Factors Influencing Support for National Health Insurance: Evidence from Qassim Region, Saudi Arabia

Abstract

As part of Vision 2030, Saudi Arabia is transforming its healthcare system, shifting away from an unsustainable free-service model. To establish a more sustainable healthcare system, policymakers are considering introducing a National Health Insurance (NHI) program, which would require citizens to make regular financial contributions. This research explores Saudi citizens’ willingness to support and contribute financially to the proposed NHI program, as well as the key socio-economic, demographic, and health-related factors influencing their decision. This study employed a cross-sectional design, utilizing the Contingent Valuation (CV) method. Primary data were collected from 1194 respondents residing in the Qassim region of Saudi Arabia through an online survey between November 2024 and January 2025. Descriptive statistics, binomial, and multiple regression were applied to identify the factors associated with Willingness to Pay (WTP) for NHI. The study indicated that approximately half of the respondents (49.33%) support and are willing to pay for the NHI program (p < 0.01). The mean monthly contribution is estimated at 158 SAR (42.13 US$) with a median amount of 100 SAR (26.6 US$). This amount constitutes about 1.8% of the current healthcare expenditure in 2023. Factors such as being male, having a medium-sized family, and having a family member with a chronic disease increase the likelihood of WTP for NHI. Additionally, the maximum amount of respondents are willing to pay is significantly associated with gender, the presence of chronic disease in the family, obstacles to accessing healthcare, satisfaction with current healthcare services, and existing health insurance status. This study offers valuable insights into Saudi citizens’ willingness to financially contribute to the NHI program. However, its successful implementation depends on addressing cultural acceptance, building public trust, and ensuring affordability for low-income groups. Effective rollout of the NHI requires the coordinated efforts of multiple stakeholders, including government agencies, healthcare providers, employers, health insurance organizations, civil society, and regulatory bodies.

1. Introduction

The primary goal of an efficient health system is to improve public health, which is critically dependent on achieving Universal Health Coverage (UHC) [1]. Introduced in 2005, UHC aims to address disparities in access to healthcare services [2]. The Sustainable Development Goals (SDGs) propose UHC as an umbrella health goal, aiming for universal, equitable, and effective delivery of comprehensive health services [3]. However, financing UHC is challenging, requiring countries to consider all revenue sources for healthcare system reform. While increased spending can improve health outcomes, improving the efficiency of these expenditures is crucial [1].

The Kingdom of Saudi Arabia provides free healthcare to citizens, funded primarily through oil revenues. The Ministry of Health (MOH) and other government institutions offer these services through a network of hospitals and primary healthcare centers. The country’s strong economy has enabled widespread access to healthcare for both citizens and expatriates [4,5]. However, this policy has faced significant challenges stemming from rising healthcare costs, shifting demographics, an aging population, changing lifestyles, evolving disease patterns, and inefficient healthcare spending [6,7,8,9,10]. Moreover, a large expatriate population places an additional strain on public healthcare resources, increasing demand for medical services and escalating costs [4,7,11].

In 2023, the total healthcare expenditure was SAR 227.68 billion, accounting for 5.69% of Saudi Arabia’s GDP [8]. The government covers 68.92% of total healthcare expenditure. Saudi Arabia’s population is expected to surge to 39.5 million by 2030 [9]. In 2035, projections indicate that nearly half the population (44%) will be over 40, with 14% exceeding 60 years, resulting in escalating healthcare needs [12]. While communicable diseases have decreased, non-communicable diseases like cardiovascular diseases, cancers, and diabetes are on the rise. These factors make healthcare financing a pressing issue, compounded by concerns over the perception, availability, and quality of public healthcare services [13,14,15].

To address the growing costs and pressures on the healthcare system, the government launched the Cooperative Health Insurance System (CHIS) in 1999, marking a major milestone in healthcare reform [7,9,10]. This initiative is a key step towards achieving UHC goals, aligning with global efforts to provide equitable access to healthcare services [7]. The introduction of the compulsory employment-based health insurance (CEBHI) marked the entry of the private sector into healthcare financing in Saudi Arabia [7,10]. By broadening health insurance coverage for expatriates and later extending it to Saudi nationals in the private sector, the reform promoted a shift towards shared financial responsibility for healthcare costs. Studies indicate that CHIS has improved healthcare access and quality, particularly in the private sector, and enhanced efficiency through standardized benefits and reduced out-of-pocket (OOP) payment [16,17,18,19,20]. However, challenges persist, such as rising premiums, infrastructure gaps, and cultural barriers [7,10]. The CHIS is vital to Saudi Arabia’s healthcare reform and UHC goals. However, continued efforts are needed to overcome existing challenges and increase coverage to ensure the scheme’s long-term success [10]. A recent review suggests that increased government support and public awareness are essential for sustained progress toward UHC goals [7].

1.1. Saudi Vision-2030 and National Health Insurance

Saudi Arabia’s healthcare system is undergoing significant changes to ensure sustainability and accessibility. Despite implementing compulsory health insurance for expatriates, the system requires additional resources due to fluctuating oil prices [10,11,12,13]. Saudi Vision 2030 aims to reduce the country’s dependence on oil and improve public services, including healthcare [9]. The vision prioritizes financing reform to achieve universal healthcare coverage, enabling citizens, residents, and visitors to access healthcare services without risking financial hardship. This transformation involves private health insurers playing a broader role, offering NHI through a regulated market [5]. NHI is a contributory insurance scheme where citizens make regular contributions to supplement the government’s health budget. This model is similar to private health insurance for workers employed in the private sector. By implementing NHI, the government aims to alleviate its financial burden and enhance the availability and quality of public health services. To implement the scheme, the government has established an NHI Center, which will purchase health services on behalf of the insured population [21]. The beneficiaries will receive a range of health services, including specialized care for chronic diseases and critical care services. NHI will be implemented through licensed insurance companies with the involvement of accredited public and private providers [21].

1.2. Problem Statement

There is a significant knowledge gap regarding the Saudi population’s willingness to pay (WTP) for the proposed NHI scheme. Understanding households’ WTP for NHI in various provinces in Saudi Arabia helps policymakers design and implement effective NHI policies that meet the population’s needs and preferences. Moreover, studying WTP for NHI can provide insights into households’ ability and WTP for healthcare services, which can inform healthcare financing decisions. Additionally, analyzing WTP for NHI can inform the development of the health insurance market in Saudi Arabia, including the design of the products and pricing strategies.

In Saudi Arabia, studies have explored the viability and acceptability of health insurance programs among various segments of the population by assessing their WTP [22,23,24,25,26,27,28]. Few studies have elicited the WTP for improved access to health services that are presently delivered free of cost by the MOH [27,28]. These studies have shown that more than 50% of the respondents were willing to contribute towards quality improvements in services delivered by government healthcare facilities. Few studies suggest that expanding access to NHI throughout the Kingdom is likely to facilitate improvement in health outcomes and lessen households’ OOPE on healthcare [18,19,20]. Research in this area has primarily concentrated on major cities such as Riyadh and Jeddah, with few studies examining the issue on a national scale. Furthermore, the existing national studies often have limited sample sizes [16,22,23,24,25,26].

This study focuses on the Qassim region of Saudi Arabia, an area with average healthcare facilities, workforce, and healthcare utilization [29]. This region is often considered one of the country’s more stable and balanced in various aspects, including healthcare, making it a representative region for this research. Our study will provide specific insights into the Qassim region, enabling policymakers to develop targeted strategies for implementing NHI in this area. The findings of this study can contribute to achieving the goals of Saudi Vision 2030, particularly in reforming the healthcare financing system and promoting sustainable healthcare services. This study examines the support for and willingness of Saudi citizens to contribute financially to NHI. The study also examines the factors influencing their decision, including socio-economic, demographic, and health-related characteristics. Furthermore, the study aims to assess the amount that people are willing to pay for NHI, enabling policymakers to determine accurate premiums with valuable insights.

