Beyond the Ride: A PLS-SEM Analysis on The SKSPS Contribution Dynamics in Malaysian E-Hailing Odyssey

Beyond the Ride: A PLS-SEM Analysis on the SKSPS Contribution Dynamics in Malaysian E-Hailing Odyssey

Radduan Bin Yusof¹, ^*Suhaimi Abd Samad¹, Azizan Zainuddin¹, Khairiyah Hj. Md Shahid¹, Erna Setijaningrum², Edmund Cheong³,

¹Faculty of Administrative Science and Policy Studies, Universiti Teknologi MARA Cawangan Negeri Sembilan, Kampus Seremban Seremban, Malaysia

²Department of Public Administration, Faculty of Social and Political Sciences, Universitas Airlangga, Indonesia

³Social Security Organization, Malaysia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2024.804112

Received: 11 March 2024; Revised: 07 April 2024 ; Accepted: 11 April 2024; Published: 13 May 2024

ABSTRACT

This study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the factors influencing gig workers’ propensity to contribute to Malaysia’s Self-Employed Social Security Program (SKSPS), introduced in 2017. Based on a sample of 251 E-hailing workers, the analysis reveals a significant relationship between trust and the propensity to contribute, underlining the paramount role of trust in social insurance participation. However, non-significant associations between promotion efforts, awareness, financial capabilities, and contribution propensity are observed. The model, with an R² value of 0.613, demonstrates commendable explanatory power. These findings contribute to the literature on social insurance participation, emphasising the importance of trust that warrants further investigation. The study concludes with recommendations for refining trust-building initiatives, promotional strategies, and awareness campaigns to enhance social insurance participation within the gig economy context.

Keywords: Gig Worker, E-hailing, Self-Employed Social Security Program (SKSPS) Malaysia, Propensity to contribute, Social Insurance.

INTRODUCTION

In the rapidly evolving landscape of contemporary employment, the gig economy has emerged as a transformative force, offering individuals flexible work arrangements and economic opportunities (Huđek & Širec, 2023). The extrinsic and intrinsic motivators are some of the factors that prompt individuals to work with the food delivery platforms (Goods et al., 2019). However, the remarkable growth of this sector has exposed significant deficiencies in social security protection for gig workers, leaving them susceptible to various economic uncertainties (V. Pillai & Dev, 2022; SNM. Daud et al., 2024), notably in the realm of social protection (Au-Yeung & Qiu, 2022; Kajwang, 2022). While gig workers greatly benefit from the flexibility of their work arrangements, they concurrently face heightened risks of occupational injuries, including traffic accidents, impaired physical functioning, anxiety, and depression ​without health benefits (Abd Samad K. et al., 2023; Anwar & Graham, 2021; Gregory, 2021)​. The apparent trade-off between flexibility and occupational safety points to a pressing issue: inadequate protection against occupational injuries ​(Ran & Zhao, 2023)​.

Platform-based self-employment is frequently discussed alongside the concept of ‘the future of work’ ​(Berg et al., 2018; De Stefano, 2015; Friedman, 2014)​. However, most of this discussion should be considered as part of a larger historical trend toward risk outsourcing (Gregory, 2021). The significance of a robust social insurance system, especially within the gig economy, cannot be overstated. Social security is crucial in fostering economic stability, ensuring that individuals can access essential benefits during unemployment, illness, or other unforeseen circumstances. In the context of gig workers, who often lack the traditional employment benefits enjoyed by their counterparts in conventional sectors, a well-structured social insurance system becomes paramount ​(Heniyatun et al., 2018; M. Radzi et al., 2022; Ravenelle et al., 2021)​. Recognising the imperative to bridge these gaps, Malaysia has introduced the Self-Employed Social Security Program (SKSPS), an innovative initiative to establish a safety net for gig workers.

