Risk Allocation and Private Sector Participation in Saudi Automotive Mega-Projects

Risk Allocation and Private Sector Participation in Saudi Automotive Mega-Projects | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF correspondence Risk Allocation and Private Sector Participation in Saudi Automotive Mega-Projects Mohamed Elkhouly This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9152430/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examined whether perceived fairness of risk allocation helps explain private-sector participation intention in public–private partnership projects more effectively than the perceived salience of major risk categories. Using a quantitative cross-sectional survey design, the study analyzed 250 valid responses from practitioners involved in Saudi automotive mega-projects, including EPC contractors, suppliers, consultants, and investors. The questionnaire measured perceived fairness of risk allocation, participation intention, and five major risk-category items, and the scales showed strong internal consistency. The results indicate that perceived fairness was evaluated positively and significantly above the neutral midpoint. More importantly, perceived fairness showed a strong positive association with participation intention and explained a substantial proportion of its variance. By contrast, although all risk categories were rated as important, their explanatory power was comparatively modest when entered simultaneously in the regression model, and no individual risk category emerged as a statistically significant predictor. These findings suggest that private-sector willingness to participate may depend less on broad recognition of project risks than on whether the allocation of those risks is perceived as fair, proportionate, and credible. The study therefore highlights perceived fairness as a central governance-related factor in strengthening PPP project attractiveness and market willingness to engage. public–private partnerships risk allocation perceived fairness participation intention private-sector participation Saudi automotive mega-projects Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Public–private partnerships (PPPs) are widely used to deliver infrastructure and public services by combining public oversight with private financing, technical capability, and operational expertise (Rusmani, 2010 ). Within such arrangements, risk allocation is not a peripheral contractual detail but a central condition of project viability, as the distribution of risks directly affects bankability, financial closure, and long-term project sustainability (Resor & Tuszynski, 2012 ). The dominant principle in the PPP literature holds that risks should be assigned to the party best able to control, manage, or absorb them, a position consistently emphasized in studies of risk-sharing practice and allocation design (Ameyaw & Chan, 2015 ). When this principle is not followed, projects may encounter adversarial relationships, inefficiency, cost escalation, and weak contractual outcomes. Empirical and conceptual scholarship further indicates that PPP success depends not merely on transferring risk to the private sector, but on achieving a balanced distribution that both parties regard as workable and sustainable (Hodge, 2004 ). In this sense, risk allocation functions as a strategic governance mechanism, shaping expectations of contractual credibility, the stability of interparty relations, and the attractiveness of the project to prospective private participants (Li, 2023 ). Studies of transport and airport PPPs likewise suggest that fair allocation of risks and rewards can strengthen private-sector willingness to engage, particularly where long-term revenue and operational commitments are involved (Tolić et al., 2011 ). Despite the importance of this issue, the literature has focused more heavily on optimal risk-sharing principles, risk typologies, and formal allocation models than on how practitioners actually perceive the fairness of these arrangements (Mazher, 2025 ). A substantial body of PPP research examines major categories such as financial, regulatory, operational, and external risks, often ranking their severity or proposing methods for allocating them more efficiently (Likhitruangsilp et al., 2017). Yet firms respond not only to objective exposure, but also to whether the contractual environment appears balanced, legitimate, and acceptable. Evidence from practice-based research shows that inequitable allocation can generate frustration among private actors and weaken incentives for continued participation (Ahwireng-Obeng & Mokgohlwa, 2002 ). More recent studies similarly indicate that institutional quality, transparency, and negotiated balance shape the effectiveness of PPP risk allocation, suggesting that market actors interpret risk structures through more than purely technical calculations (Yang et al., 2024 ). In parallel, research on private investment incentives shows that better-designed risk-sharing mechanisms can encourage earlier and stronger private participation (Wang et al., 2024 ). What remains less clear, however, is whether perceived fairness of risk allocation itself explains participation intention more effectively than broad recognition of individual risk categories. This unresolved issue creates an important gap in PPP research, as it suggests that willingness to participate may depend less on the mere presence of risks than on whether those risks are perceived to be fairly shared. Against this background, the present study investigates the relationship between perceived fairness of risk allocation and participation intention in PPP projects. This focus reflects the view that risk allocation in PPPs is not merely a technical design issue, but also a negotiated governance arrangement that shapes how public and private actors assess long-term cooperation (Li, 2023 ). In prior PPP research, practitioner judgments have frequently been used to understand how risks are perceived, prioritized, and allocated in real project environments, including studies of water infrastructure (Ameyaw & Chan, 2015 ) and project attractiveness in Singapore (Hwang & Zhao, 2013 ). Building on this empirical tradition, the study draws on survey data from practitioners involved in PPP-related activities to address three objectives. First, it assesses whether perceived fairness of risk allocation is evaluated above the neutral level. Second, it tests whether perceived fairness predicts participation intention, consistent with evidence that behavioral intention is an important precursor to actual private-sector engagement in PPP settings (Yang et al., 2020). Third, it examines whether selected risk categories contribute to explaining participation intention, with particular attention to financial and commercial risk, which has often been identified as a critical dimension of PPP performance and investment decision-making (Fathi, 2024 ). This design enables the paper to test whether willingness to participate is shaped more by judgments of fairness than by the perceived salience of individual risk categories, an issue that remains insufficiently resolved in the existing literature (Hodge, 2004 ). The study contributes to PPP research by linking fairness perception directly to private-sector participation decisions, rather than treating risk allocation solely as a contractual or analytical problem. This contribution is important because recent reviews show that much of the literature has concentrated on allocation models, risk-sharing preferences, and government support mechanisms, while giving less attention to how such arrangements are interpreted by market participants themselves (Mazher, 2025 ). By drawing on practitioner evidence, the paper also responds to earlier empirical work showing that stakeholder perceptions are central to understanding PPP risk allocation in practice (Tolani, 2013 ). In addition, it clarifies whether broad awareness of major risk categories translates into actual participation intention or whether perceived fairness offers a stronger explanation of market willingness to engage. This distinction has practical significance because changes in risk allocation structures can help re-attract private participation (Garg & Mahapatra, 2019 ), while institutional conditions such as transparency and balanced arrangements also shape the effectiveness of PPP risk allocation (Yang et al., 2024 ). The next section reviews the literature and develops the hypotheses, followed by the methodology, results, discussion, and conclusion. Research question RQ1: How do private-sector stakeholders perceive the fairness of risk allocation in Saudi automotive mega-projects? RQ2: What is the relationship between perceived fairness of risk allocation and willingness to participate in such projects? RQ3: Which types of project risks most strongly influence private-sector participation decisions? Research Objectives To assess stakeholder perceptions of risk allocation fairness in Saudi automotive mega-projects. To examine the effect of perceived risk allocation on private-sector participation intention. To identify the most critical risk categories affecting participation decisions. Hypothesis H1 Private-sector stakeholders perceive the allocation of risks in Saudi automotive mega-projects as fair. H2 Perceived fairness of risk allocation has a positive and significant effect on private-sector willingness to participate in Saudi automotive mega-projects. H3 Among different project risk categories, financial and commercial risks have the strongest influence on private-sector participation decisions in Saudi automotive mega-projects. Literature Review and Hypothesis Development PPP risk allocation as a foundation of project attractiveness In practical terms, PPP risk allocation refers to the contractual assignment of responsibility for bearing, managing, and responding to uncertainties that may affect project delivery, operation, revenues, or long-term performance. It is a central feature of PPP design because private participation is typically contingent on whether the resulting risk profile remains commercially acceptable and financeable. Studies of transport PPPs show that project finance, revenue expectations, and contractual structure are closely intertwined, making risk allocation a core determinant of bankability rather than a secondary drafting issue (Estache et al., 2007 ). Similarly, research on water-sector PPPs emphasizes that appropriate allocation enhances the efficiency and practicality of contractual arrangements by matching risks to each party’s management capabilities (Ameyaw & Chan, 2015 ). When risks are misallocated, however, the consequences extend beyond technical inefficiency and may include delayed financial closure, adversarial bargaining, and diminished investor confidence (Ahwireng-Obeng & Mokgohlwa, 2002 ). Research on PPP financing further suggests that excessive risk transfer can create pricing inefficiencies that reduce project attractiveness to private capital (Makovšek & Moszoro, 2018 ). For this reason, the prevailing principle in the literature remains that risks should be assigned to the party best able to control, absorb, or mitigate them, because this allocation logic supports commercial viability, contractual sustainability, and firms’ willingness to engage (Lee, 2013 ). Perceived fairness of risk allocation Perceived fairness of risk allocation may be understood to the extent to which market participants believe that project risks are distributed in a reasonable, balanced, and equitable manner between public and private actors. This perception is related to, but distinct from, technical efficiency. A risk allocation formula may be defensible in legal or economic terms and yet still be experienced by firms as one-sided, politically imposed, or insufficiently reciprocal. Evidence from PPP governance research shows that formal allocation outcomes are shaped by power relations and institutional asymmetries, meaning that what appears rational in contract design may nevertheless be perceived as unfair by weaker parties (Chen & Hubbard, 2012 ). This distinction matters because fairness influences the relational climate within which long-term contracts operate. Studies of PPP risk management have shown that strong