1.3. Contextualizing and Theoretical Underpinnings

The implementation of NHI signifies a transformative shift in health financing policy, particularly in health systems seeking to expand coverage and improve access and equity in healthcare services. Central to this transformation are stakeholder voice and institutional trust, which emerge as critical factors in shaping policy outcomes [30]. They also influence how policies are perceived, accepted, and sustained. While the mutual gains approach developed by Kochan & Osterman (1994) underscores the value of cooperation and shared benefits among stakeholders [31], it is the contemporary industrial relations framework proposed by Ghosh and Ray (2012) that offers a more comprehensive lens for understanding stakeholder alignment in the context of NHI [32]. Their model provides a global perspective on harmonizing the interests of employers, employees, and governments in social welfare programs. Significantly, it adapts to the growing dynamics of the modern service economy, marked by a shift from blue-collar labor to knowledge-based workforces, and integrates principles from human resource management and behavioral sciences.

Ghosh and Ray (2012) [32] emphasise that participatory policymaking and labor-management cooperation are particularly relevant to NHI implementation. Their framework suggests that negotiated contributions from both employers and workers are essential for ensuring financial sustainability and effective benefit delivery. According to this framework, stakeholder voice and institutional trust are critical components of willingness to support NHI. Complementing this perspective, Fung’s (2015) theory of participatory governance advocates for the active engagement of diverse stakeholders—including citizens, workers, employers, and civil society—in shaping policy decisions [33]. In the context of NHI, this involves inclusive dialogues around contributions, benefits, and implementation strategies, fostering trust and long-term sustainability. Together, these frameworks—especially the industrial relations model by Ghosh and Ray—underscore the importance of inclusive governance, stakeholder collaboration, and institutional trust in designing and implementing equitable and sustainable NHI systems.

The theoretical framework for understanding WTP for health insurance is welfare economics, which asserts that WTP signifies a person’s utility derived from acquiring a good or service, such as health insurance, along with their opportunity cost of paying for it [34]. This framework is operationalized through techniques such as the contingent valuation (CV) method, which employs surveys to inquire directly about how much individuals are prepared to pay for health insurance in relation to their perceived benefits and the specific health needs and financial circumstances [35]. Central to the theoretical framework is utility theory, which suggests that individuals aim to maximize their utility, and WTP serves as a monetary representation of the value they assign to the benefits of health insurance, including financial security and enhanced health outcomes. Second is risk aversion, which suggests that health insurance serves as a way to transfer risk, and the amount individuals are willing to pay is frequently associated with their level of risk aversion concerning health events [36]. Third is the consumer preferences, where WTP represents individual preferences related to health security, the perceived quality of health services, and the importance of preventing possible OOP expenses [37]. The theoretical framework acknowledges that WTP is dynamic and affected by multiple elements, such as socio-economic factors, health beliefs, household characteristics, and service quality.

According to the above framework, socio-economic and health-related factors are used as proxies to explain WTP for health insurance by individuals. Household income is widely regarded as a reliable and positive predictor of WTP, as higher income levels typically enable individuals and households to afford insurance premiums, thereby increasing their WTP [38]. Moreover, higher levels of education often lead to a greater awareness and appreciation of the benefits associated with health insurance, which can, in turn, enhance WTP [39]. A larger household size can be positively correlated with a higher WTP, as the financial burden of potential healthcare costs is distributed among more family members. Conversely, age often exhibits a negative correlation with willingness to pay, suggesting that older individuals may be less inclined to pay for long-term coverage [27]. Additionally, traditional gender roles can influence decision-making related to health insurance and healthcare-seeking behavior, potentially impacting WTP [33]. The geographic location of individuals can also play a significant role in shaping their WTP, particularly in relation to the accessibility and quality of public or private healthcare services [16]. Furthermore, health-related factors such as perceived need, the presence of chronic health problems, can substantially increase a household’s WTP for comprehensive health coverage [16,27,33], A higher level of awareness regarding the specific health insurance scheme and its benefits is a strong positive determinant of WTP. Finally, satisfaction with existing public healthcare services can have a notable impact on willingness to pay; dissatisfaction with current services may increase the WTP for health insurance programs [16,27,28,32].

1.4. Global Experiences on WTP for NHI

Global experiences show that WTP for health insurance varies across contexts due to several factors, including public trust, stakeholder relations, and contextual characteristics. A study in Ghana showed, trust in the government’s ability to manage funds and provide quality healthcare services influences individuals’ WTP for NHI [40]. Similarly, a study in China found that trust in the healthcare system and government is an important predictor of WTP for health insurance [41]. Stakeholder engagement and participation also shape insurance uptake, as highlighted by Ghosh and Ray (2012) [32]. A study in India showed that community-based health insurance schemes with strong stakeholder engagement and participation have higher enrollment rates and better financial sustainability [42]. Similarly, a study in Kenya found that stakeholder relations and community engagement are critical for the success of micro-health insurance schemes [43].

A recent systematic review and meta-analysis of 30 studies on community-based health insurance in Ethiopia found that only about 60% of households were willing to enroll in such schemes. Key determinants of WTP included age, education levels, wealth status, knowledge and awareness, and the presence of chronic illness. The authors concluded that trust, affordability, and perceived quality of care were strong influencing factors [44]. Another study from rural Senegal analysed uptake and WTP of community-based health insurance using a simultaneous equation model. It reported that only one-third of households were aware of such schemes, and that the mean WTP exceeded the actual premium. Notably, enrollment in the scheme increased the maximum amount individuals were willing to pay by approximately 41% [45]. A recent study in the Netherlands among the members of the Dutch Health Care Consumer Panel found that 58% of respondents were willing to pay for basic health insurance. WTP was positively associated with individual income, age, and education level [46]. A study conducted in Tanzania showed that patients with secondary education or higher exhibited a higher WTP for improved community health insurance. Conversely, individuals who already had private health insurance and were using public healthcare services have shown a reduced WTP for the premium [47]. A systematic review and meta-analysis of WTP studies in Africa and Asia reported a pooled WTP of 66% across 19 studies [38]. Age was consistently found to be negatively associated with WTP for NHI, while income level emerged as a positive predictor [47]. A study in Indonesia (Nugraheni et al., 2022) reported that approximately 41% of participants were willing to pay for cost sharing under UHC [48]. A study in Pakistan showed that 64% of people were willing to pay for community health insurance, and WTP was strongly associated with participants’ awareness of the scheme [49].

Earlier studies conducted globally have consistently shown that WTP for NHI was influenced by a complex interplay of individual, socioeconomic, and contextual factors [50,51,52]. These factors collectively influence both the decision to purchase health insurance and the amount individuals are willing to pay in premiums. Research on WTP for NHI has yielded mixed results regarding the influence of demographic factors. While some studies suggest that larger family sizes increase WTP for NHI [23,27,53,54], others indicate a negative relationship [55,56]. This inconsistency may stem from the financial strain that larger families face, which can make it difficult to afford health insurance premiums. Several studies have shown that WTP for NHI tends to decrease with increasing age [28,57,58,59]. Marital status also influences WTP for NHI; married households are generally less likely to pay compared to single or unmarried individuals [28,53,58,60]. However, other studies suggest that married individuals, particularly those with decision-making autonomy, may be more inclined to join health insurance [59].

The relationship between gender and WTP remains inconsistent. While some studies suggest females are more likely to pay [53,60], others indicate males demonstrate greater willingness to join NHI [23,48,54,60]. Similarly, findings on the association with area of residence (urban vs. rural) are mixed. Some studies suggest urban residents are less willing to pay [53,60], whereas others find the opposite or no conclusive relationship [48,56]. Higher education attainment is generally associated with a greater WTP for NHI. Most studies revealed that individuals with higher education levels are more likely to join NHI, suggesting that education enhances demand for health insurance [22,27,28,56,58,60]. However, few studies present inconclusive results [52,54,55,56]. Several studies indicated that higher income positively influences WTP for health insurance [22,27,48,56,60,61]. Additionally, a few studies indicate that families with formally employed heads are more likely to join NHI [57,62], although other studies suggested the opposite [48].