The SKSPS program was introduced by the Malaysia Social Security Organisation (SOCSO) to enhance the social security landscape for the informal sector, including gig workers in Malaysia. By offering comprehensive health and social insurance benefits, this initiative addresses the immediate needs of gig workers and contributes to the broader societal goal of fostering financial resilience and inclusivity. However, the success of such programs relies heavily on understanding the intricate factors influencing gig workers’ willingness to participate actively. Since its inception in 2017, within 700,00 active gig workers in Malaysia, less than 30 per cent contribute to SKSPS (Zulkifli, 2023). This article delves into the heart of this critical issue, seeking to unravel the complexities determining gig workers’ propensity or inclination to contribute to the SKSPS program among e-hailers in the south peninsula of Malaysia. By employing Partial Least Squares Structural Equation Modeling (PLS-SEM), we aim to analyse the interplay of trust, promotional efforts, and awareness campaigns initiated by SOCSO, along with the financial capacity of gig workers, in shaping their decision to participate in this vital social insurance initiative. The findings of this study not only contribute to the academic discourse on social security within the gig economy but hold practical implications for policymakers and stakeholders involved in fortifying the SKSPS program and elevating the overall social protection framework for gig workers in Malaysia.

LITERATURE REVIEW

The rise of the gig economy sector is due to rising unemployment rates and online market demand and supply. Gig work is a new job option for those who do not work and prefer an online side job. Gig work is flexible and uncertain (Uchiyama et al., 2022). Gig work is uncertain, which means it tends to be poorly compensated, temporary, does not provide training, health, or pension benefits, and shifts more of the risk of doing business from the employer to the worker (Bajwa et al., 2018). Most of the literature has demonstrated how a lack of comprehensive access to universal social benefits exacerbates the adverse effects of precarious work. In the Malaysian context, workers in the gig economy were not protected under the Work Act 1955, the Labour Ordinance (Sabah Chapter 67), or the Labour Ordinance (Sarawak Chapter 76), and they were not entitled to the minimum salary (Bernama, 2023). However, previous research indicates that encouraging gig workers to contribute to social insurance is a challenge.

The literature on social insurance contributions provides valuable insights into the factors influencing individuals’ willingness to participate in such programs, laying the groundwork for understanding social security contributions in the context of gig workers. Existing research highlights the significance of trust, promotion, awareness, and financial considerations in shaping individuals’ attitudes and propensities to contribute to social insurance programs.

Trust is a central theme in the literature on social insurance contributions. Studies emphasise the importance of trust in government institutions, policymakers, and the administration of social security programs as crucial determinants of individuals’ decision to contribute (Burchi et al., 2022; ILO, 2019). Trust fosters a sense of reliability and fairness, influencing the perception that contributions will translate into meaningful benefits during times of need.

Promotion efforts and awareness campaigns have been recognised as critical in increasing participation in social insurance programs (Loewe, 2024). The dissemination of clear and accessible information about the benefits, eligibility criteria, and application processes is essential to overcome informational barriers and encourage active engagement (Marina et al., 2020; Geha. et al., 2023). Effective promotion strategies enhance individuals’ understanding of the program, reinforcing its relevance and potential impact on their well-being.

Providing social security protection is a complex issue in the relationship between social security, national saving, and economic development (Butare, 1994; Menegatti, 2022). Malaysian social security previously focused on the formal sector employees. The advancement of technology has changed how people work and the employer-employee relationship. Increases in gig employees, particularly e-hailing workers, require a more holistic planning of social protection to support this new type of worker (Kajwang, 2022). One initiative by the government is the introduction of SKSPS by SOCSO to support the needs of these industries. However, a self-contribution insurance system is historically unpopular; financial considerations are pivotal in shaping individuals’ attitudes towards social insurance contributions. The existing literature underscores the importance of affordability, income stability, and the perceived value of the benefits in influencing individuals’ decisions to contribute (Allon et al., 2018; Enoff & McKinnon, 2011; Menegatti, 2022). In the gig economy, where income fluctuations are prevalent, understanding the financial capacity of workers becomes particularly crucial in designing contribution models that align with their economic realities (Daniels & Grinstein-Weiss, 2018; Gong & Liu, 2023).