contractual frameworks are most effective when accompanied by cooperative relationships and relational skills (Chung & Hensher, 2015 ), while more recent research on trust in PPP projects argues that trust remains central to sustaining cooperation over time (Zhang et al., 2025 ). Fairness therefore matters because it shapes acceptance, supports trust, and affects whether firms are willing to commit capital and capability under conditions of long duration and uncertainty. If practitioners generally view current arrangements as balanced rather than burdensome, the mean level of perceived fairness should exceed the neutral point on the measurement scale. Accordingly, the study proposes: H1: Perceived fairness of risk allocation is significantly above the scale midpoint. Participation intentions in PPP contexts Participation intention in PPP contexts refers to the willingness of firms or industry practitioners to engage in future PPP opportunities under the contractual environment they currently perceive. It is a useful outcome variable because it captures market confidence before actual bidding or investment behavior occurs. In this sense, intention functions as an early indicator of whether the PPP environment is regarded as credible, workable, and worth entering. Empirical work on private-sector participation in healthcare PPPs shows that behavioral intention is strongly linked to actual behavior and is shaped by attitudes, facilitating conditions, and perceived control (Yang et al., 2020). In infrastructure settings, project attractiveness has likewise been linked to a combination of positive and negative factors that influence how firms evaluate entry opportunities (Hwang & Zhao, 2013 ). Participation intention is therefore not reducible to profit expectations alone. It is also shaped by governance quality, perceived risk exposure, institutional reliability, and the extent to which the rules of engagement appear balanced and predictable. Treating participation intention as the dependent variable is appropriate because it reflects the pre-decisional judgment through which private actors screen PPP opportunities before making concrete commitments. Why perceived fairness should influence participation intention. Perceived fairness should positively influence participation intention because fair allocation helps firms interpret the PPP environment as predictable, manageable, and institutionally credible. Private actors rarely evaluate PPP opportunities under conditions of complete certainty; instead, they must make judgments about whether long-term commitments are likely to remain commercially and relationally sustainable. Where risk allocation is perceived as fair, firms are less likely to fear opportunistic burden shifting by the public side and more likely to regard the contract as a foundation for stable cooperation. Research on institutional arrangements in PPPs shows that transparency, government–business relations, and market competition improve the effectiveness of risk identification and allocation, suggesting that firms interpret allocation practices as signals of governance quality rather than merely as technical terms (Yang et al., 2024 ). This signaling role is important because fairness can communicate contractual credibility even before performance outcomes are observed. Related work on PPP legislation also emphasizes contractual balance, transparency, and fair competition as foundational principles for sustainable partnership structures (Mattar et al., 2022 ). In addition, studies of private investment incentives in PPPs indicate that risk-sharing arrangements can materially influence the timing and attractiveness of private entry (Wang et al., 2024 ). Taken together, these arguments suggest that fairness operates not only as a moral judgment but also as a governance cue that simplifies decision-making under uncertainty. The study therefore proposes: H2: Perceived fairness of risk allocation positively affects participation intention in PPP projects. Risk categories and participation intention. Although fairness may provide an overarching evaluative signal, specific risk categories may also shape participation intention because not all forms of risk have the same implications for profitability, control, or uncertainty. The present study considers five items representing major PPP risk concerns: financial and commercial risk, political and regulatory risk, construction and operational risk, force majeure or external risk, and a comparative judgment item capturing respondents’ relative assessment of risk salience. Prior research consistently shows that financial and commercial issues occupy a prominent place in PPP decision environments because they bear directly on cash flow, demand, debt service, and expected returns (Hodge, 2004 ). Empirical work on risk and success factors in transport PPPs similarly identifies financial market risk among the most important critical risks affecting project outcomes (Fathi, 2024 ). At the same time, other categories also matter. Studies from Vietnam highlight the importance of tendering, payment, and regulatory issues in shaping private-sector perceptions (Likhitruangsilp et al., 2017), while ICT and other newer PPP domains show that external, regulatory, and stakeholder-related uncertainties may significantly alter project attractiveness (Nel, 2020 ). It is therefore plausible that different risk categories contribute differently to willingness to participate, even if their effects are not equally strong. On this basis, the study tests whether perceived risk categories contribute to explaining participation intention and whether financial/commercial risk is the most influential category. Accordingly, the study proposes: H3: Perceived risk categories significantly contribute to explaining participation intention, and financial/commercial risk is expected to be the most influential category. Methodology Research design and sample. This study employed a quantitative cross-sectional survey design to examine how private-sector stakeholders perceive risk allocation and how those perceptions relate to participation intention in Saudi automotive mega-projects. The target population comprised practitioners involved in PPP-related and automotive project activities, including investors and financial partners, EPC contractors, component and parts suppliers, consultants and advisors, and other relevant private-sector actors. A total of 250 valid responses were included in the analysis. The sample reflects a broad distribution of private-sector functions, with EPC contractors representing the largest group, followed by component and parts suppliers, consultants and advisors, and investors and financial partners. The respondents also demonstrated substantial professional maturity, as the largest segment reported 5 to 10 years of experience, with sizeable proportions reporting 16 to 20 years and more than 20 years of experience. In addition, most respondents reported meaningful involvement in Saudi automotive mega-projects, and 63.2% indicated previous participation in PPP arrangements. The predominance of practitioners with direct sectoral and PPP exposure strengthens the relevance of their judgments and supports the use of the sample as an informed basis for evaluating risk allocation and willingness to participate. Measures and questionnaire structure Data was collected using a structured questionnaire composed of four sections. The first section gathered demographic and professional information, including respondents’ primary role in the automotive sector, years of professional experience, level of involvement in Saudi mega-projects, prior participation in PPP projects, and organization size. The second section measured perceived fairness of risk allocation using five items rated on a five-point Likert scale, with higher values indicating stronger agreement. These items captured whether respondents viewed risk allocation as transparent, proportionate, and generally fair within Saudi automotive mega-projects. The third section measured participation intention through five Likert-scale items that assessed respondents’ willingness to engage in future PPP opportunities, consider long-term investment, and commit organizational resources under current conditions. The fourth section addressed risk-related perceptions. Rather than treating project risk as a single unified construct, the study included five items representing the perceived salience of major PPP risk categories, which were entered as explanatory variables in the risk model. These items covered financial and commercial risk, regulatory and political risk, market and demand risk, technical and delivery risk, and a comparative judgment concerning the relative decisiveness of financial and commercial risks. This structure allowed the study to examine both general fairness perceptions and the role of distinct risk concerns in shaping participation intention. Ethical and analytical considerations Participation in the survey was voluntary, and responses were treated confidentially for research purposes only. The questionnaire collected perceptual and professional information relevant to the study objectives, and the analysis was conducted at the aggregate level rather than the individual level. No claims are made beyond the scope of the reported data and statistical procedures. In interpreting the regression models, attention was given to the direction and magnitude of coefficients, statistical significance, and the proportion of variance explained, so that the substantive importance of each result was considered alongside formal statistical evidence. Reliability Analysis Reliability analysis was conducted to evaluate the internal consistency of the measurement scales used to capture perceived fairness of risk allocation, private-sector participation intention, and perceived project risk categories. Cronbach’s Alpha (α) was employed as the primary reliability indicator, given its suitability for multi-item Likert-type constructs and its widespread acceptance in construction and project management research. All analyses were based on 250 valid responses, with listwise deletion applied and no cases excluded. Reliability Analysis of Measurement Scales Dimension Number of Items Cronbach’s Alpha (α) Perceived Fairness of Risk Allocation (PF) 5 0.912 Participation Intention (PI) 5 0.919 Project Risk Categories (RC) 5 0.883 All Likert Items Combined 15 0.821 Reliability analysis was conducted to assess the internal consistency of the measurement scales used in this study using Cronbach’s Alpha (α). The results indicate strong reliability across all constructions, with alpha values exceeding the recommended threshold of 0.70. The perceived fairness of risk allocation scale (α = 0.912) and the participation intention scale (α = 0.919) demonstrate excellent internal consistency, while the project risk categories scale also shows robust reliability (α = 0.883). Although the combined scale of all Likert items yields a slightly lower alpha (α = 0.821), this outcome is expected given the conceptual distinction between the constructions and does not suggest measurement weakness. Demographic Analysis The respondent pool reflects a broad distribution of private-sector functions involved in automotive mega-projects. EPC contractors constitute the largest group, accounting for 28.0% of the sample, followed by component and parts suppliers at 24.4%. Consultants and advisors represent 18.0%, while investors and financial partners comprise 17.2% of respondents. Participants demonstrate substantial professional maturity, with the majority reporting moderate to extensive industry experience. Respondents with 5–10 years of experience form the largest segment at 28.8%, followed by those with 16–20 years at 20.8% and more than 20 years at 18.4%. Fewer respondents report less than five years of experience, suggesting that the dataset is largely shaped by practitioners with sustained exposure to complex project environments. A clear majority of respondents have prior experience with public–private partnership projects. Approximately 63.2% indicate previous participation in PPP arrangements, while 36.8% report no such involvement. The predominance of PPP-experienced respondents suggests that attitudes toward risk allocation