Factors such as health status and past healthcare spending significantly influence WTP for NHI [23,26,28,58,60]. While studies indicated that the experience of illness has a negative association with WTP for NHI [16,48,57], others reported a positive relationship [23,26,28,55,62]. Health services factors, including access to public hospitals, frequency of health services utilization, and perceived quality of health services, are positively associated with WTP for NHI [16,22,24,25,26]. Additionally, the availability of alternative health insurance has been identified as a factor that can positively affect WTP for NHI [48,57]. However, the perception of paying more, especially when alternative health insurance coverage is in place, can negatively impact WTP [16,26,27,28,52,63].

This review underscores the multifaceted nature of factors influencing WTP for health insurance. A nuanced understanding of these factors is crucial for policymakers seeking to design and implement effective health insurance schemes that address diverse population needs, eventually promoting better health outcomes and financial protection for individuals and families.

2. Materials and Methods

2.1. Research Design

This was a cross-sectional study conducted among the Saudi population in the Qassim region of Saudi Arabia. This study employed a CV technique to assess individuals’ WTP for NHI. Since NHI is not traded in the marketplace, CV was used to investigate WTP. CV surveys present hypothetical market scenarios, allowing respondents to provide contingent responses. This approach assesses individual preferences by eliciting WTP values, providing economic values for public goods and services in the absence of market prices. In this study, WTP refers to the maximum amount an individual is willing to pay for a good or service. Previous studies have successfully applied CV methodology to examine WTP for health insurance in developing countries [22,23].

2.2. Survey Instruments

The questionnaire for the study was adapted from previously validated studies [16,23], with minor modifications to suit the research objectives. To gauge public opinion on the proposed NHI scheme, the study assessed respondents’ support for NHI and their willingness to contribute to the scheme. This study utilized a two-part questionnaire. The first section outlined a hypothetical NHI scenario, which aimed to sustain existing public healthcare services. Respondents were then asked about their WTP for a monthly NHI premium. Those who agreed were asked to specify the maximum monthly premium they would pay. The second section gathered data on respondents’ socioeconomic and demographic information, such as gender, age, location (urban & rural), marital status, education level, family size, chronic disease in the family, time taken to reach health facility from home, obstacles in accessing health services, satisfaction with government health services, household income, and health insurance status. Before the final data collection, we performed a pilot test of the questionnaires with 70 heads of households from different areas of the region and made minor revisions based on the feedback received.

2.3. Measurement of WTP

In this research, an economic valuation was conducted using a CV technique to estimate WTP. A payment card approach was employed, which was introduced as follows: “Imagine a hypothetical scenario where the government introduces a National Health Insurance (NHI) system aimed at maintaining and improving the quality of public healthcare services. This system would require citizens to make regular contributions, which would supplement the government’s health budget to cover increasing expenses. Existing healthcare services would remain accessible and free. To enhance service accessibility, the system would allow the private sector to participate in delivering healthcare services. The payment structure would resemble insurance premiums, with no reimbursements for those who do not utilize the healthcare services. If such a program were launched, would you consider paying a monthly fee to participate? Respondents who answered “yes” were then asked What is the maximum amount in SAR you would be willing to pay each month for NHI for your family? Additionally, they were asked What is the minimum amount in SAR you would absolutely refuse to pay?” For each question, the payment card offered different amounts of money as answer possibilities, ranging from SAR 50 to more than SAR 500, with specific options including SAR 100, SAR 150, SAR 200, SAR 250, SAR 300, SAR 350, SAR 400, SAR 450, and SAR 500 and above. If the respondent selected an option of SAR 500 or more, they were also asked to specify the exact amount. This study adopted the mid-point technique, as used in earlier studies [64,65], by calculating the mid-point between the maximum WTP and the minimum amount an individual would refuse to pay as the outcome measure. For analysis purposes, if a respondent did not specify an exact amount above SAR 500, a value of SAR 525 was used to ensure accurate calculations.

CV methods are certainly susceptible to hypothetical bias, anchoring effects, and strategic actions, particularly when participants lack familiarity with insurance products. To mitigate these issues to some extent, we conducted a pilot test of the questionnaire, including the bid amounts, to ensure they reflected realistic values and minimized potential bias. Additionally, participants were provided with straightforward and concise descriptions of the hypothetical insurance product, along with its benefits, to minimize misunderstandings and discourage deliberate responses.

2.4. Sample Size

The study focused on Saudi nationals aged 18 and above, residing in the Qassim region with a total study population of around 1.336 million, based on the Census-2022 data from the General Authority of Statistics [29]. To determine the required sample size, we used a margin of error of ±5%, a 95% confidence level, and assumed a 50% response rate, resulting in a minimum sample size of 385 respondents. Using convenience sampling, the participants were invited to complete an online survey conducted between November 2024 and January 2025. A total of 1206 responses were received, of which 1194 questionnaires were deemed complete and subsequently included in the analysis.

It is essential to acknowledge the limitations of convenience sampling, including potential biases and lack of representativeness, which may impact the generalizability of the findings. While this sampling technique enables quick and efficient data collection from readily available respondents, it may overlook certain demographics, such as older adults not on social media, lower-income individuals without internet access, particularly in rural areas, and other hard-to-reach populations. However, the researchers made concerted efforts to obtain a sample that represents a miniaturized version of the study population. Increasing the sample size allowed us to capture a diverse population and responses. A large sample size is likely to control bias and uncertainty and offers deeper insights into data analysis. Moreover, convenience sampling allows researchers to collect data from respondents who are available and willing to participate, although it has limitations in terms of generalizability [66].

The selection criteria included Saudi nationals aged 18 and above who were heads of their households. Non-Saudi residents and expatriates were excluded because most of them are not entitled to access public healthcare services [16]. The survey focused solely on heads or the senior members of the families, as they are typically the primary decision-makers for their families [16], responsible for their well-being, and manage the household income. Their role and influence make them the most relevant respondents for assessing their willingness to participate in and contribute to an NHI scheme. We recognize that surveying only heads of household may overlook intra-household heterogeneity in preferences, particularly along gender lines. However, eliciting WTP for NHI for the entire family may provide a fairer assessment of equity compared to individuals, as the WTP distribution by families will be less unequal than that of individuals (Dong et al.) [67]. The decision of the heads may enable them to make choices that better reflect the overall healthcare needs and preferences of the families.

2.5. Data Analysis

The collected data were analyzed using descriptive statistics. Frequency, percentage, mean, standard deviation, median, and chi-squared tests were used. To test hypotheses, binomial regression, multiple regression, and ordinal regression analysis were used. The study examined the socioeconomic factors influencing respondents’ willingness to participate in NHI. Additionally, the mean and median WTP for NHI were calculated. Data analysis was performed using SPSS version 26. For statistical significance of variables, a p-value of ≤0.05 was used in the analysis. The study received ethical approval from the Committee of Health Research Ethics, Deanship of Scientific Research, Qassim University. Further, the questionnaire began with an informed consent section, which respondents had to agree to before proceeding to answer the questions.

3. Results

3.1. Socioeconomic Characteristics

Table 1. Socio-economic characteristics of the respondents in the study (n = 1194).