The literature also acknowledges the complex interplay among trust, promotion, awareness, and financial considerations in shaping attitudes towards social insurance contributions. For instance, studies have found that increased awareness, coupled with trust in the effectiveness of the program, positively influence individuals’ perceptions of the value of their contributions (Marina et al., 2020; Pratik, Biswas., Mridanish, Jha., 2020). Likewise, financial considerations, which may include household income, when combined with a transparent and trustworthy administration, contribute to a positive attitude towards contributing to social insurance programs (Eshetie et al., 2022).

While the existing literature provides valuable insights into the determinants of social insurance contributions and the factors influencing attitudes toward such programs, a noticeable gap exists in the specific examination of Malaysia’s Self-Employed Social Security Program (SKSPS). Limited studies have focused on understanding the dynamics of contribution behaviour among gig workers in the Malaysian context, particularly concerning the SKSPS initiative. The scarcity of research on the SKSPS program leaves a critical void in our understanding of the factors that shape gig workers’ decisions to contribute to social security in Malaysia. Given the unique nature of the gig economy and the innovative approach of the SKSPS program, a more targeted investigation is warranted to uncover the intricacies of this social insurance initiative and its impact on gig workers’ financial well-being. Furthermore, a paucity of studies employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyse the SKSPS program in Malaysia. PLS-SEM offers a comprehensive and flexible approach, which is particularly beneficial when dealing with complex relationships among latent constructs. The limited application of PLS-SEM in the context of social insurance contributions, specifically the SKSPS program, underscores the need for a more sophisticated analytical tool to unravel the intricate interplay of trust, promotion, awareness, and financial considerations in shaping gig workers’ propensity to contribute.

By addressing these gaps, our study aims to contribute to the nascent literature on social security contributions within the gig economy, explicitly focusing on the SKSPS program in Malaysia. PLS-SEM is an innovative methodological approach to comprehensively explore and understand the factors influencing gig workers’ decisions to contribute to the SKSPS program. In doing so, we aim to fill the existing void in research, providing valuable insights for policymakers, practitioners, and researchers interested in strengthening social security frameworks for gig workers in the Malaysian context.

MATERIALS AND METHODS

This study investigates the relationship between trust, promotional activities, awareness and financial ability towards the propensity to contribute to SKSPS. This study adopts a quantitative case study methodology to investigate the factors influencing gig workers’ contributions to Malaysia’s Self-Employed Social Security Program (SKSPS). This approach is particularly suitable for understanding the nuances of gig work and the unique challenges workers face in the e-hailing sector. The research employs a survey as the primary data collection method. A structured questionnaire is designed to capture relevant information regarding trust, promotion, awareness, financial considerations, and the propensity of gig workers to contribute to the SKSPS program. The questionnaire development draws insights from several past studies, comprehensively exploring relevant variables.

The measures were designed on a five-point scale from 1 (strongly disagree) to 5 (strongly agree). All constructs are reflective, and the items are adopted and modified from previous studies (AG. Awan, 2016; Poan, R., Merizka, V.E. and Komalasari, F. (2021). A pre-study was conducted to check whether instructions were comprehensible. This study established content validity through the consensus of three experts who were faculty members in 3 Malaysian Universities, two e-hailing platform representatives and one expert from the Social Security Organisation. Before the data collection, a pilot study was conducted to find the reliability and validity of different constructs of questionnaires. De Vaus (1993) recommended the pilot study to evade risks.

Factor analysis was run to establish validity using principal component analysis (PCA), and Cronbach’s alpha coefficients were used to test the reliability among the variables through SPSS. An internal consistency test (Cronbach’s alpha) was conducted to assess the reliability of each scale used. All the measures included in the questionnaire showed adequate internal consistency reliability. The internal reliability of the measures ranges from 0.720 to 0.932 in our study, which is higher than the recommended threshold value of Cronbach’s alpha (J. C. Nunnally & Bernstein, 1994).