and participation intention are shaped by practical exposure rather than hypothetical assessment. Consequently, the results capture experiential evaluations of PPP structures within mega-project contexts. Most respondents report meaningful engagement in automotive mega-projects, though the intensity of involvement varies. Moderate involvement is the most common category at 31.2%, followed by high involvement at 24.8%. Limited involvement accounts for 21.2%, while only a small proportion report no direct involvement. The sample encompasses organizations of varying scales, with a slight concentration among larger entities. Large national companies represent 30.0% of respondents, closely followed by multinational companies at 26.8%. Medium enterprises and small enterprises account for 22.0% and 21.2%, respectively. This relatively balanced composition indicates that insights are drawn from both resource-rich organisations and smaller firms, allowing for a nuanced understanding of how organisational capacity intersects with risk perception and participation willingness. Descriptive Analysis Descriptive statistics were employed to examine respondents’ central tendencies and response dispersion across the three study dimensions. Item-level means and standard deviations were calculated to capture overall perception patterns while identifying the degree of consensus among respondents. All items were measured on a five-point Likert scale, with higher values indicating stronger agreement. Table X Descriptive Statistics for Perceived Fairness of Risk Allocation (PF) Item Minimum Maximum Mean Std. Deviation PF1 1 5 3.88 0.921 PF2 1 5 3.90 0.900 PF3 1 5 3.82 0.928 PF4 1 5 3.91 0.916 PF5 1 5 3.86 0.899 All five items sit tightly in the 3.82–3.91 band, which is a clear tilt toward agreement rather than neutrality on a five-point scale. PF4 records the highest mean (M = 3.91, SD = 0.916), while PF3 is the lowest (M = 3.82, SD = 0.928); the gap is only 0.09, so perceptions are not fragmented across sub-themes of fairness. Standard deviations remain just under one point (SD = 0.899–0.928), implying moderate spread but no strong polarization. In practical terms, respondents tend to judge risk allocation as broadly fair, with only limited dissent embedded in the distribution. The consistency of means near four also hints that any later inferential finding in favor of “fairness” is unlikely to be driven by a single standout item. Table Y Descriptive Statistics for Participation Intention (PI) Item Minimum Maximum Mean Std. Deviation PI1 1 5 3.58 1.039 PI2 1 5 3.62 1.103 PI3 1 5 3.60 1.042 PI4 1 5 3.59 1.088 PI5 1 5 3.62 1.032 The participation items cluster around the mid-to-positive range (M = 3.58–3.62), which signals cautious willingness rather than emphatic commitment. PI2 and PI5 share the highest meaning (M = 3.62), yet the differences across items are negligible (maximum spread = 0.04), so intention appears internally coherent at the descriptive level. What is more telling is dispersion: standard deviations exceed one for every item (SD = 1.032–1.103), with PI2 showing the widest spread (SD = 1.103). This indicates pronounced heterogeneity, meaning that while the “average” respondent leans positive, a sizeable fraction likely sits at the lower end (disagree/neutral) alongside another fraction at the higher end (agree/strongly agree). Numerically, the dimension looks stable in its center but volatile in its tails, which makes it a strong candidate for explanation via predictors such as perceived fairness and risk salience. Table Z Descriptive Statistics for Project Risk Categories (RC) Item Minimum Maximum Mean Std. Deviation RC1 2 5 3.99 0.871 RC2 1 5 3.99 0.850 RC3 2 5 3.95 0.830 RC4 1 5 3.99 0.862 RC5 2 5 4.15 0.805 All risk items are high, with means concentrated between 3.95 and 4.15, which reflects strong acknowledgment that these risks are consequential. RC5 stands out as the most salient risk (M = 4.15, SD = 0.805), exceeding the next-highest cluster around 3.99 by 0.16, a non-trivial separation on a five-point scale. The remaining items are remarkably aligned (RC1, RC2, RC4 all M = 3.99; RC3 M = 3.95), suggesting a broadly shared view that multiple risk categories matter, not just one. Dispersion is comparatively lower than the PI dimension (SD = 0.805–0.871), indicating stronger consensus and fewer extreme positions. The minimum also matters: RC1, RC3, and RC5 never drop below 2, implying that outright rejection of risk importance is rare, whereas RC2 and RC4 reaching 1 suggests a small minority that perceive those particular risks as minimal. One-Sample t-test for Perceived Fairness of Risk Allocation (H1) A one-sample t-test was conducted to examine whether private-sector stakeholders perceived fairness of risk allocation (PF_Mean) differed from the neutral midpoint of the five-point Likert scale (test value = 3.00). This procedure is appropriate because H1 evaluates whether fairness perceptions are meaningfully above neutrality, thereby indicating an overall judgment of “fair” rather than “neutral” risk allocation. Table 1 One-Sample Statistics (PF_Mean) Variable N Mean Std. Deviation Std. Error Mean PF_Mean 250 3.875 0.7793 0.0493 Table 2 One-Sample t-test Results (Test Value = 3.00) Variable t df Sig. (2-tailed) Mean Difference 95% CI Lower 95% CI Upper PF_Mean 17.757 249 < .001 0.8752 0.778 0.972 Table 3 One-Sample Effect Sizes (PF_Mean) Effect Size Point Estimate 95% CI Lower 95% CI Upper Cohen’s d 1.123 0.964 1.281 Hedges’ correction (g) 1.120 0.961 1.277 Stakeholders reported a mean perceived fairness score of 3.875 (SD = 0.779), placing the construct firmly above the neutral midpoint and close to the “agree” region. The mean difference from neutrality was 0.875, and the confidence interval for this difference remained entirely positive (0.778 to 0.972), indicating a stable elevation rather than a marginal shift. The test statistics confirmed a statistically significant deviation from neutrality, t (249) = 17.757, p < .001. The standardized effect was large (Cohen’s d = 1.123), signaling that the observed difference is not only detectable but also substantively pronounced in magnitude. Accordingly, H1 is supported, indicating that private-sector stakeholders generally perceive risk allocation in Saudi automotive mega-projects as fair. Simple Linear Regression for Perceived Fairness and Participation Intention (H2) Simple linear regression was used to test H2 by estimating whether perceived fairness of risk allocation (PF_Mean) predicts participation intention (PI_Mean). The dependent variable was the PI_Mean intention score. The predictor was the PF_Mean fairness score overall. This model estimates the expected shift in participation intention for each one-point rise in fairness, while also capturing explained variance through R² and overall fit via the F-test. Statistical significance was evaluated at α = .05. Table 4 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.695 0.482 0.480 0.6609 Table 5 ANOVA Model Sum of Squares df Mean Square F Sig. Regression 100.955 1 100.955 231.128 < .001 Residual 108.325 248 0.437 Total 209.280 249 Table 6 Regression Coefficients Predictor B Std. Error Beta T Sig. (Constant) 0.434 0.212 — 2.042 0.042 PF_Mean 0.817 0.054 0.695 15.203 < .001 Perceived fairness showed a strong, positive association with participation intention. The model explains 48.2% of the variance in PI_Mean (R² = 0.482). The F-test confirmed robust overall fit, F (1, 248) = 231.128, p < .001. The slope indicates that a one-point increase in PF_Mean corresponds to a 0.817-point increase in PI_Mean, which is a sizeable movement on a five-point response scale. The standardized coefficient (β = 0.695) indicates a substantial effect magnitude, supporting H2 and implying that fairness perceptions are a central lever shaping private-sector willingness to participate. Multiple Linear Regression for Risk Categories and Participation Intention (H3) Multiple linear regression was conducted to test H3 by examining whether the five project risk categories (RC1–RC5) significantly predict private-sector participation intention (PI_Mean). This model is appropriate because H3 concerns the comparative influence of distinct risk categories on participation decisions, allowing each risk predictor to be assessed while holding the others constant. Statistical significance was evaluated at α = .05, and model performance was interpreted using R², the overall F-test, and standardized beta coefficients. Table 7 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.330 0.109 0.091 0.8741 Table 8 ANOVA Model Sum of Squares df Mean Square F Sig. Regression 22.854 5 4.571 5.983 < .001 Residual 186.426 244 0.764 Total 209.280 249 Table 9 Regression Coefficients Predictor B Std. Error Beta t Sig. (Constant) 5.284 0.329 — 16.061 < .001 RC1 0.034 0.085 0.032 0.401 0.689 RC2 -0.143 0.093 -0.133 -1.542 0.124 RC3 -0.058 0.095 -0.052 -0.605 0.546 RC4 -0.063 0.094 -0.060 -0.677 0.499 RC5 -0.185 0.105 -0.162 -1.761 0.080 The five risk categories collectively produced a statistically significant model, F (5, 244) = 5.983, p < .001, indicating that the set of risks carries explanatory value for participation intention. However, the explained variance is modest (R² = 0.109; adjusted R² = 0.091), meaning that risk-category ratings account for roughly one-tenth of the variability in PI_Mean. At the predictor level, none of the individual risk categories reached conventional significance at α = .05, although RC5 showed the largest standardized effect (β = -0.162) and approached significance (p = 0.080), suggesting a tentative negative association with participation intention. The remaining predictors exhibited small, standardized coefficients and non-significant p-values, implying limited unique contribution once the other risk categories are controlled. Consequently, H3 is not supported in its strict form, because financial and commercial risks cannot be confirmed as the strongest statistically significant drivers within this model, even though the overall risk set is jointly associated with participation intention. Discussion The findings provide a clear answer to the study’s three research questions. First, private-sector stakeholders in Saudi automotive mega-projects generally regarded the prevailing allocation of risks as fair. Perceived fairness was not only above the neutral midpoint, but substantially so, with a mean of 3.875 and a large effect size, while the five fairness items were closely clustered between 3.82 and 3.91. This pattern indicates that the positive fairness assessment was broad-based rather than driven by a single item or a narrow aspect of the allocation framework. Given that the respondent pool consisted largely of experienced practitioners, many of whom had prior PPP exposure, this result suggests that favorable fairness evaluations were grounded in practical familiarity with complex project environments rather than in purely abstract judgment. Second, perceived fairness emerged as the strongest explanatory factor in the study. The regression model revealed a substantial positive association between perceived fairness and participation intention, with perceived fairness accounting for 48.2% of the variance in participation intention and yielding a large, standardized coefficient (β = 0.695). This finding is especially important when considered alongside the descriptive profile of participation intention. Although the participation items were moderately positive on average, they also exhibited pronounced dispersion, with standard deviations above one across all items. Substantively, respondents did not appear uniformly enthusiastic about future participation; rather, they displayed cautious willingness with marked variation across the sample. The strength of the fairness effect therefore suggests that judgments regarding whether risks are allocated in a balanced and credible manner may be a major factor distinguishing respondents who are more willing to engage from those who remain hesitant. In this sample, fairness appears to represent more than a favorable opinion of contract design. It functions as a practical signal of whether the project environment is sufficiently workable to justify future commitment. Third, the results qualify the role of specific risk categories in shaping