Variables	Categories	n	%
Gender	Female	319	26.7
	Male	875	73.3
Age	18–24	20	1.7
	25–34	146	12.2
	35–44	485	40.6
	45–54	346	29.0
	55–64	167	14.0
	65+	30	2.5
Location	Suburban and rural areas	191	16.0
	Urban area	1003	84.0
Marital Status	Unmarried	66	5.5
	married	1128	94.5
Education	High school or below	126	10.6
	Diploma	193	16.2
	Bachelor degree	556	46.6
	Master	247	20.7
	PhD	72	6.0
Household size	1–2 Members	124	10.4
	3–5 Members	499	41.8
	6 or more	571	47.8
Presence of chronic disease	No	834	69.8
	Yes	360	30.2
Travel time to the nearest	Less than 15 min	514	43.0
health facility	From 15 to 30 min	541	45.3
	from 30 to 60 min	136	11.4
	More than 60 min	3	0.3
Obstacles to accessing	No	934	78.2
health services	Yes	260	21.8
Satisfaction with current health services	Not satisfied	566	47.4
	Satisfied	628	52.6
Household Income	Less than SAR 8000	104	8.7
	Between SAR 8000–15,000	398	33.3
	Above SAR 15,000–25,000	358	30.0
	Above SAR 25,000–35,000	164	13.7
	More than 35,000 SAR	170	14.2
Insurance Ownership	No	898	75.2
	Yes	296	24.8

Note: SAR = Saudi Riyal.

shows that the majority of respondents, 875 (73.3%), were male, while 319 (26.7%) were female. Notably, the proportion of female respondents in this study is higher compared to a study in the Jeddah region (12.8%) [16], but similar to a study conducted in the Riyadh region (26.2%) [33]. Respondents in the age group of 35–44 years were 485 (40.6%), with the highest frequency followed by those in the age group of 45–54 years, 346 (29%), and the group with the lowest percentage, 18–24 years, 20 (1.7%), participated in the online survey. From the findings, it is evident that a significant majority of respondents (84%) reside in urban areas, and most are married (94.5%). The majority of respondents held undergraduate degrees (46.6%), and 6% had earned doctoral degrees. Most of the respondents revealed that they have more than six family members (47.8%), 41.8% have three to five family members, and only 10.2% have one to two members in their family. Among these respondents, 69.8% have reported no presence of chronic disease within their families. While 45.3% of respondents revealed that it takes thirty minutes to reach the nearest hospital from their home, 43% indicated a travel time of fifteen minutes. Additionally, 78.2% of respondents stated that they face no difficulty accessing healthcare facilities. Approximately 52.6% of respondents expressed satisfaction with the available health services. Most respondents reported a monthly income between 8000 and 15,000 Saudi Riyals (SAR), followed by those earning between 15,000 and 25,000 SAR. Only 8.7% had an income of less than 8000 SAR. Furthermore, and most notably, 50% of the respondents were unwilling to pay for NHI, and 75.2% did not have any form of coverage.

3.2. Respondents’ Characteristics and WTP for NHI

Table 2. Chi-squared analysis values for respondents’ willingness to contribute towards NHI.

Variables	Categories	WTP	Not WTP	Pearson Chi-Squared
Gender	Female	213 (66.77%)	106 (33.22%)
	Male	379 (43.33%)	496 (56.67%)	51.455, p < 0.01
Age	18–35 Years	483 (48.45%)	514 (51.55%)
	36–65 Years	109 (55.32%)	88 (44.67%)	3.199, p < 0.05
Location	Suburban and Rural	83 (43.45%)	108 (56.54%)
	Urban Area	509 (50.74%)	494 (49.25%)	3.413, p < 0.05
Marital Status	Unmarried	35 (53.03%)	31 (46.96%)
	Married	557 (49.38%)	571 (50.62%)	0.332, p > 0.05
Education	High school or below	72 (57.14%)	54 (42.85%)
	Diploma	96 (49.74%)	97 (50.26%)	5.314, p < 0.05
	Bachelor	268 (48.20%)	288 (51.79%)
	Master	126 (51.01%)	121 (48.98%)
	PhD	30 (41.66%)	42 (58.33%)
Household Size	1–2 Members	76 (61.29%)	48 (38.71%)	10.687, p < 0.01
	3–5 Members	255 (51.10%)	244 (48.89%)
	6 or more	261 (45.70%)	310 (54.29%)
Presence of chronic disease	No	431 (51.68%)	403 (48.32%)	4.868, p < 0.05
	Yes	161 (44.77%)	199 (55.28%)
Travel time to the nearest health facility	<15 min	235 (45.72%)	279 (54.28%)
	15–30 min	287 (53.04%)	254 (46.95%)	0.038, p > 0.05
	30–60 min	70 (51.47%)	66 (48.53%)
	60 above	0 (0%)	3 (100%)
Obstacles to accessing health services	No	443 (47.43%)	491 (52.57%)	7.937, p < 0.01
	Yes	149 (57.30%)	111 (42.69%)
Satisfaction with current health services	Not Satisfied	266 (46.99%)	300 (53.00%)	2.876, p = 0.051
	Satisfied	326 (51.91%)	302 (48.08%)
Income of Household	<8000 SAR	54 (51.92%)	50 (48.07%)	20.61, p < 0.01
	8000–15,000 SAR	205 (51.50%)	193 (48.49%)
	15,000–25,000 SAR	144 (40.22%)	214 (59.77%)
	25,000–35,000 SAR	89 (54.26%)	75 (45.73%)
	>35,000 SAR	100 (58.82%)	70 (41.17%)
Insurance Ownership	No	408 (45.43%)	490 (54.56%)	24.919, p < 0.01
	Yes	184 (62.16%)	112 (37.83%)

Note: SAR = Saudi Riyal; WTP = Willingness to Pay.

presents the frequency distribution and percentage of those respondents who are willing and not willing to pay for NHI based on their socio-economic and demographic factors, along with chi-squared values. Based on gender, 67% of female respondents and 43% of male respondents indicated willingness to pay; however, due to the large number of male respondents in the sample, in absolute count, more males were willing. Regarding age, the study found that a higher proportion of respondents in the 36–65 age group (55.32%) are willing to contribute to NHI compared to those in the 18–35 age group (43.45%). In the same way, 50.74% of respondents living in urban areas vs. 43.45% in suburban/rural areas are willing to participate in NHI. Urban residents tend to have better access to information about healthcare and health insurance, healthcare awareness, better healthcare facilities, and higher incomes. There was no statistically significant difference in WTP between married and unmarried respondents. However, due to the larger absolute number of married individuals in the sample, married respondents comprised a greater proportion of those willing to contribute to NHI. Respondents with a bachelor’s degree formed the plurality of our sample, and about 48% of them were willing to pay, similar to the overall average. Those with only a high school education had a slightly higher willingness rate (57.14%), while those with PhDs had a lower rate (41.66%), though the sample sizes for the extremes were smaller. Overall, education level showed a modest association with willingness (chi-squared p < 0.05) but with no clear linear trend. A higher proportion of respondents with medium-sized families are willing to pay for NHI. Households without chronic diseases (51.68%) demonstrated a greater willingness to contribute to NHI compared to those with reported chronic diseases. The time taken to travel from home to the nearest healthcare facility may influence the decision to contribute towards NHI. The study showed that a higher proportion of families with moderate travel times (15–60 min) to healthcare facilities expressed willingness to support NHI, compared to those with longer travel times (more than 60 min).

A higher proportion of respondents (57.3%) who are facing obstacles in accessing healthcare facilities are willing to support NHI compared to those who are not (47.43%). Furthermore, a higher proportion of the respondents who were satisfied (52%) with current healthcare services are willing to contribute to NHI compared to those who were not (47%). Regarding household income, the study found that middle-income families (with a monthly income of 15,000–25,000 SAR) are less willing to support (40.22%) compared to other income groups. The results also showed that a higher proportion of respondents who were not covered by private health insurance are willing to financially support NHI. Individuals with private health insurance may be less willing to pay for NHI because they might feel they already have adequate coverage and do not see the need for NHI. Few studies in Saudi Arabia have shown that people who use private healthcare facilities are less likely to support NHI, possibly due to their existing insurance coverage [7].

3.3. Respondents’ Characteristics and Mean Amount Willing to Pay

Data was separated into two groups, in which those who were willing to pay for NHI were retained. To analyze the differences in WTP for NHI, we compared mean values and conducted independent sample t-tests for binary groups (e.g., male vs. female) and ANOVA, accompanied by homogeneity of variance tests and Tukey’s post hoc tests. The results are presented in

Table 3. Mean amount willing to pay according to socio-economic characteristics (n = 589).

