truism and green innovation in the tourism industry ensuring third hypothesis. The measurement model analysis, which has strong reliability and validity statistics, highlights the stability and importance of constructs like Green Innovation, Sustainability, Tourism performance, and Vision 2030. Path analysis identifies that green innovation positively impacts sustainability, which strongly affects achieving Vision 2030 targets and tourism development in the Kingdom. These findings support existing literature and hypothesis, highlighting Vision 2030’s pivotal position as a champion for sustainable tourism growth through creativity in green strategies (Hibatullah & Syauqillah, 2021).

Despite the positive momentum, the research also reveals significant implementation challenges. The major challenges are balancing rapid tourism sector expansion and preserving environmental conservation, efficient management of natural resources and creating frameworks that support sustainable development without affecting the progress. All the above align with issues identified in previous literature, highlighting the challenge of balancing economic growth and environmental conservation in the Saudi case (Miniaoui, 2020; and Shili & Panjwani, 2020). Stakeholder perspectives in this study mirror (Aldhobaib’s, 2025) findings on sustainable tourism’s inherent tensions between growth and conservation. The central place of stakeholder commitment and multi-sector collaboration to ensure the optimal effectiveness of Vision 2030 is also supported by existing studies by (Aliedan, 2022; Alzubair, 2021; and Neffati & Jbir, 2024), which promotes composite strategies to sustainable development.

The research contributes to the scholarly literature by illustrating how Vision 2030 is a policy guideline and a force of change that facilitates green technology and sustainable tourism practices. In agreement with (Alharbi, 2023; and Remsey, 2023), stakeholders confirm Vision 2030’s significant contribution in inspiring tourism operators to embrace sustainability, accompanied by innovation. Empirical data from the present study support recent studies by (Elfakharani, 2024; Azad, 2023; and Alharb, 2023), confirming that national strategic actions supported by vision 2030 significantly influence tourism industry validating fourth hypothesis. The overall positive perception by stakeholders of Vision 2030’s impact is in stark contrast to previous reports of lagging adoption rates by (Mustun, 2022), representing significant advances in adopting international best practices. These results confirm that Vision 2030 is reframing Saudi Arabia’s tourism sector towards a sustainable and innovative direction and pinpointing areas for ongoing improvement and policy development.

Conclusion and Recommendations

The study provides an in-depth examination of green innovation and sustainable infrastructure development in the Saudi Arabian tourism industry, underlining their importance in support of the objectives of Vision 2030. The results clearly show that sustainable actions substantially reinforce the tourism sector by protecting the environment, diversifying the economy, and conserving culture simultaneously. Vision 2030 stands out as a key reference framework that guides strategic development and promotes the convergence of green innovation throughout the industry.

To facilitate sustainable tourism growth and diversification of the Saudi economy, policymakers must prioritise building regulatory frameworks that reconcile fast-growing sector growth and environmental protection. Encouraging more public-private collaboration is needed to effectively adopt green innovation technologies and sustainable infrastructure initiatives. Investment in capacity-building programs that enhance awareness and sustainability-linked skills among tourism operators will also enhance industry resilience. Additionally, there should be continuous monitoring and adaptive management mechanisms to ensure that there is development in accordance with Vision 2030 objectives as well as global requirements for sustainability. A key recommendation for the industrial players in KSA is to increase investment in green innovation and enhancing collaboration with government bodies to implement sustainable infrastructure.

Future studies should consider expanding the scope geographically by incorporating various regions from various parts of Saudi Arabia to enable the generalisability of the findings at the national level. Using mixed methods, e.g., pairing stakeholder interviews with quantitative study, would yield more nuanced findings on the particular barriers and opportunities encountered through the implementation of sustainable tourism. Further, longitudinal research is required to explore the long-term effects of Vision 2030 on different sub-sectors of tourism and to assess the overall effectiveness of green innovation policy over time. Arabic translation of research tools could also enhance data validity and participant responsiveness, thus enhancing the general quality of research findings. Upcoming studies should focus more on in-depth knowledge of policy implementation and stakeholder coordination to drive sustainable tourism development in alignment with Saudi Arabia’s ambitious Vision 2030 plans.

Author’s Contribution

A.A. contributed to the design and implementation of the study. A.A. contributed to the analysis of the results and the writing of the manuscript. A.A. conceived of the original study and supervised the project.

AVAILABILITY OF DATA AND MATERIALS

The data will be made available on reasonable request by contacting the corresponding author [A.A.].

Finding

The people who wrote this article did not receive any financial support from anyone.

Conflict of interest

The authors declare no conflicts of interest.

Appendices

Questionnaire

Section A: Demographic Profiles

• Gender

1. Male
2. Female

• Age

1. 20-29 years
2. 30-39 years
3. 40-49 years
4. 50 years and above