participation intention. Descriptively, all five risk items received high ratings, with means ranging from 3.95 to 4.15, and dispersion was lower than for participation intention, indicating strong consensus that these risks are consequential. Yet the multiple regression model presented a different pattern. While the five risk-category items were jointly associated with participation intention at the model level, the explained variance was modest at 10.9%, and none of the individual predictors reached conventional statistical significance. Even the most salient item, RC5, only approached significance and did so in a negative direction. This combination of findings suggests that respondents broadly agree that multiple forms of risk matter, but that such shared recognition does not translate into strong independent prediction of willingness to participate once the categories are considered simultaneously. A plausible interpretation, based on the observed response patterns, is that the risk items captured a widely shared perception of general project uncertainty rather than sharply differentiated concerns that uniquely drive participation decisions. Where most respondents already acknowledge that several risks are important, those ratings may have limited power to distinguish between higher and lower participation intention. These findings suggest that willingness to participate in Saudi automotive mega-project PPPs may depend less on the broad salience of risk categories than on whether the allocation of those risks is perceived to be fair. This is the study’s central substantive contribution. The findings do not imply that financial, regulatory, operational, or external risks are unimportant; on the contrary, respondents rated all of them highly. Rather, they indicate that awareness of risk alone is insufficient to explain participation intention. What appears to matter more is how the overall allocation structure is judged by market participants. In practical terms, this means that project attractiveness may be enhanced not simply by identifying risks or emphasizing their importance, but by designing allocation arrangements that stakeholders regard as proportionate, transparent, and balanced. The fact that fairness was consistently rated positively and strongly associated with willingness to participate, whereas the risk-category model was comparatively weak, points to a broader conclusion: in this context, private-sector participation is shaped more by confidence in the reasonableness of the allocation framework than by the mere presence of recognized project risks. Conclusion This study examined the relationship between perceived fairness of risk allocation and participation intention in PPP projects, with particular attention to whether perceived fairness provides a stronger explanation of market willingness to engage than the perceived salience of major risk categories. Using survey data from private-sector practitioners involved in Saudi automotive mega-projects, the study addressed three related questions concerning the general level of perceived fairness, its effect on participation intention, and the explanatory role of selected risk categories. The findings show that perceived fairness of risk allocation was evaluated positively and significantly above the neutral midpoint, indicating that respondents generally regarded prevailing allocation arrangements as balanced. More importantly, perceived fairness emerged as the strongest explanatory factor in the study, showing a substantial positive association with participation intention. By contrast, although all risk categories were rated as important, they showed comparatively weak explanatory value when entered simultaneously in the regression model. The main implication is that private-sector willingness to participate may depend less on broad recognition of project risks than on whether those risks are allocated in a manner perceived as fair, proportionate, and credible. In practical terms, the study suggests that policymakers and project sponsors may strengthen project attractiveness by developing risk allocation structures that the market regards as balanced and institutionally reliable. 8. Limitations and Future Research This study has several limitations that should be acknowledged. First, the analysis was based on cross-sectional survey data, which means that causal inferences should be made with caution. Second, the findings relied on self-reported perceptions rather than observed participation behavior, and such perceptions may be influenced by respondent expectations, prior experience, or contextual judgment. Third, the sample reflects practitioner views within the specific context of Saudi automotive mega-projects and therefore may not be fully generalizable to other PPP sectors, industries, or national settings. A further limitation concerns the measurement of risk categories. Although the included items captured major PPP risk concerns, this block could be refined in future research through clearer construction separation, multi-item subdimensions, or factor-based measurement strategies. Future studies could also examine whether perceived fairness mediates the relationship between risk perceptions and participation intention, whether this relationship varies across respondent types or institutional settings, and whether similar patterns appear in other sectors or countries. Such extensions would help clarify the broader applicability of the present findings and strengthen understanding of how risk allocation influences private-sector engagement in PPP environments. Declarations Funding Declaration: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Ethics and Consent to Participate Declaration: The study involved an anonymous, minimal-risk questionnaire. Participation was voluntary, and informed consent was obtained electronically from all participants prior to completion of the questionnaire. Data Availability Declaration: The data generated and analyzed during the current study are not publicly available due to participant privacy and confidentiality restrictions. Aggregated data supporting the findings are available from the author upon reasonable reques References Ahwireng-Obeng, F., & Mokgohlwa, J. (2002). Entrepreneurial risk allocation in public-private infrastructure provision in South Africa. South African Journal of Business Management , 33 , 29–39. https://doi.org/10.4102/sajbm.v33i4.709 Ameyaw, E., & Chan, A. (2015). Risk allocation in public-private partnership water supply projects in Ghana. Construction Management and Economics , 33 , 187–208. https://doi.org/10.1080/01446193.2015.1031148 Chen, C., & Hubbard, M. (2012). Power relations and risk allocation in the governance of public private partnerships: A case study from China. Policy and Society , 31 , 39–49. https://doi.org/10.1016/j.polsoc.2012.01.003 Chung, D., & Hensher, D. (2015). Risk Management in Public–Private Partnerships. Australian Accounting Review , 25 , 13–27. https://doi.org/10.1111/auar.12062 Estache, A., Juan, E., & Trujillo, L. (2007). Public-Private Partnerships in Transport. Organizations & Markets eJournal . https://doi.org/10.1596/1813-9450-4436 Fathi, M. (2024). A Structural Equation Model on Critical Risk and Success in Public–Private Partnership: Exploratory Study. Journal of Risk and Financial Management . https://doi.org/10.3390/jrfm17080354 Garg, S., & Mahapatra, D. (2019). Hybrid annuity model: Hamming risk allocations in Indian highway public–private partnerships. Journal of Public Affairs . https://doi.org/10.1002/pa.1890 Hodge, G. (2004). The risky business of public–private partnerships. Australian Journal of Public Administration , 63 , 37–49. https://doi.org/10.1111/j.1467-8500.2004.00400.x Hwang, B., & Zhao, X. (2013). Public private partnership projects in Singapore factors, critical risks and preferred risk allocation from the perspective of contractors /. International Journal of Project Management , 31 , 424–433. https://doi.org/10.1016/j.ijproman.2012.08.003 Lee, E. (2013). Fundamentals of public-private partnerships in the transportation sector: international methodologies of highway public-private partnerships and a framework to increase the probability of success and allocate risk. Li, Y. (2023). Evolutionary Game Analysis for the Behaviors of Risk Allocation Participants in Public–Private Partnerships. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A: Civil Engineering . https://doi.org/10.1061/ajrua6.rueng-994 Likhitruangsilp, V., T., S., & Onishi, M. (2017). A Comparative Study on the Risk Perceptions of the Public and Private Sectors in Public-Private Partnership (PPP) Transportation Projects in Vietnam. Engineering Journal , 21 , 213–231. https://doi.org/10.4186/ej.2017.21.7.213 Makovšek, D., & Moszoro, M. (2018). Risk pricing inefficiency in public–private partnerships*. Transport Reviews , 38 , 298–321. https://doi.org/10.1080/01441647.2017.1324925 Mattar, M., Al-Thani, T., Belknani, F., Abdullah, A., & Hawa, F. (2022). Public–Private Partnership: A Legislative Model from the State of Qatar. Global Journal of Comparative Law . https://doi.org/10.1163/2211906x-11010004 Mazher, K. (2025). Review of studies on risk allocation and sharing in public-private partnership projects for infrastructure delivery. Frontiers in Built Environment . https://doi.org/10.3389/fbuil.2025.1505891 Nel, D., & RISK IN PUBLIC PRIVATE PARTNERSHIPS IN INFORMATION AND COMMUNICATIONS TECHNOLOGY. (2020). ALLOCATION OF., 12, 17–32. https://doi.org/10.34111/ijebeg.202012102 Resor, R., & Tuszynski, N. (2012). Public–Private Partnerships. Transportation Research Record , 2288 , 40–47. https://doi.org/10.3141/2288-05 Rusmani, N. (2010). Public-Private Partnership in New Zealand and Malaysia. https://doi.org/10.26686/wgtn.16984690.v1 Tolani, O. (2013). An Examination of Risk Perceptions and Allocation Preferences in Public-Private Partnerships in Nigeria. https://doi.org/10.11575/prism/24878 Tolić, M., Vojvodić, K., & Martinović, M. (2011). Private sector participation in the airport industry. Wang, B., Geng, L., Moehler, R., & Tam, V. (2024). ATTRACTING PRIVATE INVESTMENT IN PUBLIC-PRIVATE-PARTNERSHIP: TAX REDUCTION OR RISK SHARING. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT . https://doi.org/10.3846/jcem.2024.21749 Yang, L., Hu, L., & Li, Y. (2024). Institutional Environment, Institutional Arrangements, and Risk Identification and Allocation in Public–Private Partnerships: A Multilevel Model Analysis Based on Data from 31 Provinces in China. Sustainability . https://doi.org/10.3390/su16156674 Zhang, W., Wang, Y., Li, E., Zhang, C., Li, H., & Hada, S. (2025). Evolutionary Mechanism of Trust for Public–Private Partnership Projects with Public Participation. Buildings . https://doi.org/10.3390/buildings15030391 Additional Declarations No competing interests reported. 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2","display":"","copyAsset":false,"role":"figure","size":17399,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Methodology section.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9152430/v1/0a4e75c7d0618820d08d5354.png"},{"id":105044247,"identity":"17570638-12e8-43af-ac4b-62f2bf703775","added_by":"auto","created_at":"2026-03-20 08:28:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":23722,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Methodology section.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9152430/v1/aa3af39370667dd08db16ea8.png"},{"id":105044248,"identity":"31c90d2a-fbf2-4291-b4c6-a8acad21ccb4","added_by":"auto","created_at":"2026-03-20 08:28:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20657,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Methodology section.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9152430/v1/4cb7f2e7b75a88e9cf384ec3.png"},{"id":105044251,"identity":"63b896f8-2aa7-4bc2-b5f1-4b160603b906","added_by":"auto","created_at":"2026-03-20 08:28:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17044,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Methodology section.