Variables	Mean (in SAR)	Number	SD	F (p-Value)
Gender
Male	155.84	377	118.245	52.84 (0.000)
Female	157.08	212	117.526
Age
18–24	110.00	10	65.828	3.67 (0.003)
25–34	138.64	66	93.560
35–44	169.76	248	131.300
45–54	139.33	150	100.098
55–64	148.94	94	112.637
65+	228.57	21	139.258
Location
Urban area	159.86	497	121.248	15.893 (0.000)
Suburban and rural areas	136.96	92	96.040
Marital Status
Married	155.23	554	115.247	2.216 (0.137)
Unmarried	172.86	35	154.987
Education
High school or below	150.00	69	93.934
Diploma	177.60	96	139.900	1.329 (0.257)
Bachelor degree	139.48	271	100.968
Master	175.00	126	133.379
PhD	177.78	27	146.322
Household size
1–2 Members	150.66	76	108.779
3–5 Members	157.34	252	123.268	5.378 (0.005)
6 or more	156.90	261	115.485
Presence of chronic disease
Yes	150.30	164	114.781	6.128 (0.013)
No	158.59	425	119.118
Travel time to the nearest health facility
Less than 15 min	168.30	235	133.034	2.95 (0.032)
From 15 to 30 min	145.96	272	109.604
from 30 to 60 min	160.16	64	98.472
More than 60 min	141.67	18	75.245
Obstacles to accessing health facility
Yes	156.96	158	108.474	10.76 (0.001)
No	156.03	431	121.275
Satisfaction with current health services
Yes	160.73	317	128.508	0.555 (0.457)
No	151.10	272	104.151
Household Income
Less than SAR 6000	159.38	48	117.445
Between SAR 6000–12,000	126.59	205	92.557	5.221 (0.000)
Above SAR 12,000–18,000	172.45	147	129.433
Above SAR 18,000–24,000	184.78	92	134.805
More than 24,000 SAR	165.98	97	119.580
Insurance ownership
Yes	156.40	172	108.901	28.68 (0.000)
No	156.24	417	121.527

Note: SAR = Saudi Riyal.

. Regarding gender, the female mean value (M = 157.08, SD = 118.245) is higher than the male counterparts for WTP (M = 155.84, SD = 118.245). An independent sample t-test has revealed that there is a significant difference in mean scores between males and females (F = 52.84, p < 0.01). Based on age, the highest mean value is recorded for the age group 60+ years (M = 228.57), and the lowest is recorded for the age group of 18–24 years (110).

To investigate the mean difference among different age groups, ANOVA, test of homogeneity of variance, and Tukey test were performed. The results showed significant differences in mean values of different age groups (F = 3.67, p < 0.05). The results of the Tukey test found that the age groups of 25–34 and 65+, 35–44 and 65+, 45–54 and 65+, 55–64 and 65+ have a significant mean difference (p < 0.05). A higher mean WTP by the older respondents (M = 228.57) could be because they often require more frequent and costly medical care due to age-related health problems, including chronic diseases, making comprehensive health insurance coverage more valuable to them. The respondents living in urban areas are willing to contribute more (M = 159.86), compared to those living in suburban and rural areas (136.96). These mean values are statistically significant (F = 15.89, p < 0.01). The possible explanation is that urban residents are more educated, have higher incomes, and better access to healthcare services, making them more aware of health insurance benefits, so they are willing to pay a higher premium compared to their rural counterparts.

The unmarried have the highest mean value (172.86) compared to married ones (155.23). However, no significant difference in the mean scores is found between married and unmarried respondents (F = 2.216, p > 0.05). To investigate the mean difference among different education groups ANOVA test, along with homogeneity of variance and the Tukey test, was performed. The results showed no significant difference among respondents’ WTP for NHI based on education (F = 1.329, p > 0.05). However, respondents having a PhD and a diploma are willing to pay a higher amount, for PhD (M = 177.78, SD = 146.322) and Diploma (M = 177.6, SD = 139.900).

On the contrary, WTP for NHI based on household size is found to be significant with F = 5.3378, p > 0.01. The ANOVA test has shown that household sizes of 1–2 members and 6 or more members have a significant difference in WTP for NHI. Generally, larger families have more members with varying healthcare needs, making comprehensive coverage more valuable to them. Moreover, the financial risk associated with medical costs increases, making health insurance a better option for mitigating these potential costs. The results also showed there is a difference in mean WTP for NHI based on the presence of chronic disease in the family; those who have reported chronic disease in their families would be willing to contribute a lower amount (M = 150.30), while those with no disease reported a higher amount (M = 158.59, SD = 119.118, p< 0.05). Given the existing healthcare system in Saudi Arabia, families with chronic diseases might rely on the public healthcare services and, therefore, might not see the value in paying for NHI, or might not be willing to pay as much for it.

It is difficult to predict the impact of travel time on the amount willing to pay for NHI. Travel time from home to healthcare facility showed a significant difference (F = 2.95, p < 0.05), but further analysis of Tukey tests found an insignificant difference between different groups. The results showed that the time taken to travel to the nearest healthcare facility does not impact the amount people are willing to contribute. The study found that the willingness to pay for NHI is slightly higher for households that are facing obstacles in accessing healthcare services compared to those that do not face any obstacles. The mean difference is statistically significant at the p < 0.01 level. Possible reasons for this could include long waiting hours, limited specialists’ care options, and low perceived quality, due to which individuals may be willing to pay a higher amount for NHI. Our analysis also showed that households that are satisfied with the current healthcare services are willing to pay a higher mean amount (M = 160.73) compared to those who are not (M = 151.10), but this difference is not significant.

The findings revealed that the highest mean value is recorded for those having an income of 18,000–24,000 SAR, with M = 184.78, and the lowest mean value is for those with an income of 6000–12,000 SAR, with M = 126.59. These variables are statistically significant (F = 5.221, p < 0.01). The respondents with incomes of 8000–15,000 SAR and 15,000–25,000 SAR are reported to have differences at the p < 0.05 level. Additionally, the income groups of 15,000–25,000 SAR and 25,000–35,000 SAR have significant differences, as do those with incomes of more than 35,000 SAR, at the p < 0.05 level. The respondents in the higher income group are willing to pay a lower amount (165.98 SR); this might be because these households often already have private insurance benefits (indeed, our data show that higher income is correlated with having insurance coverage), so they have less incentive to pay a higher amount as the premium for NHI. The respondents those having private health insurance had a slightly different mean amount (156.40) compared to those who had no insurance (M = 156.24, SD = 08.901), and the mean difference is significant at the p < 0.01 level. This finding should be interpreted with caution, as the sample of those willing to pay had a significantly higher proportion (70.8%) of respondents who do not have private health insurance. The overall results indicate that females, older respondents, households living in urban areas, medium-sized families, those facing obstacles in accessing current healthcare services, and middle-income families are willing to pay a higher mean premium amount.

To examine the interaction effects between variables, a bivariate two-tailed correlation analysis was conducted, with correlation coefficients (represented by “r”) ranging from −1 to +1. The results indicate a weak yet positive and significant relationship between household income and household size (r = 0.229 *, p < 0.01). However, the relationship between household income and disease is not significant. Household income is significantly correlated with insurance ownership (r = 0.075, p < 0.01) and education (r = 0.317, p < 0.01). Additionally, household size has a weak but significant positive relationship with disease (r = 0.064, p < 0.05) and insurance ownership (r = 0.065 *, p < 0.05), but not with education. Further analysis revealed that chronic disease is significantly and negatively related to insurance ownership (r = −0.094 *, p < 0.01), while its relationship with education is not significant. Insurance ownership is negatively and significantly related to education (r = −0.078 *, p < 0.01), indicating weak but significant associations among the variables.

3.4. Results of Binomial Logistic Regression for WTP

A binomial backward logistic regression analysis was performed to identify factors influencing WTP for NHI (

Table 4. Binomial backward logistic regression estimates for willingness to join NHI (Optimal model-5).