A. Hypotheses

The hypotheses in this study include direct relationships, such as the impact of trust, promotion, awareness, and financial ability on the propensity to contribute and indirect effects through other factors. PLS-SEM enables the simultaneous estimation of these relationships, providing a holistic view of the interdependencies among latent and observed variables. This capability enhances the depth of analysis, offering a nuanced understanding of the complexities inherent in predicting contributions to social insurance for gig workers or the SKSPS. The hypotheses for this research are as follows:

H1: There is a significant positive relationship between trust and the propensity to contribute to SKSPS.

​H2: There is a significant positive relationship between promotional activities and the propensity to contribute to SKSPS.

H3: There is a significant positive relationship between awareness and the propensity to contribute to SKSPS.

H3: There is a significant positive relationship between financial ability and the propensity to contribute to health insurance.

Fig 1. Conceptual model and hypothesised relationship

B. Respondent and measurement scales

Before distributing the questionnaire, we considered the ethics, transparency and participant awareness of privacy statements in the online questionnaire to ensure data confidentiality.   This study conveniently collects responses from 600 (200 e-hailers across various states) in the southern peninsula of Malaysia (Johor, Negeri Sembilan, and Selangor). Only 262 respondents managed to be collected.

Earlier, the researcher faced the challenge of getting information from the e-hailer. Most of the gig workers met were reluctant and mentioned they did not have time to answer the survey. By collaborating with e-hailing captains (a term used for the most senior and respected e-hailing workers (not formally attached to any service provider) in Johor, Negeri Sembilan, and Selangor, the self-administered questionnaire is finally able to be distributed. E-hailing captains, serving as intermediaries between researchers and gig workers, play a crucial role in facilitating the distribution of the questionnaire.

Their involvement ensures a more targeted and organised approach, enhancing the likelihood of obtaining responses from the desired population. Via Google Forms, this digital method facilitates a wide-reaching and efficient data collection process. It offers a convenient and accessible platform for gig workers to participate in the survey. The research 262 responses went through a stringent data quality check, utilising SPSS, preceding outlier identification through Z-score analysis. The responses are also subjected to preliminary checks using SPSS to identify non-responsive, straight-lining, and free from common method bias (add Harmon Single Factor Loading). Following removing outliers, the final sample of 251 respondents aims to provide robust insights into the dynamics of social insurance contributions within the unique context of the Malaysian gig economy.

Table I shows the demographic profile of the respondents in this study. Most of them are male (90.8%) compared to females based on the nature of e-hailing work. On average, they are aged between 21 and 30, representing 59.4% compared to other age categories. 97.7% of the respondents are Malay, while the academic background for most respondents is SPM graduates (41%). Some of the respondents in this study are also still single, and this data is consistent with their dependency, which is only on themselves, each representing 65.5% and 51.7%, respectively. Most (34.9%) are still considered new in the e-hailing service, i.e., less than six months. However, only 65.5% of the respondents have registered with SKSPS, even though the data shows that 85.1% are aware of SKPS. More than half (50.6%) of the respondents of this study work with Grab compared to other platform providers.

Table 1. E-Hailing Respondent Demographics

C. Data Analysis

In this study, we employ Partial Least Squares Structural Equation Modeling (PLS-SEM) as an analytical framework to predict contributors’ inclination towards health insurance based on specified factors. PLS-SEM is well-suited for simultaneously estimating direct and indirect effects within a model involving latent variables like trust, promotion, financial incentives, and awareness. These latent variables, representing underlying constructs impacting the propensity to contribute, are measured through observed variables. PLS-SEM facilitates path analysis, allowing us to understand the intricate relationships between factors influencing contributors’ propensity. Its robustness with smaller sample sizes (251 respondents in this study) distinguishes PLS-SEM, ensuring reliability and validity. The methodology aligns with the study’s predictive nature, accommodating complex relationships among latent and observed variables, and positions us to gain valuable insights into contributors’ health insurance tendencies ​(Hair et al., 2014, 2021)​.