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9152430/v1/606ac844f8ff3fe951b0d5a0.png"},{"id":106414693,"identity":"dc49f17b-5775-4892-94f7-aa2789dbe9f8","added_by":"auto","created_at":"2026-04-08 10:22:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1388637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9152430/v1/e1890eb5-2524-4ca7-8520-4fed8c81dd22.pdf"},{"id":105562699,"identity":"8f4580aa-e451-4984-819c-8fb9288c36fd","added_by":"auto","created_at":"2026-03-27 12:44:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16887,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9152430/v1/f55d82505170f706d41330c9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Allocation and Private Sector Participation in Saudi Automotive Mega-Projects","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePublic\u0026ndash;private partnerships (PPPs) are widely used to deliver infrastructure and public services by combining public oversight with private financing, technical capability, and operational expertise (Rusmani, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Within such arrangements, risk allocation is not a peripheral contractual detail but a central condition of project viability, as the distribution of risks directly affects bankability, financial closure, and long-term project sustainability (Resor \u0026amp; Tuszynski, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The dominant principle in the PPP literature holds that risks should be assigned to the party best able to control, manage, or absorb them, a position consistently emphasized in studies of risk-sharing practice and allocation design (Ameyaw \u0026amp; Chan, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). When this principle is not followed, projects may encounter adversarial relationships, inefficiency, cost escalation, and weak contractual outcomes. Empirical and conceptual scholarship further indicates that PPP success depends not merely on transferring risk to the private sector, but on achieving a balanced distribution that both parties regard as workable and sustainable (Hodge, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In this sense, risk allocation functions as a strategic governance mechanism, shaping expectations of contractual credibility, the stability of interparty relations, and the attractiveness of the project to prospective private participants (Li, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Studies of transport and airport PPPs likewise suggest that fair allocation of risks and rewards can strengthen private-sector willingness to engage, particularly where long-term revenue and operational commitments are involved (Tolić et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the importance of this issue, the literature has focused more heavily on optimal risk-sharing principles, risk typologies, and formal allocation models than on how practitioners actually perceive the fairness of these arrangements (Mazher, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A substantial body of PPP research examines major categories such as financial, regulatory, operational, and external risks, often ranking their severity or proposing methods for allocating them more efficiently (Likhitruangsilp et al., 2017). Yet firms respond not only to objective exposure, but also to whether the contractual environment appears balanced, legitimate, and acceptable. Evidence from practice-based research shows that inequitable allocation can generate frustration among private actors and weaken incentives for continued participation (Ahwireng-Obeng \u0026amp; Mokgohlwa, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). More recent studies similarly indicate that institutional quality, transparency, and negotiated balance shape the effectiveness of PPP risk allocation, suggesting that market actors interpret risk structures through more than purely technical calculations (Yang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In parallel, research on private investment incentives shows that better-designed risk-sharing mechanisms can encourage earlier and stronger private participation (Wang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). What remains less clear, however, is whether perceived fairness of risk allocation itself explains participation intention more effectively than broad recognition of individual risk categories. This unresolved issue creates an important gap in PPP research, as it suggests that willingness to participate may depend less on the mere presence of risks than on whether those risks are perceived to be fairly shared.\u003c/p\u003e \u003cp\u003eAgainst this background, the present study investigates the relationship between perceived fairness of risk allocation and participation intention in PPP projects. This focus reflects the view that risk allocation in PPPs is not merely a technical design issue, but also a negotiated governance arrangement that shapes how public and private actors assess long-term cooperation (Li, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In prior PPP research, practitioner judgments have frequently been used to understand how risks are perceived, prioritized, and allocated in real project environments, including studies of water infrastructure (Ameyaw \u0026amp; Chan, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and project attractiveness in Singapore (Hwang \u0026amp; Zhao, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Building on this empirical tradition, the study draws on survey data from practitioners involved in PPP-related activities to address three objectives. First, it assesses whether perceived fairness of risk allocation is evaluated above the neutral level. Second, it tests whether perceived fairness predicts participation intention, consistent with evidence that behavioral intention is an important precursor to actual private-sector engagement in PPP settings (Yang et al., 2020). Third, it examines whether selected risk categories contribute to explaining participation intention, with particular attention to financial and commercial risk, which has often been identified as a critical dimension of PPP performance and investment decision-making (Fathi, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This design enables the paper to test whether willingness to participate is shaped more by judgments of fairness than by the perceived salience of individual risk categories, an issue that remains insufficiently resolved in the existing literature (Hodge, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study contributes to PPP research by linking fairness perception directly to private-sector participation decisions, rather than treating risk allocation solely as a contractual or analytical problem. This contribution is important because recent reviews show that much of the literature has concentrated on allocation models, risk-sharing preferences, and government support mechanisms, while giving less attention to how such arrangements are interpreted by market participants themselves (Mazher, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). By drawing on practitioner evidence, the paper also responds to earlier empirical work showing that stakeholder perceptions are central to understanding PPP risk allocation in practice (Tolani, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, it clarifies whether broad awareness of major risk categories translates into actual participation intention or whether perceived fairness offers a stronger explanation of market willingness to engage. This distinction has practical significance because changes in risk allocation structures can help re-attract private participation (Garg \u0026amp; Mahapatra, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while institutional conditions such as transparency and balanced arrangements also shape the effectiveness of PPP risk allocation (Yang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The next section reviews the literature and develops the hypotheses, followed by the methodology, results, discussion, and conclusion.\u003c/p\u003e\n\u003ch3\u003eResearch question\u003c/h3\u003e\n\u003cp\u003eRQ1: How do private-sector stakeholders perceive the fairness of risk allocation in Saudi automotive mega-projects?\u003c/p\u003e \u003cp\u003eRQ2: What is the relationship between perceived fairness of risk allocation and willingness to participate in such projects?\u003c/p\u003e \u003cp\u003eRQ3: Which types of project risks most strongly influence private-sector participation decisions?\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Objectives\u003c/h2\u003e \u003cp\u003eTo assess stakeholder perceptions of risk allocation fairness in Saudi automotive mega-projects.\u003c/p\u003e \u003cp\u003eTo examine the effect of perceived risk allocation on private-sector participation intention.\u003c/p\u003e \u003cp\u003eTo identify the most critical risk categories affecting participation decisions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHypothesis\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003ePrivate-sector stakeholders perceive the allocation of risks in Saudi automotive mega-projects as fair.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003ePerceived fairness of risk allocation has a positive and significant effect on private-sector willingness to participate in Saudi automotive mega-projects.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eAmong different project risk categories, financial and commercial risks have the strongest influence on private-sector participation decisions in Saudi automotive mega-projects.\u003c/p\u003e \u003c/p\u003e"},{"header":"Literature Review and Hypothesis Development","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePPP risk allocation as a foundation of project attractiveness\u003c/h2\u003e \u003cp\u003eIn practical terms, PPP risk allocation refers to the contractual assignment of responsibility for bearing, managing, and responding to uncertainties that may affect project delivery, operation, revenues, or long-term performance. It is a central feature of PPP design because private participation is typically contingent on whether the resulting risk profile remains commercially acceptable and financeable. Studies of transport PPPs show that project finance, revenue expectations, and contractual structure are closely intertwined, making risk allocation a core determinant of bankability rather than a secondary drafting issue (Estache et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Similarly, research on water-sector PPPs emphasizes that appropriate allocation enhances the efficiency and practicality of contractual arrangements by matching risks to each party\u0026rsquo;s management capabilities (Ameyaw \u0026amp; Chan, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). When risks are misallocated, however, the consequences extend beyond technical inefficiency and may include delayed financial closure, adversarial bargaining, and diminished investor confidence (Ahwireng-Obeng \u0026amp; Mokgohlwa, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Research on PPP financing further suggests that excessive risk transfer can create pricing inefficiencies that reduce project attractiveness to private capital (Makovšek \u0026amp; Moszoro, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For this reason, the prevailing principle in the literature remains that risks should be assigned to the party best able to control, absorb, or mitigate them, because this allocation logic supports commercial viability, contractual sustainability, and firms\u0026rsquo; willingness to engage (Lee, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePerceived fairness of risk allocation\u003c/h3\u003e\n\u003cp\u003ePerceived fairness of risk allocation may be understood to the extent to which market participants believe that project risks are distributed in a reasonable, balanced, and equitable manner between public and private actors. This perception is related to, but distinct from, technical efficiency. A risk allocation formula may be defensible in legal or economic terms and yet still be experienced by firms as one-sided, politically imposed, or insufficiently reciprocal. Evidence from PPP governance research shows that formal allocation outcomes are shaped by power relations and institutional asymmetries, meaning that what appears rational in contract design may nevertheless be perceived as unfair by weaker parties (Chen \u0026amp; Hubbard, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This distinction matters because fairness influences the relational climate within which long-term contracts operate. Studies of PPP risk management have shown that strong contractual frameworks are most effective when accompanied by cooperative relationships and relational skills (Chung \u0026amp; Hensher, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while more recent research on trust in PPP projects argues that trust remains central to sustaining cooperation over time (Zhang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Fairness therefore matters because it shapes acceptance, supports trust, and affects whether firms are willing to commit capital and capability under conditions of long duration and uncertainty. If practitioners generally view current arrangements as balanced rather than burdensome, the mean level of perceived fairness should exceed the neutral point on the measurement scale. Accordingly, the study proposes: H1: Perceived fairness of risk allocation is significantly above the scale midpoint.