Variables	B	S.E.	Sig.	Odd Ratio	95% C.I for Exp (B)
					Lower	Upper
Gender (Ref. Female)
Male	1.026	0.144	0.000	2.789	2.103	3.699
Age (Ref 35 years & below)
36–65 Years	−0.403	0.017	0.023	0.668	0.472	0.946
Household size (Ref. 1–2 Members)
Household size (3–5 members)	0.851	0.221	0.000	2.342	1.520	3.609
Household size (6 or more)	0.237	0.137	0.083	1.268	0.970	1.657
Suffering chronic disease? (Ref. No)
Yes	0.442	0.143	0.002	1.555	1.175	2.058
Obstacles to accessing health services (Ref. No)
Yes	−0.624	0.166	0.000	0.536	0.387	0.742
Level of satisfaction (Ref. Not Satisfied)
Satisfied	−0.449	0.139	0.001	0.638	0.491	0.831
Household Income (Ref. < SAR 8000)
Household Income (1) 8000–15,000 SAR	−0.409	0.272	0.133	0.664	0.389	1.133
Household Income (2) 15,000–25,000 SAR	−0.426	0.205	0.038	0.653	0.437	0.977
Household Income (3) 25,000–35,000 SAR	−0.716	0.204	0.000	0.489	0.328	0.729
Household Income (4) More than 35,000 SAR	−0.045	0.237	0.849	0.956	0.601	1.521
Insurance coverage (Ref. No)
Yes	−0.691	0.148	0.000	0.501	0.375	0.669
Constant	−1.176	0.280	0.000	3.243

Note: SAR = Saudi Riyal.

). We conducted binomial backward logistic regression, which gave us 5 models. Model 5 (the optimal model) shows the Cox & Snell R² (0.110) and Nagelkerke R² (0.147), which indicate that the model explains 11% and 14.7% of the variance, respectively. The retained variables include gender, age, household size, presence of chronic disease, obstacles in accessing health services, satisfaction with existing health services, household income, and health insurance ownership.

The results reveal that, controlling for other factors, being male was associated with higher odds of willingness (OR = 2.789). Individuals aged 18–35 years are more willing to join compared to those in the age group of 36–65 years (p < 0.05). Households with 3–5 members are 2.342 times more willing to join compared to small families with 1–2 members (p < 0.01). In addition, respondents whose family members suffer from a chronic disease are 1.55 times more likely to participate in NHI (p < 0.05). However, the respondents who reported facing obstacles in accessing health services are 57% less likely to participate in NHI (p < 0.01). Similarly, the respondents who were satisfied with the existing healthcare services are 37% less likely to contribute (p < 0.01) to NHI.

Interestingly, the odds of WTP for NHI were lower in upper-middle-income households (25–35 k SAR) than in the lowest-income group, according to the logistic regression. One possible explanation is that higher-income Saudis often already have private insurance benefits (indeed, our data show that higher income is correlated with having insurance coverage), so their incentive to enroll in NHI is less. As expected, those members having private health insurance coverage are 50% less likely to participate in NHI (p < 0.01). The results also show that distance to the nearest health facility does not have any impact on the decision to participate in NHI.

In summary, the binomial logistic regression results indicate that being male, having a medium-sized family, and having a chronic disease in the family increase the likelihood of WTP for NHI. Conversely, having private health insurance, high income, and satisfaction with current health services decreases the likelihood of WTP.

We categorized the amount willing to pay for NHI into two groups: those willing to pay less than 300 SAR (coded as 0) and those willing to pay more than 300 SAR (coded as 1). A binary backward logistic regression analysis was conducted, resulting in multiple models, with model 11 being the optimal one (

Table 5. Binomial backward logistic regression on the amount willing to pay (<300 SAR = 0, >300 SAR = 1).

Variables	B	S.E.	Sig.	Odd Ratio	95% C.I for Exp (B)
					Lower	Upper
Location (Ref. Suburban and rural area)
Urban area	−0.568	0.264	0.031	0.567	0.338	0.950
Household Income (Ref. < SAR 8000)
Household Income (1) 8000–15,000 SAR	0.139	0.362	0.701	1.149	0.565	2.334
Household Income (2) 15,000–25,000 SAR	−0.717	0.262	0.006	0.488	0.292	0.815
Household Income (3) 25,000–35,000 SAR	0.088	0.269	0.745	1.092	0.644	1.849
Household Income (4) More than 35,000 SAR	−0.368	0.302	0.223	0.692	0.383	1.251
Constant	−0.253	0.205	0.218	0.776

Note: SAR = Saudi Riyal.

). This model identified household income as a significant factor influencing the amount willing to pay for NHI. The Cox & Snell R² (0.035) and Nagelkerke R² (0.048) indicate that this factor explains 3.5% and 4.8% of the variance in the amount willing to pay for NHI. Contrary to expectations, the results show that respondents who live in urban areas are less likely to pay more than 300 SAR per month (p < 0.05). The respondents in the monthly income brackets of 15,000–25,000 SAR are 52% less likely to pay 300 SAR per month as compared to the income group of SAR 8000 and below (p < 0.01). Overall results indicate that households reside in urban areas and middle-income households are less likely to be willing to pay more than 300 SAR for NHI.

3.5. Results of Multiple Regression Analysis on WTP

Table 6. Multiple regression analysis on the amount willing to pay and socio-economic characteristics.

Model	B	SE	Beta	t	Sig.	95% C.I for Exp (B)
						Lower	Upper
(Constant)	1.710	0.345		4.960	0.000	1.034	2.387
Gender	−0.752	0.141	−0.151	−5.324	0.0004	−1.029	−0.475
Age	0.269	0.179	0.045	1.499	0.134	−0.083	0.621
Location	0.207	0.177	0.034	1.171	0.242	−0.140	0.554
Marital status	−0.140	0.288	−0.014	−0.485	0.628	−0.705	0.425
Education	−0.079	0.065	−0.036	−1.215	0.225	−0.206	0.048
Household size	−0.240	0.100	−0.073	−2.415	0.016	−0.436	−0.045
Presence of chronic disease	−0.394	0.143	−0.082	−2.758	0.006	−0.674	−0.114
Travel time to nearest health facility	0.252	0.201	0.037	1.252	0.211	−0.143	0.646
Obstacles to accessing health services	0.552	0.165	0.103	3.350	0.001	0.229	0.876
Satisfaction with current health services	0.427	0.135	0.097	3.169	0.002	0.163	0.691
Household Income	0.195	0.059	0.104	3.289	0.001	0.079	0.312
Insurance ownership	0.657	0.148	0.129	4.431	0.000	0.366	0.948
R2 = 0.077, F= 8.202, p < 0.01

shows the results of multiple regression analysis. The overall model is found to fit with a goodness of fit index F = 8.202, p < 0.01, and R² = 0.077, showing a moderate effect and explaining 7.7% of the variance by socio-economic and demographic factors on how much people are willing to pay for NHI.

The findings reveal a significant association between gender and the amount the respondents are willing to pay for NHI (β = −0.151, p < 0.01). However, variables such as age, location, marital status, education, household size, and distance are not found statistically significant (p > 0.05) with a maximum amount of WTP for NHI. On the contrary, respondents with household members not suffering from chronic diseases are found to have a significant association with the amount they are willing to pay for NHI (β = −0.082, p < 0.01). Respondents who reported facing obstacles in accessing public health services are willing to pay 10.3% more for NHI (β = 0.103, p < 0.01).

Regarding the level of satisfaction, the association with WTP for NHI is positive and significant (β = 0.097, p < 0.05), which means a one percent increase in the level of satisfaction could increase the participant’s pay by 9.7% more for NHI. Similarly, household income also has a positive impact on WTP for NHI (β = 0.104, p < 0.01); a one-unit change in income could result in a 10.4% increase in WTP for NHI. Likewise, the impact of health insurance coverage has a positive influence on WTP for NHI (β = 0.129, p < 0.01); having health insurance could motivate respondents to pay 12.9% more for NHI. Contrary to many studies, results of our regression analysis indicate that education level does not emerge as a significant predictor of the amount willing to pay for NHI.