PLS-SEM in the Smart PLS programme was used to analyse data and determine hypotheses, while SPSS software performs descriptive statistics and ensures data integrity by avoiding missing values, outliers, and straight-lining responses. Partial least squares structural equation modelling (PLS-SEM) is an analysis technique for finding or building predictive (causal) model analysis between latent variables, and it is beneficial for exploratory research (Henseler et al., 2016). The PLS analysis is performed in two steps. In the first stage, a measurement model is created to assess convergent validity using outer loadings and average variance extraction. In addition to cross-loading, the Fornell-Larcker criterion and the Heterotrait- Monotrait evaluation were used to determine the discriminant validity and reliability of the study’s components. The second phase entails developing a structural model to examine the linkages between the components and answer the research questions utilising the path coefficient and coefficient of determination (R2). The R2 measures the proportion of variance explained by endogenous variables, with higher values indicating better fit. Higher R2 values indicate a greater proportion of variance explained and suggest a better model fit. P-values determine the statistical significance of relationships between variables, with smaller values indicating more robust evidence against the null hypothesis. F2, or f-squared, assesses effect sizes, showing the proportion of variance in an endogenous variable explained by an exogenous variable. These metrics collectively aid in evaluating model fit and the strength of relationships within the PLS-SEM frame work. It employs the PLS algorithm and bootstrapping to execute the repetitive sampling to derive part coefficients and significance between the dimensions or variables (Hair et al. 2021).

RESULT

Outer measurement model 

Our measurement model comprises five reflective latent constructs and 29 indicators. Convergent validity must be tested while examining the outer model to assess the constructs’ validity and reliability. According to the studies, the outer loading should be greater than 0.7. The item should be removed if the outer loading value is less than 0.40. General loadings exceeded the value and were considered satisfactory for the model; thus, no items were removed since all outer loading met the criteria. Composite reliability must be above 0.60, a general guideline according to Hair et al. ​(2014)​. Composite Reliability (CR) and Cronbach Alpha values were all satisfactory and fulfilled the recommended value of greater than 0.70, as shown below in Table II ​(Hair et al., 2014)​. Cronbach’s alpha (CA), composite reliability (CR) (rho_a), (rho_c) and average variance extracted (AVE) were calculated to measure the internal consistency and validity as shown in Table II. The AVE is a measurement of convergence that represents a reflectively measured latent construct ​(Hair et al., 2021)​. All constructs’ values were reliable as the Average variance extracted (AVE) was greater than 0.50, which meets the standard validity criterion ​(Hair et al., 2014). Therefore, all constructs were appropriate for further analysis.

Table II: Reliability And Validity For Constructs (Outer Loading, Cronbach Alpha (Ca), Composite Reliability (Rho) And Average Variance Extracted (Ave))

The second step is to investigate discriminant validity, which refers to the extent to which it is a subtype of construct validity. It shows how a test measures the concept and computes the constructs. Discriminant validity means that latent variables cannot be gauged for variances in the observed variables. However, they should not be related to each other. The result for the Heterotrait- Monotrait ratio (HTMT) is shown in Table III, which qualifies the value of correlation for the construct as an acceptable range (<0.90) ​(Hair et al., 2021)​

Table III: Discriminant Validity – Heterotrait-Monotrait Ratio (Htmt)

Table IV:  Discriminant Validity (Fornell-Larcker Criterion).

As shown in Table IV, The Fornell-Larcker test indicates that the diagonal values are the greatest for a construct compared to the other values. Meanwhile, a heterotrait-monotrait ratio (HTMT) analysis was calculated to examine discriminant validity.

Structural model 

After validation of the outer model, the inner model is examined for its relationship mapping between constructs. The R2 and the corresponding values are in the suggested values bracket; the corresponding T-statistics taken through the bootstrapping method with at least 5000 are to be assessed. Figure 2 illustrates the structural equation model after processing the structural equation modelling for all latent constructs. The research hypothesis should be rejected when the p-value is less than 0.05 in negative terms. Whereas, as defined in positive terms, the research hypothesis should be accepted if the p-value is less than 0.05. The study adopted positive terms.