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipation intentions in PPP contexts\u003c/h2\u003e \u003cp\u003eParticipation intention in PPP contexts refers to the willingness of firms or industry practitioners to engage in future PPP opportunities under the contractual environment they currently perceive. It is a useful outcome variable because it captures market confidence before actual bidding or investment behavior occurs. In this sense, intention functions as an early indicator of whether the PPP environment is regarded as credible, workable, and worth entering. Empirical work on private-sector participation in healthcare PPPs shows that behavioral intention is strongly linked to actual behavior and is shaped by attitudes, facilitating conditions, and perceived control (Yang et al., 2020). In infrastructure settings, project attractiveness has likewise been linked to a combination of positive and negative factors that influence how firms evaluate entry opportunities (Hwang \u0026amp; Zhao, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Participation intention is therefore not reducible to profit expectations alone. It is also shaped by governance quality, perceived risk exposure, institutional reliability, and the extent to which the rules of engagement appear balanced and predictable. Treating participation intention as the dependent variable is appropriate because it reflects the pre-decisional judgment through which private actors screen PPP opportunities before making concrete commitments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWhy perceived fairness should influence participation intention.\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePerceived fairness should positively influence participation intention because fair allocation helps firms interpret the PPP environment as predictable, manageable, and institutionally credible. Private actors rarely evaluate PPP opportunities under conditions of complete certainty; instead, they must make judgments about whether long-term commitments are likely to remain commercially and relationally sustainable. Where risk allocation is perceived as fair, firms are less likely to fear opportunistic burden shifting by the public side and more likely to regard the contract as a foundation for stable cooperation. Research on institutional arrangements in PPPs shows that transparency, government\u0026ndash;business relations, and market competition improve the effectiveness of risk identification and allocation, suggesting that firms interpret allocation practices as signals of governance quality rather than merely as technical terms (Yang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This signaling role is important because fairness can communicate contractual credibility even before performance outcomes are observed. Related work on PPP legislation also emphasizes contractual balance, transparency, and fair competition as foundational principles for sustainable partnership structures (Mattar et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, studies of private investment incentives in PPPs indicate that risk-sharing arrangements can materially influence the timing and attractiveness of private entry (Wang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Taken together, these arguments suggest that fairness operates not only as a moral judgment but also as a governance cue that simplifies decision-making under uncertainty. The study therefore proposes: H2: Perceived fairness of risk allocation positively affects participation intention in PPP projects.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk categories and participation intention.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough fairness may provide an overarching evaluative signal, specific risk categories may also shape participation intention because not all forms of risk have the same implications for profitability, control, or uncertainty. The present study considers five items representing major PPP risk concerns: financial and commercial risk, political and regulatory risk, construction and operational risk, force majeure or external risk, and a comparative judgment item capturing respondents\u0026rsquo; relative assessment of risk salience. Prior research consistently shows that financial and commercial issues occupy a prominent place in PPP decision environments because they bear directly on cash flow, demand, debt service, and expected returns (Hodge, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Empirical work on risk and success factors in transport PPPs similarly identifies financial market risk among the most important critical risks affecting project outcomes (Fathi, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At the same time, other categories also matter. Studies from Vietnam highlight the importance of tendering, payment, and regulatory issues in shaping private-sector perceptions (Likhitruangsilp et al., 2017), while ICT and other newer PPP domains show that external, regulatory, and stakeholder-related uncertainties may significantly alter project attractiveness (Nel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is therefore plausible that different risk categories contribute differently to willingness to participate, even if their effects are not equally strong. On this basis, the study tests whether perceived risk categories contribute to explaining participation intention and whether financial/commercial risk is the most influential category. Accordingly, the study proposes: H3: Perceived risk categories significantly contribute to explaining participation intention, and financial/commercial risk is expected to be the most influential category.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methodology","content":"\u003cp\u003e \u003cb\u003eResearch design and sample.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study employed a quantitative cross-sectional survey design to examine how private-sector stakeholders perceive risk allocation and how those perceptions relate to participation intention in Saudi automotive mega-projects. The target population comprised practitioners involved in PPP-related and automotive project activities, including investors and financial partners, EPC contractors, component and parts suppliers, consultants and advisors, and other relevant private-sector actors. A total of 250 valid responses were included in the analysis. The sample reflects a broad distribution of private-sector functions, with EPC contractors representing the largest group, followed by component and parts suppliers, consultants and advisors, and investors and financial partners. The respondents also demonstrated substantial professional maturity, as the largest segment reported 5 to 10 years of experience, with sizeable proportions reporting 16 to 20 years and more than 20 years of experience. In addition, most respondents reported meaningful involvement in Saudi automotive mega-projects, and 63.2% indicated previous participation in PPP arrangements. The predominance of practitioners with direct sectoral and PPP exposure strengthens the relevance of their judgments and supports the use of the sample as an informed basis for evaluating risk allocation and willingness to participate.\u003c/p\u003e\n\u003ch3\u003eMeasures and questionnaire structure\u003c/h3\u003e\n\u003cp\u003eData was collected using a structured questionnaire composed of four sections. The first section gathered demographic and professional information, including respondents\u0026rsquo; primary role in the automotive sector, years of professional experience, level of involvement in Saudi mega-projects, prior participation in PPP projects, and organization size. The second section measured perceived fairness of risk allocation using five items rated on a five-point Likert scale, with higher values indicating stronger agreement. These items captured whether respondents viewed risk allocation as transparent, proportionate, and generally fair within Saudi automotive mega-projects. The third section measured participation intention through five Likert-scale items that assessed respondents\u0026rsquo; willingness to engage in future PPP opportunities, consider long-term investment, and commit organizational resources under current conditions. The fourth section addressed risk-related perceptions. Rather than treating project risk as a single unified construct, the study included five items representing the perceived salience of major PPP risk categories, which were entered as explanatory variables in the risk model. These items covered financial and commercial risk, regulatory and political risk, market and demand risk, technical and delivery risk, and a comparative judgment concerning the relative decisiveness of financial and commercial risks. This structure allowed the study to examine both general fairness perceptions and the role of distinct risk concerns in shaping participation intention.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthical and analytical considerations\u003c/h2\u003e \u003cp\u003eParticipation in the survey was voluntary, and responses were treated confidentially for research purposes only. The questionnaire collected perceptual and professional information relevant to the study objectives, and the analysis was conducted at the aggregate level rather than the individual level. No claims are made beyond the scope of the reported data and statistical procedures. In interpreting the regression models, attention was given to the direction and magnitude of coefficients, statistical significance, and the proportion of variance explained, so that the substantive importance of each result was considered alongside formal statistical evidence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eReliability Analysis\u003c/h2\u003e \u003cp\u003eReliability analysis was conducted to evaluate the internal consistency of the measurement scales used to capture perceived fairness of risk allocation, private-sector participation intention, and perceived project risk categories. Cronbach\u0026rsquo;s Alpha (α) was employed as the primary reliability indicator, given its suitability for multi-item Likert-type constructs and its widespread acceptance in construction and project management research. All analyses were based on 250 valid responses, with listwise deletion applied and no cases excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReliability Analysis of Measurement Scales\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCronbach\u0026rsquo;s Alpha (α)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Fairness of Risk Allocation (PF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipation Intention (PI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProject Risk Categories (RC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll Likert Items Combined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eReliability analysis was conducted to assess the internal consistency of the measurement scales used in this study using Cronbach\u0026rsquo;s Alpha (α). The results indicate strong reliability across all constructions, with alpha values exceeding the recommended threshold of 0.70. The perceived fairness of risk allocation scale (α\u0026thinsp;=\u0026thinsp;0.912) and the participation intention scale (α\u0026thinsp;=\u0026thinsp;0.919) demonstrate excellent internal consistency, while the project risk categories scale also shows robust reliability (α\u0026thinsp;=\u0026thinsp;0.883). Although the combined scale of all Likert items yields a slightly lower alpha (α\u0026thinsp;=\u0026thinsp;0.821), this outcome is expected given the conceptual distinction between the constructions and does not suggest measurement weakness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Analysis\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe respondent pool reflects a broad distribution of private-sector functions involved in automotive mega-projects. EPC contractors constitute the largest group, accounting for 28.0% of the sample, followed by component and parts suppliers at 24.4%. Consultants and advisors represent 18.0%, while investors and financial partners comprise 17.2% of respondents.