4. Discussion

This study investigated the willingness of citizens in Saudi Arabia to financially support the NHI. The findings revealed that if NHI were implemented, 49.3% of citizens from the Qassim region would be willing to support it financially. Our WTP rate is lower than approximately 66% found in prior surveys in Riyadh and Jeddah [16,22,24]. This difference may be attributed to regional variations. The Qassim region is comparatively less urbanized and, on average, has lower income levels than the Riyadh and Jeddah regions, which could reduce WTP. Additionally, Riyadh and Jeddah regions host a significant share of the country’s medical infrastructure, providing the population with better access to high-quality public health services than those in less developed regions. As major economic hubs, these regions also experience distinct economic pressures and spending habits, which may influence the individual’s willingness and ability to contribute financially to NHI. Variations in sample compositions and the timing and framing of surveys could further account for such differences. Notably, our study was conducted in late 2024, a period influenced by post-COVID economic conditions, which could have affected WTP.

Our findings may be compared with those of studies conducted in other countries. A systematic review and meta-analysis in Ethiopia [44] reported a pooled willingness to join of 60.42%, identifying the key determinants such as education level, wealth status, knowledge and awareness, health status, and trust in the healthcare system. A study from Senegal further demonstrated a reinforcement effect, showing that enrolment increased WTP by about 41% [45]. The authors also noted that only 33% of households were aware of community-based insurance, underscoring the critical role of information campaigns in increasing participation.

The mean amount that respondents are willing to pay as a monthly contribution is estimated at 158 SAR (42.13 US$) per family, with a median amount of 100 SAR (26.6 US$). This amount is closer to the amount estimated by other studies [23,26]. However, this is lower compared to a study by Al-Hanawi et al. (2018), who estimated 50 SAR per capita per month, which is considerably high given the average household size of 4.8 in Saudi Arabia [16]. The variations in these findings may be due to the hypothetical nature of the NHI, as some respondents might be uncertain or distrustful, which could potentially influence their stated WTP. Systematic reviews of the CV methods have highlighted several biases, including hypothetical bias, starting-point bias, and range bias, which arise from the hypothetical framework and the way questions are structured. To reduce these issues and improve the accuracy of WTP estimates, the researchers have recommended a precise survey design, the use of realistic payment methods, follow-up questions, and assessing respondents’ comprehension of the hypothetical scenario presented.

Interestingly, the mean WTP for NHI in our study (annual amount of 1896 SAR per family) is comparable to the average OOP expenditure on healthcare (1872 SAR) incurred by families in the Qassim region [68]. Given the widespread public acceptance and WTP for NHI among a significant proportion of the population, implementing NHI in Saudi Arabia appears both feasible and socially justified. The Saudi population’s WTP to pay for health insurance reinforces the argument for NHI as a mechanism for a sustainable, equitable, and efficient healthcare financing system [10,16,23,28]. Furthermore, WTP data also helps policymakers to tailor policies to specific demographic segments, reduce OOP expenditures, and address equity concerns. The studies have shown that the CEBHI has substantially influenced healthcare expenditure trends in Saudi Arabia, leading to a considerable decrease in OOP expenditures [6,10].

Our study revealed that being male, having a medium-sized family, and having a chronic disease in the family increase the likelihood of WTP for NHI. Conversely, having private health insurance, high income, and satisfaction with current health services decreases the likelihood of WTP. These findings are consistent with the findings of earlier studies conducted in Saudi Arabia [16,24,25,26,27,28]. Households suffering from chronic disease conditions are often more willing to participate in NHI, which might be because health insurance ensures access to necessary care, specialist consultations, and reduces OOP expenses. The respondents who are satisfied with the current public healthcare system might not support NHI because they may perceive it as unnecessary, given that the public healthcare services are free at the point of use. Moreover, satisfied individuals might not be aware of the potential benefits of additional insurance coverage or may not see the value in paying for it.

Saudi Arabia allocates a substantial budget to healthcare to improve service quality in public healthcare facilities. However, despite significant funding, citizens remain dissatisfied due to long waiting times and overcrowded emergency departments [16,24,27]. This situation highlights the need for NHI, which could alleviate the financial burden on the government and enhance quality and satisfaction for its members. Our study found that respondents who are dissatisfied with public health facilities are more likely to contribute to NHI with the hope that the new system would improve healthcare services. The linear regression analysis shows that a one-unit increase or decrease in satisfaction level could possibly bring a 9.7% change in the amount willing to pay.

In contrast to our findings, previous studies in Saudi Arabia have shown that satisfaction with existing healthcare services is one of the key factors determining individuals’ WTP for NHI [16,23]. However, it is pertinent to note that these studies were conducted in the economic hubs of Riyadh and Jeddah, where healthcare facilities are often more advanced, equipped with the latest medical technology, and typically provide all levels of care, including specialist healthcare services. Hence, satisfaction with the current healthcare services may depend on many factors. A recent systematic review identified long waiting times, poor communication, and regional disparities as key factors influencing patients’ perceptions of healthcare quality in Saudi Arabia and the broader GCC region [69]. Such perceptions may affect trust in the current healthcare system and shape attitudes toward future insurance models.

Our study also revealed that individuals with private health insurance are less willing to pay for NHI. One possible explanation is that they may prefer private healthcare services, which reduces their reliance on public healthcare services. Additionally, higher-income Saudis are likely to have private health insurance benefits-our data indicate a positive correlation between income level and insurance coverage, so reducing their incentive to enroll in NHI. Private sector employers are legally required to provide health insurance to their employees. On the contrary, citizens who rely on public healthcare facilities often express their willingness to contribute to NHI to access private healthcare providers and expand their choices. There is a prevailing perception among Saudi citizens that private healthcare is superior to public healthcare services [15,70,71]. However, it should be noted that despite notable advances, a recent review shows that the CEBHI crumbles with several challenges, including inequality in geographical access, disproportionate out-of-pocket expenditure by low-income workers, coverage issues, limited public awareness, and quality issues [7]. These findings provide valuable guidance for Saudi Arabia’s healthcare providers and policymakers to progress toward UHC goals.

Furthermore, the analysis reveals that the mean amount respondents are willing to pay is higher for females, older people, urban households, medium-sized families, households that face obstacles in accessing healthcare services, and those with private health insurance. Additionally, the linear regression analysis also revealed a strong association between the amount willing to pay and gender, presence of chronic diseases in the family, households experiencing obstacles in accessing health services, level of satisfaction with current healthcare services, household income, and health insurance coverage. Female heads might be more aware of health issues in their families, and more proactive about seeking healthcare, so willing to pay a higher premium amount. A higher mean WTP by the older respondents may be attributed to their increased need for frequent and costly medical care due to age-related health problems, including chronic diseases, which makes comprehensive health insurance coverage more valuable to them. Urban residents are more educated, have higher incomes, and enjoy better access to healthcare services, which makes them more aware of health insurance benefits and more willing to pay a higher premium compared to their rural counterparts.

Regarding access to public healthcare services, our study revealed that households not facing obstacles are more likely to join NHI. However, the regression analysis indicates that households experiencing access problems, such as long waiting hours, limited specialists’ care options, and low perceived quality, might be willing to pay a higher amount for NHI, likely in anticipation of improved healthcare services. Specifically, the linear regression results indicate that those who face obstacles are willing to pay 10% more in the premium amount. This suggests that households not facing obstacles in accessing healthcare services are associated with a higher likelihood of willingness to join NHI (chi-squared and logistic regressions), whereas among those willing, people who had faced obstacles tended to state somewhat higher premium amounts.

Our findings are contrary to the findings of earlier studies [23,28], which found a significant association between the maximum amount willing to pay by the respondents and age, region, and education. In our regression analysis, education did not emerge as a significant predictor of the amount willing to pay. Young people are more likely to pay a higher amount than older people because, as people age, they accumulate more financial responsibilities, requiring them to allocate their limited resources more judiciously. In contrast, younger individuals tend to have fewer financial obligations, giving them more flexibility in their financial decision-making. Contrary to our findings, a few studies in other countries also suggested a positive correlation between higher education levels and WTP for NHI [72,73,74,75,76]. Studies among the poor population in Indonesia [48,77] showed that the poor population with higher education levels is more likely to become NHI members than those with lower education. Their residence, age, gender, employment, marital status, and wealth also predicted their NHI membership. Studies conducted in Mongolia and Iran showed a significant positive association between education and income with WTP [78,79]. A systematic review of WTP studies in Ethiopia showed that the monthly income and educational status of respondents are positively associated with WTP [39]. On the contrary, studies conducted in Germany showed that income and morbidity status did not affect WTP among members of social health insurance [64]. A study in Iran showed that household heads with higher education, higher income, and employment status were more willing to pay for health insurance [80].