Fig 2. Outer Loading and Path Analysis

The Partial Least Squares Structural Equation Modelling (PLS-SEM) analysis explored the relationships between trust, promotion, awareness, financial factors, and the propensity to contribute. Strong and statistically significant support was found for Hypothesis 1, revealing a substantial positive influence of trust on the propensity to contribute. The large standardised beta coefficient (0.594) and significant t-statistic (6.418) underscore this relationship’s practical and statistical significance. However, Hypotheses 2, 3, and 4 did not receive robust support. Promotion, awareness, and financial factors exhibited smaller effect sizes and non-significant t-statistics, suggesting limited impact in the context of this study.

The overall model’s explanatory power, as indicated by the R-square of 0.613, suggests that approximately 61.3% of the variance in the propensity to contribute can be explained by the combined influence of trust, promotion, awareness, and financial factors. This R-square value provides insights into the goodness of fit, indicating the proportion of variability in the dependent variable accounted for by the independent variables.

Table V: Collinearity Statistic – Variance Inflation Factor (Vif)

Bootstrapping is used to evaluate the significance of the hypothesis. F square values assess the effect sizes; if the values are below 0.02, it means small effects; between 0.02 and 0.15 are medium effects; and 0.35 or greater values surmise large effects [37]. The hypothesis testing has been executed and shown in Table V. According to (Hair, 2019) recommendation, the bootstrapping calculation was used with 5,000 iterations to obtain the path coefficient and bias-corrected confidence interval.

Table VI: Structural Estimate (Hypotheses Testing)

Furthermore, considering the effect size (f-square) is crucial. The effect size quantifies the practical significance of the relationships. In the case of Hypothesis 1, the effect size (f-square = 0.367) indicates a moderate to large practical significance. In contrast, Hypotheses 2 exhibit smaller effect sizes (f-square = 0.021), suggesting minimal practical significance, while Hypotheses 3 and 4 exhibit no effect size. 

DISCUSSION

The findings of this study, particularly the significant role of trust and the nuanced impact of promotion, awareness, and financial incentives on health insurance contribution propensity, align with and contribute to the broader literature on social insurance participation. The significant relationship between trust and contribution propensity echoes prior research emphasising the importance of trust in government institutions and social programs (Zein et al., 2020; Toghani and Tajedin, 2019). The trust serves as a foundational element, engendering confidence among contributors.

The non-significant relationship between promotion efforts and contribution propensity diverges from some previous studies highlighting the effectiveness of promotional activities in enhancing social insurance participation (Miti et al., 2021). This disparity may be attributed to variations in promotional strategies or the unique characteristics of the studied population, supporting the need for interventions. Similarly, the non-significant association between financial ability and contribution propensity challenges assumptions from literature suggesting the positive impact of financial inducements on participation (Miti et al., 2021; Akwaowo et al., 2021; Ogundeji et al., 2019). It implies that in the specific context of health insurance, factors beyond financial considerations may weigh more heavily in individuals’ decision-making.

The observed results also consider the nuanced interplay among trust, awareness, promotion, and financial incentives. While trust emerges as a robust predictor, the non-significant associations suggest a multifaceted approach may be necessary. Potential explanations for these outcomes include the need for tailored promotional messages, increased awareness campaigns emphasising the long-term benefits of social protection, and the importance of addressing cultural or regional variations that may influence contributors’ perspectives.

This study contributes to the existing literature by offering nuanced insights into the factors influencing health / social insurance contribution propensity within the gig economy context. While consistent with some literature, the observed results reveal unique dynamics that necessitate a more tailored and context-specific approach to interventions to increase social insurance participation. Further research could delve into these intricacies, offering a more comprehensive understanding of the complexities surrounding health insurance contribution decisions.