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eParticipants demonstrate substantial professional maturity, with the majority reporting moderate to extensive industry experience. Respondents with 5\u0026ndash;10 years of experience form the largest segment at 28.8%, followed by those with 16\u0026ndash;20 years at 20.8% and more than 20 years at 18.4%. Fewer respondents report less than five years of experience, suggesting that the dataset is largely shaped by practitioners with sustained exposure to complex project environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA clear majority of respondents have prior experience with public\u0026ndash;private partnership projects. Approximately 63.2% indicate previous participation in PPP arrangements, while 36.8% report no such involvement. The predominance of PPP-experienced respondents suggests that attitudes toward risk allocation and participation intention are shaped by practical exposure rather than hypothetical assessment. Consequently, the results capture experiential evaluations of PPP structures within mega-project contexts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMost respondents report meaningful engagement in automotive mega-projects, though the intensity of involvement varies. Moderate involvement is the most common category at 31.2%, followed by high involvement at 24.8%. Limited involvement accounts for 21.2%, while only a small proportion report no direct involvement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sample encompasses organizations of varying scales, with a slight concentration among larger entities. Large national companies represent 30.0% of respondents, closely followed by multinational companies at 26.8%. Medium enterprises and small enterprises account for 22.0% and 21.2%, respectively. This relatively balanced composition indicates that insights are drawn from both resource-rich organisations and smaller firms, allowing for a nuanced understanding of how organisational capacity intersects with risk perception and participation willingness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were employed to examine respondents\u0026rsquo; central tendencies and response dispersion across the three study dimensions. Item-level means and standard deviations were calculated to capture overall perception patterns while identifying the degree of consensus among respondents. All items were measured on a five-point Likert scale, with higher values indicating stronger agreement.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable X\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics for Perceived Fairness of Risk Allocation (PF)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll five items sit tightly in the 3.82\u0026ndash;3.91 band, which is a clear tilt toward agreement rather than neutrality on a five-point scale. PF4 records the highest mean (M\u0026thinsp;=\u0026thinsp;3.91, SD\u0026thinsp;=\u0026thinsp;0.916), while PF3 is the lowest (M\u0026thinsp;=\u0026thinsp;3.82, SD\u0026thinsp;=\u0026thinsp;0.928); the gap is only 0.09, so perceptions are not fragmented across sub-themes of fairness. Standard deviations remain just under one point (SD\u0026thinsp;=\u0026thinsp;0.899\u0026ndash;0.928), implying moderate spread but no strong polarization. In practical terms, respondents tend to judge risk allocation as broadly fair, with only limited dissent embedded in the distribution. The consistency of means near four also hints that any later inferential finding in favor of \u0026ldquo;fairness\u0026rdquo; is unlikely to be driven by a single standout item.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable Y\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics for Participation Intention (PI)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe participation items cluster around the mid-to-positive range (M\u0026thinsp;=\u0026thinsp;3.58\u0026ndash;3.62), which signals cautious willingness rather than emphatic commitment. PI2 and PI5 share the highest meaning (M\u0026thinsp;=\u0026thinsp;3.62), yet the differences across items are negligible (maximum spread\u0026thinsp;=\u0026thinsp;0.04), so intention appears internally coherent at the descriptive level. What is more telling is dispersion: standard deviations exceed one for every item (SD\u0026thinsp;=\u0026thinsp;1.032\u0026ndash;1.103), with PI2 showing the widest spread (SD\u0026thinsp;=\u0026thinsp;1.103). This indicates pronounced heterogeneity, meaning that while the \u0026ldquo;average\u0026rdquo; respondent leans positive, a sizeable fraction likely sits at the lower end (disagree/neutral) alongside another fraction at the higher end (agree/strongly agree). Numerically, the dimension looks stable in its center but volatile in its tails, which makes it a strong candidate for explanation via predictors such as perceived fairness and risk salience.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable Z\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics for Project Risk Categories (RC)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll risk items are high, with means concentrated between 3.95 and 4.15, which reflects strong acknowledgment that these risks are consequential. RC5 stands out as the most salient risk (M\u0026thinsp;=\u0026thinsp;4.15, SD\u0026thinsp;=\u0026thinsp;0.805), exceeding the next-highest cluster around 3.99 by 0.16, a non-trivial separation on a five-point scale. The remaining items are remarkably aligned (RC1, RC2, RC4 all M\u0026thinsp;=\u0026thinsp;3.99; RC3 M\u0026thinsp;=\u0026thinsp;3.95), suggesting a broadly shared view that multiple risk categories matter, not just one. Dispersion is comparatively lower than the PI dimension (SD\u0026thinsp;=\u0026thinsp;0.805\u0026ndash;0.871), indicating stronger consensus and fewer extreme positions. The minimum also matters: RC1, RC3, and RC5 never drop below 2, implying that outright rejection of risk importance is rare, whereas RC2 and RC4 reaching 1 suggests a small minority that perceive those particular risks as minimal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOne-Sample t-test for Perceived Fairness of Risk Allocation (H1)\u003c/h2\u003e \u003cp\u003eA one-sample t-test was conducted to examine whether private-sector stakeholders perceived fairness of risk allocation (PF_Mean) differed from the neutral midpoint of the five-point Likert scale (test value\u0026thinsp;=\u0026thinsp;3.00). This procedure is appropriate because H1 evaluates whether fairness perceptions are meaningfully above neutrality, thereby indicating an overall judgment of \u0026ldquo;fair\u0026rdquo; rather than \u0026ldquo;neutral\u0026rdquo; risk allocation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-Sample Statistics (PF_Mean)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Error Mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF_Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-Sample t-test Results (Test Value\u0026thinsp;=\u0026thinsp;3.00)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI Upper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF_Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-Sample Effect Sizes (PF_Mean)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoint Estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI Upper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHedges\u0026rsquo; correction (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStakeholders reported a mean perceived fairness score of 3.875 (SD\u0026thinsp;=\u0026thinsp;0.779), placing the construct firmly above the neutral midpoint and close to the \u0026ldquo;agree\u0026rdquo; region. The mean difference from neutrality was 0.875, and the confidence interval for this difference remained entirely positive (0.778 to 0.972), indicating a stable elevation rather than a marginal shift. The test statistics confirmed a statistically significant deviation from neutrality, t (249)\u0026thinsp;=\u0026thinsp;17.757, p \u0026lt; .001. The standardized effect was large (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;1.123), signaling that the observed difference is not only detectable but also substantively pronounced in magnitude. Accordingly, H1 is supported, indicating that private-sector stakeholders generally perceive risk allocation in Saudi automotive mega-projects as fair.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSimple Linear Regression for Perceived Fairness and Participation Intention (H2)\u003c/h2\u003e \u003cp\u003eSimple linear regression was used to test H2 by estimating whether perceived fairness of risk allocation (PF_Mean) predicts participation intention (PI_Mean). The dependent variable was the PI_Mean intention score. The predictor was the PF_Mean fairness score overall. This model estimates the expected shift in participation intention for each one-point rise in fairness, while also capturing explained variance through R\u0026sup2; and overall fit via the F-test. Statistical significance was evaluated at α\u0026thinsp;=\u0026thinsp;.05.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted R Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e231.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e209.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF_Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePerceived fairness showed a strong, positive association with participation intention. The model explains 48.2% of the variance in PI_Mean (R\u0026sup2; = 0.482). The F-test confirmed robust overall fit, F (1, 248)\u0026thinsp;=\u0026thinsp;231.128, p \u0026lt; .001. The slope indicates that a one-point increase in PF_Mean corresponds to a 0.817-point increase in PI_Mean, which is a sizeable movement on a five-point response scale. The standardized coefficient (β\u0026thinsp;=\u0026thinsp;0.695) indicates a substantial effect magnitude, supporting H2 and implying that fairness perceptions are a central lever shaping private-sector willingness to participate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMultiple Linear Regression for Risk Categories and Participation Intention (H3)\u003c/h2\u003e \u003cp\u003eMultiple linear regression was conducted to test H3 by examining whether the five project risk categories (RC1\u0026ndash;RC5) significantly predict private-sector participation intention (PI_Mean). This model is appropriate because H3 concerns the comparative influence of distinct risk categories on participation decisions, allowing each risk predictor to be assessed while holding the others constant. Statistical significance was evaluated at α\u0026thinsp;=\u0026thinsp;.05, and model performance was interpreted using R\u0026sup2;, the overall F-test, and standardized beta coefficients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted R Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e209.