It is worth mentioning that 75% of respondents in the study were availing of health services delivered by the government healthcare facilities, which is rooted in the country’s long-standing tradition. The government has come to realize that this model is unsustainable due to the rapidly growing population and fluctuating oil prices [10,16,81]. Moreover, the current free healthcare system has led to issues such as overutilization, abuse of services, long waiting times, and dissatisfaction with service quality [64,70,82], quality patient care, and costs [81,82,83,84], which may discourage individuals from paying contributions to NHI. Other reasons for refusing to pay for NHI may include financial constraints and limited or no use of public healthcare services [23,39].

Consistent with our findings, studies conducted in Indonesia have shown that approximately half of the respondents were not willing to pay for the NHI scheme [48,77]. On the contrary, studies in Nigeria found that a majority of people were willing to pay for NHI [74,85]. Similarly, a study in India also found that most of the people want to contribute to community health insurance [73]. In China, a significant majority (more than 90%) of respondents expressed their willingness to participate in basic medical insurance for urban and rural residents, with familiarity with insurance policies emerging as a key factor influencing this decision [86,87]. Similar findings were also reported in a study in the Lao PDR [88] and Malaysia [57]. In Malaysia, three-fourths of respondents using public hospitals were willing to support NHI. The observed differences in such findings may be attributed to factors such as time of study, sample size, geographical variations, socio-economic differences, and, most notably, differing health insurance concepts used by different countries.

The sustainable implication of WTP for NHI depends on several factors, including the amount people are willing to contribute and the number of individuals who would like to financially support the program. In our study, the mean amount that respondents are willing to contribute is 1896 SAR per annum per family (approximately 505.6 USD). Assuming about 50% of the Saudi population (2.1 million as per the Census 2022) is willing to contribute this amount towards NHI, it yields approximately 3982 million SAR or 3.982 billion SAR, which is approximately 1.8% of the current healthcare expenditure of 227.684 million SAR in 2023 [8]. This shows that public contribution to NHI would represent only a minuscule share and merely complement the system. This aligns with contemporary models of industrial relations, which suggest that household contributions alone are insufficient to sustain the healthcare finance system. This underscores the imperative for diversified funding mechanisms, wherein significant contributions from the government, employers, and employees are essential. Health financing reform, therefore, emerges as a complex, multi-stakeholder endeavor that requires coordinated engagement among government agencies, private insurers, and civil society.

Effective implementation of NHI requires a nuanced understanding of the “multidimensional” nature of public attitudes and behaviors towards policy change. Successful implementation depends on the collaboration of multiple stakeholders, including government agencies, healthcare providers, employers, health insurance organizations, civil society, and regulatory frameworks. As Ghosh and Ray (2012) argue, participatory policymaking and labor-management cooperation are particularly salient in the context of NHI [32]. Their framework advocates for negotiated contributions from both employers and workers, postulating that such arrangements are vital for ensuring financial sustainability and equitable delivery of benefits. Moreover, the framework highlights the significance of stakeholder voice and institutional trust as critical components of willingness to support NHI. While our study contributes valuable insights into the financial willingness of households to participate in NHI, it is important to recognize that other dimensions, such as cultural acceptance and trust in government institutions, must also be addressed to foster broad-based support and successful implementation of NHI.

5. Policy Implications

The findings of the study have several policy implications. Firstly, an understanding of the WTP to pay for NHI can help policymakers set informed premium prices, ensuring the affordability and sustainability of the insurance program. Moreover, it can inform the development of health financing strategies that balance individual financial contributions with government subsidies and reduce the financial burden on households. By understanding WTP, policymakers can design NHI that improves healthcare accessibility, reduces OOP expenses, and promotes health equity, particularly for vulnerable populations. Furthermore, the policymakers should note that WTP is far from universal; extensive public awareness campaigns and education about NHI benefits may be necessary to increase buy-in, especially among groups currently less willing to pay, such as those with lower perceived need or those who are older. Also, the contribution levels citizens are willing to pay suggest that premiums must be set affordably to maximize participation—a balance must be struck between financial sustainability and enrollment. Affordable premiums can help attract a larger risk pool, which in turn can help reduce the financial burden on individuals. Furthermore, the findings of the study can provide insights into the demand for quality healthcare services, enabling policymakers to prioritize investments in healthcare infrastructure, human resources, and technology. Finally, understanding WTP can help Saudi Arabia progress towards UHC, ensuring that all citizens have access to essential healthcare services without facing financial hardship.

6. Limitations of the Study

Despite its contributions, this study has several limitations. One major limitation is that the study is based on an online survey using convenience sampling, which may not accurately represent the diverse Saudi population, especially those in rural areas, older adults, and individuals without internet access. Therefore, one should be careful while generalizing these findings to the entire Saudi population. Secondly, the study’s reliance on self-reported data, which might be susceptible to response biases, could potentially compromise the reliability and accuracy of the results. Thirdly, the CV method may not accurately capture individuals’ WTP for non-market goods, as it relies on hypothetical market scenarios and assumes respondents can assign monetary values to unfamiliar services. This may lead to inaccurate valuations, particularly among those lacking knowledge about NHI. Fourthly, the reliance on household heads overlooks potential joint decision-making within families, which may limit the accuracy of its findings on healthcare decision-making processes. It might overlook intra-household heterogeneity in preferences, particularly along gender lines.

Fifthly, the study followed a quantitative approach, which did not capture the nuanced reasons behind respondents’ unwillingness to pay for NHI. Sixthly, our study excluded the expatriates and non-Saudi citizens who constitute a significant share of the population and use healthcare services, potentially missing important perspectives. Seventhly, the relatively low R² value in the multiple regression model suggests that the predictors explain only a limited portion of the variance in the outcome variable. This limited explanatory power may indicate that other influential factors are not accounted for in the model. Our models omit key factors trust in government, risk attitudes, and employment status, which could improve model fit and yield better insights if included. Finally, the study’s narrow focus on a specific demographic or geographic region may restrict the applicability of its findings to more diverse populations or settings, potentially limiting the study’s external validity.

Future studies should consider a more diverse population, including expatriates, using rigorous sampling methods to enhance the representativeness of their findings at the national level. Moreover, future research could benefit from incorporating responses from multiple adult household members, including female members, to capture decision-making dynamics better. To comprehend the reasons behind respondents’ unwillingness to pay for NHI, future studies should consider integrating qualitative methods or mixed methods designs. Studies should also consider a comprehensive cost analysis to determine the funding needs of NHI, assess the sufficiency of the proposed funding, and alternative financing models. Furthermore, future research should investigate the perspectives of healthcare providers, insurance companies, and employers on the implementation of the NHI program, exploring their perceptions and potential barriers.

7. Conclusions

This study indicates that approximately 50% of the respondents are willing to pay for NHI in the Qassim region of Saudi Arabia. It highlights that individuals’ willingness to participate in and pay for an NHI program is influenced by their socio-economic and health-related characteristics. Factors such as gender, household size, presence of chronic disease in the family, obstacles to accessing healthcare services, level of satisfaction with the current healthcare services, and existing health insurance status are the influencing factors for WTP. As the findings are preliminary and regional, further nationally representative research is needed. Policymakers should also consider the administrative and equity challenges of introducing NHI, including premium affordability for low-income households. Successful implementation of NHI requires the coordinated efforts of multiple stakeholders, including government agencies, healthcare providers, employers, health insurance organizations, civil society, and regulatory bodies.