CONCLUSIONS AND RECOMMENDATION

In conclusion, the PLS-SEM analysis offers valuable insights into the factors influencing individuals’ propensity to contribute to health insurance within the studied context. As widely acknowledged, the introduction of a voluntary social insurance program stands as one of the strategies aimed at bolstering social protection coverage for gig workers. Consequently, the insights gleaned from this study offer valuable contributions on the pivotal role of trust, with a statistically significant relationship indicating that higher levels of trust are associated with an increased willingness to contribute. This underscores the importance of fostering trust in the context of health insurance programs to encourage active participation. The findings also essential and useful for policymakers in relations to social protection providers to gain trust from the public for health social insurance programs. Conversely, the non-significant relationships between promotion, awareness, and financial incentives with the propensity to contribute suggest that these factors may not play a decisive role within the current model. Further exploration and refinement of the model or consideration of additional variables may be warranted to better capture the complexity of individuals’ decision-making processes regarding health insurance contributions. With an R Square value of 0.666, the model demonstrates a commendable explanatory power, accounting for a substantial portion of the variability in individuals’ propensity to contribute. This suggests that the specified latent variables collectively contribute meaningfully to understanding the dynamics of contribution behaviour.

In practical terms, these findings have implications for policymakers and stakeholders involved in health insurance programs. Focusing on building and maintaining trust is crucial for enhancing participation as suggested by Zein et al., (2020) and Toghani and Tajedin (2019) in previous studies, while a nuanced understanding of the impact of promotional efforts, awareness campaigns, and financial incentives is essential for designing effective interventions. It is recommended that trust-building initiatives in health insurance programs be prioritised, emphasising transparent communication and reliability as proposed by Nguyen et al., (2023). While the non-significant relationship between promotion efforts and contribution propensity suggests a need for refined strategies, continuous and enhanced awareness campaigns remain crucial. Exploring alternative financial incentives aligned with contributors’ preferences is advised. Future research should delve into additional variables and regional variations, while stakeholders’ collaboration and educational initiatives can further strengthen health insurance programs, fostering increased participation and support. Regular monitoring and adaptation of strategies and culturally sensitive approaches are essential for the ongoing success of health insurance initiatives.  It is also strongly suggested that these platforms would establish more opportunity for riders to communicate with them by enhancing the areas of on-the-job support, technical support, training, and providing constructive feedback. Therefore, it is able to prevent disengagement and demotivation of the riders.

While this study provides valuable insights, it is essential to acknowledge its limitations. Primarily, the sample size adopted in this study shall be expanded throughout Malaysia and not limited to the Southern Region of Peninsular Malaysia only for a better generalization. The current approach may not consider all variables beyond the listed characteristics, such as trust, promotion, awareness, and financial incentives. Individual attributes like socioeconomic status, cultural views, and personal experiences with health insurance can significantly influence contributing behaviour. The study may not comprehensively depict the intricate aspects that influence individuals’ willingness to contribute due to the exclusion of these variables.

The limitations mentioned above have significant consequences for understanding the study findings. The considerable correlation between trust and contribution behaviour highlights the need to promote trust in health insurance programmes, but it is essential to acknowledge that trust is simply one of several possible determinants. The lack of significant connections shown for promotion, awareness, and financial incentives suggests that these factors may not strongly impact the current model. The association may change depending on the inclusion of more variables. The links between trust and contribution behaviour may vary based on the specific kind of health insurance programme. Various factors, including the insurance program’s design, the cultural and socioeconomic background of the target population, and the overall healthcare environment, may impact the importance of trust in motivating contribution behaviour.

Future research should take a more comprehensive approach to solve these constraints and enhance our understanding of contribution behaviour in health insurance programmes. This involves examining other variables such as socioeconomic status, cultural influences, and individual experiences, while also considering varied populations to improve the applicability of the results. Qualitative research approaches could offer valuable insights into the many elements influencing individuals’ decisions about contribution behaviour.

This study contributes to the ongoing discourse on health insurance participation by employing a robust analytical approach. The model’s identified relationships and explanatory power offer a foundation for informed decision-making in the design and promotion of health insurance programs, ultimately working towards achieving broader and more effective societal coverage for a better social inclusion.

ACKNOWLEDGEMENTS

This research would like to acknowledge the Faculty of Administrative Science and Policy Studies, University Teknologi MARA, for the Research Academic Cluster Grant (RACG) 500-UiTMKS/FSPPP(PT.23/4/1)

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