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe five risk categories collectively produced a statistically significant model, F (5, 244)\u0026thinsp;=\u0026thinsp;5.983, p \u0026lt; .001, indicating that the set of risks carries explanatory value for participation intention. However, the explained variance is modest (R\u0026sup2; = 0.109; adjusted R\u0026sup2; = 0.091), meaning that risk-category ratings account for roughly one-tenth of the variability in PI_Mean. At the predictor level, none of the individual risk categories reached conventional significance at α\u0026thinsp;=\u0026thinsp;.05, although RC5 showed the largest standardized effect (β = -0.162) and approached significance (p\u0026thinsp;=\u0026thinsp;0.080), suggesting a tentative negative association with participation intention. The remaining predictors exhibited small, standardized coefficients and non-significant p-values, implying limited unique contribution once the other risk categories are controlled. Consequently, H3 is not supported in its strict form, because financial and commercial risks cannot be confirmed as the strongest statistically significant drivers within this model, even though the overall risk set is jointly associated with participation intention.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings provide a clear answer to the study’s three research questions. First, private-sector stakeholders in Saudi automotive mega-projects generally regarded the prevailing allocation of risks as fair. Perceived fairness was not only above the neutral midpoint, but substantially so, with a mean of 3.875 and a large effect size, while the five fairness items were closely clustered between 3.82 and 3.91. This pattern indicates that the positive fairness assessment was broad-based rather than driven by a single item or a narrow aspect of the allocation framework. Given that the respondent pool consisted largely of experienced practitioners, many of whom had prior PPP exposure, this result suggests that favorable fairness evaluations were grounded in practical familiarity with complex project environments rather than in purely abstract judgment.\u003c/p\u003e \u003cp\u003eSecond, perceived fairness emerged as the strongest explanatory factor in the study. The regression model revealed a substantial positive association between perceived fairness and participation intention, with perceived fairness accounting for 48.2% of the variance in participation intention and yielding a large, standardized coefficient (β = 0.695). This finding is especially important when considered alongside the descriptive profile of participation intention. Although the participation items were moderately positive on average, they also exhibited pronounced dispersion, with standard deviations above one across all items. Substantively, respondents did not appear uniformly enthusiastic about future participation; rather, they displayed cautious willingness with marked variation across the sample. The strength of the fairness effect therefore suggests that judgments regarding whether risks are allocated in a balanced and credible manner may be a major factor distinguishing respondents who are more willing to engage from those who remain hesitant. In this sample, fairness appears to represent more than a favorable opinion of contract design. It functions as a practical signal of whether the project environment is sufficiently workable to justify future commitment.\u003c/p\u003e \u003cp\u003eThird, the results qualify the role of specific risk categories in shaping participation intention. Descriptively, all five risk items received high ratings, with means ranging from 3.95 to 4.15, and dispersion was lower than for participation intention, indicating strong consensus that these risks are consequential. Yet the multiple regression model presented a different pattern. While the five risk-category items were jointly associated with participation intention at the model level, the explained variance was modest at 10.9%, and none of the individual predictors reached conventional statistical significance. Even the most salient item, RC5, only approached significance and did so in a negative direction. This combination of findings suggests that respondents broadly agree that multiple forms of risk matter, but that such shared recognition does not translate into strong independent prediction of willingness to participate once the categories are considered simultaneously. A plausible interpretation, based on the observed response patterns, is that the risk items captured a widely shared perception of general project uncertainty rather than sharply differentiated concerns that uniquely drive participation decisions. Where most respondents already acknowledge that several risks are important, those ratings may have limited power to distinguish between higher and lower participation intention.\u003c/p\u003e \u003cp\u003eThese findings suggest that willingness to participate in Saudi automotive mega-project PPPs may depend less on the broad salience of risk categories than on whether the allocation of those risks is perceived to be fair. This is the study’s central substantive contribution. The findings do not imply that financial, regulatory, operational, or external risks are unimportant; on the contrary, respondents rated all of them highly. Rather, they indicate that awareness of risk alone is insufficient to explain participation intention. What appears to matter more is how the overall allocation structure is judged by market participants. In practical terms, this means that project attractiveness may be enhanced not simply by identifying risks or emphasizing their importance, but by designing allocation arrangements that stakeholders regard as proportionate, transparent, and balanced. The fact that fairness was consistently rated positively and strongly associated with willingness to participate, whereas the risk-category model was comparatively weak, points to a broader conclusion: in this context, private-sector participation is shaped more by confidence in the reasonableness of the allocation framework than by the mere presence of recognized project risks.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the relationship between perceived fairness of risk allocation and participation intention in PPP projects, with particular attention to whether perceived fairness provides a stronger explanation of market willingness to engage than the perceived salience of major risk categories. Using survey data from private-sector practitioners involved in Saudi automotive mega-projects, the study addressed three related questions concerning the general level of perceived fairness, its effect on participation intention, and the explanatory role of selected risk categories.\u003c/p\u003e\u003cp\u003eThe findings show that perceived fairness of risk allocation was evaluated positively and significantly above the neutral midpoint, indicating that respondents generally regarded prevailing allocation arrangements as balanced. More importantly, perceived fairness emerged as the strongest explanatory factor in the study, showing a substantial positive association with participation intention. By contrast, although all risk categories were rated as important, they showed comparatively weak explanatory value when entered simultaneously in the regression model. The main implication is that private-sector willingness to participate may depend less on broad recognition of project risks than on whether those risks are allocated in a manner perceived as fair, proportionate, and credible. In practical terms, the study suggests that policymakers and project sponsors may strengthen project attractiveness by developing risk allocation structures that the market regards as balanced and institutionally reliable.\u003c/p\u003e\u003cp\u003e \u003cb\u003e8. Limitations and Future Research\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThis study has several limitations that should be acknowledged. First, the analysis was based on cross-sectional survey data, which means that causal inferences should be made with caution. Second, the findings relied on self-reported perceptions rather than observed participation behavior, and such perceptions may be influenced by respondent expectations, prior experience, or contextual judgment. Third, the sample reflects practitioner views within the specific context of Saudi automotive mega-projects and therefore may not be fully generalizable to other PPP sectors, industries, or national settings.\u003c/p\u003e\u003cp\u003eA further limitation concerns the measurement of risk categories. Although the included items captured major PPP risk concerns, this block could be refined in future research through clearer construction separation, multi-item subdimensions, or factor-based measurement strategies. Future studies could also examine whether perceived fairness mediates the relationship between risk perceptions and participation intention, whether this relationship varies across respondent types or institutional settings, and whether similar patterns appear in other sectors or countries. Such extensions would help clarify the broader applicability of the present findings and strengthen understanding of how risk allocation influences private-sector engagement in PPP environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study involved an anonymous, minimal-risk questionnaire. Participation was voluntary, and informed consent was obtained electronically from all participants prior to completion of the questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated and analyzed during the current study are not publicly available due to participant privacy and confidentiality restrictions. Aggregated data supporting the findings are available from the author upon reasonable reques\u003c/p\u003e\n"},{"header":"References ","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhwireng-Obeng, F., \u0026amp; Mokgohlwa, J. (2002). 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Evolutionary Mechanism of Trust for Public\u0026ndash;Private Partnership Projects with Public Participation. \u003cem\u003eBuildings\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/buildings15030391\u003c/span\u003e\u003cspan address=\"10.3390/buildings15030391\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"public–private partnerships, risk allocation, perceived fairness, participation intention, private-sector participation, Saudi automotive mega-projects","lastPublishedDoi":"10.21203/rs.3.rs-9152430/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9152430/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examined whether perceived fairness of risk allocation helps explain private-sector participation intention in public\u0026ndash;private partnership projects more effectively than the perceived salience of major risk categories. Using a quantitative cross-sectional survey design, the study analyzed 250 valid responses from practitioners involved in Saudi automotive mega-projects, including EPC contractors, suppliers, consultants, and investors. The questionnaire measured perceived fairness of risk allocation, participation intention, and five major risk-category items, and the scales showed strong internal consistency. The results indicate that perceived fairness was evaluated positively and significantly above the neutral midpoint. More importantly, perceived fairness showed a strong positive association with participation intention and explained a substantial proportion of its variance. By contrast, although all risk categories were rated as important, their explanatory power was comparatively modest when entered simultaneously in the regression model, and no individual risk category emerged as a statistically significant predictor. These findings suggest that private-sector willingness to participate may depend less on broad recognition of project risks than on whether the allocation of those risks is perceived as fair, proportionate, and credible. The study therefore highlights perceived fairness as a central governance-related factor in strengthening PPP project attractiveness and market willingness to engage.\u003c/p\u003e","manuscriptTitle":"Risk Allocation and Private Sector Participation in Saudi Automotive Mega-Projects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 08:27:50","doi":"10.21203/rs.3.rs-9152430/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"916c8673-2143-4048-bf1c-21a94dc5e818","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T20:44:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 08:27:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9152430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9152430","identity":"rs-9152430","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}